Kharisma Aji Saputra Wardani, Miftahur Rohman

Design And Development of An IoT-Based Automatic Magnetic Balance Testing Device for Three-Phase Transformers at PT. Bambang Djaja

Introduction

Design and development of an iot-based automatic magnetic balance testing device for three-phase transformers at pt. Bambang djaja . Develops an IoT-based automatic magnetic balance testing device for three-phase transformers. Uses Arduino Mega & Wemos D1 mini, logs data to Google Spreadsheet for remote monitoring & early defect detection, reducing human error.

Abstract

Magnetic Balance testing on three-phase transformers that are still done manually has the potential to cause human error and delays in defect identification. This research aims to develop an automatic Magnetic Balance testing tool based on the Internet of Things (IoT) using Arduino Mega 2560 microcontroller and Wemos D1 mini (ESP8266) connected to Google Spreadsheet as an online data logger. Voltage regulation on each phase is done automatically through a relay, while the measurement results are displayed on the LCD and recorded in real-time to the Spreadsheet. Based on the test results, the device functions properly and meets the Magnetic Balance testing standards, this is evidenced when we test one of the phases, the voltage value read in the other two phases when summed is close to or in accordance with the phase that is given a voltage. The successful integration of the system with Spreadsheet allows remote monitoring of test results and supports early detection of potential transformer damage. Keywords: Arduino Mega 2560, Google Spreadsheet, Internet of Things, Magnetic Balance, Wemos D1 mini ESP8266.

Review

This paper presents an innovative IoT-based automatic device for Magnetic Balance testing of three-phase transformers, directly addressing critical challenges of human error and delays associated with conventional manual testing methods. The proposed solution offers a significant step forward in industrial asset management by automating a vital diagnostic procedure. By transitioning from a manual approach to an IoT-enabled system, this research aims to substantially enhance the accuracy and efficiency of transformer defect identification, thereby fostering more reliable and predictive maintenance practices in an industrial setting. The methodology employed centers on an Arduino Mega 2560 microcontroller and a Wemos D1 mini (ESP8266) for robust connectivity, seamlessly integrating with Google Spreadsheet for real-time online data logging. A key feature of the design is the automatic voltage regulation on each phase, managed through relays, with measurement results concurrently displayed on an LCD and meticulously recorded in the cloud-based spreadsheet. The abstract highlights successful functional testing, confirming the device's adherence to Magnetic Balance testing standards, particularly evidenced by the characteristic voltage summation rule across the phases. This effective integration of hardware with an online data platform is crucial for enabling remote monitoring capabilities, which are essential for proactive asset management. The primary strength of this work lies in its direct practical application and the successful integration of IoT technologies to address a tangible industrial problem. The capacity for automated tests and remote result monitoring significantly mitigates the potential for human error and accelerates defect identification, leading to improved operational efficiency and reduced downtime for critical transformer assets. While the abstract confidently asserts the device meets testing standards, a full paper could further quantify performance metrics, such as the exact reduction in testing time or a comparative analysis of defect identification accuracy against manual methods. Nonetheless, this research offers a robust and valuable contribution, providing a scalable solution for modernizing and enhancing transformer maintenance protocols.

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