Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

Research on Slot-pole Combination in High-power Direct-drive PM Vernier Generator for Fractional Frequency Transmission System

Offshore wind energy is an important part of clean energy,and the adoption of wind energy to generate electricity will contribute to the implementation of the carbon peaking and carbon neutrality goals.The combination of the fractional frequency transmission system(FFTS) and the direct-drive wind turbine generator will be beneficial to the development of the offshore wind power industry.The use of fractional frequency in FFTS is beneficial to the transmission of electrical energy,but it will also lead to an increase in the volume and weight of the generator,which is unfavorable for wind power generation.Improving the torque density of the generator can effectively reduce the volume of the generators.The vernier permanent magnet machine(VPM) operates on the magnetic flux modulation principle and has the merits of high torque density.In the field of electric machines,the vernier machine based on the principle of magnetic flux modulation has been proved its feasibility to reduce the volume and weight.However,in the field of low-speed direct-drive machines for high-power fractional frequency power generation,there are still few related researches.Therefore,this paper studies the application of magnetic flux modulation in fractional frequency and high-power direct-drive wind turbine generators,mainly analyzes the influence of different pole ratios and different pole pairs on the generator,and draws some conclusions to provide reference for the design of wind turbine generators.

Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.

Virtual Synchronous Generator Adaptive Control of Energy Storage Power Station Based on Physical Constraints

The virtual synchronous generator(VSG)can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter,so that an electrochemical energy storage power station has the ability to actively support the power grid,from passive regulation to active support.Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control,energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning.In a classic VSG control,its virtual inertia and damping coefficient remain unchanged.When the grid load changes greatly,the constant control strategy most likely result in the grid frequency deviation beyond the stable operation standard limitations.To solve this problem,a comprehensive control strategy considering electrified wire netting demand and energy storage unit state of charge(SOC)is proposed,and an adaptive optimization method of VSG parameters under different SOC is given.The energy storage battery can maintain a safe working state at any time and be smoothly disconnected,which can effectively improve the output frequency performance of energy storage system.Simulation results further demonstrated the effectiveness of the VSG control theoretical analysis.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods,a back-propagation artificial neural network(BP-ANN) based method for short-term load forecasting is presented in this paper.The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data.Then the short-term load forecasting model of Shanxi Power Grid(China) based on BP-ANN method and correlation analysis is established.The simulation model matches well with practical power system load,indicating the BP-ANN method is simple and with higher precision and practicality.

Analysis on Effect of Parameters of Different Wind Generator on Power Grid Transient Stability

To analyze the factors which affecting transient stability of power system, the dynamic model of doubly-fed induction generator and direct-drive PM synchronous generator has been built using PSCAD. Impact of different wind farm integration on grid typically in China has been presented. The influence of the variations of transient reactance, negative sequence reactance and rotary inertia on critical clearing time of power system transient stability is analyzed by time-domain simulation. Mixture operation of DFIG and PMSG to optimize the stability of system has been analyzed firstly. The digital simulation results show that doubly-fed induction wind turbines is a better choice to meet the requirement of system instability due to large wind farm integration in comparison with direct-drive PM synchronous wind turbines. With a rather large rotary inertia, the proper ratio of direct-drive PM synchronous wind turbines used in wind farm could be comprehensive planning by optimized the stability of system. Analysis of this paper should be provided as academic reference for improving design of wind farm system.

Parameter Test and Analysis for Dynamic Compensation Devices in Wind Power Farms

The dynamic reactive power compensation equipment in Jiuquan Wind Power Base of above 10 GW consists of three different types of compensation devices,including:static var generator(SVG),thyristor controlled compensator(TCR)and magnetically controlled reactor(MCR).The lack of experimental verif ication of performance is not conducive to voltage/var management or full utilization of device capacities.In order to solve the above problems,the compensation device performance test was performed.The test items and procedures were selected based on related national standards with the consideration for different grid structures and wind farm operation modes.The testing contents included dynamic regulating range, active power loss,dynamic response time,and harmonic voltage level.Three types of compensation devices installed in different wind farms,namely SVG,TCR and MCR,were chosen and tested.The performances were compared and analyzed according to the field test results.

Research on DCC Strategy for Grid-Connected Inverter of D-PMSG under Unbalanced Network Voltage Conditions

According to performance analysis of a three-phase grid-connected inverter mathematical model of a directly-driven wind turbine with a permanent magnet synchronous generator (D-PMSG) under unbalanced network voltage conditions, a dual current-loop control strategy (DCC) oriented on positive voltage and negative current is proposed to inhibit the DC voltage fluctuation. Meanwhile, a notch filter is introduced into the conventional control strategy of a phase-locked loop to complete the low voltage ride through (LVRT) ability of the wind generator. A 1.5-MW D-PMSG with a back-to-back IGBT frequency converter was simulated in the PSCAD/EMTDC environment, and simulation results showed that: the maximum wind power tracking was achieved in this system and the proposed DCC strategy could successfully inhibit the rising aging of DC voltage and enhance the ride-through capability of D-PMSG wind generation system under unbalanced network voltage conditions.

AC-DC Fuzzy Linear Active Disturbance Rejection Control Strategy of Front Stage of Bidirectional Converter Based on V2G

Aiming at the problems of output voltage fluctuation and current total harmonic distortion(THD)in the front stage totem-pole bridgeless PFC of two-stage V2G(Vehicle to Grid)vehicle-mounted bi-directional converter,a fuzzy linear active disturbance rejection control strategy for V2G front-stage AC-DC power conversion system is proposed.Firstly,the topologicalworkingmode of the totem-pole bridgeless PFC is analyzed,and themathematical model is established.Combined with the system model and the linear active disturbance rejection theory,a double closed-loop controller is designed with the second-order linear active disturbance rejection control as the voltage outer loop and PI control as the current inner loop.The controller can realize self-adaptive tuning of the proportional gain coefficient of the active disturbance rejection controller through fuzzy reasoning and realize self-adaptive control.Simulation and experimental results show that this method can better solve the problems of slow system response and high total harmonic distortion rate of input current and effectively improve the system’s robustness.

Preparation, mechanical properties and wear behaviors of novel aluminum bronze for dies

A modified single melt technique involving joint charging was developed for preparation of aluminum bronze, Cu-14%Al-X(mass fraction) alloy, which could be used as die materials. The mechanical properties and wear behavior of the developed alloy under boundary-lubrication conditions was investigated. The results demonstrate that all the phases disperse homogeneously in the bronze matrix with a significant amount of discrete and spherical brittle and hard γ2 phase, moreover, the dispersed κ phase are the dominant factor that improves the anti-deformation properties of the soft matrix, after a solution treatment at 920 ℃ for 2 h and followed by aging at 580 ℃ for 3 h, thus remarkably improves the mechanical properties and wear resistance of the developed alloy. The Cu-14%Al-X alloy can be used as materials for static precise stretching and squeezing dies.

Wind power prediction based on variational mode decomposition multi-frequency combinations

Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.

Multi-objective interval prediction of wind power based on conditional copula function

Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind power.However,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction accuracy.Therefore,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution function.We use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution set.The particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical examples.Finally,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

Multi-features fusion for short-term photovoltaic power prediction

In recent years,in order to achieve the goal of“carbon peaking and carbon neutralization”,many countries have focused on the development of clean energy,and the prediction of photovoltaic power generation has become a hot research topic.However,many traditional methods only use meteorological factors such as temperature and irradiance as the features of photovoltaic power generation,and they rarely consider the multi-features fusion methods for power prediction.This paper first preprocesses abnormal data points and missing values in the data from 18 power stations in Northwest China,and then carries out correlation analysis to screen out 8 meteorological features as the most relevant to power generation.Next,the historical generating power and 8 meteorological features are fused in different ways to construct three types of experimental datasets.Finally,traditional time series prediction methods,such as Recurrent Neural Network(RNN),Convolution Neural Network(CNN)combined with eXtreme Gradient Boosting(XGBoost),are applied to study the impact of different feature fusion methods on power prediction.The results show that the prediction accuracy of Long Short-Term Memory(LSTM),stacked Long Short-Term Memory(stacked LSTM),Bi-directional LSTM(Bi-LSTM),Temporal Convolutional Network(TCN),and XGBoost algorithms can be greatly improved by the method of integrating historical generation power and meteorological features.Therefore,the feature fusion based photovoltaic power prediction method proposed in this paper is of great significance to the development of the photovoltaic power generation industry.