Control Strategy of DC Link Voltage Flywheel Energy Storage for Non Grid Connected Wind Turbines Based on Fuzzy Control

Abstract

The large-scale development of wind power is an important means to reduce greenhouse gas emissions, alleviate environmental pollution and improve the utilization rate of renewable energy. At the same time, large-scale non grid connected wind power generation theory avoids the technical difficulties of wind power integration [1]. However, due to the randomness and uncontrollability of wind energy, the output power of the wind power generation system will fluctuate accordingly [2]. Therefore, the corresponding energy storage devices are arranged in the non-grid-connected wind power generation system to ensure the power quality, and it has become the key to full utilization of renewable energy. In the case of wind speed fluctuation, the DC bus control strategy of the wind turbine is proposed in this paper. It can reduce the impact on the unit converter and the power load; this ensures safe and stable operation of non-grid connected wind turbines.

Retracted: Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality‐sensitive hashing

“…15 In the case of wind speed fluctuation, the DC bus control strategy of the wind turbine is proposed in this paper. 16 To tackle this problem, least squares support vector machine (LSSVM) is developed by using equality constraints instead of inequality constraints for SVM. 17 The LSSVM model based on different kernel functions is proposed to forecast the wind power, and the gravitational search algorithm (GSA) is employed to optimize the hyperparameters of LSSVM.…”

“…But it takes a relatively long time to implement the optimization process 15. In the case of wind speed fluctuation, the DC bus control strategy of the wind turbine is proposed in this paper 16. To tackle this problem, least squares support vector machine (LSSVM) is developed by using equality constraints instead of inequality constraints for SVM 17.…”

Effect of PI controllers’ parameters on machine‐network interaction of grid‐connected PMSG system

“…The output of wind power, PV and other renewable energy sources has the characteristics of strong volatility, randomness and time series [16]. In the case of wind speed fluctuation, the DC bus control strategy of the wind turbine is proposed in this paper [17]. Least-squares support-vector machine combined with selforganising maps is developed for time series forecasting [18].…”

“…ΔX PMSG , A PMSG , B PMSG and Δu PMSG are, respectively, the small-signal model of the state vector (17 × 1 order), the system matrix (17 × 17 order), the input matrix (17 × 5 order) and the input vector (5 × 1 order); subscript '0' represents the value of the steady-state point; ω b is the reference frequency of the system; H 1 , H 2 are the inertia constants of the masses 1 and 2, respectively; D 1 , D 2 are the self-damping coefficients of the masses 1 and 2, Due to the adoption of 'back-to-back' full power converter, there are two PI controllers in the control unit of the rectifier, so the small-signal system still has17 state variables Δθ 2 , Δω 1 , Δω 2 , Δψ ds , Δψ qs , Δx 1 , Δx 2 , ΔU DC , Δx 3 , Δx 4 , Δx 5 , Δx 6 , Δi Lx , Δi Ly , Δu cx , Δu cy…”

Study on Fundamental Technology of Multi Directional Vibration Piezoelectric Power Generation

“…In the case of wind speed fluctuation, the DC bus control strategy of the wind turbine is proposed in this paper. It can reduce the impact on the unit converter and the power load; this ensures safe and stable operation of non-grid connected wind turbines [16].…”