Design and implementation of an intelligent digital pitch controller for digital hydraulic pitch system hardware-in-the-loop simulator of wind turbine
TL;DR: A study shows that IDPC controlled DHPS exhibits better performance than an ML-Proportional Integral (PI) controlled HPS with proportional flow control valve.
Abstract: Digital hydraulics is a potential technology for the Hydraulic Pitch System (HPS) in Wind Turbine (WT). Digital Hydraulics Pitch System (DHPS) uses Digital Flow Control Units (DFCU) to develop the ...
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TL;DR: In this article , a chaotic-based moth swarm algorithm (CMSA) is introduced by incorporating chaos into the MSA algorithm to overcome slow convergence feature of MSA, and a newly developed fractional order tilt integral derivative double integral filter plus (FOTIDF-II) controller is proposed in this research work.
Abstract: In the upcoming era, when conventional sources will mostly be replaced by renewable energy sources, engineers may face huge problem during controller design. For this large interconnected hybrid automatic generation control (AGC) system, high proficient and intelligent control techniques are needed. Hence, a newly developed fractional order tilt integral derivative double integral filter plus (FOTIDF-II) controller has been proposed in this research work. To overcome slow convergence feature of moth swarm algorithm (MSA), chaotic-based moth swarm algorithm (CMSA) is introduced by incorporating chaos into the MSA algorithm. Afterward, to upgrade the performance of AGC, the newly proposed controller is incorporated to the said system. The gains and fractional order parameters such as order of integrator, differentiator and filter coefficients of the proposed controller are optimized using CMSA optimizing technique. It is observed that the dynamic performance of the proposed CMSA optimized FOTIDF-II controller is better than the CMSA optimized PID. Furthermore, the performance of CMSA in the presence of different flexible AC transmission system (FACTs) and storage devices such as distributed power flow controller (DPFC), redox flow battery (RFB), and Tesla power wall (TPWA) battery (i.e Storage devices) along with proposed controller is compared with MSA-based PID controller with same combination of FACTs controller. Afterwards, superiority of the proposed controller has been judged in three area renewable-based AGC system under realistic environment.
3 citations
TL;DR: DC microgrids (DCMGs) are gaining popularity mainly in shipboards and isolated power systems for their modest control and higher efficiency.
Abstract: DC microgrids (DCMGs) are gaining popularity mainly in shipboards and isolated power systems for their modest control and higher efficiency. However, the newer conception and less literature are fo...
3 citations
TL;DR: In this article , a high-speed digital hydraulic valve-based Digital Fluid Power Pitch System (DFPPS) test rig was proposed to improve the load mitigation performance without compromising the generator performance at high wind speeds.
Abstract: ABSTRACT The pitch control system plays a vital role in generating rated power and mitigates mechanical loads during strong wind gusts. Generally, the hydraulic pitch system deployed in a large-scale wind turbine uses a proportional/servo valve to adjust the pitch angle. However, these valves suffer from low operating efficiency which leads to poor pitching performance. Thus, a high-speed digital hydraulic valve-based Digital Fluid Power Pitch System (DFPPS) test rig was proposed in this paper. Moreover, the conventional controller was not able to produce adequate pitching performance during high wind flux. Therefore, a machine learning-based pitch controller was implemented in the developed test rig. The developed DFPPS test rig comprises simulation loop (Wind Generator System (WGS) model, pitch gear model, Predictive Load Mitigation Controller (PLMC), and pitch load model), and DFPPS-hardware interfaced through an I/O interface. Here, PLMC was developed to improve the load mitigation performance without compromising the generator performance at high wind speeds. The proposed PLMC is a cascade arrangement of Recurrent Elman Neural Network (RENN) tuned Fractional-Order Proportional Integral Derivative (FOPID). Further, for collecting the optimal datasets to train the RENNs in PLMC, a Customized Particle Swarm Optimization (CPSO) algorithm was utilized. Two experiments are performed considering the developed WGS model and benchmark Fatigue Aero-elastic Structure Turbulence (FAST) simulator deployed in the DFPPS test rig. The experimental results of PLMC (experiment) were compared to simulation results PLMC (simulation). Further, the experimental output of the PLMC was compared to established controllers in the literature (Radial Basis Function tuned Fractional-Order Proportional Integral Derivative (RBF-FOPID) and baseline Proportional Integral (PI)). In the experiment using FAST, relative to RBF-FOPID, the PLMC (experiment) decreased the Standard Deviation (STD) of blade and tower moment by 81.5% and 81.4%, respectively. Consequently, compared to baseline PI, the PLMC (experiment) reduced the STD of blade and tower moment by 85.6%, and 84.4%, respectively. The proposed controller effectively mitigated the load and also the generator performance was close to the rated value than its counterparts.
1 citations
Journal Article•
TL;DR: In this paper, a neuro-fuzzy Digital Hydraulic Pitch Controller (NFDPC) was employed in the hydraulic pitch system of a wind turbine for uniform power generation at above-rated wind speeds.
Abstract: Digital Hydraulics has been implemented in the hydraulic pitch system of Wind Turbine (WT) which aids in uniform power generation at above-rated wind speeds A novel Digital Hydraulic Pitch System (DHPS) model which uses a hydraulic motor as the end actuator was established and a wind generator system model was developed Further, a Neuro-Fuzzy Digital Pitch Controller (NFDPC) with two distinct neuro-fuzzy controllers was employed in the developed models Two comparative studies are conducted and in these studies, the proposed NFDPC was compared with Proportional Integral Derivative Cascade Controller (PIDCC) where PID is implemented in inner and outer loop In the first comparative study (considering custom wind speed), results revealed that the proposed controller maintains rated generator speed than PIDCC The Root Mean Square Error (RMSE) (between rated speed and generated speed) of the generator was 123 and 373 for the proposed and PIDCC, respectively Thus, uniform power was generated Consequently, in the second case study (real-time effective wind speed), the proposed controller maintains optimal generator speed with a minimal RMSE of 066 when compared to PIDCC with an RMSE of 355 Thus, uniform power was developed by the proposed controller Therefore from these results, the proposed controller generates uniform power at above-rated wind speed with large wind fluctuations
1 citations
References
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01 Dec 2007
TL;DR: In this paper, the authors describe the development, verification, and application of a comprehensive simulation tool for modeling coupled dynamic responses of offshore floating wind turbines, which is used to simulate the dynamic response of wind turbines.
Abstract: This report describes the development, verification, and application of a comprehensive simulation tool for modeling coupled dynamic responses of offshore floating wind turbines.
677 citations
TL;DR: This paper describes mathematical and software developments needed for the effective solution of differential algebraic equations of index 1 in the integrated computing environment MATLAB and the dynamic simulation package Simulink.
Abstract: This paper describes mathematical and software developments needed for the effective solution of differential algebraic equations of index 1 in the integrated computing environment MATLAB and the dynamic simulation package Simulink. The developments are applicable to other problem-solving environments and some are applicable to general scientific computation.
430 citations
TL;DR: In this paper, a nonlinear approach, using a two-mass model and a wind speed estimator, for variable-speed wind turbine (WT) control is presented. But, their performance is weak, as the dynamics aspects of the wind and aeroturbine are not taken into consideration.
Abstract: The paper presents a nonlinear approach, using a two-mass model and a wind speed estimator, for variable-speed wind turbine (WT) control. The use of a two-mass model is motivated by the need to deal with flexible modes induced by the low-speed shaft stiffness. The main objective of the proposed controllers is the wind power capture optimization while limiting transient loads on the drive-train components. This paper starts by an adaptation of some existing control strategies. However, their performance are weak, as the dynamics aspects of the wind and aeroturbine are not taken into consideration. In order to bring some improvements, nonlinear static and dynamic state feedback controllers, with a wind speed estimator, are then proposed. Concerning the wind speed estimator, the idea behind this is to exploit the WT dynamics by itself as a measurement device. All these methods have been first tested and validated using an aeroelastic WT simulator. A comparative study between the proposed controllers is performed. The results show better performance for the nonlinear dynamic controller with estimator in comparison with the adapted existing methods.
330 citations
TL;DR: An exhaustive review of artificial neural networks used in wind energy systems is presented, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases.
305 citations
TL;DR: A new algorithm with neuron-by-neuron computation methods for the gradient vector and the Jacobian matrix that can handle networks with arbitrarily connected neurons, which can be more efficient than commonly used multilayer perceptron networks.
Abstract: This paper describes a new algorithm with neuron-by-neuron computation methods for the gradient vector and the Jacobian matrix. The algorithm can handle networks with arbitrarily connected neurons. The training speed is comparable with the Levenberg-Marquardt algorithm, which is currently considered by many as the fastest algorithm for neural network training. More importantly, it is shown that the computation of the Jacobian, which is required for second-order algorithms, has a similar computation complexity as the computation of the gradient for first-order learning methods. This new algorithm is implemented in the newly developed software, Neural Network Trainer, which has unique capabilities of handling arbitrarily connected networks. These networks with connections across layers can be more efficient than commonly used multilayer perceptron networks.
148 citations