2018/10/03 by Longfei Wei, Arif I. Sarwat, Wei, Longfei +1 · 5 citations
Engineering · Mathematics · #Applications (stat.AP) #Artificial intelligence #Artificial neural network #Computer science #Electric power system #Energy Load and Power Forecasting #Engineering #Extreme learning machine #FOS: Computer and information sciences #Geography #Machine learning #Mathematics #Meteorology #Multilayer perceptron #Optimal Power Flow Distribution #Parametric statistics #Perceptron #Power (physics) #Power System Reliability and Maintenance #Reliability (semiconductor) #Reliability engineering #Smart grid #Statistics #Time series #Wind speed #stat.AP
paper · pdf · doi:10.48550/arxiv.1810.05004
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/10/03 · openalex publication_date 2018/10/03 · arxiv updated 2018/10/12 · openalex created_date 2018/10/26 · openalex updated_date 2026/08/06
The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions.