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All Journal Journal of Mechanical Design and Testing (JMDT)
Prayogo, Bobby
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Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Multi-Criteria Decision-Making The Selection of IoT-Based Inverter Smart Grid System and Smart Meter for Solar Photovoltaic and Wind Turbine Installations in Pelabuhan Ratu CFPP using AHP & TOPSIS Method Prayogo, Bobby; Yudisaputro, Hendra
Journal of Mechanical Design and Testing Vol 6, No 2 (2024): Articles
Publisher : Department of Mechanical and Industrial Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jmdt.101112

Abstract

The selection of appropriate inverters is pivotal in maximizing the efficiency and performance of solar photovoltaic (PV) and wind turbine systems, as they directly impact the overall energy conversion efficiency and system output by influencing efficiency and reliability. Inverter selection also encompasses critical criteria like cost, compatibility with renewable energy sources, and environmental considerations. Thus, an exhaustive and systematic approach is essential to effectively evaluate and compare different inverter options. This study employs multi- criteria decision-making to address these challenges, evaluating the identified criteria using the Analytical Hierarchy Process (AHP) and ranking them to determine the optimal solution via Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS). Consequently, the investigation identifies smart grid and smart meter inverters as the ideal solutions, successfully addressing concerns regarding power stability, communication and connectivity, security, data management, and cost. The proposed methodology yields a substantial total equity portion of USD 9,325.71, accompanied by impressive earnings before interest, taxes, depreciation, and amortization (EBITDA) of USD 1,734.09 per year. The estimated payback period is 6.85 years, and the return on investment (ROI) reaches a remarkable 338.07%. Additionally, the net income significantly reduces the production cost, with USD 40,853.34 in a single period.
Generator Early Warning System Based on Partial Discharge & Operation Parameters to Prevent Catastrophic Failure Mahardhika, Aditya; Prayogo, Bobby
Journal of Mechanical Design and Testing Vol 7, No 1 (2025): Articles
Publisher : Department of Mechanical and Industrial Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jmdt.108701

Abstract

Generators are essential components in the power generation industry, responsible for maintaining a continuous and reliable electricity supply. Ensuring their health is critical to avoid costly downtime and catastrophic failures. Traditional offline health assessments delay the detection of potential issues and may not provide accurate diagnostics. Partial Discharge (PD) analysis has become a valuable tool for identifying insulation faults in generator stators by measuring discharge magnitudes. However, despite the implementation of PD technology, catastrophic failures still occur, often due to a lack of understanding of PD analysis and the absence of an effective early warning system. To address these issues, an innovative online early warning system has been developed, utilizing Digital Signal Input Modules (DSIM) connected to Program Vision for real-time data collection and PD analysis. This system significantly enhances diagnostic capabilities by not only monitoring PD magnitude trends but also incorporating operational parameter comparisons to swiftly identify the source of any anomalies. The creation of a comprehensive online monitoring dashboard, which integrates all generator operational parameters, enables real-time health assessments and provides operators with actionable insights, thereby improving maintenance strategies and drastically reducing the risk of unexpected failures. This enhanced system empowers operators to proactively address potential issues, ensuring greater generator reliability and minimizing operational disruptions.
Co-Authors Mahardhika, Aditya Yudisaputro, Hendra