All classes Solar Panel MPPT Fuzzy-Neural Syllabus · Episode 3 of 6

Fuzzy-Neural Solar Panel MPPT Class #3: Classical MPPT: Perturb & Observe and Incremental Conductance

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Tutorial on Perturb and Observe and Incremental Conductance MPPT with Python, tested on partial shading. Episode 3 of the Fuzzy-Neural Solar Panel MPPT series.

Software: Python (NumPy, Fuzzy, ANFIS)
Level: Intermediate
Duration: ± 10 min
Series total: 1 h 6 min · 6 videos
Published: October 25, 2026

Episode 3 of 6 · view syllabus

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Transcript

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Introduction

This is the final result, two of the most popular MPPT algorithms, Perturb and Observe and Incremental Conductance, tracking maximum power when irradiation changes. When the weather is clear, both are perfect, but when there are shadows, both get stuck at a local maximum, and lose up to 30% of power. Let's get started. Hello, welcome to episode three of the Fazi Neural Solar Panel MPPT series. Most commercial inverters use Perturb and Observe or Incremental Conductance because they are simple and cheap. Today we are writing both from scratch, and testing them on four changing irradiation conditions, including two shadow conditions.

Step 1: MPPT Simulation Framework

Create the MPPT.py file, The scenario contains four irradiation conditions, each with 150 sampling steps, uniform, heavy shadow, light shadow, then three different module patterns. The simulation function runs any MPPT algorithm, at each step, the operating area is at the reference voltage, power and current are read, then the algorithm determines the next reference voltage. Efficiency is the total energy obtained divided by the total global maximum energy.

Step 2: P&O and Incremental Conductance

Perturb and Observe is very simple. Shift the voltage slightly, then observe the power. If the power increases, continue in the same direction. If it decreases, reverse the direction. As a result, the operating point always oscillates around the peak. Incremental conductance uses the mathematical property of the peak, at the maximum power point, where the derivative of power with respect to voltage is equal to minus current over voltage. To the left of the peak, the value is larger, to the right it is smaller, so we know the direction without needing to try.

Step 3: Efficiency Tracking

Create a classic perturb and observe test file called underscore classic dot py and run it. The total efficiency of both algorithms is around 89%. When uniform and under the last condition, both reach 100%. But when the shadow is heavy, the power eventually only reaches around 78% of the maximum, and when the shadow is light, only around 69%. Let's take a look at the graph.

Step 4: Stuck at a Local Maximum

The black dashed line is the global maximum power. Under shadow, both algorithms stop far below it because it's stuck at the right peak, around 85 volts, while the global peak is around 54 volts. The voltage graph shows the characteristic continuous oscillation of PNO. For this algorithm, the local peak looks exactly the same as the global peak.

Done!

Done. We have built the MPPT simulation framework, written perturb and observe as well as incremental conductance from scratch, and proved both are stuck at the local peak during partial shedding. In the next episode, we create a smoother fuzzy logic MPPT. Currently working on a thesis, dissertation, or research in the field of solar energy and renewable energy? Cerdas Inovasi Digital helps with the implementation of solar panel simulations, from the one-diode model, partial shedding, MPPT perturb and observe and incremental conductance, fuzzy logic, up to ANFIS, as well as opening Python programming courses for electrical engineering. Want to master the entire series, from solar panel models up to comparisons of all MPPT methods? All six episodes, complete with PDF step-by-step guides and source code Available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, Save 28% with the 12-month package, Or pay once Rp999.000 for lifetime access. Want just one class? Buy single classes with lifetime access. Subscribe now on the premium page cerdasinovasidigital.com. Contact our WhatsApp, Or visit cerdasinovasidigital.com. Thank you for watching, See you in the next episode.

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Transcript

0:03Introduction

0:46Step 1: MPPT Simulation Framework

3:23Step 2: P&O and Incremental Conductance

5:08Step 3: Efficiency Tracking

6:51Step 4: Stuck at a Local Maximum

8:31Done!

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My notes

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Transcript

0:03Introduction

0:46Step 1: MPPT Simulation Framework

3:23Step 2: P&O and Incremental Conductance

5:08Step 3: Efficiency Tracking

6:51Step 4: Stuck at a Local Maximum

8:31Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: MPPT Simulation Framework

Step 2: P&O and Incremental Conductance

Step 3: Efficiency Tracking

Step 4: Stuck at a Local Maximum

Done!

Introduction

Step 1: MPPT Simulation Framework

Step 2: P&O and Incremental Conductance

Step 3: Efficiency Tracking

Step 4: Stuck at a Local Maximum

Done!