Aiming at the disadvantages that numerous insulated gate bipolar transistor (IGBT) switching loss are difficult to accurately measure online in the cascaded energy storage application area, switching loss prediction model is established based on the error back propagation neural network. Firstly, dynamic test system of switching loss is built with cascaded H bridge power module, the massive switching loss data is obtained with changing the direct current bus voltage, alternating current and coolant temperature of power module. 3 main factors including collector-emitter voltage, collector current and device junction temperature are taken as the input of IGBT switching loss prediction model. The particle swarm optimization is used to optimize the initial weight and threshold of prediction model, improving prediction accuracy and accelerating the convergence of learning laws. The optimized performance of this model is compared and analyzed with the prediction model that the initial weight and threshold are given randomly. The results show that the prediction accuracy of the model proposed in this paper is higher. The maximum percentage error for 50 sets of random validation data is 3.3%.

A short term electricity price prediction method based on variational mode decomposition and hybrid deep neural network is proposed to address the characteristics of nonlinearity, volatility, and timeliness in electricity price data in the electricity market. Firstly, the original electricity price sequence is decomposed into multiple stationary subsequences using variational mode decomposition (VMD). Secondly, a hybrid deep neural network prediction model is used to predict and superimpose each subsequence separately, obtaining the final electricity price prediction result. This model combines convolutional neural network (CNN) and bidirectional long short term memory (BiLSTM) network to effectively extract spatial and temporal features of the original electricity price data, and combines attention mechanism to effectively distinguish the importance of electricity price data at different times in the original electricity price sequence. Finally, simulation analysis is conducted using actual electricity price data from the PJM electricity market in the United States, and the effectiveness of the proposed method is verified by comparing multiple electricity price prediction models.

The image acquisition of substations in low light environments can lead to problems such as low visual quality, loss of details, and low contrast, which in turn affect the subsequent detection and monitoring of equipment. A fusion method based on low light image enhancement and nonsubsampling contourlet transform (NSCT) and discrete cosine transform (DCT) technology is proposed in this paper. Firstly, adaptive image adjustment is performed on visible light images based on gamma parameters to enhance visibility. Then NSCT decomposes the image into high and low frequency coefficients. For high-frequency coefficients, edge information extraction based on Sobel operator is used, and for low-frequency coefficients, improved DCT-DFT is used for decomposition and integration. The decomposed amplitude spectrum and the phase spectrum are fused using contrast enhancement weighting and local energy optimization rule based on singular value decomposition (SVD), respectively. Finally, the fused image is obtained by NSCT inverse transformation. Three sets of images of common equipment in substations are used to compare the proposed method with other algorithms. The results show that this proposed method performs better in indicators such as average gradient, information entropy and mutual information.

Against the backdrop of the continuous development of new power system, various regions across the country are actively carrying out new energy storage construction to enhance the regulation capacity of the power system and meet the peak shaving and demand of the power grid. With the characteristics of bidirectional transmission and fast response speed, it is a key issue that should be considered in the site selection stage of energy storage planning, which involves how to effectively improve the security of the power grid and enhance the ability to resist the impact of faults after the new energy storage is access to the power system. A multi-objective decision-making model for energy storage site selection is constructed to address the impact of new energy storage access on the vulnerability of partitioned power grids. Faced with the shortcomings of traditional power grid vulnerability analysis methods, a new vulnerability assessment method based on k-core decomposition is proposed. The system vulnerability indicators under different typical operation scenarios after energy storage access to the power grid are taken as decision-making sub-targets, and the technique for order preference by similarity to an ideal solution (TOPSIS) decision method is used to comprehensively evaluate the optimal solution of energy storage target access point. The rationality and effectiveness of the proposed vulnerability assessment method and the energy storage site selection method are verified through the analysis of the IEEE 39-node system.

As the distribution network industry evolves towards greener and low-carbon solutions, environmentally friendly gases are progressively substituting traditional SF₆ gas as the insulation medium for medium-voltage inflatable cabinets. Given the limitations associated with the insulation efficacy of currently used environmentally friendly gases and the challenges related to the sustainable recycling of epoxy resin insulation components, this paper introduces an environmentally friendly insulation model that utilizes thermoplastic materials and integrated injection molding technology, specifically applied to 12kV dry air insulated inflatable cabinets. Through mechanical simulations, the study confirms that the mechanical properties of the proposed insulation model meet the required standards. Additionally, electric field simulations optimize the insulation model’s structure, effectively reducing the localized maximum electric field intensity and enhancing insulation performance. Finally, the insulation reliability of the environmentally friendly insulation model is substantiated through comprehensive insulation test and partial discharge assessments.

Aiming at the problems of poor working environment and low working efficiency of secondary cable manual threading, this paper proposes a design method of secondary cable auxiliary threading lead mechanism based on the principle of electromagnetic adsorption. Firstly, the principle of electromagnetic adsorption is analyzed, and the structure of traction end, threading end and electromagnetic adsorption are designed. Then, the finite element model of the adsorption device is constructed to analyze the relationship between the magnetic flux density in the inner and outer magnetic poles, the adsorption force and the air gap. The adsorption force of the designed adsorption device is determined to be 77.8N. Finally, the experimental platform is built to carry out related experimental studies such as primitive verification, repeatability and efficiency comparison. The experimental results show that the designed mechanism can work stably for about 12h, and the efficiency is increased by 87.99% compared with the manual threading method.

Large-scale electric vehicles are connected to the park integrated energy system (PIES), in order to improve the energy utilization rate, reduce the pressure on the park’s power grid, and realize low-carbon operation, this paper proposes a two-tier low-carbon optimized operation strategy that combines electric vehicles and efficient hydrogen use. Firstly, the disordered charging of the electric vehicles is simulated based on the spatio-temporal feature correlation, on the basis of which real-time tariffs are utilized to guide the electric vehicles for orderly charging. Combining the improved power-to-gas (P2G) two-phase technology, the park participates in the carbon trading market. The laddering carbon trading mechanism is introduced to minimize the system’s cost of purchasing energy, the cost of carbon trading, and the cost of abandoning the wind as a target function. The improved whale optimization algorithm (IWOA) is adopted for solving the problem. Finally, the scenarios are compared to verify the economy and environmental benefits of the two-tier optimal scheduling strategy proposed in this paper.

In recent years, active phased array radar has imposed increasingly demanding requirements in aspects such as size, weight, and power density, presenting considerable challenges to the volume, power, and heat dissipation of the transmitter and receiver components. This paper proposes a thermal-electric co-design scheme for high density integrated transmitter and receiver components and designs a system in package with high efficiency heat dissipation, high output power, and high integration to achieve the transmitter and receiver front-end functions of the components which has been verified in the components.

This paper proposes a DC distribution network fault location method combining current integral variation trend and temporal convolutional network (TCN)-support vector machine (SVM), to distinguish and locate DC distribution network faults, and lay the foundation for DC distribution network protection. Firstly, the integral sequence of fault current is calculated, and the integral sequence is decomposed by variational mode decomposition (VMD) algorithm. The eigenvalues of the decomposed high frequency intrinsic mode function are used as the input eigenvectors of the combination model of TCN and SVM, and the fault lines are located and the fault types are determined. The simulation results show that the scheme can not only locate the fault line quickly and identify different faults accurately, but also has good adaptability and certain anti-interference ability.

In response to the intermittency and uncertainty of renewable energy generation, which leads to changes in the model parameters of the electric spring (ES) system and a decrease in control performance, this paper proposes a model-free adaptive control (MFAC) strategy applied to the ES system. This strategy involves updating the control law equation and pseudo-derivative estimation equation in real-time using only the input-output data of ES. Through the compact form dynamic linearization algorithm, the input-output data of ES is described as a compact form dynamic linearization data model to replace the non-linear system of ES, thus achieving model-free adaptive control of ES. To verify the superiority of the control strategy proposed in this paper, the voltage stabilisation effect of the ES system is simulated by Matlab/Simulink. The results show that the voltage stabilisation response speed is improved by 0.07s, and the voltage waveform distortion rate is reduced by 6.43%, compared with the traditional proportional integral (PI) control strategy.