Strong inverter fault analysis and protection strategy based on advanced diagnostic technology

As a key equipment in the power electronic system, the stability and reliability of the strong inverter directly affects the operation efficiency of the whole power system. However, since the inverter is in the high-voltage and high-current working environment for a long time, fault problems occur frequently, how to effectively diagnose the faults and formulate a reasonable protection strategy has become an urgent technical problem to be solved. The purpose of this paper is to study the fault analysis and protection strategy of strong power inverter based on advanced diagnosis technology. Firstly, the common fault types and traditional diagnostic methods of strong power inverters are reviewed, and their limitations and shortcomings are analyzed. Subsequently, it focuses on the application of signal processing techniques, artificial intelligence algorithms and multi-source data fusion techniques in inverter fault diagnosis, and proposes a fault diagnosis framework that comprehensively utilizes these advanced techniques. Finally, based on the diagnostic results, this paper designs a set of intelligent protection strategies that can realize adaptive protection adjustment according to the fault types, thus improving the operation reliability of the inverter. The research results show that the fault analysis method and intelligent protection strategy based on advanced diagnostic techniques can effectively improve the fault detection accuracy and response speed of the strong power inverter, which provides a strong guarantee for the safe operation of the power system.

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Publication Details

Journal
Transactions on Engineering and Technology Research
Published
2024-09-13
DOI
https://doi.org/10.62051/ahmkts36
Primary Topic
Smart Grid and Power Systems
Type
article
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article

Strong inverter fault analysis and protection strategy based on advanced diagnostic technology

X. Wang
Transactions on Engineering and Technology Research
Smart Grid and Power Systems
article

Strong inverter fault analysis and protection strategy based on advanced diagnostic technology

X. Wang
article en

Abstract

As a key equipment in the power electronic system, the stability and reliability of the strong inverter directly affects the operation efficiency of the whole power system. However, since the inverter is in the high-voltage and high-current working environment for a long time, fault problems occur frequently, how to effectively diagnose the faults and formulate a reasonable protection strategy has become an urgent technical problem to be solved. The purpose of this paper is to study the fault analysis and protection strategy of strong power inverter based on advanced diagnosis technology. Firstly, the common fault types and traditional diagnostic methods of strong power inverters are reviewed, and their limitations and shortcomings are analyzed. Subsequently, it focuses on the application of signal processing techniques, artificial intelligence algorithms and multi-source data fusion techniques in inverter fault diagnosis, and proposes a fault diagnosis framework that comprehensively utilizes these advanced techniques. Finally, based on the diagnostic results, this paper designs a set of intelligent protection strategies that can realize adaptive protection adjustment according to the fault types, thus improving the operation reliability of the inverter. The research results show that the fault analysis method and intelligent protection strategy based on advanced diagnostic techniques can effectively improve the fault detection accuracy and response speed of the strong power inverter, which provides a strong guarantee for the safe operation of the power system.

Transactions on Engineering and Technology ResearchVol. 3
Ovarian Cancer Canada (CA)
Openalex Percentile: Top 86%
Smart Grid and Power Systems
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Strong inverter fault analysis and protection strategy based on advanced diagnostic technology — X. Wang · Transactions on Engineering and Technology Research (2024) | TGRS Research Map | TGRS