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 steadystate
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 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.
