RETRACTED: A review of intelligent algorithm approaches and neural-fuzzy stability               criteria for time-delay tension leg platform systems

Frequent variation in the wind flow affects the Wind Turbine (WT) to generate fluctuating output power and this can negatively impact the entire power network. This paper aims at modelling an Enhanced-Elman Neural Network (EENN) based pitch angle controller to mitigate the output power fluctuation in a grid connected Wind Energy Conversion System. The outstanding aspect of the proposed controller is that, they can smoothen the output power fluctuation, when the wind speed is above or below rated speed of the WT. The proposed EENN pitch controller is trained online using Gradient Descent (GD) algorithm and the network learning is carried out using Customized-Particle swarm optimization (C-PSO) algorithm. The C-PSO is adopted, in order to increase the learning capability of the training process by adjusting the networks learning rate. Further, the node connecting weights of the EENN is updated by means of GD algorithm using back-propagation methodology. The performance of the proposed controller is analysed using the simulation studies carried out in MATLAB /Simulink environment.

Section: Introductionmentioning

confidence: 99%

An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS

Sitharthan¹,

Geethanjali²

2016

“…On the other hand, artificial intelligence techniques, such as fuzzy logic, neural network, and genetic algorithms, have recently shown a lot of promise in the application of wind turbines (Ganesh and Patnaik 2012;Chen 2013Chen , 2014Chen et al, 2013;Kuo et al, 2013;Liu et al, 2013;Mitchell et al, 2013;Shanmugasundram et al, 2013;Wang et al, 2014;Eski and Temurlenk, 2013;). For example, Kasiri et al (2012) proposed fuzzy rule extraction from neural network using a genetic algorithm (FRENGA) which rejects wind disturbance in wind energy conversion systems (WECS) input with pitch angle control generation.…”

Section: Introductionmentioning

confidence: 99%

A CMAC-PID based on pitch angle controller for direct drive permanent magnet synchronous wind turbine

Chen

Zhou

2014

The efficiency of wind power conversions systems can be greatly improved using an appropriate control algorithm. This paper deals with a cerebellar model articulation controller coupling with a proportional-integral-derivative controller for a direct drive permanent magnet synchronous wind turbine generator. First, the aerodynamic characteristics of the wind turbine have been studied in detail. Moreover, the transfer function from pitch angle to the speed of the wind turbine is established. Finally, some tests on the proposed method are carried out including random wind speed using numerical simulation. From these tests, several operating conditions are simulated, and satisfactory results are obtained.

“…Like in many other models where optimization algorithms can be used to find parameters, in T-S fuzzy models, optimization algorithms can be used to find the fuzzy consequent parameters. For example, algorithms such as neural networks (NNs) learning algorithms and genetic algorithms (GAs) have shown a good adaptation to search for optimal T-S fuzzy model parameters (Chen, 2014;Mendes et al, 2012). Cazarez-Castro et al (2010) presented a hybrid architecture, which combines Type-1 or Type-2 fuzzy logic system and GAs for the optimization of the membership function parameters.…”

Section: Introductionmentioning

confidence: 99%

A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems

Rastegar

Araújo

Mendes

2014

This paper proposes a new unsupervised fuzzy clustering algorithm (NUFCA) to construct a novel online evolving Takagi–Sugeno (T-S) fuzzy model identification method and an adaptive predictive process control methodology. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. The NUFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modelling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system; then the recursive least squares method is exploited to obtain initialization type consequent parameters and to construct a method for on-line fuzzy model identification. The integration of the proposed adaptive identification method with the generalized predictive control results in an effective adaptive predictive fuzzy control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP); and to control a simulated continuous stirred tank reactor (CSTR), and a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the generalized predictive controller. It is also shown that the algorithm is robust to changes in the initial parameters, and to unexpected disturbances.