Nelson River Pole 1 Thyristor Leakage Problem

Li, Xin; Sintra, Luis; Dhaliwal, Narinder

doi:10.1109/tpwrd.2017.2715330

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Therefore, when comparing two groups of data with different mean values, the coefficient of variation rather than the standard deviation should be used as the comparison index. Let a group of output signals be expressed as x i (i = 1, 2, …, n), the mean m and standard deviation v are calculated as shown in ( 6) and ( 7), and the coefficient of variation cv is calculated as shown in (8).…”

Section: Key Eigenvector Optimization Based On Coefficient Of Variationmentioning

confidence: 99%

“…The health status of thyristor valve is mainly determined by the parameters of components such as damping capacitance and leakage resistance in thyristor module [8,9]. With the increase of working time, the parameters of various components at thyristor level gradually shift under the action of various environmental stresses, and multiple components will degrade at the same time, which directly affects the working performance of thyristor converter valve.…”

Section: Introductionmentioning

confidence: 99%

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Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Pang

Zhang

Wang

et al. 2022

IET Generation Trans & Dist

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Online health monitoring of thyristor converter valve is very important for the stable operation of high voltage direct current (HVDC) system. However, in practical projects, only offline annual maintenance can be realized. This paper proposes a method for parameter identification of thyristor valve based on wavelet packet key feature extraction and Elman neural network. By preprocessing the measurable characteristic parameters of thyristor valve in practical engineering, such as wavelet packet time-frequency domain feature extraction, variation coefficient optimization and so on, the characteristic vector that can characterize the health state of thyristor valve is obtained and sent to Elman neural network for training, which realizes the rapid online identification of multiple electrical parameters of thyristor modules in converter valve. Simulation and comparative experiments show that the key feature extraction method proposed in this paper has significant advantages in identification accuracy, and can better identify the damping capacitance and leakage resistance of all thyristor modules. Among them, the deterioration of the damping capacitance is more easily identified, the identification accuracy is the highest and the relative identification error is less than 5%. The parameter identification method proposed in this paper has the advantages of fast identification speed, strong model generalization ability and wide application. This provides a new idea for the online condition monitoring of thyristor valves.

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Section: Key Eigenvector Optimization Based On Coefficient Of Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Pang

Zhang

Wang

et al. 2022

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…The failure rate of thyristors is generally considered to satisfy the law of the bathtub curve. Long-term operation failure is usually caused by degradation of the thyristor 7 , which is in the third stage of the bathtub curve. After a long period of stable operation, with increasing operating years, the failure rate of thyristors substantially increases, which in uences the stable operation of the HVDC transmission system.…”

Section: Introductionmentioning

confidence: 99%

Reliability Research of Thyristors for HVDC Transmission System

XU,

Pang,

Xie

et al. 2024

Preprint

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The long-term operation performance of HVDC transmission systems is significantly influenced by the reliability of thyristors. However, the current reliability research of thyristors is mainly based on the leakage current and there is no comprehensive degradation characteristics of thyristors. In this article, the degradation characteristics of thyristors under voltage and temperature accelerated ageing tests are obtained, including the reverse recovery characteristic, on-state characteristic, blocking characteristic and gate characteristic. The reverse recovery characteristic obviously degrades under the voltage accelerated ageing tests. The reverse recovery charge and reverse recovery time decrease by over 10% after the tests. Seven thyristors operating for a certain number of years from two HVDC stations are tested, and the results verify the thyristor degradation characteristics obtained from the ageing tests. Moreover, the mechanisms of the leakage current and reverse recovery characteristic are analyzed in detail.

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“…In various semiconductor components, thyristors, as the core component of the converter valve, play a key role in the rectification of the inverter [1]- [4]. Thyristors are affected by complex electrical, temperature, humidity, vibration, and other factors during operation, and performance degradation will inevitably occur after long-term operation [5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method for Reverse Recovery Characteristics of Thyristors Based on Charge Measurement

Zha,

Gu,

Guo

et al. 2023

J. Phys.: Conf. Ser.

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The change of operation mode of the HVDC project causes a change of the external characteristic of the converter valve, and the reverse recovery characteristic of the thyristor inside the converter valve changes accordingly. Therefore, the reverse recovery charge measurement values of thyristors under different operating conditions cannot be directly compared, and the status of thyristors cannot be evaluated online. Therefore, a method of measuring the reverse recovery charge of series thyristor based on the turn-off voltage is presented in this paper. Then, the operation mode of the HVDC project is analyzed, and the external characteristics of the converter valve under different operation modes are discussed. Secondly, the external factors that affect the reverse recovery characteristics of thyristors are analyzed essentially, and the relationship between the external factors and the external characteristics of the converter valve is discussed. A reverse recovery charge model considering external factors is established, and a method to reduce the error is proposed. Finally, the reverse-recovery charge normalization coefficient is derived for the normalization of the measured values in different working conditions. Based on the charge online measurement method and the normalization method proposed in this paper, the reverse-recovery characteristic evaluation method of the thyristor is proposed, and the evaluation steps are introduced.

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Nelson River Pole 1 Thyristor Leakage Problem

Cited by 12 publications

References 8 publications

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Parameter identification of key components of thyristor converter valve based on wavelet packet key feature extraction and Elman neural network

Reliability Research of Thyristors for HVDC Transmission System

Evaluation Method for Reverse Recovery Characteristics of Thyristors Based on Charge Measurement

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