Biologically Inspired Intelligent Fault Diagnosis for Power Distribution Systems

配电系统的仿生智能故障诊断

• 批准号: 0245383
• 负责人: Mo-Yuen Chow
• 金额: --
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-15 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0245383&HistoricalAwards=false
• 关键词: Biologically; Inspired; Intelligent; Fault; Diagnosis

This project will investigate and develop a Biologically Inspired Intelligent Fault Management System using Artificial Immune System (AIS) technologies on top of a Neural Network - Fuzzy Logic (NN-FZ) structure to actively manage power distribution system faults, including diagnosis, prognosis, and data mining. The proposed approach can answer the challenges of discovering new fault diagnosis knowledge based on the dynamic operating environments, in addition to providing accurate fault diagnosis and prognosis. The NN-FZ technology will be used to aggregate information collected from sources such as SCADA system, circuit database, fault database, etc., to perform fault diagnosis and prognosis. Then an AIS algorithm will be used to guide the NN-FZ to learn, absorb, adapt, evolve and improve the fault diagnosis performance based on existing information such as the distribution system's network topology, operating conditions and weather conditions, and depending upon newly acquired information, such as new faults and operating conditions. This proposed system can data mine the existing outage database and explain heuristics about the fault diagnosis and prognosis process as preferred by operators and engineers. It can disseminate learned information to other power distribution centers, thereby preventing the reoccurrence of "learned" problems. This system would revolutionize the Fault Diagnosis process for power distribution systems, to significantly increase system reliability and reduce operation costs. The proposed activities and architectures are not only limited to power distribution system, but are also applicable to other industries such as communication networks and transportation system that are large scale nonlinear system with uncertain operating environments.

该项目将研究和开发一个受生物启发的智能故障管理系统，该系统使用人工免疫系统(AIS)技术，建立在神经网络-模糊逻辑(NN-FZ)结构之上，以主动管理配电系统故障，包括诊断、预测和数据挖掘。该方法除了能够提供准确的故障诊断和预测外，还可以应对基于动态运行环境发现新的故障诊断知识的挑战。NN-FZ技术将用于汇总从SCADA系统、电路数据库、故障数据库等来源收集的信息，以进行故障诊断和预测。然后利用AIS算法指导神经网络FZ根据配电网的网络拓扑、运行状况和天气状况等现有信息，以及新获得的新故障和运行状况等信息，学习、吸收、适应、进化和提高故障诊断性能。该系统可以对现有的停电数据库进行数据挖掘，并对故障诊断和预测过程中的启发式规则进行解释，这是操作员和工程师所喜欢的。它可以将学习到的信息传播到其他配电中心，从而防止“学习”问题再次发生。该系统将彻底改变配电系统的故障诊断流程，显著提高系统可靠性，降低运行成本。所提出的活动和体系结构不仅限于配电系统，还适用于其他行业，如通信网络和交通运输系统，这些行业是具有不确定运行环境的大型非线性系统。