Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory
AbstractThe reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately. View Full-Text
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Ali Kadhem, A.; Abdul Wahab, N.I.; Aris, I.; Jasni, J.; Abdalla, A.N. Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory. Energies 2017, 10, 343.
Ali Kadhem A, Abdul Wahab NI, Aris I, Jasni J, Abdalla AN. Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory. Energies. 2017; 10(3):343.Chicago/Turabian Style
Ali Kadhem, Athraa; Abdul Wahab, Noor I.; Aris, Ishak; Jasni, Jasronita; Abdalla, Ahmed N. 2017. "Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory." Energies 10, no. 3: 343.
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