Adaptive PSO MPPT for Solar PV System Under Partial Shading
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Adaptive PSO MPPT for Solar PV System Under Partial Shading
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧
Adaptive PSO MPPT for Solar PV System Under Partial Shading
Solar photovoltaic power generation depends strongly on environmental conditions such as:
Solar irradiance
Temperature
Uniform illumination
Partial shading
Rapid variation in sunlight
Under uniform irradiance, the PV power-voltage characteristic normally contains a clear maximum power point. However, under partial shading, multiple local power peaks can appear.
Conventional MPPT techniques may become trapped around a local peak. Optimization-based techniques such as PSO are therefore useful for searching a wider operating region.
The proposed implementation improves conventional PSO by introducing adaptive PSO parameters during the optimization process.
𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰
The MATLAB/Simulink model consists of the following main sections:
Solar PV array
Irradiance inputs for individual PV sections
PV voltage measurement
PV current measurement
Adaptive PSO MPPT controller
Duty-cycle generation
PWM generator
Boost converter
DC load
Load voltage measurement
Load current measurement
PV and load power monitoring
The basic power flow is:
PV Array → Boost Converter → DC Load
The Adaptive PSO controller provides the optimum duty-cycle command to the PWM generator controlling the boost converter switch.
𝐏𝐕 𝐒𝐲𝐬𝐭𝐞𝐦 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬
The demonstrated PV configuration contains four PV sections connected in series.
Parameter | Value / Configuration |
Number of PV sections | 4 |
Approximate power per section | 62–63 W |
Total PV maximum power | Approximately 250 W |
Connection | Series |
Main measured inputs | PV voltage and PV current |
Converter | DC-DC boost converter |
MPPT technique | Adaptive PSO |
Controller output | Duty cycle |
Load type | DC load |
Demonstrated optimization iterations | 4 |
This configuration is particularly useful for demonstrating the effect of non-uniform irradiance across individual PV sections.
𝐖𝐡𝐲 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐏𝐒𝐎?
In conventional PSO-based MPPT, several important optimization parameters are normally maintained at fixed values.
These include:
Inertia weight
Personal learning coefficient
Global learning coefficient
In the adaptive approach, these parameters are modified during the optimization process.
Feature | Conventional PSO | Adaptive PSO |
Inertia weight | Fixed | Updated adaptively |
Personal learning coefficient | Fixed | Updated during iterations |
Global learning coefficient | Fixed | Updated during iterations |
Response to changing irradiance | Moderate | Improved |
Search flexibility | Fixed behavior | Adaptive behavior |
Partial shading capability | Good | Better suited for changing conditions |
Global MPP search | Possible | Enhanced through parameter adaptation |
The main objective is to improve the balance between exploration and exploitation during the MPPT search.
𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲
The Adaptive PSO controller receives two important feedback signals:
PV voltage
PV current
From these measurements, PV power is evaluated internally by the controller.
The Adaptive PSO algorithm then searches for the duty cycle that produces the highest PV power.
The control sequence can be summarized as:
Measure PV voltage and current.
Initialize a set of candidate duty-cycle values.
Apply each candidate duty cycle to the converter.
Observe the corresponding PV power.
Store the best solution obtained by each particle.
Determine the best solution among all particles.
Update the adaptive PSO parameters.
Update particle movement and duty-cycle candidates.
Repeat the optimization process.
Apply the optimum duty cycle to the PWM generator.
This allows the operating point of the PV array to move toward the maximum available power point.
𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐈𝐧𝐞𝐫𝐭𝐢𝐚 𝐖𝐞𝐢𝐠𝐡𝐭
One of the important differences between conventional and Adaptive PSO is the treatment of the inertia weight.
Instead of keeping the inertia weight constant, the controller considers information such as:
Particle fitness
Average fitness
Minimum fitness
Maximum fitness
Current optimization iteration
The inertia weight is then modified depending on the quality of the particle solution.
This adaptive behavior can help the algorithm perform:
Wider searching when required
Finer searching near a promising solution
Faster adjustment to changing PV conditions
Improved convergence toward the global power point
𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐬
The personal and global learning coefficients are also varied during the optimization iterations.
These coefficients influence how much each particle follows:
Its own previous best solution
The best solution found by the complete swarm
The adaptive update provides a better balance between individual particle searching and global swarm searching.
This is particularly valuable during partial shading because the controller may need to distinguish between local power peaks and the global maximum power point.
𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬
1. PV Power Generation
The PV array receives separate irradiance inputs for its individual sections.
The operating conditions of each section can therefore be changed independently.
2. Voltage and Current Measurement
The PV voltage and current are continuously measured and supplied to the Adaptive PSO controller.
3. Initial Duty-Cycle Search
Several candidate duty cycles are generated during initialization.
Each duty cycle produces a different PV operating point.
4. Fitness Evaluation
For every candidate operating point, the corresponding PV power is evaluated.
Higher PV power represents a better particle solution.
5. Best Solution Identification
The controller determines:
Particle best solution
Global best solution
6. Adaptive Parameter Update
The PSO parameters are modified according to the optimization condition and current iteration.
7. Duty-Cycle Update
New duty-cycle candidates are generated based on the updated swarm information.
8. PWM Generation
The selected duty cycle is converted into switching pulses for the boost converter.
9. Maximum Power Extraction
The boost converter adjusts the PV operating point so that maximum available solar power can be delivered to the load.
𝐔𝐧𝐢𝐟𝐨𝐫𝐦 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐧𝐜𝐞 𝐓𝐞𝐬𝐭
The first operating condition applies identical irradiance to all four PV sections.
PV Section | Irradiance |
Section 1 | 1000 W/m² |
Section 2 | 1000 W/m² |
Section 3 | 1000 W/m² |
Section 4 | 1000 W/m² |
Under this condition:
All PV sections receive uniform sunlight.
The system operates close to its rated maximum power.
The Adaptive PSO determines the required converter duty cycle.
PV voltage reaches its optimum operating region.
PV power settles near the maximum available value.
For the demonstrated approximately 250 W PV array, the controller successfully tracks the high-power operating region.
𝐏𝐚𝐫𝐭𝐢𝐚𝐥 𝐒𝐡𝐚𝐝𝐢𝐧𝐠 𝐓𝐞𝐬𝐭
To evaluate the robustness of the Adaptive PSO algorithm, the irradiance of one PV section is reduced.
An example condition demonstrated in the model is:
PV Section | Irradiance |
Section 1 | 1000 W/m² |
Section 2 | 800 W/m² |
Section 3 | 1000 W/m² |
Section 4 | 1000 W/m² |
This creates a partial shading condition.
The resulting PV characteristic differs from the uniform irradiance condition, and the available maximum power decreases.
The Adaptive PSO controller responds by calculating a new optimum duty cycle and shifting the PV operating point toward the new maximum-power region.
𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐧𝐜𝐞 𝐓𝐞𝐬𝐭
The controller is also tested under changing irradiance.
A representative dynamic condition is:
Time | Section 2 Irradiance | Other Sections |
Before 1 s | 1000 W/m² | 1000 W/m² |
After 1 s | 800 W/m² | 1000 W/m² |
At approximately 1 second, the irradiance of one PV section changes from 1000 W/m² to 800 W/m².
After this change:
PV power decreases according to the available solar energy.
The optimum operating voltage shifts.
The required boost converter duty cycle changes.
Adaptive PSO begins a new search.
The controller converges toward the new maximum-power operating point.
This demonstrates the dynamic tracking capability of the MPPT controller.
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
The main simulation outputs include:
PV Voltage
The PV voltage initially changes while the Adaptive PSO searches for the optimum operating point.
After convergence, the voltage becomes stable around the selected maximum-power operating region.
When partial shading is introduced, the controller moves the voltage toward a new optimum value.
Load Voltage
Because a boost converter is used, the load-side voltage is higher than the PV-side voltage.
The voltage changes according to the converter duty cycle determined by the MPPT controller.
Duty Cycle
The duty-cycle waveform demonstrates the searching and convergence process of the Adaptive PSO algorithm.
During initialization, noticeable variations occur.
After the optimum operating point is identified, the duty cycle becomes relatively stable.
When irradiance changes, a new duty cycle is determined.
PV Power
PV power increases rapidly during the MPPT search and settles close to the available maximum power.
Following partial shading, the available power decreases and the controller tracks the new maximum-power point.
Load Power
The load power follows the energy extracted from the PV source after conversion through the boost converter.
𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
Adaptive PSO-based MPPT
Designed for solar PV systems
Handles uniform irradiance
Handles partial shading
Supports dynamic irradiance variation
Adaptive inertia-weight operation
Adaptive personal learning behavior
Adaptive global learning behavior
Particle-best and global-best searching
Automatic optimum duty-cycle generation
PWM-controlled boost converter
PV voltage and current feedback
Real-time PV power evaluation
Global maximum power searching capability
MATLAB/Simulink implementation
Suitable for studying MPPT behavior under complex PV conditions
𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐀𝐝𝐚𝐩𝐭𝐢𝐯𝐞 𝐏𝐒𝐎 𝐌𝐏𝐏𝐓
Adaptive PSO offers several benefits for PV maximum power extraction:
Better adjustment to changing environmental conditions
Improved search flexibility compared with fixed-parameter PSO
Reduced possibility of remaining around an undesirable local peak
Effective operation under non-uniform irradiance
Good balance between global searching and local refinement
Automatic adjustment of optimization behavior
Suitable for nonlinear PV characteristics
Effective for converter-based PV energy systems
𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
The Adaptive PSO MPPT concept can be applied to:
Standalone solar PV systems
Grid-connected PV systems
Solar battery charging systems
PV-powered DC microgrids
Renewable energy conversion systems
Solar-powered DC loads
Hybrid renewable energy systems
PV energy storage systems
Solar charging infrastructure
Research on global MPPT techniques
Comparative studies of optimization-based MPPT controllers
𝐖𝐡𝐨 𝐂𝐚𝐧 𝐔𝐬𝐞 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥?
This MATLAB/Simulink implementation is useful for:
Engineering students
Research scholars
Renewable energy researchers
Power electronics engineers
Control-system researchers
MATLAB/Simulink learners
Engineers working with solar converters
Researchers studying intelligent MPPT algorithms
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧
The Adaptive PSO MPPT for Solar PV System Under Partial Shading provides an effective approach for extracting maximum available power from a PV array operating under both uniform and non-uniform irradiance.
Unlike conventional PSO, where important optimization coefficients remain fixed, the adaptive method modifies its searching behavior during successive iterations. This helps the algorithm respond effectively when the PV operating condition changes.
The MATLAB/Simulink results demonstrate successful operation during 1000 W/m² uniform irradiance, partial shading with one PV section reduced to 800 W/m², and a dynamic irradiance change occurring around 1 second.
By combining Adaptive PSO with a PWM-controlled boost converter, the system provides a practical platform for studying global MPPT, partial shading, converter control, and intelligent solar PV energy extraction.
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