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M. R. Aghamohammadi. Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network. IJEEE. 2008; 4 (3) :104-114
URL: http://ijeee.iust.ac.ir/article-1-71-fa.html
آقا محمدی . Static Security Constrained Generation Scheduling Using Sensitivity Characteristics of Neural Network. . 1387; 4 (3) :104-114

URL: http://ijeee.iust.ac.ir/article-1-71-fa.html


چکیده:   (7726 مشاهده)

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.

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