GWO MPPT Fuzzy MPPT P&O MPPT in MATLAB

GWO MPPT Fuzzy MPPT P&O MPPT in MATLAB

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

𝐆𝐫𝐚𝐲 𝐖𝐨𝐥𝐟 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (𝐆𝐖𝐎) 𝐌𝐏𝐏𝐓, 𝐅𝐮𝐳𝐳𝐲 𝐌𝐏𝐏𝐓, and 𝐏𝐞𝐫𝐭𝐮𝐫𝐛 & 𝐎𝐛𝐬𝐞𝐫𝐯𝐞 (𝐏&𝐎) 𝐌𝐏𝐏𝐓 are compared in MATLAB/Simulink for a solar PV system operating under uniform irradiance and partial shading conditions.

The main objective of this simulation is to determine which MPPT technique can track the global maximum power point quickly and accurately when different PV modules receive different solar irradiance levels.

GWO MPPT Fuzzy MPPT P&O MPPT in maltab

₹10,000.00₹5,000.00

Buy Now

Maximum Power Point Tracking (MPPT) is an essential control technique used in solar photovoltaic systems.

The available PV power changes continuously because of:

• Solar irradiance variation

• Temperature variation

• Cloud movement

• Partial shading

• Different operating conditions of PV modules

Under normal uniform irradiance, conventional MPPT techniques can provide satisfactory performance. However, under partial shading, the PV power characteristic can contain several local maximum points.

This makes it difficult for conventional controllers to identify the actual Global Maximum Power Point (GMPP).

In this MATLAB/Simulink study, three MPPT techniques are compared:

• GWO MPPT – Gray Wolf Optimization MPPT

• Fuzzy Logic MPPT

• P&O MPPT – Perturb and Observe MPPT

𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰

The developed MATLAB/Simulink model consists of:

• Four solar PV modules

• Different irradiance inputs for each PV module

• Partial shading arrangement

• GWO MPPT controller

• P&O MPPT controller

• Fuzzy Logic MPPT controller

• PWM generator

• DC–DC boost converter

• IGBT switching device

• DC load

• PV voltage measurement

• PV current measurement

• PV output power measurement

The same PV system and converter configuration are used to evaluate all three MPPT techniques.

𝐏𝐕 𝐀𝐫𝐫𝐚𝐲 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬

Parameter	Value
Number of PV modules	4
Connection	Series
Rated power of one module	249 W
Voltage at maximum power point	30.96 V
Current at maximum power point	8.07 A
Approximate total rated PV power	1000 W
Standard irradiance	1000 W/m²
Standard temperature	25 °C

The four PV modules are provided with independent irradiance inputs. This makes it possible to reproduce both uniform irradiance and partial shading conditions.

𝐏𝐚𝐫𝐭𝐢𝐚𝐥 𝐒𝐡𝐚𝐝𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐕 𝐒𝐲𝐬𝐭𝐞𝐦

Partial shading occurs when individual PV modules receive different levels of solar irradiance.

For example:

• One module may receive 1000 W/m²

• Another may receive 500 W/m²

• Another may receive only 300 W/m²

Under this condition, multiple power peaks can appear.

Therefore, the MPPT controller should distinguish between:

• Local Maximum Power Point (LMPP)

• Global Maximum Power Point (GMPP)

An effective MPPT algorithm should reach the global point instead of becoming trapped around a local peak.

𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬

The basic operating sequence of the MATLAB/Simulink system is:

1. The PV array generates DC power according to irradiance conditions.

2. PV voltage and PV current are measured.

3. These measurements are supplied to the selected MPPT controller.

4. The MPPT controller determines the required converter duty cycle.

5. The duty cycle is sent to the PWM generator.

6. The PWM generator creates switching pulses for the IGBT.

7. The boost converter changes its operating point according to the duty cycle.

8. The PV operating point moves toward the maximum power point.

9. Output power is measured and compared for the three MPPT methods.

𝐆𝐖𝐎 𝐌𝐏𝐏𝐓 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

GWO stands for Gray Wolf Optimization.

It is a population-based optimization technique inspired by the hunting and social hierarchy behavior of gray wolves.

In this simulation, the GWO MPPT controller receives:

• PV voltage, Vpv

• PV current, Ipv

The algorithm searches for the most suitable converter duty cycle corresponding to maximum PV power.

𝐌𝐚𝐢𝐧 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐆𝐖𝐎 𝐌𝐏𝐏𝐓

• Suitable for partial shading conditions

• Capable of global power-point searching

• Reduced possibility of remaining at a local maximum

• Fast tracking in the presented simulation

• Smooth steady-state response

• Suitable for nonlinear PV characteristics

The GWO-generated duty cycle is supplied to the PWM generator, which controls the IGBT of the boost converter.

𝐏&𝐎 𝐌𝐏𝐏𝐓 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

The Perturb and Observe MPPT method is one of the most commonly used MPPT techniques.

The controller continuously observes changes in:

• PV voltage

• PV current

• PV power

Based on changes in PV operating power, the duty cycle is increased or decreased.

𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬

• Simple implementation

• Low computational requirement

• Easy MATLAB/Simulink implementation

• Suitable for normal operating conditions

𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐔𝐧𝐝𝐞𝐫 𝐏𝐚𝐫𝐭𝐢𝐚𝐥 𝐒𝐡𝐚𝐝𝐢𝐧𝐠

Under partial shading, conventional P&O can take more time to identify the desired operating point and may not always distinguish the global maximum from nearby local peaks as efficiently as a global optimization method.

𝐅𝐮𝐳𝐳𝐲 𝐌𝐏𝐏𝐓 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

The Fuzzy Logic MPPT controller uses a rule-based control strategy.

The PV voltage and current measurements are used to obtain control information related to:

• Change in power

• Change in voltage

• Tracking error

• Change in error

The fuzzy inference system processes these inputs and determines the required change in converter duty cycle.

𝐌𝐚𝐢𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

• Does not require an exact mathematical PV model

• Suitable for nonlinear systems

• Can provide fast dynamic response

• Flexible rule-based implementation

• Performance can be improved through membership-function and rule tuning

The quality of Fuzzy MPPT performance depends strongly on the selected:

• Membership functions

• Fuzzy rules

• Scaling factors

• Input ranges

• Output ranges

Therefore, careful tuning is required for reliable global power-point tracking.

𝐁𝐨𝐨𝐬𝐭 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐚𝐧𝐝 𝐏𝐖𝐌 𝐂𝐨𝐧𝐭𝐫𝐨𝐥

A DC–DC boost converter is connected between the PV array and load.

Its switching operation is controlled using the duty cycle generated by the MPPT controller.

The major converter components include:

• Inductor

• IGBT

• Diode

• Input/output capacitors

• Load resistance

• PWM generator

For all three controllers, the objective of the converter is the same: adjust the PV operating point so that maximum available solar power is extracted.

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬

Three major operating conditions are considered.

𝐓𝐞𝐬𝐭 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬

Condition	PV1	PV2	PV3	PV4
Uniform irradiance	1000 W/m²	1000 W/m²	1000 W/m²	1000 W/m²
Partial shading – Case 1	1000 W/m²	1000 W/m²	1000 W/m²	300 W/m²
Partial shading – Case 2	1000 W/m²	1000 W/m²	500 W/m²	300 W/m²

These operating points allow the MPPT algorithms to be evaluated under both simple and difficult PV conditions.

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬

𝐂𝐚𝐬𝐞 𝟏 – 𝐔𝐧𝐢𝐟𝐨𝐫𝐦 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐧𝐜𝐞

All four PV modules are operated at:

1000 W/m²

Under this condition:

• GWO MPPT reaches the maximum power region quickly.

• GWO produces a comparatively smooth response.

• The demonstrated GWO tracking reaches the maximum region at approximately 1.2 seconds.

• P&O requires comparatively more tracking time.

• Fuzzy MPPT performance depends on its rule design and tuning.

The uniform irradiance condition confirms the normal MPPT behavior before introducing partial shading.

𝐂𝐚𝐬𝐞 𝟐 – 𝐏𝐚𝐫𝐭𝐢𝐚𝐥 𝐒𝐡𝐚𝐝𝐢𝐧𝐠

The irradiance values are:

Module	Irradiance
PV1	1000 W/m²
PV2	1000 W/m²
PV3	1000 W/m²
PV4	300 W/m²

𝐏𝐨𝐰𝐞𝐫 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧

MPPT Technique	Extracted Power
GWO MPPT	≈742 W
P&O MPPT	≈729 W
Fuzzy MPPT	≈723 W

For this operating condition, GWO MPPT provides the highest measured power among the three methods.

It also reaches the high-power operating region faster in the demonstrated response.

𝐂𝐚𝐬𝐞 𝟑 – 𝐒𝐞𝐯𝐞𝐫𝐞 𝐏𝐚𝐫𝐭𝐢𝐚𝐥 𝐒𝐡𝐚𝐝𝐢𝐧𝐠

The irradiance values are changed to:

Module	Irradiance
PV1	1000 W/m²
PV2	1000 W/m²
PV3	500 W/m²
PV4	300 W/m²

𝐏𝐨𝐰𝐞𝐫 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧

MPPT Technique	Extracted Power
GWO MPPT	≈486 W
P&O MPPT	≈485 W
Fuzzy MPPT	≈424 W

Again, GWO MPPT produces the highest measured power in the demonstrated test.

P&O also reaches a power level close to GWO in this case, while the Fuzzy controller requires further tuning to obtain improved global tracking.

𝐎𝐯𝐞𝐫𝐚𝐥𝐥 𝐌𝐏𝐏𝐓 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧

Feature	GWO MPPT	P&O MPPT	Fuzzy MPPT
Basic principle	Optimization-based	Perturbation-based	Rule-based
Implementation complexity	Higher	Low	Medium
Global searching capability	Strong	Limited	Depends on design
Partial shading suitability	Very good	Moderate	Good when properly tuned
Tracking speed in demonstrated model	Fast	Slower	Depends on tuning
Local maximum issue	Lower possibility	Possible	Possible if not optimized
Controller tuning	Algorithm parameters	Relatively simple	Membership functions and rules
Computational requirement	Higher	Low	Moderate
Performance in tested cases	Best overall	Good	Requires improvement

𝐊𝐞𝐲 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐬

• GWO MPPT provides the best overall performance for the tested operating conditions.

• GWO demonstrates effective global maximum power-point searching.

• P&O MPPT is simple and effective under conventional operating conditions.

• Under partial shading, P&O may require more time to locate the best operating point.

• Fuzzy MPPT can provide good dynamic performance when properly designed.

• Fuzzy MPPT performance strongly depends on membership functions and inference rules.

• Poorly tuned fuzzy rules can cause the controller to remain around a local maximum.

• Global optimization techniques are especially useful when several PV power peaks exist.

• The boost converter provides the necessary interface between the PV source and DC load.

• Independent irradiance inputs make it possible to test different partial shading patterns.

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

This MATLAB/Simulink model includes:

• GWO-based MPPT

• P&O-based MPPT

• Fuzzy Logic MPPT

• Four series-connected PV modules

• Individual irradiance control

• Partial shading simulation

• Boost converter control

• PWM pulse generation

• IGBT switching

• PV voltage measurement

• PV current measurement

• PV power measurement

• Global maximum power tracking comparison

• Dynamic response comparison

• Multiple irradiance test conditions

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

This simulation concept is useful for studying:

• Solar PV power conversion

• Partial shading effects

• Global MPPT algorithms

• Intelligent MPPT controllers

• Optimization-based renewable energy control

• DC–DC converter control

• Smart solar power systems

• Rooftop PV systems

• Building-integrated PV systems

• PV-fed DC microgrids

• Standalone solar systems

• Renewable energy research

• Comparative analysis of MPPT techniques

𝐖𝐡𝐲 𝐆𝐖𝐎 𝐌𝐏𝐏𝐓 𝐢𝐬 𝐔𝐬𝐞𝐟𝐮𝐥 𝐔𝐧𝐝𝐞𝐫 𝐏𝐚𝐫𝐭𝐢𝐚𝐥 𝐒𝐡𝐚𝐝𝐢𝐧𝐠

The main challenge with partial shading is the presence of multiple possible power peaks.

A conventional controller may identify a nearby peak and treat it as the maximum operating point.

GWO uses an optimization-based search strategy and can explore a wider operating region.

In the demonstrated MATLAB/Simulink cases, this allows GWO to:

• Locate a higher-power operating point

• Reduce local maximum trapping

• Provide fast tracking

• Produce a smooth response

• Extract slightly or significantly higher power depending on the shading condition

This makes GWO particularly attractive when the PV array is exposed to non-uniform solar irradiation.

𝐖𝐡𝐲 𝐅𝐮𝐳𝐳𝐲 𝐌𝐏𝐏𝐓 𝐍𝐞𝐞𝐝𝐬 𝐏𝐫𝐨𝐩𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠

Fuzzy Logic MPPT performance is directly influenced by controller design.

For improved results, attention should be given to:

• Input membership functions

• Output membership functions

• Number of fuzzy sets

• Fuzzy rule base

• Input scaling

• Output scaling

• Sampling time

• Duty-cycle limits

A well-tuned Fuzzy MPPT controller can provide fast and smooth performance. However, inappropriate rules can result in poor global maximum power-point tracking.

𝐆𝐖𝐎 𝐯𝐬 𝐅𝐮𝐳𝐳𝐲 𝐯𝐬 𝐏&𝐎 𝐌𝐏𝐏𝐓 – 𝐖𝐡𝐢𝐜𝐡 𝐢𝐬 𝐁𝐞𝐭𝐭𝐞𝐫?

There is no single MPPT technique that is ideal for every implementation.

For this particular MATLAB/Simulink comparison:

• Choose GWO MPPT when global tracking under partial shading is the main priority.

• Choose P&O MPPT when simplicity and low computational effort are important.

• Choose Fuzzy MPPT when intelligent rule-based nonlinear control is required and sufficient controller tuning can be performed.

Based on the demonstrated operating conditions, GWO MPPT gives the best overall power-tracking performance.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

The MATLAB/Simulink comparison of GWO MPPT, Fuzzy MPPT, and P&O MPPT demonstrates the importance of selecting an appropriate tracking algorithm for solar PV systems operating under partial shading.

The developed system uses four series-connected PV modules, a DC–DC boost converter, PWM control, and three different MPPT techniques.

The simulation results show that:

• GWO MPPT achieved approximately 742 W for the 1000–1000–1000–300 W/m² shading condition.

• P&O MPPT achieved approximately 729 W.

• Fuzzy MPPT achieved approximately 723 W.

• For the 1000–1000–500–300 W/m² condition, GWO achieved approximately 486 W.

• P&O achieved approximately 485 W.

• Fuzzy MPPT achieved approximately 424 W.

Therefore, among the tested controllers and operating conditions, GWO MPPT provides the strongest overall global maximum power-point tracking performance, particularly when partial shading creates multiple possible operating peaks.