Data-driven fault detection and isolation scheme for a wind turbine benchmark

Bessa, Iury; Palhares, Reinaldo M.; D’Angelo, Marcos Flávio Silveira Vasconcelos; Filho, João Edgar Chaves

doi:10.1016/j.renene.2015.10.061

Cited by 67 publications

(40 citation statements)

References 22 publications

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“…To ensure that the state is not affected by the unknown fault inputs, namely the robustness of the estimation algorithm, f (k) is decoupled from estimation error (23) and residual dynamics (24). The decoupling problem is equivalent to…”

Section: The Problem Of Stochastic Hybrid Estimation Algorithmmentioning

confidence: 99%

“…Subject to: Constraint (25) and (26) being satisfied, that is, the actuator fault input is decoupled from the system estimation error (23) and residual (24). Theorem 1.…”

Section: Individual Robust Estimator Designmentioning

confidence: 99%

“…A lot of research has been started in recent years for the fault diagnosis and isolation of wind turbines [22][23][24][25][26][27][28][29]. Karim [22] designed an observer scheme for FDI, which is integrated with a maximum-shift strategy and a time-varying Kalman filter for the additive and multiplicative measurement failures of voltage and current.…”

Section: Introductionmentioning

confidence: 99%

“…Silvio [23] used fuzzy models to estimate fault residuals for diagnosis and isolation. Iury [24] uses data-driven methods for fault detection, and a fuzzy/Bayesian network to distinguish faults. However, current research works do not systematically consider the stochastic hybrid characteristics of wind turbines and do not estimate the continuous and discrete states of the wind turbine during fault diagnosis and isolation.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

et al. 2018

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Abstract:In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators' faults of the WECS. Both the system status and the actuators' faults can be detected by the FDI method and it can effectively quantify the actuators' fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

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Section: The Problem Of Stochastic Hybrid Estimation Algorithmmentioning

confidence: 99%

“…Subject to: Constraint (25) and (26) being satisfied, that is, the actuator fault input is decoupled from the system estimation error (23) and residual (24). Theorem 1.…”

Section: Individual Robust Estimator Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

et al. 2018

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“…Generally, it is not restricted by a specific form of application and/or learning machine. Hence, it could be easily applied to other types of applications such as explanation of most expressive electrode-combination in hand movement recognition with EMG signals [31], change point/anomaly detections in time series for fault detections in wind turbines [32, 33], explanation of important pixel patches in computer vision [6], quantum chemistry [34], and extraction of latent brain states [35]. However, there are two main shortcomings: first, it does not take any non-linear correlations of features into account and second, the number of samples depends on the complexity of the problem.…”

Section: Applications and Limitationsmentioning

confidence: 99%

ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines

et al. 2017

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High prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. For computational biology, positional oligomer importance matrices (POIMs) have been successfully applied to explain the decision of support vector machines (SVMs) using weighted-degree (WD) kernels. To extract relevant biological motifs from POIMs, the motifPOIM method has been devised and showed promising results on real-world data. Our contribution in this paper is twofold: as an extension to POIMs, we propose gPOIM, a general measure of feature importance for arbitrary learning machines and feature sets (including, but not limited to, SVMs and CNNs) and devise a sampling strategy for efficient computation. As a second contribution, we derive a convex formulation of motifPOIMs that leads to more reliable motif extraction from gPOIMs. Empirical evaluations confirm the usefulness of our approach on artificially generated data as well as on real-world datasets.

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Fault estimation of wind turbines using combined adaptive and parameter estimation schemes

Witczak

Rotondo

Puig

et al. 2017

Adaptive Control & Signal

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This paper addresses the problem of fault estimation in wind turbines using a joint fault and state estimation scheme. The scheme assumes a set of possible faults affecting the dynamics of the wind turbine. Then, a joint process fault and state estimation scheme is developed considering that process disturbances and sensor noises are unknown but bounded in an ellipsoid. Two subcases are considered depending of the satisfaction of a rank condition. The proposed scheme is applied to a well-known wind turbine benchmark proposed in a international competition. From the results obtained, a satisfactory fault estimation is achieves against a set of pre-defined fault scenarios.

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Data-driven fault detection and isolation scheme for a wind turbine benchmark

Cited by 67 publications

References 22 publications

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines

Fault estimation of wind turbines using combined adaptive and parameter estimation schemes

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