Probabilistic Load Flow Algorithm with the Power Performance of Double-Fed Induction Generators

Probabilistic load flow(PLF)algorithm has been regained attention,because the large-scale wind power integration into the grid has increased the uncertainty of the stable and safe operation of the power system.The PLF algorithm is improved with introducing the power performance of double-fed induction generators(DFIGs)for wind turbines(WTs)under the constant power factor control and the constant voltage control in this paper.Firstly,the conventional Jacobian matrix of the alternating current(AC)load flow model is modified,and the probability distributions of the active and reactive powers of the DFIGs are derived by combining the power performance of the DFIGs and the Weibull distribution of wind speed.Then,the cumulants of the state variables in power grid are obtained by improved PLF model and more accurate power probability distributions.In order to generate the probability density function(PDF)of the nodal voltage,Gram-Charlier,Edgeworth and Cornish-Fisher expansions based on the cumulants are applied.Finally,the effectiveness and accuracy of the improved PLF algorithm is demonstrated in the IEEE 14-RTS system with wind power integration,compared with the results of Monte Carlo(MC)simulation using deterministic load flow calculation.

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.

Maximum Power Point Tracking in Variable Speed Wind Turbine Based on Permanent Magnet Synchronous Generator Using Maximum Torque Sliding Mode Control Strategy

The present study was carried out in order to track the maximum power point in a variable speed turbine by minimizing electromechanical torque changes using a sliding mode control strategy. In this strategy, first, the rotor speed is set at an optimal point for different wind speeds. As a result of which, the tip speed ratio reaches an optimal point, mechanical power coefficient is maximized, and wind turbine produces its maximum power and mechanical torque. Then, the maximum mechanical torque is tracked using electromechanical torque. In this technique, tracking error integral of maximum mechanical torque, the error, and the derivative of error are used as state variables. During changes in wind speed, sliding mode control is designed to absorb the maximum energy from the wind and minimize the response time of maximum power point tracking(MPPT). In this method, the actual control input signal is formed from a second order integral operation of the original sliding mode control input signal. The result of the second order integral in this model includes control signal integrity, full chattering attenuation, and prevention from large fluctuations in the power generator output. The simulation results, calculated by using MATLAB/m-file software, have shown the effectiveness of the proposed control strategy for wind energy systems based on the permanent magnet synchronous generator(PMSG).

Battery Energy Storage to Strengthen the Wind Generator in Integrated Power System

The wind energy generation,utilization and its grid penetration in electrical grid are increasing world-wide.The wind generated power is always fluctuating due to its time varying nature and causing stability problem.This weak interconnection of wind generating source in the electrical network affects the power quality and reliability.The localized energy storages shall compensate the fluctuating power and support to strengthen the wind generator in the power system.In this paper,it is proposed to control the voltage source inverter （VSI） in current control mode with energy storage,that is,batteries across the dc bus.The generated wind power can be extracted under varying wind speed and stored in the batteries.This energy storage maintains the stiff voltage across the dc bus of the voltage source inverter.The proposed scheme enhances the stability and reliability of the power system and maintains unity power factor.It can also be operated in stand-alone mode in the power system.The power exchange across the wind generation and the load under dynamic situation is feasible while maintaining the power quality norms at the common point of coupling.It strengthens the weak grid in the power system.This control strategy is evaluated on the test system under dynamic condition by using simulation.The results are verified by comparing the performance of controllers.

Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

Difference between grid connections of large-scale wind power and conventional synchronous generation

In China, regions with abundant wind energy resources are generally located at the end of power grids. The power grid architecture in these regions is typically not sufficiently strong, and the energy structure is relatively simple. Thus, connecting large-capacity wind power units complicates the peak load regulation and stable operation of the power grids in these regions. Most wind turbines use power electronic converter technology, which affects the safety and stability of the power grid differently compared with conventional synchronous generators. Furthermore, fluctuations in wind power cause fluctuations in the output of wind farms, making it difficult to create and implement suitable power generation plans for wind farms. The generation technology and grid connection scheme for wind power and conventional thermal power generation differ considerably. Moreover, the active and reactive power control abilities of wind turbines are weaker than those of thermal power units, necessitating additional equipment to control wind turbines. Hence, to address the aforementioned issues with large-scale wind power generation, this study analyzes the differences between the grid connection and collection strategies for wind power bases and thermal power plants. Based on this analysis, the differences in the power control modes of wind power and thermal power are further investigated. Finally, the stability of different control modes is analyzed through simulation. The findings can be beneficial for the planning and development of large-scale wind power generation farms.

Enhancement of Wind Energy Conversion Using Axial Flux Generator

This paper investigates the application of the axial flux machine (AFM) to the wind energy conversion systems (WECS) to obtain high power and torque at reduced cost. By developing mathematical equations using the phase and active transformations, the three-phase model is transformed to two-phase equations by making both the stator and rotor as reference frames, finally converting to arbitrary reference frame, which is useful for the modelling of the axial flux machine. The torque, current, and voltage equations are expressed to improve the simulation reliability. Based on the developed equations, the mathematical model for the axial flux machine is developed using the MATLAB/Simulink. Starting with the axial flux motor model, when the load on the motor increases, how the parameters like torque, current, and speed of the motor vary are explored in this paper. Then for the axial flux generator model, when the wind speed exceeds the rated speed how the torque, line voltages, currents, power and speed of the generator behave are investigated and presented in this paper. The developed model in this paper could be extended to a twin-rotor axial flux synchronous machine, which will lead to the development of more efficient WECS.

An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System

In this paper, a dynamic generation scheduling model is formulated, aiming at minimizing the costs of power generation and taking into account the constraints of thermal power units and spinning reserve in wind power integrated systems. A dynamic solving method blended with particle swarm optimization algorithm is proposed. In this method, the solution space of the states of unit commitment is created and will be updated when the status of unit commitment changes in a period to meet the spinning reserve demand. The thermal unit operation constrains are inspected in adjacent time intervals to ensure all the states in the solution space effective. The particle swarm algorithm is applied in the procedure to optimize the load distribution of each unit commitment state. A case study in a simulation system is finally given to verify the feasibility and effectiveness of this dynamic optimization algorithm.

Important Issues and Results When Considering the Stochastic Representation of Wind Power Plants in a Generation Optimization Model: An Application to the Large Brazilian Interconnected Power System

Wind power has an increasing share of the Brazilian energy market and may represent 11.6% of total capacity by 2024. For large hydro-thermal systems having high-storage capacity, a complementarity between hydro and wind production could have important effects. The current optimization models are applied to dispatch power plants to meet the market demand and optimize the generation dispatches considering only hydroelectric and thermal power plants. The remaining sources, including wind power, small-hydroelectric plants and biomass plants, are excluded from the optimization model and are included deterministically. This work introduces a general methodology to represent the stochastic behavior of wind production aimed at the planning and operation of large interconnected power systems. In fact, considering the generation of the wind power source stochastically could show the complementarity between the hydro and wind power production, reducing the energy price in the spot market with the reduction of thermal power dispatches. In addition to that, with a reduction in wind power and a simultaneous dry-season occurrence, this model, is able to show the need of thermal power plants dispatches as well as the reduction of the risk of energy shortages.

Research on Power Control of Wind Power Generation Based on Neural Network Adaptive

因为风发电系统的特征是 multivariable ，非线性、随机，在这篇论文，神经网络 PID 适应控制是沥青的 adopted.The 尺寸角度及时被调整改进力量 control.The PID 参数的性能被坡度降下方法改正，并且光线的基础 Functinn ( RBF )当在这 method.Simulation 的系统标识符结果，神经网络被使用杂木林由使用神经适应 PID 控制器生成器力量控制罐头

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

Stabilization of Wind Farm by Using PMSG Based Wind Generator Taking Grid Codes into Consideration

This paper presents a new operational strategy for a large-scale wind farm (WF) which is composed of both fixed speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) and variable speed wind turbines with permanent magnet synchronous generators (VSWT-PMSGs). FSWT-SCIGs suffer greatly from meeting the requirements of fault ride through (FRT), because they are largely dependent on reactive power. Integration of flexible ac transmission system (FACTS) devices is a solution to overcome that problem, though it definitely increases the overall cost. Therefore, in this paper, a new method is proposed to stabilize FSWT-SCIGs by using VSWT-PMSGs in a WF. This is achieved by injecting the reactive power to the grid during fault condition by controlling the grid side converter (GSC) of PMSG. The conventional proportional-integral (PI)-based cascaded controller is usually used for GSC which can inject small amount of reactive power during fault period. Thus, it cannot stabilize larger rating of SCIG. In this paper, a suitable fuzzy logic controller (FLC) is proposed in the cascaded controller of GSC of PMSG in order to increase reactive power injection and thus improve the FRT capability of WF during voltage dip situation due to severe network fault. To evaluate the proposed controller performance, simulation analyses are performed on a modified IEEE nine-bus system. Simulation results clearly show that the proposed method can be a cost-effective solution which can effectively stabilize the larger rating of SCIG compared to conventional PI based control strategy.

The Measuring System for Estimation of Power of Wind Flow Generated by Train Movement and Its Experimental Testing

The measuring system for estimation of power of wind flow generated by the train movement has been created. The advantages of the proposed system are the cheapness and simple design. With its simplicity of design and easy build-up of channels, designed measuring system can be used for a wide range of technical problems. This paper describes the design process, validation and conducting the first field test of this measuring system.

Wind Generator Stabilization with Doubly-Fed Asynchronous Machine

This paper investigates the function of doubly-fed asynchronous machine(DASM)with emphasis placed on its ability to the stabilization of the power system including wind generators.P(active power)and Q(reactive power)compensation from DASM can be regulated independently through secondary-excitation controlling.Simulation results by power system computer aided design(PSCAD)show that DASM can restore the wind-generator system to a normal operating condition rapidly even following severe transmission-line failures.Comparison studies have also been performed between wind turbine pitch control and proposed method.