Abstract:

False data injection attack (FDIA) is one of the major factors threatening the operational security of power grids. It primarily targets communication links within power grids, misleading the state estimation results of the power system and posing significant risks to grid security. Addressing the challenges of effectively detecting FDIA and the non-positive definite covariance matrix of process noise and measurement noise in power system state estimation, this paper introduces the vector auto-regression (VAR) model into power system state estimation and proposes an FDIA detection method based on VAR and weighted least squares (WLS). Firstly, a VAR state estimation model is established, treating measurement noise as a stable quantity and estimating only process noise, thereby resolving the non-positive definite issue of the covariance matrix. Secondly, both VAR and WLS are used for power system state estimation, and the results of the two methods are detected using consistency checks and measurement residual tests to determine the presence of FDIA. Finally, simulation results from IEEE 14-bus and IEEE 30-bus systems demonstrate that the proposed detection method can successfully detect FDIA with a high success rate, verifying the feasibility and effectiveness of the method.

Abstract:

Aiming at the problem of system state estimation under false data injection attacks, the mathematical model of power system was established according to the third-order model of generator and the model of automatic voltage regulator, taking the cyber-physical power system as the research object. The exponential smoothing method was used to predict the measured value, and by comparing the predicted value with the actual measured value, it detected whether there were false data injection attacks in the system. If the detection results determine that the system being subjected to false data injection attacks, the predicted value is used instead of the bad data input state estimation algorithm to restore corrupted data cansed by these attacks. Combining the exponential smoothing method with the cubature Kalman filter algorithm, an improved cubature Kalman filter algorithm was proposed to estimate the state of the system. Taking a typical five-machine power system as an example, the simulation results show that the proposed method can effectively prevent the adverse effects of false data on system state estimation.

Abstract:

Aiming at the difficulties that the existing fault location methods are complex to implement and the traveling wave transmission speed cannot be accurately calculated due to the influence of frequency-dependent changes in distribution network line parameters, a fault location method for complex distribution networks is proposed based on the multi-terminal traveling wave frequency matrix, which does not rely on wavefront time information. By analyzing the relationship between the inherent frequency principal component of the fault traveling wave and the transmission distance, the reference terminals of each branch node are defined, and the difference between the benchmark inherent frequency matrix before the actual fault occurs and the fault inherent frequency matrix after the actual fault occurs is calculated, ultimately obtaining the corresponding fault branch determination principle. Based on the determination of the fault branch, the reference terminal is selected according to the principle that the path from the fault point to the reference terminal does not pass through branch nodes or passes through the fewest number of branch nodes, and the wave speed under the principal component of the fault inherent frequency of the corresponding reference terminal is calculated, thereby accurately locating the fault point. Simulation results show that the proposed method does not require detecting the wavefront time of the traveling wave, accurately describes the situation of any branch fault in the distribution network topology by constructing a multi-terminal frequency matrix, ensures reliable determination of the fault branch, and achieves matching between the frequency component and the wave speed, greatly improving the accuracy of fault location. Moreover, the location results are not affected by the fault location, type, transition resistance or initial phase angle.

Abstract:

Due to the complex topology, multiple line branches, and dense spatial distributions of distribution networks, the potential disturbances and failures cannot be eliminated. Thus, a protection system is required to ensure a high level of both reliability and stability. In that case, new challenges in the monitoring and identification of these potential abnormal operation statues must be worked out. To this end, a data-driven-based real-time anomaly detection model is proposed in this paper. To start with, the kernel principal components analysis (KPCA) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of normal operating states of each feature. Thus, the IF can maintain a high detection performance in the biased or sparse distributions, and react swiftly to those outliers. Finally, the operation data of a relay system in one regional distribution network are utilized in the case study. The results verify the better performance of the proposed model in practical applications, and therefore can be utilized to assist in the automatic identification and response of the risks of distribution networks.

Abstract:

With the rapid increase in the scale of low-voltage distribution system and user demand, leakage faults of user lines and household electrical equipment occur frequently, which increase the risk of electric shock and electrical fire accidents. Residual current protector is a common method to detect low-voltage leakage fault. In recent years, due to the existence of leakage current to ground of lines (or equipment), it also frequently false operates, which greatly reduces the operation rate and reliability of protective equipment. To overcome these issues, this paper proposes a leakage detection technology for low-voltage distribution system based on random forest （RF） algorithm. In order to closely simulate the real leakage fault scenario, the original residual current data close to the real fault scenario can be obtained by fully considering the interference factors such as excessive normal leakage current and frequent switching of load in the adjacent branch of the fault scenario. Through data preprocessing of the original residual current data, the frequency domain and time domain characteristics of the residual current are analyzed, and the time-frequency characteristics are extracted using the Fourier transform algorithm to complete the establishment and training of the low-voltage system leakage detection model. The leakage detection model is tested under the condition of multiple interference factors, and the results show that the detection accuracy of the leakage fault can reach 99.98%, realizing the leakage fault detection of low-voltage distribution system under the condition of multiple interference factors. Finally, support vector machine (SVM) algorithm, K?nearest neighbor (KNN) algorithm and the leakage fault detection accuracy based on random forest algorithm are compared to verify the accuracy and feasibility of the proposed leakage fault detection model of low-voltage system.

Abstract:

When a ground fault occurs on double-circuit transmission lines, the conventional tripping strategy may inject negative-sequence components into the system. The traditional autoreclosing scheme does not determine the fault feature before the reclosing, and the stability of the power system will be affected if the reclose fails. To solve the above problems, an improved tripping strategy suitable for ground faults on double-circuit transmission lines is proposed, where phase-separation adaptive autoreclosing is used in the tripping strategy. Firstly, by establishing and analyzing the phase-to-phase coupling model of double-circuit transmission lines, an improved tripping strategy that can avoid the injection of negative-sequence components into the power system is proposed. Secondly, the capacitive coupling voltage characteristics are analyzed under transient and permanent faults respectively, and the fault feature identification criteria based on the capacitive coupling voltage of double-circuit transmission lines is proposed. Finally, the improved tripping and closing strategy are combined to form a phase-separation adaptive autoreclosing strategy suitable for ground faults on double-circuit transmission lines. The PSCAD/EMTDC simulation verifies that the proposed phase-separation adaptive autoreclosing strategy can avoid the injection of negative-sequence components into the system and ensure the reclosing success rate of the transmission line under different ground fault types, fault locations and transition resistances.

Abstract:

To enhance the speed and reliability of low-voltage protection for DC transmission lines in hybrid multi-terminal HVDC systems, a novel low-voltage protection strategy based on an improved cumulative sum (CUSUM) algorithm is proposed. When faults occur at different locations in a ±800 kV hybrid multi-terminal HVDC system, the electrical quantities measured at the protection installation exhibit certain differences. Traditional low-voltage protection lacks a setting basis and has the risk of maloperation. This paper introduces the CUSUM algorithm to extract the characteristics of electrical quantity differences during different faults, thereby ensuring the reliability of protection. At the same time, to improve the speed of protection, the CUSUM algorithm window is improved with fractal theory, making the window adaptive and enhancing the speed of protection. A hybrid multi-terminal HVDC model is established using PSCAD/EMTDC, and the proposed new protection strategy is verified with MATLAB. Simulation results show that the proposed scheme can operate quickly and reliably, with good speed and reliability; it can withstand higher transition resistance and has good tolerance to transition resistance; the criterion relies on single-ended electrical quantities, avoiding interference from noise and data anomalies in communication, and can quickly identify internal and external faults and operate reliably.

Abstract:

The rapid development of the electric vehicle industry will definitely bring great pressure to the power grid operation, which needs to be tackled by the precise study of the electric vehicles impact on the distribution grid. Therefore, this paper constructs a spatial and temporal distribution prediction model of EV charging load and a comprehensive evaluation system of EV access to the distribution network. Firstly, multi-source data, multi-factor energy consumption model, dynamic road network model and road-electricity coupling theory are used to predict the spatial and temporal distribution of EV charging load. Secondly, a comprehensive evaluation system is established by constructing quantitative indicators based on five criteria: rationality, safety, economy, reliability and environmental protection. Thirdly, the comprehensive weighting method is applied to obtain the index weight set, and the fuzzy Borda method is applied to synthesize various comprehensive evaluation methods in order to obtain the comprehensive evaluation results. Finally, the feasibility of the proposed method is proved by simulating a case in a region of Shanghai.

Abstract:

A strategy and evaluation method for enhancing the resilience of distribution networks, which considers load importance under extreme weather disasters, is proposed to improve the resilience enhancement strategy and evaluation system of distribution networks after the occurrence of small-probability， high-risk extreme weather disasters. Firstly, taking typhoon weather as a representative event of extreme weather disasters, the line failure probability is determined based on the typhoon weather model and the failure probability model of overhead lines. Secondly, a three-stage joint optimization strategy model for resilience enhancement is established to determine the joint optimization strategy for resilience enhancement, the faulty lines, and the minimum load shedding rate based on the line failure probability. Then, a resilience evaluation method considering load importance is proposed to assess the resilience of distribution networks based on the load shedding rate and load importance of each node under the resilience enhancement strategy. Finally, simulations are conducted using the IEEE 33-node system to compare the resilience enhancement effects of single resilience enhancement measure with those of joint optimization of multiple measures, verifying the effectiveness and superiority of the proposed resilience enhancement strategy and evaluation method.

Abstract:

Addressing the issue of low accuracy in predicting short-term power generation and consumption data for the photovoltaic-storage-direct-flexible distribution system using existing time series models, a multi-data prediction model based on grey relation analysis (GRA), ensemble empirical mode decomposition (EEMD), and Informer, namely the GRA/EEMD-Informer is proposed. This model effectively captures the precise long-range correlation coupling between outputs and inputs through grey relation analysis, modal decomposition, combined with a self-attention distillation mechanism. It reduces spatiotemporal complexity and significantly alleviates the limitations of traditional encoding and decoding methods. Using data from a photovoltaic power station, the electricity consumption of typical office building, and an electric vehicle charging station for a certain month as the original data, the model is tested using evaluation metrics such as mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). Ablation experiments and analyses are conducted, and the results are compared with those of long short-term memory (LSTM), particle swarm optimization-based LSTM (PSO-LSTM), and the transformer time series prediction method. The results indicate that the proposed method exhibits significantly higher fitting accuracy than other prediction methods, verifying the effectiveness and practicality of the GRA/EEMD-Informer algorithm in enhancing prediction capabilities.

Abstract:

Time of use (ToU) price is an important means for power utilities to implement demand response. In order to shave peak load of the power grid based on coordination interests of all participants, it is necessary to analyze the electricity usage data of users and optimize the configuration of ToU price. The cosine similarity is employed to develop a peak load transfer evaluation coefficient index that can visually evaluate user responsiveness in a straightforward way. The index is employed to evaluate the users’ response degree of a power utility by industry. It is uncovered that majority of industrial and commercial users of various industries cannot participate in demand response due to their industry and electricity usage characteristics. Once all days in the summer are set as day of peak electricity prices, these users suffer substantial economic loss inevitably. The meteorological data and load data are analyzed to find out the way to optimize configuration of day of peak electricity prices. It is uncovered that the peak load can be shaved effectively without causing unnecessary electricity bill of these users by setting work day with temperature over 35℃ as day of electricity price.

Abstract:

The access of large-scale distributed resources makes the regulation and control of the distribution grid more difficult, and how to reasonably and effectively utilize diversified resources to reduce the operating cost of the distribution grid has become a key technical problem to be solved. Considering the complementary characteristics of removable energy storage systems and electric vehicles, a day-ahead optimal scheduling strategy for distribution grids with the support of temporal and spatial flexibility of multiple types of energy storage systems is proposed to improve the operating economy of the distribution grid system. Firstly, the scheduling models of electric vehicles and removable energy storage systems are established and the electric vehicle parking generation rate model is built, which simplifies the complexity of the model and improves the solution efficiency. Secondly, the ensemble-based improved particle swarm algorithm is introduced and adapted to be suitable for the optimal scheduling of distribution grids, which improves the solution efficiency of its optimization search in discrete space. Lastly, simulation analysis conducted on the IEEE 33-bus distribution system verifies the effectiveness of the proposed coordinated optimization scheduling strategy.

Abstract:

Due to the excessive width of the main lobe after windowed Fourier transform of the cable input impedance spectrum, there is an excessive ‘dead zone’ in defect positioning near the cable end. To address this issue, an improved method for distribution cable defect positioning based on Chebyshev window is proposed in this paper. Firstly, a distributed parameter model of the cable is established, and the principle of local defect positioning is elaborated based on the cable input impedance spectrum. Subsequently, simulation studies are conducted on the cable input impedance spectrum using Hamming window, Blackman window, and Chebyshev window, comparing and analyzing the effectiveness of each window function. Finally, a diagnostic function is constructed from the cable impedance phase spectrum and combined with the Chebyshev window to achieve defect positioning and defect type identification. Simulation study on a 10 kV XLPE cable, 100 m in length, demonstrates that compared with the direct treatment of cable impedance spectrum with Chebyshev window, the proposed method not only significantly improves defect positioning accuracy: reducing the relative error of defect positioning from 0.467% to 0.029% at 2 m from the cable’s first end, but also shrinking the positioning ‘dead zone’ from 6 m to 3 m away from the cable’s end. Moreover, the proposed method can identify different types of defects according to the waveform characteristics.

Abstract:

The water tree in XLPE distribution network cables is a threat to the power grid. In order to study the location mechanism of water tree based on broadband impedance spectrum, an experiment is carried out to accelerate the water tree, and the changes of cable micro-element parameters in different stages of water tree are measured. Then a finite element model of cable water tree section is established and the experimental measurement results are verified. The optimal window function and incident waveform in the location algorithm are determined to get an optimized location algorithm. After that, the effectiveness of algorithm is verified according to the experimental results. The results show that the growth of water tree will cause the increase of conductance and capacitance. The location method based on broadband impedance spectroscopy is more sensitive to small changes in local capacitance caused by water trees, but not sensitive to changes in conductance. The localization map of capacitance changes is a in typical double peak structure, and the reflection amplitude is proportional to the local capacitance change.

Abstract:

In order to provide technical support to the reinforcement for old angle steel transmission towers, studies of the effects on the ultimate bearing carrying capacity of angle steel leg member under different reinforcement methods were conducted. In this paper, two main reinforcement methods of fixture and perforation types for angle steel leg member were proposed. The failure mode, bearing capacity and reinforcement effect of reinforced members are studied under different reinforcement methods, reinforcement positions and connector spacing by static load test and parametrization finite element simulation, and then the reinforcement mechanism of members is analyzed. The results show that: the finite element simulation results are in good agreement with the test results, the ultimate bearing capacity and damage pattern are similar to the test results. The reinforcement changes the damage pattern of the original member, from overall instability to local instability. The bearing capacity is increased by about 7 % ~ 8 % when the two ends of the reinforced member are connected, and the reinforcement effect is doubled after the middle connection is added. Both types of reinforcement are effective in increasing the bearing capacity of the elements, and with the addition of intermediate joints increasing the bearing capacity even more significantly.

Abstract:

To achieve safe and stable grid connection of the direct-driven permanent magnet wind power generation system, a grid-connection control strategy combining dual compensation droop with multiple quasi-proportional-resonances (MQPR) is proposed. This strategy originates from traditional droop control, introduces DC voltage compensation in the voltage control loop to quickly adjust and stabilize the DC bus voltage, and introduces capacitive current compensation in the current control loop to effectively reduce the impact of current errors caused by filter capacitors. Meanwhile, a MQPR controller is designed to replace the PI controller of the inner-loop current, which can filter out multiple harmonic currents in the system. By establishing a simulation model and comparing it with the double closed-loop PI and traditional droop control strategies, the effectiveness of the proposed control strategy is verified.

Abstract:

In this paper, an adaptive virtual inertial-damping control method of battery energy storage for maximum wind power point tracking is proposed to increase the frequency stability of wind farms. First, adjustment coefficients of both the inertia control parameter and damping parameter are introduced to the virtual synchronous control strategy. Then, the value range of both control parameters is given by small signal analysis. Finally, based on the combined wind and storage system, the effectiveness of the control method proposed in this paper is verified when facing different types of faults. The results show that compared with the traditional virtual synchronous control method, the proposed method can significantly improve the frequency stability of the merging points, and realize the adaptive smooth adjustment of inertia and damping.

Abstract:

With the increasing proportion of renewable energy devices in the energy collection system, the uncertainty of photovoltaic and load pose power quality issues and challenges to the stable operation of the grid. The voltage and power compensation control of the system can be realized through energy storage access. First, the coupling influence mechanism between voltage and power is analyzed. Then, the series-connectded mode and the parallel-connectded mode energy storage are used to compensate the fluctuation of grid voltage and power through coordinated control. Finally, based on the coordinated control of energy storage, the capacity of energy storage equipment with different integration modes is optimized to meet the compensation requirements of energy collection system and ensure the stable operation of the system. The effectiveness of the proposed coordinated control and optimization design method is verified by simulation examples, which can reduce the total amount of energy storage and has practical application value.

Abstract:

The basic mathematical model of doubly-fed induction generator (DFIG) participating in grid frequency regulation is presented. The relationship between wind power penetration, the proportion of wind turbines with adjustable frequency ability, the frequency regulation coefficient of wind turbines, and wind power utilization is derived under different operating modes (such as inertia control or maximum power tracking) after large-scale wind power is connected to the grid. The control strategies for wind turbines to participate in grid frequency regulation under different operating modes are proposed. Taking a grid in southern China as an example, the impact characteristics of wind power injection ratio on grid frequency regulation under changing wind speed and load conditions are analyzed, and the effectiveness of renewable energy generation with adjustable frequency ability in grid frequency regulation is verified. By connecting wind turbines with adjustable frequency ability to a 4-machine 2-area grid model, the minimum proportion of required wind turbines and the comprehensive carrying capacity of the grid for renewable energy under different wind power injection ratios are presented. Simulation results show that when large-scale wind farms participate in frequency regulation, it is advisable to involve turbines operating near rated wind speed to minimize grid frequency deviation and maintain frequency within the safe operating range.

Abstract:

Quantitative analysis of additional losses in low-voltage distribution networks is one of the necessary ways to achieve energy conservation and loss reduction in power systems, in response to the serious increase in additional losses caused by the disorderly access of household photovoltaics. This paper focuses on the quantitative relationship between the additional losses of household photovoltaic power generation and distribution lines after being connected to the power grid. Firstly, a theoretical analysis is conducted on the changes in characterization of additional losses for household photovoltaic (PV) access. An equivalent circuit and mathematical model are established for active low-voltage distribution networks with three-phase four-wire systems. Secondly, the study investigates the disturbance of multiple types of power quality under different capacities and phase sequences of household PV and establishes a composite power quality disturbance additional loss model. Finally, based on the typical daily PV power output curve and additional loss model, the accuracy of the proposed quantification evaluation method for household PV's additional losses in distribution networks is analyzed and verified through case studies.

Abstract:

The conditions in long-distance offshore areas are complex, and surface wind speeds are highly susceptible to the influence of mesoscale oceanic events. The resulting anomalous data points and bump events will decrease the accuracy of ramp-up detection, affecting the short-term forecasting precision of offshore wind power in long-distance sea areas. Therefore, a short-term forecasting method for offshore wind power in long-distance sea areas is proposed, which simultaneously considers ramp-up events and long-distance sea meteorological factors. Firstly, an improved parameter and resolution adaptive algorithm （PRAA） based on state marker and sliding window is designed to detect ramp-up events and extract features. Secondly, the correlation of multiple factors such as wind speed, wind direction and temperature in the long-distance offshore is analyzed to expand the dimension of the feature samples of the meteorological factors, and the potential features are deeply explored by principal component analysis (PCA). Finally, based on the measured data of a domestic offshore wind farm, the light gradient boosting machine (LightGBM) considering ramp-up and meteorological factors in long-distance sea areas is used to complete the short-term prediction of long-distance offshore wind power. Simulation results verify the effectiveness of the proposed method.

Abstract:

With the large capacity of photovoltaic power generation connected to the grid, in order to reduce the randomness of photovoltaic power generation output, a long short-term memory (LSTM) based on an improved moth-flame optimization (IMFO) algorithm is proposed to predict photovoltaic power generation power. Firstly, through data preprocessing, grey relational analysis is conducted to reduce the dimensionality of input variables. Then, based on the selected input variables, similar-day sample selection is performed using the grey relational analysis method. Secondly, the position update formula are improved to enhance the performance of the moth algorithm. Then, the improved moth algorithm is used in the optimization of the number of network layers and learning rate of the LSTM to improve its prediction accuracy and reduce randomness. Finally, based on the pre-processed samples of similar days, the optimized LSTM is adopted for power prediction. Simulation results show that the prediction accuracy of the model has been improved to a certain extent, which meets the actual engineering requirements.

Abstract:

The volatility and randomness of photovoltaic (PV) output will affect the security and reliability of power system dispatching operation. In order to accurately simulate the PV output over a long time scale, this paper proposes a time-series power simulation model of PV output based on Markov chain. Firstly, a photovoltaic output model is established, and the uncertainty and regularity characteristics of output are analyzed. Then, considering the output relationship between adjacent days of the year on the basis of the first-order Markov chain model, the historical data is sampled in a rolling way with 10 days as a sampling interval based on the season and weather factors, and a multi-state transition probability matrix is established, and then the annual time series output model is constructed; Finally, based on the output data and the annual historical meteorological monitoring data of a PV plant, the simulation of the annual output is conducted and the results are compared with the traditional methods. The example results verify the effectiveness of the proposed method, which shows that the method can simulate the PV output under the influence of season and weather, and is consistent with the historical actual situation.

Abstract:

A multi-timescale optimal scheduling strategy for integrated energy system considering source-load uncertainty and user demand response is proposed to address the problems of source-load uncertainty and equipment power fluctuation in the energy system. First, according to the respective characteristics of the source and load, the robust optimization method and the stochastic optimization method are used to deal with the uncertainty of the source and load. Second, based on the scheduling characteristics and response differences of electricity, heat and gas loads in different time scales, a demand response model corresponding to different time scales is designed, and a multi-timescale optimization model is developed for the day-ahead and intraday phases, with the day-ahead scheduling objective of minimizing the cost of purchasing energy, the system operating cost, and the cost of carbon emission, and the intraday scheduling phase further considers the penalty of equipment regulation and the reduction of subsidy demand response costs. Finally, the model and methodology are validated through examples, and under the consideration of source-load uncertainty and demand response, the proposed methodology is able to take advantage of the complementary advantages of multiple energy sources, effectively promote the balance of energy supply and demand, and reduce the impact of source-load uncertainty on the integrated energy system.

Abstract:

Building clean and efficient integrated rural energy systems is of great significance to the comprehensive promotion of the rural revitalization strategy, an integrated energy system operation model with hydrogen mixing-carbon capture coupling is constructed, and a low-carbon economic scheduling strategy that takes into account the parameter-adaptive stepped carbon trading and stepped incentive-based demand response is proposed in this paper. Firstly, a refined model of gas-fired units after hydrogen doping reform is established, and two-stage electricity-to-gas conversion, carbon capture technologies are integrated to form a coupled hydrogen mixing-carbon capture operation mode; secondly, stepped incentive demand response is introduced to promote the transformation of users' energy use and alleviate the pressure of energy supply during the peak load period of the integrated energy system; finally, a step carbon trading mechanism is introduced to construct an operation optimization model with the aim of minimizing the operating cost, and CPLEX is adopted to optimize the operation mode of the integrated energy system. Finally, the stepped carbon trading mechanism is introduced and the operation model is constructed with the goal of minimizing the system operation cost, and the CPLEX is used to solve the model, while the total carbon emission is calculated to minimize the carbon emission as the optimization goal of the parameters of the carbon trading mechanism, and the particle swarm algorithm is used for finding the optimal parameters of the optimal carbon trading mechanism and the operation strategy. By setting different scenarios for comparison, it is verified that the proposed scheduling strategy can effectively reduce carbon emissions and realize economic operation.

Abstract:

The identification of key links in a comprehensive energy system is of great significance for the research and improvement of system vulnerability. Aiming at the problem that the traditional reductionism method can not explain the system level and the influence of energy flow is not considered in the analysis of complex network, a key link identification method of the IES based on power flow and complex network structure is proposed. Firstly, a comprehensive energy complex network model is established based on power flow, and the mathematical model parameters and power flow results of the IES are introduced into the modeling of complex network to make the model more suitable for realistic operating conditions. Secondly, based on the power flow calculation results and complex network parameters, two evaluation indicators, i.e. node energy degree and energy edge intermediate, are proposed to improve the identification accuracy of key links. Finally, based on the established model, the key links are destroyed and the changes in network efficiency are observed. Compared with other attack modes, the feasibility of the proposed method is verified.

Abstract:

For at the DC electric energy router in the MVDC distribution network, there are some issues when renewable energy sources fluctuate. Under these conditions, the traditional control strategy has a general control effect on the DC bus and the voltage is easy to violate. Based on the modular input?series-output-parallel (ISOP) type topology, a DC electric energy router control strategy combining the input voltage sharing and virtual DC machine is proposed. First, the power balancing control characteristics between the modules in the input voltage sharing control process are studied and compared with the output current sharing control; then, the virtual DC machine control is applied to the control algorithm so that the converter can simulate the inertia characteristics of DC machine; then, a small signal model of the virtual DC machine is established to analyze its working principle and the influence of parameters on the system. Finally, a simulation model is built in MATLAB/Simulink for verification. The results show that the proposed control strategy can achieve power balancing among the modules of the DC electric energy router while having inertia characteristics and damping characteristics similar to the DC machine, which can significantly improve the DC bus voltage stability of the DC distribution network.

Abstract:

The corrosion rate of grounding grid is an important aspect of corrosion state evaluation. The artificial intelligence algorithm model can predict the corrosion rate of the grounding grid well. In view of the problem that the selection of the characteristic input in the current prediction model is not comprehensive enough, based on the theoretical analysis of the grounding grid, the corrosion sampling point of the grounding grid is determined. The physical and chemical properties of the soil and the average growth rate of the grounding grid resistance are proposed as the characteristic input of the prediction model. The genetic algorithm (GA) is used to optimize the back propagation （BP） neural network, and the prediction model of the corrosion rate of the grounding grid is established. Compared with the unoptimized BP neural network model and the BP neural network model optimized by fruit fly optimization algorithm （FOA）, the prediction performance of the proposed model is better and has better applicability.