TransFNN: A Novel Overtemperature Prediction Method for HVDC Converter Valves Based on an Improved Transformer and the F-NN Algorithm

Zhou, Sihan; Qin, Liang; Sun, Hanyu; Peng, Bo; Ruan, Jiangjun; Wang, Jing; Tang, Xu; Wang, Xiaole; Liu, Kaipei

doi:10.3390/s23084110

Cited by 1 publication

(2 citation statements)

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“…Currently, there are a few mature technical solutions to address the challenges posed by the unmanned operation and maintenance of the main circulation pump in the cooling system of the VSG-HVDC system [5][6][7][8][9][10][11][12][13]. Specifically, a relevant study focused on estimating the health state of the pump [5].…”

Section: Introductionmentioning

confidence: 99%

“…While these studies shed light on the physical mechanism of the main circulation pump, there is a lack of implementation of specific tasks related to fault diagnosis. In [13], a novel approach to predicting the overtemperature state of some core devices of converter valves, including the main circulation pumps, was proposed. However, this study only focused on the fault of overtemperature without taking into consideration of other fault modes.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems

Zhou

Qin

Yang³

et al. 2023

Sensors

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The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.

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