Category Solar PV and Wind Energy Conversion Systems

Neural Networks for MPP Tracking

The human brain mainly inspires artificial neural networks. This doesn’t mean that Artificial Neural Networks are exact simulations of the biological neural networks inside our brain because the actual working of human brain is still a mystery. Neural

Network is a machine that is designed to model the way to which the brain performs a particular task. The network is implemented using electronic components or is simulated in software on digital computer. Neural networks perform usual compu­tations through process of learning.

Read More

Simulation and Results

The SIMULINK model of fuzzy logic based maximum power tracking for a solar panel with 60 W is shown in Fig. 3.5. The performance of the SIMULINK model is validated with a PV panel,, buck converter and a resistive load. The objective of the modeled FLC using SIMULINK is to track maximum power irrespective of panel voltage variations. Accordingly FLC uses two input variables: change in PV array Power (APpv) and change in PV array voltage (AVpv) corresponding to the two sampling time instants. Equations (3.3, 3.4, and 3.5) determine the required power, change in array voltage and reference variable. The change in control variable (AVref) is considered as the output variable. (AVref) is integrated to achieve desired Vref value...

Read More


The output of the fuzzy controller is a fuzzy variable. However a crisp quantity is needed. Thus the output of the fuzzy controller ought to be defuzzified. The centroid method of defuzzification is one of the normally utilized defuzzification routines and is the one being utilized for the framework. This system has great averaging proper­ties and recreation effects have demonstrated that it gives the best outcome.

Read More

Inference Method

The Inference technique decides the yield of the fuzzy controller. Mamdani’s inference system is utilized within the recognized framework alongside the max-min creation strategy. This is on the grounds that this system is computation­ally more proficient and has preferable interpolative properties over techniques dependent upon other suggestion capacities. Henceforth, Mamdani’s deduction system is normally prominent for most control building provisions.

Table 3.2 Rule base for the fuzzy model

Read More

Rule Base

The Fuzzy algorithm tracks the maximum power based on the rule-base: If the last change in the reference voltage (Vref) has caused the power to increase keep changing the reference voltage in the same direction; else if it has caused the power to drop, move it in the opposite direction. A rule base consisting of 49 rules is designed as shown in Table 3.2.

Read More


The fuzzy model is developed on a trial-and-error basis to meet the desired performance criteria.

Fig. 3.3 Block diagram of the FLC

The universe of discourse for input variable 1 (APpv) is divided into seven Fuzzy sets: PL (Positive Large), PM (Positive Medium), PS (Positive Small), Z (Zero), NS (Negative Small), NM (Negative Medium) and NL (Negative Large). Here, the Fuzzy set PS assumes a membership value greater than zero beginning at the origin, in order to speed up the start-up process and at the same time prevent variation of the reference voltage at the MPP. Additional Fuzzy sets PM and NM has been added to improve the control surface.

The universe of discourse for input variable 2 (Д Vpv) is divided into seven fuzzy sets: PL (Positive Large), PM (Positive Medium), PS (Positi...

Read More

Description and Design of FLC

Fuzzy model of the framework is planned based on master information of the fuzzy framework. The fuzzy logic controller is partitioned into four segments: Fuzzification, rule base, inference and defuzzification. The inputs to the fuzzy logic controller are change in PV array power (APpv) and change in PV array voltage (AVpv) and the output is the change in reference voltage (AVref) (Fig. 3.3).

Read More


To beat a percentage of the disservices mentioned in past MPPT strategies, fuzzy logic controller is utilized for most extreme force following the PV Panel. The primary distinction from the past routines is that the correct depiction of the framework to be controlled is not needed. Fuzzy logic permits the determination of the principle base by etymological terms and consequently, the tuning of the controller is an extremely basic way which is qualitatively not quite the same as accepted configuration systems. Moreover, fuzzy control is nonlinear and versatile in nature, which provides strong execution under parameter variety, load and supply voltage...

Read More