A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter

Mahesh, P. Venkata; Meyyappan, S.; Alla, Rama Koteswara Rao

doi:10.37936/ecti-eec.2022202.246909

Cited by 15 publications

(8 citation statements)

References 22 publications

(29 reference statements)

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“…Fuzzy logic control (FLC) [1,27] handles the system's nonlinearities in a better way, no need a precise mathematical model and also works with defective inputs. The variation in D (ΔD) is FLC output.…”

Section: With Flc Methodsmentioning

confidence: 99%

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Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Mahesh

Meyyappan

Alla

2023

Int. j. electr. comput. eng. syst. (Online)

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Photovoltaic panels use the sun’s radiation on their surface to convert solar energy into electricity. This process is dependent on the temperature of the surface and the intensity of the sun's radiation. To escalate the energy transformation, the solar system must be functioned at its maximum power point (MPP). Every maximum power point tracking (MPPT) technique has a distinct mechanism for tracking maximum power point (MPP). The support vector machine (SVM) regression algorithm is used in this work to develop a novel method for tracking the MPP of a PV panel. The solar panel technical parameters were used to prepare the data for training and testing the SVM model. The SVM algorithm predicts the PV panel's maximum power and relevant voltage for specific irradiation and temperature. The duty cycle of the boost converter corresponding to the maximum power was evaluated using the predicted values. The result of the simulation shows that the proposed control strategy forces the solar panel to work near the predicted MPP. The SVM regression control strategy gives the MPP tracking efficiency of more than 94% for the solar PV system despite variable climatic conditions during its stable state operation. In addition, a comparative analysis of the proposed method was carried out with the existing approaches to confirm the effective tracking of the proposed technique.

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Section: With Flc Methodsmentioning

confidence: 99%

“…For the calculation of RMSE, SSE, and R 2 , the following equations Eq. ( 1), (2), and (3) [26,27] are used.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Mahesh

Meyyappan

Alla

2023

Int. j. electr. comput. eng. syst. (Online)

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“…The linear regression method is a simple ML technique that is suitable for predicting real numbers from available data. It works by predicting unknown data, which is also known as dependent data, from the features, which are referred to as independent data [12,13]. If the data has a single feature, then the univariate linear regression algorithm gives a straight line that predicts the data in a two-dimensional space.…”

Section: Multivariate Linear Regressionmentioning

confidence: 99%

“…On the other hand, if there are multiple features, the MLR algorithm provides a plane in multidimensional space. The general form of the multiple linear regression planes [12] can be expressed as:…”

Section: Multivariate Linear Regressionmentioning

confidence: 99%

Maximizing solar photovoltaic system efficiency by multivariate linear regression based maximum power point tracking using machine learning

Paquianadin,

Navin Sam,

Koperundevi

2024

Electrical Engineering & Electromechanics

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Introduction. In recent times, there has been a growing popularity of photovoltaic (PV) systems, primarily due to their numerous advantages in the field of renewable energy. One crucial and challenging task in PV systems is tracking the maximum power point (MPP), which is essential for enhancing their efficiency. Aim. PV systems face two main challenges. Firstly, they exhibit low efficiency in generating electric power, particularly in situations of low irradiation. Secondly, there is a strong connection between the power output of solar arrays and the constantly changing weather conditions. This interdependence can lead to load mismatch, where the maximum power is not effectively extracted and delivered to the load. This problem is commonly referred to as the maximum power point tracking (MPPT) problem various control methods for MPPT have been suggested to optimize the peak power output and overall generation efficiency of PV systems. Methodology. This article presents a novel approach to maximize the efficiency of solar PV systems by tracking the MPP and dynamic response of the system is investigated. Originality. The technique involves a multivariate linear regression (MLR) machine learning algorithm to predict the MPP for any value of irradiance level and temperature, based on data collected from the solar PV generator specifications. This information is then used to calculate the duty ratio for the boost converter. Results. MATLAB/Simulink simulations and experimental results demonstrate that this approach consistently achieves a mean efficiency of over 96 % in the steady-state operation of the PV system, even under variable irradiance level and temperature. Practical value. The improved efficiency of 96 % of the proposed MLR based MPP in the steady-state operation extracting maximum from PV system, adds more value. The same is evidently proved by the hardware results.

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“…In order to provide the appropriate output current and voltage as required by the load, huge PV arrays are typically built by connecting a multiple of modules in series and parallel [12]. To reduce the hotspot's heat stress on the array's output voltage, the bypass diodes are also positioned in parallel with the PV modules [13], [14]. Figure 2 shows various types of solar PV array configurations.…”

Section: Pv Array Configurationsmentioning

confidence: 99%

Traditional and hybrid solar photovoltaic array configurations for partial shading conditions: perspectives and challenges

Narne

Kumar

Alla³

2023

Bulletin EEI

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he role of photovoltaic (PV) array in converting solar energy to electrical energy is very much important to get maximum power. Current challenge in solar PV systems is to make them energy efficient. Partial shading conditions (PSCs) is one of the main causes for performance degradation of PV array. It not only effects the shaded region but also effect the overall output of the PV array. Proper selection of configuration is essential to overcome such type of challenges. There exist various types of traditional configurations such as series (S), parallel (P), series parallel (SP), total-cross-tied (TCT), bridge-link (BL), and honeycomb (HC). Hybrid configurations also available such as series-parallel-total-cross-tied (SPTCT), bridge-link-total-cross-tied (BL-TCT), honey-comb- total cross-tied (HC-TCT), and bridge-link-honey-comb (BL-HC). This paper presents an overview on various types of configurations available with their merits and demerits under various partial shading situations. This paper also insights recent advancements in PV array configurations with their future trends to benefit the researchers working in this domain.

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A New Multivariate Linear Regression MPPT Algorithm for Solar PV System with Boost Converter

Cited by 15 publications

References 22 publications

Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Support Vector Regression Machine Learning based Maximum Power Point Tracking for Solar Photovoltaic systems

Maximizing solar photovoltaic system efficiency by multivariate linear regression based maximum power point tracking using machine learning

Traditional and hybrid solar photovoltaic array configurations for partial shading conditions: perspectives and challenges

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