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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Yulbarsovich, Usmonov Shukurillo
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Control & Systems Engineering Electrical & Electronics Engineering

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Increase the operational reliability of the electric drive of the weaving machine Yulbarsovich, Usmonov Shukurillo; Ugli, Sultonov Ruzimatjon Anvarjon; Ugli, Mamadaliev Musulmonkul Imomali; Saleem, Adeel; Toptiyevna, Kuchkarova Dilnoza
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp704-714

Abstract

The main purpose of this research work is to analyze malfunctions, power consumption, engine overheating and vibrations based on the loadings of electrical circuits through artificial neural networks. The reliability of artificial intelligence systems was proven on the basis of a model-based system depending on the task, and the obtained values were experimentally compared in the electrical operation of existing equipment in general industrial enterprises. An imitation model of the real object was developed. A concept of increasing productivity was set up to identify malfunctions, in contrast to the existing annular method. The article developed an algorithm for increasing the operational reliability of the electrical operation of the weaving machine on the basis of integrated indicators of excitation to determine the probability of failure of electrical operation. The article proposes the possibility of directly processing the diagnostic energy parameters through artificial neural networks. An experimental combination of signals resulted in a model based on input power and torque, and was based on an asynchronous motorized electrical circuit. It has been proven that intellectual reliability can be increased by 3-5% compared to operational reliability in traditional methods.
Advanced control and optimization strategies for a 2-phase interleaved boost converter Samad, Muhammad Adnan; Yulbarsovich, Usmonov Shukurillo; Anvarjon Ugli, Sultonov Ruzimatjon; Siddiqui, Saima
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1421-1429

Abstract

Renowned for their adeptness in smoothing current flow and maintaining balanced operation, 2-phase interleaved boost converters (IBC) demonstrate remarkable efficiency, especially when confronted with demanding loads. This makes them a preferred choice for high-power applications such as renewable energy systems, high-power supplies, and electric vehicle power trains. In contrast, standard boost converters are typically favored in low-power, low-demand scenarios. The control of a 2-phase IBC involves running two boost converters in parallel but with a phase shift to reduce ripple currents, improve efficiency, and increase power handling capabilities. To ensure stability and optimal performance, the control strategies for these converters focus on achieving balanced operation between the phases. Hence, the control of 2-phase IBC presents a significant challenge due to their non-minimum phase behavior. The core focus of this article is the implementation of a composite model predictive control (MPC) technique to regulate a 2-phase interleaved boost converter. It introduces a novel approach, model predictive sliding mode control (MPSMC), which leverages the strengths of both MPC and sliding mode control (SMC). The benefits of this hybrid method, termed MPSMC, are thoroughly developed and simulated using MATLAB/Simulink. The results, as discussed in the respective section, provide an in-depth understanding of its effectiveness in practical applications.
Co-Authors Anvarjon Ugli, Sultonov Ruzimatjon Saleem, Adeel Samad, Muhammad Adnan Siddiqui, Saima Toptiyevna, Kuchkarova Dilnoza Ugli, Mamadaliev Musulmonkul Imomali Ugli, Sultonov Ruzimatjon Anvarjon