Dual active bridge (DAB) converter has been widely used in Electric Vehicles (EVs) with the advantages of its simple structure and high performance. Traditional optimization strategies based on triple phase shift (TPS) of DAB cannot optimize both soft-switching and current stress. To overcome this challenge, a comprehensive optimization strategy based on TPS (TPS-CO) adapted to whole operating range is proposed in this paper. The power range, current stress and soft-switching features for TPS are detailed, and the mathematical model of TPS-CO is established. Accordingly, the optimal path method for seeking the minimal current stress in zero voltage switching (ZVS) region over full power range is proposed. Therein, it solves the problem of zero current switching (ZCS) operation of TPS at light loads and significantly extends the ZVS range. The proposed strategy is implemented through closed-loop control. Finally, experimental results on a 1.6 kW laboratory prototype demonstrate the significant efficiency advantages of the proposed TPS-CO strategy of the DAB converter with widely varying voltage ratios and full power range compared with the conventional strategies, the peak efficiency can be promoted up to 2% at light loads as reported in the experimental statistics.

Nonintrusive load monitoring is a technology that can identify the users’ internal energy consumption by using the data measured at a single point on the bus and event detection is a key technical problem that needs to be solved. An algorithm combining probability and expert heuristic models is proposed for event detection in this study, including an event predetection subalgorithm called voting improved isolated forest (VIIF) for high-sensitivity event predetection and an event verification subalgorithm called time shift downsampling matching (TSDM) for high-accuracy event verification. VIIF is used to detect suspicious events quickly from the low-frequency characteristics of the signal; TSDM identifies real events from suspicious events by analyzing high-frequency characteristics of the signal. To evaluate the proposed algorithm, three datasets are used. Compared with the state-of-the-art algorithms, the proposed algorithm has great adaptability and accuracy to long-transient events and small-signal events.

Electromagnetic field has a great impact on the performance of Wireless Sensor Networks. The influence of electromagnetic field generated by power equipment on wireless sensor nodes is designated as two parameters: environmental electromagnetic interference factor and path loss index. Environmental electromagnetic interference factor (EEIF) is used to describe the probability in the sensor node probability sensing coverage model; Path loss index (PLI) is substituted into node communication path loss model, then node packet receiving success rate model can be obtained by above models, which is used to evaluate node communication quality in electromagnetic interference environment. Combined with service cost model of wireless sensor networks in complex electromagnetic environment, the performance evaluation function of wireless sensor networks is established, and constraints are determined. Solved by the PSO-GWO algorithm, optimal deployment of wireless sensor networks in complex electromagnetic environment is realized. Experimental results show that proposed method can achieve best performance.