In response to the problem of poor accuracy in short-term electricity load forecasting models, this paper proposes a Short-term power load forecasting model that utilizes the variational mode decomposition (VMD) technique to extract deep features from short-term load data, followed by optimizing the hyperparameters of the Random Forest (RF) load forecasting model using the sparrow search algorithm (SSA). Firstly, in the data processing part, VMD is used to decompose the load data to obtain multiple modal components, and the decomposed modal components are analyzed, and the modal components that are seriously affected by noise and cause excessive waveform fluctuation are merged to reduce the calculation cost of the model. Then, the sparrow search algorithm is used to optimize the hyperparameters of the random forest prediction model. The optimal prediction model is constructed for the multiple modal components obtained after VMD decomposition, and their results are reconstructed to obtain the final prediction outcomes. Through the analysis of examples, it is verified that the proposed prediction model has higher prediction accuracy than the commonly used intelligent prediction model.

In recent years, cable failure due to the ablation of the high-voltage cable buffer materials has posed a serious threat to the safe operation of the cable system. The ablation process of buffer materials generates characteristic gases, which enables the prediction and diagnosis of buffer layer defects through characteristic gas detection. The experimental platform was set up to simulate the discharge ablation of the cable buffer layer and to collect gases. Using different structures of electrodes, the experiment of the buffer layer with typical defects was carried out. The gases produced during the discharge and ablation process were analyzed with a gas chromatograph to study the evolution pattern of the product concentrations. The results show that the gas products produced during the buffer layer ablation include nine kinds of gases, such as CO2, H2, CO and low molecular weight hydrocarbon gases, and the concentrations of different kinds of gases are directly related to the type of buffer layer defect, ablation time, and contact state between the buffer layer and metal sheath. The method based on characteristic gas detection is prospective in diagnosing latent defects in the buffer layer of high-voltage cables.

In recent years, internal short-circuit faults inside ultra-high voltage (UHV) converter transformers have led to a spate of explosive fires, posing a serious threat to the safe and reliable operation of power systems. In this paper, taking a typical single-phase four-limb converter transformer of ±800 kV UHV converter station as a research case, modeling and simulation of winding interturn short-circuit fault are carried out on ANSYS Maxwell & Simplorer platform. The variation characteristics of radial leakage flux, the short-circuit current and the arcing fault energy under different fault scenarios are investigated. The results show that the normal axial flux distribution is disturbed by a large amount of radial leakage flux inside the converter transformer with the occurrence of interturn short-circuit fault, and high-amplitude fault circulating current emerges in the short-circuit loop. Under a 1.98% interturn short-circuit fault, the magnetic leakage flux density reaches 2.76 T at peak, the short-circuit circulating current reaches 80.59 kA at peak, and the fault energy released over four cycles reaches 1.22 MJ. According to the law of ampere-turn balance, the more the shorted turn number, the lower the circulating current amplitude. However, since the average arc voltage increases monotonically with the arc length, the overall arc energy is positively correlated with the number of shorted turns. In addition, interturn short-circuit faults closer to the middle part of the winding are more serious due to the difference in leakage inductance caused by the leakage distortion.

Optimizing the design of train operation curve and reasonably configuring super-capacitor energy storage is an effective solution to the high energy loss and significant power impact of urban railway traction power supply system. The train operation curve will directly determine the current operation status of the train, and change the overall energy consumption of the power supply system by adjusting the efficiency of the motor on the one hand, and influence the optimal capacity configuration of the energy storage by adjusting the power difference between the traction process and regenerative braking process of the train on the other hand. Considering the mutual constraints of the above two factors, this study proposes a comprehensive method for optimal capacity configuration of urban railway energy storage system, aiming at both energy-saving operation and reduction of energy storage capacity. Firstly, the mathematical model of train operation is used to establish the energy consumption function considering the variation of motor efficiency. Then, the power difference function between traction and regenerative braking is determined considering the energy interaction during multi-vehicle operation. Further, the composite cost function containing fixed time minutes, comprehensive energy consumption of the system, and energy storage capacity is reconstructed, and the operation curve is optimized based on an improved particle swarm algorithm. Finally, given that the composite cost function involves different physical quantity, the influence of each economic factor on the optimization objective is studied in depth. The examples show that the operation curve obtained based on the optimization method of this study can significantly reduce the configuration capacity of super-capacitor while saving energy.

Due to the randomness and volatility of wind and photovoltaic power generation, and the more complicated structure of the distribution network after being connected, the reliability of power supply of the distribution network is reduced. This paper proposes a distribution network reconstruction scheme combining the quasi-Newton genetic algorithm with robust optimization to address this issue. Firstly, based on the output model of wind and photovoltaic power generation, probability multi-scenarios are generated. Secondly, a robust reconstruction optimization model of the distribution network with wind and photovoltaic power generation access is established, and the quasi-Newton genetic algorithm is used to solve the model. The risk value of voltage out of limit in the distribution network before and after reconstruction is compared. Finally, an example is given to verify that the reconstruction method in this paper can effectively improve the reliability of power supply of the distribution network.

As an electromechanical energy conversion device, motors are widely used in electric vehicles, aerospace, energy metallurgy and other fields, but their operating scenarios vary widely. In order to adapt to the operating characteristics of the load under different scenarios and working conditions, the motor usually needs to have a certain torque overload operation ability. Accurate analysis and utilization of the torque overload capacity of the motor is of great significance to improve the economy of the system and realize energy saving and consumption reduction. This paper firstly introduces the motor torque overload capacity and utilization methods, and summarizes the application scenarios and practical value of high torque overload capacity motors. The electromagnetic characteristics, loss characteristics and temperature limit of motor torque overload operation are analyzed. The constraint conditions and utilization boundary of motor design with high torque overload capacity are summarized. The research status of permanent magnet synchronous motor and asynchronous motor with high torque overload capacity is analyzed and summarized respectively, and the strategies to improve the torque overload capacity of the motor are sorted out. Finally, the development direction of research and utilization of high torque overload capacity motor is prospected.

Offshore wind power is an important trend for the development and utilization of new energy. The rational planning of offshore wind power systems has an important impact on the operating economy and safety of the system. The planning and evaluation of the existing offshore wind power system is difficult to balance the power quality of the offshore wind farm side and the operational safety of the transmission system side, and cannot comprehensively reflect the performance of the offshore wind power system. Therefore, the differences between the power quality of wind farms and the fault characteristics of transmission systems caused by different planning schemes of offshore wind power systems are analyzed, the inferior evaluation indicators of offshore wind farms and transmission systems are established, and the differences and correlations between the indicators are fully considered, and then a TOPSIS comprehensive evaluation method of offshore wind power system is proposed to improve the CRITIC entropy weight combination weighting method, and is practical in combination with the early planning and design of an offshore wind power system. The results show that the proposed comprehensive evaluation method is consistent with the actual situation, and has reference value for the planning and decision-making of offshore wind power system.

This article optimizes the design of the medium-voltage DC Solid State Circuit Breaker (SSCB) snubber circuit based on normally-on Silicon Carbide (SiC) junction field-effect transistor (JFET). First, considering the factors such as parasitic inductance inside the medium-voltage SiC SSCB main switch SiC JFET device, printed circuit board inductance, and power circuit inductance, RCD-type, MOV-type, and RCD+MOV hybrid-type three typical snubber circuits applied to SiC SSCB are analyzed for their operation principles and performance evaluation. Based on this, a MOV-RC snubber circuit design suitable for SiC SSCB is proposed, and the operation principle and parameter design method of the circuit are described in detail. Finally, a 1.5 kV/38 A SSCB experimental prototype based on three 1200 V/38 A normally-on SiC JFET devices in series was built to verify the effectiveness of the snubber circuit design scheme. The simulation and experimental results show that the snubber circuit design scheme not only effectively suppresses overvoltage during SiC SSCB fault isolation, but also has the advantages of fast fault clearance time and low cost.

Medium-voltage power distribution network in China is dominated by small-current grounding systems, with single-phase-ground faults most. Existing single-phase-to-ground protection faces the challenges of difficult to discriminate fault sections, insufficient reliability caused by interference, and low sensitivity for faults through high resistance. This paper proposes a precise protection scheme for single-phase-to-ground fault section in power distribution network using only local fault information. The scheme is based on the characteristics of phase current changes before and after the fault to startup of protection relay, combined with the characteristics of the voltage and zero-sequence current change and the time delay to selectively trip the circuit breaker at the power-side of faults; and in accordance with the trip time delay of opposite-end protection relay and circuit breaker, combined with the three-phase voltage change to trip the circuit breaker at the load side of the ground fault section using local information. Simulation results show that the proposed scheme can operate accurately without communication and even for faults through high resistances.

In order to establish the mapping relationship between key electromagnetic force harmonics and structural parameters in the motor NVH design stage, and achieve an orderly, efficient and rapid optimization of high-density and low-noise IPMSM, this paper proposes a multi-layer surrogate model IPMSM optimization method based on structural parameter sensitivity classification. First, the key-order electromagnetic force of the multi-operating electromagnetic noise source of the motor is obtained through the “finite element method + unit force wave response” hybrid model, taking its amplitude, IPMSM’s average output torque, and torque pulsation as optimization targets at the same time, analyzing the sensitivity of structural parameters through the random forest algorithm to achieve screening and grading of structural parameters, integrating multi-objective particle swarm algorithm, multi-island genetic algorithm, parametric scanning and other optimization methods are used for hierarchical optimization. Compared with the traditional motor multi-field coupling optimization method, this method saves computing power and reduces the calculation time by 549%. After optimization, the average output torque is increased by 346% as compared with before optimization, the electromagnetic force amplitude of the key order of the motor decreased by 137% as compared with before optimization, and the torque ripple decreased by 678% as compared with before optimization.

According to the development needs of smart grid for advanced sensing and measurement technology and the needs of grid voltage monitoring in different scenarios, D-dot sensor based on electromagnetic coupling measurement principle can realize the non-contact measurement of grid voltage, which, as an important device for obtaining voltage signals from the grid, will influence the effect of the whole measurement system. In order to solve the problem that the D-dot sensor has only one single measurement direction, as well as to fulfill the requirements of the installation angle and other issues, in this paper, the basic principle and differential input structure of the D-dot sensor are discussed in depth, and the segmentation of the electrode and the synthesis law of the vector under the coupled magnetic field are investigated. Finally a new design of the D-dot circumferential angle sensor under the self-integrating operating state is proposed and experimentally verified by the finite element simulation software with the aim of obtaining a more accurate measurement result. Compared with the traditional sensors, the D-dot circumferential angle sensor can achieve the purpose of multi-angle measurement, and it is also more capable of meeting the demand for the measurement of electrical parameters in all directions in various practical situations.

In distributed energy storage applications, hybrid control is an effective approach to expand the wide-range voltage gain of L-LLC resonant converters, while multi-tube parallel synchronous rectification technology is the main method to reduce switch device on-state losses and improve efficiency. This paper analyzes the different operating modes in hybrid control and concludes that the converter operates with the rectifier-side current in an intermittent state. Furthermore, it is found that the synchronization rectification driving timing needs to be coordinated differently with the inverter-side switching timing based on the control method. Therefore, a synchronous rectification control strategy is proposed, which combines the inverter-side switching timing with external detection of the rectifier-side current. Compared to other control strategies, the proposed method does not require complex external detection circuits, and the control approach is simple and less susceptible to stray parameter effects. It is suitable for bi-directional converters in wide-range and high-current applications. Finally, an experimental prototype with a high-side voltage range of 280~430 V, low-side voltage range of 36~54 V, forward power transmission of 25 kW, and reverse power transmission of 2 kW is constructed to verify the proposed method. The experimental results demonstrate the feasibility and reliability of the proposed synchronous rectification control strategy.

With the increasing scale of distributed photovoltaic power generation, accurate prediction of photovoltaic power generation is very important for the safe and stable operation of power systems. In order to improve the accuracy of photovoltaic power prediction, an integrated model based on mechanism model and extreme gradient boosting algorithm is proposed for short-term distributed photovoltaic power probability interval prediction. Firstly, combined with the meteorological data, a density-based spatial clustering of applications with noise algorithm is used to design a photovoltaic power data governance method to filter out abnormal data in historical data. Then, the integrated model is constructed based on the optimized samples after screening. Specifically, the basic photovoltaic power prediction model is constructed based on the mechanism model for preliminary prediction, and the prediction results and other environmental data are used as input variables of the XGBoost model, and then the prediction error generated by the basic prediction model is corrected. Different feature data are extracted to train the mechanism model and XGBoost model and forecast respectively. Finally, the prediction error probability density function is established by non-parametric kernel density estimation, and the fluctuation range of photovoltaic power is predicted at a certain confidence level. The accuracy and effectiveness of the method are verified by the actual data of photovoltaic power station.

In this paper, a wide-area damping control strategy and corresponding design scheme is proposed such that grid-connected converters can be used to improve the small signal rotor angle stability of the system. Based on the small signal stability model of power system with grid-connected converters, the modal linear quadratic regulator (MLQR) is applied to design the state feedback control strategy, which can achieve the decoupling control of different rotor angle oscillation modes. Considering that different grid-connected converters have different regulation capabilities, this paper further builds a damping effort allocation model, which can be solved by using the genetic algorithm (GA). In the allocation model, the weight matrix of MLQR is regarded as the variable to be optimized, meanwhile the damping ratio of key oscillation modes and the regulation ability of grid-connected converters are used as constraints. Finally, simulation tests are carried out in the Power System Analysis Toolbox (PSAT) based on the IEEE 68 bus system, and the results show that the damping control scheme can effectively use each grid-connected converter to provide sufficient damping support to the system within their control capabilities.

Aiming at the problem of low accuracy of real-time diagnosis of power transformer mechanical faults, this paper proposes a power transformer fault diagnosis method based on vibration signal and deep learning. Firstly, the vibration signal on the surface of the power transformer case is decomposed by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to obtain the reconstructed signal, and the fuzzy entropy value is introduced to construct the vibration eigenvectors. Then, a Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is used to form a basic classification network to achieve feature classification, and an Efficient Channel Attention Mechanism (ECAM) is introduced to improve the CNN learning performance. Finally, a Multi-strategy Co-optimization Bald Eagle Search (MSCOBES) algorithm is designed based on the hybrid improvement of ICMIC chaotic mapping, adaptive dynamic perturbation and elite inverse learning, and the improved algorithm is applied to realize hyper-parameter optimization of CNN-BiGRU to obtain the optimization of power transformer fault diagnosis based on MSCOBES-CNN-BiGRU-ECAM model. In the experiment for the test transformer, the experimental results show that the proposed method can reach an accuracy up to 994% for the power transformer with different types of mechanical fault.

The modeling of new energy prediction error analysis will help to describe the uncertainty of new energy prediction more accurately. The traditional prediction error analysis process is mostly fitted by a fixed mathematical model, which has low prediction accuracy and performs poorly in practice. This paper proposes a new energy prediction error modeling method based on naive Bayesian classification. The model is divided into data discretization process and naive Bayesian classifier training process. Firstly, the kernel density estimator is utilized here to estimate the probability density distribution of the actual observation of new energy, new energy prediction data and new energy prediction error data. Using self-organizing map neural network training, the number of clusters and classification boundaries of the three types of data are obtained, and the three types of new energy data are discretized. Based on the cross-validation model training method, the naive Bayesian classifier model is used to construct the mapping relationship between the day-ahead actual output data and the prediction data to the prediction error evaluation of the new energy. The test results on the real new energy data of Northwest Power Grid in 2021 show that this method can effectively describe the characteristics of new energy data and the autocorrelation and cross-correlation between the predicted and actual output data, which greatly improves the modeling accuracy of new energy prediction error.

Scientific planning and optimized configuration of comprehensive energy distribution systems have significant practical engineering implications for improving the operation and overall performance of integrated energy systems in large buildings. This article proposes a multi-objective optimization method for comprehensive energy distribution systems that includes wind and solar energy, gas, and hybrid energy storage. First, a composite objective function is proposed that takes into account operating economics, life-cycle costs of energy storage systems, and low carbon emissions over multiple periods. A constraint model is established that considers the coordinated operation of new energy and energy storage systems for heating and power generation. Then composite objective optimization planning is carried out in several typical scenarios during the year when new energy output corresponds to heating and power generation needs, and the optimal design of the hybrid energy storage system is obtained. Finally, a large-scale building energy example is illustrated using YALMIP+CPLEX to verify the feasibility of this method. The results show that this method can be used to plan source-network-load-storage for comprehensive energy distribution systems, thereby improving the overall performance and efficiency of the system.

At high altitudes, the low air density, reduced pressure, and weak convection make traditional air cooling insufficient for meeting the heat dissipation requirements of the energy storage converter PCS. To enhance the heat dissipation performance of the IGBT module in high-altitude areas, a novel optimization design method for PCS liquid cooling radiators using the multi-objective gray wolf optimization algorithm (MOGWO) has been proposed. Initially, a simulation model of an NPC three-level LCL grid-connected inverter was developed using PLECS software to calculate the total power loss of the IGBT module. The IGBT module was then modeled in three dimensions using SolidWorks. Subsequently, a liquid-cooled radiator with a snake-shaped flow path was designed. The radiator’s internal and external structural variables have been optimized using the multi-objective gray wolf optimization algorithm, multi-objective particle swarm optimization algorithm, and multi-objective genetic algorithm. A three-dimensional model of the radiator was created based on these optimization results. Following this, fluid-structure coupling simulations were conducted using finite element analysis in ANSYS-Fluent. The simulation results have been compared, revealing that the heat sink performance is improved the most after MOGWO optimization. Finally, hardware has been assembled based on the optimized design, and its reliability was tested and verified in a high-altitude environment.

A large amount of new energy generation connected to the grid changes the stability problem of the power transmission system. New types of stability problems and new mechanisms appear, which fundamentally affect the feasibility of the planning scheme. However, only the transient stability simulation for the large power grid is employed to check the power supply planning schemes because the traditional transient stability simulation is not applicable to the new stability forms. Due to the enormous workload and high complexity, the electromagnetic transient simulation is unsuitable for checking new energy generation planning schemes for actual power systems. Thus, based on a review of the new stability problems of the power grid containing a high proportion of new energy generation, this paper summarizes three scenarios where new stability problems emerge in the new energy planning schemes. Then the mechanism of the stability problems restricting the new energy sending and transmission is analyzed. Furthermore, quantitative risk assessment methods are proposed indicators for assessing stability risk that are more efficient and feasible than existing assessment methods. Finally, the simulation verifies the existence of three types of new stability risks, and also verifies that the methods proposed in this paper can quickly and effectively assess the stability risks.

There are a large number of sub-modules ( SM ) in the modular multilevel converter ( MMC ), and its reliability directly affects the safe and stable operation of the whole system. In this paper, a new redundant SM with fault tolerance is introduced, and on this basis, a real-time open-circuit fault diagnosis strategy for MMC redundant sub-modules based on improved event-triggered and one-dimensional convolutional neural network (1-D CNN) is proposed. Firstly, the capacitor voltage of the integrated unit composed of specific SM in MMC is selected as the event-triggered object, which greatly reduces the number of capacitors that need to be observed in the traditional diagnosis strategy. Secondly, from the perspective of effectively reducing the computational burden, the event-triggered process is improved, and the 1-D CNN algorithm is used to detect the fluctuation of the capacitor voltage and the bridge arm current of the integrated unit, and the MMC fault real-time diagnosis system is constructed. Finally, considering the fault integration unit and the fault bridge arm position, the open-circuit sub-module is located, and then the fault-tolerant operation of MMC under the new redundant SM condition is completed. The effectiveness of the proposed real-time diagnosis strategy is verified by the 19-level MMC model built by Matlab/Simulink.

Hydrogen energy as one of the essential carriers of global energy transition development will play a key role in the future low-carbon integrated energy system. In this paper, based on the mixed integer linear programming (MILP) model, the investment planning model in the hydrogen-coupled residential integrated energy system of hydrogen energy is established on the basis of consideration of the equipment models, operational characteristics, and investment, operation, and maintenance costs. Firstly, the long-term operational profit of the system is clarified by the typical day method, and the planning objective function is determined according to the income from electricity, heat, and hydrogen energy supply, investment cost, and operation and maintenance cost. Then, it is proposed to ensure the real-time balance of the system power load, hydrogen load, and thermal energy load under the premise of meeting the requirements of different types of energy (electricity, heat, and hydrogen). Finally, the investment planning and optimal allocation of fuel cells, hydrogen refueling stations, and various other types of hydrogen-related equipment and components in the hydrogen-coupled residential integrated energy system are considered. The analysis of the case shows that the proposed planning model also considers the economic operation of the system on the basis of the equipment selection and capacity allocation. By comparing the system economy of multiple possible scenarios under the influence of different external factors, it is shown that with the decrease in the cost of the core equipment of the hydrogen energy, the net income of the system can be significantly improved.

In broadband voltage measurement, transient overvoltage components will seriously affect line power quality and power system security. The traditional measurement method often uses a single microprocessor to process data. It is difficult to realize the real-time and synchronization of multi-channel high-speed data acquisition. It cannot realize the acquisition of transient overvoltage signals and cannot meet the requirements of broadband voltage measurement. The voltage measurement system designed in this paper uses the advantages of fast operation speed, low internal delay, hardware parallel processing of FPGA and rich ARM communication interface, many peripheral resources and strong control ability to realize high-speed conditioning acquisition, processing and transmission of broadband voltage signals. In the data input layer, edge nodes are introduced to perform data pre-screening, which solves the problem that the cloud pressure in data processing is large and it is difficult to achieve real-time data evaluation, thereby improving the efficiency of data transmission.

In recent years, the frequent occurrence of wildfires in China has seriously threatened the safe and stable operation of the power systems. As an important flexible scheduling resource, mobile emergency generators can guarantee the power supply of critical loads under the spread of wildfires and enhance the resilience of the distribution network to cope with wildfire disasters. Hence, this paper proposes a resilience enhancement strategy for distribution network based on mobile emergency generators supply under wildfire spreading. First, a distribution line conductor heat model and heat balance equation based on the consideration of wildfire heat radiation are established, and clustering is applied to generate typical scenarios with uncertain parameters; then, with the optimization objectives of minimizing the power purchase cost of the distribution network, the load outage compensation cost, and the operation cost of the dispatchable resources, the distribution and operation constraints considering the distance traveled by the mobile emergency generators to the access point are proposed, and then the optimal allocation of the mobile emergency generators is established in conjunction with a disaster whole process operation strategy model. Considering that the wildfire model is a nonlinear equation, a linearization method based on two-stage relaxation is proposed to transform it into a mixed-integer linear programming problem for fast solution. Finally, the effectiveness of the proposed method is verified by simulation using the IEEE 33-node test system.

In recent years, mechanical failures in domestic power transformer windings have been frequent. Utilizing vibration analysis to monitor the mechanical operation of power transformer windings can effectively mitigate the risk of transformer failures. This article first analyzes and solves the vibration sources and acceleration theory of power transformer windings, establishes a finite element model of a 110 kV power transformer, studies the vibration characteristics of the transformer windings under rated normal load conditions, and finds that the radial vibration intensity of the windings under load conditions is much greater than the axial vibration, with a main frequency of 100 Hz in the winding vibration signal. With fixed constraints at the upper and lower ends of the windings, the vibration intensity at the upper and lower ends is less than that in the middle of the windings. Next, by altering the geometric shape of the high-voltage windings to cause buckling deformation, a comparison analysis of the vibration signals in the undeformed state of the windings reveals that when deformation occurs, the vibration signal spectrum shows a significant amount of high-order harmonic components. As the degree of deformation of the windings increases, the main frequency of the vibration signal continuously increases, and the distribution of high-order harmonic components becomes more complex. Finally, by defining characteristic parameters such as the fundamental frequency ratio and high-frequency ratio, the article further analyzes the changes in vibration characteristics before and after the deformation of the transformer windings, providing a basis for evaluating the mechanical state of transformer windings using vibration analysis in actual field conditions.

With the widespread use of lithium-ion batteries, rapid diagnosis of the health state of lithium-ion batteries has attracted extensive attention. Aiming at the traditional impedance diagnostic methods, which are affected by the change of state of charge (SOC) during online measurement, involving complex calculations, and less impedance information obtained, this paper conducts electrochemical impedance spectroscopy on lithium-ion batteries with different states of charge (SOH) and different states of health (SOH) and establishes a more physically interpretable equivalent circuit model by decoupling the complex dynamics in lithium batteries through the distribution of relaxation time (DRT) method. Based on the dependence of the characteristic peaks of the relaxation time distribution function on the SOC and SOH, three characteristic frequency points are screened for online impedance measurements. A fast online SOH estimation method with multi-point impedance is proposed, which can infer the state of health of the battery based on the characteristic impedance measurement data injected by the superposition of multiple feature frequency points at a time. It is experimentally verified that the method has good accuracy and practicality in online battery characteristic parameter measurement and health state estimation.

Hydrogen-based integrated energy systems have emerged as effective solutions to address the depletion of fossil fuels and the increasingly pressing challenges posed by climate change. This paper introduces an adaptive optimal energy dispatch method based on Deep Deterministic Policy Gradient (DDPG) to enhance the operational efficiency of hydrogen-based integrated energy systems. The optimal dispatch problem is formulated as a Markov Decision Process with action space, environmental states, and action-value function. Leveraging policy gradients and neural networks, we propose the DDPG-based optimal energy dispatch method, which enables adaptive optimization based on the dynamic responses of the hydrogen-based integrated energy system. Finally, the effectiveness of the proposed approach is validated through case studies.

To address the voltage quality issue caused by the high proportion of photovoltaic (PV) in urban distribution network, this paper proposes a voltage optimization strategy considering the flexible regulation capability of the photovoltaic integrated energy storage system (PIESS). Firstly, three typical operation scenarios of “photovoltaic + general energy storage” are analyzed. Furthermore, the operation models of general energy storage and PIESS under the three scenarios are established, successively. Then, the flexible regulation potential of PIESS is analyzed, and the general formulations of the active and reactive power constraints for PIESS are proposed. Finally, the voltage optimization model of distribution network, which incorporates the operation models and power constraints of PIESS, is reformulated as a mixed-integer second-order cone programming problem. The effectiveness of voltage regulation for the proposed model is verified in a modified IEEE-33 bus test system. The results indicate that PIESS can effectively optimize the voltage profile of distribution network, and the flexible regulation capabilities of different “photovoltaic + general energy storage” systems can be completely exploited to ensure the security and economic operation of the distribution network.

The problem of harmonic pollution has attracted more and more attention as the ‘double high’ characteristics of the power system becoming more and more obvious. For the harmonic resonance amplification phenomenon on the transmission line, the resistive active filter (RAPF) can well suppress this situation. It is usually necessary to connect the step-up transformer in series between the RAPF and the distribution network in order to match the voltage. However, the structure of the transformer will have a certain impact on the original control, which will lead to the weakening of the harmonic suppression effect. Therefore, it is necessary to analyze the equivalent model of the transformer in the harmonic transmission and compensate the influence of the transformer on the control. In this paper, the equivalent model of transformer under harmonic transmission is analyzed and established, and then its influence mechanism on current control is analyzed. Finally, a compensation strategy is proposed to eliminate the influence of transformer on control and improve the ability of RAPF to suppress harmonics.

Hydrogen energy system contains multiple hydrogen energy equipment with different operating characteristics, which will have an interactive impact on the economy of distribution network operation and planning. In this paper, a planning method of electric-hydrogen coupling system considering the characteristics of multiple operating conditions is proposed. Based on the physical principle, the start-stop characteristics of electrolytic cell and methanation equipment, the time and energy consumption characteristics of multi-condition conversion and the variable efficiency characteristics of hydrogen fuel cell were studied. Considering the three operating conditions of cold reserve, hot reserve and normal operation of electrolytic cell and methanation equipment, and the two operating conditions of low and high operating power of hydrogen fuel cell, the operation model of hydrogen energy equipment considering the operating characteristics of multiple operating conditions was systematically established. In order to verify the effectiveness of the proposed method, a regional integrated energy testing system based on IEEE 33-node power grid and 10-node gas network is simulated. The simulation results show that the flexibility of hydrogen energy equipment will be overestimated by ignoring the operation characteristics of hydrogen energy equipment under multiple operating conditions, which leads to a large deviation between the planned capacity and the planned cost and the actual situation.

In an integrated energy system, there are complex and strong coupling relationships between the multi-energy loads, and multi-energy loads have strong volatility and randomness. In view of the above characteristics, a multi-energy load short-term forecasting model based on graph neural network, attention mechanism and variational mode decomposition is proposed. Firstly, the variational mode decomposition of multi-energy loads is carried out to weaken the volatility and randomness. Then through the graph learning network improved by the attention mechanism, a graph structure that fully reflects the coupling connection of multi-energy loads and the correlation between multi-energy loads and meteorology is established, and the graph prediction network is used to analyze the graph structure and the historical data of multi-energy loads to realize the prediction of multi-energy loads. Finally, the proposed model is compared with other models based on the actual data of Arizona State University. The results show that the proposed model has higher prediction accuracy.

Due to the relatively flat curve between the open-circuit voltage and SOC of LiFePO4 battery, the electrical signal is less sensitive to the change of SOC, which seriously affects the estimation accuracy. Ultrasound technology can detect differences in the acoustic properties of batteries due to changes in the physical properties of the material and thus characterize the battery state. In this paper, a method for SOC estimation of lithium iron phosphate battery based on BiGRU network model with high correlation ultrasonic characteristics and Apollo optimization algorithm is proposed. First, ultrasonic detection experiments during battery charging and discharging were carried out, and highly correlated ultrasonic time-frequency domain features were extracted and selected based on waveform and statistical perspectives .Then, comparing a variety of state-of-the-art data-driven models and optimization algorithms, and the SOC estimation method based on the Apollo-BiGRU deep network model was studied .Finally, the validation of the accuracy and reliability of the method was realized at different current multiplicities. The results show that the average absolute error and root mean square error of SOC estimation at different current multiplicities are lower than 1.26% and 1.46%, respectively, which verifies the feasibility of the method.

There is a common scenario in the northern region where wind power, photovoltaic, and thermal power units are integrated into a coupled system at the point of common coupling. In this scenario, considering the demand response of the load is conducive to reducing the pressure of thermal power deep peak shaving in the coupled system, so that the coupled system can better respond to the dispatching instructions of the superior power grid. Firstly, based on the traditional thermal power unit model, this paper considers the unit operation characteristics that the ramp rate changes with the load in different time scales, establishes the output model of the thermal power unit with deep peak shaving, and establishes the demand response model of load shedding and load shifting for the scenario where the coupling system as a whole participates in auxiliary peak shaving service. Secondly, considering the impact of demand response on the scheduling strategy and comprehensive operation benefits of the coupled system, a two-level optimal scheduling model of the coupled system is constructed. Finally, a local power grid is taken as an example for simulation analysis. The results of the example show that the characteristics and advantages of thermal power and new energy can be more fully utilized taking into account the demand response.

In order to reduce the current stress of the double-active-bridge (DAB) converter while taking into account the soft switching of the switching tubes zero-voltage switch (ZVS), this paper proposes a triple-phase-shifted double-active-bridge converter combined with the ZVS to minimize the current stress control strategy. The strategy first introduces the triple phase-shift control and combines the mathematical model and power characterization of the DAB converter under this control mode, and it is found that the triple phase-shift control enables the DAB converter to transfer energy efficiently in the full power range. In order to seek the optimal solution for current stress minimization, the strategy adopts the optimization method based on the Karush-Kuhn-Tucker (KKT) condition and fully considers the ZVS characteristics of the DAB converter. The optimal phase-shift angle combination that can minimize the current stress is successfully determined in the full power range. Comparing and analyzing the proposed control strategy with the traditional triple phase-shift control strategy, the proposed strategy significantly reduces the current stress while realizing the ZVS capability of all the switching tubes, which significantly improves the overall performance and efficiency of the DAB converter. Finally, an experimental prototype is built based on the proposed control strategy, and the experimental results demonstrate its effectiveness in reducing current stress and realizing ZVS.

This article analyzes the impact of transient response characteristics of doubly-fed induction generation on traditional protection from the perspective of phasor extraction. Firstly, this article analyzed the relationship between the transient response of doubly-fed induction generation and the rotor side converter as well as crowbar circuit under different fault depths, and obtained conclusions on the transient response analysis of doubly-fed induction generation under different voltage drop levels. Secondly, based on the conclusion of the fault analysis, the impact of the transient response of doubly-fed induction generation on the differential protection and distance protection of the AC line under different fault depths was analyzed from the perspective of phasor extraction. Finally, scenarios of protection malfunction were provided in the article, and reasons for malfunction were explained combined with the fault analysis conclusion.

With the extensive use of the new generation of synchronous condensers, the security issues of condensers have become increasingly prominent. The inter-turn short circuit of excitation winding is a common fault, which directly affects the normal operation of the synchronous condenser. In this paper, aiming at the analysis of the fault characteristics of the inter-turn short circuit of the excitation winding of the synchronous condensers in the unbalanced state of the grid voltage, the unified mathematical characterization of the air gap flux density and the unbalanced magnetic pull of the rotor under the four working conditions of the normal operation of the synchronous condensers and the occurrence of the inter-turn short circuit fault of the excitation winding, the failure of the unbalanced voltage synchronous condensers and the occurrence of the inter-turn short circuit fault of the excitation winding is established. The rotor vibration characteristics of the synchronous condensers under different working conditions are analyzed, and the harmonic content and harmonic amplitude are compared and analyzed, and the fault mechanism of the condenser is revealed. Based on the TTS-300-2 new synchronous condenser of a station, the finite element simulation model under four working conditions is established, and the experimental verification is carried out in the dynamic simulation laboratory with the MJF-30-6 fault simulation synchronous motor to simulate the operation of the condenser.The results indicate that under unbalanced voltage conditions, a decrease in air gap magnetic density occurs during faults, and the rotor experiences three harmonic components of magnetic imbalance forces. Through simulation analysis and experimental verification, the accuracy of the theoretical research results has been fully confirmed, which lays a solid theoretical foundation for the fault diagnosis of the synchronous condenser.

The switching timing design of hybrid on load tap changer (OLTC) has a significant impact on its operating loss, device life, and operational reliability. Taking a hybrid OLTC with asymmetric-star topology as the research object, this paper analyzes the working state and action process of the tap changer, constructs a numerical simulation model for the tap changer switching process, and further studies the impact of asymmetric topology and initial phase difference on the loss during forward and reverse switching timing. The results show that the transition loss generated by reverse switching is smaller than that of forward switching. By analyzing the action errors of mechanical switches and semiconductor components and their impact on the switching process, the switching action buffer is reasonably set to improve switching reliability. In order to shorten the duration of the switching process and reduce the loss of transition resistance, this paper establishes a switch action tolerance model, normalizes and integrates the fault tolerance range of the action, removes redundant switch margins, and optimizes the overall action sequence of the hybrid OLTC, proposing an action sequence with high error tolerance and shorter switching time. These above results can provide theoretical guidance for improving the operational reliability and fast switching ability of hybrid OLTC, and reducing circuit losses.

To address the need for operational risk assessment and resilience improvement for power systems under extreme disasters, a resilience assessment method for power systems considering mobile energy storage pre-arrangement is proposed in this paper. First, a fault probability prediction model for grid equipment and a meteorological condition dependent output model for distributed generation are constructed with input from strong convective weather forecast information to assess the time-varying risk level of the power system. Second, a pre-arrangement scheme for mobile energy storage is proposed with the goal of minimizing the amount of mobile energy storage and its transportation cost and load shedding cost by considering the constraints of mobile energy storage resources, spatial-temporal scheduling, path planning, and network traffic. The resilience of the power system is then evaluated using the missing area of the system function curve, the load level of the continuous power supply, and the load loss rate as resilience metrics. Finally, for a severe convective weather process, the proposed method is validated in a modified IEEE 30-bus power system. The results show that, compared with the case of not using the mobile energy storage pre-arrangement scheme, the total cost can be reduced by 7805% and the resilience of the power system can be improved, which can provide reference for the emergency plan of the power system under severe convective disasters.

Temperature is a predominant variable to identify the operating state of buried cables, while ampacity is a performance metric for the thermal stability limit of cables. Because power cables are laid underground, precise constitutive parameters such as soil thermal conductivity are essential and fundamental for accurate calculation of the cable temperature field. However, the thermal conductivities of cable backfill soil and mother soil are affected by cable heat dissipation, region, climate and other factors, which result in significant differences and uncertainties, making it relatively difficult to accurately calculate the ampacity. In order to accurately calculate the temperature field and ampacity of high-voltage power cables under engineering operating conditions, this paper proposes a mathematical model and solution methodology for the real-time inversion of soil thermal conductivity for power cables. First, a distributed optical fiber temperature measurement system for the outer surface temperature of cables is developed to invert the soil thermal conductivity for power cables; then, based on the real-time temperature of typical sampling points on the cable skin, a mathematical model for the real-time inversion of the layered soil thermal conductivity is constructed. Finally, an improved genetic algorithm for solving the inverse problem is proposed. The numerical results show that the real-time or dynamic ampacity calculated using the proposed methodology is about 38.08% higher than the nominal ampacity.

Bus voltage adjustment is an important measure to ensure power quality and safe and stable operation of power systems. However, the data processing and analysis efficiency and correctness of automatic voltage control systems are still difficult to meet the increasingly complex requirements of large power grids. Based on this, this paper proposes a binary neural network knowledge experience fusion (BNN-IKAE) double-layer multi-agent deep reinforcement learning algorithm, On the basis of deep reinforcement learning, the bus voltage of the power grid is adjusted. This article first introduces the conventional adjustment process and builds a Markov Decision Process (MDP) model based on it; Then, a double-layer multi-agent structure is designed to address the complex parameters of adjustable components in the large power grid. By introducing a binary neural network (BNN), the network complexity is reduced and the problem of slow model calculation speed can be solved. The knowledge and experience fusion (IKAE) module based on expert experience is integrated, and the convergence and reward value of the model are improved through expert experience pool and stock judgment mechanism. Finally, the bus voltage adjustment ability of the proposed BNN-IKAE based double-layer multi-agent deep reinforcement learning model is simulated and verified in the Northeast power grid. Compared with conventional methods, the adjustment time can be reduced by 79.331%, and the success rate of adjustment increased by 19.23%. The results showed that the intelligent automatic adjustment method for bus voltage in the large power grid based on BNN-IKAE double-layer multi-agent deep reinforcement learning can improve calculation speed and success rate.