- Email: [email protected]
A decision support system for electricity distribution network refurbishment projects
ELECTRIC POUIER SYSTEM RESEClRCH
Electric Power Systems Research 40 (1997) 27-35
ELSEVIER
A decision support system for electricity distribution refurbishment projects
network
Glare J. Oatley a, B. Ramsay a,*, A. McPherson b, R. Eastwood b, C.S. Ozveren c ’ Department
of Applred
Ph.vstcs and Electrome & Mechanrc~al Engmeermg, Unrcersrt!~ of’ Dundee. ‘Scottish Hydra-Electric pk.. Per-t/z, UK ’ School of Engineering, Uniwrsrt.v qf’ Ahertay Dundee, Lkmdee, UK
Dundee,
L/K
Received 21 June 1996: accepted 8 July 1996
Abstract The electricity distribution network in the UK is currently undergoing major refurbishment due to the expiration of equipment installed in the 1950s and 1960s during the large-scale expansion of the network. Regional Electricity Companies are attempting to minimise and control the expenditure required to comply with the distribution licence, to maintain a sufficient level of system reliability and balance the costs of refurbishment with those incurred in operating a sub-standard network. Scottish Hydro-Electric plc. has a five year plan for refurbishment of these networks which involves assessment to identify sections requiring attention followed by proposals to implement the necessary work within the objectives of network refurbishment. The planning engineer faces many pressures in making decisions to adopt the best scheme for a particular section of network and the paper seeks to show how decision analysis methods. in the form of current computer software, may assist him or her in assessing influencing factors and their effect on the outcome of the project. The paper discusses these factors primarily for overhead rural networks. and develops a set of variable and non-variable attributes, including costs, fault clearance, security, wayleaves, visual impact. political impact, etc., to form the basis of a decision
model, The application of Artificial Intelligence is introduced using a decision analysis package to produce a decision support system (DSS), codenamed PRODEX, and a computer mode1 is developed on a PC to demonstrate the feasibility of the DSS. A spreadsheet is integrated with the decision analysis through Dynamic Data Exchange to input. store, manipulate and output data. The Visual Basic programming language is used to demonstrate the possibilities for creating a customised, user-friendly front-end to the system. The final DSS shows how the interfaces between applications can be created to enable a customised system to be developed so that no specialised knowledge of the applications is required by the user. A case study is given in the form of a 33 kV refurbishment project in the Highlands and Islands region of Scottish Hydro-Electric plc’s system. The study considers the problems of a section of the network. and details four options proposed for improvement of the supply. The PRODEX system implementation is explained. along with its analysis of the options through the use of tools such as outcome distribution probability graphsand event sensitivity comparison diagrams. Q 1997 Elsevier Science S.A. Keywords:
Decision support systems: Electrical power distribution
1. Decision
analysis
- the decision
making
process
Decision-making is the culmination of the decision analysis. This analysis involves defining objectives, gathering information, evaluating the relevance and quality of information in reaching objectives and con-
* Corresponding author. 0378-7796/97:$17.00 0 1997 Elsevier Science S.A. All rights reserved PII SO378-7796(96)01 129-7
sideration of the penalties that will result from a nonoptimum decision or the occurrence of an unfavourable event. From this information and the assumptions based on it a number of alternatives can be proposed to meet the objectives. The final decision is based on the option which maximises the objectives according to their importance. Decision analysis aims to improve decision-making through better understanding and preparation of the
28
C.J. Oatley
et al.
Electric
Power
problem leading to a more informed evaluation and choice of options. Firstly, the objectives are defined, which may have different weightings of importance, e.g., the desire to minimise costs may have priority over providing security of supply. Normally a balance of objectives is required and this is termed satisjicing. The objectives lead to a stage of gathering information sufficient to describe the problem in order to produce possible solutions which can then be interrogated to discover the strengths and weaknesses of each option and their susceptibility to uncertainty. Uncertainty is the main constituent problem in decision making because if the outcomes of a decision were definite then the skills of assessing alternative options would be much reduced. However, the outcomes can be predicted with some measure by considering the uncertainty of each attribute and its associated outcome value. The uncertainty of an event occurring is usually modelled through probability theory - the likelihood that an event will or will not occur. There are three identifiable theories of probability as described by Klein and Methlie [l]. The theory used for the purposes of the decision analysis is the subjective theory which describes probability as the degree of belief an individual has in a proposition which is dependent on the individual’s knowledge and subjective perception of the matter being considered.
2. Knowledge
acquisition
The idea behind the decision analysis model is that it is provided with information and their relationships which it then analyses. It is therefore logical to assume that the quality of the analysis depends not only on the decision analysis method but also on the quality of the information supplied for interpretation. The quality of the knowledge depends on the right amount of relevant, correct data that effectively describes the problem being provided to the decision model developer. The object of knowledge ucquisition is to compile this quality information on the problem in question and process it to a standard to adequately describe the problem.
There are different sources for collecting information such as domain experts, books and databases. The main types used in knowledge acquisition are defined below, as in Durkin [2], and these were used to obtain information on refurbishment projects.
S~wcm
Resecrrch
40 (1997)
-77-35
The domuin expert: The domain expert, as the main source of information on a project, will have an understanding of all aspects involved and the problems that occur. The expert can, in theory, describe the problem completely. However, obtaining the information from the domain expert is not as simple as reading a book and this process is known as knowledge elicitation, it produces the knowledge required to emulate the expert’s behaviour. End-user: The end-user of the system can provide input as to what is required from the decision model. When the system has been prototyped the end-user can test and identify any faults which may not be apparent to the developer or may be able to suggest improvements to the system. Multiple experts: Other experts can give different views on the problem which can help clarify the understanding of important aspects and identify other relevant aspects. Literature: Documents can provide concrete information such as reports, regulations and recommendations Books, journals, etc. can give background information, latest developments, new technologies, historical information and further insight into the problem and its related topics. The process may never be considered complete as once the knowledge gained is sufficient to construct a decision model the information can be further enhanced and the system performance improved. However a practical conclusion will often be required which can be stated as when the system performance meets the initial specifications. 2.2. Knowledge
elicitation
Knowledge elicitation is the iterative process of acquiring information from a domain expert on a problem. It requires the collection of data which is interpreted and analysed by the problem developer, the knowledge engineer, which then leads to further gathering of more detailed knowledge which can the be incorporated into the decision model and system design. 2.2. I. Problems of’ knowledge elicitation To be able to synthesise a problem in a decision model requires in-depth knowledge of the problem. The domain expert, as the main source of this knowledge, is not readily accessible to the knowledge engihave difficulty in neer, as humans often communicating their actions and the reasoning and knowledge involved in those actions. The main reasons identified below as the cause of the knowledge elicitation errors are related to the cognitive psychology of humans, as in Klein and Methlie [l]:
C.J. Oarley
et ~1. Elecrric
Power
S~stcms
Reseavdl
40 (1997)
27-35
29
30
C.J. Ontley
et al.
Electric
Powr
Systems
Rrseard~
40 (1997)
27-35
0
REV1
Fig. 2. Influence
(a) Humans are often not aware of their actions as they are performed through repetition, e.g., tying a shoelace, or they are unaware of the mental processes involved when solving a problem which leads to the omission of important information to the knowledge engineer. (b) If the human cannot describe the process this can lead to a knowledge bottleneck. Humans, e.g., experts who acquire their knowledge by experience often base their decisions on intuition supported by their deep knowledge which they must access to be able to de-
diagram
scribe the problem fully. This has been described as the knowledge engineering paradox in that: “The more competent domain experts become, the less able they are to describe the knowledge they used to solve problems”. (c) Humans do not always execute actions in the way that they describe and contradict definitions; for example, rules in grammar that are broken by exceptions.
C.J. Oarle>, rr al. :‘Elecrric
Powrr
S~~.s/c’m Research
40 (1997)
27-35
31
1 0.9 0.6 > 0.7 : 0.6 .-lFj 0.5 33 0 I 0.4 a 0.3 0.2 0.1
0 540
560
580
600
620
640 Value
Fig. 3. Distribution
(d) Human judgement may be biased by a heuristic principle which the human has established even though this may be incorrect or cause the omission of other details. (e) The short-term memory of the human is limited and not every detail relating to a problem can be stored, so in the long-term memory only significant details relating to the problem will be stored. This can lead to incomplete knowledge being provided to the knowledge engineer. In summary. all these problems can lead to errors in the knowledge elicitation process if experts: l Are unaware of the knowledge used l Cannot verbalise the knowledge l Provide irrelevant knowledge l Provide incomplete knowledge l Provide incorrect knowledge l Provide inconsistent knowledge The effects of these problems can be reduced by the use of methods of knowledge elicitation which have evolved for these reasons. 2.3. Methods of knowledge elicitution Methods can be used by the knowledge engineer to be able to communicate with the domain expert to be able to understand and then describe the problem correctly. The main techniques for knowledge elicitation are [2]: Interview method: The interview method involves discussion sessionsbetween the expert and the knowledge engineer. Initially, the expert will provide information on the problem background and describe how the problem is resolved. These sessionscan develop, as the
660
660
700
720
740
760
Range probability
graph.
engineer’s knowledge increases, to interactive discussions that involve the exchange of ideas which will clarify the problem definition, This method uncovers concepts, structure of the domain, qualifications of variables, justifications and explanations of the problem area. The method is useful for defining the basic structure and analysing the tasks involved in the problem. Case study method: The case study method requires the knowledge engineer to shadow the expert through an actual problem. This method can be particularly useful where the problem is not easily defined and can improve the quality of the information already established. Verbal protocols: Verbal protocols are used to obtain knowledge of the cognitive processes of the expert. It can be an easier method for the expert than the interview method as it is a ‘thinking aloud’ process which provides great detail into the behavioural aspects of the expert. This is also time consuming and the method can interfere with the expert’s normal actions. Prototyping: The knowledge gathered is implemented in a prototype system for the expert to test. It can identify missing or incorrectly interpreted information and gives the expert some visualisation of the use of the knowledge.
3. Knowledge acquisition process for refurbishment projects As the planning engineer (the domain expert) had a busy schedule, the knowledge acquisition process was applied mainly through case studies which facilitated short interview sessionsto discuss and clarify methods and aspects of the projects.
32
C.J. Ootlq~
rr ~1. Elrctrir,
Po~vr
.SJ,srrm.s Research
40 (1997)
27-35
Future Accuracy-
580
590
600
610
620
630
640
650
660
670
680
VCI1CIC nanqe Fig. 4. Event
sensitivity
Documentation such as regulations was also used to provide a firm base for certain attributes, e.g., security of supply, and journals provided information on the latest ideas and developments. Verbal protocols and prototyping would be very useful methods to develop the model further in two ways: l to assist the knowledge engineer in identifying areas that are incorrect or need further information; l to assist the end-user in understanding the aim of the model and creating further ideas for investigation.
4. The decision support system (DSS) The environment in which decisions are made is often stressful, information intensive (largely due to the capacity of computers to store and correlate data), time constrained and limited in resources. Refurbishment project planning is no exception to this. These pressures have led to the development of computer decision-making tools, using artificial intelligence, known as Decision Support Systems, which can handle the amount of data involved and make decisions based on an established algorithm without bias or external influence. 4.1. The decision model attributes The decision model attributes are described so that some insight is given into the various attributes which affect the problem solution. The non-variable attributes are generally facts that are known about the proposed option which will be inherent in carrying out that option and can be evaluated without prolonged discussion. The variable attributes consider the uncertain events in the project and often require the use of intuition and subjective analysis by the engineer in conjunction with knowledge of company policies, past projects and engineering expertise. Non-tlariable l Revenue
attributes:
comparison.
Internal Rate of Return 0 Project Cost l Project Length 0 Cost of continuity of supply 0 Cost of new connections 0 Voltage limits cost 0 Security cost 0 Value of support l Loss factor l Faults eliminated l Fault rectification l Type of installation Vuriuhle attributes: 0 Security of supply l Liveline working l Obtaining wayleaves l Accuracy in forecasting projects costs l Expected lifespan l Future demand l Expected proximity l Continuity of supply during refurbishment 0 Visual impact 0 Political impact l
4.2. PRODEX
decision support system
PRODEX operates as a decision support system by utilising three computer applications to elicit and analyse data. and to return the results of the analysis. The applications are able to interact through the dynamic data exchange facility. The facility is available to all the applications through the Windows operating environment which is responsible for controlling communications between the applications. The Microsoft Visual Basic programming application was used as the operating environment to the PRODEX system. This was to illustrate how the user friendliness of the system can be improved with the use of Visual Basic functions. The application provided an introduction to the PRODEX system and main menu for managing the system. The data for the analysis is obtained via Lotus l-2-3 for Windows. This application has spreadsheet, data-
C.J. Outle!,
et al. I Elecrric
Power
base and graphing functions available which makes it versatile in creating custom operations. The application allows the automatic control of the data input and reporting functions through the use of the l-2-3 macro language. The actual decision analysis is performed in the decision analysis application, DPL (decision programming language), supplied by Applied Decision Analysis of Palo Alto, which defines the decision model and allows the user to evaluate the decision model for each refurbishment option and then to interrogate these options further to view the effects of uncertainty of attributes on the outcome. l-2-3 is also used to display the outcome model values to the user as this serves as a good interface and is also where the non-variable attribute data is stored. The report provides a table of values for comparison of project options by the user. 4.3. Decision programming
language (DPL)
application
DPL provides graphical and code programming environments to build complete models of the alternatives. uncertainties and values associated with a problem. The problem is structured through the creation of an influence diagram which describes the types of events and details of the relationships between these events. The influence diagram also provides for the specification of values or mathematical formulae relating to events and the probabilities of chance events occurring. The decision tree is created automatically from the influence diagram but usually requires restructuring, e.g., to define decisions whose outcomes follow different subsequent paths. Once the graphical decision tree and influence diagrams have been used to create the model this is translated into DPL program code. Additional programming code can then be used to instruct the model in more complex handling of information. This analysis follows three phases to arrive at the outcome value for the problem [3]. Rollforward: Starting at the root node of the decision tree each path is followed and appropriate calculations are made to produce independent endpoint values and probabilities. This is known as the outcome model. Model evaluation: Then the outcome values are analysed and combined in accordance with the defined objective, e.g., to minimise costs, and if a risk function is used the objective function is adjusted accordingly to produce a value model. Roll back: The third phase is where the analysis really does its work. the output of the second phase is used to produce the optimal expected value-based decision policy from the defined tree. The optimum will be adjusted if a risk factor is included. At a chance node the
System
Resead
40 (1997)
33
27-35
evaluation is the sum of the products of probabilities and values associated with each event outcome. At decision nodes the choice is the minimum or maximum option, as required. Once the attributes considered relevant to the project decisions had been compiled they were defined in the DPL decision model. The process of defining the model was complex and the final model was produced through iterative assessment and adjustments to the model. 5. Case study The project under consideration for refurbishment was the 33 kV Annatt Lochailortt Mallaig network and associated low voltage networks (Data courtesy of Scottish Hydro-Electric plc., Transmission Planning Group, Shore Road, Perth, Scotland). The network is located to the west of Fort William in the Highlands and Islands Region of Hydro-Electric (Fig. 1). The project appraisal was carried out in 1992 and approved in 1993. 5.1. Objectives of the refurbishment
project
The objectives specified for the refurbishment project were: 1. to bring the network within standards for security of
supply; 2. to ensure continuity of supply to customer within company limits during refurbishment; 3. standardisation of the network by replacing ageing and obsolete equipment, e.g. reduction of overhead spans to company specification, maximum of 70 m, to reduce faults from conductor clashing; 4. to allow future maintenance, extension of system and systematic repair of the system without widespread interruption of supplies: 5. to ensure a standard of network capable of supporting existing customers and the forecast load for the expected lifespan of the refurbished network; The proposed options which were seen to meet the project objectives were: Option 1: l Build a new line between Fort William and Lochailort to the new overhead specification along a parallel route to the existing line. l The existing line could then be refurbished on a like for like basis. l The Lochailort-Morar line would be rebuilt to new specification and re-routed at Arisaig to overcome clearance problems. Option 2: l Build a new line between Fort William and Lochailort and refurbish the existing as per option 1.
34 Table 1 Refurbishment
C.J. Oatley
project
decision
analysis
et al.
Electric
Ponw
Cost value Engineering value Cost weighting from Decision analysis Engineering weighting from Decision analysis Customer weighting from Decision analysis
40 (1997)
27-35
1
Option
2
Option
3
Option
- 188 160 -100 390 200
-267 240 -110 350 300
-53 240 - 110 340 200
-103 320 -110 300 200
462
513
617
607
Establish new 33/l 1 kV substation at Arisaig to replace 33 kV busbar and transformer at Morar. l Rebuild 33 kV line to new primary to new specification and rebuild remainder of line to Morar at 11 kV busbar for more economic distribution. Option 3: l Establish an additional 33 kV feed to Lochailort from Salen to comply with security standards. l Refurbish Annat-Lochaiiort on like for like basis. l Rebuild Lochailort-Morar to new specification as per option 1. Option 4: l Establish an additional 33 kV feed to Lochailort from Salen. l Refurbish Annat-Lochailort on like for like basis. l Establish new 33/l 1 kV sub-station at Arisaig to replace 33 kV busbar and transformer at Morar. l Rebuild 33 kV line to new primary to new specification and rebuild remainder of line at 11 kV using 70 mm2 conductor. l Convert Arisaig network for operation at 11 kV. l
5.2. PRODEX
Research
report Option
Total
Systems
analysis
A base model was analysed with the constant control nodes set and other attributes uncontrolled to provide some information on the range of outcome values possible. Fig. 2 shows the influence diagram, depicting the relationship envisaged. Non-financial values have been applied to attributes, such as environmental impact, which cannot easily be costed. As the study is comparative, this was believed to be an acceptable approach at this stage. Fig. 3 shows the probability distribution graph for the model, with a mean expected value, given by a line, of approximately 680. Fig. 4 shows the comparison of event sensitivities, with a clear predominance in the present model of environmental attributes; these weightings were arbitrarily chosen in the first instance, but can of course be altered with further insight. The PRODEX system was then used to analyse the values for the four options whose results are shown in
4
Table 1, together with the non-variable attribute values. Options 3 and 4 scored notably highest and were therefore preferred. The option that Hydro-Electric adopted for the project was Option 4 as the engineering and customer benefits could be justified against the extra expenditure. The case study was a useful exercise in that the attributes that had been described were assigned actual values which indicated ways in which the model could be improved. Some attributes were found only to consider the cost aspect and did not consider the benefit that these might be make to either the engineering or customer values such as voltage costs, whilst others were found to be constant across the project options, of supply, and not relevant to the e.g., continuity analysis.
6. Conclusions The model uses the influences considered relevant to refurbishment project decisions to evaluate each option and calculate values for each project option. The outcome value of each option is considered as three values - cost, engineering and customer value - which give the user an insight in to the advantages and disadvantages of each option. Information is also provided on the influences that are most critical to the outcome and how changes in the option influences will affect the outcome. The model shows how the interfaces between the applications can be created with the view to a customised system being developed so that no specialist knowledge is required by the user. It also allows a general system to be developed so that it can be used on different projects without the need for remodelling. The use of the different software packages in this project reflects the current trend in linking multiple tools to provide a complete analytical environment. This concept is now being extended in the University of Dundee to encompass a Geographical Information Sys-
C.J. 0arle.v
et al.
Electric
Power
tern and Virtual Engineering to provide accurate visual display for the Decision Support System.
S.~~terns Research
The support of Scottish fully acknowledged.
Hydro-Electric
plc. is grate-
35
27-35
References [I]
Acknowledgements
40 (1997)
M. Klein and L.B. Methlie, Experr Systems Supporr Approach, ISBN o-201-17562-2. [2] J. Durkin, Expert Systems, Design and Dewlopment, 02-330970-9. [3] DPL User’s Guide, Applied Decision Analysis Alto.
a Decision ISBN Inc.,
OPalo
Electric Power Systems Research 40 (1997) 27-35
ELSEVIER
A decision support system for electricity distribution refurbishment projects
network
Glare J. Oatley a, B. Ramsay a,*, A. McPherson b, R. Eastwood b, C.S. Ozveren c ’ Department
of Applred
Ph.vstcs and Electrome & Mechanrc~al Engmeermg, Unrcersrt!~ of’ Dundee. ‘Scottish Hydra-Electric pk.. Per-t/z, UK ’ School of Engineering, Uniwrsrt.v qf’ Ahertay Dundee, Lkmdee, UK
Dundee,
L/K
Received 21 June 1996: accepted 8 July 1996
Abstract The electricity distribution network in the UK is currently undergoing major refurbishment due to the expiration of equipment installed in the 1950s and 1960s during the large-scale expansion of the network. Regional Electricity Companies are attempting to minimise and control the expenditure required to comply with the distribution licence, to maintain a sufficient level of system reliability and balance the costs of refurbishment with those incurred in operating a sub-standard network. Scottish Hydro-Electric plc. has a five year plan for refurbishment of these networks which involves assessment to identify sections requiring attention followed by proposals to implement the necessary work within the objectives of network refurbishment. The planning engineer faces many pressures in making decisions to adopt the best scheme for a particular section of network and the paper seeks to show how decision analysis methods. in the form of current computer software, may assist him or her in assessing influencing factors and their effect on the outcome of the project. The paper discusses these factors primarily for overhead rural networks. and develops a set of variable and non-variable attributes, including costs, fault clearance, security, wayleaves, visual impact. political impact, etc., to form the basis of a decision
model, The application of Artificial Intelligence is introduced using a decision analysis package to produce a decision support system (DSS), codenamed PRODEX, and a computer mode1 is developed on a PC to demonstrate the feasibility of the DSS. A spreadsheet is integrated with the decision analysis through Dynamic Data Exchange to input. store, manipulate and output data. The Visual Basic programming language is used to demonstrate the possibilities for creating a customised, user-friendly front-end to the system. The final DSS shows how the interfaces between applications can be created to enable a customised system to be developed so that no specialised knowledge of the applications is required by the user. A case study is given in the form of a 33 kV refurbishment project in the Highlands and Islands region of Scottish Hydro-Electric plc’s system. The study considers the problems of a section of the network. and details four options proposed for improvement of the supply. The PRODEX system implementation is explained. along with its analysis of the options through the use of tools such as outcome distribution probability graphsand event sensitivity comparison diagrams. Q 1997 Elsevier Science S.A. Keywords:
Decision support systems: Electrical power distribution
1. Decision
analysis
- the decision
making
process
Decision-making is the culmination of the decision analysis. This analysis involves defining objectives, gathering information, evaluating the relevance and quality of information in reaching objectives and con-
* Corresponding author. 0378-7796/97:$17.00 0 1997 Elsevier Science S.A. All rights reserved PII SO378-7796(96)01 129-7
sideration of the penalties that will result from a nonoptimum decision or the occurrence of an unfavourable event. From this information and the assumptions based on it a number of alternatives can be proposed to meet the objectives. The final decision is based on the option which maximises the objectives according to their importance. Decision analysis aims to improve decision-making through better understanding and preparation of the
28
C.J. Oatley
et al.
Electric
Power
problem leading to a more informed evaluation and choice of options. Firstly, the objectives are defined, which may have different weightings of importance, e.g., the desire to minimise costs may have priority over providing security of supply. Normally a balance of objectives is required and this is termed satisjicing. The objectives lead to a stage of gathering information sufficient to describe the problem in order to produce possible solutions which can then be interrogated to discover the strengths and weaknesses of each option and their susceptibility to uncertainty. Uncertainty is the main constituent problem in decision making because if the outcomes of a decision were definite then the skills of assessing alternative options would be much reduced. However, the outcomes can be predicted with some measure by considering the uncertainty of each attribute and its associated outcome value. The uncertainty of an event occurring is usually modelled through probability theory - the likelihood that an event will or will not occur. There are three identifiable theories of probability as described by Klein and Methlie [l]. The theory used for the purposes of the decision analysis is the subjective theory which describes probability as the degree of belief an individual has in a proposition which is dependent on the individual’s knowledge and subjective perception of the matter being considered.
2. Knowledge
acquisition
The idea behind the decision analysis model is that it is provided with information and their relationships which it then analyses. It is therefore logical to assume that the quality of the analysis depends not only on the decision analysis method but also on the quality of the information supplied for interpretation. The quality of the knowledge depends on the right amount of relevant, correct data that effectively describes the problem being provided to the decision model developer. The object of knowledge ucquisition is to compile this quality information on the problem in question and process it to a standard to adequately describe the problem.
There are different sources for collecting information such as domain experts, books and databases. The main types used in knowledge acquisition are defined below, as in Durkin [2], and these were used to obtain information on refurbishment projects.
S~wcm
Resecrrch
40 (1997)
-77-35
The domuin expert: The domain expert, as the main source of information on a project, will have an understanding of all aspects involved and the problems that occur. The expert can, in theory, describe the problem completely. However, obtaining the information from the domain expert is not as simple as reading a book and this process is known as knowledge elicitation, it produces the knowledge required to emulate the expert’s behaviour. End-user: The end-user of the system can provide input as to what is required from the decision model. When the system has been prototyped the end-user can test and identify any faults which may not be apparent to the developer or may be able to suggest improvements to the system. Multiple experts: Other experts can give different views on the problem which can help clarify the understanding of important aspects and identify other relevant aspects. Literature: Documents can provide concrete information such as reports, regulations and recommendations Books, journals, etc. can give background information, latest developments, new technologies, historical information and further insight into the problem and its related topics. The process may never be considered complete as once the knowledge gained is sufficient to construct a decision model the information can be further enhanced and the system performance improved. However a practical conclusion will often be required which can be stated as when the system performance meets the initial specifications. 2.2. Knowledge
elicitation
Knowledge elicitation is the iterative process of acquiring information from a domain expert on a problem. It requires the collection of data which is interpreted and analysed by the problem developer, the knowledge engineer, which then leads to further gathering of more detailed knowledge which can the be incorporated into the decision model and system design. 2.2. I. Problems of’ knowledge elicitation To be able to synthesise a problem in a decision model requires in-depth knowledge of the problem. The domain expert, as the main source of this knowledge, is not readily accessible to the knowledge engihave difficulty in neer, as humans often communicating their actions and the reasoning and knowledge involved in those actions. The main reasons identified below as the cause of the knowledge elicitation errors are related to the cognitive psychology of humans, as in Klein and Methlie [l]:
C.J. Oarley
et ~1. Elecrric
Power
S~stcms
Reseavdl
40 (1997)
27-35
29
30
C.J. Ontley
et al.
Electric
Powr
Systems
Rrseard~
40 (1997)
27-35
0
REV1
Fig. 2. Influence
(a) Humans are often not aware of their actions as they are performed through repetition, e.g., tying a shoelace, or they are unaware of the mental processes involved when solving a problem which leads to the omission of important information to the knowledge engineer. (b) If the human cannot describe the process this can lead to a knowledge bottleneck. Humans, e.g., experts who acquire their knowledge by experience often base their decisions on intuition supported by their deep knowledge which they must access to be able to de-
diagram
scribe the problem fully. This has been described as the knowledge engineering paradox in that: “The more competent domain experts become, the less able they are to describe the knowledge they used to solve problems”. (c) Humans do not always execute actions in the way that they describe and contradict definitions; for example, rules in grammar that are broken by exceptions.
C.J. Oarle>, rr al. :‘Elecrric
Powrr
S~~.s/c’m Research
40 (1997)
27-35
31
1 0.9 0.6 > 0.7 : 0.6 .-lFj 0.5 33 0 I 0.4 a 0.3 0.2 0.1
0 540
560
580
600
620
640 Value
Fig. 3. Distribution
(d) Human judgement may be biased by a heuristic principle which the human has established even though this may be incorrect or cause the omission of other details. (e) The short-term memory of the human is limited and not every detail relating to a problem can be stored, so in the long-term memory only significant details relating to the problem will be stored. This can lead to incomplete knowledge being provided to the knowledge engineer. In summary. all these problems can lead to errors in the knowledge elicitation process if experts: l Are unaware of the knowledge used l Cannot verbalise the knowledge l Provide irrelevant knowledge l Provide incomplete knowledge l Provide incorrect knowledge l Provide inconsistent knowledge The effects of these problems can be reduced by the use of methods of knowledge elicitation which have evolved for these reasons. 2.3. Methods of knowledge elicitution Methods can be used by the knowledge engineer to be able to communicate with the domain expert to be able to understand and then describe the problem correctly. The main techniques for knowledge elicitation are [2]: Interview method: The interview method involves discussion sessionsbetween the expert and the knowledge engineer. Initially, the expert will provide information on the problem background and describe how the problem is resolved. These sessionscan develop, as the
660
660
700
720
740
760
Range probability
graph.
engineer’s knowledge increases, to interactive discussions that involve the exchange of ideas which will clarify the problem definition, This method uncovers concepts, structure of the domain, qualifications of variables, justifications and explanations of the problem area. The method is useful for defining the basic structure and analysing the tasks involved in the problem. Case study method: The case study method requires the knowledge engineer to shadow the expert through an actual problem. This method can be particularly useful where the problem is not easily defined and can improve the quality of the information already established. Verbal protocols: Verbal protocols are used to obtain knowledge of the cognitive processes of the expert. It can be an easier method for the expert than the interview method as it is a ‘thinking aloud’ process which provides great detail into the behavioural aspects of the expert. This is also time consuming and the method can interfere with the expert’s normal actions. Prototyping: The knowledge gathered is implemented in a prototype system for the expert to test. It can identify missing or incorrectly interpreted information and gives the expert some visualisation of the use of the knowledge.
3. Knowledge acquisition process for refurbishment projects As the planning engineer (the domain expert) had a busy schedule, the knowledge acquisition process was applied mainly through case studies which facilitated short interview sessionsto discuss and clarify methods and aspects of the projects.
32
C.J. Ootlq~
rr ~1. Elrctrir,
Po~vr
.SJ,srrm.s Research
40 (1997)
27-35
Future Accuracy-
580
590
600
610
620
630
640
650
660
670
680
VCI1CIC nanqe Fig. 4. Event
sensitivity
Documentation such as regulations was also used to provide a firm base for certain attributes, e.g., security of supply, and journals provided information on the latest ideas and developments. Verbal protocols and prototyping would be very useful methods to develop the model further in two ways: l to assist the knowledge engineer in identifying areas that are incorrect or need further information; l to assist the end-user in understanding the aim of the model and creating further ideas for investigation.
4. The decision support system (DSS) The environment in which decisions are made is often stressful, information intensive (largely due to the capacity of computers to store and correlate data), time constrained and limited in resources. Refurbishment project planning is no exception to this. These pressures have led to the development of computer decision-making tools, using artificial intelligence, known as Decision Support Systems, which can handle the amount of data involved and make decisions based on an established algorithm without bias or external influence. 4.1. The decision model attributes The decision model attributes are described so that some insight is given into the various attributes which affect the problem solution. The non-variable attributes are generally facts that are known about the proposed option which will be inherent in carrying out that option and can be evaluated without prolonged discussion. The variable attributes consider the uncertain events in the project and often require the use of intuition and subjective analysis by the engineer in conjunction with knowledge of company policies, past projects and engineering expertise. Non-tlariable l Revenue
attributes:
comparison.
Internal Rate of Return 0 Project Cost l Project Length 0 Cost of continuity of supply 0 Cost of new connections 0 Voltage limits cost 0 Security cost 0 Value of support l Loss factor l Faults eliminated l Fault rectification l Type of installation Vuriuhle attributes: 0 Security of supply l Liveline working l Obtaining wayleaves l Accuracy in forecasting projects costs l Expected lifespan l Future demand l Expected proximity l Continuity of supply during refurbishment 0 Visual impact 0 Political impact l
4.2. PRODEX
decision support system
PRODEX operates as a decision support system by utilising three computer applications to elicit and analyse data. and to return the results of the analysis. The applications are able to interact through the dynamic data exchange facility. The facility is available to all the applications through the Windows operating environment which is responsible for controlling communications between the applications. The Microsoft Visual Basic programming application was used as the operating environment to the PRODEX system. This was to illustrate how the user friendliness of the system can be improved with the use of Visual Basic functions. The application provided an introduction to the PRODEX system and main menu for managing the system. The data for the analysis is obtained via Lotus l-2-3 for Windows. This application has spreadsheet, data-
C.J. Outle!,
et al. I Elecrric
Power
base and graphing functions available which makes it versatile in creating custom operations. The application allows the automatic control of the data input and reporting functions through the use of the l-2-3 macro language. The actual decision analysis is performed in the decision analysis application, DPL (decision programming language), supplied by Applied Decision Analysis of Palo Alto, which defines the decision model and allows the user to evaluate the decision model for each refurbishment option and then to interrogate these options further to view the effects of uncertainty of attributes on the outcome. l-2-3 is also used to display the outcome model values to the user as this serves as a good interface and is also where the non-variable attribute data is stored. The report provides a table of values for comparison of project options by the user. 4.3. Decision programming
language (DPL)
application
DPL provides graphical and code programming environments to build complete models of the alternatives. uncertainties and values associated with a problem. The problem is structured through the creation of an influence diagram which describes the types of events and details of the relationships between these events. The influence diagram also provides for the specification of values or mathematical formulae relating to events and the probabilities of chance events occurring. The decision tree is created automatically from the influence diagram but usually requires restructuring, e.g., to define decisions whose outcomes follow different subsequent paths. Once the graphical decision tree and influence diagrams have been used to create the model this is translated into DPL program code. Additional programming code can then be used to instruct the model in more complex handling of information. This analysis follows three phases to arrive at the outcome value for the problem [3]. Rollforward: Starting at the root node of the decision tree each path is followed and appropriate calculations are made to produce independent endpoint values and probabilities. This is known as the outcome model. Model evaluation: Then the outcome values are analysed and combined in accordance with the defined objective, e.g., to minimise costs, and if a risk function is used the objective function is adjusted accordingly to produce a value model. Roll back: The third phase is where the analysis really does its work. the output of the second phase is used to produce the optimal expected value-based decision policy from the defined tree. The optimum will be adjusted if a risk factor is included. At a chance node the
System
Resead
40 (1997)
33
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evaluation is the sum of the products of probabilities and values associated with each event outcome. At decision nodes the choice is the minimum or maximum option, as required. Once the attributes considered relevant to the project decisions had been compiled they were defined in the DPL decision model. The process of defining the model was complex and the final model was produced through iterative assessment and adjustments to the model. 5. Case study The project under consideration for refurbishment was the 33 kV Annatt Lochailortt Mallaig network and associated low voltage networks (Data courtesy of Scottish Hydro-Electric plc., Transmission Planning Group, Shore Road, Perth, Scotland). The network is located to the west of Fort William in the Highlands and Islands Region of Hydro-Electric (Fig. 1). The project appraisal was carried out in 1992 and approved in 1993. 5.1. Objectives of the refurbishment
project
The objectives specified for the refurbishment project were: 1. to bring the network within standards for security of
supply; 2. to ensure continuity of supply to customer within company limits during refurbishment; 3. standardisation of the network by replacing ageing and obsolete equipment, e.g. reduction of overhead spans to company specification, maximum of 70 m, to reduce faults from conductor clashing; 4. to allow future maintenance, extension of system and systematic repair of the system without widespread interruption of supplies: 5. to ensure a standard of network capable of supporting existing customers and the forecast load for the expected lifespan of the refurbished network; The proposed options which were seen to meet the project objectives were: Option 1: l Build a new line between Fort William and Lochailort to the new overhead specification along a parallel route to the existing line. l The existing line could then be refurbished on a like for like basis. l The Lochailort-Morar line would be rebuilt to new specification and re-routed at Arisaig to overcome clearance problems. Option 2: l Build a new line between Fort William and Lochailort and refurbish the existing as per option 1.
34 Table 1 Refurbishment
C.J. Oatley
project
decision
analysis
et al.
Electric
Ponw
Cost value Engineering value Cost weighting from Decision analysis Engineering weighting from Decision analysis Customer weighting from Decision analysis
40 (1997)
27-35
1
Option
2
Option
3
Option
- 188 160 -100 390 200
-267 240 -110 350 300
-53 240 - 110 340 200
-103 320 -110 300 200
462
513
617
607
Establish new 33/l 1 kV substation at Arisaig to replace 33 kV busbar and transformer at Morar. l Rebuild 33 kV line to new primary to new specification and rebuild remainder of line to Morar at 11 kV busbar for more economic distribution. Option 3: l Establish an additional 33 kV feed to Lochailort from Salen to comply with security standards. l Refurbish Annat-Lochaiiort on like for like basis. l Rebuild Lochailort-Morar to new specification as per option 1. Option 4: l Establish an additional 33 kV feed to Lochailort from Salen. l Refurbish Annat-Lochailort on like for like basis. l Establish new 33/l 1 kV sub-station at Arisaig to replace 33 kV busbar and transformer at Morar. l Rebuild 33 kV line to new primary to new specification and rebuild remainder of line at 11 kV using 70 mm2 conductor. l Convert Arisaig network for operation at 11 kV. l
5.2. PRODEX
Research
report Option
Total
Systems
analysis
A base model was analysed with the constant control nodes set and other attributes uncontrolled to provide some information on the range of outcome values possible. Fig. 2 shows the influence diagram, depicting the relationship envisaged. Non-financial values have been applied to attributes, such as environmental impact, which cannot easily be costed. As the study is comparative, this was believed to be an acceptable approach at this stage. Fig. 3 shows the probability distribution graph for the model, with a mean expected value, given by a line, of approximately 680. Fig. 4 shows the comparison of event sensitivities, with a clear predominance in the present model of environmental attributes; these weightings were arbitrarily chosen in the first instance, but can of course be altered with further insight. The PRODEX system was then used to analyse the values for the four options whose results are shown in
4
Table 1, together with the non-variable attribute values. Options 3 and 4 scored notably highest and were therefore preferred. The option that Hydro-Electric adopted for the project was Option 4 as the engineering and customer benefits could be justified against the extra expenditure. The case study was a useful exercise in that the attributes that had been described were assigned actual values which indicated ways in which the model could be improved. Some attributes were found only to consider the cost aspect and did not consider the benefit that these might be make to either the engineering or customer values such as voltage costs, whilst others were found to be constant across the project options, of supply, and not relevant to the e.g., continuity analysis.
6. Conclusions The model uses the influences considered relevant to refurbishment project decisions to evaluate each option and calculate values for each project option. The outcome value of each option is considered as three values - cost, engineering and customer value - which give the user an insight in to the advantages and disadvantages of each option. Information is also provided on the influences that are most critical to the outcome and how changes in the option influences will affect the outcome. The model shows how the interfaces between the applications can be created with the view to a customised system being developed so that no specialist knowledge is required by the user. It also allows a general system to be developed so that it can be used on different projects without the need for remodelling. The use of the different software packages in this project reflects the current trend in linking multiple tools to provide a complete analytical environment. This concept is now being extended in the University of Dundee to encompass a Geographical Information Sys-
C.J. 0arle.v
et al.
Electric
Power
tern and Virtual Engineering to provide accurate visual display for the Decision Support System.
S.~~terns Research
The support of Scottish fully acknowledged.
Hydro-Electric
plc. is grate-
35
27-35
References [I]
Acknowledgements
40 (1997)
M. Klein and L.B. Methlie, Experr Systems Supporr Approach, ISBN o-201-17562-2. [2] J. Durkin, Expert Systems, Design and Dewlopment, 02-330970-9. [3] DPL User’s Guide, Applied Decision Analysis Alto.
a Decision ISBN Inc.,
OPalo