Identification of Static and Dynamic Distribution System Aggregate Load Using Probabilistic Neural Networks

本研究的目的是：1） 开发基于神经网络的实时负荷模型， 动态和分配系统分析;以及 2)设计一个“智能”监控器， 配电系统电容器，电压调节器， 和有载抽头变换变压器。 研究 将确定动态和分配系统 将负载表示为电压函数的模型 和频率。 测试将在中环进行 缅因州电力公司馈线获取数据， 用来训练概率神经网络 的 神经网络学习算法将 用数字信号处理器实现， “智能”监视器，将能够映射矢量的 测量和其他相关变量加载 参数将用于动态系统研究 并将允许实时电压无功控制， 配电馈线 的重要目标 建议的研究是生产负荷模型或 可用于标准的建模技术 其他研究人员应用众所周知的 分析方法

The objectives of the proposed research are: 1) develop a neural net-based real-time load model for both dynamic and distribution system analyses; and 2) design a "smart" monitor for controlling distribution system capacitors, voltage regulators, and load tap changing transformers. The research will determine dynamic and distribution system models that represent load as a function of voltage and frequency. Tests will be made on Central Maine Power Company feeders to obtain data with which to train a probabilistic neural network. The neural network learning algorithm will be implemented with a digital signal processor in a "smart" monitor that will be able to map a vector of measured and other relevant variables to load parameters will be used for dynamic system studies and will allow real-time volt-var control on a distribution feeder. An important goal of the proposed research is to produce load models or modeling techniques that can be used in standard ways by other researchers applying well-known analysis methods.