考虑扰动影响因子的多能融合微电网谐波责任区分

Multi-energy Fusion Microgrid Harmonic Responsibility Differentiation Considering Disturbance Influence Factor

与传统大电网相比,多能融合微电网系统具有多样性、复杂性和随机性等特点,其直接测量所得的实时谐波数据存在随机波动性大、受扰动强等问题,无法准确反映关注谐波源的真实谐波责任。为此,在传统谐波责任模型中引入扰动影响因子,分析了扰动影响因子的建模依据及其对谐波责任区分精度的影响。为减弱扰动影响因子的影响,采用基于BP神经网络拟合的均衡谐波数据代替实时谐波数据,采用改进尺度参数的M估计稳健回归算法评估谐波责任。最后,利用基于Benchmark 0.4 kV标准低压电网仿真模型和宁波某工业园区的微电网实验工程实测数据,验证了该方法的有效性。

The real-time multi-energy fusion microgrid system harmonic data has problems such as large random volatility and strong disturbance,which cannot accurately reflect the real harmonic responsibility.The disturbance influence factor(DIF)is introduced,and the modeling basis of DIF and the influence on the accuracy of harmonic responsibility are given.The equilibrium harmonic data calculated by BP neural network is proposed to replace the real-time harmonic data to reduce the influence of DIF,and the M-estimated robust regression algorithm is used to evaluate the harmonic responsibility.The effectiveness of the proposed method is verified by simulation model based on Benchmark 0.4 kV standard low-voltage power grid and the micro-grid experimental engineering in Ningbo.