An Alternative Design Approach for DMS Using Knowledge Engineering
Metropolitan Electricity
Abstact:- With greater network complexity of power distribution, this makes it more difficult for the system operators to manage their network. Distribution Management System (DMS) assists the system operators to effectively operate and control distribution system through better decisions near real time. Typically, many functions can be included in DMS depending on utility’s requirements. These are for example, real time distribution network monitoring, load flow simulation for planning and analysis, or management of disturbances. Since such requirements may vary from utility to utility, their individual distribution system knowledge and experience results in lots of difficulties placed on DMS design engineers. This paper presents an alternative approach to overcome these design problems. This approach utilizes expert knowledge relating to DMS requirement such as functionality, architecture, and customization. By using CommonKADS framework, this expert knowledge can be captured systemically and knowledge based system can then be developed. In this paper, the knowledge from members of Metropolitan Electricity Authority (MEA) expert group will be captured and presented as a case study. Results of this reseach will show that knowledge for DMS design exists. Moreover, by developing the knowledge based system this approach can assist engineers to further design an effective DMS.
Keywords:- Distribution Management System (DMS), Expert Knowledge, Knowledge Analysis and Data Structuring (KADS), Knowledge Based System
I. INTRODUCTION
Today many utilities in liberalization market increase their performance by applying digital control to reduce overall cost. [1]
Supervisory Control and Data Acquisition for Distribution Management System (SCADA/DMS) or DMS with many functional tools, as shown in Figure 1, will help their system operators to control the distribution system effectively. The features allow simplified management for large distribution networks with frequent modifications and updating operations. Utility will focus on system reliability, power quality, system losses, customer communications and customer billing. The DMS functions [2] can be grouped into :-
- SCADA: data acquisition, data processing and supervisory control
- Substation Automation: control device within the substation such as service restoration, bus voltage control, parallel transformer control, automatic reclosing etc.
- Feeder Automation: control device on the feeder such as fault location, fault isolation, service restoration, feeder reconfiguration etc.
- Distribution System Analysis: basic distribution power flow and advance functions such as contingency load transfer, load and voltage profile, and distribution losses etc.
- Interface to other computer system: such as Customer Information System (CIS), Geographic Information System (GIS), Energy Management System (EMS) via, Middleware, a software layer that provides a level of interconnection.
Standard hardware architecture can be centralized or distributed (multi-center), redundancy workstation (vendor UNIX or Linux) or personnel computer (Window or Linux). The software can be proprietary or open. The communication in metropolitan area is mainly fiber optic. Specific or open protocols and error detection philosophies are used for efficient and optimum transfer of data. [3]
Figure 1 Typical SCADA/DMS system [4]
This paper offers a knowledge engineering approach to develop knowledge based system for DMS design and is arranged into six sections which consist of introduction in section I, problems in designing DMS in section II, system thinking for DMS design in section III, knowledge engineering for DMS design in section IV, case study in section V, and conclusion in section VI.
II. PROBLEMS IN DESIGNING DMS
According to an individual utility distribution system, DMS design can vary from system to system. The design process which interacts between designer and user is generalized as followed :-
- Acquire DMS knowledge
- Capture DMS requirement
- Requirement Analysis
- Propose DMS design
- Check proposed design with requirement
- Revise design
Actually, the users’ requirement is always dynamic and there is a knowledge gap between user and designer which can cause the problem in designing DMS. Therefore, three kinds of problem in designing DMS are discussed in this paper.
One problem is that key knowledge is not transfered from generation to generation. New engineer have no idea about old distribution primary equipments because experts’ key knowledge is not transfered from time to time. Utility expert has an individual knowledge, skill and experience related to action, problem solving and decision making opportunities. This knowledge is called Tacit knowledge, which is dynamic process of justifying personal belief toward their truth and specialization. Tacit knowledge is valuable but hard to articulate in formal language. It is quite impossible to get all experts’ tacit knowledge which is massive ; however, it is necessary to transfer key experts’ tacit knowledge to know what is made or changed, know how it make or change, know why the system is made or changed, and especially who made or changed.
Figure 2 Knowledge diversification
Another problem is knowledge diversification across the whole organization. This is illustrated in Figure 2. An individual tacit knowledge among primary equipment, protection, information technology and communication people in functional department such as planning, design, construction, operation and maintenance will make up this knowledge diversification within the organization because their internal use of language is full of jargon. Moreover, their individual heuristic requirement is different and dynamic. This will make them lack of their unity. It will place the difficulties on DMS designer to acquire and map real knowledge and requirement.
The last problem is a little DMS knowledge within the organization. Moreover, DMS capabilities vary from system to system especially DMS in developing country. Before getting the system, technical problems might occur when they buy different DMS system under price consideration only. Many different suppliers and techniques with a lot of different spare parts will bring a big difficult situation when small suppliers do no longer exist.
In the DMS system product life-cycle, utilities have to learn how to get the optimum system that adequate to their distribution network and organization. They use DMS system as a tool to control and monitor their distribution network in order to increase their effective system performance and reduce their operation and maintenance cost. However, the utility staffs still need to acquire some new system knowledge and develop skill in action. The DMS system organizational learning process is intensively developed during system planning, designing, installing, commissioning, operation and maintenance period. [5]
To overcome this problem, this paper introduces system thinking and knowledge engineering methodology for DMS design in the following section.
III. SYSTEM THINKING FOR DMS DESIGN
This section discusses an organization learning and introduce a system thinking technique to assist in DMS design.
Purpose of organizational learning is to convert individual knowledge into organization knowledge, to collaborate for exchanging intellectual material, and to collect intellectual material such as person, document, information etc. There are five basic ingredients for a learning organization [6]:-
- Team Learning : work together to achieve that vision
- Share Vision : form a plan everyone can agree on
- Mental Model : put aside their old ways of thinking
- Personal Mastery : learn to be open with others
- System Thinking : understand how the company really works
System thinking is looking for cause and effect in systematic way. Unlike reductionist, a good system thinker is someone who see events, system, patterns of behavior, and mental model operating simultaneously. For example, one can try to improve protection system on primary feeder by not only looking in great detail at individual relay setting for primary feeder but also the components of the feeder, type of feeder, location of feeder, type of customer, position of lateral on feeder, sectionalizer and fuse characteristic, fault indicator, position of lightning arrestor, minimum fault condition, overload load condition, operation procedure and considering the interactions between them.
System thinking technique such as ‘Storytelling’ is used for knowledge elicitation in this paper. It can help the organization collaborate between individual knowledge by reasonably story looking at the whole system picture. This method can generate the useful solution by alligning the main organization objective, reducing individual conflict and then compromizing the idea. When using ‘Storytelling’ technique to determine causes and effects for DMS lifecycle from distribution planning to decommissioning, the whole picture of distribution management system can be captured.
In the next section, knowledge engineering techinique will provide a useful template to capture and analyze the requirement.
IV. KNOWLEDGE ENGINEERING FOR DMS DESIGN
This section, knowledge engineering method-ology is applied to capture, analyze, and model the knowledge from the DMS design process. Because key successful factor for DMS software function integration and implementation is an enterprise level architecture describing how information is shared, the technology should consider not only information and communication but also knowledge. [7,8] Knowledge engineering can assist in maintaining and making systematically use of otherwise lost knowledge. In other words, it provides heuristic approach to capture, analyze, model and utilize key expert knowledge within the organization.
Moreover, Knowledge oriented technology is concerned in which people acquire, create, store and use knowledge within the organization. Knowledge engineering will provide tools and techniques to manage organization DMS design knowledge.
Knowledge engineering approach such as Knowledge Analysis and Data Strucure (KADS) and Knowledge Based System (KBS) can be shown in more details in the following subsection.
A. Knowledge Analysis and Data Structure (KADS)
KADS: Knowledge Analysis and Data Structuring is a knowledge engineering methodology supporting the development of knowledge systems. In principle, a KADS knowledge model has three kinds of knowledge [6]:-
- Task Knowledge : ‘Book’ contains knowledge about how elementary inference can be combined to achieve a certain goal. It can commit to achieve a particular goal. Tasks represent fixed strategies for achieving problem-solving goals.
- Inference Knowledge : ‘Chapter’ controls knowledge that we abstract from the domain theory and describe the inference that we want to make the reason in this theory.
- Domain knowledge : ‘Theory’ embodies the conceptualization of a domain for a particular application in the form of a domain theory. It can be viewed as a declarative theory of the domain. In fact, adding a simple deductive capability would enable a system in theory to solve all solvable problems by the theory.
CommonKADS is the EU de facto standard methodology for supporting design and implementation of knowledge systems. CommonKADS or KADS (previous version of CommonKADS) has been broadly applied in power business, for instances, Knowledge Management for planning, operation, maintenance, pricing negotiation, asset management and regulatory issues.
Figure 3 ‘Propose and Revise’ Inference Template [7]
It provides a standard inference knowledge template for configuration design to support knowledge model process which can often be reused for feasible system construction. It assumes that all components of the artifact are predefined like building a boat from a set of Lego blocks.
This paper uses this synthesis task type for DMS design which is called “Propose and Revise” as a standard template to collect DMS design knowledge and requirements. This is illustrated in Figure 3 so that MEA heuristic knowledge on distribution equipment, operation, control and protection, and their requirements on DMS system can be transcripted, analyzed, and reused systematically.
B. Knowledge Based System (KBS)
Knowledge based system has 4 major components which are [9]:-
- Dialog: user interface
- Infererence engine: control structure of the system
- Knowledge base: contains facts and rules of thumb in a given area
- Explanation facilities: allow user to ask how and why.
Eventhough experts’ tacit knowledge does not exist in explicit form; the most common way to deal with tacit knowledge is yellow pages which refer to the owner of the tacit part of knowledge. There are six process roles in knowledge engineering and management which relate to [7]:-
- Knowledge Provider : ‘Expert’ in application domain
- Knowledge User : makes use directly or indirectly of knowledge based system
- Knowledge Engineer : ‘Analyst’ elicits knowledge from experts and requirement from users
- Knowledge System Developer : is responsible for knowledge based system design and implementation
- Project Manager : is in charge of knowledge based system development project
- Knowledge Manager : formulates knowledge strategy
This is shown in Figure 4. From Propose and Revise template in previous subsection, Knowledge base that contains the facts and rules of thumb in DMS design can be developed and modified as the amount of DMS problem-specific knowledge existing within the organization. [10]
Figure 4 Knowledge Base System [7]
In summary, learning organization and knowledge engineering methodology is used to help DMS configuration design on both hardware and software in the following processes:-
- Knowledge elicitation: capture knowledge and requirement this is both heuristic and tacit
- Knowledge analysis and creation: determine DMS function and architecture from require-ment
- Knowledge utilization: use CommonKADS ‘Propose and Revise’ standard template and guideline to customize and get appropriated design from the skeleton design.
- Design Validation: to approve and evaluate the design
The next section will present MEA case study.
V. CASE STUDY
Metropolitan Electricity Authority (MEA)’s DMS design is used as a case study to show the applicability and benefit of using system thinking and CommonKADS to capture knowledge and requirements systematically. The methodology and technique as described in previous sections are used to capture knowledge and requirement in Metropolitan Electricity Authority (MEA)
A. General Description of MEA DMS
MEA, is responsible for power distribution to customers in Bangkok Metropolis, Nontaburi, and Samutprakan provinces, has 18 district service centers covering an area of 3,192 Sq.km. and distribution feeders of 13,471 cct-km. Primary feeders are energized at 12 or 24 kV. For overhead, the primary feeder configuration is radial or loop. For underground, the primary feeder configuration is radial, loop, primary selective, or special spare line. MEA plans to get DMS to enhance their service quality in the near future. By this reason, MEA expert team is settled to work with the specialized consultant company and to implement an effective DMS.
B. Reseach Methodology
A knowledge engineering exercise that used previous methodology and technique was conducted on MEA expert team. The knowledge elicitation was done from MEA expert in Engineering department, Design and Commissioning department, Control and Operation department, and District Service department by using both unstructured interview by ‘Storytelling’ and structured interview on CommonKADS ‘Propose and Revise’ template.
Knowledge elicitation, analysis and creation for DMS design is shown in this paper to prove that there is a useful knowledge within the organization for DMS design. Moreover, knowledge utilization and validation will be done in the future.
C. Results and Future Works
This paper uses system thinking technique and CommonKADS standard templete to capture MEA heuristic knowledge and requirements on their distribution system such as distribution equipment, operation, control and protection.
Firstly, ‘Storytelling’ technique is used to identify MEA heuristic knowledge in their distribution system. This is illustrated in Figure 5.
Figure 5 Heuristic knowledge on distribution system
Although, this knowledge is tacit and it distributes within the organization, the concept of system thinking can be used to collaborate and collect their organization reasonable knowledge. However, Information Technology and Communication knowledge and requirements will be captured in future work.
Secondly, CommonKADS ‘Propose and Revise’ template can help knowledge engineer to capture their snapshot reasonable organization domain knowledge and requirement and construct domain schema for knowledge based system for DMS design.
In the template, the skeletal design in Figure 6 was created and based on MEA general requirement, consultance and manufacturer data.
Figure 6 DMS Skeleton Design
Moreover, soft and hard requirement from the DMS user and existing DMS supplier can be initially put into knowledge based system and helped them propose the design extension by this template via knowledge system developer. This requirement is an external input to DMS design system; therefore, we can get system preferences from soft requirements and system constraints from hard requirements for design extension. The example on such requirements, constraints and preferences for distribution system can be shown in Figure 7. However, violation and action list for critique and select in the template will be done in future work.
Figure 7 Soft and Hard requirement
Anyway, it is shown that MEA has enough useful knowledge and requirement within the organization and is ready for DMS design.
Finally, this initial knowledge based system can help MEA transfer knowledge, integrate organization knowledge, compromize the conflict, and bridge the gap for DMS design and implementation. Moreover, this specific knowledge can be reused for their future modification, expansion and migration.
VI. CONCLUSION
Knowledge Engineering is another effective approach for capturing the specific cost effective knowledge on the distribution system, building up the community of practice and motivating the organization learning to achieve their effective performance with cost reduction. Knowledge based system can be used as collaboration tools for organization DMS design from learning, knowing, doing and revising process. It will be benefit for all utility, consultance and manufacturer to keep, transfer, update, and reuse the knowledge and requirement so that they can together design DMS effectively.
With the organization knowledge staffs, DMS will be the intelligent tools to manage their distribution system and get the customer satisfaction in liberalization market.
ACKNOWLEDGEMENT
The author wishes to acknowledge Dr. Tirapot Chandarasupsang and Dr. Nopasit Chakpitak from
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