Control Strategy to Smooth Wind Power Fluctuations of PMSG Wind Turbine based on the Secondary Exponential Moving Average Method

With the rapid increase in the proportion of the installed wind power capacity in the total grid capacity, the state has put forward higher and higher requirements for wind power integration into the grid, among which the most difficult requirement is the zero-voltage ride through (ZVRT) capability of the wind turbine. When the voltage drops deeply, a series of transient processes, such as serious overvoltage, overcurrent, or speed rise, will occur in the motor, which will seriously endanger the safe operation of the wind turbine itself and its control system, and cause large-scale off-grid accident of wind generator. Therefore, it is of great significance to improve the uninterrupted operation ability of the wind turbine. Doubly fed induction generator (DFIG) can achieve the best wind energy tracking control in a wide range of wind speed and has the advantage of flexible power regulation. It is widely used at present, but it is sensitive to the grid voltage. In the current study, the DFIG is taken as the research object. The transient process of the DFIG during a fault is analyzed in detail. The mechanism of the rotor overcurrent and DC bus overvoltage of the DFIG during fault is studied. Additionally, the simulation model is built in DIgSILENT. The active crowbar hardware protection circuit is put into the rotor side of the wind turbine, and the extended state observer and terminal sliding mode control are added to the grid side converter control. Through the cooperative control technology, the rotor overcurrent and DC bus overvoltage can be suppressed to realize the zero-voltage ride-through of the doubly fed wind turbine, and ensure the safe and stable operation of the wind farm. Finally, the simulation results are presented to verify the theoretical analysis and the proposed control strategy.

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An internal parallel capacitor control strategy for DC-link voltage stabilization of PMSG-based wind turbine under various fault conditions

Wind Engineering ◽

10.1177/0309524x211060684 ◽

2021 ◽

pp. 0309524X2110606

Author(s):

Mohamed Metwally Mahmoud ◽

Mohamed M Aly ◽

Hossam S Salama ◽

Abdel-Moamen M Abdel-Rahim

Keyword(s):

Wind Turbine ◽

Wind Power ◽

Control Strategy ◽

Low Voltage ◽

Harmonic Distortion ◽

Synchronous Generator ◽

Parallel Capacitor ◽

Wind Generators ◽

Fast Fourier Transform Analysis ◽

Energy Conversion Systems

In recent years, wind energy conversion systems (WECSs) have been growing rapidly. Due to various advantages, a permanent magnet synchronous generator (PMSG) is an appealing solution among different types of wind generators. As wind power penetration level in the grid increases, wind power impacts the grid and vice versa. The most essential concerns in the system are voltage sag and swell, and grid code compliance, particularly for low voltage ride-through (LVRT) and high voltage ride-through (HVRT) capability, is a pressing necessity. This paper presents a parallel capacitor (PC) control strategy to enhance the LVRT and HVRT capability of PMSG. Furthermore, this study presents a method for the sizing of a PC system for the reduction of the overvoltage of the DC-link during voltage sags and swell. Fast Fourier transform analysis is used to determine the total harmonic distortion (THD) for the injected current into the grid. The obtained results illustrate the effectiveness of the proposed system in keeping the DC-link voltage below the limit, power quality improvement, and increasing the LVRT and HVRT capability. Models of wind turbine, PMSG, and PC control system are built using MATLAB/SIMULINK software.

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Study on Medium-Term and Short-Term Wind Power Forecasting Methods

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.361-363.318 ◽

2013 ◽

Vol 361-363 ◽

pp. 318-322

Author(s):

Gui Zhong Wu ◽

Yuan Biao Zhang ◽

Cheng Su ◽

Yu Jie Liu

Keyword(s):

Neural Network ◽

Wind Power ◽

Average Method ◽

Moving Average ◽

Forecasting Model ◽

Power Prediction ◽

Medium Term ◽

Accuracy Rate ◽

Short Term ◽

Weighted Moving Average

In the paper, the wind power prediction is devided into medium-term forecasts and short-term forecasts. For medium-term forecasts, we use the weighted moving average method and BP neural network forecasting model, while for short-term forecasts, the ARMA model and combination forecasting model based on the maximum entropy principle are used. The application example shows that the weighted moving average method is easy and can precisely obtain the fluctuation trend of the wind power, while the accuracy rate of the BP neural network forecasting model is 91.23%, which is better than the former. The predictive results of the ARMA model are similar with actual trends and its accuracy rate is 88.98%. The combination model integrates the advantages of the BP neural network and ARMA model, and its accuracy rate is up to 92.58%.

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Wind Turbine Anomaly Identification Based on Improved Deep Belief Network with SCADA Data

Mathematical Problems in Engineering ◽

10.1155/2021/8810045 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xiafei Long ◽

Shengqing Li ◽

Xiwen Wu ◽

Zhao Jin

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Wind Power ◽

Wind Turbines ◽

Domain Knowledge ◽

Wind Farm ◽

Prediction Models ◽

Moving Average ◽

Pearson Correlation ◽

Whale Optimization

This article presents a novel fault diagnosis algorithm based on the whale optimization algorithm (WOA)-deep belief networks (DBN) for wind turbines (WTs) using the data collected from the supervisory control and data acquisition (SCADA) system. Through the domain knowledge and Pearson correlation, the input parameters of the prediction models are selected. Three different types of prediction models, namely, the wind turbine, the wind power gearbox, and the wind power generator, are used to predict the health condition of the WT equipment. In this article, the prediction accuracy of the models built with these SCADA sample data is discussed. In order to implement fault monitoring and abnormal state determination of the wind power equipment, the exponential weighted moving average (EWMA) threshold is used to monitor the trend of reconstruction errors. The proposed method is used for 2 MW wind turbines with doubly fed induction generators in a real-world wind farm, and experimental results show that the proposed method is effective in the fault diagnosis of wind turbines.

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Optimal Inertia Reserve and Inertia Control Strategy for Wind Farms

Energies ◽

10.3390/en13051067 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1067

Author(s):

Youming Cai ◽

Zheng Li ◽

Xu Cai

Keyword(s):

Wind Turbine ◽

Wind Power ◽

Control Strategy ◽

Wind Farm ◽

Power Grid ◽

Wind Farms ◽

Exit Point ◽

Inertia Control ◽

The Impact ◽

Inertia Response

It is important to reduce the impact of the high penetration of wind power into the electricity supply for the purposes of the security and stability of the power grid. As such, the inertia capability of wind farms has become an observation index. The existing control modes cannot guarantee the wind turbine to respond to the frequency variation of the grid, hence, it may lead to frequency instability as the penetration of wind power gets much higher. For the stability of the power grid, a simple and applicable method is to realize inertia response by controlling wind farms based on a high-speed communication network. Thus, with the consideration of the inertia released by a wind turbine at its different operating points, the inertia control mechanism of a doubly-fed wind turbine is analyzed firstly in this paper. The optimal exit point of inertia control is discussed. Then, an active power control strategy for wind farms is proposed to reserve the maximum inertia under a given power output constraint. Furthermore, turbines in a wind farm are grouped depending on their inertia capabilities, and a wind farm inertia control strategy for reasonable extraction of inertia is then presented. Finally, the effectiveness of the proposed control strategy is verified by simulation on the RT-LAB (11.3.3, OPAL-RT TECHNOLOGIES, Montreal, Quebec, Canada) platform with detailed models of the wind farm.

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Study on Transient Stability of Circulation Economy Power System with High Penetration of Wind Power

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.197 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 197-201

Author(s):

Jian Ping Liu ◽

Chun Liu ◽

Ting Rui Lu ◽

Hai Yan Tang ◽

Xin Shou Tian ◽

...

Keyword(s):

Optimal Control ◽

Power System ◽

Wind Turbine ◽

Wind Power ◽

Control Strategy ◽

Transient Stability ◽

Power Grid ◽

Demonstration Project ◽

Optimal Control Strategy ◽

High Penetration

On the basic of deeply analyzing the characteristic of circulation economy power system with high penetration of wind power, and researching the model and transient characteristics of DFIG, the problem on transient stability of circulation economy power grid was given through simulating and analyzing the huolinhe circulation economy demonstration project on wind power consumed in the local in the paper. At last, an optimal control strategy of wind turbine based on circulation economy power system with high penetration of wind power was proposed.

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Study on the Control Strategy of Wind Farms Connected to Power Grid by VSC-HVDC

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.3446 ◽

2014 ◽

Vol 687-691 ◽

pp. 3446-3449

Author(s):

Min Xue ◽

Hui Ping Zheng ◽

Shu Yong Song ◽

Xin Jie Hao

Keyword(s):

Power System ◽

Wind Turbine ◽

Wind Power ◽

Control Strategy ◽

Wind Farms ◽

Long Distance ◽

Voltage Level ◽

Hvdc Transmission ◽

The Stability ◽

High Degree

The technology of VSC-HVDC transmission has a high degree of flexibility and controllability.This paper researches the control strategy of VSC-HVDC and the operating principle of the wind turbine, which can effectively enhance the stability of power system in the region of wind farms, improve the long-distance transmission capacity of wind power and stabilize the voltage level of public connection point.

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