Novel Magnetization Hysteresis-Based Power-Transformer Protection Algorithm

Jiao, Zaibin; Li, Zongbo

doi:10.1109/tpwrd.2018.2837022

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…Finally, the average energy is used as input to an SVM to identify the operating states of the power transformer. (5) Geometric features of the VICur and SVM-based protection scheme (Scheme 5) [27]. The inclination angle, the major axis and the ellipticity of the VICur are calculated by the methods provided in [27].…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

“…In summary, to improve the generalizability of AIbased protection schemes, it is critical to decrease the data ergodicity requirement of AI. In this paper, a novel deep neural network called a denoising-classification neural network (DCNN) is proposed and used to develop an AI-based transformer protection scheme by identifying the exciting voltage-differential current curve (VICur) [27][28][29]. Typical VICurs are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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A denoising-classification neural network for power transformer protection

Jiao

et al. 2022

Prot Control Mod Power Syst

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Artificial intelligence (AI) can potentially improve the reliability of transformer protection by fusing multiple features. However, owing to the data scarcity of inrush current and internal fault, the existing methods face the problem of poor generalizability. In this paper, a denoising-classification neural network (DCNN) is proposed, one which integrates a convolutional auto-encoder (CAE) and a convolutional neural network (CNN), and is used to develop a reliable transformer protection scheme by identifying the exciting voltage-differential current curve (VICur). In the DCNN, CAE shares its encoder part with the CNN, where the CNN combines the encoder and a classifier. Based on the interaction of the CAE reconstruction process and the CNN classification process, the CAE regards the saturated features of the VICur as noise and removes them accurately. Consequently, it guides CNN to focus on the unsaturated features of the VICur. The unsaturated part of the VICur approximates an ellipse, and this significantly differentiates between a healthy and faulty transformer. Therefore, the unsaturated features extracted by the CNN help to decrease the data ergodicity requirement of AI and improve the generalizability. Finally, a CNN which is trained well by the DCNN is used to develop a protection scheme. PSCAD simulations and dynamic model experiments verify its superior performance.

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Section: Comparisons With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A denoising-classification neural network for power transformer protection

Jiao

et al. 2022

Prot Control Mod Power Syst

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“…It is capable of classifying in-zone and out-of-the-zone faults more reliably compared to ANNbased algorithms. In [90], an SVM-based protection technique is proposed. It monitors magnetization hysteresis loop by voltage-differential current (V-DCurr) curves.…”

Section: Relays Based On Support Vector Machinementioning

confidence: 99%

Protection and Monitoring of DESO

Hamidi

2022

Preprint

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This chapter first accurately reviews the basics of smart microgrid protection since the definitions vary from a reference to another. Then it focuses on the fault responses of Inverter-Based Resources (IBR’s) as those are emerging technologies that will be playing the great role of interfacing primary energy resources and the grid. In particular, for materialization of net-zero Carbon emission in electricity generation, the use of IBR’s is indispensable. However, the non-universal and software-defined IBR fault responses make conventional relays such as overcurrent, directional, and distance relays inapplicable in heavily IBR-based grids. Therefore, the discrepancy between conventional and IBR fault responses is elaborated and possible solutions to the looming protection issues are discussed. The shortcomings and merits of each solution are also discussed.

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“…Magnetisation hysteresis-based power transformer protection has been proposed by Jiao and Li [18]. The authors observed that the B-H curve decline during a fault condition and this inclination could be identified by the SVM classifier method.…”

Section: Introductionmentioning

confidence: 99%