Machine Learning Based Real-Time Monitoring of Long-Term Voltage Stability Using Voltage Stability Indices
Kalana Dharmapala, Athula Rajapakse, Krish Narendra, Yi Zhang
Abstract
This article presents a machine learning approach to predict the long-term voltage stability margin as represented by the Loadability Margin (LM). LM is an intuitive and easily understandable indicator of voltage stability. The unique feature of the proposed technique is the use of different Voltage Stability Indices (VSI) proposed in the literature as inputs to an ensemble of machine learning models which predict the LM. The VSIs used are carefully selected to include those based on different principles and computable using real time synchrophasor measurements. In addition, the paper presents a methodology to generate training data under different operational conditions and N-1 contingencies to train the machine learning models. The best machine learning algorithm and the categories of input VSIs are selected through a comparative study. These studies were conducted on the IEEE 14 bus system and IEEE 118 bus system and led to the selection of Random Forest Regression machine learning algorithm, and confirmed the accuracy and robustness of the proposed method. The system was implemented on real time PhasorSmart <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> synchrophasor application platform and validated using RTDS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> real-time simulator. The impact of synchrophasor measurement errors on the proposed technique were also analyzed.