Ground Fault Detection Method for Variable Speed Drives

Sánchez‐Palencia, Yolanda; Navarro, Gustavo; Mahtani, Kumar; Platero, Carlos A.

doi:10.1109/tia.2021.3064001

Cited by 22 publications

(2 citation statements)

References 29 publications

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“…The fault diagnosis scheme using a variable speed drive is introduced by Guerrero et al 24 for the effective handling of the electronic system. The fault in the variable speed drive is detected by analyzing the terminal voltage signal near the grounding resistor that connects the transformer and ground.…”

Section: Related Workmentioning

confidence: 99%

Support vector machine based fault detection in inverter‐fed electric vehicle

Mestha,

Prabhu

2024

Energy Storage

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Inverters play a prominent role in the power train system of electric vehicles (EVs). Devices in EV connected power system are threatened by faults due to the continuous working and varying speed range of motors in EVs. Hence, in the EV connected application, the detection of fault is essential since it secures the system from severe damage and dangerous operating conditions. This paper deals with fault detection in inverter‐fed EV using a dual‐tree complex wavelet transform (DTCWT) based squeeze net (SN) and optimized support vector machine (SVM). Due to the simple structure and high power density, most EV models on the market are equipped with induction motors. In the proposed work, the voltage, current, and speed signals are measured at different faulty conditions, and then the features are extracted through the DTCWT‐based SN. Extracted data are processed and classified through the sucker‐vulture optimization algorithm (SVOA) based SVM. In the proposed SVOA, the exploration phase of remora optimization algorithm is used for the exploitation phase of African vulture optimization algorithm (AVOA). Thus, the convergence speed of AVOA is improved. The proposed method is implemented in MATLAB/SIMULINK, and the results are used for different scenarios. The accuracy and F1‐score for the proposed methodology are attained as 99.92441% and 99.92441%. From the obtained results, it is clear that the proposed DTCWT‐based SN effectively detects the faults in the inverter.

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Section: Related Workmentioning

confidence: 99%

Support vector machine based fault detection in inverter‐fed electric vehicle

Mestha,

Prabhu

2024

Energy Storage

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“…• "Ground Fault Detection Method for Variable Speed Drives," by Guerrero, Navarro, Mahtani, and Platero, presents a method for detecting ground faults in drive circuitry [12].…”

Section: Industrial Drives Committeementioning

confidence: 99%