Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

Bansal

Int Trans Electr Energ Syst

2020

Summary Nonlinear load results in inferior power quality and a non‐uniform power demand curve. This gets aggravated due to the charging of electric vehicles. This paper presents a new control approach of integrating a photovoltaic (PV) cell with battery storage to a shunt active power filter (SAPF) for electric vehicle (EV) applications. The multifunctional PV‐Battery‐integrated SAPF (PV‐Battery‐SAPF) performs harmonic mitigation along with clean power generation, energy storage, uniform load demand curve, and battery swapping. It is achieved through a two‐stage topology. In the first stage, maximum power point (MPP) of a PV array is robustly tracked using metaheuristic algorithms like cuckoo search algorithm and particle swarm optimization. In the second stage, an ant colony optimization‐tuned controller is developed which adaptively controls the SAPF to improve the power quality. The suggested method provides efficient MPP tracking, lesser total harmonic distortion, better dynamic system performance, and appropriate charging/discharging of battery leading to increased system reliability. The proposed system is validated in real time on a hardware‐in‐the‐loop (HIL) testing platform.

Section: Motivationmentioning

confidence: 99%

Improving power quality and load profile using PV‐Battery‐SAPF system with metaheuristic tuning and its HIL validation

Bansal

Int Trans Electr Energ Syst

2020

“…Control of the DClink voltage and minimization of THD using PI and fuzzy logic-based control strategy is presented. 15 The ANN-based MPPT methods have faster tracking speed, no oscillations around the MPP, high tracking efficiency, and can be used for all types of power converters. The SAPF has been effectively controlled to minimize at the same time the THD, reactive power, and the current imbalance of nonlinear load with the generated renewable power injection to the grid.…”

Section: Introductionmentioning

confidence: 99%

“…13 A two-stage MPPT controller using ANN for PV systems under fluctuating solar irradiation and temperature was designed. 15 The ANN-based MPPT methods have faster tracking speed, no oscillations around the MPP, high tracking efficiency, and can be used for all types of power converters. The performance of traditional MPPT algorithms is poor as compared with AI-based methodology, particularly in partial shading and fast changing climatic conditions.…”

Section: Introductionmentioning

confidence: 99%

Real‐time implementation of adaptive PV‐integrated SAPF to enhance power quality

Int Trans Electr Energ Syst

Bansal

2019

Summary This paper presents the design and implementation of a photovoltaic‐integrated shunt active power filter (SAPF) to improve the power quality and to generate clean power. The system uses adaptive neuro‐fuzzy inference system (ANFIS)‐based maximum power point tracking and control of synchronous reference frame theory–based SAPF. Various control schemes are implemented in MATLAB and then validated in real‐time using FPGA‐based computation engine of OPAL‐RT 4510. Control techniques built around the artificial neural network, fuzzy logic control, and ANFIS are compared for balanced and unbalanced loads on parameters like total losses with/without compensation, voltage drop, power factor, and total harmonic distortion.

“…On the other hand, in order to tackle the disadvantages of the classical methods, several approaches for MPPT based on artificial neural networks (ANN) have been considered. The first class of the ANN approaches uses the ambient climatic conditions (irradiation and temperature) to estimate the optimum voltage/current, or both the voltage and the current [27][28][29][30][31][32], or uses the climatic conditions to estimate the duty cycle [33,34], in order to ensure the maximum power point operation. A second approach uses the PV-module current and voltage to estimate the optimum voltage [35][36][37], or duty cycle [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

et al. 2019

In this article, a novel maximum power point tracking (MPPT) controller for a photovoltaic (PV) system is presented. The proposed MPPT controller was designed in order to extract the maximum of power from the PV-module and reduce the oscillations once the maximum power point (MPP) had been achieved. To reach this goal, a combination of fuzzy logic and an adaptive radial basis function neural network (RBF-NN) was used to drive a DC-DC Boost converter which was used to link the PV-module and a resistive load. First, a fuzzy logic system, whose single input was based on the incremental conductance (INC) method, was used for a variable voltage step size searching while reducing the oscillations around the MPP. Second, an RBF-NN controller was developed to keep the PV-module voltage at the optimal voltage generated from the first stage. To ensure a real MPPT in all cases (change of weather conditions and load variation) an adaptive law based on backpropagation algorithm with the gradient descent method was used to tune the weights of RBF-NN in order to minimize a mean-squared-error (MSE) criterion. Finally, through the simulation results, our proposed MPPT method outperforms the classical P and O and INC-adaptive RBF-NN in terms of efficiency.