基于孪生分支网络的非侵入式冲击负荷辨识方法

传统边缘侧电力设备无法有效检测出对电网影响较大的冲击性负荷的设备类别与功率启停信息。为此,提出一种基于孪生分支网络的非侵入式冲击负荷辨识方法。通过总线入口处的高频采样数据提取波形的V-I轨迹特征和对角高斯谐波特征;预设多种先验信息对不同设备的冲击负荷特性进行训练,特别地,设计一种基于孪生分支结构的卷积神经网络,利用二分类交叉熵损失函数实现冲击负荷的分类辨识,同时引入最小平方误差损失函数对冲击负荷功率进行分解;使用非侵入式的方式并基于ARM Cortex-M4平台进行算法部署与识别测试。对比不同识别算法对冲击负荷的辨识能力,结果表明,当电网发生大功率冲击性波动时,孪生分支网络可以更准确地识别冲击负荷的设备类别,有效提高了对冲击负荷的辨识效果。

Non-intrusive impact load identification method based onsiamese-architecture network

Traditional electric equipment cannot effectively detect the type of equipment and power start-stop information of impact load which has a great influence on the power grid. Therefore, a non-intrusive identification method of impact load based on siamese-architecture network is proposed. Firstly, the V-I trajectory characteristics and diagonal Gaussian harmonic characteristics of the waveform are extracted from the high-frequency sampling data at the inlet of the power edge equipment. On this basis, using the strong learning ability of convolutional neural network, a variety of prior information is preset to train the impact load characteristics of different equipment. In particular, a siamese-architecture network structure with shared network weight is designed to intelligently monitor and identify the occurrence of impact load and decompose its power by using different loss functions. The algorithm is deployed and tested based on ARM Cortex-M4 platform in a non-invasive way, and the identification ability of different identification algorithms for impact load was compared. The results show that the siamese-architecture network can be more accurate when high-power impact fluctuations occur in the power grid, the siamese-architecture network can more accurately identify the equipment category of the impact load and decompose its power, which effectively improves the identification effect of the impact load.