Application of New Optimization Techniques for Fault Section Estimation in Power Systems

Abstract

Reliability and continuity of supplying electrical energy to the customers are one of the vital tasks of power system operation. The greatest problem facing this goal is the occurrence of faults. Thus, the determination of the faulty section becomes a vital task. This thesis introduces two algorithms for solving Fault Section Estimation (FSE) problem. The first algorithm is the Artificial Bee Colony (ABC) algorithm and the second one is the Improved Honey Bee Mating Optimization (IHBMO) algorithm. The two algorithms are applied on a 10-section system and a 28-section system using various test scenarios for each system. Then, the results obtained by the introduced algorithms are compared to other methods. The comparison is based on three tests: computation time test, convergence test and robustness test. The results show the validity of the two introduced algorithmsfor the detection of faulty section for both study systems. Therefore, the introduced algorithms can be used for larger systems. The robustness of the ABC and IHBMO algorithms is ensured as the algorithms are tested for 100 independent trials and acceptable results are obtained. The ABC algorithm has a main advantage than other techniques that it has only 2 parameters to be tuned which are the colony size and the maximum number of iterations. The IHBMO algorithm proves high computation efficiency, robustness and convergence characteristics when compared to other methods.