Jinliang Yin

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

Partial shading (PS) is an unavoidable condition which significantly reduces the efficiency and stability of a photovoltaic (PV) system. With PS, the system usually exhibits multiple-peak output power characteristics, but single-peak is also possible under special PS conditions. In fact it is shown that the partial shading condition (PSC) is the necessary but not sufficient condition for multiple-peak. Based on circuit analysis, this paper shows that the number of peak points can be determined by short-circuit currents and maximum-power point currents of all the arrays in series. Then the principle is established based on which the number of the peak points is to be determined. Furthermore, based on the dynamic characteristic of solar array, this paper establishes the rule for determination of the relative position of the global maximum power point (GMPP). In order to track the GMPP within an appropriate period, a reliable technique and the corresponding computer algorithm are developed for GMPP tracking (GMPPT) control. It exploits a definable nonlinear relation has been found between variable environmental parameters and the output current of solar arrays at every maximum power point, obtained based on the dynamic performance corresponding to PSC. Finally, the proposed method is validated with MATLAB^®/Simulink^® simulations and actual experiments. It is shown that the GMPPT of a PV generation system is indeed realized efficiently in a realistic environment with partial shading conditions.