Adaptive fault-tolerant control of variable pitch system of wind power generator based on clustering-type fuzzy neural network

2020 ◽  
Author(s):  
Hongwei Wang ◽  
Qian Zhang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Fuzzy Neural Network ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Power Generator ◽  
Variable Pitch ◽  
Fuzzy Neural ◽  
Wind Power Generator ◽  
Pitch System

scholarly journals Intelligent Wind Power Smoothing Control using Fuzzy Neural Network

2018 ◽  
Vol 69 ◽  
pp. 01006
Author(s):  
Faa-Jeng Lin ◽  
Shih-Gang Chen ◽  
Jin-Kuan Chang
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Digital Signal Processor ◽  
Fuzzy Neural Network ◽  
Storage System ◽  
Minimum Energy ◽  
Power Generator ◽  
Power Smoothing ◽  
Fuzzy Neural ◽  
Wind Power Generator

An intelligent wind power smoothing control using fuzzy neural network (FNN) is proposed in this study. First, the modeling of wind power generator and the designed battery energy storage system (BESS) are introduced. The BESS is consisted of a bidirectional interleaved DC/DC converter and a 3-arm 3-level inverter. Then, the network structure of the FNN and its online learning algorithms are described in detail. Moreover, actual wind data is adopted as the input to the designed wind power generator model. Furthermore, the three-phase output currents of the wind power generator are converted to dq-axis current components. The resulted q-axis current is the input of the FNN power smoothing control and the output is a gentle wind power curve to achieve the effect of wind power smoothing. The difference of the actual wind power and smoothed power is supplied by the BESS. Comparing to the other smoothing methods, a minimum energy capacity of the BESS with a small fluctuation of the grid power can be achieved by the FNN power smoothing control. In the experimentation, a digital signal processor (DSP) based BESS is built using two TMS320F28335. From the experimental results of various wind variation sceneries, the effectiveness of the proposed intelligent wind power smoothing control is verified.

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

scholarly journals Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Network for Quadcopter

2021 ◽  
Vol 15 (1) ◽  
pp. 109-122
Author(s):  
Dejie Li ◽  
◽  
Pu Yang ◽  
Zhangxi Liu ◽  
Zixin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Failure Model ◽  
Aircraft Control ◽  
Actuator Failure ◽  
Fuzzy Neural ◽  
The Stability

This paper proposes a fault-tolerant aircraft control method based on a self-constructed fuzzy neural network for quadcopters with multiple actuator faults. We first introduce the actuator failure model and the model uncertainty. Subsequently, we establish a framework for a self-constructed fuzzy neural network observer with an adaptive rate to obtain the estimated value of the nonlinear term of the module uncertainty. We also design a multivariable sliding mode fault-tolerant controller to ensure the stability of the aircraft under this fault condition. Finally, we conduct experiments using the Pixhawk 4 flight controller installed on the QBall-X4 UAV experimental platform, such that the use of the flight controller’s fault coprocessor and redundant sensor design reduces the crash that occurs during the debugging of the control algorithm. Compared to the existing intelligent fault-tolerant control technology, our proposed method employs fewer fuzzy rules, and the number of these rules can be adaptively adjusted when the system model changes. In the experimental test, the aircraft was still able to fly stably under multi-actuator failure and interference conditions, thereby proving the stability of the proposed controller.

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