Hybrid expert system and simulated annealing approach to optimal reactive power planning

Jwo, Wu-Shun

doi:10.1049/ip-gtd:19951958

Cited by 64 publications

(29 citation statements)

References 4 publications

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“…GA, by contrast, access deep knowledge of systems problem by well-established models. GA has much more potential in power systems analysis and are also latest entry into the Hybrid AI techniques Application area/power system problems Fuzzy neural network systems Generation and distribution [180], relaying [181], fault diagnosis [182], load forecasting [183,184], reactive power control [185,186], generator maintenance scheduling [187] Fuzzy genetic systems Stability [188], Power systems control [189,190], economic dispatch [191] Fuzzy expert systems Power system planning [192] Fuzzy/ neural/expert/genetic systems Load forecasting [193,194], generation expansion planning [195], power system stabilizer [196] Simulated annealing with fuzzy/genetic/expert systems Reactive power planning [197], generator maintenance scheduling [198][199][200] AI fields and are getting most of the current attention. GA needs to be understood in relation to the computation requirements and convergence properties.…”

Section: Discussionmentioning

confidence: 99%

Optimization Methods for Electric Power Systems: An Overview

Bansal¹

2005

International Journal of Emerging Electric Power Systems

119

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Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article.

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Section: Discussionmentioning

confidence: 99%

Optimization Methods for Electric Power Systems: An Overview

Bansal¹

2005

International Journal of Emerging Electric Power Systems

119

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“…Var planning was presented as a multi-objective optimization problem in terms of maximum system security and minimum operation cost (Jwo et al, 1995). An effective algorithm based on hybrid expert system and SA was proposed to obtain the global optimal solution considering both quality and speed.…”

Section: Reactive Power Planningmentioning

confidence: 99%

Applications of Simulated Annealing-Based Approaches to Electric Power Systems

Huang¹,

Sun²,

Huang³

2012

Simulated Annealing - Advances, Applications and Hybridizations

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“…If both security and cost are included in the same objective function, then the weights can not be decided directly and easily. The objective in [20], [21] includes Var investment cost minimization, power loss reduction, and voltage deviation reduction as follows:…”

Section: F Multi-objective (Mo)mentioning

confidence: 99%

“…The two-layer simulated annealing (TLSA) [20], and the hybrid expert system simulated annealing (ESSA) [21] are proposed to improve the CPU time of SA while retaining the main characteristics of SA. SA is combined with many other approaches such as the genetic algorithm (GA) [12], the -constraint method [26], [34], the goal-attainment approach [23], the weighted-norm approach [24], and the fuzzy logic in [25] to deal with MO problems.…”

Section: ) Simulated Annealing (Sa)mentioning

confidence: 99%