Extreme Learning Machine Approach for On-Line Voltage Stability Assessment
In recent years, voltage instability has become a major threat for the operation of many power systems. This paper proposes a scheme for on-line assessment of voltage stability of a power system for multiple contingencies using an Extreme Learning Machine (ELM) technique. Extreme learning machines are single-hidden layer feed- forward neural networks, where the training is restricted to the output weights in order to achieve fast learning with good performance. ELMs are competing with Neural Networks as tools for solving pattern recognition and regression problem. A single ELM model is developed for credible contingencies for accurate and fast estimation of the voltage stability level at different loading conditions. Loading margin is taken as the indicator of voltage instability. Precontingency voltage magnitudes and phase angles at the load buses are taken as the input variables. The training data are obtained by running Continuation Power Flow (CPF) routine. The effectiveness of the method has been demonstrated through voltage stability assessment in IEEE 30-bus system. To verify the effectiveness of the proposed ELM method, its performance is compared with the Multi Layer Perceptron Neural Network (MLPNN). Simulation results show that the ELM gives faster and more accurate results for on-line voltage stability assessment compared with the MLPNN.
KeywordsExtreme learning machine loading margin voltage stability assessment
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