A Review of Fuzzy Logic and Artificial Neural Network Technologies Used for MPPT

Abstract

Solar electric power generating stations play a major role in meeting the growing demand for electric power. These generating stations make use of solar photovoltaic (PV) panels to perform the conversion of solar energy to electric energy. However, the solar panel output is highly unpredictable because the output is a function of number of factors; some of which are not in the control of humans like the weather conditions, and the output is also a function of the age of PV panel, dust and other debris collected on the panel, direction and angle of elevation and so on. The solar panels exhibit a low efficiency. Currently, a lot of research is going on to overcome these issues. This paper represents a review of two modern techniques used in solar photovoltaic systems which enhance the extraction of maximum output power in an efficient manner. The Artificial Intelligence Based MPPT Techniques for PV Applications, and, a Forecasting System of Solar PV Power Generation using Wavelet Decomposition and Bias- compensated Random Forest are reviewed and compared in this paper.

An Innovative MPPT Method Based on the Estimated Model of the Photovoltaic Cell considering the Environmental Conditions

“…For the simulation, a standard 57 mm 2 silicon-type PV cell for R.T.C company (French) is selected. For this PV cell at standard conditions of solar radiation (1000 W/m 2 ) and temperature (33 5.…”

“…In recent years, intelligent methods such as fuzzy logic [28], neural networks [29][30][31], neural fuzzy [32,33], and model predictive [34,35] are used more for the PV system MPPT. The intelligent MPPT methods have high accuracy in finding MPP if they are designed accurately and optimally.…”

Implementation of fuzzy logic MPPT based on Arduino in small scale PV pumping system

“…The design of fuzzy logic controller Figure 1 require three steps: the first step; fuzzifcation of input voltage and current with trapezoidal function, the second step: the fuzzy rules as shown in Table 1 which defines the system responses for each condition, then the last step is the defuzzifcation to go back to reel value in this case the duty cycle output for the boost converter insulated gate bipolar transistor (IGBT) switch [10], [11].…”

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

“…Kumar et al [24] show the advantages of using the GA over P&O and IC. The authors [25], that machine learning, FL, and AI techniques appear to be the most useful and promising in the process of harvesting the most power from a solar PV system. According to [26] and [27], different intelligent MPPT approaches are grouped into three categories: offline, online, and hybrid methods.…”