Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network

M Aghamohammadi
2008 Iranian Journal of Electrical & Electronic Engineering   unpublished
This paper proposes a novel approach for generation scheduling using sensitivity characteristic of a Security Analyzer Neural Network (SANN) for improving static security of power system. In this paper, the potential overloading at the post contingency steady-state associated with each line outage is proposed as a security index which is used for evaluation and enhancement of system static security. A multilayer feed forward neural network is trained as SANN for both evaluation and enhancement
more » ... on and enhancement of system security. The input 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 for generation rescheduling aimed to enhance security. Economic characteristic of generation pattern is also considered in the process of rescheduling to find an optimum generation pattern satisfying both security and economic aspects of power system. One interesting feature of the proposed approach is its ability for flexible handling of system security into generation rescheduling and compromising with the economic feature with any degree of coordination. By using SANN, several generation patterns with different level of security and cost could be evaluated which constitute the Pareto solution of the multi-objective problem. A compromised generation pattern could be found from Pareto solution with any degree of coordination between security and cost. The effectiveness of the proposed approach is studied on the IEEE 30 bus system with promising results.