基于优化神经网络算法的大数据专家系统的继电保护二次回路故障定位系统(RSFLS)的研究与实现

为解决继电保护二次回路故障排查耗时耗力、考虑因素不全面、受限于人员经验等问题,本文提出基于优化神经网络的大数据专家系统的方法，并搭建继电保护二次回路故障定位系统（RSFLS）。在对继电保护二次回路抽象分类的基础上，建立基于实体-关系模型（E-R模型）的缺陷数据库模型，利用优化神经网络进行随机化和自学习，并融合继电保护人员的经验库，形成基于优化神经网络的专家系统架构，从而匹配定位故障点。该方法有效解决了传统二次回路故障排查方法所存在的准确性不高、随机性较大、时效性较低等问题。基于该方法开发的RSFLS系统已投入实际应用，能有效缩短继电保护二次回路故障排查时间、提高排查准确性。测试结果证明所述方法和系统在提升继电保护系统可靠性方面有显著效果。

Relay Protection Secondary Loop Fault Location System (RSFLS) Based on Big Data Expert System Using Optimized Neural Network Algorithm

The disadvantages of the traditional troubleshooting method for secondary loop faults are: first, it takes a lot of manpower and time; second, it is difficult to fully consider various factors at the fault site; third, it is highly dependent on the technical skills of relay protection personnel. This paper proposes a method based on optimized neural network for big data expert system, and builds relay protection secondary loop fault location system (RSFLS). Based on the abstract classification of the relay protection secondary loop, the defect database model of the entity-relationship model (E-R model) is established, and the optimized neural network is used for randomization and self-learning. In addition, establish a library of relay protection personnel experience. An expert system architecture based on optimized neural networks is formed to locate fault points. The method effectively solves the problems of low efficiency and high randomness in the troubleshooting of the relay protection secondary circuit. The RSFLS system developed based on this method has been put into practical use, which can effectively shorten the troubleshooting time of the relay protection secondary circuit and improve the troubleshooting accuracy. The test results prove that the method and system have significant effects in improving the reliability of the relay protection system.