Power System Event Classification and Localization Using a Convolutional Neural Network

Abstract

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

Frequency Event Detection and Mitigation in Power Systems: A Systematic Literature Review

“…The 3120-bus Polish system was simulated by Ren et al for four types of faults in different zones [131]. Frequency data obtained from synchronous generators provide input to a CNN model.…”

Electricity Theft Detection in Power Consumption Data Based on Adaptive Tuning Recurrent Neural Network

“…Therefore, the development of power industry needs to strengthen the development of new artificial intelligence and information and automation technology. With the continuous improvement of dynamic monitoring and acquisition technology of power consumption data of power grid users, it is of great engineering significance to study the intelligent anti-power-theft algorithm based on the big data of the power consumption to identify the power theft behavior (Ren et al, 2020;Zhang et al, 2021).…”

Power quality disturbances classification based on Gramian angular summation field method and convolutional neural networks

“…In Reference 20, 1D and 2D convolutional neural networks are combined to extract both visual features and signal features simultaneously. The work described in Reference 21 is based on processing Phasor Measurement Unit (PMU) monitoring time series in three different ways for generating picture datasets for CNN model training and testing, which would include time‐domain stacking, frequency domain stacking, and GAF stacking, in order to facilitate a powerful, image‐based CNN for pattern detection and extraction. In Reference 22, a novel approach based on optimized Bayesian CNN is presented to classify the real power quality events such as sag, swell, interruption, and harmonics which is gathered from the nation‐wide power quality monitoring system.…”