Extreme Learning Machine Approach for Real Time Voltage Stability Monitoring in a Smart Grid System using Synchronized Phasor Measurements

Abstract

-Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

“…ELM has shown better performance and lower training times than ANN and SVM facing regression problems [34]. In [35][36][37], ELM is used as a tool to evaluate the long-term online voltage stability, considering different electric parameters as input vectors. These include voltage magnitudes and angles, power flows, as well as active and reactive power injections.…”