非侵入负荷辨识的谐波特征量提取改进方法研究

基于事件分解的负荷匹配方法计算快内存少，工程应用适应性强。首先面向事件分解型负荷辨识方法分析了稳态特征量的提取方法，梳理出典型家用电器辨识特征库，并指出谐波特征量是空调及小功率电器复杂工况下负荷辨识的重要判据。然后分析了工程应用中谐波特征量提取影响因素，基于快速傅里叶变换（FFT）算法频谱泄露原理，研究了电网频率动态变化和电器谐波相角抖动对于谐波特征量提取的影响。提出了针对性解决方法，通过多点均值方法解决电网频率波动导致的非同步采样问题，并提出极值差量方法解决电器谐波相角影响，两种方法结合，可有效将基次谐波误差降到1%以下，偶次谐波误差降到2%～4%。最后通过实验平台和工程实证，验证了谐波改进提取方法的有效性，相对于改进前可有效提升负荷辨识精度5%以上。

Improved method research on extracting load harmonic feature for non-intrusive load monitoring

Non-intrusive load monitoring based on event decomposition is widely application for less storage and fast calculation speed. Firstly, methods on extracting load steady state feature were given, typical household appliance identification feature library was concluded. From the library, load harmonic features were important for air conditioner and other small power appliance which have even harmonics. Then, influencing factors of harmonic feature extraction in engineering application were researched. Based on the principle of spectrum leakage of FFT algorithm, the influence of grid frequency dynamics and electrical harmonic phase angle jitter on harmonic feature quantity extraction were studied. A Multi-point mean solution is proposed to solve the problem of non-synchronous sampling caused by grid frequency fluctuations, and the method of extreme value difference is proposed to solve the influence of appliance harmonic phase angle. With the two methods, the base harmonic error can be reduced to less than 1%, and the even harmonic error to 2%~4%. Finally, with the experimental platform and engineering, the effectiveness of the harmonic improvement extraction method is verified, and the load identification accuracy can be effectively improved by more than 5%.