SVD和神经网络在孤岛检测中的应用

Application of SVD and neural network in islanding detection

针对传统被动式检测方法存在较大检测盲区(Non-detection Zone,NDZ)、阈值难以确定以及易受电能质量扰动影响的缺陷,研究了一种基于奇异值分解(Singular Value Decomposition,SVD)和神经网络的被动式孤岛检测方法。该方法首先对公共连接点(Point of Common Coupling,PCC)处电压和逆变器输出电流进行S变换,提取相应的谐波幅值后,对其进行SVD并构成特征向量,最后运用BP神经网络对孤岛以及非孤岛情况进行分类识别。仿真结果表明,该方法可以有效检测出功率平衡情况下发生的孤岛,而且能防止电能质量扰动对检测准确性的影响,具有很高的准确性、可靠性和实用性。

Aiming to the existing defects of traditional passive islanding detection methods,such as large non-detection zone,thresholds difficult to determine and the results easily affected by power quality disturbances,a novel passive islanding detecting method based on singular value decomposition（SVD） and neural network for distributed generation（DG） is proposed.Initially,the voltage at point of common coupling（PCC） and output current of inverter are processed through S-transform to derive harmonic amplitudes matrixes.Then,the feature vector is formed by applying SVD to the matrixes.Further,it is determined by BP neural network whether there is an islanding phenomenon.The simulation results show that the method is faster than the traditional passive methods in islanding detection and can still accurately detect islanding in the state of power equilibrium,and is not easily affected by power quality disturbances,owning high accuracy,reliability and practicability.