Mirza Ateeq Ahmed Baig , Naeem Iqbal Ratyal , Adil Amin , Umar Jamil , Haris M. Khalid , Muhammad Fahad Zia
{"title":"电能质量扰动分类的多模态深度学习框架:一维时间序列信号和二维尺度图的集成","authors":"Mirza Ateeq Ahmed Baig , Naeem Iqbal Ratyal , Adil Amin , Umar Jamil , Haris M. Khalid , Muhammad Fahad Zia","doi":"10.1016/j.compeleceng.2025.110716","DOIUrl":null,"url":null,"abstract":"<div><div>Power quality (PQ) is crucial for the dependable functioning of electrical systems, requiring stable voltage, frequency, and waveform integrity. However, power quality disturbances (PQDs), resulting from faults, nonlinear loads, and switching events, can degrade system performance, damage equipment, and reduce operational efficiency. Accurate identification and classification of PQDs are therefore critical for effective mitigation. Traditional methods that rely solely on either one-dimensional (1D) time-series signals or two-dimensional (2D) waveform images often fail to capture the full characteristics of disturbances, leading to reduced accuracy. To address this limitation, a multi-modal deep learning framework is proposed that integrates 1D time-series data with corresponding 2D scalogram images. The proposed model employs parallel 1D and 2D convolutional neural networks (CNNs), each enhanced with attention mechanisms to enhance feature extraction by focusing on modality-specific salient information. The proposed model is evaluated on a comprehensive synthetic dataset of sixteen PQD types. Experimental results demonstrate that the proposed approach achieves an average classification accuracy of 99.99%, a sensitivity of 99.98%, and a specificity of 99.99%, outperforming existing methods. These results demonstrate the framework’s robustness and its potential as an effective solution for PQD monitoring and classification in smart grid environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110716"},"PeriodicalIF":4.9000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-modal deep learning framework for power quality disturbance classification: An integration of 1D time-series signals and 2D scalograms\",\"authors\":\"Mirza Ateeq Ahmed Baig , Naeem Iqbal Ratyal , Adil Amin , Umar Jamil , Haris M. Khalid , Muhammad Fahad Zia\",\"doi\":\"10.1016/j.compeleceng.2025.110716\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Power quality (PQ) is crucial for the dependable functioning of electrical systems, requiring stable voltage, frequency, and waveform integrity. However, power quality disturbances (PQDs), resulting from faults, nonlinear loads, and switching events, can degrade system performance, damage equipment, and reduce operational efficiency. Accurate identification and classification of PQDs are therefore critical for effective mitigation. Traditional methods that rely solely on either one-dimensional (1D) time-series signals or two-dimensional (2D) waveform images often fail to capture the full characteristics of disturbances, leading to reduced accuracy. To address this limitation, a multi-modal deep learning framework is proposed that integrates 1D time-series data with corresponding 2D scalogram images. The proposed model employs parallel 1D and 2D convolutional neural networks (CNNs), each enhanced with attention mechanisms to enhance feature extraction by focusing on modality-specific salient information. The proposed model is evaluated on a comprehensive synthetic dataset of sixteen PQD types. Experimental results demonstrate that the proposed approach achieves an average classification accuracy of 99.99%, a sensitivity of 99.98%, and a specificity of 99.99%, outperforming existing methods. These results demonstrate the framework’s robustness and its potential as an effective solution for PQD monitoring and classification in smart grid environments.</div></div>\",\"PeriodicalId\":50630,\"journal\":{\"name\":\"Computers & Electrical Engineering\",\"volume\":\"128 \",\"pages\":\"Article 110716\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Electrical Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0045790625006597\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790625006597","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
A multi-modal deep learning framework for power quality disturbance classification: An integration of 1D time-series signals and 2D scalograms
Power quality (PQ) is crucial for the dependable functioning of electrical systems, requiring stable voltage, frequency, and waveform integrity. However, power quality disturbances (PQDs), resulting from faults, nonlinear loads, and switching events, can degrade system performance, damage equipment, and reduce operational efficiency. Accurate identification and classification of PQDs are therefore critical for effective mitigation. Traditional methods that rely solely on either one-dimensional (1D) time-series signals or two-dimensional (2D) waveform images often fail to capture the full characteristics of disturbances, leading to reduced accuracy. To address this limitation, a multi-modal deep learning framework is proposed that integrates 1D time-series data with corresponding 2D scalogram images. The proposed model employs parallel 1D and 2D convolutional neural networks (CNNs), each enhanced with attention mechanisms to enhance feature extraction by focusing on modality-specific salient information. The proposed model is evaluated on a comprehensive synthetic dataset of sixteen PQD types. Experimental results demonstrate that the proposed approach achieves an average classification accuracy of 99.99%, a sensitivity of 99.98%, and a specificity of 99.99%, outperforming existing methods. These results demonstrate the framework’s robustness and its potential as an effective solution for PQD monitoring and classification in smart grid environments.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.