Modern power systems may experience untypical forced wideband oscillations, and traditional methods are difficult to identify the source of such forced oscillations. This paper proposes a universal identification method for forced oscillation sources based on voltage oscillation ratio (VOR), using wide-frequency measurement data provided by the broadband measurement system. The VOR is the ratio of the oscillation voltage amplitude relative to the steady-state voltage amplitude, which can effectively reflect the physical characteristics that the forced oscillation source has the maximum relative amplitude of oscillation voltage. This method is suitable for identification of low-frequency oscillation and sub/super-synchronous oscillation. This method is applicable in different voltage levels and can reduce the impact of measurement errors of transformers and broadband measurement devices. The effectiveness of this proposed method is verified through simulation cases and on-site cases.

To comprehensively consider the benefits of both the supply and demand sides in the scheduling process of a microgrid, an island microgrid dual-layer optimal scheduling model considering demand response is established. The upper level optimizes the output of each unit with the goal of maximizing the net revenue of the microgrid. The lower level optimizes the load curve with the goal of maximizing residents' overall comfort. An improved dung beetle optimizer is used to solve the dual-layer optimization model. The population is initialized using a sinusoidal mapping and optimized with quasi-oppositional learning to increase population diversity. During the update phase, the Harris hawks' besiege strategy and adaptive t-distribution perturbation are introduced to enhance the optimization capability and improve the solution quality. The superiority of the improved algorithm is verified by comparing its convergence on test functions with other algorithms. The case study results show that the improved algorithm not only improves the system's economic benefits but also enhances the users' electricity and energy comfort. Comparing the results with those obtained by the original dung beetle optimizer confirms the effectiveness of the im-proved method.

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.

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.

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.

Recursive Fourier algorithm is a commonly used digital signal processing algorithm for protection relays. Its characteristic is to use the last calculation result to calculate the current result. Its calculation efficiency is very high. However, the recursive algorithm has a memory effect. Errors caused by accidental factors will be kept and be difficult to detect, bringing hidden dangers to the operation of the protection device. In this paper, a fault-tolerant mechanism suitable for recursive algorithm is proposed to eliminate the memory effect and prevent irrecoverable calculation deviation caused by occasional or accumulated errors, thus effectively preventing such problems from causing misoperation or failure of protection devices.

With the proposal of China’s “dual carbon” goal and the construction of a novel power system, how to reduce the total carbon emissions of power generation enterprises and increase the share of renewable energy generation is an important issue facing China at present. In this paper, the conventional electric energy market trading paradigm is improved, and an optimization model for electricity trading considering carbon markets and renewable energy quota system is proposed. The optimization model is constructed with the goal of minimum carbon emissions of power generation enterprises and minimum customer purchase cost of electricity, and the model is solved by using the Cplex solver. The optimized trading paradigm is compared with the conventional electric energy market trading paradigm, and the case analysis verifies that the optimization model corresponding to the trading paradigm in this article can well decline the carbon emission and customer purchase cost of electricity.

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.