Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network

This paper proposes a novel approach for generation scheduling using sensitivitycharacteristic of a Security Analyzer Neural Network (SANN) for improving static securityof power system. In this paper, the potential overloading at the post contingency steadystateassociated with each line outage is proposed as a security index which is used forevaluation and enhancement of system static security. A multilayer feed forward neuralnetwork is trained as SANN for both evaluation and enhancement of system security. Theinput of SANN is load/generation pattern. By using sensitivity characteristic of SANN,sensitivity of security indices with respect to generation pattern is used as a guide line forgeneration rescheduling aimed to enhance security. Economic characteristic of generationpattern is also considered in the process of rescheduling to find an optimum generationpattern satisfying both security and economic aspects of power system. One interestingfeature of the proposed approach is its ability for flexible handling of system security intogeneration rescheduling and compromising with the economic feature with any degree ofcoordination. By using SANN, several generation patterns with different level of securityand cost could be evaluated which constitute the Pareto solution of the multi-objectiveproblem. A compromised generation pattern could be found from Pareto solution with anydegree of coordination between security and cost. The effectiveness of the proposedapproach is studied on the IEEE 30 bus system with promising results.