𝐇𝐲𝐛𝐫𝐢𝐝 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁

𝐇𝐲𝐛𝐫𝐢𝐝 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

𝐇𝐲𝐛𝐫𝐢𝐝 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁 is a complete solar PV simulation model designed for efficient maximum power extraction, battery energy management, DC bus voltage control, and grid-connected inverter operation.

Solar PV output is 𝐧𝐨𝐧-𝐥𝐢𝐧𝐞𝐚𝐫 because it changes with irradiation, temperature, and load condition. Therefore, direct connection of a PV panel to the load cannot always extract maximum power. To solve this problem, an 𝐌𝐏𝐏𝐓 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐛𝐨𝐨𝐬𝐭 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 is used between the PV panel and load.

This model combines:

• 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓• 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐛𝐚𝐬𝐞𝐝 𝐌𝐏𝐏𝐓• 𝐁𝐨𝐨𝐬𝐭 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 control• 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 storage with bidirectional DC-DC converter• 𝐒𝐢𝐧𝐠𝐥𝐞-𝐩𝐡𝐚𝐬𝐞 grid-connected inverter

𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰

The proposed MATLAB/Simulink model is developed to study the performance of a solar PV system under changing irradiation conditions. The PV panel output is connected to a 𝐛𝐨𝐨𝐬𝐭 𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫, which is controlled by a hybrid MPPT technique.

The generated power is supplied to a 𝐃𝐂 𝐛𝐮𝐬, where battery storage helps maintain voltage stability. A single-phase inverter converts DC power into AC power for grid connection.

𝐒.𝐍𝐨	𝐒𝐲𝐬𝐭𝐞𝐦 𝐏𝐚𝐫𝐭	𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧
01	Solar PV Panel	Generates DC power from solar irradiation
02	Boost Converter	Increases PV voltage and supports MPPT
03	INC MPPT	Tracks maximum power using voltage and current changes
04	Neural Network MPPT	Predicts suitable maximum voltage using irradiation and temperature
05	PI Controller	Processes voltage error and supports duty cycle control
06	Battery Storage	Stores or supplies energy based on power balance
07	Bidirectional DC-DC Converter	Performs battery charging and discharging
08	DC Bus	Maintains regulated DC voltage
09	Single-Phase Inverter	Converts DC power into AC power
10	LC Filter	Reduces harmonics and improves waveform quality
11	Grid	Receives synchronized AC power

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬

𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫	𝐕𝐚𝐥𝐮𝐞
PV Panel Rating	250 W
Number of Series Strings	4
Approximate PV Power	1000 W
Maximum Power Point Voltage	13.7 V
Maximum Power Point Current	8.85 A
Standard Irradiation	1000 W/m²
Standard Temperature	25°C
Battery Voltage	48 V
DC Bus Voltage	220 V
Load Power	1200 W
Grid Voltage	110 V RMS
Grid Frequency	50 Hz
Inverter Carrier Frequency	7000 Hz

𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬

The working process of this model is simple and practical:

• Solar irradiation is applied to the 𝐏𝐕 𝐩𝐚𝐧𝐞𝐥• PV voltage and current are measured continuously• The 𝐈𝐍𝐂 𝐌𝐏𝐏𝐓 algorithm identifies the maximum power direction• The 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 estimates the reference maximum voltage• The PI controller reduces the difference between actual and reference voltage• The combined duty cycle controls the boost converter IGBT• The DC bus voltage is regulated at 𝐵220 V• The battery charges when PV power is high• The battery discharges when PV power is low• The inverter synchronizes the output with the grid

𝐏𝐕 𝐏𝐨𝐰𝐞𝐫 𝐮𝐧𝐝𝐞𝐫 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧

The PV panel produces different power levels based on solar irradiation. At higher irradiation, the panel generates more power. At lower irradiation, the output power decreases.

𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧	𝐌𝐚𝐱𝐢𝐦𝐮𝐦 𝐏𝐕 𝐏𝐨𝐰𝐞𝐫
1000 W/m²	1001 W
800 W/m²	799.6 W
600 W/m²	598.5 W
400 W/m²	395.9 W

These results show that the PV power changes almost proportionally with irradiation. The MPPT controller helps the system operate near the maximum power point during these changes.

𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲

𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓

The 𝐈𝐍𝐂 𝐌𝐏𝐏𝐓 algorithm uses PV voltage and PV current measurements to decide whether the operating point should move forward or backward. Based on this decision, the duty cycle is increased or decreased.

𝐈𝐍𝐂 𝐌𝐏𝐏𝐓 𝐒𝐞𝐭𝐭𝐢𝐧𝐠	𝐕𝐚𝐥𝐮𝐞
Initial Duty Cycle	0.42
Maximum Duty Cycle	0.85
Minimum Duty Cycle	0.1
Control Action	Increase or decrease duty cycle
Main Objective	Extract maximum PV power

𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐌𝐏𝐏𝐓

The 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 is trained using irradiation and temperature data. It provides the suitable maximum voltage reference for the PV system.

This improves the tracking performance because the controller receives intelligent support from trained data.

𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐩𝐮𝐭	𝐎𝐮𝐭𝐩𝐮𝐭
Irradiation	Maximum voltage reference
Temperature	Maximum voltage reference

𝐇𝐲𝐛𝐫𝐢𝐝 𝐌𝐏𝐏𝐓 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡

The hybrid MPPT method combines the duty cycle response from the 𝐈𝐍𝐂 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 and the reference voltage support from the 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤.

This helps to achieve:

• Faster tracking• Better power extraction• Improved response under irradiation variation• Reduced power loss• Smooth boost converter operation

𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐚𝐧𝐝 𝐃𝐂 𝐁𝐮𝐬 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧

The model uses a 𝐛𝐚𝐭𝐭𝐞𝐫𝐲 𝐞𝐧𝐞𝐫𝐠𝐲 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 system to maintain stable DC bus voltage. The battery is connected through a bidirectional DC-DC converter.

𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧	𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐌𝐨𝐝𝐞	𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐀𝐜𝐭𝐢𝐨𝐧
PV power is higher than load demand	Buck mode	Battery charging
PV power is lower than load demand	Boost mode	Battery discharging
PV power matches load demand	Balanced mode	Low battery current
DC bus variation occurs	Regulating mode	Supports voltage stability

𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬

𝐏𝐕 𝐏𝐨𝐰𝐞𝐫	𝐋𝐨𝐚𝐝 𝐃𝐞𝐦𝐚𝐧𝐝	𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐮𝐫
1000 W	600 W	Battery charges using excess power
400 W	600 W	Battery supplies remaining power
300 W/m² irradiation condition	Load demand continues	Battery enters discharge mode

𝐆𝐫𝐢𝐝 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧

For grid connection, the system uses a 𝐬𝐢𝐧𝐠𝐥𝐞-𝐩𝐡𝐚𝐬𝐞 𝐟𝐮𝐥𝐥-𝐛𝐫𝐢𝐝𝐠𝐞 𝐢𝐧𝐯𝐞𝐫𝐭𝐞𝐫. The inverter converts the regulated DC bus voltage into AC voltage.

An 𝐋𝐂 𝐟𝐢𝐥𝐭𝐞𝐫 is used after the inverter to reduce harmonics and produce a smooth sinusoidal waveform. The inverter is synchronized with the grid using a PLL.

𝐆𝐫𝐢𝐝 𝐒𝐞𝐜𝐭𝐢𝐨𝐧	𝐑𝐨𝐥𝐞
Single-Phase Inverter	Converts DC into AC
LC Filter	Reduces harmonics
PLL	Synchronizes inverter output with grid voltage
DQ Controller	Controls inverter current
PI Controller	Generates control voltage signals
PWM Generator	Produces inverter switching pulses
Grid	Receives synchronized AC power

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬

The simulation is tested under constant and variable irradiation conditions. The results show that the system maintains the 𝐃𝐂 𝐛𝐮𝐬 𝐯𝐨𝐥𝐭𝐚𝐠𝐞 at 220 V even when solar irradiation changes.

𝐂𝐨𝐧𝐬𝐭𝐚𝐧𝐭 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭

𝐓𝐞𝐬𝐭 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧	𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧
Irradiation at 1000 W/m²	PV generates high power
Current reference increases from 1 to 2	Battery charging current decreases
Current reference decreases to 0.5	Grid current increases
DC bus voltage	Maintained near 220 V

𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭

𝐓𝐢𝐦𝐞 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧	𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧	𝐒𝐲𝐬𝐭𝐞𝐦 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞
Initial condition	1000 W/m²	PV power is high and battery can charge
After change	500 W/m²	Battery charging current becomes nearly zero
Further reduction	300 W/m²	Battery discharges to support the load
Overall response	Variable irradiation	DC bus remains stable at 220 V

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

• Complete 𝐌𝐀𝐓𝐋𝐀𝐁/𝐒𝐢𝐦𝐮𝐥𝐢𝐧𝐤 model for solar PV system analysis• Hybrid 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 + 𝐈𝐍𝐂 𝐌𝐏𝐏𝐓 control• Boost converter controlled using IGBT switching• Battery storage with bidirectional DC-DC converter• Stable 𝐃𝐂 𝐛𝐮𝐬 voltage regulation at 220 V• Single-phase grid-connected inverter model• PLL-based synchronization with grid voltage• DQ reference control for inverter current regulation• Suitable for variable irradiation analysis• Includes PV voltage, PV current, PV power, battery current, DC bus voltage, inverter voltage, and grid current results

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

This model is useful for:

• Solar PV MPPT control analysis• Renewable energy system simulation• Neural Network based controller study• Boost converter design and control• Battery energy storage system analysis• DC bus voltage regulation study• Grid-connected inverter control• Power electronics and renewable energy research• MATLAB/Simulink based technical learning• PV system performance comparison under irradiation changes

𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥 𝐢𝐬 𝐔𝐬𝐞𝐟𝐮𝐥

This simulation model gives a clear understanding of how a solar PV system works with intelligent MPPT control. It helps users study the interaction between PV generation, converter control, battery storage, and grid synchronization.

𝐁𝐞𝐧𝐞𝐟𝐢𝐭	𝐄𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧
Better MPPT Tracking	Hybrid method improves maximum power extraction
Smooth DC Bus Control	Battery and PI control maintain stable voltage
Practical Grid Connection	Inverter and PLL support grid synchronization
Easy Result Analysis	Scope outputs show power, voltage, current, and battery response
Useful Learning Model	Simple structure for understanding PV, converter, and grid operation

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

𝐇𝐲𝐛𝐫𝐢𝐝 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁 is a complete and effective simulation model for studying intelligent solar PV power extraction. The model combines classical INC MPPT with Neural Network support to improve PV tracking performance.

With boost converter control, battery storage, bidirectional converter operation, 220 V DC bus regulation, and grid-connected inverter synchronization, this system provides a practical platform for students, researchers, and engineers working in solar PV and power electronics.