标题： 多层感知机与参数模型在智能电网可靠性预测中的混合集成 显示英文标题

标题： Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid

摘要： 可靠电力系统运行是电力公司的重要目标，这需要准确的可靠性预测以最小化停电持续时间。 由于天气状况通常是智能电网中停电的主要原因，特别是其配电网络，本文全面研究了各种天气参数对配电网络可靠性性能的综合影响。 特别地，提出了一种基于多层感知器（MLP）的框架，利用常见的天气数据时间序列来预测一个配电管理区域内的持续和瞬时停电每日数量。 首先，实施了参数回归模型来分析停电每日数量与各种常见天气参数之间的关系，例如温度、降水量、气压、风速和雷电。 然后，所选的天气参数和相应的参数模型被整合为输入，以制定一个MLP神经网络模型来预测停电的每日数量。 引入了一种改进的极限学习机（ELM）为基础的分层学习算法，使用佛罗里达州一家电力公司的实时可靠性数据和国家气候数据中心（NCDC）的常见天气数据来训练该模型。 此外，进行了敏感性分析，以确定不同天气参数对停电每日数量的各种影响。 显示英文摘要

摘要： 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.