The article introduces a case of analyzing and processing abnormal ultrasonic signals in the busbar air chamber of a 330kV gas insulated switchgear (GIS). By locating the amplitude and changing the operating status of the equipment, it is determined that the defect comes from the busbar chamber on the main transformer side. By analyzing the scatter plot and phase amplitude plot of the partial discharge ultrasonic signal, it is believed that the discharge of metal foreign objects at the bottom of the gas chamber is the main cause of the abnormal ultrasonic signal. After inspection of the manhole, it is found that there is a silver white metal sharp substance at the measuring point on the bottom of the tank body, which is generated during the production. This GIS busbar air chamber discharge is a typical case of metal tip discharge treatment, providing reference for the treatment of similar abnormal defects in the future.

With the improvement of electrical equipment performance and the popularization of intelligence, electrical monitoring has gradually moved from traditional decentralized management to intelligent centralized monitoring. Under this trend, this article develops a centralized electrical equipment management system that meets the needs of the subway commercial sector to improve managment quality and reduce maintenance costs. Starting from the perspective of business needs, this article analyzes the development ideas of the system, introduces relevant modules and upper computer development achievements, and proposes expansion directions and optimization suggestions.

In response to the challenges faced by third-party inspection agencies during on-site photovoltaic testing, such as device diversity, high data storage costs, low processing efficiency, and data synchronization issues, this paper proposes a standardized module detection system for photovoltaic power stations based on the internet of things and cloud platforms. The aim is to enhance inspection efficiency, reduce costs, and improve data interconnectivity. By deploying standardized detection modules that include various sensors and data collectors, and utilizing network protocols for time synchronization, all measurements are ensured to occur within the same reference framework. Concurrently, a data acquisition and management system built upon cloud computing technology is developed to achieve cloud-based data storage, sharing, and collaboration with excellent scalability. Research findings indicate that the proposed detection system can effectively address existing problems encountered during inspection processes. Furthermore, it significantly lowers equipment deployment expenses, saving considerable manpower and material resources. Suitable for third-party inspectors conducting on-site photovoltaic station assessments, it holds broad application prospects.

In order to improve the selectivity and quickness of the metro direct current feeder protection at the same time, a direct current feeder protection scheme based on differential current theory is proposed. The features of the metro direct current feeder, the existing fault recording waveforms, the details of differential protection, the differential communication data transmission mode, the differential data calculation mode, and the feasibility of clock synchronization are analyzed. The existing available technologies are discussed. The feasible implementation methods are given, as well as the device failure mode and the blocking response mode.

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.

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.

With the growth of renewable energy and the increase of low carbon demand, alumina industry is facing the challenge of optimizing energy consumption. Targeting industrial production at high renewable energy ratios, it is optimized through electric energy substitution and demand response. In this paper, other green power real-time regulation is taken as the object of demand response, and a multi-objective demand response model of alumina production electricity consumption is established on the basis of guaranteeing that the rate of wind and light abandonment is minimized, and with the goal of satisfying the system economy and guaranteeing the output. The normal boundary intersection (NBI) method and nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) are used to optimize and solve the model. According to the analysis results of actual cases, the NBI algorithm performs better in reducing the electricity cost and the rate of power abandonment, with a cost reduction of 73% and a rate of power abandonment of 16.65 percentage points, compared to 70% and 15.65 percentage points, respectively, for NSGA-Ⅱ.

In order to improve the efficiency of identification and diagnosis of distribution network line faults and improve the reliability of power supply, this paper adopts a method based on wavelet entropy to construct a distribution network line operating condition characteristic gene bank. Firstly, the distribution network line simulation model is built to extract the operation data, and then, combined with the effective simulation data, the feature extraction algorithm model based on wavelet entropy is built, and finally, the characteristic gene bank is developed based on the feature data. The simulation results show that the feature extraction algorithm and characteristic gene bank based on wavelet entropy can effectively identify and diagnose a variety of operating conditions of distribution network lines, which meets the requirements of sustainable development of distribution network.

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.

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.