Download Knowledge base approach in relation to risk

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Session 2004
© CIGRÉ
Knowledge Base Approach
in Relation to Risk Management of Distribution and Transmission Assets
PH.WESTER∗
NUON
E.GULSKI
TU Delft
THE NETHERLANDS
E.R.S.GROOT
NUON
I.RING
ESBI
IRELAND
Present asset management processes are going in the direction of risk management. Risk management
expresses the relation between consequences/acceptance in function of probability of occurrence of
supply failures. The translation from accepted risk levels into activities necessary to maintain the
supply function through the upkeep of the asset performance is thus of extreme importance. Methods
to measure the quality/performance of assets strongly depend on the information with respect to the
specific asset type. This would be maximal if the relevant data per asset type is shared by the owners.
Application of data-mining techniques are than practiced to gain knowledge from a growing amount of
asset type information in order to interpret future asset behaviour and performance properly.
Key-words: Asset management – Risk strategies – Knowledge sharing – Data mining – Performance
measurement
1.
INTRODUCTION
The asset management strategy promises to achieve the optimum balance between investment return
and other stakeholders’ values as availability and environmental load. Risk decisions are based upon,
at the one hand, the judgement of the acceptable risk and at the other the (assumed or predicted)
performance of the asset “string” concerned. To be informed about the related assets performances
condition based maintenance is applied intensively. A FMECA (failure mode effect and criticality
analysis) based approach that, if designed correctly, supports a standardized and human independent
environment for the execution of condition measurements and maintenance. The approach also
provides data that, if analysed properly, give insight in future asset performance.
The Delft University of Technology, High Voltage and Management group together with her partner
InfraCore designed an expert system capable of analysing condition data. It is obvious that
maximization of asset type data population strongly improves the quality of the analysis mentioned.
The designed expert system has this capability of gathering a large amount of (measured or registered
condition and fault data representing the actual or historic performance of the asset concerned on a
type and piece basis. In order to maximize the relevant data a business model to create an environment
where other utilities support by the disposal of relevant data was implemented and checked via a world
wide executed survey. Stored historic and type data are run through a data mining process thus
generating new knowledge. The approach can as such be considered a knowledge based, even ISO
certificated, system supporting the risk analysis process of asset management divisions.
2.
RISK MANAGEMENT MAINTENANCE
Risk management is generally characterized by assessing the consequences for all stakeholders, not
only the more result driven shareholder. The secret of a well designed and applied risk management
process lies in finding the optimal balance of value added versus risk taken between shareholders,
regulation, employees, consumers and society. It will thus depend on decisions upon defining the
∗
Ph.Wester, NUON, [email protected]
results in financial, quality, environmental and strategic point of view. Compared to the often used
concept of reliability centered maintenance (RCM), risk management also means a process of a
continuous improvement cycle. Based upon the risk analysis, control measures, execution of actions
and evaluation and actualization improvement steps are taken in a more or less continuous process. It
implies that decisions are not only based upon realizing the reliability level agreed or profit promised
but that all aspects are judged including the measures that limit the risk relevant for a combination of
economical, technical, environmental, customer and employee values.
impact assessment
3.
RELIABILITY OF POWER SYSTEMS
As the issue of risk management is a constant and complex theme for a transmission grid asset
manager. The Cigre brochure on principles for risk assessments [1] poses that the best way to manage
is the separation of the observations and studies into a system approach and a component approach
with reliability management as linking process. This approach eases the handling of data, distinguishes
between the “system risk” and the “asset failure mode” part and guarantees the interaction between
supply demand and decisions regarding executing of (maintenance) activities. Cigre JWG B3/C2-14
shows in her publication on “Information strategy for asset management” [2] that the asset manager
basically applies a step by step decision process as shown in figure 1. The decision process again is
separated into a (network) risk and a
(component) condition orientation with
Decision
corporate level
reliability management as the link
between both.
Risk management
Based on the information available
system level
(external, financial, asset information
Safety
Reliability management
source) different “impact” assessments
Social
asset level
System
are executed. The result: an optimum
balancing of the different variables and
S3 Risk assessment
Condition assessment
alternatives, leading to the best
solution given the situation.
Reliability management aims to
Societal information Financial information
Asset information
identify the processes of degradation,
while describing and quantifying their
effects on the reliability of the
figure 1: risk decision process JWG B3/C2-14
components of a system. It is, in other
words, an approach to set the (expected) condition of the system elements into a financial consequence
setting. Too weak results can concede into a change in design of the system as well as a replacement
of a component or another maintenance interval applied. Taking into account societal information one
can ultimately speak from risk management. Items like environmental and personnel safety as well as
consequences at social/strategical level is taken into account at this level.
The described impact assessment approach necessitates three elementary types of information:
1. feedback from corporate level: analysis reports of effects applied investment and upkeep activities
on corporate “strategic” level related to stakeholder satisfaction
2. feedback from system level: information on interruptions, quality of the supply to customers,
reports on environmental consequences, energy not supplied
3. feedback from component level: information on equipment failures and measured condition, in
order to maintain the network in good operating condition.
4.
RISK ASSESSMENT APPROACH
Where figure 1 covers a more or less theoretic model of a risk decision process a more practical format
for risk assessment is visualized in figure 2. Both the risks accepted at network level as the
performance at component level are addressed. The left matrix can be considered as representing the
consequences of a certain risk at network level, the right as the related activity depending on the
performance of the asset or circuit concerned. The two matrices are linked via the consequence
judgement “vital”, “critical” or “non-critical”.
2
figure 2: Matrices visualizing both the risk accepted at network level and the performance at
component level.
The left matrix shows the probability of failures through the assessment of susceptability of a circuit.
Subjects like aggressive soil, distance to sea, influence of sand or ice, age of the circuit, redundancy
etc. are covered. By measuring this susceptability against classified consequences for (all)
stakeholders including penalties and constraint costs a non-critical, critical or vital situation is judged
and transponded to the risk axis of the right positioned matrix. Obviously decisions regarding
maintenance and/or re-investment priorities are consequences this judgement process. In line with the
previously mentioned Cigre publications the left hand matrix could be regarded as risk analysis
connected to the right, asset directed matrix through reliability management. The basic idea: translate
the network failure consequences for stakeholders into decisions at component level
5.
DATA MAXIMIZATION AND OPTIMIZATION
As shown in chapter 4 it is clear that data regarding the performance of assets/components are of
extreme importance for realizing the best possible decisions in a risk strategy. Apart from the
information regarding the consequences of a system failure it is the only controllable and steering
variable in the decision approach. An important difference with the system allowable risks is that
performance information is not only depending on the local situation. One can use information from
other assets as well, especially those assets of the same type and operational requirements, thus
decreasing the subjectivity part of this part of the decision process.
A maximum objective decision regarding asset performance is supported by broadly collected asset
condition information, analysed and translated into knowledge. For this reason the initiators of the iCore data bank stared a knowledge platform (KSANDR, knowledge sharing and research) with the
pure aim to store a maximum amount of measured condition data for analysis purposes. [6]
By making the platform and the database accessible for all interested participants responsible for the
management of maintenance and investment of high- and medium voltage equipment it is the intention
to maximize the relevant condition and performance data. As a consequence a far better judgment
could be taken by the participants with regard to risk based prioritization of maintenance. As shown in
figure 3 and stated earlier, unlike the local judgment process regarding acceptable risks (expected)
asset performance can be objectified by the maximization and sharing of condition data. Chapter 6
describes the further possibilities that this approach is given to the data mining process resulting in
new expert rules and efficiency increase due to experienced correlations.
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at disposal for other
utilities to maximize
data population and
improve decisions
new, independent,
non for profit
organization
operates platform
The ultimate goal: combining all practical,
design, scientific know-how and measurement
results in a data-base, thus creating an
independent knowledge platform in the field of
medium- and high voltage equipment.
Figure 3: knowledge sharing platform
growing knowledge,
applied analysis
i-
Data sources
6.
DATA MINING AND DECISION SUPPORT
In general the data mining involves the earlier stages of data selection and data transformation and the
subsequent stages of validation and interpretation [4]. Furthermore data mining aims to provide an
alternative to the traditional scientific method, where data analysis is largely directed by hypothesis
and theory. The aim of data mining is to find intelligible patterns which are not predicted by
established theory [6]. Formatting the output data in a visual form that human intelligence can
interpret is important. In particular evaluation existing experiences in data mining applications for
condition monitoring, including validity and relevance of results is of importance [4].
In the next sections, use of partial discharge data of
(A)
Knowledge
power cables is discussed in the scope of these two
Failure
rules
data
aspects. Partial discharges, because of their wide
variation with equipment type, and with electrical
Condition
configuration and material type, provide a very
Based
Component
Data-mining (B)
suitable vehicle for detailed data mining
Maintenance
data
approach
Activities
applications. In addition to the example given
relating to cables, partial discharges can be used as
Condition
the basis for data mining applications.
Norms &
data
criteria
(C)
The full potential of condition and maintenance
information cannot always be realized by using
traditional techniques of data handling and
analysis. There are often underlying trends or
features of the data that are not evident from the
usual analysis techniques. Such detail and trends can be important for the assessment of the equipment
operation and there are increasing demands from operators and asset managers to fully exploit the
capabilities of this data in order to optimise the utilization of high voltage electrical plant. The method
of extracting full value from such extensive databases, using new analysis techniques, is commonly
called data mining [6]. In its basic essence, data mining is the application of relatively novel datadriven approaches to find patterns in data obtained from electrical equipment. The data mining
techniques are then used to relate these patterns to the operational condition of the equipment and to
provide new knowledge about aging mechanisms, norms, and required maintenance activities. In order
to support the data mining process utility may apply the knowledge expert database in which all the
measured data and failure information is stored and analysed. The general features of this approach are
shown in figure 4.
This process provides three outcomes (A, B and C) of the data mining approach. Outcome (A) refers
to new knowledge about aging mechanisms of power cable components and outcome (B) to
recommended maintenance activities on power cable components, resulting from the database
analysis. Due to the large amount of measurement data stored in the software data system, operating
norms and criteria are continuously updated and fed back to workers in the field as determined by
result (C).
Figure 4: Schematic structure of data-mining
process for condition-based maintenance of
HV assets, e.g. power cables.
4
6.1 Examples of Insulation Condition Knowledge Rules
In [2] schematic example of interpretation rules for PD measurements on power cables are shown.
These are rules of thumbs supporting the analysis of the measurement results from a cable section.
Different aspects of the ‘fingerprint’ are used in these interpretation rules.
After measuring and analysing PD activity in a cable section, the second step is to make a decision on
the insulation condition of the tested cable sample. Using the measured PD quantities and their
interpretation rules (3) three condition classes can be derived from the analysis:
• cable section NOT OK: weak spot in the cable section should be replaced;
• cable section NOT OK?: trending on the cable component is required (e.g. 1 year 3 years);
• cable section OK: no weak spots in the cable section, cable section is OK.
Analysing the derived measurement data through a decision diagram, the cable systems insulation
condition can be determined in one of the four classes. The decision diagram is
Table 3: Indication of some typical PD levels for based on all PD quantities for measurements
different types of cable insulation and accessories on power cables. Not concentrated PD in the
cable insulation is not an indication of aging
based on experiences from the Netherlands.
of the cable insulation materials. For PILC
Type
Trend values
Cable
related insulation, certain levels of PD can be
element
accepted in the cable insulation, joint and
PILC
10.000pC
insulation
termination, depending on the design of the
XLPE
<20 pC
component. Also, XLPE related accessories
Oil-joint
5.000pC
splices
could stand certain PD levels (generally lower
Type
1(resin 500pC
pC levels compared to PILC). However,
insulation
(asymmetric)
XLPE cable insulation is required to be PD
(oil insulation)
>10.000 pC
free (PDIV>1.3U0, PDEV>1.1U0), where the
(oil/resin
5.000 pC
background noise during the measurement is
insulation)
not allowed to exceed 20pC. The typical PD
Type 2 (resin 4.000 pC
values for the different components can be
insulation)
derived from the statistical analysis of all
6.000 pC
terminations Oil-termination
required measurement data in a database. As a
Dry termination
3.500 pC
result for particular components threshold
Type
3 250 pC
values can be derived to support the CBM, see
termination
table 3
6.2.
examples of data mining approach and norms
Statistical evaluations of diagnostic parameters e.g. PD level at the service voltage or PD inception
voltage (PDIV) are powerful means to determine the component typical values and the statistical
significance limits. Based on these parameters norms can be estimated. Based on a large population of
measuring data as obtained on one type of cable accessory the average PD level and the 95%
significant limits can be calculated.
In figure 5 for two different types of splice insulation the population of PD level at U0 is shown. It
follows from these examples that for resin insulated splices the average PD level is 2.4 nC and for oil
filed splices is 3.6 nC. Respectively, the significance limits are 4.7nC for resin-insulated splices and
8.2nC for oil filed splices.
As a result of the above mentioned examples it can be concluded that using large population of
measuring data, database selection of different insulation types result in sensitive distinction in
threshold values for decision support.
6.3.
examples of separation of construction effects and aging
Using large populations of PILC power cables and PD diagnosis provides support in separating the
aging processes and effects of different power cable insulation types.
As known, a PILC power cable is not always PD free and a certain level in the range of nC’s of PD
activity is accepted. Figure 6 shows an example of PD amplitudes as detected at U0 on a large
population of PILC cables. It follows from this figure that at U0 (service voltage) the typical (average)
5
-PD amplitude in PILC power cable insulation is in the range of 4.7 nC. As a result, cable section with
PD larger as 12.9 nC are significantly different from the cable population.
It has been observed that in contrast to mineral oil impregnated insulation in the case of synthetic oil
impregnated cables (manufactured after 1980) due to temperature/pressure effects (T/P) (load changes
or switching off the cable section) result in additional PD activities [3]. Large discharge amplitudes
characterize these PD processes and they are of temporary art and they do not indicate insulation
degradation.
These situation forces to take during data evaluating and condition assessment into account the
construction aspects, e.g. type of insulation impregnation. As a result, selecting in the database the
cables influenced by T/P effects shows that the typical parameters like the average PD and the
significance levels are changing.
(a)
(b )
Figure 5: PD levels as observed at U0 for PILC
database population of two different type of
cable splices; (a) splice type resin insulated with
the norm 4.7nC; (b) splice type oil filed with the
norm 8.2 nC
(a)
(b)
Figure 6: Database selection of all PILC power
cables showing PD activity at U0 level: (a)
definition of the database filter; (b) population of
cable section and the PD levels; norm 12.9nC
Figure 7 shows an example of the database selection of PD amplitudes as detected at U0 on a large
population of PILC cable only where the T/P effects have been observed. It follows from this analysis
that at U0 (service voltage) the typical (average) PD amplitude in PILC power cable insulation is in the
range of 9,3 nC. As a result, cable section with PD larger as 18 nC are significantly different from the
cable population. In order to obtain typical PD amplitudes in relation to insulation degradation, those
cables where no TP effect is measured should be obtained.
Figure 8 shows an example of the database selection of PD amplitudes as detected at U0 on a large
population of PILC cable, where the cable sections the T/P effects are left out the observation. It
follows from this analysis that at U0 (service voltage) the typical (average) PD amplitude in a PILC
power cable insulation is in the range of 3,7 nC. As a result, cable section with PD larger as 11 nC are
significantly different from the cable population. From these examples, it follows that as a result of
T/P effects, the norm level is influenced. If all PILC insulation is used for norm calculations, the norm
would be 12nC. The right norm level should be 11nC, obtained by filtering only the degradation
effects. This example shows that results of statistical analysis are sensitive to different construction
parameters. As a result, condition assessment and decision support need data-mining support, see
figure 4.
6
(a)
(a )
(b)
Figure 7: Database selection of those PILC
power cables showing PD activity at U0 level,
where the temperature/pressure effects have
been observed; (a) definition of the database
filter; (b) population of cable sections and the
PD levels; norm 18nC.
7.
(b )
Figure 8: Database selection of those power
cables showing PD activity at U0 level where the
temperature/pressure effects are not observed;
(a) definition of the database filter; (b) population
of cable sections and the PD levels; norm 11 nC
EXAMPLE OF DECISION SUPPORT
Figure 9: Example of using knowledge rules and database to
support the decision process of asset managers in making
distinction between cable networks with different insulation
conditions; the reed marked cable section shows increased
insulation degradation as defined by the knowledge rules.
Combining the component
data with the condition data as
obtained using diagnostic
inspections can be used to
develop decision support for
asset management. In figure 9
two examples are shown of
such decision support. Based
on the following information:
PD parameters as detected for
the particular cable type,
significance limits (norms) of
those cable section can be
selected (marked in red) from
the whole cable population
where statistical deviating
condition is observed.
Conform the figures 1 and 2 of
this paper this technical
information now can be further
combined to economic and
strategic decision processes as
defined by the asset
management of the power
utility.
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8.
CONCLUSIONS
Cost pressure on utilities’ will force in the near future the asset managers to apply risk strategies, often
resulting in more or less sophisticated condition based maintenance activities. Data resulting from
these condition measurements would be far better applicable for decision purposes if analysed against
a larger asset data population through an overall data mining analysis process.
Risk management processes are based upon the relation between risk acceptance estimation at system
level, being a decision fully depending on “subjective” local circumstances, and “objectified”
knowledge about (expected) asset performance. A possible approach to find this relation between
network risk and asset directed activities is set by applying a matrix approach. Stakeholders’ values
are assessed against the susceptibility for failures at circuit/system level and transposed to the relation
where the asset condition is investigated.
The latter judgement is strongly influenced by asset type based knowledge that can be generated if fed
by condition information from a maximum number of users of the specific asset type.
A generic applicable software system should be used to gather and store a maximum of condition data
providing there would be an independent operating organisation that offers the facilities for such a
platform. Such an independent platform does exist nowadays supporting real knowledge base
approach for risk management of distribution and transmission assets. The application of data mining
and analysis techniques on large amounts of data that this platform is able to generate offers many
possibilities for a better judgement of maintenance and investment priorities and actions related to real
practical risk management processes.
References
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[2]
[3]
[4]
[5]
[6]
[7]
[8]
[8]
[9]
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