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.

该项目将使用人工免疫系统（AIS）技术在神经网络 - 模糊逻辑（NN -FZ）结构上进行研究并开发具有生物学启发的智能故障管理系统，以积极管理功率分配系统故障，包括诊断，预后和数据挖掘。提出的方法还可以回答基于动态工作环境的新故障诊断知识的挑战，此外还可以提供准确的故障诊断和预后。 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，以执行故障诊断和预后。然后，将使用AIS算法来指导NN-FZ学习，吸收，适应，进化和改善故障诊断性能，这些诊断性能基于现有信息，例如分配系统的网络拓扑，操作条件和天气条件，并取决于新获得的信息，例如新的故障和工作条件。该提出的系统可以数据挖掘现有的停电数据库，并解释有关操作员和工程师首选的故障诊断和预后过程的启发式方法。它可以将学习的信息传播到其他配电中心，从而防止“学到”问题的再发生。该系统将彻底改变功率分配系统的故障诊断过程，从而大大提高系统可靠性并降低操作成本。拟议的活动和体系结构不仅限于发电系统，而且还适用于其他行业，例如通信网络和运输系统，它们是具有不确定操作环境的大型非线性系统。