Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

Simani, Silvio; Castaldi, Paolo; Farsoni, Saverio

doi:10.3390/en10070866

Cited by 14 publications

(21 citation statements)

References 34 publications

(80 reference statements)

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“…The same datasets above will be applied to train the three models and the proposed selective SWNN ensemble model. Monte-Carlo analysis [37] is used to calculate the indices and to test the robustness of the comparison approaches. Particularly, a rigorous test simulation based on 10,000 runs has been executed, during which realistic wind turbine uncertainties have been considered.…”

Section: Comparative Analysismentioning

confidence: 99%

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Wang

2019

Energies

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Pitch system failures occur primarily because wind turbines typically work in dynamic and variable environments. Conventional monitoring strategies show limitations of continuously identifying faults in most cases, especially when rapidly changing winds occur. A novel selective-ensemble monitoring strategy is presented to diagnose the most pitch failures using Supervisory Control and Data Acquisition (SCADA) data. The proposed strategy consists of five steps. During the first step, the SCADA data are partitioned according to the turbine’s four working states. Correlation Information Entropy (CIE) and 10 indicators are used to select correlation signals and extract features of the partition data, respectively. During the second step, multiple Small-World Neural Networks (SWNNs) are established as the ensemble members. Regarding the third step, all the features are randomly sampled to train the SWNN members. The fourth step involves using an improved global correlation method to select appropriate ensemble members while in the fifth step, the selected members are fused to obtain the final classification result based on the weighted integration approach. Compared with the conventional methods, the proposed ensemble strategy shows an effective accuracy rate of over 93.8% within a short delay time.

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Section: Comparative Analysismentioning

confidence: 99%

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Wang

2019

Energies

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“…This objective is often known as power regulation for nominal power generation [2]. Accordingly, control design of wind turbines in power regulation has gained significant importance during the last decades [3]. Several industrial controllers for power regulation use the PID-type control, as the linear controller [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Habibi¹,

Rahimi²,

Howard³

et al. 2019

Preprint

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This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust adaptive and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

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“…This objective is often known as power regulation for nominal power generation [2]. Accordingly, the control design of wind turbines in power regulation has gained significant importance during the last decades [3]. Several industrial controllers for power regulation use the Proportional-Integral-Derivative (PID) type control, as the linear controller [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

show abstract

Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

Cited by 14 publications

References 34 publications

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

Fault-Tolerant Neuro Adaptive Constrained Control of Wind Turbines for Power Regulation with Uncertain Wind Speed Variation

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