Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.91-1995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem. Index Terms-Adaptive Network-Based Fuzzy Inference System (ANFIS), transformer asset management, data-driven model, loss of life estimation.
Abstract-Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.
The utilisation of smart streetlights is gaining more attention in urban planning, as it potentially reduces streetlights' operation and maintenance costs, offers additional benefits in terms of safety, security, efficiency, versatility, and scalability, and sets the stage for further smart city applications. This study provides an extensive overview of state-of-the-art research on smart streetlights from various perspectives, including smart city applications, communications, control strategies, and cybersecurity, with the objective of laying down a foundation for future improvements of smart streetlight systems as well as establishing a basis for comparing existing technologies.
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