Research on line loss analysis and intelligent diagnosis of abnormal causes in distribution networks: artificial intelligence based method

• Published in: PeerJ. Computer science Vol. 9; p. e1753
• Main Authors: Liao, Yaohua, En, Wang, Li, Bo, Zhu, Mengmeng, Li, Zhengxing, Gu, ZhiMing
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 15.12.2023; PeerJ, Inc; PeerJ Inc
• Subjects: Abnormal causes; Accuracy; Adaptive and Self-Organizing Systems; Algorithms; Algorithms and Analysis of Algorithms; Anomalies; Artificial Intelligence; Data Mining and Machine Learning; Diagnosis; Distribution network; Electricity distribution; Energy distribution; Energy sources; Identification; Intelligent diagnosis; Line loss; Load; Methods; Power supply; Search algorithms; Sparrow search algorithm; Support vector machine; Support vector machines; Technology application; China; Support vector machine; Sparrow search algorithm; Abnormal causes; Distribution network; Line loss; Intelligent diagnosis
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1753

The primary source of energy losses in distribution networks (DNs) is rooted in line losses, which is crucial to conduct a thorough and reasonable examination of any unusual sources of line losses to guarantee the power supply in a timely and safe manner. In recent studies, identifying and analyzing abnormal line losses in DNs has been a widely and challenging research subject. This article investigates a key technology for the line loss analyses of DNs and intelligent diagnosis of abnormal causes by implementing artificial intelligence (AI), resulting in several prominent results. The proposed algorithm optimizes the parameters of the support vector machine (SVM) and suggests an intelligent diagnosis algorithm called the Improved Sparrow Search Algorithm and Support Vector Machine (ISSA-SVM). The ISSA-SVM algorithm is trained to calculate the data anomalies of line losses when changing loads and exhibiting exceptional performance to identify abnormal line losses. The accuracy of abnormality identification employing the ISSA-SVM algorithm reaches an impressive 98%, surpassing the performances of other available algorithms. Moreover, the practical performance of the proposed approach for analyzing large volumes of abnormal line loss data daily in DNs is also noteworthy. The ISSA-SVM accurately identifies the root causes of abnormal line losses and lowers the error in calculating abnormal line loss data. By combining different types of power operation data and creating a multidimensional feature traceability model, the study successfully determines the factors contributing to abnormal line losses. The relationship between transformers and voltage among various lines is determined by using the Pearson correlation, which provides valuable insights into the relationship between these variables and line losses. The algorithm’s reliability and its potential to be applied to real-world scenarios bring an opportunity to improve the efficiency and safety of power supply systems. The ISSA that incorporates advanced techniques such as the Sobol sequence, golden sine algorithm, and Gaussian difference mutation appears to be a promising tool.