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Abstract
In modern industrialized society, a supply of electric energy is expected to be reliable and continuous since a high availability of secure power system is essential for its operation. A secure power system is expected to be free from risk or danger and to have the ability to withstand any one of the pre-selected list of credible contingencies. The objective of this work is to investigate the reliability of the Static Security Assessment (SSA) in determining the security level of power system from serious interference during operation. Therefore, back-propagation Artificial Neural Network (ANN) is implemented to classify the security status in the test power systems (secure or insecure) then estimate the degree of severity by ranking the contingency cases. Offline Newton-Raphson load flow is employed to gather the input data for the ANN. This method has been tested using two different power system models, the first is IEEE-14 bus test system and the second is a part of Egyptian Network 61-bus, 102 line. The Power World Simulator (PWS) software is an advanced package used to simulate the power system models.