Neural Network control of grid connected PV system
Neural Network control of grid connected PV system
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧
The Neural Network control of grid connected PV system is a MATLAB/Simulink-based solar photovoltaic system designed to study grid integration, power-quality improvement, MPPT operation, nonlinear loads, and unbalanced load conditions.
The system combines:
Solar PV array
Incremental Conductance MPPT
DC-link capacitor
Three-phase Voltage Source Inverter
Coupling inductor
Neural Network-based load-current estimation
Nonlinear load
Unbalanced-load testing
Grid voltage and current control
A major objective of the control strategy is to maintain a near-sinusoidal grid current even when the connected load current is distorted or unbalanced. Pasted text
𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰
The simulation represents a grid-connected PV system with neural-network-assisted inverter control.
The PV array is directly connected to the DC side of the Voltage Source Inverter through a DC-link capacitor. The inverter is then interfaced with the three-phase utility grid through a coupling inductor. Pasted text
Main system components
Component | Function |
Solar PV Array | Generates DC electrical power |
Incremental Conductance MPPT | Determines the PV reference voltage for maximum power extraction |
DC-Link Capacitor | Supports and stabilizes the inverter DC voltage |
Voltage Source Inverter | Converts PV DC power into three-phase AC power |
Coupling Inductor | Connects the inverter to the AC grid |
Neural Network | Estimates the required load-current-related control quantity |
Nonlinear Load | Creates distorted load-current conditions |
Circuit Breaker | Creates an unbalanced load condition during testing |
Grid | Receives active power while the inverter controller supports power quality |
𝐏𝐕 𝐀𝐫𝐫𝐚𝐲 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧
The transcript describes a PV array formed using 213 W modules, with 20 modules connected in series and 18 parallel strings. The resulting PV array power is approximately 95.9 kW under standard operating conditions. Pasted text
PV Parameter | Value |
Single PV module rating | 213 W |
Series-connected modules | 20 |
Parallel strings | 18 |
Approximate PV array capacity | 95.9 kW |
Initial irradiance | 1000 W/m² |
Reduced irradiance | 500 W/m² |
Irradiance change time | 3 s |
Simulation duration | 6 s |
Unbalanced-load initiation | 5 s |
𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬
The overall operating sequence is straightforward.
The solar PV array generates DC power according to irradiance and temperature.
PV voltage and current are measured continuously.
The Incremental Conductance MPPT controller processes these measurements.
Instead of directly generating a duty ratio, the MPPT block produces a PV reference voltage.
The DC-link voltage is measured and filtered.
The neural network processes three-phase load-current information.
The inverter reference-current magnitude is generated using PV power, load information, and the DC-link control signal.
Unit-vector signals are used to convert the current magnitude into three-phase sinusoidal reference currents.
The reference currents are compared with the actual inverter currents.
The resulting switching pulses control the Voltage Source Inverter.
The inverter transfers PV power to the AC system while helping improve grid-current quality. Pasted text
𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓
The PV side uses an Incremental Conductance MPPT algorithm.
MPPT inputs
PV voltage
PV current
MPPT output
Reference PV voltage
A key detail in this model is that the MPPT output is not directly treated as a converter duty cycle. Instead, voltage limits and voltage increments are used to determine a reference operating voltage for the PV system. Pasted text
Why this is useful
Tracks the PV maximum-power operating region
Responds to irradiance variation
Provides a reference for the DC-side inverter controller
Supports stable PV power extraction during changing environmental conditions
𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
The Neural Network is trained using load-current data obtained under different load conditions.
The network receives three-phase load-current information:
Phase-A load current
Phase-B load current
Phase-C load current
Training data are generated from different operating situations including:
Normal load condition
Nonlinear load condition
Unbalanced load condition
After training, the Neural Network produces the load-current-related reference used by the inverter controller. Pasted text
Neural Network role
The controller helps the inverter recognize changes in load behavior and contributes to the generation of the required reference current.
This makes the inverter capable of operating not only as a renewable-energy interface but also as a power-quality-supporting converter.
𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲
The inverter control combines several quantities.
Main control inputs
PV power
Load-current information
DC-link voltage
Grid/inverter terminal voltage
Actual inverter current
The DC-link voltage is passed through a low-pass filter to reduce measurement noise. It is then compared with the desired reference, and a controller processes the voltage error.
The resulting loss-related current component is combined with the load-related and PV-related components to determine the reference current magnitude. Pasted text
𝐏𝐋𝐋-𝐅𝐫𝐞𝐞 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧
An interesting feature of this controller is that the described implementation does not use a conventional Phase-Locked Loop for reference-current generation.
Instead, measured three-phase terminal voltages are used to create normalized unit-vector voltage signals.
These unit vectors are multiplied by the required current magnitude to obtain three-phase sinusoidal current references. Pasted text
Advantages of this approach
Reduces dependence on a conventional PLL stage
Generates sinusoidal three-phase reference currents
Supports synchronization with the grid voltage
Simplifies the reference-current generation structure
Works with the neural-network-assisted current controller
𝐏𝐨𝐰𝐞𝐫 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭
One of the most important functions of the controller is reducing the influence of a nonlinear load on grid current.
The nonlinear load produces a visibly nonsinusoidal current waveform. Without suitable compensation, this distorted current can deteriorate utility-side power quality.
In the presented model, the PV inverter controller attempts to compensate for this behavior so that the grid current remains sinusoidal even though the load current is distorted. Pasted text
Control objective
Distorted load current → Neural Network and inverter control → Improved grid current
This demonstrates that the inverter can simultaneously perform:
PV power transfer
Grid current regulation
Harmonic compensation
Load disturbance compensation
Power-quality improvement
𝐓𝐞𝐬𝐭 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬
The model is tested under multiple operating scenarios. Pasted text
Operating Condition | Test Description |
Normal operation | PV supplies power while the grid-connected inverter operates normally |
Irradiance variation | Irradiance changes from 1000 to 500 W/m² |
Nonlinear load | Distorted load current is introduced |
Unbalanced load | One load phase is disconnected |
MPPT test | PV operating point changes with irradiance |
Power-quality test | Grid current is monitored under distorted load conditions |
𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐧𝐜𝐞 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭
Initially, the PV system operates at an irradiance of 1000 W/m².
The MPPT controller moves the PV system toward its maximum-power operating point during the initial transient.
At approximately 3 seconds, the irradiance is reduced to 500 W/m². Pasted text
Observed response
PV current decreases significantly.
PV power reduces as solar irradiance decreases.
PV voltage experiences a temporary change.
The MPPT controller searches for the new operating point.
The PV system settles at the new available power level.
The transcript reports PV maximum power at the higher irradiance at roughly 95.7 kW. Pasted text
𝐍𝐨𝐧𝐥𝐢𝐧𝐞𝐚𝐫 𝐋𝐨𝐚𝐝 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧
During nonlinear-load operation, the load current becomes distinctly nonsinusoidal.
However, the grid-side current remains much closer to a sinusoidal waveform because of the inverter control strategy. Pasted text
Quantity | Observed Behaviour |
Load current | Distorted / nonsinusoidal |
Grid current | Maintained near sinusoidal |
Inverter current | Controlled by reference-current strategy |
PV system | Continues supplying available solar power |
Controller | Compensates for load-related current distortion |
𝐔𝐧𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐋𝐨𝐚𝐝 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧
The unbalanced load condition is introduced at approximately 5 seconds by disconnecting one of the load phases. Pasted text
This creates a sudden disturbance in the electrical system.
Observed effects
Temporary oscillation in PV voltage
Temporary disturbance in PV current
Temporary reduction in PV power
Recovery toward the previous operating region
Grid current remains controlled
Inverter current continues to maintain a sinusoidal characteristic
The transcript indicates that the system requires approximately 0.3 seconds to return toward the normal operating condition after the unbalanced-load disturbance. Pasted text
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
The MATLAB/Simulink model monitors several important electrical variables.
PV-side results
Solar irradiance
PV temperature
PV voltage
PV current
PV power
Maximum-power tracking response
DC-side results
DC-link capacitor voltage
DC-link transient response
AC-side results
Grid voltage
Grid current
Inverter voltage
Inverter current
Load voltage
Load current
Overall result interpretation
The simulation demonstrates that:
Incremental Conductance MPPT tracks changes in solar conditions.
PV power responds correctly to irradiance reduction.
Nonlinear load current contains distortion.
The inverter control reduces the influence of this distortion on the grid.
The neural-network-assisted controller responds to normal, nonlinear, and unbalanced load conditions.
Grid and inverter currents remain substantially more sinusoidal during the tested disturbances. Pasted text
𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤-assisted inverter control
𝐆𝐫𝐢𝐝-𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 solar PV operation
𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 MPPT
Approximately 𝐏𝐕 𝐚𝐫𝐫𝐚𝐲 𝐜𝐚𝐩𝐚𝐜𝐢𝐭𝐲 𝐨𝐟 𝟗𝟓.𝟗 𝐤𝐖
Nonlinear-load compensation
Unbalanced-load testing
DC-link voltage regulation
Three-phase inverter current control
Unit-vector-based reference-current generation
No conventional PLL used for the described reference-current generation
PV irradiance-change analysis
Grid-current power-quality improvement
MATLAB/Simulink implementation
𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
This simulation is useful for studying:
Grid-connected photovoltaic systems
Artificial Neural Network-based power-electronic control
Renewable-energy integration
Smart inverter control
Solar PV power-quality enhancement
Harmonic mitigation
Nonlinear-load compensation
Unbalanced-load analysis
MPPT controller performance
Three-phase Voltage Source Inverters
Intelligent control of renewable-energy converters
MATLAB/Simulink-based power-system research
𝐖𝐡𝐨 𝐂𝐚𝐧 𝐔𝐬𝐞 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥?
The topic is particularly useful for:
Electrical engineering students
Power electronics learners
Renewable-energy researchers
MATLAB/Simulink users
Power-system researchers
Engineers working with PV inverters
Researchers studying Neural Network control
Researchers working on grid power quality
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧
The Neural Network control of grid connected PV system demonstrates an intelligent approach for integrating solar photovoltaic power with the utility grid while improving current quality.
The system combines Incremental Conductance MPPT, Neural Network-based load-current processing, DC-link voltage regulation, unit-vector reference generation, and three-phase inverter control.
Simulation results described in the source show that the system can respond to irradiance variation, nonlinear loading, and unbalanced loading while maintaining controlled grid and inverter currents. This makes the model a useful platform for understanding AI-assisted renewable-energy control and grid power-quality improvement in MATLAB/Simulink. Pasted text
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