Single Axis Solar Tracker for Maximizing Power Production and Sunlight Overlapping Removal on the Sensors of Tracker

Nahar, Mst Jesmin; Sarkar, Md Rasel; Uddin, Moslem; Hossain, M. F.; Rana, Masud; Tanshen, Md. Riyad

doi:10.31763/ijrcs.v1i2.333

Cited by 14 publications

(5 citation statements)

References 26 publications

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“…NNs model of the plant should be constructed at the system identification step. Then, the controller is designed or trained using the developed model [19], [27], [18], [28].…”

Section: Neural Network In Process Modeling and Controlmentioning

confidence: 99%

Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

Abougarair

Aburakhis

Edardar

2022

IJRCS

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The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system TWMR is stabilized utilizing a robust output feedback control called LQOSEIC. This controller allows a linearized model to emulate a model reference for the original nonlinear system. However, it works for a limited range of operations and will fail if the plant characteristics are unknown or uncertain. An adaptive neural network is used to overcome this problem. The adaptive neural controller is trained offline using LQOSEIC to obtain the initial weights of neurons for the network's hidden layers. After finishing the training, the LQOSEIC will be replaced by the adaptive neural controller. The main advantage of a neuro-controller is its ability to update the weights online depending on the error signal. If there are any disturbances or uncertainties that arises within the concerned nonlinear system, the neuro-controller will be able to handle it because of online learning that compensates for the effect of unpredictable conditions. The proposed adaptive neural network improves control performance and ensures the robust stability of the closed-loop control system. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.

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“…NNs model of the plant should be constructed at the system identification step. Then, the controller is designed or trained using the developed model [19], [27], [18], [28].…”

Section: Neural Network In Process Modeling and Controlmentioning

confidence: 99%

Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

Abougarair

Aburakhis

Edardar

2022

IJRCS

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“…Teknologi Photovoltaic (PV) adalah konversi energi matahari menjadi listrik menggunakan sel surya. Sekarang, dalam pertumbuhan energi terbarukan sistem ini banyak digunakan dalam kehidupan sehari-hari [12]. Energi listrik yang di hasilkan dari panel surya tergantung pada beberapa faktor yaitu bahan pembuatan, intensitas cahaya matahari temperatur dan posisi panel terhadap arah datangnya matahari.…”

Section: Pendahuluanunclassified

“…Arduino Mega adalah jenis produk Arduino dengan mikroprosesor ATMega 2560 yang terpasang pada papan sirkuit. Dibandingkan dengan Arduino Nano dan Arduino Uno, board ini memiliki lebih banyak pin I/O, terdapat 54 pin I/O digital di papan, termasuk 16 pin input analog (15 di antaranya adalah PWM) dan 4 pin UART (port serial perangkat keras) [12].…”

Section: Desain Sistemunclassified

Prototipe Solar Tracking Berbasis Arduino dan Sensor Light Dependent Resistor (LDR)

Saputra

Ma’arif

2022

Buletin Ilmiah Sarjana Teknik Elektro

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The government prioritizes the availability of electrical energy, especially in the industrial sector and urban areas, but the infrastructure to provide access to electricity in rural areas, especially street lighting, is still very lacking as a result of the need for a different energy source to overcome this problem. Solar cells are power plants that can convert solar energy into electricity, but most solar panels are installed statically which results in less than optimal absorption of solar energy. The purpose of this research is to make a prototype solar tracking based on the Arduino Mega 2560 microcontroller with the fuzzy logic method with an LDR sensor as a light sensor and a servo motor as a driver so that the solar panel can track the movement of the sun to get the maximum light intensity. The results of this study there are four test conditions which indicate that the percentage of the comparison value of solar panels statically and tracking in cloudy conditions is 34.07% while the test in cloudy sunny conditions is 25.603%. Pemerintah memprioritaskan ketersediaan energi listrik khususnya di sektor industri dan wilayah perkotaan tetapi infrastruktur untuk memberikan akses listrik di daerah pedesaan terutama penerangan jalan masih sangatlah kurang akibatnya perlunya sumber energi yang berbeda diperlukan untuk mengatasi masalah ini. Sel surya adalah pembangkit listrik yang dapat mengubah energi matahari menjadi listrik namun sebagian besar Panel surya dipasang secara statis yang mengakibatkan penyerapan energi matahari kurang optimal. Tujuan dari penelitian ini adalah membuat Prototype solar tracking berbasis mikrokontroler Arduino Mega 2560 dengan metode fuzzy logic dengan sensor LDR sebagai sensor cahaya dan motor servo sebagai penggerak sehingga panel surya dapat melacak pergerakan matahari untuk mendapatkan intensitas cahaya yang maksimal. Hasil dari penelitian ini terdapat empat kondisi pengujian yang menunjukkan bahwa persentase nilai perbandingan panel surya secara statis dan tracking dalam kondisi mendung sebesar 34,07% sedangkan pengujian dalam kondisi cerah Berawan sebesar 25,603%.

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“…Changes in the direction of light emitted by the sun and sensor detection can be utilized to monitor the movement of the sun. Sensors commonly used in this research context include photodiodes or LDRs (Light Dependent Resistors) [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Two-Axis Solar Panel Tracking Device Prototype With Iot-Based Monitoring

Awan,

Sofwan,

Multi

2024

IJST

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The aim of this project is to develop a dual-axis solar tracker equipped with an IoT monitoring system with the BLYNK platform using Arduino. Overall, solar energy refers to technologies that generate power from sunlight. The utilization of solar energy has been around for centuries and has been widely used without relying on other energy supplies. However, its importance is increasing as environmental awareness grows and the availability of other energy sources, such as fuel, becomes limited. Sun tracking systems are an effective technology that can improve the efficiency of solar panels by following the movement of the sun. With the help of this system, solar panels can automatically orient themselves to sunlight, improve sunlight detection, and increase electricity collection because solar panels can always be in a bright position. This project focuses on developing a two-axis sun tracker using Arduino Uno as the main controller. For its implementation, four light-sensitive resistors (LDRs) are used to detect sunlight and maximum light intensity. Two servo motors are used to move the solar panel according to the direction of sunlight detected by the LDRs. Furthermore, an ESP8266 WIFI device is used as an intermediary between the device and the IoT monitoring system. The IoT monitoring system is a website that serves to store data. The efficiency of this system is tested and compared with single axial sun tracking. The results show that the two-axis solar tracking system generates more power, voltage, and current compared to the single axial solar tracker.

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Single Axis Solar Tracker for Maximizing Power Production and Sunlight Overlapping Removal on the Sensors of Tracker

Cited by 14 publications

References 26 publications

Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

Prototipe Solar Tracking Berbasis Arduino dan Sensor Light Dependent Resistor (LDR)

Two-Axis Solar Panel Tracking Device Prototype With Iot-Based Monitoring

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