Hardware-in-the-Loop to Test an MPPT Technique of Solar Photovoltaic System: A Support Vector Machine Approach

Abstract

This paper proposes a new method for maximum power point tracking (MPPT) of the photovoltaic (PV) system while using a DC-DC boost converter. The conventional perturb and observe (P&O) method has a fast tracking response, but it presents oscillation around the maximum power point (MPP) in steady state. Therefore, to satisfy transient and steady-state responses, this paper presents a MPPT method using support vector machines (SVMs). The use of SVM will help to improve the tracking speed of maximum power point of the PV system without oscillations near MPP. A boost converter is used to implement the MPPT method, where the input voltage of the DC-DC converter is regulated using a double loop where the inner loop is a current control that is based on passivity. The MPPT structure is validated by hardware in the loop, a real time and high-speed simulator (PLECS RT Box 1), and a digital signal controller (DSC) are used to model the PV system and implement the control strategies, respectively. The proposed strategy presents low complexity and it is implemented in a commercial low-cost DSC (TI 28069M). The performance of the MPPT proposed is presented under challenging experimental profiles with solar irradiance and temperature variations across the panel. In addition, the performance of the proposed method is compared with the P&O method, which is traditionally most often used in MPPT under demanding tests, in order to demonstrate the superiority of the strategy presented.

Fuzzy Logic Based Synchronization Method for Solar Powered High Frequency On-Board Grid

“…This is achieved by implementing the maximum power point tracking (MPPT) algorithm which, based on the actual value of photovoltaic current i PV and voltage v PV , controls the operation of the boost converter. In this project, the popular MPPT Perturb & Observe method, described in Reference [26], was used. Obtaining maximum power from photovoltaic sources is therefore an independent process, whereby the power ripples resulting from the instantaneous power equilibrium can be decoupled from the photovoltaic cells.…”

Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm

“…Therefore, the capability of the local search is improved for both behaviors. The reproductive and foraging formulas updated after the introduction of the spiral position-updating strategy are presented as Equations ( 14) and (15), respectively.…”

“…At present, the commonly used methods of machine learning include Support Vector Machines (SVMs) [15], neural networks [16], Extreme Learning Machines (ELM) [17], etc. Meng proposed a method in which a correlation analysis was performed to select the factors closely correlated with PV power as inputs before a prediction model was constructed using an improved SVM to enhance the accuracy of prediction [18].…”

“…If an individual belongs to the breeding beetles category, its position is updated according to Equation (14). If the individual belongs to the foraging beetles category, its position is updated according to Equation (15). If the individual belongs to the stealing beetles category, its position is updated according to Equation ( 16).…”

Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review

“…Other algorithm improvements use different branches of artificial intelligence, such as those using Support Vector Machines (SVM) [ 16 , 17 ] or Fuzzy Logic (FL) [ 18 , 19 ]. FL algorithms have the advantage of being based on knowledge bases of the PV system and environmental conditions.…”