Vulnerability assessment and control of large scale interconnected power systems using neural networks and neuro-fuzzy techniques

Vulnerability Assessment and control are some of the essential requirements for maintaining security of modern power systems, particularly in competitive energy markets. This paper presents intelligent computational techniques for vulnerability assessment of power systems and recommends preventive control measures. Accurate techniques for vulnerability assessment and control of power systems are developed. In vulnerability assessment, power system loss index is used as a vulnerability parameter, neural network weight extraction is employed as the feature extraction method and the generalized regression neural network is used to predict vulnerability of a power system. As for vulnerability control, load shedding is considered by using the neuro-fuzzy technique. Finally, the paper presents and discusses the results from this research with recommendations.