Power quality disturbances analysis of BLDC motor drive using wavelet transform

Jose, Shobha; Singh, Ritu

doi:10.1063/5.0000434

Cited by 1 publication

(1 citation statement)

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“…The indices were compared with some different techniques to detect the Inter -turn fault occur in BLDC drive. Edwin jose.s et al [22] had proposed wavelet transform (WT) technique to overcome the power quality disturbances present in BLDC drive. They had determined the Hysteresis Current Controller (HCC) and Digital proportional integral controller (DPIC) in BLDC drive to predict the issues with the help of WT algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prognosis Of BLDC Drive Faults Using Autoregressive Integrated Moving Average Algorithm

Sarman¹,

Madhu

Prasad

2023

Preprint

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Generally, Brushless DC (BLDC) machines attract many industrialists due to their unique characteristics like better output, stabilized performance, and high torque to current ratio. BLDC drive has a long life, and they do not need maintenance; however, the drive has low starting torque and high cost. Thus, Non-stop monitoring and future prediction methods can reduce fault occurrence and improve system performance. In this paper, we have proposed the prognosis of BLDC drive faults using the Autoregressive Integrated Moving Average (ARIMA) Algorithm. Here, we consider the open circuit (OC) and short circuit (SC) faults in BLDC drive to prognosis by ARIMA technique. The ARIMA has a fixed structure, and it is particularly built for time series data. By data acquisition system, the drive parameters such as current, torque, and speed will be continuously obtained. Filtering out the high-frequency noise present in the data is the main principle of the ARIMA model. Matlab/Simulink platform is used to implement the process and analyze the results using prediction efficiency, the fault analysis in speed, flux, torque, current, and voltage.

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Section: Introductionmentioning

confidence: 99%