RESEARCH PAPER
DBN-Based MPPT Algorithm with FOPID Control for Optimal Power Tracking, Reliability, and Efficiency in Solar PV Systems under Shaded Conditions
1
Department of Electronics and Communication Engineering,, Loyola- ICAM College of Engineering and Technology,Chennai India
2
Department of Electrical and Electronics Engineering, Government College of Engineering, Bargur, India
Submission date: 2024-10-21
Final revision date: 2024-11-14
Acceptance date: 2024-12-20
Online publication date: 2024-12-26
Publication date: 2024-12-26
Corresponding author
Jenifer Suriya L
Department of Electronics and Communication Engineering,, Loyola- ICAM College of Engineering and Technology, Nungambakkam, Chennai, Tamil Nadu, India
Department of Electronics and Communication Engineering,, Loyola- ICAM College of Engineering and Technology, Nungambakkam, Chennai, Tamil Nadu, India
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(3):199421
HIGHLIGHTS
- Utilizing a grid integration model.
- Proposed intelligent MPP is utilized to locate the overall MPP.
- Utilizing the Green Anaconda Optimization based DBN.
- Increase efficiency and reducing harmonics.
KEYWORDS
maximum power point trackingdeep belief networkpower efficiencyPV panelsFOPID controllergreen anaconda optimization
TOPICS
ABSTRACT
Monitoring the Maximum Power Point Tracking (MPPT) is one among the most crucial jobs in a solar system. Significant issues with solar power generation, including the installation and operation of PV panels, include harmonic distortion and electromagnetic radiation interference. As a result, the MPPT controller in the proposed work utilizes deep learning to track the MPP. Developing a Deep Belief Network (DBN) using the generated dataset allows it to determine the MPPT. Utilizing the Green Anaconda Optimization (GAO) based DBN to provide the reference or attained voltage, the fractional order controller is then used to generate the converter’s pulse. The suggested methods outperform prior MPPT methods in terms of steady state response, oscillation-free operation, loss reduction, and time to settle down at MPP. The suggested model provides very low harmonic values, such as 0.78% for voltage THD and 0.781% for current THD, and accuracy of 99.8%. The outcomes show that, the optimal DBN MPPT approach works better than the controllers.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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