Backbone-network reconfiguration for power system restoration using genetic algorithm and expert system

During the last few years many blackouts have been experienced throughout the world. It seems that modern power systems are more exposed to major blackouts. This raises the necessity of having an obvious restoration plan to rebuild the power system as soon as possible. This problem is characterized by a large solution space which can be constrained with expert knowledge. This paper describes a new power system restoration algorithm jointly using genetic algorithms (GA) and expert systems (ES). GA's are used to obtain optimized skeleton networks for power systems, while ES acts as an effective system operator to constrain the solution space for the GA. Also ES allows the GA to be more informed about the overall power system physical performance. This includes, for example, frequency response to sudden load pick up, reactive power balance, load-generation balance, stability limits, high and low voltage levels limits, MW and MVAR reserve requirement and line transfer capability, etc. In order to show the advantages of combining the GA and ES to this problem, this paper presents a comparative result between the hybrid algorithm and pure ES method. The case study presented in this study is 39 IEEE bus systems. The results presented in this paper show that the application of ES can be significantly enhanced by the stated combination.