Generating System Wellbeing Index Evaluation Using Genetic Algorithm

Reliability assessment of generation system is a crucial task used to be done using deterministic approaches. However, due to the practical limitations of these approaches, they have been gradually replaced by probabilistic techniques. Nevertheless, there is a considerable reluctance in many electric power utilities to completely abandon deterministic considerations. To fulfill the industry need, wellbeing analysis has been developed to combine the deterministic and the probabilistic approaches in a single framework. Analytical techniques or Monte Carlo Simulation have been used to evaluate wellbeing indices. However, analytical approaches are complicated and mathematically demanding and simulation technique requires a huge amount of computing time, and large memory size. This still prevents the utilities to benefit from the wellbeing framework. This paper presents a novel Genetic Algorithm (GA) based technique to calculate the wellbeing indices. Hopefully, this will encourage the industry to benefit from the wellbeing analysis. The features of the GA are utilized to collect and identify the health, marginal and at risk wellbeing states and to calculate the associated wellbeing indices. The proposed technique is applied to the IEEE-RBTS and the resulting wellbeing indices are compared to those obtained using a conventional analytical technique. The results show that the outcome of both techniques is virtually identical. The effect of the GA parameters on the wellbeing indices is examined. The proposed GA based technique in the manner applied in this study is simple, practical and valid to calculate the wellbeing indices.