Development of a Knowledge Based System for Power System Restoration

Sakaguchi,; Matsumoto, Tadahiko

doi:10.1109/mper.1983.5519623

Cited by 17 publications

(20 citation statements)

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“…In applying artificial-intelligence techniques to the control of power systems, several expert systems, mainly in production-rule form, have been built to aid and train operators in diagnosing a network fault [4], and to assist operators to make decisions in restoring a blackout system [5], in the control of reactive power and voltage in a network [6], as well as in processing alarms in a control centre [7]. On the power-system planning side, an intelligent load-flow engine [8] has been developed.…”

Section: Symbols For Variables In Prolog Clausesmentioning

confidence: 99%

Artificial intelligence approach to load allocation in distribution substations

Wong

Cheung

1987

IEE Proc. C Gener. Transm. Distrib. UK

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Based on the set theory and the Prolog programming language, the paper develops methods by which the allocation of loads to the busbars of distribution substations may be determined automatically. Starting from the initial possible allocation of loads to the substation busbar sections, a formulation for load allocation is first developed using set theory. The 'generate evaluate and test' technique and the depth-first search method are then applied to the solution-seeking process. The formulation is implemented and recursive search procedures are derived using the Prolog language. The Prolog software aid developed includes a complete solution generator from which all hypothetical solutions are generated, an evaluator which prunes invalid solutions, and a tester which produces feasible solutions by checking the load allocation constraints related to the firm capacity of the distribution substations. An application of the methods developed is given for two 11 kV interconnected substations which include 5 busbar sections and 10 ring loads. List of principal symbols Symbols in formulationB t = set containing ring loads which may be allocated to busbar section i C, = set containing ring loads which may be allocated to busbar section i and to other busbar sections S t = set containing ring loads which can only be allocated to busbar section i Ri = set containing ring loads which have been allocated to busbar sections j , where; # i p. = power set of a set that contains ring loads which are allocated to busbar section i £, = set containing ring loads which are allocated to busbar section i Qi = set containing all the sets each of which contains all the ring loads that have been allocated to busbar section i A { = set containing ring loads which are to be allocated to busbar section i Symbols for variables in Prolog clauses Ring_id = ring identifier Paper 5528C (P9, C4),

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Section: Symbols For Variables In Prolog Clausesmentioning

confidence: 99%

Artificial intelligence approach to load allocation in distribution substations

Wong

Cheung

1987

IEE Proc. C Gener. Transm. Distrib. UK

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“…Various approaches have been presented to solve power system restoration problem [1][2][3][4][5][6][7][8][9]. These techniques may very in several major types: automated restoration, Heuristics system, mathematical programming, and computer aided restoration.…”

Section: Introductionmentioning

confidence: 99%

Navy ship power system restoration using multi-agent approach

Momoh

2006

2006 IEEE Power Engineering Society General Meeting

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This paper presents a multi-agent system framework that performs system restoration for Navy ship. If Navy surface combatants' electric power supply interruption is caused by a fault, it is important to restore the power system as soon as possible to a target network configuration after the fault. The system views the problem as the interaction of simple independent agents. The approach is implemented using visual c++/JAVA and KQML programming languages. Simulation results have demonstrated that this method is able to reach target configurations.

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“…The first paper in this area is [1], it is a rule-based expert system. In [2] the knowledge engineering technology combined with conventional programming, in [3] an operator training simulator was exemplary full integrated of the restoration system as EMS function.…”

Section: Introductionmentioning

confidence: 99%

A Case-Based Reasoning System for Black-start

Zhou

2006

2006 International Conference on Power System Technology

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the basic strategy of case-based reasoning (CBR) is retrieving the most relevant cases and adapting them to fit new situation, and solutions would be stored for future reuse. A conceptual framework for a CBR system to scheme black-start plan is presented. The CBR has a hierarchical structure, cases in abstract level present the general characters of black-start, cases in the detail level include the restoration operations of the plants and substation in the subsystem. Conventional CBR system have had limited in revising ability. We developed "path finding" and "generation ability dispatch" algorithms for detail level case revising, and used qualitative process theory (QPT) to revise the abstract cases, the qualitative process model of black-start also can act as an explaining tool. This paper uses the CBR as the main frame, which combines qualitative analysis and quantitative analysis, and the theoretics research and field experience are well organized in this frame.

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Development of a Knowledge Based System for Power System Restoration

Cited by 17 publications

References 0 publications

Artificial intelligence approach to load allocation in distribution substations

Artificial intelligence approach to load allocation in distribution substations

Navy ship power system restoration using multi-agent approach

A Case-Based Reasoning System for Black-start

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