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Journal : Green Engineering: Journal of Engineering and Applied Science

Water Level Monitoring Device with Hybrid Solar Power Based on IoT for River Safety Monitoring Mohammad Ilham Adi Saputra; Sri Arttini Dwi Prasetyowati; Sauqie Fairoozy Firdaus; Imam Rachmat Widodo
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 3 (2025): July : Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i3.217

Abstract

 The Karanggeneng River in Rembang Regency, Central Java, serves as the main water source for the surrounding community but is vulnerable to seawater contamination during the dry season due to decreasing river elevation. To address this issue, this study aims to design and implement a river water elevation monitoring device based on the Internet of Things (IoT) powered by a hybrid Solar Power Plant (PLTS). The device utilizes the MB7360 ultrasonic sensor connected to an ESP32 microcontroller to measure water elevation in real-time and display the data through an LCD and the Blynk application on a smartphone. The methodology includes literature review, device design, system implementation, and field performance testing. Test results show that the sensor can measure water height accurately within a range of 30 cm to 5 meters, and the PLTS system is capable of supplying the required 0.56 Watts of power. The study compared two alternative solutions and selected the ESP32-based system as the best option due to its efficiency, cost-effectiveness, and easy-to-source components. The conclusion of this research indicates that the developed device can provide accurate and continuous information, support monitoring of river conditions to prevent the risk of seawater intrusion, flooding, or drought, and has the potential to be applied as a mobile system in various other river locations across Indonesia.
Motor Speed Control for River Sediment Volume Measurement Using a Fuzzy Logic Controller Nur Azizah Maghfiroh; Muhammad Kevin Hardiansyah; Sri Arttini Dwi Prasetyowati; Nugroho, Agus Adhi; Bustanul Arifin
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 4 (2025): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i4.235

Abstract

The DC motor serves as the main drive of the vessel and is equipped with a rotary encoder that functions to regulate the movement of the sensor in measuring sediment levels. This rotary encoder is also used to monitor and represent the rotational speed of the DC motor. System testing was carried out by implementing a Fuzzy Logic Controller (FLC) algorithm to control the DC motor speed in moving the vessel, ensuring stable motion. This fuzzy logic–based approach is expected to improve accuracy and efficiency in sediment volume calculations, while also reducing potential errors that commonly occur in manual methods. Simulating motor speed control using the fuzzy logic algorithm in MATLAB, the best test results were achieved over several trials, with a rise time of 376.310 ms and an overshoot of 83.33%. Motor speed measurements using both a tachometer and Arduino produced consistent results, with an average relative error of 0.18%.
Feature Extraction Using Discrete Wavelet Transform and Zero Sequence Current for Multi-Layer Perceptron Based Fault Classification Khoirudin, Irfan; Sri Arttini Dwi Prasetyowati
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 4 (2025): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i4.239

Abstract

Application of Multi-Layer Perceptron neural network to fault classification in high-voltage transmission lines is demonstrated in this paper. Different fault types on protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of Discrete Wavelet Transform energy features combined with zero sequence current magnitude as input features for neural network classifier. The proposed method uses eight extracted features to learn hidden relationship in fault signal patterns. Using proposed approach, fault detection and classification of all 11 fault types could be achieved with high accuracy. Improved performance is experienced once the neural network is trained sufficiently with 1188 fault samples, thus performing correctly when faced with different system conditions. Results of performance studies show that proposed neural network-based classifier achieves 96.18% average accuracy, which demonstrates that it can improve the performance of conventional fault classification algorithms, which in turn can provide more efficient solutions in the management and protection of high voltage electrical systems.