To solve the problem of low accuracy of the previous electricity theft detection methods, the authors propose a multi-domain feature (MDF) fusion electricity theft detection method based on improved tensor fusion (ITF). Firstly, the original electricity consumption series is transformed by gram angle field (GAF) to obtain the time-domain matrix. The original electricity consumption series is converted into frequency-domain by Maximal Overlap Discrete Wavelet Transform (MODWT) to obtain the frequency-domain matrix. Then, the convolutional neural networks (CNN) are used to extract features of the time-domain matrix and frequency-domain matrix, respectively. Next, in order to fuse single-domain feature information and MDF interaction information while reducing redundant information, the authors propose an ITF method to obtain a multi-domain fusion tensor. Finally, the multi-domain fusion tensor is input into the electricity theft inference module to judge whether the user implements electricity theft behaviour. The authors simulate six electricity theft types and evaluate the method's performance separately for each electricity theft type. The results show that the proposed method outperforms other methods.

For the optimal power distribution problem of battery energy storage power stations containing multiple energy storage units, a grouping control strategy considering the wind and solar power generation trend is proposed. Firstly, a state of charge (SOC) consistency algorithm based on multi-agent is proposed. The adaptive power distribution among the units started can be realized using this algorithm. Then, considering the trend of wind and solar power generation, a reasonable grouping control strategy is formulated. The grouping situation of the units is determined by using the probability distribution characteristics of energy storage charging and discharging, which reduces the number of charging and discharging conversions and extends the power station life. Finally, the actual data of a wind–solar energy storage microgrid is used to verify the method. The simulation results demonstrate that the proposed method has certain advantages in terms of control effect, SOC consistency, and extending the power station life.

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Traditional offline cable diagnosis methods need power outages during detection, affecting power supply reliability. Here, a hierarchical diagnosis method of cable aged segment based on transfer function is proposed. Firstly, the calculation model of cable transfer function with the aged segment is established; on this basis, the correlation between transfer function and cable aging is analysed. Then, a structure with combined sparse autoencoder and convolutional neural network is trained to estimate the aging location, and a hierarchical diagnosis model of distribution cable based on transfer function is proposed. The sensitivity and accuracy of aged segment detection are improved after hierarchical diagnosis. Finally, the simulation results show that the method proposed in this paper can effectively realize the online identification and location of the cable aged segment. The proposed method makes use of the advantage that the cable transfer function can be obtained online. Compared with the existing methods, this method does not need power outages in the diagnosis process, and the aged segment can be located without a lot of additional equipment.