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

Quantification of Dynamic Parameters of Flexible Rotor Partially Levitated on Active Magnetic Bearing

An unavoidable fault that generally present in the rotor system is residual unbalance, and plenty of research has been carried out to develop off-line balancing techniques in the past. But, as present industrial need is moving towards a very high speed (supercritical) and light weight drivelines, it is important to use on-line balancing techniques instead of off-line balancing. Therefore, present work is focused on the on-linebalancing of a rotor system by estimating the distributed unbalance parameters with the help of active magnetic bearings (AMBs). The flexible rotor model having helicallydistributed unbalances along its length and supported by two conventional bearing at both ends is well thought-out here for the analysis. Two active magnetic bearings are considered to generate controlling force for suppressing the unbalance. Then, an equivalent discretized modelis formulated by utilizing seven numberof mass-less thin discs having an eccentricity equivalent to distributed unbalance in theflexible rotor.The finite element approachhas been used to obtain a unified model. In the present work, an identification algorithm has been developed to estimate unbalance parameters in addition with dynamic parameters of bearings i.e. conventional as well as AMBs. Equation of motion of the system hasbeen derived by considering only linear DOFs at bearing locations as generalized coordinates. Displacement at various nodes as well as current signals at AMB locations are calculated by using MATLAB Simulink. Since the present proposed algorithm requires frequency domain data to estimate unknown parameters, the time domain signal obtained from Simulink has been transformed into frequency domain by using Fast Fourier Transformation (FFT). The proposed algorithm is developed on the premise of the least-squares fit method in the frequency domain. Fourth order Runge-Kutta solver is used during numerical simulation. Gyroscopic effect has been neglected in the present work

Use of Modal Representation for the Supporting Structure in Model Based Fault Identification of Large Rotating Machinery: Part 1 – Theoretical Remarks

Fault identification by means of model-based techniques, both in frequency and time domain, is often employed in diagnostics of rotating machines, when the main task is to locate and to evaluate the severity of the malfunction. The model of the fully assembled machine is composed by the submodels of the rotor, of the bearings and of the foundation, while the effect of the faults is modelled by means of equivalent force systems. Some identification techniques, such as the least squares identification in frequency domain, proposed by the authors, have proven to be quite robust even if the submodels are not fine-tuned. Anyhow, the use of a reliable model can increase the accuracy of the identification. Normally a supporting structure is represented by means of rigid foundation or by pedestals, i.e. 2 d.o.f. mass–spring–damper systems, but these kind of models are often not able to reproduce correctly the influence of the dynamical behaviour of the supporting structure on the shaft, especially in large machines where coupled modes are present. Therefore, peculiar aspect of this paper is the use of a modal foundation to model the supporting structure of the machine and the method is discussed in detail in this first part. The modal representation of the foundation is then introduced in the least squares identification technique in frequency domain

Wind turbine asymmetrical load reduction with pitch sensor fault compensation

Offshore wind turbines suffer from asymmetrical loading (blades, tower, etc), leading to enhanced structural fatigue. As well as asymmetrical loading different faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance. Individual pitch control (IPC) can achieve rotor asymmetric loads mitigation, but this is accompanied by an enhancement of pitch movements leading to the increased possibility of pitch system faults, which exerts negative effects on the IPC performance. The combined effects of asymmetrical blade and tower bending together with pitch sensor faults are considered as a “co‐design” problem to minimize performance deterioration and enhance wind turbine sustainability. The essential concept is to attempt to account for all the “fault effects” in the rotor and tower systems, which can weaken the load reduction performance through IPC. Pitch sensor faults are compensated by the proposed fault‐tolerant control (FTC) strategy to attenuate the fault effects acting in the control system. The work thus constitutes a combination of IPC‐based load mitigation and FTC acting at the pitch system level. A linear quadratic regulator (LQR)‐based IPC strategy for simultaneous blade and tower loading mitigation is proposed in which the robust fault estimation is achieved using an unknown input observer (UIO), considering four different pitch sensor faults. The analysis of the combined UIO‐based FTC scheme with the LQR‐based IPC is shown to verify the robustness and effectiveness of these two systems acting together and separately

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Robust Active Learning Multiple Fault Diagnosis of PMSM Drives with Sensorless Control under Dynamic Operations and Imbalanced Datasets

Authors accepted manuscript© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper proposes an active learning scheme to detect multiple faults in permanent magnet synchronous motors in dynamic operations without using historical labelled faulty training data. The proposed method combines the self-supervised anomaly detector based on a local outlier factor (LOF) and a deep Q-network (DQN) supervised reinforcement learner to classify interturn short-circuit, local demagnetisation and mixed faults. The first fault, which is detected by LOF and verified by an expert during maintenance, is used as training data for the DQN classifier. From that point onward, the LOF anomaly detector and DQN fault classifiers are working in tandem in the identification of new faults, which require expert intervention when either of them identifies a fault. The robustness of the scheme against dynamic operations, mixed fault and imbalanced training datasets is validated via a comparative study using stray flux data from an inhouse test setup.acceptedVersio

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

Wide-area monitoring and control of future smart grids

Application of wide-area monitoring and control for future smart grids with substantial wind penetration and advanced network control options through FACTS and HVDC (both point-to-point and multi-terminal) is the subject matter of this thesis. For wide-area monitoring, a novel technique is proposed to characterize the system dynamic response in near real-time in terms of not only damping and frequency but also mode-shape, the latter being critical for corrective control action. Real-time simulation in Opal-RT is carried out to illustrate the effectiveness and practical feasibility of the proposed approach. Potential problem with wide-area closed-loop continuous control using FACTS devices due to continuously time-varying latency is addressed through the proposed modification of the traditional phasor POD concept introduced by ABB. Adverse impact of limited bandwidth availability due to networked communication is established and a solution using an observer at the PMU location has been demonstrated. Impact of wind penetration on the system dynamic performance has been analyzed along with effectiveness of damping control through proper coordination of wind farms and HVDC links. For multi-terminal HVDC (MTDC) grids the critical issue of autonomous power sharing among the converter stations following a contingency (e.g. converter outage) is addressed. Use of a power-voltage droop in the DC link voltage control loops using remote voltage feedback is shown to yield proper distribution of power mismatch according to the converter ratings while use of local voltages turns out to be unsatisfactory. A novel scheme for adapting the droop coefficients to share the burden according to the available headroom of each converter station is also studied. The effectiveness of the proposed approaches is illustrated through detailed frequency domain analysis and extensive time-domain simulation results on different test systems