Frequency domain based DC fault analysis for bipolar HVDC grids

Wang, Mian; Beerten, Jef; Hertem, Dirk Van

doi:10.1007/s40565-017-0307-y

Cited by 29 publications

(20 citation statements)

References 16 publications

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“…An average two-level VSCs value model is employed [56]. DC voltage droop and reactive power controls are applied to onshore VSC stations [57,58]. At offshore VSC stations, active power and AC voltage controls are used to ensure a constant power flow in the grid via AC voltage regulation [59].…”

Section: Mt-hvdc Test System Understudymentioning

confidence: 99%

A Support Vector Machine Learning-Based Protection Technique for MT-HVDC Systems

Muzzammel

Raza

2020

Energies

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High voltage direct current (HVDC) transmission systems are suitable for power transfer to meet the increasing demands of bulk energy and encourage interconnected power systems to incorporate renewable energy sources without any fear of loss of synchronism, reliability, and efficiency. The main challenge associated with DC grid protection is the timely diagnosis of DC faults because of its rapid built up, resulting in failures of power electronic circuitries. Therefore, the demolition of HVDC systems is evaded by identification, classification, and location of DC faults within milliseconds (ms). In this research, the support vector machine (SVM)-based protection algorithm is developed so that DC faults could be identified, classified, and located in multi-terminal high voltage direct current (MT-HVDC) systems. A four-terminal HVDC system is developed in Matlab/Simulink for the analysis of DC voltages and currents. Pole to ground and pole to pole faults are applied at different locations and times. Principal component analysis (PCA) is used to extract reduced dimensional features. These features are employed for the training and testing of SVM. It is found from simulations that DC faults are identified, classified, and located within 0.15 ms, ensuring speedy DC grid protection. The realization and practicality of the proposed machine learning algorithm are demonstrated by analyzing more straightforward computations of standard deviation and normalization.

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Section: Mt-hvdc Test System Understudymentioning

confidence: 99%

A Support Vector Machine Learning-Based Protection Technique for MT-HVDC Systems

Muzzammel

Raza

2020

Energies

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“…2 shows the three-terminal bipolar test system used for the studies in this paper. The system parameters and models are taken from [15], except the dc circuit breaker model and the values of the series inductors, which are taken from [10]. The breaker opening delay is assumed to be 2 ms for hybrid dc circuit breakers [10] in the reference scenario.…”

Section: Fast Breaker Backup Protection 21 Fast Breaker Failure Backmentioning

confidence: 99%

“…During the time frame of interest for the backup protection, not only the travelling wave phenomenon but also the converter control, converter protection and ac in-feed can play a role in determining the fault voltages and currents. In order to incorporate all these factors, the robustness of the backup protection algorithm is evaluated via simulation studies on a detailed three-terminal bipolar test system implemented in PSCAD [15].…”

Section: Introductionmentioning

confidence: 99%

Robustness Evaluation of Fast Breaker Failure Backup Protection in Bipolar HVDC Grids

Wang¹,

Leterme²,

Beerten³

et al. 2017

13th IET International Conference on AC and DC Power Transmission (ACDC 2017)

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To ensure a reliable fault clearing, a backup protection scheme is required for selective HVDC grid protection. One way to achieve this is to use both voltage and current measurements to distinguish uncleared faults from cleared ones during the fault clearing process of the primary protection. This paper studies the applicability of such a backup protection algorithm in meshed bipolar HVDC grids and evaluates the robustness of the algorithm against system operating conditions and breaker opening delays. The influence of different operating conditions on the fault waveforms is analysed using a three-terminal bipolar test system. The robustness of the fast breaker failure backup protection algorithm is evaluated via simulation studies on the bipolar test system in PSCAD. The simulation results show that the fast breaker failure backup protection algorithm can distinguish between uncleared and cleared faults with sufficient margin for all considered operating conditions.

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“…In previous researches, the two problems have been clearly illustrated in single-infeed HVDC systems [7]. However, HVDCs will interact with each other when fed into an AC system in a close proximity [8,9], and the problems therefore become quite complex in MIDC systems.…”

Section: Introductionmentioning

confidence: 99%

An on-line power/voltage stability index for multi-infeed HVDC systems

Lan

Liao

et al. 2019

J. Mod. Power Syst. Clean Energy

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High-voltage direct current (HVDC) transmission is playing an increasingly important role in modern power systems, and the resulted power/voltage stability issue has raised widespread concern. This paper presents an on-line power/voltage stability index (PVSI) for multi-infeed HVDC (MIDC) systems. Different from the existing indices which are developed mainly for off-line and static analysis, the proposed PVSI can be applied in real time.Effects of system changes on stability assessment such as change of system states and control strategies are considered. Thus, helpful guidance can be provided for on-line HVDC stability and controls. The PVSI is originally deduced for single-infeed HVDC systems in an ''AC way'' by analyzing the power and voltage stability of both pure AC systems and HVDC systems. Moreover, its on-line application in practical MIDC systems is realized by building an equivalent single-infeed model, and utilizing nowadays measurement and communication infrastructures such as wide-area measurement system (WAMS). The effectiveness of the PVSI is verified through simulations in real-time digital simulator (RTDS).

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Frequency domain based DC fault analysis for bipolar HVDC grids

Cited by 29 publications

References 16 publications

A Support Vector Machine Learning-Based Protection Technique for MT-HVDC Systems

A Support Vector Machine Learning-Based Protection Technique for MT-HVDC Systems

Robustness Evaluation of Fast Breaker Failure Backup Protection in Bipolar HVDC Grids

An on-line power/voltage stability index for multi-infeed HVDC systems

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