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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. -werfelli graduated from Al-fateh University (Tripoli-Libya) in Electrical Power Engineering in 2000 and obtained a Master's degree with distinction in 2005 from University of Newcastle upon Tyne in the same field. He worked for three years (2000)(2001)(2002)(2003) as a planning engineer at General Electricity Company of Libya (GECOL). Currently, he is pursuing a PhD at the University of Bath. His areas of interest are power system stability assessment and power system optimization problems using artificial intelligence. DrRod Dunn received his B.Sc. and Ph.D. in electrical Engineering from the University of Bath in 1981 and 1984 respectively. He became a lecturer in computing and control at the University of Bath, where he is now a senior lecturer in the Power and Energy System Group. His research areas include parallel and real time computing, power system modelling and control using AI methods. He has published over 70 technical papers, and is a member of the IEEE Pejman Iravani is a research fellow at the University of Bath working on artificial intelligence methods and their application to robotics and other systems that require adaptability. He gained his PhD in 2005 at the Open University where he developed architectures for multi robotic control and machine learning.

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