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Showing 2 results for Static Security

M. R. Aghamohammadi,
Volume 4, Issue 3 (10-2008)

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.

D. S. Javan, H. Rajabi Mashhadi,
Volume 7, Issue 4 (12-2011)

Deregulation of power system in recent years has changed static security assessment to the major concerns for which fast and accurate evaluation methodology is needed. Contingencies related to voltage violations and power line overloading have been responsible for power system collapse. This paper presents an enhanced radial basis function neural network (RBFNN) approach for on-line ranking of the contingencies expected to cause steady state bus voltage and power flow violations. Hidden layer units (neurons) have been selected with the growing and pruning algorithm which has the superiority of being able to choose optimal unit’s center and width (radius). A feature preference technique-based class separability index and correlation coefficient has been employed to identify the relevant inputs for the neural network. The advantages of this method are simplicity of algorithm and high accuracy in classification. The effectiveness of the proposed approach has been demonstrated on IEEE 14-bus power system.

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