基于改进遗传算法的高压电气设备故障信号低复杂度快速提取模型

Low-complexity and Fast Extraction Model of High-voltage Electrical Equipment Fault Signal Based on Improved Genetic Algorithm

提取速度过慢,提取位置不准确会直接影响电气设备的正常运行,为了解决这个问题,基于改进遗传算法提出高压电气设备故障信号低复杂度快速提取模型。建立了目标函数,构建了适用于高压电气生产设备故障信号提取的信息网络模型,对高压电气生产设备运行中的数据信息进行采集整理,根据数据类型和关键特征进行分类提取,代入运算程序进行故障检测运算模型,代入电气设备运行数据;引入改进后的遗传算法,选择适当的改进过程,实现对高压电器生产设备故障信号的低复杂度、准确、快速的提取。实验结果表明,该模型能够精准地判断故障类型,提取速度提高了20%~30%,复杂度较低,工作效果更好,应用性能更强。

The problems that the extraction speed is too slow,the extraction position is inaccurate directly affect the normal operation of electrical equipment,hence,a low-complexity and fast extraction model for fault signals of high-voltage electrical equipment based on improved genetic algorithm is proposed.We establish an objective function,construct an information network model which is suitable to the extraction of fault signals of high-voltage electrical production equipment.It can collect and organize data information in the operation,classify and extract according to data types and key features,and substitute it into the operation program to perform fault detection operation model.We introduce the improved genetic algorithm,select the appropriate improvement process,and realize the low-complexity,accurate and fast extraction of fault signals of high-voltage electrical equipment.The experimental results show that the model can accurately determine the fault type,the extraction speed is increased by 20%~30%,the complexity is lower,the work effect is better,and the application performance is stronger.