Search

Transformer live fault diagnosis is of great significance to ensure the safe and stable operation of power transformers. In response to the problem of complex working environment and limited fault types characterized by a single parameter, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and feature entropy weights method (EWM) is proposed for fault diagnosis. The correlation coefficient weighted kurtosis (CCWK) principle is used to filter the CEEMDAN components and reconstruct the signal to achieve an improved characterisation of transformer vibration signal features while eliminating redundant components. The EWM is used to construct feature determination coefficients (FDC) to achieve a single data diagnosis of transformer fault types. The principal component analysis (PCA) is used to reduce the scale of mixed domain features and the chicken swarm optimization (CSO) algorithm is used to optimize support vector machine (SVM) model for fault diagnosis. The analysis is performed on a 110 kV three-phase oil-immersed transformer in a certain substation, and the results show that compared with other transformer fault diagnosis methods such as probabilistic neural network (PNN) and SVM, the proposed method not only provides early qualitative fault type identification but also improves the accuracy and efficiency of transformer fault diagnosis.

To solve the problems of low accuracy, difficult diagnosis and long time consuming of the current fan bearing fault diagnosis, an improved grey wolf optimization (IGWO) algorithm and a support vector machine (SVM) fault diagnosis method are proposed. In order to accurately extract the fault features, the wavelet packet decomposition method in the time-frequency domain analysis is used to extract the fault vibration signal. Take the wavelet packet decomposition energy of the eight frequency bands as the fault feature， the eigenvectors are constructed. Then, the fault model of SVM is established and the parameters of the SVM model are optimized by the IGWO algorithm, which avoids the defects of local optimum and slow convergence. According to the experimental analysis result, the average fault recognition rate of the IGWO algorithm is up to 99.3%, and it can identify fault types more quickly, more efficiently and more accurately, which provides a good support for the development of fault diagnosis.

In order to meet the grid connection requirements of new energy power generation and ensure the stable operation of the power system, in view of the security problems brought by the uncertainty and volatility of intermittent new energy power generation to the stable operation of power grid equipment, a bilevel programming model aimed at compensating the output error and stabilizing the grid connection power fluctuation is proposed. First of all, the capacity and error satisfaction rate characteristic curve is constructed to obtain the optimal energy storage capacity to improve the economy of the energy storage system. Then, the upper planning model takes the minimum output error as the goal. The charging and discharging power distribution strategy of the energy storage system is established, the cycle life of the energy storage battery is considered and unnecessary compensation values is set to avoid its overcharge and overdischarge. The lower planning model takes the minimum grid connection fluctuation rate as the objective function, and the model predictive control algorithm is adopted to carry out the advanced rolling optimization control for the compensated photovoltaic output, so as to achieve the smooth fluctuation of photovoltaic output. Finally, based on the above model, the model evaluation index function is established, and a 21 MW photovoltaic power station in Xinjiang is taken as an example to verify the feasibility of the strategy.

As a clean energy source, hydrogen not only helps to reduce the use of fossil fuels but also promotes the transformation of energy structure and sustainable development. This paper firstly introduces the development status of green hydrogen at home and abroad and then focuses on several advanced green hydrogen production technologies. Then, the advantages and shortcomings of different green hydrogen production technologies are compared. Among them, the future source of hydrogen tends to be electrolysis water hydrogen production. Finally, the challenges and application prospects of the development process of green hydrogen technology are discussed, and green hydrogen is expected to become an important part of realizing sustainable global energy development. [ABSTRACT FROM AUTHOR]

Accurately predicting traffic flow is crucial for optimizing traffic conditions, reducing congestion, and improving travel efficiency. To explore spatiotemporal characteristics of traffic flow in depth, this study proposes the MFSTBiSGAT model. The MFSTBiSGAT model leverages graph attention networks to extract dynamic spatial features from complex road networks, and utilizes bidirectional long short-term memory networks to capture temporal correlations from both past and future time perspectives. Additionally, spatial and temporal information enhancement layers are employed to comprehensively capture traffic flow patterns. The model aims to directly extract original temporal features from traffic flow data, and utilizes the Spearman function to extract hidden spatial matrices of road networks for deeper insights into spatiotemporal characteristics. Historical traffic speed and lane occupancy data are integrated into the prediction model to reduce forecasting errors and enhance robustness. Experimental results on two real-world traffic datasets demonstrate that MFSTBiSGAT successfully extracts and captures spatiotemporal correlations in traffic networks, significantly improving prediction accuracy. [ABSTRACT FROM AUTHOR]

Regular inspection is an important task to ensure the normal production and safe operation of high-voltage power corridors. In view of the large space of power corridor, complex internal environment, high air humidity and harmful gas, a multi-sensor, remotely-controlled intelligent detection robot is designed to overcome the constraints of time and environment and guarantee the accuracy of the inspection results. The test shows that the inspection robot can not only complete the inspection task, but also has the advantages of strong adaptability, high reliability, flexible control and high anti-interference.

According to the non-stationary and nonlinearity of the fault signals from rolling bearing,a recognition method based on Modified Ensemble Empirical Mode Decomposition（ MEEMD） and Double Hiddenlayer Elman Neural Network（ DHENN） is proposed. The vibration signal is decomposed by MEEMD,and using variance contribution to select out the Principal Intrinsic Mode Function（ PIMF）,one can calculate the permutation entropy of the IMF which obtained by using Complementary Ensemble Empirical Mode Decomposition（ CEEMD）,it is basic to deal with the illusive component,and the others are reconstructed and decomposed by using EMD,the variance contribution is calculated,through Hilbert transform,characteristic matrix is constructed. The DHENN network model is constructed to recognize the fault type,getting the best combine of the double hidden neural network. Finally,compare EMD-DHENN and MEEMD-ENN,the result show that the proposed MEEMD-DHENN method has a high accuracy rate of 100% and only need 26 steps.

A novel identification algorithm is proposed to deal with the modelling issues of linear switched systems under the switching modes being unknown. The main contributions of this paper embody the following aspects: (1) for simplifying the identification problems, the label functions are proposed to replace the switching states of the modes; (2) The recursive least square algorithm is used to identify the system parameters, and the convergence performance of the proposed algorithm is studied. By using convergence analysis, the parameter estimation error can converge to zero point when the input signals satisfy persistent excited conditions and the total time of mode mismatch is limited. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]