Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

Abstract

Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.

A comprehensive review of fault diagnosis and fault-tolerant control techniques for modular multi-level converters

“…In [54][55][56] the authors implemented an ANN-based technique to remove the unbalancing in capacitor voltage. The proposed approach reduces ripple from capacitor voltage and quickly finds circulating current without analytical computations.…”

Regulated 2D Grayscale Image for Finding Power Quality Abnormalities in Actual Data

“…As of now, categorization and disturbance detection have been found to be crucial steps in preserving power quality. In the context of the smart grid, it is feasible to develop a power quality system based on the Internet of things [1] and deploy it along with the distribution network with the aim of informing utilities about consumption and disruptions via a two-way communication infrastructure. A general architecture can be seen in Figure 1.…”

Submarine cable fault identification based on FCN-GRU-SVM

“…Fault signal classification methods can be divided into deep learning methods and machine learning methods according to the different classifiers. In terms of traditional machine learning fault classification, a large number of mature classifiers have been available [4], such as K-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), etc. According to relevant studies, the combination of feature extraction and mature classifiers can achieve higher classification accuracy in fault signal classification.…”