Implementation of an MPPT technique of a solar module with supervised machine learning

Sharmin, Ruhi; Chowdhury, Sayeed Shafayet; Abedin, Farhana; Rahman, K.M.

doi:10.3389/fenrg.2022.932653

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…The dataset contains both the input values (irradiance and temperature) and the output target MPP voltage (Vmpp). All the data needed for training in offline mode can be found in the MATLAB workspace after executing the program MATLAB [43]. To increase prediction accuracy, datasets used to train neural networks must contain a large set of measurements [4].…”

Section: Artificial Neural Network Mppt Algorithmmentioning

confidence: 99%

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

2023

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Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.

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Section: Artificial Neural Network Mppt Algorithmmentioning

confidence: 99%

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

2023

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“…This is possible because, like Hill Climbing algorithms, PSO only requires current and voltage sensors typically included in converters and a microprocessor in order to implement the MPPT algorithm [32]. This provides a significant advantage over other intelligent algorithms, which often require additional sensors to measure environmental conditions, high processing power, or a preliminary study of the system in which they will operate, thus increasing the installation and maintenance costs and time of the plants [33].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

del Rio,

Barambones,

Uralde

et al. 2023

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Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system’s performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm’s search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm’s remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO.

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“…However, these methods have certain limitations, including slow response times and reduced accuracy in tracking the maximum power point. Recognizing the need for more efficient and accurate MPPT techniques, researchers have turned to artificial intelligence (1)(2)(3) . Artificial intelligence, particularly neural networks (NN), has demonstrated immense potential for enhancing the MPPT process in PV systems.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking in A Photovoltaic System Based on Artificial Neurons

Ourici,

Abderaouf

2023

IJST

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Objectives: This study seeks to assess the efficacy of an approach utilizing artificial neural networks for tracking the maximum power point (MPP) in photovoltaic systems. The main objective is to compare this method with the traditional perturb and observe technique, evaluating its effectiveness, particularly when faced with changing weather conditions. Methods: An artificial neural network was employed to model the relationship between the weather conditions such as irradiation and temperature and the maximum power point of the 100 Kw photovoltaic system. They collected data from the installation, including performance measurements and corresponding environmental conditions. The artificial neural network was trained using this dataset to accurately estimate and track the MPP. In our study we have only one input variable which is the power of the photo voltaic generator and a single output which is the cyclic ratio' D', we collected these data from the solar panel simulation with perturb and observe control. The network we built has an input layer with two neurons, a hidden layer with 15 neurons and an output layer with one neuron, we performed a learning on 100 data using Matlab software, we trained and tested this neural network until we obtained a very small quadratic error compared with similar researches. Compared with a P&O method , the maximum power was riched. Findings: The deep learning model exhibited enhanced efficiency in extracting the maximum power point ;particularly ; in the presence of climatic variations. It successfully captured the complex relationships between weather conditions and the MPP, leading to improved power generation and minimized energy losses. The maximum power reached with perturb and observe method (P&O) is 95 kw for a response time of 0.1752 s, on the other hand the neural method is faster with a response time of 0.1567 s and at the same time the maximum power of 100 kw has been reached. Novelty: This study is unique in a way that it employed an artificial neural network for MPP extraction in photovoltaic installations. The neural network we used to simulate our photocoltaic installation produced excellent results: we can clearly see that the PV output voltage remained constant and PV maximum power reached 100 kw despite climatic variations.

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Implementation of an MPPT technique of a solar module with supervised machine learning

Cited by 12 publications

References 36 publications

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

Maximum Power Point Tracking in A Photovoltaic System Based on Artificial Neurons

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