Abstract: A single type of knowledge and reasoning method is often not sufficient for a decision support system to address the variety of tasks a user performs. It is often necessary to determine which reasoning method would be the most appropriate for each task, and a combination of different methods has often shown the best results. In this study CBR was mixed with other RBR and MBR approaches to promote synergies and benefits beyond those achievable using CBR or other individual reasoning approaches alone. Each approach has advantages and disadvantages, which are proved to be complementary in a large degree. So, it is well-justified to combine these to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we briefly outlined popular case-based reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rule based reasoning, and model based reasoning.Further we examined the strengths and weaknesses of var

• Publication Date: 24-Feb-2013
• DOI: 10.15224/978-981-07-5461-7-32
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Abstract: In a power system the detection of the faults is very important. The faults occurred should be detected and corrected as soon as possible in order to protect further damage to the system. The faults can cause very adverse effects to the system. However the fault detection is not an easy task. In this paper we have considered a sub-station and the detection of the faults within that sub-station is done with the help of Artificial Neural Networks. The purpose of using ANN in this work is very simple, As ANN has various advantages like it can handle huge amount of data without any difficulty and it also takes less execution time. The sub-station considered in this work is the 500kV gas-insulated substation of the Itaipu system. The MATLAB software and the ANN toolbox within it is used here and the network is trained and tested.

• Publication Date: 24-Feb-2013
• DOI: 10.15224/978-981-07-5461-7-33
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• Downloads: 0