基于IGA-BP神经网络的智能电能计量设备状态自动检测系统

Intelligent Energy Measurement Equipment Status Automatic Detection System Based on IGA-BP Neural Network

摘要:

面对不断扩大的电网规模及愈加复杂的外部环境,影响智能电能计量设备安全稳定运行的因素也在不断增加,,因此,设计基于IGA-BP神经网络的智能电能计量设备状态自动检测系统。硬件中,设计数据采集模块、信号处理模块、数据传输模块与数据分析模块。软件中,通过采集智能电能计量设备检测数据,选取基础电能计量误差、电压波动幅度、电流波动幅度、功率因数为智能电能计量设备状态量,引入改进遗传算法-反向传播算法(Improved Genetic Algorithm-Back Propagation,IGA-BP)神经网络对状态量进行迭代运算,实现智能电能计量设备状态自动检测。实验结果显示:应用设计系统的智能电能计量设备状态检测时间最小值为2s,检测结果与实际结果一致,充分证实了设计系统具备较好的设备状态检测效率与精度。

Abstract:

Faced with the constantly expanding scale of the power grid and the increasingly complex external environment, the factors affecting the safe and stable operation of intelligent energy metering equipment are also increasing. Therefore, a smart energy metering equipment status automatic detection system based on IGA-BP neural network is designed. In hardware, design data acquisition module, signal processing module, data transmission module, and data analysis module. In the software, by collecting detection data from intelligent energy metering equipment, basic energy metering error, voltage fluctuation amplitude, current fluctuation amplitude, and power factor are selected as the state variables of intelligent energy metering equipment. An improved genetic algorithm backpropagation (IGA-BP) neural network is introduced to iteratively calculate the state variables and achieve automatic detection of intelligent energy metering equipment status. The experimental results show that the minimum detection time for the intelligent electric energy metering equipment using the designed system is 2 seconds, and the detection results are consistent with the actual results, fully confirming that the designed system has good equipment status detection efficiency and accuracy.