{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:16:29Z","timestamp":1771002989591,"version":"3.50.1"},"reference-count":14,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,2,4]]},"abstract":"<jats:p>Power quality analysis and governance need the identification of power quality issues. With the use of smart meters and various smart collection devices, more and more power quality data are collected, and the massive data collection brings pressure on communication, storage and computation to the conventional algorithm for identifying and classifying power quality disturbances based on cloud computing. In the paper, a classification algorithm for power quality disturbance identification based on edge computing and fusion model is proposed. The algorithm\u2019s key concept is to compress and sense the power quality signals at the edge side, and then transmit the compressed power quality data to the cloud, which uses an improved Dense-Net and LSTM fusion model to identify and classify the compressed power quality data. Through experiments, it is proved that the method can compress the power quality signal to 70% of the original signal size while satisfying the recognition and data on power quality disturbance categorization accuracy, reducing the communication cost of data transmission, lowering the computational pressure and caching pressure on the cloud, and having certain robustness.<\/jats:p>","DOI":"10.3233\/jcm226494","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T11:38:28Z","timestamp":1668166708000},"page":"391-403","source":"Crossref","is-referenced-by-count":0,"title":["Research on power quality disturbance classification algorithm based on edge computing"],"prefix":"10.1177","volume":"23","author":[{"given":"Min","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jinhao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Tengxin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Huiqiang","family":"Zhi","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Huipeng","family":"Li","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/JCM226494_ref1","doi-asserted-by":"crossref","first-page":"107682","DOI":"10.1016\/j.epsr.2021.107682","article-title":"Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model","volume":"204","author":"Gao","year":"2022","journal-title":"Electric Power Systems 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network","volume":"9","author":"Cai","year":"2019","journal-title":"Applied Sciences."},{"issue":"19","key":"10.3233\/JCM226494_ref15","doi-asserted-by":"crossref","first-page":"6755","DOI":"10.3390\/app10196755","article-title":"A comparison of power quality disturbance detection and classification methods using CNN, LSTM and CNN-LSTM","volume":"10","author":"Garcia","year":"2020","journal-title":"Applied Sciences."},{"issue":"99","key":"10.3233\/JCM226494_ref20","first-page":"1","article-title":"A new convolutional network structure for power quality disturbance identification and classification in micro-grids","volume":"PP","author":"Gong","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/JCM226494_ref21","doi-asserted-by":"crossref","unstructured":"Rodriguez Miguel Angel, Sotomonte John Felipe, Cifuentes Jenny, Bueno L\u00f3pez Maximiliano. 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