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All Journal Brilliance: Research of Artificial Intelligence
Elgallai, Tareg
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Decision Sciences, Operations Research & Management Mathematics Other

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Fault Detection and Condition Monitoring in Induction Motors Utilizing Machine Learning Algorithms Elgallai, Tareg
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3539

Abstract

Electric induction motors (IM) are considered to be a highly significant and extensively utilized category of machinery within contemporary industrial settings. Typically, powerful motors, which are frequently essential to industrial processes, are equipped with integrated condition-monitoring systems to support proactive maintenance and the identification of faults. Typically, the cost-effectiveness of such capabilities is limited for tiny motors with a power output of less than ten horsepower, given their relatively low replacement costs. Nevertheless, it is worth noting that several little motors are commonly employed by large industrial facilities, mostly to operate cooling fans or lubricating pumps that support the functionality of larger machinery. It is possible to allocate multiple small motors to a single electrical circuit, so creating a situation where a malfunction in one motor could potentially cause damage to other motors connected to the same circuit. Hence, there exists a necessity to implement condition monitoring techniques for collections of small motors. This paper presents a comprehensive overview of a continuous effort aimed at the development of a machine learning-driven solution for the identification of faults in a multitude of small electric motors.
Modeling and Power Quality Enhancement of PV Interfaced with Power Grid System Elgallai, Tareg
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3417

Abstract

Recently, photovoltaic (PV) technology has significantly contributed to the utilization of renewable energy sources. It plays a crucial role in addressing the challenges posed by climate change, enhancing energy affordability, ensuring power supply stability, and facilitating energy accessibility. PV technology represents a significant opportunity for addressing the issue of energy access among distant areas that now lack reliable access to electricity. Using a traditional grid is limited due to cost and feasibility constraints. These difficulties are to be addressed in the appropriate timeframe to improve the dependability of the supply. The expansion of the grid into these regions diminishes the overall resilience of the grid system. This leads to a situation where PV systems are integrated into an AC grid with low power quality (PQ) or limited capacity. Nevertheless, the integration of solar power into an AC grid with low capacity. The presence of PQ problems imposes limitations on the levels of penetration. Other factors impose limitations and penetration levels encompass several factors such as non-linear loads, dynamic loads, fluctuating irradiations, and partial shade, among others. This article aims to address the PQ difficulties that arise from a weak electrical infrastructure. The purpose of this compilation is to provide engineers with a readily accessible resource, offering them a distinct advantage in their professional endeavors and scholars engaged in this field of investigation. In this context, the photovoltaic (PV) array is methodically organized into 86 parallel strings. The aforementioned strings are meticulously constructed, consisting of a sequence of seven SunPower SPR-415E solar modules that are intentionally interconnected.
Fault Detection and Condition Monitoring in Induction Motors Utilizing Machine Learning Algorithms Elgallai, Tareg
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.3539

Abstract

Electric induction motors (IM) are considered to be a highly significant and extensively utilized category of machinery within contemporary industrial settings. Typically, powerful motors, which are frequently essential to industrial processes, are equipped with integrated condition-monitoring systems to support proactive maintenance and the identification of faults. Typically, the cost-effectiveness of such capabilities is limited for tiny motors with a power output of less than ten horsepower, given their relatively low replacement costs. Nevertheless, it is worth noting that several little motors are commonly employed by large industrial facilities, mostly to operate cooling fans or lubricating pumps that support the functionality of larger machinery. It is possible to allocate multiple small motors to a single electrical circuit, so creating a situation where a malfunction in one motor could potentially cause damage to other motors connected to the same circuit. Hence, there exists a necessity to implement condition monitoring techniques for collections of small motors. This paper presents a comprehensive overview of a continuous effort aimed at the development of a machine learning-driven solution for the identification of faults in a multitude of small electric motors.
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