RESUMO
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.
RESUMO
The various social issues that accompany economic development pose new challenges for leaders to integrate economic benefits, social responsibility, and environmental protection. In this context, various new leadership styles have emerged. Among them, sustainable leadership reveals the key role of leaders in balancing the triple goals of economy, society and environment, and has become an important part of leadership theory research in recent years. We searched the literature related to sustainable leadership in databases such as Web of Science, EBSCO and CNKI. Based on the existing literature, we systematically review the origins, connotations, analytical perspectives, measurement methods, and conceptual comparisons of sustainable leadership. And we also construct an integrated analytical framework of sustainable leadership on the premise of sorting out and summarizing the antecedents and consequences of sustainable leadership. Finally, we provide an outlook on the future research areas of sustainable leadership in order to further promote research of sustainable leadership.
