Prediction method of successive commutation failure for multi-infeed high voltage direct current systems under fault of weak receiving-end grid

Ouyang, Jinxin; Pang, Mingyu; Zhang, Zhen; Ye, Junjun; Diao, Yanbo

doi:10.1016/j.ijepes.2021.107313

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…where ω is the rotational angular frequency; and V L is the root-mean-square (RMS) phase-to-phase commutation voltage. Thus, by integrating both sides of (20) in commutation range, and taking the dynamic change of the commutation current i 1 , i 3 into account, the extinction angle can be expressed as: (22) where k is the converter transformer ratio; and β is the firing advance angle.…”

Section: B Proposed Strategy For Scf Mitigationmentioning

confidence: 99%

“…However, the fundamental basis for the extinction angle control is an accurate quantification of advance trigger angle. It is reported that excessive advance triggering will increase the reactive power consumption of the converter, which poses threat to commutation on the contrary [22]. On the other hand, researchers attempt to utilize DC current controls to reduce fault current, which can thus mitigate SCF [23].…”

Section: Introductionmentioning

confidence: 99%

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Improved Identification Method and Fault Current Limiting Strategy for Commutation Failure in LCC-HVDC

Liu

Lin

Zhong

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Line-commutated converter based high-voltage direct-current (LCC-HVDC) transmission systems are prone to subsequent commutation failure (SCF), which consequently leads to the forced blocking of HVDC links, affecting the operation of the power system. An accurate commutation failure (CF) identification is fairly vital to the prevention of SCF. However, the existing CF identification methods cause CF misjudge or detection lag, which can limit the effect of SCF mitigation strategy. In addition, earlier approaches to suppress SCF do not clarify the key factor that determines the evolution of extinction angle during system recovery and neglect the influence. Hence, this paper firstly analyzes the normal commutation process and CF feature based on the evolution topology of converter valve conduction in detail. Secondly, the energy in the leakage inductance of converter transformer is presented to characterize the commutation state of the valves. Then a CF identification method is proposed utilizing the leakage inductance energy. Thirdly, taking the key variable which is crucial to the tendency of extinction angle during the recovery process into account, a fault current limiting strategy for SCF mitigation is put forward. Compared with the original methods, the proposed methods have a better performance in CF identification and mitigation in terms of detection accuracy and mitigation effect. Finally, case study on PSCAD/EMTDC validates the proposed methods.

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Section: B Proposed Strategy For Scf Mitigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Identification Method and Fault Current Limiting Strategy for Commutation Failure in LCC-HVDC

Liu

Lin

Zhong

et al. 2022

Journal of Modern Power Systems and Clean Energy

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show abstract

“…An approach involving the transients due to HVDC is proposed to evaluate the CF risk (Yang et al, 2020). After the dynamic reactive power of inverter stations as well as the induced successive CF is analyzed (Ouyang et al, 2021), presents a rapid prediction method based on the extinction angle of LCC-HVDC affected by the adjacent one. However, the criterion established on the analytical extinction angle is more or less difficult to be specified.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Convolutional Network Based Identification of Voltage-Coupling Commutation Failures in Multi-Infeed HVDC Systems

et al. 2022

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Cascading commutation failures (CFs) pose severe risks in multi-infeed high voltage direct current (HVDC) systems. Different from the single or concurrent CF, not only the time-relevance of signals but also the spatio coupling and even control correlation of HVDCs will attribute to the cascading CFs. The conventional approaches to identify them tend to fall into a dilemma due to their complicated dynamics, wide-area coupling and vague threshold of judgement. In this paper, a deep-learning method based on the data-driven idea is proposed to recognize the cascading CFs. It analyzes the crucial factors leading to the cascading relationship of multiple HVDCs, while classifying them into time and space signals. To extract the inherent correlation between HVDCs as well as the time relevance in question, a spatio-temporal convolutional network (STCN) is formulated. The data generated in case of faults with diverse severity are applied to train STCN. Finally, the proposed framework and STCN method are validated by a customized IEEE 39 bus system and a practical power grid.

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A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data scenarios

et al. 2022

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Prediction method of successive commutation failure for multi-infeed high voltage direct current systems under fault of weak receiving-end grid

Cited by 5 publications

References 13 publications

Improved Identification Method and Fault Current Limiting Strategy for Commutation Failure in LCC-HVDC

Improved Identification Method and Fault Current Limiting Strategy for Commutation Failure in LCC-HVDC

Spatio-Temporal Convolutional Network Based Identification of Voltage-Coupling Commutation Failures in Multi-Infeed HVDC Systems

A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data scenarios

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