MPPT Techniques

Introduction

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Nowadays, the production of electricity from renewable sources is widespread, and it is a constantly growing sector throughout the world. Climate change has led all of us over the years to promote policies that aim to limit the impact of human activities on the environment and to research new energy sources with low environmental impact.

From the various existing renewable sources photovoltaic is among the most popular as the sun is a source of energy present throughout the planet.

MPPT (Maximum Power Point Tracking) Technique

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As mentioned above, photovoltaic is considered one of the most reliable and promising renewable energies for the future. However, since the efficiencies are still - at the time of writing - not much more than 20%, it is very important to try to optimize the maximum possible energy from these generators. One technique adopted in converters is the MPPT technique.

The MPPT (Maximum Power Point Tracking) is a technology widely used in photovoltaic systems to optimize the energy efficiency of solar panels. Each solar module has a maximum power point, which varies based on environmental conditions such as sunlight (solar irradiance) and temperature. The main goal of MPPT is to locate and maintain the operating system at this point, to maximize the power extracted from the solar panels.

PV System Overview

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A photovoltaic module produces a characteristic curve that relates voltage and current (I-V curve), from which the power curve (P-V curve) is derived. The point where the power is maximum is known as the maximum power point (MPP). However, since this point varies based on environmental factors, such as sunlight irradiance intensity and temperature, dynamic algorithms must be used to constantly track it.

A complete discussion of photovoltaic (PV) panels and how they work can be found here

MPPT Algorithms

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There are several algorithms used for MPPT, but the most common ones include:

1. Perturb and Observe (P&O).
2. Incremental Conductance (INC)
3. Fuzzy Logic & Neural Networks

Among them, the Perturb and Observe (P&O) algorithm is the simplest and most widely used due to its ease of implementation and good compromise between performance and efficiency. For this reason we’ll focus on this algorithm in more detail.

Perturb and Observe (P&O) Algorithm

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The operating principle is relatively simple:

1. A small perturbation is applied to the voltage or current of the system.
2. The resulting power after the perturbation is measured.
3. If the power increases, the perturbation is maintained.
4. If the power decreases, the perturbation is changed.

In other words, if a voltage change is introduced which increases the power, the algorithm continues to vary the voltage in the same direction. If the power decreases, the direction of the voltage change is reversed.

Detailed Operation

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1. Initial Measurement.

At the beginning, the algorithm measures the voltage and current coming from the solar panel and calculates the power output.

2. Perturbation.

The algorithm introduces a small perturbation to the voltage (for example, a small increase or decrease in voltage).

3. New Power Calculation.

After the perturbation, the algorithm measures the power again. At this point, it compares the current power with the previous one.

4. Comparison.

If the new power is greater than the previous power, it means that the perturbation has made the system towards the maximum power point. Then the algorithm continues to perturb in the same direction.If the new power is less, the algorithm changes direction and starts to perturb in the opposite direction

5. Iteration.

This process is repeated continuously until the system converges on the maximum power point, adapting to the changes in environmental conditions.

Simplified Block Diagram

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This process is shown in flowchart form below

Benefits of the P&O Algorithm

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1. Simplicity.

The algorithm is easy to implement in both software and hardware. This makes it popular for low-cost applications.

2. Efficiency.

Despite its simplicity, the algorithm is able to track the maximum power point with good results, especially under stable irradiance conditions.

3. Flexibility.

It can be applied to a wide range of photovoltaic applications

Problems with the P&O Algorithm

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1. Oscillations around the MPPT.

Due to the iterative nature of the algorithm, the system does not converge exactly on the MPP but oscillates around it. This can slightly reduce the overall efficiency of the system.

2. Reduced performance in variable conditions.

When irradiance conditions change rapidly (e.g., due to clouds), the algorithm may not be fast enough to follow the new maximum power point

3. Convergence Error.

In some situations, especially when the change in power is small, the algorithm may fail to correctly determine the optimal direction of the perturbation.

P&O Algorithm Optimisations

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While not represented in the simulation examples of this article, several optimisations have been developed to overcome some of the limitations above. Whether any of these is required and which to prioritise then become application specific decisions for the firmware designer. If required, please refer to specialized texts for the discussion of these optimized algorithms.

1. Adaptive Perturbation.

Instead of using a fixed perturbation, the size of the perturbation can be adapted based on the distance from the MPP. In this way, near the maximum power point, the perturbation can be reduced to minimize oscillations.

2. Hybrid Algorithms.

Some systems combine P&O with other algorithms, such as INC (Incremental Conductance), to improve the response speed and accuracy in variable irradiance conditions.

Proteus Simulation

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A playground project for simulating and learning about MPPT in Proteus VSM can be downloaded below.

The example uses simulation models provided in the standard Proteus library; for example ARDUINO UNO, Photovoltaic Panel (PV), INA286 for measuring input and load currents and others.

In the circuit, L1, D2, Q1 and C6//C2 represents a DC-DC Boost Converter circuit for which:

D = 1-(Vs/Vo) L1 = Vs D/ Δ Io f C6//C2 = Vo D/ ΔVo Rf

where:

• D: Duty Cycle.
• Vs: Input voltage from solar PV.
• Vo: Output voltage at load side.
• ΔIo: Ripple of output current.
• ΔVo: Ripple of output voltage.

The Boost Converter is driven in PWM mode by ARDUINO pin (IO5) and this provides the perturbation used to track MPP. The circuit displays the simulation results both in real time, on a 4-line, 16 columns, alphanumeric LCD display, and on a couple of Transient Analysis Graphs

The variation of the solar irradiance is simulated by a PWlin generator that injects the signal into the simulation model of the Photovoltaic Panel through a hidden pin named IRRADIANCE. This signal is proportional to the solar irradiance such that 1V = 1 W/m^2. The simulation model of the photovoltaic panel, therefore, provides a variable output voltage corresponding to its characteristic I/V curve proportional to the solar irradiance itself.

Then, in the source code for ARDUINO UNO the variable “deltaPower” determines the size of the perturbation. If it is too large, the system will converge faster but will oscillate excessively around MPP (Maximum Power Point). On the other hand, if it is too small, the convergence of the system will be too large and the system will respond too slowly to sudden variations in ambient brightness

Conclusion

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The MPPT P&O (Perturb and Observe) algorithm represents a simple and effective solution for MPPT in PV systems. Although it has some limitations, such as oscillations around the maximum power point and performance degradation under variable conditions, it remains a popular choice due to its simplicity and ease of implementation.

Even in this case, we note how the complete provision of appropriate simulation models, allow Proteus to simulate, in a relatively simple way, the growing techniques of modern energy production.

Head over to the Sample Design Browser to quickly find -made embedded simulation sample projects that are included with the Proteus demo version.

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