Hafidz Nindhom Zen, Ibrohim, Endryansyah, Subuh Isnur Haryudo

Arduino-Based Capacitor Bank Automation for Power Factor Optimization

Introduction

Arduino-based capacitor bank automation for power factor optimization. Optimize power factor and energy efficiency with an Arduino-based automatic capacitor bank system. Dynamically compensates reactive power to reduce power losses & electricity bills.

Abstract

Electrical energy efficiency in PLN customers in the R1 category is a crucial issue due to the low value of the power factor (cos φ), which is caused by the dominance of the use of inductive equipment. This condition not only causes significant energy waste but also puts a strain on the power grid, where the urgency is amplified by various economic factors. This resaerch designed an automatic capacitor bank system to dynamically correct the power factor. By integrating the Arduino Nano microcontroller and the PZEM-004T sensor, the system monitors electrical parameters such as voltage, current, and cos φ in real-time. Based on this data, the system autonomously activates the relay to connect capacitors with the most optimal capacitance value to compensate for reactive power precisely. Its main innovation is an adaptive automation mechanism that is able to respond to load fluctuations. The implementation aims to increase the cost value φ close to 1.0, so that it has great potential to reduce power losses, reduce electricity bills, and improve the overall efficiency of the electrical system.

Review

The paper titled "Arduino-Based Capacitor Bank Automation for Power Factor Optimization" presents a timely and relevant solution to the pervasive issue of low power factor, particularly in settings like PLN R1 category customers where inductive loads dominate. The authors correctly identify that this condition leads to substantial energy waste and grid strain, underscoring the necessity for effective power factor correction. By proposing an automatic capacitor bank system, the research aims to dynamically address these challenges, with the clear objective of improving electrical energy efficiency and mitigating associated economic and operational drawbacks. The proposed system's methodology centers on integrating an Arduino Nano microcontroller with a PZEM-004T sensor for real-time monitoring of critical electrical parameters, including voltage, current, and power factor. This data then drives the autonomous activation of relays to connect capacitors of optimal capacitance, precisely compensating for reactive power. A notable aspect highlighted is the "adaptive automation mechanism," which purports to enable the system to dynamically respond to load fluctuations. This promises a more sophisticated approach than traditional static correction methods, offering a responsive and precise control over power factor optimization. From the abstract, the practical implications of this research appear significant, with the potential to deliver tangible benefits such as reduced power losses, lower electricity bills for end-users, and an overall improvement in the efficiency and stability of the electrical system. The use of an Arduino platform suggests a cost-effective and accessible solution, making it appealing for widespread implementation. While the abstract effectively lays out the conceptual framework and anticipated advantages, a full manuscript would be expected to provide detailed validation results, a comprehensive discussion of the control algorithms, and a thorough cost-benefit analysis to fully substantiate the projected impacts.

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