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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Arunachalam Sundaram
Jubail Industrial College
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Assessment of off-shore wind turbines for application in Saudi Arabia Arunachalam Sundaram; Abdullahi Abubakar Mas’ud; Hassan Z. Al Garni; Surajudeen Adewusi
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (395.761 KB) | DOI: 10.11591/ijece.v10i5.pp4507-4513

Abstract

This paper presents models and economic analysis of ten different wind turbines for the region of Yanbu, Saudi Arabia using the hybrid optimization models for energy resources (HOMER) software. This study serves as a guide for decision makers to choose the most suitable wind turbine for Yanbu to meet the target of 58.7GW of renewable energy as part of Saudi Vision 2030. The analysis was carried out based on the turbines initial capital cost, operating cost, net present cost (NPC) and the levelized cost of energy (LCOE). Additionally, the wind turbines were compared based on their electricity production, excess energy and the size of the storage devices required. The results show that Enercon E-126 EP4 wind turbine has the least LCOE (0.0885 $/kWh) and NPC ($23.8), while WES 30 has the highest LCOE (0.142 $/kWh) and NPC ($38.3) for a typical load profile of a village in Yanbu.
Rotating blade faults classification of a rotor-disk-blade system using artificial neural network Abdullahi Abubakar Mas’ud; Ahmad Jamal; Surajuddeen Adewusi; Arunachalam Sundaram
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 12, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v12.i3.pp1900-1911

Abstract

In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
Co-Authors Abdullahi Abubakar Mas’ud Ahmad Jamal Hassan Z. Al Garni Surajuddeen Adewusi Surajudeen Adewusi