Weighted ensemble learning for real‐time short‐term voltage stability assessment with phasor measurements data
Amir Hossein Babaali, Mohammad Taghi Ameli
Abstract
Abstract Voltage stability assessment based on machine learning has become an important challenge in power systems. This paper presents real‐time short‐term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data and machine learning (ML). The database is created through time series of measurement data to involve system time‐temporal and dynamics. Then multiple operating states of the power system are classified through the calculation of the Lyapunov exponent and dynamic voltage index according to the database. This paper presents a weighted combination of random forest (RF) and LightGBM (LGBM) classifiers to train a time‐series database. One of the main advantages of this paper is using the gradient concept in data preprocessing, which has enhanced performance metrics and reduced the defect of data noise. Also, hyperparameter optimization is conducted to improve machine performance. Studies on the IEEE 118bus and a real local grid (RLG) demonstrate that the proposed method improves the performance metrics such as accuracy and F1‐score. Also, this approach is robust against PMU data noise and topology changes in the network.