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.

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.

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 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.

Given the vast number and diverse needs of electric vehicles, as well as their varying decision-sensitive factors, a multi-vehicle interaction evolutionary game model within the EV community was established to address potential free-riding behaviors during vehicle-grid interactions. This model analyzes the strategic choice paths of individual EVs driven by expected returns and examines the formation process of interaction strategies among the EV community from a micro perspective. It also establishes the relationship between the average abundance of electric vehicles and influencing factors. Case analysis indicates that increasing the profit-sharing coefficient and discharge price, as well as reducing charging costs and loss coefficients, can enhance the average abundance of electric vehicles. Notably, the profit-sharing coefficient has a more significant effect on abundance adjustment. The study of the internal behavioral evolution mechanism can provide a basis for formulating incentive measures for vehicle-grid interactions.

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.

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.

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.

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.

With the deepening development of new power systems, the demand for grid-forming technical equipment, such as inertia response, transient support, and rapid frequency and voltage regulation, has become increasingly urgent to address the system stability issues caused by the decreasing proportion of synchronous generator units. Flywheel energy storage, as a rotational mechanical inertia device, possesses inherent advantages for grid-forming operation. At present, flywheel energy storage can operate as an independent energy storage unit connected to the grid through power conversion devices or in combination with synchronous condensers and other equipment. The concept of grid-forming flywheel energy storage is proposed, encompassing typical technological directions such as inertial flywheel synchronous condensers, power-electronics-based grid-connected high-speed flywheel energy storage, and synchronous-machine-based grid-connected high-speed flywheel energy storage. This paper analyzes the grid-forming operation mechanisms, key technologies, and application scenarios, providing valuable references for the research and application of flywheel energy storage.

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.

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.

Addressing the problem of low arc-extinguishing accuracy of passive devices and high arc-extinguishing cost of active devices, this paper proposes a flexible distribution network interconnection system to address single-phase ground faults. The system consists of a phase-separated networking device and two power distribution networks, which can improve the power quality and enhance the load rate of power supply transformers during normal operation of the distribution network. Upon occurrence of a single-phase ground fault, the system disconnects the non-fault phase circuit breakers used to access the faulty distribution network, achieving reliable arc extinction by injecting current through the fault phase bridge arm. The control modes of the flexible distribution network interconnection system in various operating states of the distribution network and the necessity of mode switching are analyzed. Simulation results demonstrate that the proposed method can achieve fault arc extinction without the need for additional arc extinction devices, while maintaining the function of regulating the power quality of the non-fault power distribution network, thus exhibiting superior arc extinction effectiveness and cost-effectiveness.

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.

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.

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.

In response to the challenges of computational burden and lengthy switching behavior encountered when employing Finite-Control-Set Model Predictive Control (FCS-MPC) for diode Neutral Point Clamped (NPC)-type three-level grid-connected inverters, this paper proposes a simplified model predictive control strategy based on an event-triggered mechanism. The strategy effectively reduces the number of switching vectors that need to be traversed and optimized by introducing a large sector division and judgment mechanism based on the traditional model predictive control method, thereby reducing computation time. Simultaneously, active damping techniques are applied to mitigate the risk of LCL-type filter resonance during system operation. Leveraging inequality approximation theory, the preset threshold of state error for event-triggered control is derived. Upon reaching the event-triggered boundary, the control rule is altered, triggering FCS-MPC, thus avoiding redundant operations and reducing the number of switching transitions, consequently lowering the system’s switching losses. The superiority of the proposed control method over traditional proportional-integral space-vector pulse-width modulation control and cost function optimization model predictive control methods is validated through comparative experiments.

In order to more effectively control the real-time power flow of power grid interfaces, the optimization method of demand side resources participating in real-time control of interface power flow was studied. Firstly, the identification method of key interfaces in the power grid was studied, and a key interface identification method that takes into account both topology structure and power flow transfer ratio was proposed; Secondly, taking two types of demand side resources, interruptible loads and electric vehicles, as examples, the adjustable ability to participate in real-time control of interface power flow was analyzed and evaluated; Subsequently, a real-time control optimization model for interface power flow considering demand side resources was established, and a two-step solution method was proposed that takes into account both solution speed and calculation accuracy; Finally, the IEEE118 power grid model was used as an example for calculation and analysis. Research shows that the collaborative participation of demand side resources in real-time control of interface power flow has a lower adjustment cost as compared to solely through generators. The fundamental reason is that the power grid power flow is controlled mainly by increasing or decreasing the load of is less changed, so that generators can operate near the economic optimal range. demand side resources, and the output of generators

Traction transformer is an indispensable power equipment in the operation system of electric locomotive, and its operation state determines whether the electric locomotive can run safely and normally. The effective simulation analysis and estimation of the aging status of oil paper insulation are of great significance for the safe operation of traction transformers, as the operating environment of traction transformers is different from that of power transformers due to their different operating times and other factors. In this paper, an accelerated thermal aging test was carried out on oil-immersed insulating paper samples to measure their dielectric properties under accelerated thermal aging condition. The Davidson-Cole model was employed to establish the complex dielectric constant and the parameters of the model were obtained. By establishing the quantitative relationship between the polymerization degree of solid insulation, dielectric loss and aging time under accelerated thermal aging, the aging prediction curve and model with dielectric loss as the characteristic quantity were constructed, which provided relevant experimental data and mathematical model for the effective prediction of traction transformer insulation aging.

With the increase of the proportion of large-scale distributed photovoltaic in the distribution network, higher requirements are placed on the distributed photovoltaic fault ride-through capability. The dynamic behavior of distributed photovoltaic power generation system changes under fault conditions, which has an impact on the comprehensive load characteristics of the distribution network side. Based on PSCAD / EMTDCD, a simulation model of distributed photovoltaic power generation system is built to analyze the dynamic characteristics of system under fault conditions. By comparing the similarity between the output characteristics of the first-order integral circuit and the dynamic characteristics of photovoltaic, an equivalent model based on the first-order integral circuit is proposed. The model has the advantages of lower structural complexity and less identification parameters. The equivalent model proposed in this paper is used to carry out simulation analysis under different voltage drop faults. The simulation results show that the equivalent model proposed in this paper can accurately characterize the dynamic characteristics of the system.

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.

As the modern power system continues to evolve, the formation of large-scale interconnected network structures has become increasingly prominent. This development has led to a significant increase in low-frequency oscillation phenomena within the power system, posing a serious challenge to its safe and stable operation. Identifying the modes in low-frequency oscillation signals is a critical prerequisite for implementing appropriate measures or strategies to suppress these oscillations in the power system. To this end, this paper proposes a novel method for the modal identification of low-frequency oscillations in power systems based on deep learning and variational mode decomposition. This method initially employs the variational mode decomposition algorithm for noise reduction in low-frequency oscillation signals. Subsequently, a convolutional neural network is utilized to recognize the order of the denoised low-frequency oscillation signals. This recognition is then combined with the variational mode decomposition algorithm to separate the modes of the low-frequency oscillation signals. Finally, a multilayer perceptron is used to identify the parameters of each separated low-frequency oscillation mode, thereby accomplishing the modal identification of low-frequency oscillations. The effectiveness and accuracy of the proposed method in identifying low-frequency oscillation modes in power systems are validated through multiple simulation case studies.

Multi-energy systems consisting of electricity, heating and natural gas networks are recognized as an important strategy for reducing carbon emissions from the energy sector. The combination of hydrogen utilization and hydrogen-doped transportation offers great potential to achieve deep decarbonization without adding additional construction costs. However, traditional steady-state models and ideal dynamic cases are not sufficient to accurately characterize the real thermal properties of multi-energy systems, and there is a need for a more in-depth study of the dynamic energy transport process in the network. In this paper, a dynamic model of a multi-energy system containing hydrogen-rich compressed natural gas (HCNG) based on the generalized phase volume modeling approach is proposed. The model analyzes the thermodynamic behavior of energy media in various energy networks. Numerical studies demonstrate the benefits of the proposed multi-energy system model, especially in terms of operational cost reduction and renewable energy consumption.

In recent years, increasingly frequent meteorological disasters such as typhoons, ice disasters, earthquakes, and high temperatures have seriously threatened the safe and reliable operation of the power system. Large-scale power grid accidents under extreme meteorological disasters have resulted in extremely high social and economic losses. Therefore, accurate and effective power system failure prediction method is of great significance. However, the traditional method considers a relatively single type of failure influencing factors and fails to simultaneously consider multiple factors such as meteorology, geography, and power grid. Considering the spatial distribution and temporal evolution characteristics of extreme meteorological disasters, the spatiotemporal correlation of failures is also a key factor in prediction. Therefore, this paper proposes a power system failure prediction method meteorological disasters based on convolution long-short term memory neural network. A power system failure prediction data set is established containing meteorological, geographical, and power grid data. This paper proposes a multi-source data analysis method based on convolutional neural network which can efficiently extract the spatial correlation of failures. A failure sequential prediction method with a double-layer network structure is designed based on the long-short term memory algorithm, which achieves effective characterization of failure temporal correlation. Finally a CNN-LSTM framework is proposed to improve accuracy of failure prediction under meteorological disaster. The effectiveness and accuracy of the proposed method are verified through the historical meteorological data of typhoons Mikala and Lubi as well as the geographical and power grid data of a certain area on the southeastern coast of China.

In this paper, based on the T100a test of a certain type of UHV pneumatic SF6 circuit breaker, the T100a breaking test of EHV pneumatic SF6 circuit breaker is tested by using a nanosecond current zero zone measuring device. The variation rules of arc key parameters such as arc peak, pre-zero arc conductance and arc current are studied when interrupting high DC component asymmetric short circuit current under different arc time. At the same time, based on the internal structure of the circuit breaker, the physical simulation model of gas arc enthalpy flow is established to study the pressure and distance change of the pressure chamber at different arc times. The results show that under the same on-off current, the longer the arc burning time is, the larger the arc quenching peak is and the smaller the pre-zero arc conductance is. At the same time, the pressure of the compressor chamber and the contact distance also increase positively with the arc time, which makes it easier for the circuit breaker to break, and that provides a theoretical basis for the SF6 circuit breaker to break the asymmetric short-circuit current in practical application.

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.

To guarantee the stability and reliability of low-voltage circuit breakers within power supply systems, this paper proposes a fault diagnosis method that is based on a combination of Genetic Algorithm (GA) and Deep Belief Network (DBN). The proposed method employs features derived from multi-physical domain information as input to the network for the purpose of diagnosing faults in low-voltage circuit breakers. Firstly, vibration and current signals from the circuit breakers are collected via sensors and denoised using wavelet soft thresholding to preserve feature information effectively, avoiding losses typical in conventional methods. Subsequently, time-domain and frequency-domain features are extracted from both signal types to form a multi-physical domain feature matrix, which serves as input for subsequent modeling. The multi-physical domain feature matrix is then fed into a DBN neural network for fine-tuning, with GA optimizing the network’s weights and thresholds to enhance fault diagnosis accuracy. Experimental results demonstrate that the proposed model achieves a diagnostic accuracy of 9846%. Compared to models using vibration or current signals alone, this represents an improvement of 149% and 679%, respectively. This fully demonstrates the superior performance of the multi-physical domain-based GA-DBN diagnostic model in fault diagnosis of low-voltage circuit breakers, further validating the criticality and practicality of this technology in the intelligent power supply system of subways.

Aiming at the load spike problem caused by large-scale electric vehicles (EV) entering the grid, especially the fast charging behavior, this paper proposes a two-stage optimal scheduling strategy for microgrids that takes into account the characteristics of EV charging behavior. Firstly, based on the V2G characteristics and time-sharing tariff system, a function model considering EV charging and discharging costs and the peak-to-valley difference of microgrid loads is constructed, and an optimal EV charging and discharging strategy to achieve peak shaving and valley filling of loads is formulated; secondly, the output of each power generation unit or grid with the goal of minimizing the microgrid operating costs and environmental costs is optimized, and the optimal solution is determined by the entropy weighting method; lastly, the two-phase optimization model is divided into grid-connected and islanded modes for joint solution using CPLEX solver and improved MOIDBO algorithm, respectively. The simulation results prove that the strategy proposed in this paper realizes peak shaving and valley filling while guaranteeing the EV charging demand, reduces the charging cost of EV users, and improves the economy of the microgrid system containing a high proportion of EV charging loads.

Acetylene is a characteristic gas generated by high temperature overheating and various discharge faults inside transformers. Acetylene causes oil degradation and reduces insulation performance, as well as acid corrosion of metal surface inside transformers; The alarm value for acetylene content during operation of ultra-high voltage transformers is determined to be 1 μL/L, but the current research on dissolved gas prediction models in transformer oil has a significant prediction error for gas concentrations below 1 μL/L. This paper proposes a VMD-LSTM prediction model to accurately predict the low concentration of acetylene in transformer oil. Variational mode decomposition(VMD)is used to decompose acetylene time series data into intrinsic mode function(IMF), and the tolerance parameters are adjusted to reduce the impact of data noise on the model. The long short-term memory(LSTM)is combined for prediction. The prediction performance of the proposed model is verified, the root mean square error of the prediction model is 0010 9, the mean absolute error is 0008 7, and the mean absolute percentage error is 1641%, it means that a very ideal predictive effect is achieved.

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.

Zero-sequence voltage and current based single phase grounding fault detection and location methods in non-effectively grounded distribution network have limitations in practical use due to the lack of zero-sequence CT and PT in some cases. To deal with this problem, transient characteristics of the fault components of phase current under single phase grounding fault are analyzed based on the Karrenbauer Transformation and the single phase grounding fault detection method and section location method are proposed. PSCAD-based simulation results and verification results based on fault recording data show that the proposed method behaves well when single phase grounding fault with different impedances occurs in different sections. Besides, the proposed method can select the faulty phase automatically without using voltage signals, which is suitable for all IEDs.

Slide mode variable structure control is widely used in permanent magnet motor control system due to its good robustness and response speed. However, the traditional sliding mode control still has problems such as convergence speed and vibration. In order to further improve the control performance, a modified fuzzy integral terminal sliding mode variable structure controller is designed based on the traditional sliding mode variable structure control. By combining the linear function and the nonlinear function, the error has faster convergence, faster output speed response and smaller steady-state error. By introducing fuzzy control and using continuous saturation function sat(s) instead of the symbol function sgn(s), the system speed is dynamically adjusted and smoothly switched, and the overall anti-interference ability of the system is improved and the system output vibration is weakened. The results of simulation and physical experiment show that fuzzy integral terminal sliding mode variable structure control effectively reduces chattering while improving system response speed. It proves that the designed control strategy has the advantages of improving the robustness of the system and weakening the vibration of the system.

In offshore wind power via VSC-HVDC systems, the surplus power generated after an AC grid fault can lead to overvoltage in DC cables. In order to prevent the generation of DC overvoltage, a DC Chopper is generally configured at the side of the receiver converter station, which is put into consumption of surplus power under fault conditions to realize the balance of the system’s power at the sending and receiving ends. In this paper, three DC energy consumption programs, namely, switching valve section series centralized resistor, distributed resistor, and sub-module series centralized resistor, were simulated and compared, and the topologies and working principles of the three types of DC Choppers were introduced. Based on the actual parameters of the Rudong offshore wind power via VSC-HVDC project, a system model was constructed under PSCAD/EMTDC to realize the working process of the above three DC Choppers. The characteristics and FRT performance during the operation of the three DC Choppers were compared, and the advantages and disadvantages of the three DC Choppers were analyzed. The research results show that the distributed energy consumption resistor scheme provides the best control effect but at the highest cost; the series-connected centralized energy consumption resistor for switch valve sections offers the worst control effect but at the lowest cost; the submodule series-connected centralized energy consumption resistor scheme falls between the two in terms of various indicators. This study can provide a reference for the selection of DC energy consumption devices in offshore wind farm VSC-HVDC grid-connected systems.

As the construction of the novel power system continues to advance, the cooperative development of source-grid-load-storage is inevitable. However, under the background of “dual-carbon”, the existing source-grid-load-storage cooperative optimization scheduling method does not take into account enough factors such as carbon emission. It is difficult to support the low-carbon development of the novel power system. In this paper, firstly, dynamic carbon emission factors are introduced to propose a low-carbon demand response mechanism driven by both benefits and low carbon, and a low-carbon demand response model is built to accommodate the economic cost and low carbon emission. Secondly, based on the theory of carbon emission flow, a source-grid-load-storage synergistic day-ahead economic dispatch model is constructed considering the carbon flow constraints, which helps the power system to operate with carbon reduction. Subsequently, in order to deal with the nonlinearities introduced by the consideration of carbon flow constraints, a decomposition method is used to deal with them, which is solved by alternating iterations of the two subproblems: low-carbon scheduling and carbon flow calculation. Finally, through the IEEE-14 node system, we implemented the cooperative optimization scheduling for source-grid-load-storage coordination considering low-carbon demand response. The results show that the proposed model and method can effectively balance the carbon and economy, and promote the cooperative development of each section of source-grid-load-storage in the context of the “dual-carbon”.

Axial flux permanent magnet motor (AFPMM) has some characteristics including wide speed regulation range, flexible control, and so on, which exhibit good prospect in the field such as electric tools and electric vehicles. It has advantages in the field of electric tools and electric vehicles. In order to achieve the goal of low vibration, low noise and high performance of the motor, the source and temporal and spatial distribution characteristics of axial electromagnetic force of the motor are analyzed, and the harmonic amplitude under different temporal and spatial orders is determined. Combined with the harmonic amplitude of axial electromagnetic force at different time and space orders, the changes of vibration acceleration and noise amplitude under AFPMM load are analyzed. Finally, the axial electromagnetic force is taken as the optimization goal, the genetic algorithm is used to optimize the motor parameters in layers, and on the basis of finite element simulation, experimental verification is carried out, and the vibration acceleration and noise value before and after optimization are analyzed by comparative analysis, which proves that the parameter optimization effectively improves the electromagnetic vibration noise of AFPMM.

With the integration of large-scale clean energy sources such as wind/light and the operation of more and more DC lines, the voltage safety issue of AC/DC power grids is becoming increasingly prominent, and how to determine the safe range of grid voltage under N-1 faults is an important problem to be selected it. The existing methods mostly focus on the static characteristics of the power system, and fail to fully consider its dynamic characteristics and fail to achieve an effective balance between calculation speed and accuracy. In response to this issue, this article proposes an adaptive dynamic dimensionality reduction method, which involves dimensionality reduction from the fault dimension, reactive voltage dimension, and reactive equipment dimension. The original voltage safety domain problem is simplified using the first-order trajectory sensitivity method, and error analysis is performed to verify the dimensionality reduction effect. Finally, the accuracy and effectiveness of the proposed method were tested using an improved IEEE 39 node system. And the accuracy of the proposed method is further verified in terms of reactive power reserve optimization.

With the surge in the number of new energy vehicles, the stable operation of electric vehicle charging facilities has become particularly critical for grid security and user rights protection. In this study, an in-depth analysis is conducted for the fault prediction of EV charging piles. Firstly, the user’s charging behavior is studied based on kernel density estimation, and the temporal correlations of charging onset, duration, and end moments are explored, and a non-Euclidean domain data modeling method is proposed accordingly. Further, the study introduces Graph Convolutional Neural Network (GCN) and Convolutional Neural Network (CCN), develops a GCN-CNN joint deep learning model to effectively capture the complex nonlinear relationship between fault classification and data features. Through ablation and algorithm comparison experiments on real datasets, this model achieves a superior performance of 0.844 for both F1score and G-mean on the validation set, which improves the average performance over other models by 6.28% and 6.04%, respectively. This study provides an innovative solution for charging pile fault prediction, which helps to reduce O&M costs and improve detection efficiency.

Open-circuit faults in gate drivers are typical failures of power devices, and accurate diagnosis of such faults can help improve the operational reliability of power electronic converters. Aiming at the deficiencies of existing fault diagnosis methods, a novel inverter power device gate driver open-circuit fault diagnosis method based on a DenseNet-ViT network is proposed: Firstly, normalization and augmentation processing are performed on sampled data to form three types of datasets: training set, validation set, and test set. Secondly, a DenseNet-ViT model is constructed to achieve enhanced extraction of fault features, and the model is trained using fault data. Finally, the validation set is used to conduct model testing, selecting the best-performing model. This method was applied to a three-level NPC inverter, detailing its modulation principle and the modeling process of the fault diagnosis model for this specific power converter topology, explaining the stochastic gradient descent function used in the model training process, and setting up an experimental platform for verification. Experimental results show that compared with other mainstream diagnostic methods, the proposed method has certain advantages in terms of floating-point computations, model parameters, and algorithm runtime.