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.

The stator cooling water system of a turbine generator must maintain optimal operating conditions to ensure the reliability and safety of the generator. Typically, thermal faults are detected using methods such as shutdown maintenance or temperature difference thresholds, but these methods cannot effectively detect faults in real time while the generator is in operation. To more accurately identify stator thermal faults, this paper proposes a temperature prediction algorithm based on the Transformer architecture. Using the predicted temperatures from multiple measurement points, the future temperature difference is estimated, and a diagnosis model for stator thermal faults is established. To address the issue of limited fault operation data samples, this paper utilizes Gaussian processes with different kernel functions to generate various types of time series, which are then combined with the original data, significantly expanding the training sample space. Finally, experiments are conducted using existing test data. The results indicate that the predictive algorithm proposed in this paper outperforms traditional autoregressive integrated moving average (ARIMA) and long short term memory (LSTM) algorithms. Moreover, the diagnostic model based on this predictive algorithm achieves an accuracy rate of 91.9% in identifying operational states, while also maintaining high precision and recall rates, ensuring low false alarm and missed alarm rates.

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.

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.

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.

This paper aims to explore the application of isolation technology in high voltage design of energy storage systems (ESS), with a focus on analyzing the characteristics of optical isolation, magnetic isolation, and capacitive isolation technologies and their performance in different application scenarios. By combining technical comparison, application analysis, and case studies, this paper elaborates in detail on the application of isolation technology in improving system safety, reducing electromagnetic interference, achieving signal and energy transmission, and facilitating measurement and control in high voltage environments. The results indicate that reasonable selection of isolation technologies can effectively improve the safety and reliability of high voltage energy storage systems, providing theoretical basis and practical guidance for the design and optimization of high voltage energy storage systems.

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.

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.

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.