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All Journal International Journal of Electrical and Computer Engineering
Sanjay Kumar Maurya
GLA University
Computer Science & IT Electrical & Electronics Engineering

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A Literature review on industrially accepted MPPT techniques for solar PV system Indresh Yadav; Sanjay Kumar Maurya; Gaurav Kumar Gupta
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v10i2.pp2117-2127

Abstract

Solar energy is a clean renewable energy and it is available around 89,000 TW on the earth surface. To get maximum power from a solar PV system with minimum power transfer loss is one of the main design objectives of an energy transferring network. Power electronic devices perform a very important character for an efficient PV power tracking system control and either incorporates to transfer the generated power to the ac/dc grid or battery storage system. In this case the duty of the power electronics devices used in PV system is to track maximum power point under different operating conditions of environment, so that power tracking efficiency of solar PV system can be improved. This paper encapsulates based the on performance comparisions on the behavior of MPP under uniform and nonuniform operating conditions and selects the optimum duty cycle for industrially accepted MPPT techniques with their algorithm.
Stator interturn short circuit fault identification in doubly-fed induction generator and its analysis using artificial neural network Vivek Kushwaha; Sanjay Kumar Maurya; Arvind Kumar Yadav
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2379-2392

Abstract

Inter-turn short circuit (ITSC) issues are a common electrical failure mainly caused by the deterioration of winding insulation in the machine over time. Failing to detect such issues early can lead to catastrophic consequences. This article investigates the interturn fault in the stator winding of a doubly-fed induction generator (DFIG) used in wind turbines. A flux linkage difference vector (FLDV) model is introduced in this study for fault detection. Additionally, an artificial neural network (ANN) model is proposed to classify these faults. Specifically, a short-circuit fault is induced in each phase of the stator winding, and the faults are classified by assigning different magnitudes to the respective phases. The ANN is trained to identify which phase contains an interturn fault, with output waveform amplitudes of 1, 2, or 3 corresponding to faults in phases "a," "b," and "c." If no fault is present, the waveform magnitude is designated as "0." This approach enables early fault diagnosis by analyzing waveform patterns, thereby preventing overheating caused by short circuits and avoiding severe, irreversible damage to the windings.
Co-Authors Arvind Kumar Yadav Gaurav Kumar Gupta Indresh Yadav Vivek Kushwaha