Saggu, Tejinder Singh
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THD analysis and its mitigation using DSTATCOM integrated with EV charging station in the distribution network Dhami, Kavita; Saggu, Tejinder Singh
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1990-1997

Abstract

With the increase in carbon emissions, noise pollution and other environmental impacts caused by conventional vehicles, the demand for electric vehicles (EVs) is continuously increasing in the market. The transport sector has also been revolutionized with the use of EVs. The unique features such as reduction in noise pollution, carbon emissions and running costs and the capability of EVs to work in both grid-vehicle (G2V) and vehicle-grid (V2G) have made EVs popular nowadays. Still, it has several effects on the power distribution grid. There are several power issues due to the incorporation of electric vehicles (EVs) in the distribution network such as voltage instability, harmonics, and voltage fluctuations. This research paper focuses mainly on the harmonics caused in the system when EVs are connected to the distribution side. A distributed static compensator (DSTATCOM) based on the d-q theory is introduced to mitigate the harmonics along with the improvement in the voltage profile of the distribution side. By using MATLAB Simulink, the performance of DSTATCOM is validated and the comparison of the proposed approach is also done with that of similar work already existing in the literature.
Fault diagnosis of electric motors using vibration signal analysis Singh, Mandeep; Saggu, Tejinder Singh; Dhingra, Arvind
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp300-307

Abstract

In industrial applications, especially in manufacturing environments, electric motors are employed practically everywhere. They are necessary for many different sectors, which can sometimes make it challenging to prevent malfunctions and keep them operating at their best. Numerous defects can affect how well they work, but bearing-related errors are the most frequent reasons for motor failures. This research uses temporal and frequency domain analysis of vibration signals to identify motor faults. A public domain database has been used for the investigation and analysis. The findings show that electric motor problems, including inner raceway, outer raceway, and rolling element fault, can be identified and diagnosed using the time and frequency domain features extracted from the vibration signals. The effectiveness of the proposed technique is shown by comparing it with both the time domain and frequency domain techniques. The accuracy of the time domain and frequency domain techniques is 85.4% and 91.6% respectively. However, the proposed hybrid technique has a far better accuracy of 95.8% as compared to the two techniques.