Neural network based estimation of maximum power generation from PV module using environmental information

1997 ◽  
Vol 12 (3) ◽  
pp. 241-247 ◽  
Author(s):  
T. Hiyama ◽  
K. Kitabayashi
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Environmental Information ◽  
Maximum Power ◽  
Pv Module

Incremental Learning on a Budget and its Application to Quick Maximum Power Point Tracking of Photovoltaic Systems

2014 ◽  
Vol 18 (4) ◽  
pp. 682-696 ◽  
Author(s):  
Koichiro Yamauchi ◽  
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Incremental Learning ◽  
Maximum Power ◽  
General Regression Neural Network ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point ◽  
General Regression

Recent improvements in embedded systems has enabled learning algorithms to provide realistic solutions for system identification problems. Existing learning algorithms, however, continue to have limitations in learning on embedded systems, where physical memory space is constrained. To overcome this problem, we propose a Limited General Regression Neural Network (LGRNN), which is a variation of general regression neural network proposed by Specht or of simplified fuzzy inference systems. The LGRNN continues incremental learning even if the number of instances exceeds the maximum number of kernels in the LGRNN. We demonstrate LGRNN advantages by comparing it to other kernel-based perceptron learning methods. We also propose a light-weighted LGRNN algorithm, -LGRNNLight- for reducing computational complexity. As an example of its application, we present a Maximum Power Point Tracking (MPPT) microconverter for photovoltaic power generation systems. MPPT is essential for improving the efficiency of renewable energy systems. Although various techniques exist that can realize MPPT, few techniques are able to realize quick control using conventional circuit design. The LGRNN enables the MPPT converter to be constructed at low cost using the conventional combination of a chopper circuit and microcomputer control. The LGRNN learns the Maximum Power Point (MPP) found by Perturb and Observe (P&O), and immediately sets the converter reference voltage after a sudden irradiation change. By using this strategy, the MPPT quickly responds without a predetermination of parameters. The experimental results suggest that, after learning, the proposed converter controls a chopper circuit within 14 ms after a sudden irradiation change. This rapid response property is suitable for efficient power generation, even under shadow flicker conditions that often occur in solar panels located near large wind turbines.

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

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2827 ◽  
Author(s):  
Bouarroudj ◽  
Boukhetala ◽  
Feliu-Batlle ◽  
Boudjema ◽  
Benlahbib ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Radial Basis Function ◽  
Basis Function ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Pv Module ◽  
Mppt Controller ◽  
Power Point

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.

scholarly journals Manufacturing Cost Optimization of Photovoltaic Enterprises Based on Neural Network

2018 ◽  
pp. 167-173
Author(s):  
Weiping ZHANG ◽  
Shuming LI ◽  
Junfeng YU ◽  
Yihua MAO
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power Generation ◽  
Rbf Neural Network ◽  
Cost Optimization ◽  
Maximum Power ◽  
Manufacturing Cost ◽  
Maximum Power Point ◽  
Photovoltaic Power ◽  
Power Point

How to reduce the cost of photovoltaic power generation is the core issue of the survival and development of photovoltaic enterprises. Based on this, the manufacturing cost optimization of photovoltaic enterprises is studied based on neural network. Through the design of cost accounting control of photovoltaic enterprises, a genetic algorithm is proposed to optimize the manufacturing cost of photovoltaic enterprises, which is predicted at the maximum power point of the same photovoltaic power generation system. The results show that the RBF neural network optimized by genetic algorithm not only improves the prediction speed, but also improves the prediction accuracy. Thus, the maximum power point tracking control of photovoltaic power generation can be achieved better, and the manufacturing cost of photovoltaic enterprises can be optimized.

scholarly journals A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 483
Author(s):  
Novie Ayub Windarko ◽  
Muhammad Nizar Habibi ◽  
Bambang Sumantri ◽  
Eka Prasetyono ◽  
Moh. Zaenal Efendi ◽  
...  
Keyword(s):  
Power Generation ◽  
Duty Cycle ◽  
Operating Conditions ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Photovoltaic Panel ◽  
Maximum Power Point Tracker ◽  
Photovoltaic Power ◽  
Power Point

During its operation, a photovoltaic system may encounter many practical issues such as receiving uniform or non-uniform irradiance caused mainly by partial shading. Under uniform irradiance a photovoltaic panel has a single maximum power point. Conversely under non-uniform irradiance, a photovoltaic panel has several local maximum power points and a single global maximum power point. To maximize energy production, a maximum power point tracker algorithm is commonly implemented to achieve the maximum power operating point of the photovoltaic panel. However, the performance of the algorithm will depend on operating conditions such as variation in irradiance. Presently, most of existing maximum power point tracker algorithms work only in a single condition: either uniform or non-uniform irradiance. This paper proposes a new maximum power point tracker algorithm for photovoltaic power generation that is designed to work under uniform and partial shading irradiance conditions. Additionally, the proposed maximum power point tracker algorithm aims to provide: (1) a simple math algorithm to reduce computational load, (2) fast tracking by evaluating progress for every single executed duty cycle, (3) without random steps to prevent jumping duty cycle, and (4) smooth variable steps to increase accuracy. The performances of the proposed algorithm are evaluated by three conditions of uniform and partial shading irradiance where a targeted maximum power point is located: (1) far from, (2) near, and (3) laid between initial positions of particles. The simulation shows that the proposed algorithm successfully tracks the maximum power point by resulting in similar power values in those three conditions. The proposed algorithm could handle the partial shading condition by avoiding the local maxima power point and finding the global maxima power point. Comparisons of the proposed algorithm and other well-known algorithms such as differential evolution, firefly, particle swarm optimization, and grey wolf optimization are provided to show the superiority of the proposed algorithm. The results show the proposed algorithm has better performance by providing faster tracking, faster settling time, higher accuracy, minimum oscillation and jumping duty cycle, and higher energy harvesting.

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