The Effects of Controls on Fatigue Loads in Two-Bladed Teetered Rotor Wind Turbines

1996 ◽  
Vol 118 (4) ◽  
pp. 228-234 ◽  
Author(s):  
K. C. Wu ◽  
R. De La Guardia
Keyword(s):  
Fuzzy Logic ◽  
Wind Turbine ◽  
Fuzzy Logic Controller ◽  
El Paso ◽  
Operating Point ◽  
Simulation Data ◽  
Satisfactory Performance ◽  
Full State ◽  
Power Regulation ◽  
Fatigue Loads

This paper presents a quantitative analysis of the fatigue loads in a down wind, yaw-controlled, fixed pitch, two-bladed teetered-rotor wind turbine using proportional-integral, full-state optimal, and fuzzy logic controllers. Time-domain simulation data is generated using the EASY5x/WT software developed at the University of Texas at El Paso. The simulation data reveal that the choice of controller type, or the controller dynamics, can play a very important role in the fatigue life of a wind turbine and should be considered early in the design process of the wind turbine. In summary, the fuzzy logic controller is the most robust controller under a wide regime of wind conditions. It provides the best overall performance in terms of power regulation capability and minimum fatigue loads. The optimal controller with a full-state Kalman filter observer provides a satisfactory performance interms of power regulation capability and loads when the operating condition is close to the design point at which the controller was optimized. It fails to regulate the power output when the actual operating point deviated too far, about 30 percent in our computer simulations, from the designed operating point. The PI controller provided satisfactory performance in power regulation. However, it produced the worst fatigue loads to the wind turbine among the three controllers.

scholarly journals Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

2015 ◽  
Vol 64 (2) ◽  
pp. 291-314 ◽  
Author(s):  
Maziar Izadbakhsh ◽  
Alireza Rezvani ◽  
Majid Gandomkar
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Hybrid System ◽  
Pitch Angle ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Control Technique ◽  
Power Curves

Abstract In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator

Data-Tuned Fuzzy Logic Controller Applied to a Horizontal Axis Wind System

2020 ◽  
Author(s):  
Lorenzo Dambrosio
Keyword(s):  
Fuzzy Logic ◽  
Wind Turbine ◽  
Control Algorithm ◽  
Fuzzy Logic Controller ◽  
Tracking Error ◽  
Synchronous Generator ◽  
Horizontal Axis ◽  
Fuzzy Rules ◽  
Wind System ◽  
Horizontal Axis Wind Turbine

Abstract This paper deals with the control problem concerning the output voltage frequency and amplitude regulation of a wind system power plant not connected to the supply grid. The wind system configuration includes a horizontal-axis wind-turbine which drives a synchronous generator. An appropriate modeling approach has been adopted for both the wind-turbine and the synchronous generator. The proposed controller makes use of the fuzzy logic environment in order to take advantage of the wind plant system informations integrated into a limited number of equilibrium condition points (input variable - output variable pairs). The fuzzy logic controller described in the present paper merges the most appropriate fuzzy rules clusters, based on the steady state working conditions. Then, thanks to a Least Square Estimator algorithm, the proposed control algorithm evaluates, for each sample time, the linear relation between control law correction and control tracking error levels. In order to demonstrate robustness of the suggested fuzzy control algorithm, two sets of results have been provided: the first one consider a fuzzy base with equally spaced rules, whereas, in the second set results, the number of fuzzy rules is reduced by a 25%.

A Nonlinear Fuzzy Logic Controller Developed for an Autonomous Surface Boat

2005 ◽  
Author(s):  
Thomas A. Bean ◽  
Akira Okamoto ◽  
John R. Canning ◽  
Dean B. Edwards
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Optimization Method ◽  
Operating Point ◽  
Linear Feedback ◽  
Control Law ◽  
Linear Feedback Control ◽  
Linear Controller ◽  
Optimal Linear ◽  
Optimal Set

This paper presents an optimized nonlinear fuzzy logic controller designed for an autonomous surface craft and describes the process by which it was found. The nonlinear fuzzy logic controller described herein was developed to maintain the linear feedback control of an optimal set of controller gains when the state is near the operating point. The simplex optimization method was utilized to find the optimal fuzzy logic parameters that define the shape of the control law away from the normal operating point. The resultant controller showed approximately a 20% improvement over the optimal linear controller.

Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation

2020 ◽  
Vol 265 ◽  
pp. 121659
Author(s):  
O. Zamzoum ◽  
A. Derouich ◽  
S. Motahhir ◽  
Y. El Mourabit ◽  
A. El Ghzizal
Keyword(s):  
Fuzzy Logic ◽  
Performance Analysis ◽  
Wind Turbine ◽  
Fuzzy Logic Controller ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy Logic ◽  
Robust Adaptive

scholarly journals An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1457 ◽  
Author(s):  
Shehab Al-Sakkaf ◽  
Mahmoud Kassas ◽  
Muhammad Khalid ◽  
Mohammad A. Abido
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Fuel Cell ◽  
Wind Turbine ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Economic Dispatch ◽  
Energy Management System ◽  
Battery Energy Storage ◽  
Battery Energy

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

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