Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

Liu, Mingping; Chen, Yue; Zhang, Zhen; Deng, Suhui

doi:10.1109/access.2022.3233767

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…Liu et al [87] proposed an innovative approach to classifying power quality disturbances in the system using a deep convolutional neural network, multi-class support vector machine (MSVM) and segmented and modified S-transform (SMST). The test results showed that this method is characterised by high efficiency.…”

Section: Power Qualitymentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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Section: Power Qualitymentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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“…The model was evaluated on synthetic and experimental data collected from process-adaptive VMD data. The overall The algorithm of a CNN for PQ detection and analysis comprises five major stages [11]. Stage-1 convolution stage: the input image is convolved with multiple filters to extract features.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

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

et al. 2023

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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.

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“…In this case, the Long Short Term Memory (LSTM) network is used to categorize the signals based on their properties as a succession of disturbances. In [16], based on segmented and modified Stransform, deep convolutional neural network, and multiclass support vector machine, a novel method for classifying PQD is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Neural Dynamics Models for Motor Temperature Prediction

Liao,

Chen,

Long

et al. 2023

Preprint

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<p> The permanent magnet synchronous motor finds extensive use in industrial applications, and the development of effective thermal management solutions is crucial to enhance its power density. Accurate temperature prediction of the permanent magnet synchronous motor serves as the fundamental basis for designing effective thermal management strategies. Model-based prediction methods exhibit superior real-time performance, but the intricate modeling process requires substantial expert knowledge guidance and lacks versatility. Conversely, data-driven prediction methods, while offering flexibility, often lack physical implications in terms of system dynamics. This paper proposed a structured linear neural dynamics model for motor temperature prediction. This model is data-driven, with prior knowledge integrated into its structure, which preserves flexibility while guaranteeing system stability through the Perron-Frobenius theorem. Additionally, this paper achieves the decoupling of control input from state transitions and the embedded deployment of this model. The method is validated with a real dataset. The lightweight feature is demonstrated by the implementation of an STM32 Microcontroller with 1.808 KB and 27 mW. The paper is accompanied with open source data and code at GitHub https://github.com/ms140429/Explainable-Neural-Dynamics-Model</p>

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Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

Cited by 17 publications

References 38 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

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

Explainable Neural Dynamics Models for Motor Temperature Prediction

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