Wavelet and Hilbert Huang Transforms Applied to Park’s Vector for Fault Detection in a PMSG Wind Turbine

Ortiz-Medina, Raúl Arturo; Sanabria-Villamizar, Mauricio; Lopez-Garcia, Irvin; Villalobos-Piña, Francisco Javier; Beltran-Carbajal, Francisco; Maldonado-Ruelas, Victor Arturo

doi:10.1109/ccac58200.2023.10333368

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Temporarily disregarding the impact of the input signal and input noise on the system, the bistable system's approximate Langevin equation at this juncture is [16] x ′ = ax − bx 3 (8) Solving this differential equation gives an expression for the amplitude of the output x(t) of the system,…”

Section: Output Saturation Limitmentioning

confidence: 99%

“…Analyzing the parameters of harmonics to obtain real-time and accurate harmonic data is key to harmonic detection. Currently, power system harmonic analysis primarily relies on time-domain analysis [5], frequency-domain analysis [6], wavelet transform method [7], and Hilbert-Huang transform method [8]. In classical time-frequency domain analysis methods, spectral overlap occurs when weak power harmonic signals are overwhelmed by a wide spectrum of strong background noise.…”

Section: Introductionmentioning

confidence: 99%

“…The study [7] proposed an improved wavelet transform method but did not address the issue of frequency aliasing, and the harmonic suppression effect is inadequate. The study [8] utilized the Hilbert-Huang transform method, which suffers from modal aliasing and noise sensitivity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

Sun,

Qi,

Yuan

et al. 2024

Applied Sciences

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The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise.

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Section: Output Saturation Limitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

Sun,

Qi,

Yuan

et al. 2024

Applied Sciences

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“…PM machines, in general, are susceptible to several types of faults that can impact their functionality (Choi et al, 2018;Kudelina et al, 2021). Stator failures are common, which can include insulation faults and issues with the connections in the stator windings (Wang et al, 2014;Nyanteh et al, 2013;Hoang Nguyen et al, 2023;Ortiz-Medina et al, 2023). Airgap eccentricity can cause operational disturbances such as vibrations and noise and may lead to mechanical stress and uneven wear (Valavi et al, 2013;Ebrahimi et al, 2009Ebrahimi et al, , 2014Tong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised anomaly detection of permanent magnet offshore wind generators through electrical and electromagnetic measurements

Dibaj,

Valavi,

Nejad

2024

Preprint

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Abstract. This paper investigates fault detection in offshore wind permanent magnet synchronous generators (PMSG) for demagnetization and eccentricity faults (both static and dynamic) at various severity levels. The study utilizes a high-speed PMSG model, on the NREL 5-MW reference offshore wind turbine, and at the rated wind speed, to simulate healthy and faulty conditions. An unsupervised convolutional autoencoder (CAE) model, trained on simulated signals from the generator in its healthy state, serves for anomaly detection. The main aim of the paper is to evaluate the possibility of fault detection by means of high-resolution electrical and electromagnetic signals, given that the typically low-resolution standard measurements used in SCADA systems of wind turbines often impede the early detection of incipient failures. Signals analyzed include three-phase currents, induced shaft voltage, electromagnetic torque, and magnetic flux (airgap and stray) from different directions and positions. The performance of CAE models is compared across time and frequency domains. Results show that in the time domain, stator three-phase currents effectively detect faults. In the frequency domain, stray flux measurements, positioned at the top, bottom, and sides of outside the stator housing, demonstrate superior performance in fault detection and sensitivity to fault severity levels. Particularly, radial components of stray flux can successfully distinguish between eccentricity and demagnetization.

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Operational State Recognition of Continuous Worm Gear Grinding Process Based on Wavelet Packet Energy Ratio and Double-Layer SVM Classifier

He,

Zhao,

Zhu

et al. 2024

J. Vib. Eng. Technol.

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Wavelet and Hilbert Huang Transforms Applied to Park’s Vector for Fault Detection in a PMSG Wind Turbine

Cited by 4 publications

References 17 publications

Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

Unsupervised anomaly detection of permanent magnet offshore wind generators through electrical and electromagnetic measurements

Operational State Recognition of Continuous Worm Gear Grinding Process Based on Wavelet Packet Energy Ratio and Double-Layer SVM Classifier

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