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A maximum power point tracking (MPPT) method based on improved mayfly algorithm (IMA) with shading detection is proposed to realize the global MPPT with multiple-peak P-V characteristic curve under partial shading condition (PSC) and rapid MPPT with single-peak P-V characteristic curve under uniform irradiance condition (UIC) for photovoltaic array in this paper. Firstly, the characteristic of current-voltage curve in the ideal current source region for photovoltaic array is analyzed, and a shading detection strategy is proposed to monitor the shading condition of photovoltaic array to identify the multi-peak and single-peak on P-V curve. Secondly, the IMA with the elimination strategy is proposed to realize multi-peak MPPT. Meanwhile, the IMA with the bisection searching strategy is utilized to realize quick single-peak MPPT. Finally, simulation and experiment are conducted to verify the effectiveness of the proposed IMA MPPT method, and the results show that the IMA MPPT method can not only identify multi-peak and single-peak effectively, but also improve the tracking efficiency and accuracy in single-peak and multi-peak MPPT scenarios.

With the integration and development of sensor technology and control technology, the construction of smart grid is in the ascendant. But existing technology is still insufficient in the field of equipment monitoring. Therefore, it is difficult to accurately determine if SF6 breaker or other equipment have faults in its operational mechanisms which can cause false action and thus impact the safety of power grids. In this paper, the IBAS (Improved Beetle Antennae Search) algorithm and the BP neural network are combined and used in the monitoring system for the first time. To improve the beetle search algorithm, a single beetle is improved into a population in the iterative process of algorithm. The measured opening (closing) current data is used to verify the accuracy of different algorithms. The results show that compared with PSO-BP (particle swarm optimization, PSO) model, GA-BP (genetic algorithm, GA) model and BAS-BP model, the IBAS-BP model not only effectively avoids the possibility of local minimums but also has higher prediction accuracy and better robustness. The number of iterations in the IBAS-BP algorithm is only 38, and the average error is only 0.1%. According to this, it is possible to make real-time diagnosis of faulty states in SF6 breaker including iron core jams, low operating voltages, poor contact of auxiliary switches, jammed operating mechanisms, and barometer failures in SF6. At the same time, the IBAS-BP algorithm can be applied to wind turbine power prediction and other occasions since model regression determination coefficient R2 of the training set is up to 0.9753 and the relative average error is only 0.25%. The results prove that the IBAS-BP algorithm has obvious advantages and fairly good universality. It can be further promoted and applied to power systems to provide reference for optimizing the online monitoring of power equipment.

LLC resonant converter is utilized for a 3.3-kW on-board charger (OBC), where high efficiency and wide output voltage are required. In this paper, the resonant frequency and the magnetizing inductor of the converter are first optimized based on the required efficiency. Then, the resonant inductor and the resonant capacitor are designed considering the required maximum output voltage. The energy equations and the KCL and KVL equations about the converter are established by the time domain method to find out the information about peak resonant current, peak magnetizing current, and minimum switching frequency. The above-mentioned information is used to accurately design the converter`s magnetic components. A 3.3-kW prototype applied for OBC is developed. Its output voltage ranges from 230 to 430 V. Its peak efficiency is higher than 97.6%. The experimental results verify that the proposed method can accurately design the efficiency, the voltage gain, and the magnetic components of LLC resonant converter in a wide output voltage range.