Implementation of Incremental Conductance MPPT Algorithm Using MATLAB

The Incremental Conductance method is a widely used algorithm for Maximum Power Point Tracking (MPPT) in photovoltaic systems. This algorithm determines the maximum power point location by comparing changes in the photovoltaic array's conductance, offering fast response speed and high precision characteristics.

Algorithm Concept The core principle of the Incremental Conductance method is based on the current-voltage (I-V) characteristic curve of photovoltaic arrays. At the maximum power point (MPP), the change in conductance equals zero, expressed as: [ dI/dV + I/V = 0 ] where (dI/dV) represents the rate of current change with respect to voltage, and (I/V) is the instantaneous conductance value.

When the conductance change is negative, it indicates the operating point is to the right of the MPP (voltage too high), requiring voltage reduction. Conversely, a positive conductance change necessitates voltage increase to approach the MPP.

Algorithm Implementation Steps: Real-time sampling of photovoltaic array voltage (V) and current (I) measurements. Calculation of conductance change (dI/dV) and comparison with instantaneous conductance (I/V). Adjustment of duty cycle (controlling DC-DC converters) based on comparison results, progressively approaching the MPP.

MATLAB Implementation Key Aspects Variable Sampling: Acquire photovoltaic array voltage and current values through sensors or simulation models using functions like 'read' or simulated data arrays. Conductance Calculation: Approximate derivative calculation (dI/dV) using differences between current and previous cycle values, typically implemented with difference equations: dI/dV ≈ (I(k)-I(k-1))/(V(k)-V(k-1)). Logic Decision: Adjust output voltage based on conductance change direction, commonly implemented using PI controllers or direct duty cycle stepping algorithms with conditional statements (if-else structures). Convergence Conditions: Set thresholds or iteration limits using while loops or conditional breaks to ensure stable convergence near MPP.

Advantages and Extensions The Incremental Conductance method maintains good tracking performance even under rapidly changing light conditions. It can be combined with other optimization algorithms (like Perturb and Observe) to enhance dynamic response speed through hybrid algorithm structures. Practical applications require consideration of noise filtering (using moving average filters) and parameter adaptive adjustment to improve robustness.

For MATLAB implementation, it's recommended to encapsulate the algorithm as a function module with clearly defined input/output parameters, facilitating integration into photovoltaic system simulation models (like Simulink) or actual hardware control code deployment.