In order to solve the problems of oil‐paper insulation deterioration of oil‐immersed power transformers, including the instability of performance degradation quantity at the early stage, non‐linear deterioration process and uncertainty of life prediction, an improved Wiener stochastic process based on strong tracking filter (STF) is proposed to predict the remaining life of transformer oil‐paper insulation in this paper. Firstly, in order to grasp the dynamic process from healthy state to current deterioration state, a deterioration model is established based on Wiener random process. Secondly, in order to improve the accuracy, the initial values of model parameters are estimated based on expectation maximization (EM) algorithm, then with the increase of monitoring data, model parameters are updated by using STF algorithm, and the probability density function of remaining life is derived to predict the remaining life of oil‐paper insulation. Finally, the accuracy of the proposed method is verified by using furfural content as performance degradation index and accelerated thermal aging experimental data. Compared with the Bayesian–Wiener algorithm, the MSE of the proposed algorithm is reduced to 0.2138 and its MAPE is reduced more than 4%. Besides, the proposed method has the advantages of strong robustness and low uncertainty of residual life prediction.

This study is for the case where the available data of power transformer oil-paper insulation is limited to a small amount furfural data, to solve the problems in oil-paper insulation degradation modelling, such as few samples available, unknown function form of the degradation process, differences of individual transformers among degradation processes, and commonality of degradation trends. A power transformer oil-paper insulation degradation modelling and prediction method based on functional principal component analysis (FPCA) is proposed. First, discrete furfural data of oil-paper insulation degradation are converted into continuous functional data, and the common degradation information of transformers is extracted based on functional time warping technology. Second, the principal components of insulation degradation are extracted based on FPCA method, and the difference of degradation information of individual transformers is obtained by analysing the differential of principal component scores. Subsequently, power transformer oil-paper insulation degradation model is constructed, and finally, the degradation model is updated based on Bayesian theory and the oil-paper insulation degradation is predicted. The example results show that compared with traditional transformer oil-paper insulation degradation modelling method, the proposed method has obvious superiority in model accuracy.

To solve the oxygen supply problem of proton exchange membrane fuel cell (PEMFC), this paper presents a reduced-order active disturbance rejection control method for PEMFC air intake system based on the estimation of oxygen excess ratio(EOER-RADRC). First, a control-oriented third-order nonlinear model was established based on PEMFC air intake system. Then, based on the control-oriented model, the disturbance extended state observer was designed to solve the problem of unmeasured cathode pressure parameters in the calculation of oxygen excess ratio (OER), and realize the real-time estimation of OER. Next, aiming at the large phase lag of active disturbance rejection control (ADRC), a reduced-order ADRC method based on reduced-order extended state observer was proposed by a variable substitution technique. So, the proposed method reduces the observation phase lag and improves the dynamic response performance under good antidisturbance ability. Finally, the simulation and hardware-in-the-loop experience results showed that the proposed method has an excellent control effect compared with other methods.

Aiming to meet the low-carbon demands of power generation in the process of carbon peaking and carbon neutralization, this paper proposes an optimal PV-hydrogen zero carbon emission microgrid. The light–electricity–hydrogen coupling utilization mode is adopted. The hydrogen-based energy system replaces the carbon-based energy system to realize zero carbon emissions. Firstly, the mathematical models of photovoltaic, hydrogen and electric energy storage systems in a microgrid are built. Then, the optimal allocation model of the microgrid source storage capacity is established, and a scheduling strategy considering the minimum operational cost of energy storage equipment is proposed. The priority of equipment output is determined by comparing the operational costs of the hydrogen energy storage system and the electric energy storage system. Finally, the proposed scheme is compared with the scheduling scheme of the battery priority and the hydrogen energy system priority in an actual microgrid. It is verified that the scheme can ensure stable power-generating, zero carbon operation of a microgrid system while reducing the total annual power costs by 9.8% and 25.1%, respectively.

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.