560019, India.

Received: 04 April 2026; 09 April 2026; Published: 25 April 2026

This paper presents the implementation of Incremental Conductance (IC) Maximum Power Point Tracking

(MPPT) for photovoltaic (PV) system integrated with a DC-DC boost converter to enhance energy harvesting

efficiency. Output power varies with environmental conditions such as solar irradiance and temperature due to

The proposed system consists of a PV array, an IC based MPPT controller and a boost converter that steps up

the output voltage to a required level for efficient load utilization. The boost converter is controller through pulse

width modulation (PWM) signals generated by the MPPT algorithm, enabling optimal power extraction from

the PV panel. MATLAB/Simulink is used to model and simulate the system, validating its performance under

varying irradiance conditions.

Simulation outcome indicates that the IC MPPT technique achieves improved efficiency, quicker tracking of

MPP and minimizes steady-state fluctuations when compared to conventional approaches like Perturb and

for clean power generation. However, the electrical characteristics of PV modules are inherently nonlinear and

strongly influenced by environmental factors such as solar irradiance and temperature. As a result, the operating

point of a PV system varies continuously, and maximum power can only be obtained at a specific point known

as the maximum power point (MPP). If the system does not operate at this point, energy extraction becomes

inefficient. To overcome this limitation, Maximum Power Point Tracking (MPPT) techniques are implemented

to dynamically adjust the operating conditions of the PV system. Among the available methods, the Incremental

Conductance (IC) algorithm is widely adopted due to its capability to accurately locate the MPP under both

steady-state and rapidly changing conditions. This method relies on the relationship between incremental

conductance and instantaneous conductance to determine the direction of operation, thereby reducing

oscillations and improving tracking precision.

efficiency, response time, and system stability. A comparative evaluation with the conventional Perturb and

Observe (P&O) method is also included to demonstrate the effectiveness of the proposed approach.

Several studies have focused on improving the performance of Incremental Conductance(IncCond) based MPPT

techniques for photovoltaic (PV) systems operating under dynamic environmental conditions. In [1], The

modified approach improves tracking precision by refining the decision-making mechanism under varying

irradiance and temperature, resulting in better steady-state stability and reduced power loss. The work presented

in [2] investigates the effectiveness of the IncCond method under rapidly changing atmospheric conditions. The

study demonstrates that the algorithm can accurately estimate the MPP by utilizing the slope of the PV curve,

thereby achieving improved dynamic response and reduced oscillations compared to traditional MPPT strategies.

reduces system complexity and improves energy conversion efficiency by eliminating intermediate conversion

stages while maintaining effective MPP tracking. Furthermore, adaptive enhancements such as step-size control

combined with holding mechanisms have been explored in [6] to suppress unnecessary perturbations near the

MPP. This method significantly improves tracking stability and reduces power fluctuations under rapidly varying

conditions. Hebchi et al. [7] proposed an improved version of the IC algorithm aimed at enhancing tracking

accuracy and reducing steady-state oscillations. Their work demonstrates better performance compared to

conventional IC, particularly during rapid changes in solar irradiance. Hemavathi et al. [8] focused on defect

detection in polycrystalline solar cells using electroluminescence imaging. Their study highlights how

identifying defects can significantly improve the reliability and efficiency of PV systems. Chawda et al. [9]

introduced a hybrid approach combining Incremental Conductance with Particle Swarm Optimization (PSO) to

results confirm that IC performs better than simpler methods like Perturb and Observe under steady-state

conditions. In another study, Hemavathi et al. [12] explored the simulation of a SEPIC DC–DC converter using

LabVIEW. Their work highlights the importance of efficient power converters in PV systems, as they play a key

role in implementing MPPT algorithms effectively.

Overall, the reviewed literature indicates that although the conventional IncCond algorithm provides reliable

MPP tracking, recent advancements primarily focus on adaptive control strategies to enhance dynamic response,

minimize steady-state oscillations and improve overall system efficiency.

The system consists of a photovoltaic (PV) array, an IC MPPT controller, a DC-DC boost converter, and a load

point using the condition

. Based on this, the IC algorithm adjusts the duty cycle to ensure optimal

The photovoltaic (PV) array is represented using a single-diode equivalent circuit, which captures the nonlinear

electrical characteristics of a solar cell. The model consists of a current source, a diode, and series (R

) and shunt

(R

) resistances to account for internal losses. The output current of the PV cell is expressed as:

where I

is the photo-generated current, Io is the diode saturation current, a is the diode ideality factor and

where N

and N

is obtained by scaling the single-cell current, while the diode and resistive effects are adjusted based on the array

configuration. This equation models the overall behavior of the PV array under different operating conditions.

Maximum Power Point Tracking (MPPT) is essential for maximizing energy extraction from photovoltaic (PV)

systems under varying irradiance and temperature. The Incremental Conductance (IC) method determines the

maximum power point (MPP) by evaluating the slope of the power–voltage characteristic, which becomes zero

at the MPP:

) with instantaneous conductance (

), the algorithm identifies the

operating region. When

, the system operates at MPP; if

, the operating point is left of MPP,

changes and updates the duty cycle of the DC-DC converter accordingly. This adaptive adjustment enables fast

convergence and reduces steady-state oscillations, improving overall tracking efficiency.

Figure 2 presents the flowchart of the Incremental Conductance (IC) MPPT algorithm. The process begins with

measuring the PV voltage and current, followed by calculating their incremental changes. The algorithm

1. Acquire the instantaneous PV voltage V(k) and current I(k).

2. Determine incremental variations:

Figure 5 illustrates the dynamic response of the photovoltaic system employing the Incremental Conductance

(IC) MPPT algorithm, showing the variation of output power, voltage and current.

The system demonstrates fast convergence to the maximum power point (MPP) with high stability and minimal

oscillations. Under varying irradiance conditions, the proposed method achieves a tracking efficiency of

approximately 96–98%, indicating effective utilization of available solar energy. The convergence time is

observed to be less than 0.05 seconds, reflecting a rapid dynamic response. Furthermore, steady-state oscillations

around the MPP are significantly reduced compared to conventional methods such as Perturb and Observe

P

= Output power obtained (from simulation graph)

P