Comparative Study of ANN and Traditional MPPT Techniques in PV Applications

The sun offers a boundless supply of clean energy, but capturing it efficiently requires more than just placing panels on a roof. Solar cells are sensitive devices whose ability to generate electricity changes constantly with the weather. When the sun's intensity shifts or the temperature rises, the point at which a panel produces its maximum power moves, much like a target shifting on a moving wall. To harvest the most energy possible, a system must constantly hunt for this shifting target. This process is known as maximum power point tracking. For decades, engineers have relied on traditional methods to guide solar systems to this sweet spot, using algorithms that nudge the system slightly and watch how the power output reacts. While these established techniques work, they can be slow to react to rapid weather changes and often wobble around the ideal setting, wasting a portion of the available energy.

In a recent study, researchers at Near East University explored whether a different approach, one inspired by how the human brain learns, could outperform these long-standing methods. They focused on a specific type of artificial intelligence called a deep neural network. Unlike traditional algorithms that follow a fixed set of rules, this neural network was trained on vast amounts of historical weather and solar data. The researchers fed the system information about sunlight intensity, air temperature, and the electrical current and voltage flowing from the panels. The goal was to teach the computer to predict the exact best operating point instantly, without needing to guess and check. By comparing this new, data-driven method against the standard techniques used in the industry, the team sought to determine if artificial intelligence could make solar power generation significantly more efficient and responsive.

The researchers built their experiment around a digital simulation of a solar power system. They gathered detailed records of sunlight and temperature from a specific location in North Cyprus, using data from a European Union database that tracks solar resources. To create a realistic test environment, they used these weather records to generate the expected electrical behavior of a solar panel. They then ran three different control strategies against this same weather data. The first two were the traditional methods: one that simply perturbs the system to see if power goes up or down, and another that calculates the rate of change in electrical conductance to find the peak. The third strategy was the new artificial intelligence model. This model had been trained beforehand to recognize the complex, non-linear relationship between the weather conditions and the optimal electrical setting. Once trained, the neural network acted as a controller, taking real-time measurements of sunlight and temperature and immediately predicting the perfect voltage setting for the solar panel.

The results of the comparison were clear and favored the artificial intelligence approach. The traditional methods, while functional, struggled with speed and precision. The standard "perturb and observe" technique, which relies on trial and error, took an average of 8.5 steps to find the maximum power point after a change in conditions. The slightly more advanced conductance-based method improved on this, needing 6.2 steps. In contrast, the neural network model found the optimal setting in just 2.8 steps. This speed meant the system could adapt to changing clouds or shifting temperatures almost instantly, rather than lagging behind. Furthermore, the traditional methods tended to oscillate, or wobble, around the target, constantly overshooting and undershooting the ideal power level. The neural network, having learned the pattern of the data, settled on the correct setting with far less movement, maintaining a steady and efficient operation.

In terms of overall efficiency, the artificial intelligence controller captured 98.7 percent of the available power, a figure that surpassed the 95.8 percent achieved by the conductance method and the 94.2 percent of the traditional trial-and-error approach. The study also measured how closely the predicted settings matched the actual best possible settings. The neural network achieved an accuracy of 99.57 percent, meaning it rarely missed the mark. The researchers noted that the traditional methods produced more error in their predictions, leading to a loss of potential energy. The neural network model was able to handle the complex, shifting nature of solar power generation without the hesitation or confusion that often plagues rule-based systems. It did not need to wait for a change to happen before reacting; it could anticipate the best setting based on the current conditions.

This work demonstrates that data-driven learning can offer a tangible improvement over established engineering rules for solar energy. The study confirms that a system trained on historical data can navigate the complex behavior of solar panels more effectively than algorithms that rely on fixed mathematical formulas. While the results come from a simulation rather than a physical hardware test, the consistency of the data suggests that the neural network approach holds significant promise for real-world applications. By reducing the time it takes to find the best power setting and minimizing the energy lost to oscillation, such a system could help solar farms and rooftop installations generate more electricity from the same amount of sunlight. The researchers plan to take these findings further by testing the system on actual hardware and exploring ways to combine this intelligent tracking with other protective measures, aiming to make solar power even more reliable and productive.