Download The analysis carried out to date can be seen in Section 4 Reduction

New Thames Valley Vision
SSET203
LCNF Tier 2 SDRC 9.2(d) Evidence Report
Develop and Trial Method of Optimising Network Monitoring
Based on Installation of First 100 Substation Monitors
Prepared By
Document Owner(s)
Project/Organization Role
Gideon Evans
Project Manager
Nigel Bessant
Project Delivery Manager
Employment Manual Version Control
Version
Date
Author
Change Description
1.0
30 04 2014
Gideon Evans
Final
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Blank Page
Page 2
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Contents
Page
1
Summary
1.1
Criteria 9.2 (b)
6
1.2
Background
7
1.3
Link to Methods and Learning Outcomes
9
2
Substation Monitor Installations
2.1
Substation Selection Process
11
2.2
Data from substation Monitoring
13
2.3
Substation Monitoring Data in PowerOn Fusion
14
2.4
Substation Monitoring Data in Pi Process Book
15
3
Optimised Substation Monitoring – Substation
Categories
3.1
Substation categories
18
3.2
Analysis of Substation Data by Category
18
3.3
Recommendations for Future Substation Monitoring
19
4
Optimised Substation Monitoring – DNO
Operational Requirements
4.1
Availability of Data
21
4.2
Summary of Data
21
43
Uses for Streamed Data
25
4.4
Uses for Periodic Data
26
Page 3
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
4.5
Uses for Alarms
28
4.6
Local Depot Review of Data
31
4.7
Recommendations for Future Substation Monitoring
31
5
Optimised Substation Monitoring – Virtual
Monitoring
5.1
Virtual Monitoring (Buddying and Aggregation)
35
5.2
Comparison with Real Substation Monitoring
35
5.3
Reducing Monitoring at Substations
36
5.4
Recommendations for Future Substation Monitoring
36
6
Conclusions
6.1
Convergence of Recommendations - Location
37
6.2
Convergence of Recommendations – What to Monitor
38
7
Next Steps
7.1
Project Substation Monitoring Installations
39
7.2
Refinement of Recommendations
40
Appendices
42
8
Appendix 1 University of Reading Categorisation of Substations
Attached
Appendix 2 University of Reading Selection Procedure for Substation Attached
Monitor Locations
Appendix 3 Substations Selected
Attached
Page 4
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Appendix 4 Data from Substation Monitoring – Pi Process Book
Appendix 5 University of Reading
Attached
Initial Analysis of First 100 Attached
Substations Where Monitoring was Installed
Appendix 6 University of Oxford 12 Month Analysis of First 100 Attached
Substations Where Monitoring was Installed
Appendix 7 Schedule of Available Substation Monitoring Analogues.
Attached
Appendix 8 First 100 Substations – Operational Data Overview
Attached
Appendix 9 Operational Use of Substation Monitoring Data - Case Attached
Studies
Appendix 10 Strategy for the Use of Alarms on the LV Network
Attached
Appendix 11 University of Reading Feeder Demand Predictions
Attached
Page 5
SRDC 9.2 (d) Evidence Report
1
SSET203 NTVV
New Thames Valley Vision
Summary
1.1
Criteria 9.2(d)
Successful Delivery Reward Criteria 9.2 (d)
Criterion:
Develop and Trial Method of Optimising Network Monitoring Based on Installation of
First 100 Substation Monitors
Evidence:
Prepare Report reviewing optimal deployment of monitors based on installation of first
100 substation monitors.
SSEPD confirms that this criterion has been met.
This document provides details of the review of the deployment of the first 100
substation monitors, and presents the findings identified in line with the evidence criteria
specified for the Successful Delivery Reward Criteria (SDRC).
It is confirmed that:

The 100 substation monitors installed by April 2013 have been operating and
providing data for analysis for a year.

The data provided has been analysed against the original selection criteria to
recognise if these criteria usefully inform the selection of future substations to be
monitored.

The data provided has been compared with aggregated end point data to
understand if the availability of smart meter data in the future can influence the
substations that need monitoring.

The data has been reviewed in terms of operational relevance and direct input to
network planning decision making.

The outcome of learning points from each analysis stage has been described in
terms of its influence on the optimisation of future substation monitoring
deployments.
Page 6
SRDC 9.2 (d) Evidence Report
1.2
SSET203 NTVV
New Thames Valley Vision
Background
The NTVV project requires half hourly energy data to be captured from substations (and
end points) on the low voltage network so that energy usage patterns can be identified,
categorised and forecast, and compared with aggregated data from end points. With this
information it is expected that meaningful forecasts can be made regarding the future
loading of the low voltage network.
Some LV feeders have little or no headroom whilst others have a reasonable amount
available for historic network reasons. The substation monitoring will provide real data
for comparison with the estimates and forecasts. The substation monitoring will also
provide data to support the development of the most appropriate deployment strategy
and operating regimes for new network operating methods identified.
The data provided from substation monitoring is clearly important for the NTVV project to
be able to better understand the LV network, but it is accepted that one of the learning
outcomes required from each area of analysis is how the LV network can be most
effectively managed using the least amount of substation monitoring data, and how to
recognise which substation locations bring the greatest benefit to the DNO from the data.
SDRC 9.2 (d) was established to acknowledge the clear focus given to the optimisation
of substation monitoring, both in terms of quantity and location. In particular, the project
has sought to:

minimise the hardware deployed so that the cost is minimised while providing the
maximum ultimate benefit to customers

take a phased approach to monitoring deployments, and has targeted
substations with larger numbers of feeders and connected customers (to
increase the quantity of data received per installation) while facilitating the
targeting of substation categories proposed by the University of Reading team for
the best statistical analysis

Review the data from the first 100 substation monitoring installations to allow the
next batch of 100 substations to be selected for the greatest data benefit to the
project
Page 7
SRDC 9.2 (d) Evidence Report

SSET203 NTVV
New Thames Valley Vision
Identify any correlations between substation energy usage data and other
publicly available data such as council tax bands to better perform above a
standard statistical approach
Only very weak data correlation between substation energy data and property size,
homogeneity and density was found and it was calculated that from a relevant substation
population of 500, to achieve a 99% level of confidence that the correlation is correct, a
minimum of 294 substations would need to be monitored. Hence it was decided to
deploy all 325 substation monitors, allowing for anticipated difficulties at some substation
sites, typically with communications signal strength.
Consideration was also given to the optimal use of this substation data by business-asusual teams in both operational and planning environments, and new approaches to
minimising monitoring which will be revealed throughout the course of this project
Page 8
SRDC 9.2 (d) Evidence Report
1.3
SSET203 NTVV
New Thames Valley Vision
Link to Methods and Learning Outcomes
Method 3 as defined for NTVV (see SET203 New Thames Valley Vision bid submission)
proposes the development of mathematical techniques to reduce the need for new and
extensive low voltage network monitoring that might be required to manage and design
low voltage networks to meet the needs of the new low carbon technologies.
Mathematical models are to be developed by the University of Reading using data from
end point monitors now installed in customers’ premises, and subsequently from devices
measuring the energy profiles of low voltage feeder cables from distribution substations.
The substation monitors were installed in line with SDRC 9.2 (b).
To meet SDRC 9.2 (b) 100 substation monitors were installed at distribution substations
in the Bracknell area, commissioned and data collection established. The substation
monitors are GE Digital Energy Multilin DGCM devices. These were configured for use
at distribution substations with up to 6 low voltage feeders, with communications
achieved using a Westermo MR-310 modem, and GPRS/3G SIM card connected to the
Vodafone network.
The substation data is transmitted to an SSEPD server via a Vodafone access point, and
fed into a front end processor (FEP) of the PowerOn Fusion system, established in the
shadow environment in the SSEPD control room. From PowerOn Fusion the data is
available for storage in SSEPD’s PI Process Book, and is subsequently available for
sharing with the University of Reading.
Successful completion of Learning Outcome 1 requires an understanding of the
interaction between the network and individual customers in order to optimise network
investment. The selection of the initial 100 substations locations to be monitored was
made with guidance from the University of Reading to ensure that the mix of customer
types fed from the substations was a representative sample. Combined with end point
monitoring data this substation data will be used to predict future demand on the network
and better inform investment decisions relating to the low voltage network.
Successful completion of Learning Outcome 2 requires improved modelling to enhance
network operation procedures and to provide a management tool for planning and
Page 9
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
investment on the low voltage distribution network. The University of Reading are using
the data from the substation monitors to develop the model.
Successful completion of Learning Outcome 3 looks to optimise the deployment of
monitoring through the use of modelling to reduce the need for and enhance the
information provided by monitoring.
Page 10
SRDC 9.2 (d) Evidence Report
2
2.1
SSET203 NTVV
New Thames Valley Vision
Substation Monitor Installations
Substation Selection Process
To be able to install 100 substation monitors, SSEPD consulted with the University of
Reading to identify appropriate locations. Their objective was to achieve substation
monitoring for a good mix of properties in Bracknell that were statistically relevant, and to
achieve a good coverage of one or more high voltage feeders. Substations were
allocated to a matrix of property categories based on density (number of customers per
feeder) and homogeneity (mix of types of property connected to the feeder). The matrix
of substations is shown in Appendix 1 University of Reading Categorisation of
Substations.
Actual substations to be monitored were selected from the matrix, taking into account the
coincident requirements for end point monitoring (as previously indicated for End Point
Monitors, SDRC 9.2(a)) and practical considerations identified during a survey of the
substations. A more detailed description of the criteria is found in Appendix 2
University of Reading Selection Procedure for End Point Monitor Locations.
The actual substations selected are listed in Appendix 3 Substations Selected
Figure 1 Substation Monitoring Installed at Merryhill Substation
Page 11
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Figure 2 Installed Substation Monitor
Page 12
SRDC 9.2 (d) Evidence Report
2.2
SSET203 NTVV
New Thames Valley Vision
Data from Substation Monitoring
Three types of data have been obtained from substation monitors. These are:
Real time
Immediate transmission (streaming) of measured values
These include current, voltage, power and harmonic content
Periodic
Half hourly calculated values
These include Current (maximum, minimum and mean), Voltage
(maximum, minimum and mean) energy (real positive and negative,
reactive positive and negative) and harmonic content)
Alarms
These include Voltage (high and low) and Current (high high and high)
The real time values are transmitted from the substation monitor direct to the PowerOn
Fusion system for storage in Pi Historian. Any gap in communications in the GSM /
UMTS systems due to signal strength fluctuations or communication network congestion
will result in loss of data; there is no storage of this type of data. As might be expected,
the volume of data is large and some gaps in the data are inevitable for the reasons
described.
Periodic data is calculated in the substation monitor and stored for subsequent
transmission to PowerOn Fusion when polled. Storage in the device is limited to 14 days
and it would be expected that the data is polled at least once a day. When the substation
monitor is streaming real time values the half hourly data will naturally be polled as soon
as it is available, logically just after the completion of each half hour. The University of
Reading team requested that this data be calculated on a half hourly basis for
consistency with end point monitor data (smart meter data), although the period can be
varied (1, 5, 10, 30 and 60 minute options are possible).
Page 13
SRDC 9.2 (d) Evidence Report
2.3
SSET203 NTVV
New Thames Valley Vision
Substation Monitoring Data in PowerOn Fusion
PowerOn Fusion is the primary tool in which data monitored at substations is visible to
operational users.
Figure 3 PowerOn Fusion – Substation Monitor Data – Trevelyan Substation
It can be seen that all analogues for every phase of every feeder and the busbar are
visible. Real time streamed analogues are shown in yellow boxes and calculated
periodic (half hour) data is shown in white boxes. Alarms are shown in squares.
This presentation of data is intended to allow any user with access to the PowerOn
Fusion system to quickly view actual values in real time, and to make immediate
operational decisions based on current circumstances. An example could be the
connection of a back feed that is only permitted when the load current has dropped
below a threshold; the load can be monitored at the substation that is to be off-loaded,
and similarly, the substation that will pick-up the load can be checked to confirm that the
load current does not exceed the threshold.
PowerOn Fusion is a useful operational data viewing medium, but it does not store the
data;
it
sends
the
data
to
Pi
Page 14
Process
Book
for
storage.
SRDC 9.2 (d) Evidence Report
2.4
SSET203 NTVV
New Thames Valley Vision
Substation Monitoring Data in Pi Process Book
Many operational decisions are made following some analysis of trends in data, and for
this purpose Pi Process Book is used. Real time information alone in PowerOn Fusion is
insufficient for this purpose and Pi Process Book allows many different analogues to be
displayed individually or in multiple on a graph, and for any time period from seconds to
months or years.
Figure 4 Pi Process Book – Trevelyan Substation Busbar Analogues
For periodic data that has been obtained on a half hourly basis there are 48 values in a
day, 336 in a week and over 17,000 in a year. It is practical to display these data
volumes for these periods, and most users are able to identify relevant trends in the data
in these periods. Figure 5 below shows typical half hour values, in this case the mean
busbar voltages at Trevelyan substation.
Page 15
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Figure 5 Trevelyan Substation Busbar Mean (Half Hour) Voltage Analogues
For streamed data there are over 17,000 values for each analogue in a day, 121,000 in
a week and 6,300,000 in a year. It is possible to view such volumes of data, but system
performance is inevitably a limitation, and most benefit is gained from such high
resolution data by viewing shorter periods. Figure 6 blow shows the corresponding
streamed data (Busbar Voltages) for comparison with Figure 5.
Figure 6 Trevelyan Substation Busbar (Instantaneous) Voltage Analogues
Page 16
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Further images of the different types of data, viewed graphically in Pi Process Book can
be seen in Appendix 4 Data from Substation Monitoring – Pi Process Book.
A further key feature of the Pi Process Book is that it allows data to be exported for use
in other applications such as Microsoft Excel or Matlab. For the NTVV project, the
majority of DNO operational users use the data directly in Pi Process Book and the
majority of University users access the data in Matlab.
Page 17
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
3
Optimised Substation Monitoring – Substation Categories
3.1
Substation categories
At the start of the NTVV project it was recognised that it would not be possible to fit
substation monitoring at all substations, so a choice would have to be made as to which
substations to target. The University of Reading team chose to group substations into a
matrix that considered the types of properties connected in terms of homogeneity and
density. This could be achieved from information that was widely available in the public
domain (e.g. council tax bands, Google Earth, etc) and did not require any DNO data
other than the connectivity of properties to their feeding substation.
The names of the substations selected and their groupings are shown in Appendix 1
University of Reading Categorisation of Substations. Further details about the
substations including the number of feeders and numbers of customers connected is
included in Appendix 3 Substations Selected.
It was expected that analysis of the energy consumption data collected would reveal
some trends that may inform the selection of substations for subsequent monitoring
installations for the NTVV project, and also potential learning that will inform DNOs about
the benefits of monitoring particular categories of substations.
3.2
Analysis of Substation Data by Category
The University of Reading have received data from the first 100 substation monitors
since April 2013 and made initial observations in July 2013. At that stage the joint priority
was to establish whether further substation monitoring was required for the NTVV
project, and if so, which substations should be targeted so far as this could be informed
from the data received at that point in time. The observations made are included in
Appendix 5 University of Reading Initial Analysis of First 100 Substations where
Monitoring was Installed.
It was apparent that more data would be required, and the monitors would need to
operate for longer than the three months that they had operated at that time, before
definite conclusions could be drawn. The primary recommendation for the NTVV project
was that “more data was required in each of the groups and the benefit from more
Page 18
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
information (substations with more feeders, say) regardless of group far outweighs a
focus on any one given group”. This has led to a second and third tranche of monitoring
installations in the project being targeted at substations with four, five and six feeders (in
preference to two and three feeder substations).
Now that the first 100 substation monitoring installations have been operating for nearly
a year, it is expected that relevant trends and observations may become more apparent
and lead to conclusions that may allow a DNO to target future substation monitoring. The
detailed analysis in Appendix 6 University of Oxford 12 Month Analysis of First 100
Substations Where Monitoring was Installed provides further details of observations
made. The main observation is that the 282 feeders examined show no strong
correlation between substation energy data and property size, homogeneity and density.
Random sampling was therefore recommended.
3.3
Recommendations for Future Substation Monitoring
In the absence of any tangible correlation the University of Oxford team1 recommended
that a random sampling approach be adopted, aiming for a 99% confidence level for a
normally distributed population. The quantity of substations required for analysis is
understood as:
99% confidence, population size of 500, install monitoring at up to 294 substations
95% confidence, population size of 500, install monitoring at up to 223 substations
There are over 600 substations in Bracknell, but after excluding the HV switching
stations and single customer (LV or HV) substations the actual number of relevant
substations is approximately 500 (the actual number changes on a weekly basis
reflecting developments in the area). To achieve the optimum statistical target number of
installations (to achieve 99% confidence) therefore required monitors to be installed at
294 substations.
University of Reading is a project partner with the New Thames Valley Vision. To perform some of the analysis required in support
of the New Thames Valley Vision, the University of Reading has placed a contract with the University of Oxford. Scottish and
Southern Energy Power Distribution is pleased to have the benefit of the combined expertise from both the Universities of Reading
and Oxford in this project.
Page 19
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
In practise, experience to date has confirmed that data losses due to communications
problems and other technical difficulties may reduce the effective count of installed
substation monitors by up to 10%, so if data is required from 294 sites, installations
would have to reach approximately 110% of 294, ie 323 sites. For the NTVV project this
meant that all 325 substation monitors allowed for would be deployed.
As no feeder or substation level correlation has been identified at this stage it is not
possible to make a recommendation, based on substation categorisation, for substation
monitoring deployments beyond the project. At the end of the NTVV project when
significantly more data has been assessed, both by count of substations and by duration
of operation, a recommendation may be possible.
Page 20
SRDC 9.2 (d) Evidence Report
4
SSET203 NTVV
New Thames Valley Vision
Optimised Substation Monitoring – DNO Operational
Requirements
4.1
Availability of Data
The existing low voltage network is operated predominantly on a reactive basis, with
depot teams responding to requests for new supplies, or responding to incidents,
typically where customers have experienced a loss of supply. Any data relating to the
network that supports these existing priorities is valued. Statistically, only a small number
of substations or low voltage feeders are involved in such issues in a town the size of
Bracknell in any given period, and it follows that the number of locations where direct
operational benefit is gained from substation monitoring is also small. The locations of
interest on a given day are typically unknown in advance, and it is therefore not possible
to target the installation of monitoring on this basis.
Streamed data in particular is very vulnerable to communications difficulties (e.g. poor
GSM signal strength), as this results in gaps in the data. This is not ideal, but experience
to date, where users are unaccustomed to receiving any centralised data, this has not
proved to be a problem. It might be anticipated that expectations will rise as users
become more familiar with what data is routinely available and how they can use this
data to operate the network in a manner that brings the greatest benefits to customers.
4.2
Summary of Data
A summary of the types of analogue available at each substation is included in
Appendix 7 Schedule of Available Substation Analogues. A summary of the data
collected from each substation since the date of installation (January 2013 to March
2013) can be seen in Appendix 8 First 100 Substations – Operational Data
Overview. This schedule records, for each substation, and for each analogue, the
highest of the high values, the lowest of the low values, and the mean of the average
(RMS) values. Energy values are cumulative and for these the most recent (highest
value) is logged in the schedule. For each analogue type, rules were set that resulted in
individual values being highlighted. In this schedule highlight colours based on the
following rules were applied:
Page 21
SRDC 9.2 (d) Evidence Report
Voltage
Harmonic
Content
Harmonic
Content
SSET203 NTVV
New Thames Valley Vision
254 < V < 216
> 5%
>8%
Reactive Energy
>10% of Energy +ve
Energy -ve
>0
Maximum
Demand
Maximum
Demand
Alarm Count
>100%
>130%
>0
Table 1 Colour Key for Analogue Thresholds
The objective of the highlighting was to give immediate visibility to those substations or
feeders that have operated outside of thresholds of interest, and to allow those
substations or feeders to be investigated further. In aspects where large numbers of
substations or feeders are highlighted, further consideration was given to how many
phases (one, two or all three) were showing the same characteristic. A single minor
breach of a threshold was seen as less important than a large breach involving two or
possibly all three phases. This was about prioritising the effort of investigation of a
potentially large count of substations or feeders.
A statistical analysis reveals that of 100 substations (with 382 feeders in total) monitored:
Page 22
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Busbar – Thresholds Exceeded Count
L1
L2
L3
Average
Voltage greater than 254 volts
6
16
9
10
Harmonic Content greater than 5% or 8%
56
53
45
51
Reactive Energy greater than 10% of real energy
40
26
29
33
Energy (-ve) greater than zero
20
17
20
19
Maximum Demand greater than 100% or 130%
15
18
18
17
Feeders - Thresholds Exceeded Count
L1
L2
L3
Average
Reactive Energy greater than 10% of real energy
91
96
126
104
Energy (-ve) greater than zero
85
89
85
86
Maximum Demand greater than 100% or 130%
29
23
30
27
Table 2 Count of Analogue Limits Exceeded During First 12 Months of Operation
The investigation into each substation or feeder with highlighted analogues started with
an assessment of the data recorded in Pi Process Book which records all the actual
values in the period of analysis (January 2013 to January 2014). If this revealed that the
excursion was “seldom” and in the first few months of the period under review, then little
further investigation is justified. On the other hand, if the excursion was seen to be
“frequent” and “recent” (within the last month or two of the period under review), then the
justification to pursue the investigation further was made. It was then appropriate to
assess if there was a pattern (e.g. did the excursion happen at the same time each day
or week? To what extent? On one or all phases simultaneously?)
It was possible for any substation or feeder that the data was corrupted due to
communications problems, equipment or sensor malfunctions, or manual errors
introduced in the early stages of the installation and commissioning works. All such
issues were resolved where possible, but the data stored prior to the point of resolution
is vulnerable to misinterpretation. Clearly it was important to distinguish substations and
feeders from further investigation where the problem could be quickly and directly linked
to the monitoring process or equipment; in this case it followed that the monitoring
process and equipment became the subject of the investigation and the issues had not
already been identified and resolved.
Page 23
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
High Voltage was seen to occur at approximately 10% of substations at least once
during the 12 month period. Note that this refers to high voltage at the substation and
not to any customer’s supply point (to which the Electricity Safety Quality and Continuity
Regulations apply). In one example (Cannon Hill substation), the circumstances were
analysed in detail. It was found that the transformer was operating on the wrong tap
position; this was promptly corrected by the local depot team. Further details can be
found in Study 1 in Appendix 9 Operational Uses of Substation Monitor Data – Case
Studies.
High Harmonic Content occurred at 51% of substations. Historic data about harmonic
content is limited to specific sites investigated for very specific reasons, so it is not
possible to make any comparison. Closer inspection has revealed that the occurrences
of high harmonic content are mostly at times when the load on the network is less (i.e.
over night). It is suspected that this may be caused by street lighting, and the effect on
the voltage is seen as the non lighting load on the network is much less.
Large quantities of reactive energy may be expected at substations feeding non
domestic customers. This would be expected where there are large motors or significant
quantities of traditional ‘low-frequency’ fluorescent lighting; in an area like Bracknell it is
likely that this is linked to air conditioning and catering type equipment. The proliferation
of switched mode power supplies may also result in reducing power factors as the
current draw from these devices is often smoothed through the use of reactive
components.
Real Energy (-ve) is a measure of embedded generation on the low voltage network
feeding back from the feeder into the substation busbars, or from the substation busbars
through the transformer into the high voltage system. In small quantities, and with no
adverse impact on voltage anywhere on the network, this is not a problem. However, as
the take-up of solar panels and other embedded generation technologies grows, this
parameter is likely to grow and may be an important indicator of the feeders or
substations where high voltage related problems are likely to be encountered first,
particularly at remote ends of a feeder. An example is analysed in Study 14 in
Appendix 9 Operational Uses of Substation Monitor Data – Case Studies.
Page 24
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Maximum Demand is calculated by dividing the maximum current measured by the
feeder or transformer rating in amps. Correct calculations require correct fuse size and
transformer rating information to be loaded into the database. Large overload concerns
may reflect lag in updating the database following a recent reinforcement project, rather
than a true overload. Most distribution transformers in the SEPD area are designed to
withstand a 30% overload on a cyclic basis, and transformers loaded in the range 100%
to 130% may be completely satisfactory if the nature of the loading is within the cyclic
capability. The concern at these sites is the extent to which the loading will further
increase, and clearly there is no headroom left to pick up the load from electric vehicle
charging if this coincides with the periods when the load already exceeds 100%.
Having full access to the maximum demand information on a continuous basis allows
considerably greater analysis of the actual loading compared with the quarterly reads
that may be expected from traditional maximum demand indicators (MDIs). An example
analysis is given in Study 8 in Appendix 9 Operational Uses of Substation Monitor
Data – Case Studies.
4.3
Uses for Streamed Data
Streamed data is very onerous to retrieve from site in terms of site monitoring equipment
functionality, communications systems, head end processing and storage. Ultimately
these aspects manifest as costs, and it follows that a DNO would expect to avoid
drawing on streamed data unless it is actually very valuable.
Operational staff find access to actual real time current and voltage values invaluable in
understanding the operational status of the network. Benefits include:
Supply Restoration

Provision of immediate information for decision making

Provision of immediate information to keep customers informed

Records of actual times when faults occurred, and supplies restored
Network Abnormalities (Voltage or Power Quality)

Quick initial assessment of the state of the network

Provision of immediate information to keep customers informed
Page 25
SRDC 9.2 (d) Evidence Report

SSET203 NTVV
New Thames Valley Vision
Records of actual times when issues occurred
Operations (Construction or Maintenance)

Quick initial assessment of the state of the network at the time (eg linking for a
shutdown)

Records of actual times when trends show that operations can or cannot be
carried out.
Control of Network Management Tools

Accurate real time information about current and voltage may become crucial to
the control of energy storage (See Section 7 Next Steps).

Accurate real time information about current and voltage may become a trigger to
activate some form of Automated Demand Response (See Section 7 Next
Steps).
4.4
Uses for Periodic Data
Periodic data is derived from original measured values in the monitoring device, and for
a half hour period, only 48 values are created, transmitted, managed and stored in a 24
hour period. From a data management point of view, there are 360 streamed values for
every periodic value; clearly periodic values are less onerous to handle and the marginal
cost of each analogue is considerably less than for a streamed value. Benefits of
periodic values include:
Operational Use

Records of “maximum” or “minimum” values can be easily assessed for
significant periods (days, weeks, months, and possibly even years). This may be
helpful in building a “big picture” view of the network performance to support
decisions such as the suitability of a backfeed, or generator sizing for planned
works.

Speed of access to data for large periods. To make operational decisions,
operational users have to strike a balance between seeking more accurate
information (e.g. going to site to take readings of current or voltage) compared to
relying on experience, intuition and judgement. The quicker they can access real
Page 26
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
trustworthy information (from monitoring equipment) the more likely they are to
follow the more objective approach to decision making.
Network Planning

Accurate and detailed historic loading information from monitoring, particularly
maximum and mean currents can be used to inform planners as they respond to
requests from customers requesting new connections and connections of
increased capacity.

As for operational users, speed of access to data for large periods will bring a
benefit. To make planning decisions, planners have to strike a balance between
seeking site based readings or employing more expedient heuristic techniques.
The quicker they can access real trustworthy information (from monitoring
equipment) the more likely they are to follow the more objective approach to
decision making.

Power quality information such as voltage harmonic content can be drawn upon,
both to assist in initial decision making (e.g. permitting a particular load to be
connected) and also in assessing the consequence (e.g. observing that
harmonics have not exceeded an agreed threshold). The monitoring system
provides good quality information of “before” and “after” that can be used to
objectively inform commercial and technical discussions with customers.
Customer Service

Enquiries from customers are diverse, including requests for new connections,
concerns about their own service (load or voltage), or concerns about the
performance of the network. The availability of comprehensive periodic data can
allow the DNO to respond objectively, and if a network performance issue is
identified, this can be addressed in an informed manner, in line with other agreed
procedures.

Speed of response is crucial in meeting customer expectations and in complying
with agreed procedures. Having access to a database that already contains the
relevant periodic analogues is much quicker than visiting site for each request,
deploying localised monitoring equipment for a week, then returning to site to
recover the equipment, and downloading the data.
Page 27
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
For the NTVV project, values such as energy (all four quadrants of real and reactive
energy) are being recorded. They will be used within the project to support project
aspects of “Forecasting” and “Aggregation”. It is possible that an outcome of these
investigations is that a DNO requires these analogues for future use, but at this stage
operational and planning users have not shown any interest in these parameters.
4.5
Uses for Alarms
The traditional mechanism of alarms on the low voltage network is a phone call from a
customer informing the DNO that their “lights had gone out” or that they were suffering
from some other problem such as “an intermittent supply”. Protection is predominantly
provided with fuses, and there is no electronic relay or communications functionality
provided at distribution substations.
The installation of distribution substation monitoring equipment inherently provides an
electronic relay (with ability to compare real measured electrical values with
predetermined thresholds), transmission of data via GSM or UMTS (a means to transmit
alarms back), and a head end system (Distribution Management System) to receive,
display and process for storage large quantities of data. It follows that if the infrastructure
and costs are justified by the value of the substation monitoring data, then the additional
cost of providing alarms can be thought of as negligible. In practice, allowance must also
be made for managing alarms (where are they displayed, who is responsible for
responding to them, how are they cleared etc).
Alarms Available
The following alarms were established at the time of commissioning the first 100
substation monitors:
Feeder High Current (80% of fuse rating)
Feeder Low Current
(5% of 100A, ie 5A)
Busbar High High Current (120% of rating)
Busbar High Current (95% of rating)
Busbar High Voltage (110% of 230, ie 254 Volts)
Busbar Low Voltage (94% of 230, ie 216 Volts)
Busbar No Volts (5% of 230, ie 11.5 Volts)
Page 28
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Observations
For the project to date, only one user has been reviewing alarms in detail, and this role
has been focussed on ensuring that the alarms were functioning correctly in line with
expectations, and this has taken place during working hours only. As other users gain
access to alarms expectations will evolve, and this will influence policy, procedure and
training recommendations accordingly. For now, observations are linked to functionality.
Busbar – Alarm Count
L1
L2
L3
Average
High Voltage Alarms
26
38
38
34
Low Voltage Alarms
3
5
6
5
High High Current Alarms
2
3
2
2
High Current Alarms
8
3
4
5
Table 3 Count of Alarms Triggered During First 12 Months of Operation

Operation – alarms are communicated on an “unsolicited” basis (i.e. alarms are
transmitted by the site equipment without waiting to be polled by the front end
processor (FEP)). Alarms are received correctly and in a timely manner during
normal system operation. The absence of sufficient GSM or UMTS signal
strength will clearly hold up the transmission of the alarms; this is not critical for
the project, but if they become relied upon to support a business process, then
this could completely change expectations of the communications system
deployed.

Response – as above actual receipt of alarms in real time (in practice, within a
minute or two) of an event occurring, is possible most of the time, but may not be
all of the time. The real question is whether this is actually necessary? Apart from
alarms that indicate a loss of supply, little benefit is gained from trying to respond
immediately. For example, if two or three high voltage alarms came in overnight
from two or three different substations, and the recorded voltage is seen to be
one volt over the threshold, value is gained by recognising the pattern of what
has occurred (e.g. are all substations fed from the same high voltage feeder,
timing of alarms consistent, extent of excursion etc), and then despatching a
team to investigate on site in an informed manner a day or two later (this
scenario could be the result of a defect in high voltage control scheme). Alarms
Page 29
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
draw attention to abnormal network values, but do not necessarily require an
emergency response (analogous to HV switching carried out in response to HV
alarms).

Volume of Alarms – experience of operation for the first 100 substations quickly
indicated that many feeders operate frequently in the current range zero to 5
amps. 5 Amps is the minimum value that the GE DGCM monitor can respond to,
and the consequence was that there were large numbers of low current alarms
that provided absolutely no value to the project team or other prospective users.
The original objective was to receive an alarm when a fuse had operated by
establishing that the current was (or very close to) zero. 5 Amps was perceived
as an acceptable compromise, but the learning outcome is that typical low
voltage network feeders operate to very low loading levels and any measuring or
alarming capability should take this into account. Low current alarms have now
been disabled to avoid burdening the project systems and users with unwanted
information.

Real Network Issues – actual alarms (e.g. high voltage) do raise concerns that
the network is not operating correctly and give a reason to investigate further and
resolve actual network problems BEFORE the problem manifests itself as a
larger problem that actually causes a problem to the customers connected to the
network. Such issues can typically be resolved in a planned manner during
normal working hours, rather than in a reactive manner following a complaint
from a customer, typically involving higher costs due to out of hours working and
extensive effort required to keep the customer informed.

Alarm Development – recognising that a fuse has blown by means of an alarm
would add immediate benefit to existing depot users and to the network
management centre. Provision of this type of alarm is not a primary requirement
of the project, and is constrained by the equipment used (5 Amp small current
limitation) and the inability to make a voltage connection to the feeder cables on
the outgoing side of the fuses. It is hoped to develop and refine a virtual alarm
that can be calculated in the DGCM monitor that assesses the neutral current
and provides a reliable indication. Other similar ideas may evolve as depot users
start to become familiar with what is possible.
Page 30
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Further comments on the experiences gained with the provision of alarms on the low
voltage network are given in Appendix 10 Strategy for the Use of Alarms on the LV
Network.
4.6
Local Depot Review of Data
The Slough Depot team that are responsible for the low voltage network in the Bracknell
area have been given access to the substation monitoring data, as well as the summary
of data seen in Appendix 8 First 100 Substations – Operational Data Overview. As
an operational team, they are familiar with PowerOn Fusion, so access to the system is
not an area requiring significant learning, but access to the data and recognising when
they can benefit from exploring the data around their business as usual responsibilities is
a new area of focus. To support the process a project team representative has been
working in the Depot Environment on a weekly basis, both to help the Depot team when
they take interest, and also to understand their day-to-day issues with a view to leading
them to the data that can answer their queries when they have not already thought to do
so.
Specific analysis and observations are recorded in Appendix 9 Operational Use of
Substation Monitoring Data – Case Studies.
4.7
Recommendations for Future Substation Monitoring
Taking into account the practical possibilities and limitations of equipment,
communications and systems, and consideration of the benefits obtained by existing
users and prospective users, the recommended substation monitoring for future
deployments would be as follows.
Choice of Locations to Monitor
Operational users of monitoring data (including alarms) have an inevitable desire (if not
expectation) to have data from all distribution substation sites. Justification for
deployment at all sites cannot be made from the observations, experience and trials to
date (based on 12 months’ worth of data), but logical suggestions are:
Page 31
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Deploy monitoring at substations where:

New substations are built to provide new supplies to customers as these have
uncertain loading (and the marginal cost of monitoring equipment pre-fitted on
new substation plant would be assumed to be less than for retrofitting);

Existing substations with known problems, particularly relating to load, but not
necessarily significantly overloaded on three phases;

Existing substations feeding a very dynamic customer base (those where
customers frequently change), more likely in non-domestic, or mixed use areas

Existing substations where the take up of low carbon technologies is already
advanced or forecast to be problematic
This combination of substation sites will give a DNO the operational data that it needs
most of the time, without the volume of data and associated costs for monitoring all
substations.
Requirement for Streamed Data
The benefits of streamed data are clear at the time that they are called upon, but for
most substations (statistically) no problems occur for many years and the
disproportionate communications and data storage costs outweigh the benefits for most
sites. A suggested deployment strategy with regard to streamed data would be as
follows.

Modify substation monitoring device to store a rolling one month period of
streamed data at the device, and configure the FEP to retrieve this data only
when requested.

For those substations where there are known ongoing operational issues, turn on
the streaming;

For those substations where energy storage or ADR calls upon streamed data
directly turn on the streaming.
The default arrangement should be that streaming is turned off, but that any substation
can have it turned on, and the rolling month’s worth of data held in the device can be
retrieved if required following a fault, planning requirement or query from a customer.
Page 32
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
Requirement for Periodic Data
Periodic data is very efficient at conveying useful information to operational and planning
users. By definition, if monitoring is deployed then this is the data that would be
expected to be retrieved from site by default. The choice to be made is more about
which analogues provide benefit, and which ones do not. The suggestion is as follows.
Analogues that should be monitored:

Feeder current analogues (maximum, mean and minimum)

Busbar current analogues (Maximum and mean)

Busbar voltage analogues (maximum, mean and minimum)

Busbar voltage harmonic content
The justification and benefit from energy values has yet to be established (for use
beyond the project), and if required, are more likely to benefit the modelling of the
network to support medium term and longer term planning and investment decisions.
Requirement for Alarms
Of the alarms available to date the following alarms would be expected to be available in
a future deployment.
Busbar high voltage
Busbar low voltage
Busbar high high current
Busbar high current
Feeder high current
The identification of a blown fuse is a fundamentally desirable notification that would be
valued as an alarm, but is not directly available since this would require additional
voltage measurements on the outgoing side of each fuse. Further analysis of neutral
currents and the establishment of an alarm based on a neutral current threshold being
reached is to be investigated for radial LV circuits.
Further “virtual” alarms may be derived in PowerOn Fusion for the benefit of users by
recognising the features of other analogues. Examples include:
Page 33
SRDC 9.2 (d) Evidence Report

SSET203 NTVV
New Thames Valley Vision
Phase to Phase Fault (one or two phase currents drop to zero, while another
phase current increases)

Current Sensor Failure (half hour mean, maximum and minimum currents
converge on same value for two or more consecutive half hours)
These will be further investigated during the remainder of the project.
Page 34
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
5
Optimised Substation Monitoring – Virtual Monitoring
5.1
Virtual Monitoring (Buddying and Aggregation)
By successfully categorising customers, buddying and aggregating demand profiles, it is
anticipated that a DNO will be able to model the load on the low voltage network using
only very small amounts of real monitored data. The applicability of buddied profiles to
individual customers in the absence of real (smart meter type) data has been established
(see SDRC9.5(c) Evidence Report). It was then necessary to aggregate the data at the
feeder level, and in particular, to compare the predicted power flow characteristics
against substation monitoring data. A close correlation between aggregated data (virtual
monitoring) and real monitored data suggested that less real monitoring may be
required.
Further consideration is being given to how far virtual monitoring could reduce the
necessary number of substation monitoring points and estimate the optimal location of
the remaining monitoring points.
5.2
Comparison with Real Substation Monitoring
To be able to emulate aggregated energy profiles on the low voltage network a
number of steps have been followed:

Feeder identified for analysis

End point data obtained from the 250 monitored customers (where the data was
successfully transmitted)

For each customer on the selected feeder the Least-Error Matching algorithm
was run. This was developed at the University of Reading, producing an
allocation of a buddy to each customer. This is described in detail in Section 3
of Appendix 11 University of Reading Feeder Demand Predictions.

The energy profiles allocated to each customer were run through the network
modelling environment (NME) to calculate the total energy profile of the feeder;

The last stage was to use the figures to calculate the error in the model as well
as comparing to real substation monitoring.
Page 35
SRDC 9.2 (d) Evidence Report
5.3
SSET203 NTVV
New Thames Valley Vision
Reducing Monitoring at Substations
It was expected that if the buddying and aggregation is successful (accurate enough to
allow a DNO to make relevant decisions), then buddying may be thought of as “virtual
monitoring”, and for those feeders where buddying has been applied to end points, less
substation monitoring may be required. To study this it was necessary to:

Assess the error measures of the buddying technique against actual substation
monitor energy readings to assess the accuracy of the buddying.

Assess the stresses on the network under the buddying scenario and suggest
roughly how accurate the buddying needs to be in order to be sure of whether or
not there are network issues.

Compare the buddying accuracy with the accuracy required.

Identify any patterns in which properties and/or feeders are potential issues.
The analysis carried out to date can be seen in Section 4 Reduction in the necessary
number of network monitoring points of Appendix 11 University of Reading Feeder
Demand Predictions.
5.4
Recommendations for Future Substation Monitoring
Only three substations have been considered to date. The buddied profiles created have
been successfully loaded and run in the network modelling environment, and
demonstrated that when simulated increased loading scenarios are applied, network
excursions can be successfully revealed. Assessment of buddying accuracy has not
revealed conclusively the scale of reduction in monitoring that could be achieved.
It remains expected that for some networks the buddying will be sufficiently good and the
network headroom known to be significant, in which case substation monitoring
equipment will not be required. On the other hand, some substations are very
unpredictable, in which case buddying is less likely to be successful; such substations
may always need to be monitored.
Page 36
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
6
Conclusions
6.1
Convergence of Recommendations - Location
For Substation Categorisation no recommendation can be made at this stage
regarding location as no correlation between feeder demand and customer criteria have
been identified to date. Some correlation may become apparent following further
analysis with much more data throughout the project.
The main recommendations from DNO Operational Requirements with regard to
choice of location for monitoring were:

New substations built to provide new supplies to customers;

Existing substations with known problems, particularly relating to load, but not
necessarily significantly overloaded on three phases;

Existing substations feeding a very dynamic customer base (those where
customers frequently change), more likely in non-domestic, or mixed use areas

Existing substations where the take up of low carbon technologies is already
advanced or forecast to be problematic
Assessment of Virtual Monitoring has started and will continue for large quantities of
substations throughout the project. Buddying and aggregation of customers’ energy
profiles has been seen to work, but the judgement of precise criteria for when real
monitoring is required cannot be made at this stage. The generalised expectation that
substations regarded as predictable and with reasonable headroom available may not
require monitoring, and those where the demand is volatile and close to limits will require
monitoring does remain reasonable.
The anticipated monitoring requirements of based on Virtual Monitoring are convergent
with the proposed monitoring requirements for DNO Operational Requirements. For
the NTVV project all 325 available substation monitors have been committed to
substations predominantly with the greatest count of feeders; this approach has been
adopted to obtain the greatest amount of data using the available resources, and this will
help to validate the recommendations made at the end of the project.
Page 37
SRDC 9.2 (d) Evidence Report
6.2
SSET203 NTVV
New Thames Valley Vision
Convergence of Recommendations – What to Monitor
Streamed Data
In terms of DNO Operational Requirements, Operational users will always have a
preference for streamed data (eg current, voltage and power) being fully available but
the costs and resources required to do so are substantial. The suggested strategy was
that monitoring equipment installed should have the capability for streaming built in and
available to turn on, but this would be turned off by default except in certain
circumstances such as specific operational needs (eg faults), energy storage control,
and possibly ADR.
The analysis of Substation Categories and Virtual Monitoring do not explicitly draw
on streamed data and, when completed later in the project, are not expected to inform
this recommendation.
Periodic Data
The analysis of Substation Categories and Virtual Monitoring both draw on half
hourly real and reactive energy values, and for every site where monitoring is installed, it
would be expected that these values are available.
The provision of current and voltage analogues (maximum, mean and minimum) for
feeders and busbars is fundamental to DNO Operational Requirements, providing
benefit to operational and planning users. Other calculated values such as energy and
harmonic content provide a more selective value, but can be provided for negligible extra
resources, given that the primary currents and voltages are already being monitored.
Alarms
Alarm functionality is generally inherently available in monitoring systems, and
appropriately managed current and voltage alarms for feeders and busbars provide a
helpful management tool for DNO Operational Requirements. Added value can be
gained by further establishment of virtual alarms. The real challenge is to identify the
most appropriate strategy for which alarms to respond to in real time, and which ones to
assess purely on a retrospective statistical basis.
The analysis of Substation Categories and Virtual Monitoring do not require the
provision of alarms.
Page 38
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
7
Next Steps
7.1
Project Substation Monitoring Installations
Following the initial assessment of substation monitoring data by the University of
Reading team in July 2013, as recorded in Appendix 5 University of Reading Initial
Analysis of First 100 Substations Where Monitoring is Installed, it was agreed that
targeting substations with larger numbers of customers and feeders would provide the
greatest benefit to the project. With this in mind a second batch of 110 sites were
identified, substation monitors ordered and preparations made for installation. These
installations are being carried out in parallel with this optimisation review.
One of the learning outcomes of the first batch of installations was that practical
difficulties including low signal strength could be expected to affect 10% of sites; to
provide effective complete data from 100 sites required an overpopulation of 10 further
sites, bringing the total to 110 sites. The total number of substation monitors supplied is
now 220 with just over 200 installed to date. In the light of the Substation Categorisation
analysis (random sampling requiring a monitoring installation count of 294 sites to
achieve a confidence level of 99%), the total project allowance of 325 monitors will be
deployed. This should provide confidence that quality continuous data can be obtained
from approximately 300 installations; for those sites where communication difficulties
prove impractical or uneconomic to resolve, those devices can be relocated to other
unmonitored sites to optimise the availability of data for the project.
The learning gained during the first batch of 100 installations in various aspects has
informed the installation process for the second batch. Examples include the mounting
arrangements of the antennas (detached from enclosures and mounted as high as
possible), setting the resolution of the voltage analogues to 0.1 volt, and ensuring that
the periodic values could be polled such that only the required 48 values are transmitted
per day. These aspects will now be applied retrospectively to the first batch of substation
monitors to improve the quality of the data.
For the project the primary objective is to ensure that the data from all selected
substations is measured correctly and transmitted completely, and so far as possible, the
Page 39
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
operation of devices on site and the PowerOn Fusion and Pi Process Book systems are
operated as efficiently as possible to the end of the project. It is possible that as learning
is derived in other aspects of the project (eg the deployment of energy storage devices)
new requirements for monitoring may be identified (eg location, type of data, resolution
of data etc) and it is equally important that the systems are managed with a degree of
flexibility to allow for these new needs to be assessed and fulfilled with the available
resources where possible.
7.2
Refinement of Recommendations
There are many operational uses for the substation monitoring data and the DNO
Operational Requirements section details the possibilities and recommendations.
The analysis of Substation Categories has been unable to show a strong correlation
between energy demand at the substation level and customer attributes; there is a clear
requirement to adopt a random sampling technique, and this requires approximately 200
monitors to be installed in addition to the 100 monitors installed for the initial
assessment. The data from a much larger quantity (approximately 300 sites) operating
over a longer period (up to 3 years) will allow any correlation to be fully assessed, and
recommendations made.
To date, operational users have had access to the substation data under supervision of
the project team. Operational users need time to make their own discoveries with the
data and to grow their confidence in using it. An ongoing programme of hands-on use,
combined with targeted discussions with the project team is planned for the next few
months to test the actual benefits of the data, and to test the recommendations
regarding the locations where the greatest benefit from monitoring is derived.
Virtual Monitoring has been demonstrated to work, but considerably more studies are
required to be run and assessed before conclusions can be drawn about the confidence
with which virtual monitoring can be used in lieu of real monitoring. Further studies will
be run with the remainder of the existing 100 substation monitor sites (only three have
Page 40
SRDC 9.2 (d) Evidence Report
SSET203 NTVV
New Thames Valley Vision
been used to date), and also the 200 additional substation monitors currently being
installed.
These next steps will allow the recommendations to be refined and to establish a
methodology for determining the number and distribution of substation monitoring for
maximum benefit to a DNO.
Page 41
SRDC 9.2 (d) Evidence Report
8
SSET203 NTVV
New Thames Valley Vision
Appendices
Appendix 1
University of Reading Categorisation of Substations
See separate document attached.
Appendix 2
University
of
Reading
Selection
Procedure
for
Substation Monitor Locations
See separate document attached.
Appendix 3
Substations Selected
See separate document attached.
Appendix 4
Data from Substation Monitoring – Pi Process Book
See separate document attached.
Appendix 5
University of Reading Initial Analysis of First 100
Substations Where Monitoring is Installed
See separate document attached.
Appendix 6
University of Oxford 12 Month Analysis of First 100
Substations Where Monitoring was Installed
See separate document attached.
Appendix 7
Schedule of Available Substation Monitoring Analogues.
See separate document attached.
Appendix 8
First 100 Substations – Operational Data Overview
See separate document attached.
Appendix 9
Operational Use of Substation Monitoring Data – Case
Studies
See separate document attached.
Page 42
SRDC 9.2 (d) Evidence Report
Appendix 10
SSET203 NTVV
New Thames Valley Vision
Strategy for the Use of Alarms on the LV Network
See separate document attached.
Appendix 11
University of Reading Feeder Demand Predictions
See separate document attached.
Page 43