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.

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.

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.

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.

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.

A small-signal stability preventive control method based on convolutional neural network (CNN) sensitivity analysis is presented in the paper, to improve the developing speed of small- signal stability preventive control measures. For poor or negative damping low frequency oscillation modes (i.e., the damping ratios are smaller than a threshold), first, an optimization model with small- signal stability constraints is established; second, the sensitivities of the damping ratios with respect to control variables (the active power of adjustable generators) based on CNN model of damping ratio prediction are calculated and then the optimization model is transformed into a quadratic programming model by linearizing small-signal stability constraints through sensitivities; finally, the adjustment amounts of generator active power are obtained. Several iterations are needed to make the damping ratios meet specific requirements. Analysis results of WEPRI 36-node case show that the effective control measures can be obtained by the presented method, which is more precise than that of the support vector machine method. The computing speed of the presented method is faster than that of the traditional eigenvalue analysis method. The ideas presented in this paper can also be applied to transient stability preventive control.

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 problem of the conventional cooling early warning of wind turbine generator, this paper puts forward the cooling early warning method of wind turbine generator based on multiple linear regression, which makes effective use of the existing new energy centralized control system environment, adopts Pearson coefficient analysis and establishes the early warning framework, and forms the early warning model by multiple linear regression calculation. At the same time, according to different models and different working conditions, different evaluation indexes of generator cooling warning threshold are established, which makes the warning model more flexible and more accurate. The verification results of the example show that this method can correctly achieve the cooling early warning for wind turbine generators.

Electric heavy truck charging and swapping stations are developing rapidly, and battery charging strategies have an important impact on station-side operating costs and user battery swapping experience. How to meet the daily battery swapping needs of electric heavy trucks while minimizing station-side operating costs and shortening user battery swapping waiting time is a key research direction. First, a certain electric heavy truck charging and swapping station is taken as the experimental object, and statistical analysis methods are used to obtain user battery swapping needs at different times of the day. Secondly, a charging strategy optimization control model is proposed with the goal of reducing station-side battery charging costs and life loss costs. Combined with battery swapping demand and time-of-use electricity prices, a genetic algorithm is used to solve the charging rate matrix and charging cut-off voltage of the battery charging compartment at different times of the day. Finally, the effectiveness of the model is verified through experimental examples, which also provides reference for its wide application in actual charging and swapping stations.