Assessment and Enhancement of Power System Security using Soft Computing and Data Mining Approaches

Abstract

The power system is a complex network with numerous equipment’s interconnected for it’s reliable operation. These power system networks are forced to operate under highly stressed conditions closer to their limits. One of the key objective of the power system operators is to provide safe, economic and reliable power to it’s consumers. However, such network experiences perturbations due to many factors. These perturbations may lead to system collapse or even black out, which impacts the reliability of the system. Thus, one of the major aspect for the secure operation of the system can be achieved through security assessment. In this context, the power system static security assessment is necessary to evaluate the security status under contingency scenario. One of the approach for the security assessment is by contingency ranking, where the severity of a specific contingency is computed and ranked with highest severity to the lowest one. Initially, this approach is implemented using several load flow methods in order to identify the limit violations. However, these approaches are complex, time consuming and not feasible for real time implementation. These approaches are applied to a specific system operating condition. Thus in this context, this thesis focusses to implement soft computing and data mining approaches for security assessment by contingency ranking and classification approach. Along with the security assessment, this thesis also focusses on a control mechanism approach for the security enhancement under contingency scenario using evolutionary computing techniques.
In this thesis, the various aspects of the power system security such as it’s assessment, and it’s enhancement are studied. The conventional contingency ranking approach by NRLF method is presented for the security assessment. In order to predict the system severity, a ranking module is designed with two neural network models namely, MFNN and RBFN for security assessment under different load conditions. Both neural network models are quite accurate in predicting the performance indices in less time.
Another aspect of power system static security assessment is by classification approach, where the security states are classified into secure, critically secure, insecure and highly insecure. This approach helps in proper security monitoring. Thus, this thesis also presents the design and implementation of two security pattern classifier models namely the decision tree and the random forest classifiers. The classifiers are trained and tested with several security patterns generated in an offline mode. The proposed models are compared with MLP, RBFN and SVM classifier models in order to prove their efficiency in classifying the security levels.
Further, this thesis work also focusses on a control mechanism for security enhancement under N-1 line outage contingency scenario. Initially contingency analysis is carried out under N-1 line outage case and critical contingencies are identified. The objective is to reschedule the generators with minimum fuel cost in such a way that the overloaded lines are relieved from stress. In order to enhance the power system security, an evolutionary computing algorithm, namely an enhanced cuckoo search algorithm is proposed for the contingency constrained economic load dispatch. To study the robustness and effectiveness of the proposed algorithm, the results are compared with CS, BA and PSO algorithms.