Special protection and control scheme based on generation rescheduling using differential evolution and electromagnetism-like algorithm

A power system contingency phenomenon known as N−1 security criterion is a great concern in power system analysis as it involves loss of any of the system components such as line, transformer, and generator while having no loss of system demand. However, upgrading utility infrastructure including building new transmission lines to arrest the N−1 contingency condition is costly and time-consuming. Accordingly, Special Protection Scheme (SPS) remedial strategies such as generation rescheduling, load shedding, phase shift transformers and transmission line switching can be adopted by utilities to reduce the impacts of risks caused by the line overloading without any infrastructure expansion. The main problem with the current generation rescheduling strategies, which is the main SPS technique implemented in this research, is their inefficiency in the sense that they are slow in corrective decision and costly. The main objective of this research is designing a Special Protection and Control Scheme (SPCS) based on a hybrid approach of combining Differential Evolution (DE) and Electromagnetism-Like algorithms. This SPCS strategy, novel in the generation rescheduling application, is called as Differential Evolution with Adaptive Mutation (DEAM). The specific aims in employing DEAM-based SPCS are to investigate a strategy to resolve the line overloading issue through the N-1 contingency based on the load flow analysis and the severity index develop SPCS scheme through the generation rescheduling strategy based on the hybrid DEAM algorithm. The proposed algorithm is evaluated on the IEEE 30-bus test system. The performance of the proposed DEAM algorithm is validated with both the normal DE and Genetic Algorithm (GA) in terms of the fitness convergence and generation fuel cost. The results showed that the DEAM based scheme gives better performance than DE and GA in terms of less generation fuel cost and faster fitness convergence

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