Ladder‐Wise calculation method for <i>z</i>‐coordinate of transformer PD source based on planar layout UHF antenna sensors

Zhang, Guozhi; Zhang, Xiaoxing; Cheng, Hansong; Tang, Ju

doi:10.1002/tee.23061

Cited by 36 publications

(24 citation statements)

References 21 publications

(26 reference statements)

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“…In the future work, we will further investigate whether these algorithms can be applied to systems with missing data. The iterative algorithm in this paper is proposed for bilinear stochastic systems but the idea can be extended to other linear and nonlinear stochastic systems with colored noises [66][67][68][69][70][71][72][73][74][75] and can be applied to other literatures [76][77][78][79][80][81][82][83] such as signal modeling, pattern cognition, information processing, and engineering application systems.…”

Section: Discussionmentioning

confidence: 99%

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Liu

Zhang

Ding

et al. 2020

Adaptive Control & Signal

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Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.

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Section: Discussionmentioning

confidence: 99%

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Liu

Zhang

Ding

et al. 2020

Adaptive Control & Signal

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“…The simulation results indicate that the proposed algorithms are effective for parameter estimation and that the ML-ADE algorithm is capable of improving the parameter estimation accuracy of the maximum likelihood-based differential evolution algorithm. The proposed maximum likelihood-based adaptive differential evolution algorithm in this article can be applied to other control and schedule areas such as transportation communication systems, [65][66][67][68] engineering application systems, [69][70][71][72] and so on. System identification includes two main aspects: model structure identification and parameter estimation.…”

Section: Discussionmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Ding

et al. 2020

Adaptive Control & Signal

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Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

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“…Keep the experimental voltage stable, use Tek DPO 7000 oscilloscope (Tektronix, Beaverton, OH, USA) to simultaneously acquire the AC phase voltage from the voltage divider capacitor and the high frequency electromagnetic wave signal which is captured by the Vivaldi antenna sensor [34,35]). The sampling rate is 50 MHz, and the positive half-cycle zero-crossing point of the AC voltage signal is used as the trigger signal.…”

Section: Construction Of Transformer Pd Modelmentioning

confidence: 99%

Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm

Zhou

Pei

et al. 2019

Energies

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In view of the fact that the statistical feature quantity of traditional partial discharge (PD) pattern recognition relies on expert experience and lacks certain generalization, this paper develops PD pattern recognition based on the convolutional neural network (cnn) and long-term short-term memory network (lstm). Firstly, we constructed the cnn-lstm PD pattern recognition model, which combines the advantages of cnn in mining local spatial information of the PD spectrum and the advantages of lstm in mining the PD spectrum time series feature information. Then, the transformer PD UHF (Ultra High Frequency) experiment was carried out. The performance of the constructed cnn-lstm pattern recognition network was tested by using different types of typical PD spectrums. Experimental results show that: (1) for the floating potential defects, the recognition rates of cnn-lstm and cnn are both 100%; (2) cnn-lstm has better recognition ability than cnn for metal protrusion defects, oil paper void defects, and surface discharge defects; and (3) cnn-lstm has better overall recognition accuracy than cnn and lstm.In recent years, with the rapid development of recognition technologies in the research fields such as images, texts, and videos, classification algorithms based on deep learning, for example, dbn (deep belief networks), dnn (deep neural networks), cnn (convolutional neural networks), have gradually begun to be applied in the field of PD pattern recognition [19][20][21]. In 2016, Mingzhe Rong used convolutional neural network for PD pattern recognition. The simulation results show that the recognition accuracy of cnn is better than the Hilbert-Huang transform and wavelet entropy-characterized support vector machine (SVM) [22]. In 2017, Zheng used cnn to identify the pattern of transformer PD. The experimental results show that the recognition rate of cnn is better than the traditional identification method [18]. In 2018, Jiang and Sheng applied cnn to the pattern recognition of GIS PD. The PRPS (phase resolved pulse sequence) map and the whole-period time-domain waveform map were used as input. The research shows that the recognition accuracy of cnn is better than SVM and BP (back propagation) neural network [23,24]. In the same year, Peng used cnn for pattern recognition of cable PD. The results showed that the overall recognition accuracy of cnn increased by 3.71% and 4.06%, respectively, compared with SVM and BP neural network [25]. Nguyen used lstm to identify the partial discharge pattern of GIS (Gis Insulation Switchgear). The experimental results show that the recognition accuracy of lstm is better than SVM [26]. Adam applied lstm to the identification of different PD defect types in insulating oil. The time domain single-pulse electrical signal was used as input. Experimental results show that the recognition accuracy of lstm is slightly lower than the random forest method. However, since the lstm method has the advantage of not requiring human statistical recognition features, it is still recommended by the a...

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Ladder‐Wise calculation method for z‐coordinate of transformer PD source based on planar layout UHF antenna sensors

Cited by 36 publications

References 21 publications

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm

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