Showing 5 results for Wind Energy
M. Heidari,
Volume 13, Issue 3 (9-2017)
Abstract
In this paper, a new type of multi-variable compensation control method for the wind energy conversion systems (WECS) is presented. Based on wind energy conversion systems, combining artificial neural network (ANN) control and PID, a new type of PID NN intelligent controller for steady state torque of the wind generator is designed, by which the steady state torque output is regulated to track the optimal curve of wind power factor and the blade pitch angle is regulated to keep the stable power output. Also, the LPV model of the WECS, LPV compensator for the wind generator is designed to effectively compensate output of the wind generator torque and the blade pitch angle. Finally, simulation models of the control system based on a realistic model of a 8kw wind turbines are built up based on the Dspace platform. The results show that the proposed method can reduce interferences caused by disturbed parameters of the WECS, mechanical shocks of the wind generator speed are reduced while capturing the largest wind energyfluctuation range of wind generator power output is reduced, and the working efficiency of the variable pitch servo system is improved.
S. Heshmatian, D. Arab Khaburi, M. Khosravi, A. Kazemi,
Volume 14, Issue 1 (3-2018)
Abstract
Wind energy is one of the most promising renewable energy resources. Due to instantaneous variations of the wind speed, an appropriate Maximum Power Point Tracking (MPPT) method is necessary for maximizing the captured energy from the wind at different speeds. The most commonly used MPPT algorithms are Tip Speed Ratio (TSR), Power Signal Feedback (PSF), Optimal Torque Control (OTC) and Hill Climbing Search (HCS). Each of these algorithms has some advantages and also some major drawbacks. In this paper, a novel hybrid MPPT algorithm is proposed which modifies the conventional methods in a way that eliminates their drawbacks and yields an improved performance. This proposed algorithm is faster in tracking the maximum power point and provides a more accurate response with lower steady state error. Moreover, it presents a great performance under conditions with intensive wind speed variations. The studied Wind Energy Conversion System (WECS) consists of a Permanent Magnet Synchronous Generator (PMSG) connected to the dc link through a Pulse-Width Modulated (PWM) rectifier. The proposed algorithm and the conventional methods are applied to this WECS and their performances are compared using the simulation results. These results approve the satisfactory performance of the proposed algorithm and its notable advantages over the conventional methods.
H. Ahmadi, A. Rajaei, M. Nayeripour, M. Ghani,
Volume 14, Issue 4 (12-2018)
Abstract
Considering the increasing usage of the clean and renewable energies, wind energy has been saliently improved throughout the world as one of the most desired energies. Besides, most power houses and wind turbines work based on the doubly-fed induction generator (DFIG). Based on the structure and the how-ness of DFIG connection to the grid, two cases may decrease the performance of the DFIG. These two cases are known as a fault and a low-voltage in the grid. In the present paper, a hybrid method is proposed based on the multi-objective algorithm of krill and the fuzzy controller to improve the low-voltage ride through (LVRT) and the fault ride through (FRT). In this method, first by using the optimal quantities algorithm, the PI controllers’ coefficients and two variables which are equal to the demagnetize current have been calculated for different conditions of fault and low voltage. Then, these coefficients were given to the fuzzy controller. This controller diagnosed the grid condition based on the stator voltage and then it applied the proper coefficients to the control system regarding the diagnosed condition. To test the proposed method, a DFIG is implemented by taking the best advantages of the proposed method; additionally, the system performance has been tested in fault and low voltage conditions.
E. Heydari, M. Rafiee, M. Pichan,
Volume 14, Issue 4 (12-2018)
Abstract
Among a multitude of diverse control methods proposed for doubly fed induction generator (DFIG) based-wind energy conversion systems, direct power control (DPC) method has demonstrated superior dynamic performance and robustness in presence of disturbances. However, DPC is not a flawless method and shortcomings like necessity for high sampling frequency, high-speed sensors and less noise-affected sampling circuit need to be mitigated by utilizing fuzzy controllers. Parameter setting in a fuzzy controller plays a vital role, especially under non-ideal grid conditions. In this paper, a fuzzy-genetic algorithm-based direct power control (FGA-DPC) method is proposed for DFIG, while, the parameters of the fuzzy controller are optimized by genetic algorithm. The objective of the optimization is to minimize the stator active and reactive power errors to increase the precision of reference tracking. The objectives of the controller are also optimizing active power absorption based on the zone of operation and adjustment of reactive power according to grid requirements. The proposed method improves the overall precision and speed of transient response as well as significantly reducing power oscillations under non-ideal grid conditions. Finally, to demonstrate the effectiveness of the proposed method, extensive simulations are performed in Matlab/Simulink under different conditions.
P. Bhat Nempu, J. N. Sabhahit,
Volume 16, Issue 4 (12-2020)
Abstract
The hybrid AC-DC microgrid (HMG) architecture has the merits of both DC and AC coupled structures. Microgrids are subject to intermittence when the renewable sources are used. In the HMG, since power fluctuations occur on both subgrids due to varying load and unpredictable power generation from renewable sources, proper voltage and frequency regulation is the critical issue. This article proposes a unique method for operating a microgrid (MG) comprising of PV array, wind energy system (WES), fuel cell (FC), and battery in HMG configuration. The control scheme of the interlinking converter (ILC) regulates frequency, voltage, and power flow amongst the subgrids. Power management in the HMG is investigated under different scenarios. Proper power management is accomplished within the individual subgrids and among the subgrids by the control techniques adopted in the HMG. The system voltage and frequency deviations are found to be minimized when the FC system acts as the backup source for DC subgrid, reducing the power flow through the ILC.
