Sensor-less maximum power extraction control of a hydrostatic tidal turbine based on adaptive extreme learning machine

In this paper, a hydrostatic tidal turbine (HTT) is designed and modelled, which uses more reliable hydrostatic transmission to replace existing fixed ratio gearbox transmission. The HTT dynamic model is derived by integrating governing equations of all the components of the hydraulic machine. A nonlinear observer is proposed to predict the turbine torque and tidal speeds in real time based on extreme learning machine (ELM). A sensor-less double integral sliding mode controller is then designed for the HTT to achieve the maximum power extraction in the presence of large parametric uncertainties and nonlinearities. Simscape design experiments are conducted to verify the proposed design, model and control system, which show that the proposed control system can efficiently achieve the maximum power extraction and has much better performance than conventional control. Unlike the existing works on ELM, the weights and biases in the ELM are updated online continuously. Furthermore, the overall stability of the controlled HTT system including the ELM is proved and the selection criteria for ELM learning rates is derived. The proposed sensor-less control system has prominent advantages in robustness and accuracy, and is also easy to implement in practice

Development of a robust nonlinear pitch angle controller for a redesigned 5MW wind turbine blade tip

Power in wind turbines are traditionally controlled by varying the pitch angle at high wind speeds in region 3 of the wind turbine operation. The pitch angles controllers are normally driven by electrical or hydraulic actuators. The motivation of this research is to design and implement a pitch angle control strategy at the outer section of the blade via a separated pitch control at blade tip (SePCaT). A pneumatic actuator is implemented to drive the pitch angle control mechanism by incorporating pneumatic actuated muscles (PAM) due to its high power/mass ratio, high specific work, and good contraction ratio while maintaining low weight at the tip of the blade. A sliding mode controller (SMC) is modeled and implemented on a redesigned 5MW wind turbine numerically. The hypothesis is that the SePCaT control strategy is effective and satisfactory pitch angle trajectory tracking is achievable. The method is adopted, the system is modeled, and the response was observed by subjecting the model dynamics to desired pitch angle trajectories. Initially comparative controller response with respect to desired trajectory revealed satisfactory pitch angle tracking but further investigation revealed chattering characteristics which was minimized by incorporating a saturation function. SePCaT offers an effective pitch angle control strategy which is smaller, lighter, reliable and efficient

Comparative study of back-stepping controller and super twisting sliding mode controller for indirect power control of wind generator

© 2021 Springer. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s13198-019-00905-7This paper presents the application nonlinear control to regulate the rotor currents and control the active and reactive powers generated by the Doubly Fed Induction Generator used in the Wind Energy Conversion System (WECS). The proposed control strategies are based on Lyapunov stability theory and include back-stepping control (BSC) and super-twisting sliding mode control. The overall WECS model and control scheme are developed in MATLAB/Simulink and the simulation results have shown that the BSC leads to superior performance and improved transient response as compared to the STSMC controller.Peer reviewe

Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Actuator fault tolerant offshore wind turbine load mitigation control

Offshore wind turbine (OWT) rotors have large diameters with flexible blade structures which are subject to asymmetrical loads caused by blade flapping and turbulent or unsteady wind flow. Rotor imbalance inevitably leads to enhanced fatigue of blade rotor hub and tower structures. Hence, to enhance the life of the OWT and maintain good power conversion the unbalanced loading requires a reliable mitigation strategy, typically using a combination of Individual Pitch Control (IPC) and Collective Pitch Control (CPC). Increased pitch motion resulting from IPC activity can increase the possibility of pitch actuator faults and the resulting load imbalance results in loss of power and enhanced fatigue. This has accelerated the emergence of new research areas combining IPC with the fault tolerant control (FTC)-based fault compensation, a so-called FTC and IPC “co-design” system. A related research challenge is the clear need to enhance the robustness of the FTC IPC “co-design” to some dynamic uncertainty and unwanted disturbance. In this work a Bayesian optimization-based pitch controller using Proportional–Integral (PI) control is proposed to improve pitch control robustness. This is achieved using a systematic search for optimal controller coefficients by evaluating a Gaussian process model between the designed objective function and the coefficients. The pitch actuator faults are estimated and compensated using a robust unknown input observer (UIO)-based FTC scheme. The robustness and effectiveness of this “co-design” scheme are verified using Monte Carlo simulations applied to the 5MW NREL FAST WT benchmark system. The results show clearly (a) the effectiveness of the load mitigation control for a wide range of wind loading conditions, (b) the effect of actuator faults on the load mitigation performance and (c) the recovery to normal load mitigation, subject to FTC action

Output power levelling for DFIG wind turbine system using intelligent pitch angle control

Blade pitch angle control, as an indispensable part of wind turbine, plays a part in getting the desired power. In this regard, several pitch angle control methods have been proposed in order to limit aerodynamic power gained from the wind turbine system (WTS) in the high-windspeed regions. In this paper, intelligent control methods are applied to control the blade pitch angle of doubly-fed induction generator (DFIG) WTS. Conventional fuzzy logic and neuro-fuzzyparticle swarm optimization controllers are used to get the appropriate wind power, where fuzzy inference system is based on fuzzy c-means clustering algorithm. It reduces the extra repetitive rules in fuzzy structure which in turn would reduce the complexity in neuro-fuzzy network with maximizing efficiently. In comparing the controllers at any given wind speed, adaptive neuro-fuzzy inference systems controller involving both mechanical power and rotor speed revealed better performance to maintain the aerodynamic power and rotor speed at the rated value. The effectiveness of the proposed method is verified by simulation results for a 9 MW DFIG WTS

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Fault-tolerant load reduction control for large offshore wind turbines

Offshore wind turbines suffer from asymmetrical loading (blades, tower etc.), leading to enhanced structural fatigue. As well as asymmetrical loading different types of faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance and enhanced fatigue. Individual pitch control (IPC) provides an important method to achieve mitigation of rotor asymmetric loads, but this may be accompanied by a resulting enhancement of pitch movement leading to increased possibility of pitch system faults, which negative effects on IPC performance.This thesis focuses on combining the fault tolerant control (FTC) techniques with load reduction strategies by a more intelligent pitch control system (i.e. collective pitch control and IPC) for offshore wind turbines in a system level to reduce the operation & maintenance costs and improve the system reliability. The scenario of load mitigation is analogous to the FTC problem because the action of rotor/tower bending can be considered as a fault effect. The essential concept is to attempt to account for all the "fault effects" in the rotor and tower systems which can weaken the effect of bending moment reduction through the use of IPC.Motivated by the above, this thesis focuses on four aspects to fill the gap of the combination between FTC and IPC schemes. Firstly, a preview control system using model predictive control with future wind speed is proposed, which could be a possible alternative to using LiDAR technology when using preview control for load reduction. Secondly, a multivariable IPC controller for both blade and tower load mitigation considering the inherent couplings is investigated. Thirdly, appropriate control-based fault monitoring strategies including fault detection and fault estimation FE-based FTC scheme are proposed for several different pitch actuator/sensor faults. Furthermore, the combined analysis of an FE-based FTC strategy with the IPC system at a system level is provided and the robustness of the proposed strategy is verified