Abstract
AbstractThis paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PIMVA) and Voltage-reactive power performance index (PIVQ). SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ) that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system). The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.
Highlights
Modern power system is a complex interconnected network having multiple utilities of different nature at generation, transmission, and distribution ends
The least square support vector machine (LS-support vector machines (SVMs)) outperformed over the recent available topologies of neural networks (NNs) in prediction of performance indices
This paper proposes a supervised learning model based on least square loss function with RBF Kernel function to estimate the contingency ranking in a standard IEEE 39 bus system
Summary
With the increase in population and ongoing demand of the electricity, power utilities are working near to the operating limits. Radial Basis Function Neural Networks (RBFNN) was used in the approaches (Devaraj et al, 2002; Singh & Srivastava, 2007; Srivastava et al, 2000) These networks exploited as a supervised agent to estimate the line loadings and bus voltages of different power systems. To aggregate research in a more promising way, two major thrust areas are identified and those are as follows: firstly, development of an intelligent feature selection algorithm which can map dependent and independent variables and secondly to employ fast and accurate supervised learning model to contemporary power system for accurate contingency ranking. (i) To develop a supervised learning based model which can predict the performance indices based on MVA power flow and line voltage reactive power flow for a large interconnected standard IEEE 39 bus test system under a dynamic operating scenario.
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