基于模糊控制和大数据算法模型的电力运维故障诊断设备方法

针对现有技术中对电力运维故障检测灵敏度低、诊断误差大等问题,设计了一种新型故障诊断方案。该方案将PID模糊控制计算器与大数据算法模型相结合,并采用实时布线的方法减少诊断面积,基于改进型大数据算法模型提取电力运维设备故障数据特征,对电力运维设备运行工况构建诊断网络,通过分析电力运维设备工况的功能系统完成数据诊断。为了减少诊断误差,该研究设计了一种故障诊断设备,采用集成芯片化设计和算法程序,减小体积的同时保证检测结果的准确性。实验结果表明,该研究方法故障诊断误差小,准确率最高达到98.6%。

Design of power operation and maintenance fault diagnosis scheme based on improved big data algorithm model

Aiming at the problems of low sensitivity and large diagnosis error in power operation and maintenance fault detection in the existing technology, a new fault diagnosis scheme is designed. In this scheme, the PID fuzzy control calculator is combined with the big data algorithm model, and the real-time wiring method is used to reduce the diagnosis area. Based on the improved big data algorithm model, the fault data characteristics of power operation and maintenance equipment are extracted, the diagnosis network is constructed for the operating conditions of power operation and maintenance equipment, and the data diagnosis is completed by analyzing the functional system of the operating conditions of power operation and maintenance equipment. In order to reduce the diagnosis error, a fault diagnosis equipment is designed in this study. The integrated chip design and algorithm program are adopted to reduce the volume and ensure the accuracy of the detection results at the same time. The experimental results show that the fault diagnosis error of this research method is small, and the highest accuracy is 98.6%.