Reduction and control of harmonic on three-phase squirrel cage induction motors with voltage source inverter (VSI) using ANN-grasshopper optimization shunt active filters (ANN-GOSAF)

Araoye, Timothy Oluwaseun; Ashigwuike, Evans Chinemezu; Adeyemi, Adeyinka Cornelius; Egoigwe, Sochima Vincent; Ajah, Nnaemeka Genesis; Eronu, Emmanuel

doi:10.1016/j.sciaf.2023.e01785

Scientific African

2023

DOI: 10.1016/j.sciaf.2023.e01785

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Reduction and control of harmonic on three-phase squirrel cage induction motors with voltage source inverter (VSI) using ANN-grasshopper optimization shunt active filters (ANN-GOSAF)

Timothy Oluwaseun Araoye

Evans Chinemezu Ashigwuike

Adeyinka Cornelius Adeyemi

et al.

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2024

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Cited by 4 publications

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“…They are made up of artificial neurons that are networked together and can learn from data to anticipate the future or carry out tasks. Recently, various techniques for determining the characteristics of induction motors have been developed by researchers [12,21,22]. One of these techniques was used to identify the characteristics of induction motors operating in the electromechanical mode using the transient stator current.…”

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Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Gör

2024

Mathematics

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Industry is the primary application for induction machines. As such, it is essential to calculate the induction devices’ electrical properties accurately. With DC testing, no-load rotor tests, and locked rotor tests, one may empirically evaluate the electrical variables of induction motors. These tests are expensive and difficult to conduct, however. The information supplied by machine makers can also be used to accurately approximate the equivalent variables of the circuits in induction machines. This article has successfully predicted motor reactance (Xm) for both double- and single-cage models using artificial neural networks (ANN). Although ANNs have been investigated in the literature, the ANN structures were trained to use unmemorized training. Besides ANN, six other approaches have been suggested to address this issue: heap-based optimization (HBO), leagues championship algorithm (LCA), multi-verse optimization (MVO), osprey optimization algorithm (OOA), cuckoo optimization algorithm (COA), and sooty tern optimization algorithm (STOA). The efficaciousness of the suggested approaches was compared with each another. Regarding the obtained outcomes, the suggested MVO- multi-layer perceptron (MLP) technique performed better than the other five methods regarding reactance prediction, with R2 of 0.99598 and 0.9962, and RMSE of 20.31492 and 20.80626 in the testing and training phases, respectively. For the projected model, the suggested ANNs have produced great results. The novelty lies in the mentioned methods’ ability to tackle the complexities and challenges associated with induction motor reactance optimization, providing innovative approaches to finding optimal or near-optimal solutions. As researchers continue to explore and refine these techniques, their impact on motor design and efficiency will likely grow, driving advancements in electrical engineering.

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confidence: 99%

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Gör

2024

Mathematics

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Three novel machine learning-based adaptive controllers for a photovoltaic shunt active power filter performance enhancement

Azzam Jai,

Ouassaid

2024

Scientific African

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Investigation of the Composite Motor Load in the Presence of Higher Harmonics in the Electrical Network

Shklyarskiy,

Lobko,

Kuznetsova

et al. 2024

Izv. vysh. ucheb

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The article discusses the methods of substitution of a compound motor load as a consumer of higher harmonics. Two substitution schemes were used. The calculation of the complex resistance against them was compared with the results of simulation modeling. The simulation was carried out in the MATLAB Simulink environment, with the use of the Specialized Power Systems library, in which a simulation model with motors and a source of harmonic distortion was developed. Five asynchronous motors with a squirrel-cage rotor of the AIR brand with powers varying from 0.75 to 5.50 kW were selected for the study. The simulation was carried out at different motor loads. The shaft torque varied with values of 50; 70; 90 and 100 % of the nominal values of each motor. A six-pulse diode rectifier was used as a source of harmonic currents in the network, generating harmonics with numbers 5, 7, ..., 25, corresponding to a pulse rate equal to six. Oscillograms of currents and voltages were recorded at the point of common connection of asynchronous motors and the rectifier, which were subsequently used to calculate the complex resistance. Based on the results of the work, amplitude-frequency and phase-frequency characteristics of the impedance of a composite motor load were obtained, the analysis of which revealed the incongruence of calculation methods with simulation modeling. Conclusions have been drawn on the need to continue research in this direction, since there are qualitative discrepancies in the functional dependence of the complex resistance on the harmonic number with the experimental data obtained for a single asynchronous motor. The results of the work can be used in calculating the voltage distortion factor and modeling modes associated with higher harmonics both at existing enterprises and during their design, which will increase the reliability and efficiency of electrical networks.

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Reduction and control of harmonic on three-phase squirrel cage induction motors with voltage source inverter (VSI) using ANN-grasshopper optimization shunt active filters (ANN-GOSAF)

Cited by 4 publications

References 22 publications

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Feasibility of Six Metaheuristic Solutions for Estimating Induction Motor Reactance

Three novel machine learning-based adaptive controllers for a photovoltaic shunt active power filter performance enhancement

Investigation of the Composite Motor Load in the Presence of Higher Harmonics in the Electrical Network

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