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Artificial intelligence and machine learning techniques provide a qualitative as well as quantitative assessment of the power system.

1. Fuzzy Logic: The Fuzzy logic system (FLS) is a logic system which represents reasons and knowledge in a fuzzy manner for reasoning under uncertainty or describes in imprecise manner for human interpretation. Not like Boolean logic and classic logic which assumes that entire fact is either true or false, but fuzzy logic allows Boolean logic to tackle with vague and imprecise expressions of human understanding. Not like the classic logic systems, it models the reasoning for imprecision model that plays important role in ability of human knowledge to understand an estimated or inexact answer for a question which is based on store of knowledge which is approximate, not complete or totally unreliable. It is the best approach and way to go for fuzzy logic when it is too difficult to*…show more content…*

Knowledge of human experts forms the base of the accuracy of fuzzy logic systems (FIS). The results of post contingent state of line power flows and performance indices are obtained using Newton Raphson or any other load flow method .The membership functions for these post contingent quantities are first recognized and defined and with these formed membership functions, the computation of overall severity index is done to obtain the contingency ranking. For each post contingent quantities which is obtained by the conventional load flow method is known by different linguistic variable and with the membership function associated with it. The inputs to the fuzzy inference system are line loadings, and voltage profiles indices and the outputs to the same FIS are the severity indices, which are computed using the simple set of rules of Fuzzy. The post contingent quantities of line flows and bus voltage must be

1. Fuzzy Logic: The Fuzzy logic system (FLS) is a logic system which represents reasons and knowledge in a fuzzy manner for reasoning under uncertainty or describes in imprecise manner for human interpretation. Not like Boolean logic and classic logic which assumes that entire fact is either true or false, but fuzzy logic allows Boolean logic to tackle with vague and imprecise expressions of human understanding. Not like the classic logic systems, it models the reasoning for imprecision model that plays important role in ability of human knowledge to understand an estimated or inexact answer for a question which is based on store of knowledge which is approximate, not complete or totally unreliable. It is the best approach and way to go for fuzzy logic when it is too difficult to

Knowledge of human experts forms the base of the accuracy of fuzzy logic systems (FIS). The results of post contingent state of line power flows and performance indices are obtained using Newton Raphson or any other load flow method .The membership functions for these post contingent quantities are first recognized and defined and with these formed membership functions, the computation of overall severity index is done to obtain the contingency ranking. For each post contingent quantities which is obtained by the conventional load flow method is known by different linguistic variable and with the membership function associated with it. The inputs to the fuzzy inference system are line loadings, and voltage profiles indices and the outputs to the same FIS are the severity indices, which are computed using the simple set of rules of Fuzzy. The post contingent quantities of line flows and bus voltage must be

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