Grid connected PV Wind and Battery with Fuzzy MPPT
- lms editor
- 1 hour ago
- 4 min read
Introduction
With the increasing penetration of renewable energy sources into modern power systems, hybrid energy systems combining solar, wind, and energy storage have become essential for ensuring reliability, efficiency, and grid stability. However, the intermittent nature of solar irradiance and wind speed presents significant control challenges. To address these issues, intelligent Maximum Power Point Tracking (MPPT) techniques, such as fuzzy logic control, are increasingly adopted.
Overview of System Architecture
The proposed system integrates:
A Wind Energy Conversion System (WECS)
A Solar PV system
A Battery Energy Storage System (BESS)
A DC bus maintained at 400 V
A grid-connected inverter
Both DC and AC loads
All renewable sources and storage units are interfaced through power electronic converters to ensure efficient power flow and stable operation.
Wind Energy Conversion System (WECS)
Components of WECS
The wind energy subsystem consists of:
Wind turbine
Permanent Magnet Synchronous Generator (PMSG)
Three-phase rectifier
DC–DC boost converter
The wind turbine has a maximum rated power of 3 kW, and the boost converter output is connected to the common 400 V DC bus.
Fuzzy MPPT Algorithm for WECS
To extract maximum power from the wind turbine, a fuzzy logic-based MPPT controller is used. Unlike conventional MPPT techniques, fuzzy MPPT does not require an exact mathematical model, making it suitable for nonlinear wind energy systems.
Working Principle
The fuzzy MPPT algorithm operates as follows:
The rectifier voltage and current are measured.
Power change (ΔP) and voltage change (ΔV) are calculated.
The error, representing the slope of the power–voltage curve, is derived.
The error and change in error serve as inputs to the fuzzy logic controller.
The controller outputs an optimized duty cycle.
The duty cycle controls the MOSFET of the boost converter, adjusting the operating point to achieve maximum power extraction.
This intelligent approach enables fast and accurate tracking under varying wind speeds.
Solar PV System
The solar photovoltaic system, rated at 2 kW, is also connected to the DC bus through a DC–DC boost converter. Similar to the WECS, the PV system employs a fuzzy MPPT algorithm.
Fuzzy MPPT for PV System
The fuzzy MPPT for the PV array:
Uses PV voltage and current as inputs
Computes ΔP and ΔV
Determines the optimal duty cycle
Ensures the PV array operates at its maximum power point under fluctuating irradiance
This unified fuzzy MPPT strategy enhances overall system efficiency and simplifies control implementation.
Battery Energy Conversion System
The battery energy storage system plays a crucial role in power balancing and DC bus voltage regulation.
Battery Specifications
Nominal Voltage: 220 V
Capacity: 40 Ah
The battery is interfaced to the DC bus via a bi-directional DC–DC converter, allowing both charging and discharging operations.
Control Strategy
A voltage control-based method is employed:
The DC bus voltage is compared with a 400 V reference
A PI controller generates the control signal
The bi-directional converter regulates battery power flow to maintain DC bus stability
This approach ensures seamless power balancing during renewable power fluctuations.
Grid Integration and Load Management
The system is connected to a 230 V RMS, 50 Hz utility grid through a grid-tied inverter.
Load Conditions
AC Load:
1000 W initially
Additional 1400 W connected after 2 seconds (total 2400 W)
DC Load:
Constant 1000 W
Grid-Tied Inverter Control
The inverter operates using a current control strategy, adapting dynamically based on:
PV generation level
Wind power availability
Battery state of charge (SOC)
When:
Renewable generation is sufficient, the load is supplied locally
PV output is low or battery SOC falls below 10%, the grid supplies the deficit power
This ensures uninterrupted load supply and stable grid interaction.
Simulation Setup
To evaluate system performance, dynamic environmental and load conditions are applied:
Environmental Conditions
Wind Speed:
12 m/s initially
Reduced to 10.8 m/s after 2 seconds
Solar Irradiation:
Changes every 0.3 seconds
Sequence: 1000 W/m² → 500 W/m² → 10 W/m² → 1000 W/m²
Load Variation
AC load increase from 1000 W to 2400 W after 2 seconds
Constant DC load of 1000 W
Simulation Results and Performance Analysis
PV System Performance
PV power initially reaches 2000 W
Drops to 1000 W at 500 W/m²
Falls to 0 W at 10 W/m²
PV voltage remains around 245 V, reducing to 50 V under very low irradiance
WECS Performance
Rectifier output power initially at 3000 W
Reduces to 2100 W as wind speed decreases
Battery Performance
Battery current exhibits both charging and discharging behavior
Acts as a buffer to compensate for renewable power fluctuations
Grid Interaction
Grid power varies dynamically
Supplies power when renewable generation is insufficient
Maintains system reliability under low PV and wind conditions
Load Voltage and Current
Load voltage remains stable
Current variations reflect changes in available generation sources
Key Observations
Inverter behavior:
Voltage and current are in phase during grid power injection
Out of phase during grid power absorption
Fuzzy MPPT effectiveness:
Ensures fast and accurate maximum power tracking
Performs well under rapid environmental changes
System stability:
DC bus voltage maintained at 400 V
Reliable load supply under all test conditions
Conclusion
The grid-connected PV–Wind–Battery hybrid system using fuzzy MPPT demonstrates robust performance and high adaptability under varying environmental and load conditions. The fuzzy MPPT algorithms effectively maximize power extraction from both renewable sources, while the battery system ensures DC bus stability and power balance. Grid integration further enhances system reliability by supporting the load during low renewable generation.
This study highlights the potential of intelligent control techniques in hybrid renewable energy systems and underscores their importance in future smart grids and microgrid applications.
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