Abstract:In order to improve the accuracy and speed of key node identification and damage resistance analysis of power grids, the topological structure and electrical characteristics are considered comprehensively. The topological information entropy in the improved K?shell algorithm (IKS) is replaced by power flow entropy, and the value obtained by weighted summing of the K-shell (Ks) value and power flow entropy is defined as a new index for evaluating node importance, namely the comprehensive value of node importance. The comprehensive value of node importance is ranked, and the key node identification model with IKS and power flow entropy is obtained. By taking the IEEE-118 node system as an example, the proposed method and four traditional key node identification methods are used to identify nodes, and random attack and deliberate attack tests are carried out with different attack intensities. According to the node importance ranking, the maximum connectivity subgraph scale of power grids, and network efficiency analysis, the proposed key node identification method based on IKS and power flow entropy is superior to betweenness centrality and closeness centrality.

Abstract:Reflected traveling waves are generated by substation equipment, and the incident waves and reflected waves are overlapped during traveling wave signal measurement. To address these issues, a precise detection method of traveling waves based on S-transform and particle swarm optimization and generated regression neural network algorithm (PSO-GRNN) is proposed. Firstly, the S matrixes of the overlapped traveling wave signal and the real incident traveling wave signal are obtained by the S transform, respectively. Secondly, the S matrixes of the overlapped traveling wave signal and the real incident traveling wave signal are reconstructed in terms of dimensionality into a vector, which is used as the input and output of PSO-GRNN for training and learning, and the network model for separating the overlapped traveling wave signal is established. Finally, according to this model, the S matrix of the incident traveling wave signal is separated from the overlapped traveling wave signal, and the S-inverse transform is performed to obtain the pure incident traveling wave. The simulation results show that the separated incident traveling wave has higher steepness and more prominent time-frequency characteristics, which provides a new idea to improve the reliability of existing traveling wave protection and the accuracy of traveling wave positioning.

Abstract:Distributed photovoltaic (PV) is of great significance to realizing the objectives of “carbon peaking and carbon neutrality” and establishing a new power system mainly supported by new energy sources. In this study, the optimization operation of the household PV-energy storage system under the present step-peak valley tariff mechanism was investigated. Firstly, the structure of household PV-energy storage system and tariff mechanism were introduced. Secondly, by considering the impact of step-peak valley tariff on users’ energy use strategy on long time scale, a two-layer rolling optimization operation strategy of household PV-energy storage system based on model predictive control (MPC) was proposed, where the upper-layer model focused on step tariff-based annual rolling optimization with maximum annual comprehensive revenue as objective; in the lower-layer model, the operation scheme of PV and energy storage was based on the optimization results of the upper layer, and the deviation of system state caused by uncertain factors was corrected. In addition, the Informer model was used to achieve accurate prediction of the system state on a long time scale. Finally, the simulation results show that the proposed strategy can delay the arrival of high step tariffs and effectively improve the overall revenue of residential users.

Abstract:Effective use of natural runoff forecast information can increase the upper limit of storage capacity of hydropower stations during flood season, make full use of hydropower resources under the premise of ensuring flood control safety, and improve the economy of power grids. For the power system with a high proportion of small hydropower resources, an optimal scheduling strategy is proposed, which takes into account the expected runoff and the upper limit of elastic storage capacity of hydropower stations within the basin during flood season. Firstly, a screening method of meteorological and hydrological forecast factors is proposed based on the maximal information coefficient (MIC), and an Informer natural runoff forecast model based on the attention mechanism is constructed. Secondly, by considering the accuracy of forecast information and the pre-discharge capacity of hydropower stations, a method of determining the upper limit of elastic storage capacity of hydropower stations based on chance-constrained optimization is proposed to excavate storage resources of hydropower stations during flood season. Finally, a case study of a small hydropower station group in Lishui, Zhejiang Province is carried out. The results show that the proposed model has an accurate forecast effect, which can improve the utilization efficiency of storage resources of small hydropower stations and reduce the system operation cost.

Abstract:As the scale of direct current (DC) transmission projects continues to expand, the alternating current (AC)-DC interconnection system has brought certain challenges to the transient voltage recovery capability of the power grid. In order to reduce frequent operations of tap changers of converter transformers caused by voltage fluctuations in converter stations, an optimization method for reactive power control of DC converter stations based on multi-channel fusion and multi-scale dynamic adaptive residual learning (MC-MSDARL) and particle swarm optimization-bat algorithm (PSO-BA) is proposed. Firstly, research is conducted on the transient overvoltage characteristics of the converter stations, and the impact of AC filters, reactive power compensation equipment, and phase-shifting cameras on transient overvoltage is analyzed. Then, the multi-scale dynamic adaptive residual convolution method is used to dynamically update the size of the convolution kernel, improve the model’s learning ability, map the relationship between the operating state of the DC system and voltage stability, and construct a transient voltage stability prediction model. Finally, an optimization model for reactive power control of DC converter stations is established to reduce voltage fluctuations and network losses, and the PSO-BA is used to solve the model. PSASP is employed to build a DC power grid for simulation verification, and the experimental results show that the proposed method improves the transient voltage stability capability and effectively reduces frequent operations of tap changers of converter transformers.

Abstract:Relay protection device is one of the important equipment for collecting fault recording data. Comparison and analysis of homologous recording data can realize fault diagnosis, but due to the lack of standardization for the naming format of the recording channel, it is difficult to match the homologous recording channels. In this paper, a matching method for homologous recording channels of protection devices based on feature enhancement is proposed. Firstly, the recording channel names are feature-enhanced by means of synonym replacement, text expansion, and regular expression algorithms. Then, the Pkuseg word segmentation tool is used to segment the recording channel names, and a customized stop word list is utilized to remove the stop words in the recording channel names. Finally, the term frequency?inverse document frequency (TF-IDF) method is used to process the recording channel names into the form of digital vectors, and the cosine similarity algorithm is used to calculate the similarity between the recording channel names. The homologous recording channels are judged according to their similarity. The example results show that the proposed method can fully explore the key information of the recording channel names and improve the accuracy of the matching of homologous recording channels.

Abstract:Substations are important hubs for transmitting electricity in power grids, and the assessment of their flood control capabilities is crucial for improving flood warning systems of power grids. However, factors affecting the static flood control of substations are complex, and the assessment process is highly subjective and fuzzy, which poses difficulties for static evaluation. To solve this problem, a static flood control capability evaluation index system of substations is constructed, and then a fuzzy comprehensive evaluation algorithm for the static flood control capability of substations based on combination weighting is proposed. The algorithm uses game theory to combine the analytic hierarchy process and entropy weight method to weigh the evaluation indicators and finally adopts the fuzzy comprehensive evaluation method to obtain the evaluation results. The experimental results show that the game theory-based combination weighting method can balance the subjective analysis of the analytic hierarchy process and the objective analysis of the entropy weight method well, and the calculation results are consistent with the flood disaster causation mechanism. The fuzzy comprehensive evaluation method proposed in this paper is reasonable and effective, with high confidence in the conclusions, and it can provide reference and decision-making assistance for actual flood warnings of substations.

Abstract:The magnetizing inrush current will be generated when the distribution transformer is closed with no load. If its value is greater than the setting value of line protection, the circuit breaker will fail to close, Therefore, it is necessary to identify the magnetizing inrush current and the general short-circuit current so that the circuit breaker can avoid the magnetizing inrush current and achieve normal closing. A comprehensive multi-feature identification method of magnetizing inrush current in distribution transformers based on an improved Prony algorithm is proposed, and the characteristics of magnetizing inrush current of one and multiple distribution transformers are analyzed. Backward-prediction link is introduced to improve the traditional Prony algorithm and improve the accuracy of magnetizing inrush current identification. The advantages, disadvantages, and application scope of the fundamental wave attenuation factor method, the second harmonic discrimination method, and the wavelet decomposition identification method of the magnetizing inrush current are comprehensively compared. A comprehensive identification method of the magnetizing inrush current and fault current of the distribution transformers based on a multi-feature discrimination system is put forward to improve the identification accuracy of the magnetizing inrush current. The recorded samples of field fault current and magnetizing inrush current are taken as identification objects, and the proposed algorithm is compared with the single feature method and wavelet decomposition identification algorithm, verifying its effectiveness and engineering feasibility.

Abstract:The flow distribution of stray current in urban rail transit in the ground makes it intrude into the nearby alternating current (AC) power grid transformer. In order to study the influence of stray current in rail transit on the magnetic bias of substations along the line under different soil characteristics, based on a substation in Shanghai and its nearby rail transit system, CDEGS software is used to establish the coupling simulation model of “urban rail transit-AC power grid substation”, so as to study the influence of stray current on the direct current (DC) magnetic bias of the AC power grid transformer under different concrete-soil resistivity. The distribution curve of soil iso-current density amplitude is used, combined with visualization technology, to display the influence range and flow path of stray current. The results show that there is a big difference in the influence of concrete resistivity and soil resistivity on the spatial electric field distribution of the leakage current of the rail and the DC magnetic bias of the neutral point of the transformer. In the simulation model, the maximum influence range of stray current is 1 421.33 m, and the differential of distribution distance and soil resistivity is used as the influence index of soil resistivity on the flow range of stray current. The maximum soil resistivity is 1.42 m/(Ω?m). When the train moves 500.3 m after 33 s, the soil resistivity has the greatest influence on the DC magnetic bias of the neutral point of the substation, which can reach 0.003 431 A/(Ω?m).

Abstract:Due to climate change and urban layout, urban waterlogging disasters are becoming increasingly severe, posing a serious threat to the stable power supply of the distribution system. In order to minimize the impact of flood disasters, it is urgent to explore urban flood disaster prediction models to achieve distribution equipment risk prediction. However, the existing hydrodynamic model-based method has high computational complexity and is difficult to guarantee the timeliness of large-scale flooding simulation forecast. The data-driven model-based method has insufficient training data, which is insufficient to meet the requirements of fast and accurate urban waterlogging warnings. To this end, a rapid waterlogging prediction model based on multimodal data fusion is proposes. This method generates training data through a hydrodynamic model to solve the problem of insufficient training data and integrates image data such as elevation maps with rainfall sequence time series data to improve prediction accuracy. Furthermore, Guilin City is used as the research object to verify the effectiveness of the proposed method. The experimental results show that the proposed method maintains high accuracy while reducing computational complexity. This method can provide a reference for risk assessment of distribution terminals.

Abstract:In recent years, various types of online monitoring devices have been installed in the digitally intelligent cable line system for electrical equipment. However, these online monitoring devices are relatively independent, and the monitored operation data is not comprehensively analyzed. As a result, it becomes challenging to effectively prevent safety accidents. To address this issue, a dynamics safety evaluation model for digitally intelligent cable systems considering coupling effects is proposed. Firstly, the electrical and non-electrical key characteristic parameters of the digitally intelligent cable line system are selected and classified. Secondly, a causality diagram that can be used for safety evaluation is established, and model functions are designed to analyze and build a dynamics flow diagram for the cable system. To determine the variable weights, an interaction matrix is employed, allowing for the establishment of optimal model training parameters. Failure probability functions are constructed for different types of variables and devices. Additionally, the safety evaluation of the entire cable system is achieved through a tandem model, which is transformed into a system dynamics equation to create a safety evaluation model for the digitally intelligent cable system. Finally, the safety state of a regional cable line is evaluated. The results demonstrate that this model effectively handles complex cable systems by considering the coupling effects between multiple variables. It enables a comprehensive safety evaluation of digitally intelligent cables and ensures their reliable operation.

Abstract:As a mobile energy storage resource, electric vehicles can participate in the emergency control service of power grids through the aggregation of operators. The power distribution strategy of electric vehicle operators participating in emergency control is proposed, and the means of electric vehicles participating in emergency control is analyzed. The flexibility and economy of different types of electric vehicle users participating in emergency control are discussed. The power distribution optimization model of electric vehicles participating in emergency control is established by comprehensively considering the three control modes of load transfer, charge reduction, and vehicle-to-grid (V2G), and the model is further simplified to a linear programming model by relaxation. The case study shows that the proposed power distribution strategy can quickly and effectively realize the power distribution of electric vehicles for emergency control and effectively activate the flexibility of electric vehicles within stations beyond traditional orderly charging control.

Abstract:The integration of photovoltaic (PV) and electric vehicle (EV) charging stations into the distribution network affects the stable operation of the distribution network, but the respective flexible scheduling characteristics of PV and EV charging stations enhance the flexibility of the distribution network. Therefore, a distributed resource coordination control strategy (DRCCS) for the distribution network is proposed, which considers the efficiency of PV utilization and the limitation of the number of EV charging facilities. On the PV side, the V-Q droop control slope of each PV inverter in the distribution network is coordinated, and optimal output control of PV is achieved by considering the PV control capability in the distribution network. On the EV charging side, the number of charging facilities in actual charging stations is considered to achieve orderly charging of EVs. The flexibility of charging is utilized to provide auxiliary support for the distribution network while ensuring charging needs. The coordination control model considering the efficiency of PV utilization and the limitation of the number of EV charging facilities is comprehensively constructed, so as to ensure stable distribution network voltage, improve the active output of PV inverters, and satisfy the charging demand of EVs. The mixed integer nonlinear problem constructed in this paper is solved by the generalized Benders decomposition (GBD) algorithm, and its effectiveness is verified through simulation.

Abstract:The control system of the live working robot is highly susceptible to the strong electromagnetic environment of high-voltage transmission lines. In order to ensure the reliability of the robot’s operation in harsh electromagnetic environments, a simulation study is conducted on the electromagnetic protection for the control box of the live working robot for transmission lines. An electromagnetic protection control box with certain shielding efficiency is designed, which considers the influence of factors such as the shape, size, number, and spacing of the box’s holes and seams on the shielding efficiency. Through the ANSYS simulation of the electromagnetic coupling model of the holes and seams, it is verified that the design has high accuracy and shielding efficiency. The simulation results also prove that the electromagnetic environment inside the designed box is far below Chinese and international standards, and the live working robot can complete various work in this environment, which has good engineering practicability.

Abstract:A high proportion of new energy is connected to the power system through converters, which reduces the frequency support ability of the power system and affects the synchronization stability of the system. Small signal modeling and stability analysis are presented for a hybrid power system composed of a grid-forming (GFM) converter, a grid-following (GFL) converter, and a synchronous generator. Firstly, small signal modeling is conducted on a hybrid power system composed of a GFM converter controlled by a virtual synchronous generator, a GFL converter based on a phase lock loop (PLL), a synchronous generator, and other components. To verify the correctness of the signal model, the established small signal model is compared with the actual circuit model for the same step power disturbance. Secondly, the eigenvalue analysis method is used to analyze the influence of changes in the penetration rate of new energy and GFM converter on the small signal stability of the system, and the participation factor method is employed to analyze the degree of influence of relevant state variables on the system eigenvalues. Then, the influence of the virtual moment of inertia and virtual impedance in the control parameters of the GFM converter on the small signal stability of the system is analyzed. Finally, the accuracy of the theoretical analysis is verified through simulation.

Abstract:The grid-forming-based renewable energy power generation units can provide inertial support to isolated power grids, but they face transient stability issues when faults occur. To address this issue, the impact mechanism of the reactive power outer loop of the grid-forming-based renewable energy power generation units on the transient stability of isolated power grids is studied, and corresponding stability improvement strategies are proposed. Firstly, the Lyapunov direct method is employed to explore the stability boundaries of the output-end voltage of the grid-forming-based renewable energy power generation units. Secondly, a comprehensive control strategy with an adaptive droop coefficient based on the stability boundaries of the output-end voltage is proposed. This strategy not only maintains system power angle stability but also limits fault currents. It enhances the transient stability of the system while ensuring the safety of inverters. Finally, electromagnetic transient simulations are conducted by using MATLAB/Simulink, which validate the correctness of the theoretical analysis and the effectiveness of the proposed transient stability improvement strategy.

Abstract:Improper setting of control parameters during low voltage ride-through (LVRT) in photovoltaic (PV) grid-connected systems can easily cause their own disconnection, especially when large-scale PV grid connection exacerbates the degree of power electronics in the power system, which can significantly reduce the transient stability of the system. To improve the transient stability of the system, firstly, based on the operating mode of the PV grid-connected system during voltage drop, vector plots are used to analyze the transient stability characteristics of the system during the duration of the fault. Secondly, the single objective linear programming method is used to describe the absolute stability interval of active current command under transient stability constraints, and based on this interval, the geometric analysis method is used to determine the optimal interval of control parameters of the PV grid-connected system. Finally, simulation analysis is conducted on the actual power grid structure of a certain region in Jiangxi Province to verify the effectiveness of the proposed parameter optimization method in improving the transient stability of the system.

Abstract:Compared with that of the existing offshore wind farms, the impact of deep-sea offshore wind farms on the operation reliability of power systems is more complex due to varied oceanic and climatic conditions. In order to analyze the operation reliability of large-scale deep-sea offshore wind power integration, a reliability assessment model considering the effects of mesoscale eddies and tropical cyclones is proposed, which is used to evaluate the internal mechanism between critical marine factors and system reliability. The effects of mesoscale eddies, tropical cyclones, and corresponding air-sea interactions in the East China Sea on system reliability are analyzed by using the modified Roy Billinton test system (RBTS). The simulation results show that the mesoscale eddies and tropical cyclones have a significant influence on the reliability of the wind power integration system, and the influence shows a seasonal trend. Furthermore, an energy storage system is applied to the simulation case to improve the reliability level and reduce the negative impacts.

Abstract:Offshore wind power, as a clean and renewable energy source, has become an important way to realize the low-carbon transformation of offshore oilfield platforms. However, different wind power submarine cable connections have a great impact on the cost of submarine cables and the low-carbon transformation of oilfield platforms. Therefore, factors such as wind power resources and low-carbon cost are comprehensively considered, and an optimization method for wind power submarine cable routing for low-carbon energy consumption of offshore oilfield platforms is proposed. With the objective function of minimizing submarine cable construction costs, submarine cable operation losses, and carbon emissions costs of oilfield platforms, a 0-1 combination integer programming model for submarine cable routing optimization layout is established. The second-order cone relaxation is applied to the quadratic power flow equation constraints, achieving an accurate solution of the optimization model. By taking an actual offshore wind farm as an example, the simulation and comparative analysis are carried out. The results show that the method proposed in this paper can significantly improve the economy of the power collection system, select a more low-carbon scheme, and show strong practicability and feasibility.

Abstract:As the construction of new power systems proceeds, the capacity of distributed new energy has greatly increased, and more and more power electronic devices are connected to the power system, resulting in limited current amplitude and controlled phase angle of the transmission lines during short circuits. Therefore, the traditional power frequency relay protection methods are no longer applicable. In addition, existing methods are prone to the failure of protection criteria when the output of new energy is weak. To address these issues, a pilot protection method for new energy access to the power grid based on generalized S transform and Pearson correlation coefficient is proposed. Firstly, the generalized S transform is used to obtain the fault characteristic quantities in the frequency domain for currents at both ends. Then, the Pearson correlation coefficient is utilized to calculate the correlation degree of transient energy at both ends, so as to further identify intra-zone and extra-zone faults. Finally, experiments show that the proposed method can meet the requirements of protection rapidity and has a strong ability to withstand transition resistance. This method is not affected by the new characteristics of short-circuit current caused by the connection of new energy power electronic devices and can be applied to transmission lines of new energy.

Abstract:Wind turbines face various fault risks during operation, making precise fault diagnosis and prediction crucial for improving wind farm operation efficiency and ensuring system safety. Traditional fault diagnosis methods primarily rely on rule-based models or shallow machine learning algorithms, which often exhibit low accuracy and poor generalization ability when dealing with complex, nonlinear, and strongly time-dependent data. To address these challenges, this paper proposes an encoder-decoder (Seq2Seq) model based on an improved grey wolf optimizer (IGWO) for fault diagnosis and prediction of wind turbines. The model enhances the feature expression of key input moments through an attention mechanism and leverages IGWO to perform global optimization of hyperparameters, improving both prediction accuracy and generalization ability. Compared with traditional models, this approach demonstrates high efficiency and reliability in wind turbine fault prediction, providing technical support for the intelligent operation and maintenance of wind farms.

Abstract:This paper studies the direct-current bus voltage control of direct-current microgrid systems under false data injection attacks based on event-triggered fixed-time sliding mode control. Firstly, the mathematical model of the direct-current microgrid system under false data injection attacks is established. Secondly, to save network resources, event-triggered mechanisms are introduced in the sensor-to-observer channel and observer-to-controller channel. Thirdly, since the system current is difficult to be accurately measured due to the constant power load disturbance and false data injection attack, a state observer is designed. A fixed-time sliding mode controller based on the observer is designed, and the upper bound of the system convergence time is obtained. Finally, a direct-current microgrid system with two constant power loads and an energy storage unit is taken as an example for simulation. The results that the proposed control method can effectively resist the influence of constant power load disturbances and false data injection attacks on the direct-current bus voltage and save network resources effectively.

Abstract:In view of the minimum electricity cost for users in the demand response of off-grid microgrids, an economic dispatch optimization method for the demand response of off-grid microgrids based on the IGDT-Stackelberg game is proposed. Firstly, the information gap decision theory is used to solve the uncertainty interval of renewable energy generation power. Secondly, the renewable energy output expectation is calculated based on the acceptance of strategy risk on the generation side. Finally, the Stackelberg game model is used to manage the iterative interactions of the two-side strategies to determine the optimal output allocation of the generating units on the supply side, so as to reduce the cost of generation. The experimental results show that the proposed method can significantly reduce the peak-to-valley ratio of daily loads on the demand side and minimize the electricity consumption cost for users compared with the existing methods while circumventing the risk of photovoltaic (PV) unit output prediction bias.

Abstract:In response to the uncertainty of wind power and load fluctuations, as well as the insufficient system peak shaving capacity of cogeneration units during winter heating periods, a virtual power plant consisting of wind farms, power-to-gas (P2G) conversion plants, and waste incineration power plants within a certain area is formed. Firstly, to address the low energy utilization efficiency of waste incineration power plants, it is considered to adopt their cogeneration mode to participate in system peak shaving, improve energy utilization efficiency, and establish a mathematical model of waste incineration power plants under the cogeneration mode. Secondly, wind farms, P2G, and cogeneration units are introduced to establish a virtual power plant model with wind power, P2G, and waste incineration cogeneration. Finally, in response to the uncertainty of the source and load in the system, a fuzzy chance constraint is used to establish an optimized scheduling model for the virtual power plant with wind power-waste incineration cogeneration that considers the uncertainty of both the source and load sides. To verify the effectiveness of the model, two different scenarios are set up, and the model is solved by using CPLEX optimization software. The simulation results show that the proposed model can effectively promote wind power consumption, improve the energy utilization efficiency of waste incineration power plants, and reduce the total cost of virtual power plants.

Abstract:The time-of-use tariff policy is mostly implemented with a fixed time slot delineation scheme, but with the large access of renewable energy on the supply side and the enhanced flexibility of broad load on the demand side, the relationship between supply and demand shows dynamic changes. Therefore, in order to study the optimization of peak and valley time slots of dynamic time-of-use tariffs under different time scales, this paper adopts the improved fuzzy-C means (FCM) clustering algorithm to construct peak and valley time slot delineation model based on the comprehensive consideration of the large proportion of renewable energy connected to the networks and the demand-side response. The model determines the optimal time slot delineation results through the clustering analysis of the daily change curves of the net load under different time scales. According to the example analysis of Liaoning Province’s net load data from April 2021 to March 2022, renewable energy has anti-peaking characteristics, which causes the peak-to-valley difference of the net load curve to increase, and users’ response to the time slot delineation of the time-of-use tariff policy has a lag and timeliness. Therefore, it is recommended to dynamically adjust the time slot delineation of the time-of-peak tariff policy every 3?4 months to better tap the potential of users’ demand side response and promote peak shaving and valley filling. However, if the peak and valley time slots are frequently adjusted, it will be difficult for users to adjust their electricity consumption in time to respond.

Abstract:Since the operation of China’s carbon trading market is still immature, the carbon price affects the carbon capture rate and carbon trading cost of the virtual power plant (VPP) system under the combined effect of market fluctuation and government control. Therefore, this paper proposes a robust optimization model of CCS-P2G VPPs considering the penalized carbon price in the price-sensitive interval. The penalized carbon price constructed based on the price-sensitive interval that can effectively stimulate the system’s carbon capture under the marketized price will increase as the system’s actual carbon emissions increase. Therefore, it is introduced into the adjustable robust optimization model, which takes the wind power output uncertainty into account. Finally, the superiority of the proposed model is verified by comparison and analysis in four scenarios. The simulation shows that compared with the traditional carbon price, the penalized carbon price considering the carbon price-sensitive interval improves the carbon capture level of the system, and the carbon reduction effect is better; a larger robustness index indicates a more conservative system and smaller VPP gain, and vice versa. The model proposed in this paper can effectively participate in the competition of carbon markets, and the formulation of reasonable penalized carbon price interval and robustness index can realize the synergy of economy and low carbon of the VPP system.

Abstract:Cross-linked polyethylene (XLPE) cables are the first choice for urban grid line construction. Insulation defects can lead to partial discharge, which affects the insulation performance of the cables and even leads to insulation breakdown and line tripping. At the same time, the cables undergo constant hot and cold cycles during operation, and the insulation layer temperature affects the partial discharge characteristics of XLPE. In order to study this effect, a pin-to-plane electrode experimental platform is set up to measure partial discharge data of XLPE at different temperatures, and the results show that partial discharge mainly occurs at ?10°~100° and 150°~265° of the frequency cycle, and its distribution looks like “rabbit ears”; an increase in the insulation layer temperature will lead to an increase in the number of partial discharges and a decrease in the initial voltage of partial discharges. The above experimental findings are verified by simulating the partial discharge phenomena under the influence of insulation layer temperature by using a joint COMSOL-MATLAB analysis to model the pin-to-plate structure with an air gap. The results show that the introduction of an air gap can explain the variation of partial discharge of XLPE with insulation layer temperature in the pin-to-plate electrode, and the variation of XLPE dielectric constant with temperature is the key reason for the decrease in the initial voltage of partial discharge. The results help to explore the mechanism of partial discharge of XLPE affected by insulation layer temperature, which is important for the analysis of the partial discharge signal of cables and cable condition assessment.

Abstract:Under the action of high-order harmonics, the potential distribution of the multi-layer parallel winding of the dry-type air-core smoothing reactor will form abnormal distortion, resulting in local field strength concentration and accelerated electrical aging, which will affect the service life of the reactor. To solve this problem, firstly, based on Fourier analysis, the high-order harmonic characteristics of a smoothing reactor under operating conditions are obtained. Then, the physical model of the reactor with the line turn as the basic unit is established, and the potential distribution law of the dry-type air-core smoothing reactor winding under high-order harmonics and direct current (DC) superposition is studied by the finite element analysis method of field-circuit coupling. The results show that compared with the DC case, the loading of high-order harmonics will make the electric field distribution of the reactor winding uneven, and the field strength of the strongest point in the overall electric field of the reactor increases by 15%. Under the influence of high-order harmonics, the inter-turn field strength difference between the upper and lower ends of the single-layer winding of the reactor increases, and the inter-layer field strength of the windings on both sides of the encapsulation increases. The maximum electric field of the outermost winding is about 20% higher than that of the innermost winding. As a result, the outermost encapsulation of the dry-type air-core smoothing reactor is the most prone to electrical aging.