Root Cause Identification of Power System Faults using Waveform Analytics

Shuvra, Mahfuz A.; Rosso, Alberto Del

doi:10.1109/psc.2018.8664031

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…Nevertheless, the number of OC events depends on several factors, such as the geographic and economic conditions of the country, grid provider, and weather in that particular area (thunderstorms, lightning, temperature, etc.) [4,5]. Protective devices like fuses and circuit breakers can take up to 200 ms and even longer for detecting and clearing the faults [6].…”

Section: Introductionmentioning

confidence: 99%

Over-Current Capability of Silicon Carbide and Silicon Devices for Short Power Pulses with Copper and Phase Change Materials below the Chip

Bhadoria,

Dijkhuizen,

Zhang

et al. 2024

Energies

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An increasing share of fluctuating and intermittent renewable energy sources can cause over-currents (OCs) in the power system. The heat generated during OCs increases the junction temperature of semiconductor devices and could even lead to thermal runaway if thermal limits are reached. In order to keep the junction temperature within the thermal limit of the semiconductor, the power module structure with heat-absorbing material below the chip is investigated through COMSOL Multiphysics simulations. The upper limits of the junction temperature for Silicon (Si) and Silicon Carbide (SiC) are assumed to be 175 and 250 ∘C, respectively. The heat-absorbing materials considered for analysis are a copper block and a copper block with phase change materials (PCMs). Two times, three times, and four times of OCs would be discussed for durations of a few hundred milliseconds and seconds. This article also discusses the thermal performance of a copper block and a copper block with PCMs. PCMs used for Si and SiC are LM108 and Lithium, respectively. It is concluded that the copper block just below the semiconductor chip would enable OC capability in Si and SiC devices and would be more convenient to manufacture as compared to the copper block with PCM.

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Section: Introductionmentioning

confidence: 99%

Over-Current Capability of Silicon Carbide and Silicon Devices for Short Power Pulses with Copper and Phase Change Materials below the Chip

Bhadoria,

Dijkhuizen,

Zhang

et al. 2024

Energies

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“…In waveform analytics, post-fault measures are commonly conducted in the field. The use of cause identification of faults has been proposed [4,5] to assist restoration in case of momentary or temporary faults. This would guide operators in deciding on mitigation actions, and afterwards, in achieving optimized preparation and dispatching field engineers.…”

Section: Introductionmentioning

confidence: 99%

Event Sequence Model Application for Prioritization and Detection of Pre-Fault Waveforms on Power Distribution Lines

Moon

Lee

2020

IEEE Access

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In this paper, a conceptual approach is presented for pre-fault detection regarding the waveform analytic aspects of distribution monitoring and measuring devices. Also included are event patterns specifically arranged by feature and classification methods. A waveform class patterning algorithm on timeseries is applied experimentally to field waveforms that were obtained for several years. The waveforms are processed with consideration of waveform classification and event sequence processing because these detect fault-related phenomena. This approach demonstrates that conspicuous patterns in fault-related sequences can be discovered by the data-driven structure described in the paper by designing the event structure depending on the network configuration. The application is applied in a pattern-learning and pattern-detection process so that integrating both approaches provides a meaningful consolidation for detecting abnormal conditions on distribution lines. This event-based fault prevention is employed using actual acquisition data from a domestic-scale distribution system and a unique sequence model is constructed to determine normal and abnormal conditions. Event index manipulation analysis on different risk levels defines the pattern and its impact on monitoring results. The proposed model guides recognition of event patterns and waveforms that can be pre-emptively detected in advance of distribution line failures.

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