Chaotic eye‐based fault forecasting method for wind power systems

Yau, Her‐Terng; Wang, Meng Hui

doi:10.1049/iet-rpg.2014.0269

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…The controller enables the two different chaotic signals to have the same operation tracks after a period of time. This tracking system is called chaos synchronization, the basic equation of which is (22)…”

Section: Chaos Synchronization Detection Methodsmentioning

confidence: 99%

Application of Extension Neural Network Algorithm and Empirical Mode Decomposition Method to Partial Discharge Diagnosis of Power Capacitors

Wang¹,

Lu²,

Lin³

et al. 2019

Sensors and Materials

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Power capacitors are extensively used in power systems; thus, any internal capacitor fault can affect their safe operation. The most common faults include dampness, partial discharge, aging or insulation deterioration, and structural deterioration. The purpose of this study is to use a human-machine interface diagnosis system to detect the type of power capacitor fault, so as to determine the real-time status of the power capacitor. Partial discharge data are measured and diagnosed for the power capacitors that are functional and remaining in long-term highvoltage operation. The defects are handled before the capacitor is measured by a high-frequency current transformer (HFCT) sensor, and one power platform tester is used to measure partial discharge in the capacitor case. The voltage is increased until the partial discharge phenomenon stops, and the voltage and partial discharge signals are visualized by using a high-frequency oscillograph. Afterwards, the feature of the discharge signal is determined by the empirical mode decomposition (EMD) method, combined with the chaos synchronization detection analysis method to establish the chaotic error scatter map of the discharge voltage. Then, the chaos eyes are used as the features of fault diagnosis, and the extension neural network (ENN) algorithm is used for capacitor fault recognition. The advantages of this method are that the feature extraction data volume can be reduced, subtle changes in power capacitor discharge voltage signal can be detected effectively so as to detect the power capacitor's operating state, and emergency measures can be executed in advance to prevent severe disasters. The proposed method is validated by actual measurements. The detection rate of the ENN fault detection method is 95%, proving that this method is applicable to power capacitor partial discharge detection.

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Section: Chaos Synchronization Detection Methodsmentioning

confidence: 99%

Application of Extension Neural Network Algorithm and Empirical Mode Decomposition Method to Partial Discharge Diagnosis of Power Capacitors

Wang¹,

Lu²,

Lin³

et al. 2019

Sensors and Materials

View full text Add to dashboard Cite

show abstract

“…If the calculation of the classical domain and the pitch distance has been completed, then the operation of the joint function can be performed. The value of the associated function is calculated by equation (12), and equation 13is added to obtain the degree of membership of the subject, and thus the category of categorization can be clearly defined from this step…”

Section: Extension Diagnosis Algorithmmentioning

confidence: 99%

Application of chaos and extension theory to fault diagnosis of three-phase synchronous generators

Wang

Chen

2019

Journal of Low Frequency Noise, Vibration and Active Control

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This study applied an extension algorithm combined with the Chaos Theory to the fault diagnosis of the three-phase synchronous generator. First, the three-phase synchronous generator is classified, including normal, carbon brush fault, three-phase unbalance, and insulation deterioration, and then by means of hardware measurement circuit and device, electrical signals are measured for each category and a chaotic error scatter map is built through the Chaos Theory to get the chaotic eye coordinates under specific fault categories. Next, the extension algorithm is used to carry out the correlation function and the normalization calculation, evaluating the type of fault to which it belongs. The analysis results show that the proposed method can effectively identify the fault types of three-phase synchronous generators and significantly reduce the amount of feature extraction data, so as to effectively detect the change of fault signals, allowing us to know the operation state of three-phase synchronous generators.

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“…In the field of engineering, if the slave system follows the master system and the operational tracks of the two systems are adjusted to synchronize gradually, it is called a chaotic synchronous system. (18) ( )…”

Section: Chaos Theorymentioning

confidence: 99%

Application of Extension Neural Network Type 2 and Chaos Theory to the Electrocardiogram Recognition System

Wang¹,

Lu²

2019

Sensors and Materials

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LabVIEW human-machine interface, chaotic eye coordinates In this study, we combined the extension neural network type 2 (ENN2) with the chaos theory in the electrocardiogram (ECG) recognition system. The self-developed hardware measurement circuit and LabVIEW human-machine interface were used to measure and capture ECG signals. The master-slave chaos system was adopted to change the stored ECG data into a chaotic dynamic error distribution graph to obtain the chaotic eye coordinates of specific ECG signals. ENN2 was used for recognition. There were 36 research subjects. The first half of the data were measured using the signal capture circuit, while the second half were provided by the medical center of Massachusetts Institute of Technology (MIT). According to the results of analysis, the proposed method has a high accuracy when applied to the classification of ECG recognition, with a recognition rate of up to 89%. Hence, the automatic diagnosis ECG system designed in this study can effectively categorize irregular heart rhythms and reduce the huge labor cost for reading.

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Chaotic eye‐based fault forecasting method for wind power systems

Cited by 24 publications

References 33 publications

Application of Extension Neural Network Algorithm and Empirical Mode Decomposition Method to Partial Discharge Diagnosis of Power Capacitors

Application of Extension Neural Network Algorithm and Empirical Mode Decomposition Method to Partial Discharge Diagnosis of Power Capacitors

Application of chaos and extension theory to fault diagnosis of three-phase synchronous generators

Application of Extension Neural Network Type 2 and Chaos Theory to the Electrocardiogram Recognition System

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