Abstract:

With the growth of wind power grid-connected scale, power system stability is facing more challenges, necessitating efficient coordinated control techniques for wind power clusters. The basic principles of coordination in wind power cluster scenarios are analyzed, such as frequency control, power smoothing control, voltage control, and maximum power output control. On this basis, the coordinated control framework performance of decentralized, centralized, and distributed wind power clusters is analyzed, and the technical characteristics and advantages of new control frameworks are explored. Next, algorithms for coordinated control of wind power clusters are reviewed, including the fuzzy algorithm, consistency algorithm, model predictive control, alternating direction multiplier method, artificial intelligence algorithm, and swarm intelligence algorithm, and the applicable scenarios of different algorithms are discussed. Finally, the development trend of coordinated control techniques for wind power clusters is projected, and new insights into achieving interdisciplinary integration are provided.

Abstract:

Electric vehicle (EV) participating in grid ancillary frequency regulation involves leveraging the "source-load" characteristics of EVs to quickly eliminate system frequency fluctuations. However, ensuring the economic feasibility of large-scale EV-assisted frequency regulation while maintaining system performance remains a challenge. To address this, a distributed economic model predictive control (DEMPC) method is proposed for large-scale EV aggregated charging stations assisting in grid load frequency control (LFC). Based on economic model predictive control, the DEMPC method employs a single-layer control structure to oversee a two-tiered hierarchical system, enabling distributed collaborative control across multiple regional power grids. The optimization of controllers is achieved through convex relaxation of the economic cost function. The collaborative work of each subsystem controller with adjacent subsystems ensures the control performance of the entire system, and an appropriate terminal cost function guarantees the asymptotic stability of the system. Simulation results demonstrate the effectiveness and superiority of this method.

Abstract:

With the continuous advancement of flexible direct current (DC) asynchronous interconnection projects, the ultra-low-frequency oscillation problem caused by the high proportion of hydropower poses a threat to the frequency stability of the power system. To suppress the occurrence of these oscillations, an additional frequency control strategy based on a flexible DC transmission is proposed, which is also referred to as a voltage source converter-based high-voltage DC transmission (VSC-HVDC) system. Firstly, a frequency response model that includes both hydro and thermal power units is constructed. The formation mechanism of ultra-low-frequency oscillation is identified through damping torque analysis, and the influence of various governor parameters and different hydropower ratios on the occurrence of ultra-low-frequency oscillations in the system is explored. Secondly, based on the coupling characteristics of DC capacitor voltage and alternating current system frequency, the mechanism of the converter station participating in suppressing ultra-low-frequency oscillation is studied. A control method for virtual inertia and VDC-f droop of the converter station is proposed, and the virtual inertia and droop parameters are designed under constraint conditions. Finally, based on the MATLAB/SIMULINK platform, a load frequency control model that includes both hydro and thermal power units is constructed, and the effectiveness of the proposed method is verified by comparing the optimization methods of water turbine parameters.

Abstract:

The rapid development of the power system has been changing its structure, making the system stability mechanism more complex. To ensure power angle stability in the new energy power system, a policy generation method for power system stability control during emergent tripping of units based on deep reinforcement learning is proposed. Firstly, the policies for emergent tripping of units of the power system are summarized, as well as the security constraints involved. The power system stability control model is then transformed into a Markov decision process. Next, the most typical feature data are selected by feature evaluation and the Spearman rank correlation coefficient method. To improve the training efficiency of the intelligent agent of the stability control policy, a training framework for the stability control policy based on the deep deterministic policy gradient (DDPG) is put forward. Finally, tests are performed in the IEEE 39 node system and a real-life power grid for validation. The results show that the proposed method can automatically adjust and generate a stability control policy for tripping of units according to the system’s running states and fault responses, confirming its enhanced decision-making effect and efficiency.

Abstract:

With the increasing proportion of renewable energy generation, the impact of grid three-phase imbalance is becoming more and more apparent, especially the excess negative sequence is an important reason for reducing the safety of power systems. The unified power flow controller (UPFC) has the ability to adjust the output of each sequence current, which can be used to improve the balance of the system. Firstly, an optimal compensation algorithm of UPFC for positive-sequence power flow based on the decoupling-compensation principle is established. Secondly, a current control model of UPFC for negative-sequence compensation is constructed, and the optimization problem of voltage imbalance compensation is attributed to the convex quadratically constrained quadratic programming (QCQP). The primal-dual interior point method is used to obtain the optimal output value of the UPFC negative-sequence current. Finally, an optimal power flow calculation method for negative-sequence voltage compensation considering positive-sequence grid loss and negative-sequence voltage index, as well as an overall compensation strategy for regional negative-sequence voltage, are proposed. The feasibility and effectiveness of the proposed method are verified through example analysis.

Abstract:

To address the issue of weakly discernible wave heads in the reflection waves of faults in the fully parallel auto transformer (AT) traction network, compounded by difficult localization caused by refraction and reflection complexity of fault traveling waves introduced by the parallel structure of the lines, a single-ended localization method for fault traveling waves in the traction network is proposed based on variational mode decomposition (VMD) and enhanced energy operator. Firstly, the transmission characteristics of fault traveling waves in the traction network are analyzed, with a focus on the impact of the fully parallel structure on the refraction and reflection of fault traveling waves. Features of different fault types and power flows are determined, facilitating the extraction of fault features. The process of wave head identification is transformed into the extraction of abrupt energy changes. Then, VMD is used for denoising and extracting the genuine voltage traveling wave component. In view of the challenging calibration of weak wave heads in the second reflection wave, a sliding time window (STW) combined with the symmetrical differencing energy operator (SDEO) is employed to construct the second instantaneous energy spectrum of the fault signal, yielding satisfactory results. Simulation outcomes demonstrate the method’s robust resistance to transient resistances, its ability to reflect variations in electromagnetic energy under different operating conditions in the fault traction network, and its high precision in fault localization.

Abstract:

The existing distribution network node vulnerability assessment methods face problems such as the difficult selection of indicators and one-sided weights of indicators, and they thus fail to be used in the assessment of vulnerable nodes in active distribution networks. To address these issues, a vulnerable node assessment method of active distribution networks based on the improved K-shell mixed degree decomposition (MDD) is proposed. Firstly, an improved K-shell MDD based on the information entropy theory is proposed to divide the distribution network node vulnerability hierarchy; subsequently, a stochastic output model of distributed generation based on kernel density estimation and Copula theory is established by combining the geographic location and stochastic characteristics of the distributed generation; finally, a vulnerable node assessment method of distribution networks based on the risk theory is proposed with the node operation risk as a weighting correction factor. The proposed method can effectively assess the vulnerable nodes of active distribution networks and has stronger computational efficiency when facing large-scale or ultra-large-scale vulnerable node assessment of distribution networks. The feasibility and superiority of the proposed method are verified by analyzing the case of the IEEE 123 system.

Abstract:

The large-scale integration of renewable energy generation into new power systems has led to an exponential increase in operational data. Due to the severe temporal fluctuations and large disturbances of renewable energy, a large amount of bad and malicious data can emerge in the new power system. In addition, these operational data are managed in a distributed manner, leading to the poor performance of traditional centralized state estimation methods in calculation accuracy, speed, and other aspects. To solve this problem, distributed state estimation of new power systems based on edge computing is proposed. Firstly, the shortcomings of traditional centralized state estimation methods are pointed out, and a calculation approach for privacy in distributed state estimation is designed, with coordinated variables as the core. Secondly, based on traditional nonlinear state estimation models, an improved linear state estimation method is proposed to improve computational speed. Next, a multi-objective ant colony distributed algorithm based on edge computing is proposed to realize distributed state estimation. Finally, by taking the IEEE57 system integrated into a real-life new power system as an example, the proposed state estimation algorithm is simulated and verified, and the results confirm its accuracy and high computational speed.

Abstract:

Topology change will change the fault signal characteristics. The traditional fault traveling wave localization method for distribution networks is based on the fixed topology design. Through the single feature information of the time or frequency domain, the fault is located, and the localization accuracy is low under the topology change conditions. For this reason, a fault localization method based on a graph attention network is proposed. First, the distribution characteristics of fault traveling wave in time and frequency domains are quantitatively analyzed, and it is found that it is difficult to effectively distinguish different fault locations with a single time or frequency domain information, so the panoramic information representation of fault traveling wave based on wavelet transform is proposed. Then, the measurement points and overhead lines are taken as the nodes and edges of the graph. The panoramic information of traveling waves is used as the node features to construct the graph data, so as to establish the fault localization method based on the graph attention network and locate the fault of the distribution network by mining the correlation between the node features, the information of the network topology, and the fault location, thus enhancing the adaptability of the method to topological changes. Simulation results show that the method has a high localization accuracy of 98.8%. It is not affected by transition resistance, noise, and other factors and has a strong adaptive ability to topology changes.

Abstract:

To accurately and quickly locate the fault of underground cables of the distribution network and realize the fault isolation and the self-healing control of the distribution network, a fault self-healing control strategy for the distribution network based on the zero-sequence current injection method is proposed. First, a distributed zero-sequence current carrier signal is injected into the cable network, and the carrier signal is transmitted to the substation through the cable. At the substation, the signal is processed, and its characteristics are analyzed to quickly identify the faulty line and locate the fault. This enables timely fault isolation, contributing to the self-healing process for the distribution network. The signal injection device does not require an external power supply, making it easy and cost-effective to install around underground power cables and handle cable network faults. Finally, a numerical simulation is performed on MATLAB, validating the effectiveness of the proposed method.

Abstract:

In recent years, the installed capacity of photovoltaic (PV) in medium-voltage distribution networks (MDNs) and low-voltage distribution networks (LDNs) has increased rapidly, which brings about problems such as power flow direction change and overvoltage. For MDNs and LDNs with a high proportion of PV, a multi-time scale optimal dispatch method involving two stages is proposed: the day-ahead stage and the intra-day stage. Firstly, a centralized day-ahead stochastic optimization model of MDNs and LDNs is established with the objective of minimizing the network loss, the penalty cost of PV abandonment, and the charging and discharging cost of the battery energy storage system (BESS). Then, the model is transformed into a mixed-integer second-order cone programming (MISOCP) problem and solved. Secondly, in view of the different adjustment time scales of different facilities and the difficult acquisition of accurate network parameters of LDNs, a two-layer rolling optimization method for MDNs and LDNs is constructed at the intra-day stage. The upper centralized optimization model provides the benchmark strategy for the operation of MDNs, and the lower distributed control model sequentially adjusts the output of reactive power of PV inverters, reactive power and active power of BESS, and active power of PV in LDNs according to overvoltage degrees. Finally, based on the modified IEEE 33-bus MDN and 21-bus LDN, the effectiveness of the proposed method is verified.

Abstract:

Factors like low troubleshooting efficiency and untimely data updates make meter-to-transformer wiring relationships in low-voltage distribution networks deviate from the actual situation. To address this issue, a meter-to-transformer relationship identification method based on the combination of angle piecewise linear representation (APLR) and improved clustering by fast search find of density peaks (ICFSFDP) is proposed. Initially, inflection points in the voltage curve are extracted by analyzing the angle variations between neighboring segments, and the curve undergoes adaptive dimensionality reduction and reconstruction using APLR. Then, the ICFSFDP method is deployed to cluster the data sets after dimensionality reduction, and the optimal number of clusters is determined by identifying the minimum area enclosed by the fitted function and the coordinate axis within the decision graph. This allows the identification of central clustered and non-clustered consumers. Finally, the dynamic time warping (DTW) distance is utilized to measure the distance similarity between the central clustered and non-clustered consumers, obtaining meter-to-transformer relationships. The application of this method on both simulated and real data has validated its effectiveness. Results from the analytical cases indicate that this approach can analyze sequences with varied time intervals and dimensions without the need for manually setting clustering algorithm parameters, delivering a high accuracy in identifying meter-to-transformer relationships.

Abstract:

With the increase in the number of electric vehicle (EV) in recent years, the demand for charging and battery swapping facilities of new energy vehicles is also increasing. However, a single-function charging facility cannot meet the charging and battery swapping needs of different types of vehicles. To address this issue, a hybrid planning method for charging piles and battery swapping stations is proposed. First, massive ride-hailing order data is mined to extract travel features and the spatio-temporal distribution of charging and battery swapping demands. Based on the charging demand, a charging pile planning model is established to determine the scale of charging piles in each functional area, ensuring convenient charging for EVs. In view of the high cost of staggered operation of battery swapping stations, a two-layer planning model that integrates ordered charging is constructed, and each target cost is solved using an improved Grey Wolf optimization (GWO) algorithm. Finally, by taking the second ring traffic network of Chengdu City and the IEEE 33 node system as examples, the planning of the battery swapping stations under unordered and ordered charging modes is simulated and analyzed. The results show that the proposed hybrid planning method enables EV users to charge conveniently and quickly. The ordered charging mode for battery swapping stations is both economical and conducive to the stable operation of the distribution network.

Abstract:

Indoor unmanned aerial vehicle (UAV) inspection in substations can effectively reduce the intensity of manual inspection operations. Due to high flight accuracy requirements and limited carrying capacity, relying solely on UAV-mounted cameras and inertial measurement unit (IMU) data fusion to determine pose fails to meet precision requirements. Therefore, a multi-vision-inertial navigation fusion framework based on the ubiquitous IoT with existing fixed cameras in the substation is proposed. The images of UAV-mounted cameras for indoor lighting conditions are enhanced and combined with IMU data to obtain preliminary UAV position data. In addition, by deploying quick response (QR) codes on UAVs, the improved perspective?n?point (PnP) algorithm is applied to optimize UAV pose data. After the flight is completed, the cumulative error of IMU in the UAV nest is verified. Experimental results have shown that the deployment and maintenance workload of this method is small, and the flight accuracy is significantly improved compared to relying solely on cameras and IMU data fusion algorithms. It can meet the needs of UAV inspection operations in substations.

Abstract:

The fault current distribution of direct current (DC) transmission systems for large-scale offshore wind power presents new characteristics. To address this issue, the transient fault characteristics of the DC transmission system line in offshore wind power when a unipolar grounding fault occurs are analyzed, as well as its influence mechanism. The response process of unipolar grounding fault can be divided into three transient phases: DC-side capacitor discharge, grid-side current feeding, and voltage recovery. The mathematical expressions of fault currents in the transient phases of unipolar grounding fault in the DC transmission system in offshore wind power are derived, and the equivalent electrical model is established. According to the resistance value of the grounding resistor, the fault process is divided into over-damped and under-damped states, and the influence of the grounding resistor on the fault characteristics of the system is analyzed. Finally, a simulation model of ±10 kV DC transmission system in offshore wind power is constructed in MATLAB/SIMULINK simulation software, which verifies the correctness of the theoretical analysis of the transient characteristics of the system in the event of a unipolar grounding fault.

Abstract:

With higher wind speeds and broader development space, deep-sea offshore wind power demonstrates significant potential for growth. To address the reliability and optimization challenges associated with integrating deep-sea offshore wind power into marine platform power systems under the impact of wind speed and wind power fluctuations, initially, the correlation between wind speeds at different heights above sea level is investigated, establishing a relationship between wind speeds at various heights and those at 10 meters. Subsequently, an optimized wind power storage dispatching method is proposed. By considering the system reliability, energy storage limit, and other constraints, the model predictive control algorithm is used to solve the dispatching. Finally, the Roy Billinton test system (RBTS) system is utilized to verify the impact of wind power integration on system reliability and the effectiveness of the proposed wind power storage dispatching model. The results indicate that the wind speed correction at hub height is crucial for system reliability evaluation. The rational utilization and dispatching of energy storage can significantly enhance reliability, providing a valuable supplement to the reliability evaluation of deep-sea offshore wind power.

Abstract:

With the continuous development of new power systems based on new energy, large-scale and intensive wind power, photovoltaic, and other new energy access to the system has laid a solid foundation for the realization of the “carbon peaking and carbon neutrality” goals, but at the same time, it also leads to the increasing challenges faced by the dispatching operation of new power systems under extreme climates, and the most prominent problem is that the probability of wind power ramp events has increased significantly. Wind power ramp events will not only cause great fluctuations in the frequency of the system but also affect the balance of electric power and energy, threatening the safe and stable operation of the system. Through the statistical analysis of wind power ramp events, a predictive method of wind power ramp events based on a deep auto-regressive (DeepAR) model is proposed. Firstly, combined with the relationship between wind power and wind speed, the impact of wind power ramp events on power grid dispatching operations under extreme climates is analyzed. Secondly, a physical model of wind power ramp events is established to analyze the statistical characteristics of wind power when wind power ramp events occur. Then, the DeepAR model is used to perform the power prediction of wind power ramp events, and the wind power output curve under extreme climates is analyzed. Finally, combined with the measured data of the wind power field, the effectiveness of the proposed method is verified. The verification shows that the proposed method can accurately predict the occurrence probability of wind power ramp events under extreme climates in advance, which is expected to greatly improve the uncertainty faced by the dispatching operation of new power systems in the future.

Abstract:

Using a virtual synchronous generator (VSG) control technique to simulate the external characteristics of the synchronous generator can provide certain damping and inertia for the power system. However, wind turbine generator (WTG) based on VSG control can easily cause low-frequency oscillation of the power system. In this paper, the small-signal stability of grid-connected wind farm systems based on VSG control is studied. Firstly, a single-small signal model is developed for both single and multiple VSG control-based wind turbine and grid-connected wind farm systems. Then, the system’s eigenvalues are obtained by solving the state matrix, and the system’s oscillation modes are analyzed. Finally, the effects of varying key parameters on the small-signal stability, such as virtual inertia, virtual damping, and line connection reactance, are analyzed using the eigenvalue method.

Abstract:

The dispatching operation regulations have great significance in guiding the failure handling of hydro power stations. Therefore, the dispatching operation regulations of the hydro power station are used as the research object, and a top-down construction method of the dispatching operation knowledge graph of hydro power stations is proposed by means of knowledge representation, knowledge extraction, and structured management of regulations. First, through term, concept, and relationship extraction, the schema layer of the knowledge graph is constructed. Then, the data layer is built according to the schema layer with entity extraction of regulations by using a deep learning model with a bi-directional gated recurrent unit (BiGRU) network and conditional random field (CRF). Finally, by learning the dispatching operation regulations of a large-scale ladder hydro power station located in China, the dispatching operation knowledge graph of the hydro power station is constructed, and the effectiveness of the proposed method is verified with simulation results. The results show that the constructed dispatching operation knowledge graph of the hydro power station can help the staff of the hydro power station to carry out failure handling with assistant decision-making and effectively improve the level of emergency management and dispatching intelligence of the hydro power station.

Abstract:

Due to the dynamic, slowly time-varying, and strongly nonlinear characteristics of lithium-ion batteries in use, identifying the unknown parameters of first-order RC models online faces challenges, such as low accuracy and poor real-time performance. To address this issue, a chaotic system is proposed based on a charge-controlled memristor and a first-order RC model. The charge-controlled memristor parameters are adjusted to drive the system into a chaotic state, and the system’s dynamic characteristics are analyzed. Next, an adaptive control law of the unknown parameters of the chaotic system is constructed and applied to the chaotic system. This enables the online identification of unknown parameters of the first-order RC model of lithium batteries in real time, obtaining effective parameter values and overcoming shortcomings of traditional estimation algorithms that are limited by the size of data sample space and affected by factors such as ambient temperature, road conditions, load conditions, and battery materials. The experimental simulation results show that the chaotic system established in this paper possesses rich dynamic characteristics, and the proposed adaptive control algorithm for unknown parameter identification offers good real-time performance, accuracy, robustness, and fast convergence speed.

Abstract:

In order to solve the problems of environmental pollution and resource waste in the process of municipal solid waste (MSW) disposal, it is proposed to integrate MSW management and renewable power generation system with the traditional microgrid configuration scheme. In the face of the lack of source-load matching within the microgrid and the differences in microgrid output characteristics caused by different wind and solar configurations, it is proposed to formulate a demand response strategy based on the wind and solar configuration conditions of the microgrid. A multi-objective optimal allocation model is established with the objectives of minimizing the operating cost, energy waste rate and load shortage rate of equal annual value allocation, and in order to improve the convergence and distribution of the Pareto solution set, the augmented generalized ε-constraint method is used to solve the problem. Finally, the improved IEEE-33 node model of the microgrid is used for simulation, and the results of the arithmetic example show that the microgrid effectively enhances the clean energy consumption capacity and system reliability, and at the same time has good economics.

Abstract:

As more new grid-interactive devices, such as electric vehicles and photovoltaic cells, are gradually being connected to buildings, it is difficult for previous building substations to manage this complex energy flow. This results in reduced energy transfer efficiency, complexity of interfaces, and deterioration of power quality. To address this issue, a wireless energy router with a power quality control method is proposed. First, an equivalent model of a wireless energy router is proposed to design an efficient energy flow control method. Second, situations with varying loads and power quality are categorized, and their corresponding control reference values are given to ensure an efficient energy flow. Based on the control reference values, power control methods for situations with varying loads and power quality are designed. Finally, simulations that correspond to each situation are designed to verify the feasibility of the proposed structure and control method. Results show that the proposed wireless energy router can provide a unified solution for buildings’ access to grid-interactive devices, which holds significant engineering importance.

Abstract:

When the load power of a microgrid fluctuates, the energy storage system can maintain the stability of the system voltage and frequency by controlling the inverter. Energy storage inverters commonly adopt a double closed-loop control strategy with PI algorithm, but due to the lag of PI control, the dynamic response speed of the system is slow. To address this issue, a finite control set-model predictive control (FCS-MPC) strategy considering Lyapunov stability constraints is proposed. Firstly, the main control objective which is the stable control of the capacitor voltage, is achieved through Lyapunov stability constraints, and weight coefficients are set based on the severity of the total harmonic distortion constraint term of the capacitor voltage. Then, collaborative control is achieved by minimizing the objective function, solving the problems of system instability caused by the coupling of traditional FCS-MPC objective functions and difficulty in tuning weight coefficients. The improved FCS-MPC scheme is combined with droop control to control the energy storage inverter. Finally, simulation verification is performed on the MATLAB and RT-LAB platforms. Simulation results show that compared with traditional control strategies, this approach can improve the dynamic response speed of the system, achieve multi-objective collaborative control, and exhibit good robustness to weight coefficients.

Abstract:

To improve the dynamic performance of the two-stage AC/DC converter and enhance the system’s ability to resist disturbances, an improved active disturbance rejection control (ADRC) method for the two-stage converter is proposed. Firstly, small-signal analyses of the pre-stage power factor corrector (PFC) circuit and the post-stage inductor-inductor-capacitor (LLC) resonant converter circuit are carried out based on the switching cycle average method and the extended describing function method, respectively, and a mathematical model of the circuit is established. Then, by considering the double-frequency ripple as an internal disturbance, a disturbance rejection law is incorporated into the ADRC. This allows the observer to quickly predict the extended state and further study the influences of different disturbance rejection laws on the system’s dynamic performance and resistance to disturbances. A method for designing the controller’s parameters is also given. Finally, simulation experiments are conducted to test the performance of the controller. The experimental results show that, compared with the traditional ADRC and proportional integral (PI) controllers, the improved ADRC can improve the overshoot and regulation time of the output voltage when the load is abrupt under lower current harmonics, enhancing the dynamic performance of the system.

Abstract:

The new type of bilateral traction power supply system of traction substation groups can reduce the number of neutral sections, increase the distance of the power supply arm, and improve the power supply capacity. Furthermore, it provides a solution to the setting of neutral sections in the construction of electrified railways in the western and dangerous mountainous areas. At present, the system is still in the stage of theoretical verification and experimental exploration. In the simulation system, it is difficult to achieve the dynamic change process of traction network impedance caused by train movement. To address this issue, a simulation method of traction network impedance based on the voltage source converter (VSC) is proposed. By controlling the equivalent impedance of the alternating current port of VSC, the change in the impedance of the traction network at both ends of the train in the physical system is simulated, thus realizing the experimental simulation of the dynamic operation process of the trains. The proposed method is employed to build a system simulation model in MATLAB/SIMULINK. The simulation results indicate that the proposed method is able to simulate the dynamic changes in the traction network impedance when the train is under various working conditions. Moreover, the proposed traction network impedance simulation method is conducive to the further exploration and validation of the new type of bilateral traction power supply system of traction substation groups.

Abstract:

To enhance the fault diagnosis of the switch tube of the modular multilevel converter (MMC) submodule, a Sand Cat swarm optimization (SCSO) algorithm is improved. This improved SCSO (ISCSO) algorithm is employed to optimize the fault diagnosis of an extreme learning machine (ELM). Cubic chaotic mapping, a spiral search method, and a sparrow alert mechanism are used to improve the three stages of sand cat search, so as to enhance the convergence speed and search capability of the algorithm. An MMC model is developed on the MATLAB/SIMULINK platform, where the bridge arm circulation is used as the input when a fault occurs in the submodule. By comparing the fault diagnosis performance of ISSO-ELM against ELM optimized by other algorithms, the results show that the proposed method can effectively identify submodule faults. It shows feasibility and superiority in MMC fault diagnosis, offering better fault diagnosis performance.

Abstract:

During the transition of the electricity market from planned to market-based, industrial and commercial (I&C) electricity users have gradually shifted from planned electricity prices to market-based pricing. To promote this shift, an electricity purchasing mechanism via agents has been established in China. I&C users who have not directly participated in the electricity purchase market buy electricity through power grid companies, facilitating the implementation of the market-based electricity pricing policy. Firstly, the price formation mechanism of electricity purchased via power grid companies is analyzed, and a price model of electricity purchased via agents is established. To address two issues caused by the differences in electricity purchasing structure, namely the price gap between agents and market-based users and the large seasonal price fluctuations of electricity purchased via agents, an optimization method for electricity purchasing prices based on planned-to-market electricity allocation is proposed. Next, the relationship between planned-to-market electricity supply and demand within the planning cycle is studied. To minimize the price differences between I&C market users and agents, the proportion of wind and solar power generation allocated to the planned and market electricity, along with the monthly distribution ratio of purchased electricity, is optimized, with price fluctuations of electricity purchased via agents acting as a constraint. This helps dynamically adjust the price of electricity purchased via agents. An analysis of the annual source and load data of a province in China shows that the proposed optimization method for electricity price can effectively reduce the price differences between two types of users and minimize the price fluctuations of electricity purchased via agents, facilitating a smooth operation of electricity purchasing via power grid companies and supporting the planned-to-market pricing transition for I&C users.

Abstract:

The movement of free metal particles within the cavity of a direct current gas insulation transmission line (GIL) can significantly reduce the gas insulation level, and in extreme cases, it may cause partial discharge or surface flashover. The motion states of metal particles under a uniform DC electric field and their identification methods are investigated. An experimental platform is established for the movement of metal particles under DC voltage and ultrasonic detection. Through extensive experiments, the uplift voltage of metal particles between parallel plates, their motion trajectories, and the ultrasonic signal waveforms generated by their movement are recorded. The experimental results indicate that under positive polarity voltage, linear metal particles exhibit a unique state of lower plate flying firefly movement, with a higher tendency for this movement in linear metal particles with larger diameters and greater lengths. A partial discharge experimental platform of alternating current pulse is also set up to measure the partial discharge patterns of metal particles in different motion states. The results confirm that corona discharge is one of the main factors contributing to the lower plate flying firefly movement. Noise reduction processing is applied to the ultrasonic signal waveforms of metal particle movement collected by the experimental setup, and three characteristic parameters of the ultrasonic signal are extracted for comparison across different particle motion states. The findings suggest that the parameter threshold method can effectively differentiate between the size of spherical metal particles and the operational states of linear metal particles.

Abstract:

With the investment of power electronic devices in the power grid and the increasing use of nonlinear loads, the level of high-order harmonics in the system is constantly improving. High-order harmonics not only have a negative impact on the behavior of equipment and materials but also affect the online testing results of insulation parameters. Therefore, the dielectric loss characteristics (tan δ) of cable insulation under cyclic aging and online monitoring correction methods are studied through theoretical analysis and experimental testing. The dielectric loss factor of cross-linked polyethylene cables after aging in the frequency range of 1?8 times the power frequency under different voltages is tested. The research results indicate that as the degree of aging increases, the tan δ value increases, with the largest increase observed in the early stage of 20 aging treatments. For the 50 Hz condition, the tan δ value increases from 0.009?4 to 0.021?9. The degree of increase in tan δ is influenced by harmonic frequency and harmonic voltage, and the influence of harmonic frequency cannot be ignored. Based on the experimental results, a method for correcting the dielectric loss factor of cable insulation is proposed, providing correction factors at high frequencies of 50?400 Hz. The research results can be used for correcting online dielectric loss monitoring under power frequency conditions, thereby improving the reliability of online evaluation of cable aging.