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种生物启发智能故障管理系统，在神经网络-模糊逻辑（NN-FZ）结构之上使用人工免疫系统（AIS）技术来主动管理配电系统故障，包括诊断、预测和数据挖掘。除了提供准确的故障诊断和预测之外，所提出的方法还可以解决基于动态运行环境发现新的故障诊断知识的挑战。 NN-FZ技术将用于聚合从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NN-FZ根据配电系统的网络拓扑、运行状况和天气状况等现有信息以及新获取的新故障和运行状况等信息来学习、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，并根据操作员和工程师的喜好解释有关故障诊断和预测过程的启发式方法。它可以将学到的信息传播到其他配电中心，从而防止“学到的”问题再次发生。该系统将彻底改变配电系统的故障诊断过程，显着提高系统可靠性并降低运营成本。所提出的活动和架构不仅限于配电系统，还适用于其他行业，例如通信网络和交通系统，这些都是具有不确定操作环境的大规模非线性系统。