Artificial Immune System Based Reliability Appraisal Methodology of Power Generation Systems with Wind Power Penetration

Reliability appraisal of power-generating system is an effective measure to ensure proper system operations in the face of various uncertainties such as equipment failures and variations of generation and load. The integration of time-dependent sources such as wind turbine generators (WTGs) makes the reliability evaluation process more challenging. Due to the large number of system states involved in a power-generating system, it is normally not feasible to enumerate all possible failure states to calculate the reliability indices. Thus, some metaheuristics-based search algorithms, through their inherent convergence mechanisms, appear promising to And out the most meaningful system states within reasonable time. In this paper, an artificial immune system (AIS) based optimization procedure called CLONALG is adopted to assess the power-generating system adequacy including wind power integration. The most probable failure states are searched out by CLONALG and they contribute most significantly to the adequacy indices including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). A modified IEEE Reliability Test System (IEEE-RTS) is used to verify the applicability and validity of the proposed approach.