Abstract:The large fluctuation of wind power and the poor accuracy of day-ahead prediction lead to the contradiction between large-scale wind power accommodation and grid security robustness. An automatic frequency modulation scheduling method for wind power "waist load" output access is proposed to reduce wind curtailment and improve the online security of the main network under large fluctuations of wind power. Firstly, to improve the convergence of neural network for day-ahead wind power prediction, a multi-sample data preprocessing method based on singular value decomposition of time series matrix is proposed. Secondly, in order to obtain the output plan curve of waist load related to the day-ahead wind power prediction, polynomial regression fitting and reference power deviation are used to obtain a smooth curve with high correlation. Then, to reduce the frequency deviation caused by the sudden decrease of online wind power, a family of "start-up" curves for automatic frequency modulation of frequency modulation units is set. Finally, in order to maintain a reasonable active power flow distribution in the main network after automatic frequency modulation and improve the power angle security, an optimization model with the smallest equivalent power angle is adopted to obtain the optimal allocation scheme of the output increment of each frequency modulation unit. The example verifies the feasibility of the scheduling method, which has theoretical and practical significance for reducing wind curtailment of large-scale wind power and improving the safe operation level of the power grid.

Abstract:Probabilistic reliability can overcome the shortage of traditional reliability index expectation which only measures system reliability from the mean perspective. However, with the expansion of distribution network scale and the surge of data volume, a probabilistic reliability calculation method that can balance calculation accuracy and calculation speed is urgently needed. Therefore, a fast calculation method for probabilistic reliability of distribution network based on Monte Carlo method is proposed. The improved three-point estimation method and the third-order polynomial normal transformation are used to effectively reduce the size of input sample points while retaining the correlation of input variables, and the probabilistic reliability is obtained by series expansion. Firstly, the improved three-point estimation method is used to select sample points in the independent standard normal space, which are then transformed into sample points in the original variable space through the third-order polynomial normal transformation. Then, the sequential Monte Carlo method is used to calculate the reliability of the sample points considering the island division. Finally, the probability distribution of reliability index is obtained through Edgeworth series expansion. The example analysis of the improved IEEE-RBTS Bus6 F4 feeder shows that there is only a maximum deviation of 2.195% between the reliability calculation results of the proposed method and the traditional Monte Carlo method, while the calculation time of the proposed method is only 1.05% of the traditional Monte Carlo method. It proves that the proposed method can significantly improve the calculation speed while ensuring high accuracy.

Abstract:The flexibility of flexible DC control has been widely studied to suppress subsynchronous oscillation, but there is little explanation of the mechanism for the different suppressive effects of active and reactive control loops. Aiming at the subsynchronous oscillation problem of thermal power units in the vicinity of flexible DC, a suppression strategy for subsynchronous oscillation of flexible DC based on additional damping control of the power outer loop is proposed. By analyzing the negative damping frequency band of the electrical damping curve and combining the control characteristics of the flexible DC converter, the optimal position for additional control is selected, and the electrical damping of the thermal power unit is improved through additional damping control based on the power outer loop. Then, a transfer function of the complex torque coefficient is established, and it is analyzed that the damping effect of the active and reactive outer loop with additional damping control is related to the parameters of the power outer loop, mainly related to the transmitted active and reactive power, revealing the mechanism of the difference in the impact of additional damping control on the electrical damping of the d-axis and q-axis of the power outer loop. Finally, the correctness of the analysis is verified through simulation of PSCAD time-domain model.

Abstract:The new energy access ratio has rapidly increased in recent years, leading to increasingly prominent frequency stability issues in regional power grids. Therefore, this article studies the relationship between access ratio and frequency stability and proposes an improved control strategy for hydropower and thermal power-dominated frequency regulation. The analysis shows that as the access ratio increases, the equivalent inertia of the regional power grid decreases, and the equivalent power-frequency regulation ability decreases, resulting in a decrease in the static, dynamic, and transient frequency stability of the hydroelectric-dominated regional power grid; This leads to a decrease in the static and transient frequency stability of the power grid in the thermal power-dominated area, while the dynamic frequency stability remains unchanged. By reducing the adjustment coefficient and adding differential links to the frequency feedback channel, the overall frequency stability of hydropower-led frequency regulation is improved. Adding a proportional differential link to the frequency feedback channel improves the overall frequency stability of thermal power-led frequency modulation. The simulation results indicate that the analysis of the influencing factors is reasonable and that the improved control strategy is effective. This study has significant reference value for improving the frequency stability of large-scale new energy access systems.

Abstract:To improve the accuracy of electricity price forecasting and the stability of forecasting models, a short-term electricity price forecasting method based on improved VMD-PSO-CNN-LSTM is proposed. Firstly, after studying the correlation between variational mode decomposition(VMD) and the influencing factors of electricity prices, and introducing the maximum information coefficient, a parameter optimization model for VMD is constructed. Secondly, convolutional neural networks(CNN) and long short-term memory(LSTM) neural networks are used to predict the modal components obtained by VMD decomposition. As for the convolution in CNN, a extraction structure with multi-scale convolution feature is constructed, on the basis of the depth-wise separable convolution combined with the time law of electricity prices. Particle swarm optimization algorithm is then used to optimize parameters including the number of CNN convolutional layers, the number of CNN convolutional neurons, the number of LSTM hidden layers, LSTM memory time, and the number of fully connected layers, so as to improve the prediction accuracy and stability of the model. Finally, the analysis and prediction of the day-ahead electricity prices in the Australian electricity market are carried out and compared with the algorithm. The results show that the proposed algorithm has higher accuracy and better stability.

Abstract:In view of the current problems of only single operation mode, vague profit method, and low utilization rate of the energy storage business for power grid, a business operation mode combining self-operated electricity and shared energy storage is proposed. First, a multi-agent collaborative scheduling model of the power distribution network considering shared energy storage is established. This model takes into account the interests of the power distribution network, load aggregators, and energy storage providers, while effectively reducing the peak-to-valley difference of the net load and hence relieving the peak shaving pressure of the power grid. Then, the model implements a two-stage optimization. The first stage is the leasing optimization of energy storage capacity, that is, the power distribution network leases energy storage on demand for peak shaving and valley filling, minimizing leasing costs and net load variance. The second stage is multi-agent collaborative optimization, where energy storage providers arbitrage by using remaining capacity to "store at low prices and discharge at high prices" based on time-of-use electricity prices, and respond to peak shaving together with load aggregators. This minimizes the cost of the power distribution network, maximizes the benefits of load aggregators, and maximizes the arbitrage of energy storage providers, achieving mutual benefit and win-win results for all parties. Finally, the effectiveness of the proposed method is verified through example analysis.

Abstract:The power grid of megacities like Guangzhou mainly adopts a 3T wiring configuration for its 110 kV network, and its operation modes are highly variable, significantly impacting power grid planning and operation. Given the presence of numerous critical users in the megacity power grid, their power supply reliability is a crucial factor in determining the operational approach. If the arranged operation mode concentrates the power supply paths of certain essential users on the same component, it can drastically reduce the reliability of power supply for these users. An optimized decision model for power supply paths of critical users in megacity power grids is hence established. The objective is to minimize the average load factor of 220 kV lines in the grid, with the requirement that each essential user must have multiple power supply paths originating from at least two different 220 kV substations. To solve this mixed-integer nonlinear programming model quickly, a multivariate decision tree to transform the model into an integer nonlinear programming problem is introduced; subsequently, it is converted into an integer linear programming problem through variable substitution, enabling fast and accurate solutions. Finally, the feasibility and effectiveness of the proposed optimized decision model and solution method are validated using actual data from the Guangzhou power grid.

Abstract:To adapt to the future power distribution mode of AC/DC hybrid distribution network and address the lack of operational flexibility adjustment caused by the uncertainty of renewable energy, a two-stage distributionally robust optimization operation model for AC/DC hybrid distribution networks considering flexibility is proposed. Firstly, under the framework of AC/DC hybrid distribution network, the supply and demand relationship of flexible resources is analyzed, and the upward/downward flexibility evaluation index is defined. Secondly, considering various constraints in the system, an optimal operation model describing the economy, renewable energy consumption, and flexibility of the distribution network is constructed. Combined with data-driven typical wind and solar scenarios, a distributionally robust optimization method based on the comprehensive norm distance is adopted to deal with the uncertainty of wind and solar power output. And a two-stage distributionally robust optimization operation model is constructed according to the output characteristics of optimization variables. Finally, convex transformation and solution of the model are carried out, and the validity of the model is verified through a modified 33-node AC/DC hybrid distribution network.

Abstract:Distributed generation is increasingly being connected to the distribution network due to its advantages of high flexibility, low cost, green and low carbon. However, because it changes the topology of the original distribution network, it also poses new requirements for traditional current differential protection. Firstly, the impact of the access of a large number of distributed generations on relay protection in the distribution network is analyzed. Then, considering the requirements of relay protection for quickness and sensitivity, the Whale optimization algorithm with fast convergence speed is introduced, and the LM algorithm is introduced to improve it. Finally, the setting value for current protection is optimized when the distributed generation is connected to the distribution network. The Whale optimization algorithm with fast convergence speed is used for global optimization, and to avoid falling into a local optimum, it switches to the LM algorithm for further solution. Experimental analysis results show that the introduction of the Whale optimization algorithm improved by the LM algorithm can accelerate the convergence speed and improve the quickness of the relay protection device, which is suitable for relay protection setting optimization when distributed generation is connected to the distribution network.

Abstract:Accurate detection of fault traveling wave signals is an important factor to ensure accurate and reliable fault location results. Aiming at the problem that Wigner Ville distribution (WVD) is prone to produce cross terms when detecting fault traveling waves, a fault traveling wave detection method based on improved variational mode decomposition (VMD) and WVD is proposed. Firstly, the selection of VMD parameters is determined by waveform similarity, and the improved VMD is used to adaptively decompose the line mode traveling wave signal, so as to extract signals of different frequency bands of fault traveling wave while retaining important fault information. Then, the original WVD signal is obtained by the calculation and superimposition of each component signal of the WVD, thus solving the influence of cross terms due to WVD. Finally, the energy spectral density distribution of VMD-WVD is obtained, and the good time-frequency aggregation of WVD is used to detect the wave head position from 150 kHz to 170 kHz frequency band of the fault traveling wave, so as to achieve accurate detection of the fault traveling wave head. Simulation results show that the proposed method is faster and more accurate than the wavelet and HHT fault traveling wave detection methods.

Abstract:The magnetic circuit characteristics and operating equations of transformers with three different core structures: three-phase banked, five-limb, and three-limb are utilized. A numerical method is proposed to fit the circulating current in the delta windings using the zero-sequence current and voltage on the wye side. Through this approach, a deeper understanding of the characteristics of current in the delta windings and magnetizing inrush current in transformers with different core structures is gained. To validate the effectiveness of the proposed numerical method, software simulations and field waveform recordings are conducted. The results show that for three-phase banked and five-limb transformers, the differential current in each phase winding corresponds directly to the magnetizing inrush current of that phase; however, for three-limb transformers, the differential current in a phase winding is not directly correspond to its excitation current. Further theoretical analysis and simulation verification indicate that during no-load closing from the wye side of transformers with these three types of core structures, the difference between the line currents of any two phases on the wye side can be regarded as the difference in magnetizing inrush current between the two equivalent magnetic circuits. This finding provides a new basis for identifying the magnetizing inrush current of three-phase transformers with different core structures.

Abstract:With the permeability increase of distributed power supply in active distribution network, the sensitivity is reduced and the coordination of protection is difficult due to the preset setting value of traditional over-current protection. Firstly, the characteristics of different control strategy types of distributed power supply are analyzed, and relationships among the output power, control mode, grid-connected voltage and fault current of distributed power supply are studied. Secondly, multi-source data fusion of active distribution network operation information is achieved according to the relationship between fault current and voltage, and an on-line adaptive setting strategy for current protection is then proposed. Aiming at the sensitivity reduction problem of the improved protection strategy due to the increase of grid-connected points and the further permeability of distributed power supply, the fault current matching calculation is carried out to realize the fault section searching by using the collected voltage and node admittance information. Finally, the effectiveness of the presented scheme is verified by PSCAD/EMTDC simulation combined with MATLAB programming.

Abstract:The correlation analysis between meteorological factors and power load is critical to power load forecasting, and it is necessary to correct the empirical model of correlation according to actual data. Based on the comprehensive meteorological index, accumulated temperature effect, and the empirical formula of correlation analysis between power load and meteorological factors, a method for correcting the correlation model of meteorological load in summer is proposed. Load trend analysis and Python crawling are used to extract meteorological load and meteorological data to improve the accuracy of analytical data. By comparing and analyzing the correlation coefficients between load and single meteorological factors, comprehensive meteorological indices, and two kinds of accumulated temperature effect corrections, combined with the coincidence degree of load and meteorological indicators over time, the optimal index parameters suitable for correlation analysis are determined. And then the fitting relationship between meteorological load and optimal index parameters is constructed. The proposed method is applied and verified by taking the main urban area of Beijing in summer 2019 as an example. The results show that compared with single meteorological factors, there is a stronger correlation between power load, meteorological load, and comprehensive meteorological index. Among the comprehensive meteorological indices, the heat index based on daily average temperature has the highest correlation coefficient with meteorological load. Among the two accumulated temperature correction methods, the method considering the cumulative effect coefficient has a better correction effect on temperature, and the correlation coefficient between the corrected temperature and meteorological load increases by 7.39%. The correlation between the heat index based on the corrected temperature and meteorological load is higher than that before correction, which is closer to the changing trend of load. The coincidence degree between the fitting relationship of meteorological load constructed with heat index and corrected temperature as independent variables and the actual value is higher than that of the referenced empirical formula.

Abstract:The integration of distributed generation and intelligent soft open points into the distribution network has led to significant changes in its form and characteristics, gradually evolving into an active distribution network. Improving the load recovery rate in the power outage area through island partitioning is a crucial aspect of enhancing the reliability of active distribution networks during the fault recovery stage. Therefore, a fault recovery method is proposed for active distribution networks, which combines three-terminal intelligent soft open points with island partitioning. Firstly, a time-series island partitioning model based on the power supply capacity of distributed generation is established, considering the combination scheme of adjacent islands. Then, the impact of the access of intelligent soft open points on the fault recovery of active distribution networks and the influence of different access positions of three-terminal intelligent soft open points are analyzed. Through second-order cone transformation, the nonlinear problem difficult to solve is converted into a standard second-order cone model and solved. Finally, an improved IEEE 33-node active distribution network is used for analysis to verify the effectiveness of the proposed method.

Abstract:The layout about pressure plate of relay protection devices is gradually changing towards simplicity and standardization, which objectively provides conditions for intelligent inspection of the pressure plate. However, due to the actual scene, it is often impossible to provide pressure plate images with sufficient size and resolution for pressure plate recognition. To this end, a method based on image enhancement and deep neural network for target recognition is proposed to recognize pressure plate images with low resolution. The image enhancement network uses collaborative learning signals from the target recognition network to enhance extremely low-resolution images into clearer and more informative images, so that the target recognition network with high-resolution image training weights actively participates in the learning of the image enhancement network; and then the output of the image enhancement network is utilized as enhanced learning data, to improve the recognition performance for very low-resolution objects. Experiments on various benchmark datasets with low-resolution image verify that this method can improve the reconstruction and classification performance of pressure plate images.

Abstract:As a common safety hazard in low-voltage electricity use, fault arcs are difficult to perceive effectively on the eve of failure due to their concealment and randomness. Existing protection methods usually take measures after the occurrence of faults, which can easily cause electrical fires. To address these issues, a warning method for low-voltage electrical safety hazard is proposed on basis of multi-dimensional features and random forests. Next, a random forest model is built and hyper-parameters are optimized, with the goal of minimizing node information entropy to complete model training, so that enhances the overall performance and learning efficiency of the model. Finally, experimental verification shows that the proposed method achieves a prediction accuracy of over 99.4% with different loads, and its prediction accuracy is higher than that of four traditional classification prediction models.

Abstract:To ensure the normal operation and maintenance of transmission lines under meteorological conditions prone to galloping, according to the complex mapping relationship between line galloping and meteorological conditions, the improved particle swarm optimization (IPSO) is used to optimize the BP neural network, and a line galloping prediction method based on the improved particle swarm optimization BP (IPSO-BP) neural network is proposed. Text mining technology is used to analyze the influencing factors of line galloping, and an IPSO-BP neural network model with characteristic as inputs of span, ice thickness, temperature, wind speed, wind direction, relative humidity, and the angle between wind direction and line direction is determined. The model is trained through historical line galloping data to achieve the prediction function. Comparing the accuracy and stability of the IPSO-BP neural network model with other algorithm models, the results show that this method has certain advantages. Finally, this method is used to predict the line galloping in Xiezhuang area of Henan Province, which verifies the accuracy and practicability of the method.

Abstract:Aiming at the problems of low solution accuracy and efficiency in the optimal capacity allocation of energy storage system in wind farms with traditional methods, an improved multi-objective grasshopper optimization algorithm (IMOGOA) is proposed. Three strategies including Fuch chaos mapping, cosine adaptive parameters, and Levy flight are adopted for improvement, which makes the initial solution distribution of the algorithm more uniform, global exploration and local development more coordinated, and enhances the ability for algorithm to jump out of the local optimum. Performance tests are conducted to compare the improved algorithm with multiple algorithms such as multi-objective particle swarm optimization and et al. Experimental results show that the improved algorithm has better optimization accuracy and stability. When applied to the optimal capacity allocation for hybrid energy storage system in wind farms, compared with other algorithms, the improved algorithm can quickly find the Pareto optimal solution set. While meeting the system requirements, it minimizes the cost of the hybrid energy storage system, which verifies the effectiveness of the algorithm on improving strategy and its applicability to practical optimization problems.

Abstract:In large-scale photovoltaic grid-connected systems, multiple virtual synchronous generator (VSG) units are often used to coordinate and provide virtual inertia for the grid. However, the unreasonable distribution of virtual inertia will lead to poor system frequency response and source-end vulnerability in actual operation. To solve this problem, this paper proposes an inertia coordination control strategy for hybrid optical storage multiple VSG cells. By using the membership function, the strategy converts three indicators of the state of charge of the energy storage unit, i.e., the adjustable power of the converter and the adjustable power of the energy storage charge and discharge, into the same dimension, so as to solve the problem of multi-objective conversion in the moment of inertia distribution. At the same time, the parameters in the membership function can be flexibly adjusted according to the index characteristics to adapt to the inertia distribution under different working conditions. By establishing a small signal model of six-terminal AC system, the root locus method is used to analyze the stability of the dominant parameters in the membership function, and then to determine the parameter selection range. The optical storage six-terminal AC system model was built in MATLAB/SIMULINK and the time domain simulation was carried out. The simulation results verified the effectiveness of the proposed control strategy.

Abstract:To address issues such as power oscillation and frequency overshoot in the grid-connected photovoltaic and energy storage system operating at virtual synchronous generator (VSG) mode that has fixed rotational inertia, an adaptive control strategy for VSG in the grid-connected photovoltaic and energy storage system is proposed. Firstly, a model of the grid-connected power generation system which on basis of VSG control and containing photovoltaic and energy storage is established. The front stage of the grid-connected power generation system adopts photovoltaic MTTP control and energy storage control. Then, the influence of rotational inertia on the dynamic characteristics of VSG is analyzed according to characteristic curves of the power angle and the rotor angular frequency. The RBF neural network is applied to the VSG to adaptively adjust the rotational inertia. Simultaneously, based on a fixed damping ratio, the damping coefficient is adaptively adjusted as the rotational inertia changes. A simulation model about the adaptive control of VSG for the grid-connected photovoltaic and energy storage system is built in MATLAB/SIMULINK to verify the feasibility of this control strategy. Finally, a comparison between the adaptive damping control strategy for rotational inertia and other control strategies is conducted. Simulation results demonstrate that this control strategy can suppress active power oscillations in the grid-connected photovoltaic and energy storage system and improve rotor angular frequency overshoot.

Abstract:The high proportion of power electronic equipment cannot provide enough inertia for the system, posing great challenges to the power system. The energy storage system controlled by the virtual synchronous generator (VSG) can participate in grid frequency adjustment and improve frequency stability. The influence of VSG control parameters on the stability and inertia support capability of a multi-node system with large-scale energy storage is analyzed, and the basis for configuring control parameters of large-scale energy storage under different system inertia levels is provided. Firstly, the principle and implementation of VSG control are introduced, and the state space model of the system is established. Secondly, the inertia response process of the synchronous motor is analyzed, and the energy required by VSG under frequency disturbance events and the corresponding virtual inertia time constant are derived. Then, a simulation model of a 3-machine 9-node system with VSG-controlled energy storage is established, and modal analysis is used to analyze the influence of control parameters on system stability. Finally, simulations are performed to verify the inertia support capability of energy storage and the effectiveness of energy storage parameter configuration principles.

Abstract:Energy storage container is the core equipment of a power plant for lithium battery energy storage . Each container is composed of thousands of cells connected in series and parallel. Therefore, the accurate estimation of the state of charge (SOC) of lithium batteries in container cores becomes the core and basic parameter to characterize the operation of a power plant for energy storage. Moreover, in order to assist the new energy to be connected to the grid efficiently, the operating state of the energy storage system is randomness, fluctuation and uncertainty, which requires higher accuracy of the cell state estimation. In this paper, the Thevenin model of battery is firstly established on the basis of the Kirchhoff's circuit laws. The state and observation equations of the system are listed according to the ampere-time integration method, and then as the study object for the extended Kalman filter (EKF) algorithm. The EKF algorithm is used to update and iterate the estimated SOC of battery. The updated error values of the Kalman matrix and state variables derived from the EKF algorithm, and the battery data under UDDS conditions are as a training data set for long-term and short-term memory (LSTM) neural network algorithm. The joint algorithm of LSTM-EKF is thus completed to achieve an optimized estimation of the SOC of batteries in container cores. The SOC error can be reduced to less than 1% by the proposed LSTM-EKF algorithm. The optimization algorithm applied in the safe operation and monitoring platform of energy storage power station is finally introduced.

Abstract:The distribution of wind energy is uneven, and improving the assessment method of wind energy resource characteristics to enhance its accuracy and comprehensiveness is crucial for wind farm construction and efficient use of wind energy. A modeling method is proposed for the joint probability distribution of wind speed and wind direction based on a binary exponential polynomial. The parameters of the binary exponential polynomial of this model are solved by using linear least squares. A normalization constant is added to make the binary exponential polynomial satisfy the characteristics of the probability density function. It combines multiple goodness-of-fit index functions to solve the optimal index of the binary exponential polynomial, so that obtains the optimal fitting performance of the joint probability distribution of wind speed and direction. The model is used to fit the measured data of wind farms in multiple regions and compared with the Copula model for verification. The results show that due to the more fitting parameters of the binary exponential polynomial, the proposed model is superior to Copula model in the aspects of root mean square error, coefficient of determination, Akaike information criterion and average absolute percentage error. It is proved that the fitting model based on the binary exponential polynomial can more accurately fit the wind speed and direction data of the wind farm.

Abstract:In order to improve the maintenance strategy of transformers in offshore wind power system, under the premise of ensuring reliable operation, an optimal selection method for the maintenance strategy of transformers in offshore wind power system is proposed, considering fault risks and uncertainties. Firstly, the fault risk cost model is analyzed in detail , based on which a life cycle cost (LCC) model for transformers in offshore wind power system is established. And the blind number theory is introduced to quantitatively calculate the uncertain parameters in LCC using mathematical models, thereby improving the accuracy of cost estimation. Then, from the perspective of cost management, the impact of maintenance plans on LCC is analyzed, and an optimization model for the maintenance strategy of transformers with the lowest LCC as the goal is established, which is solved by an improved Cuckoo algorithm. Finally, the example verifies that the proposed model and the optimal maintenance strategy method have strong operability, which can provide a certain reference value for the reliable operation of transformers in offshore wind power system.

Abstract:At present, the operation dispatching of distributed generadion (DG) in islanded microgrids is treated uniformly as PQ model of traditional generators, which is inconsistent with the actual situation of microgrids. At the same time, few literatures consider voltage and power flow constraints. Therefore, considering the static model of droop control for DG and taking into account the nonlinear constraints of power flow, a hierarchical scheduling strategy for islanded microgrids with renewable energy output and energy storage charging and discharging is proposed. Meanwhile, considering the droop characteristics of DG and the nonlinear constraints of power flow, the economic and safe operation scheduling of the superior island microgrid adopts the centralized optimal controller to control various static parameters of the inferior DG and the charging and discharging power of each energy storage device. The inferior level uses traditional droop control to adjust active and reactive power output in real time under the control parameters of the superior level to maintain system frequency and voltage stability. The above model is solved by YALMIP+IPOPT program, and simulation is carried out with a 14-node system as an example. The simulation results prove that the proposed model and its solution method can effectively solve the economic and safe operation scheduling model of the islanded microgrid; it has significant advantages in stabilizing and improving the system voltage level and reducing the operating cost of the islanded microgrid.

Abstract:Distributed low-carbon energy stations (DLCES) can improve energy utilization efficiency and renewable energy consumption rates. Accurate prediction of the future operating status of DLCES can ensure its safe and reliable operation. Therefore, a data-driven prediction method for the status of DLCES is proposed. Firstly, the structure and operating status of DLCES are analyzed, and the operating status is divided into normal, recovery, critical, and emergency states using key state variables and deviations. Secondly, a deep long-short term memory (LSTM) model is constructed, and an improved particle swarm optimization algorithm is used for hyper-parameter optimization to improve the performance of the prediction model. Finally, the CMPSO-LSTM model is simulated using test sets data, and the results are compared with those of RNN, LSTM, and BP neural networks. The results show that the CMPSO-LSTM model can improve prediction results and has more practical significance.

Abstract:Input-series-output-parallel (ISOP) DC converters are widely used in DC grid scenarios for energy interconnection. The key challenge lies in addressing the input voltage imbalance among system modules. To address this issue, a hybrid modular ISOP DC converter with adaptive voltage balancing capability is proposed, combining resonant-type dual active bridge (SR-DAB) converters and phase-shifting dual active bridge (PS-DAB) converters. This system combines the high efficiency of SR-DAB and the flexible control capabilities of PS-DAB. By adding a passive LC resonant branch at the midpoint of the lagging arm of DAB, this resonant branch and the two half-bridge modules of adjacent sub-modules together form a non-isolated dual active half-bridge, thereby achieving adaptive voltage balancing for the system input. Additionally, a low voltage ride-through (LVRT) method is presented, which involves connecting a voltage adjustment module to the front end of DAB. The secondary side of the high-frequency transformer inside VAM is connected in series with an inductor, providing fault ride-through capability during voltage dips on the input and output sides of the system, thereby improving the transient controllability of the system. Finally, a model is built and validated in the MATLAB/SIMULINK environment, demonstrating the effectiveness of the adaptive voltage balancing performance and fault ride-through method.

Abstract:Conductive concrete applied as power transmission tower foundation grounding has been piloted in many domestic engineering projects. However, the temperature rise characteristics of conductive concrete foundation under continuous lightning impact still need further theoretical analysis and experimental research. In this paper, the spark effect of soil ionization under continuous lightning impact and its cumulative effect are considered. An ATP-EMTP simulation model for the dispersion effect of conductive concrete tower foundation is established. The thermal stability of the conductive concrete foundation suffering continuous lightning strike is calculated, which provides theoretical reference for engineering practice. The results show that the thermal effect of conductive concrete foundation changes the salinity and water content of the surrounding soil under lightning impulse. Thus, the resistivity of the surrounding soil is improved, which results in 6.56% increase of the grounding resistance. When the number of continuous lightning impulse n≤2, the temperature rise of conductive concrete foundation Δt=287.06<300 ℃, the conductive concrete foundation of tower is safe and stable. Once n≥3, the temperature rise Δt>300 ℃, the structure of conductive concrete foundation may be destructed, which may bring safety risks to the stable operation of power system. Therefore, in actual engineering applying conductive concrete foundation grounding, the resistivity of the soil around the foundation should be reduced to improve the dissipative heat capacity.