A Comprehensive Review of Deep-Learning Applications to Power Quality Analysis

Abstract

Power quality (PQ) monitoring and detection has emerged as an essential requirement due to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging stations, energy storage devices, and distributed generation energy sources in the recent smart grid and microgrid scenarios. Even though, to date, the traditional approaches play a vital role in providing a solution to the above issue, the limitations, such as the requirement of significant human effort and not being scalable for large-scale power systems, force us to think of alternative approaches. Looking at a better perspective, deep-learning (DL) has gained the main attraction for various researchers due to its inherent capability to classify the data by extracting dominating and prominent features. This manuscript attempts to provide a comprehensive review of PQ detection and classification based on DL approaches to explore its potential, efficiency, and consistency to produce results accurately. In addition, this state-of-the-art review offers an overview of the novel concepts and the step-by-step method for detecting and classifying PQ events. This review has been presented categorically with DL approaches, such as convolutional neural networks (CNNs), autoencoders, and recurrent neural networks (RNNs), to analyze PQ data. This paper also highlights the challenges and limitations of using DL for PQ analysis, and identifies potential areas for future research. This review concludes that DL algorithms have shown promising PQ detection and classification results, and could replace traditional methods.

Exploring the Synergy of Artificial Intelligence in Energy Storage Systems for Electric Vehicles

“…As an emerging topic, few contributions to the literature were retrieved. Given their non-linear behavior and the harmonic distortion generated in power grids, the integration of such components must be treated well, especially in the development of smart grids (or microgrids), where the cooperation between EV charging infrastructure, users' loads, RES generation, and ESSs is realized, as Figure 5 depicts [44]. Focusing on PQ is needed to ensure the reliability and safety of the network, stabilize the energy flow, and reduce the detrimental effect of harmonic distortion injected into the grid.…”

“…Despite that, CNNs do not fit with time-series data, and in order to improve their performance, parallel cooperation with physics-based models such as wavelet or spectrogram RNN instead are capable of dealing with time-series and sequential data for event detection. Generally, the accuracy of RNN lies between 98 and 99% [44]. In addition to CNNs and RNNs, hybrid models combining both architectures, known as Convolutional Recurrent Neural Networks (CRNNs), have emerged as powerful tools in various domains, including PQ analysis.…”

“…Upon closer examination of the bibliographic contributions, it became apparent that a significant portion addressed cross-topic research. For instance, references [38][39][40][41][42][43][44] explore the overlap between BMS and PQ, focusing on their combined effects on the grid. This underscores the importance of interdisciplinary collaboration, as the application of AI extends beyond the primary area of interest to adjacent topics.…”

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

“…Furthermore, the probability that a PQ event occurs during the operator’s observation time can be low, making the monitoring ineffective. For these reasons, recently, several devices for the automatic detection and classification of PQ events have presented in the literature [ 9 , 10 , 11 ]. The general architecture [ 12 ] is shown in Figure 1 , and it is composed of the blocks: signal acquisition and segmentation, feature extraction, classification, and decision-making.…”

Perception of power quality disturbances using Fourier, Short-Time Fourier, continuous and discrete wavelet transforms

“…A fundamental understanding of the many notations used to express power quality is required to extract significant information from signals using signal processing techniques. The definition of power quality in the Institute of Electrical and Electronics Engineers (IEEE) dictionary 19 focuses on the "powering and grounding" components of devices. Only with this basic understanding can effective signal processing be used to analyze and improve power quality.…”