
Abstract
This article proposes a novel artificial intelligence-based power maximization technique for wind energy conversion systems to face today’s rapidly changing environmental conditions. The novelty is the implementation of a fuzzy logic inference system. Based on two successive power change velocities, it schedules the next execution parameter of a classic maximum power point tracking controller called dP-P&O (Perturb and Observe). This new control strategy was validated experimentally for different wind speeds and load changing conditions.
Wind Energy Needs Smart Control
Population growth, global warming and the race to develop new technologies are inflicting severe limitations on energy demand. In fact, to decarbonize energy production, prepare for a post-oil world, and address environmental concerns and the problems associated with global warming, the world is increasingly turning to cleaner energy sources.
In the quest for cleaner energy, wind power is becoming a dominant force. Globally, wind power installations exceeded 1 TW for the first time in 2023. That year was the wind industry’s successful on record. Installations increased by 50%, connecting 117 GW of wind power capacity to the electrical grid in that year alone.
However, like all renewable energy sources, wind power faces technical challenges, particularly in maximizing energy output. A recent study proposes an advanced solution for Wind Energy Conversion Systems (WECS) to tackle this problem. It offers a new way of boosting efficiency and stabilizing power generation, even under rapidly changing wind conditions.
Wind turbines operate in ever-changing conditions, with gusts, shifts in wind speed and other environmental factors. This makes it difficult for turbines to consistently generate their maximum potential output. Historically, engineers have tackled this problem using a method called Perturb and Observe (P&O). While effective, the traditional P&O method struggles in adapting to sudden wind changes, resulting in energy loss and inefficiencies.
This is where the fuzzy adaptive Maximum Power Point Tracking (MPPT) comes into play. Inspired by fuzzy logic, this method fine-tunes turbine output adjustments, learning from power variations caused by changes in wind speed. By doing so, it ensures the turbine responds quickly and accurately to fluctuations, capturing as much energy as possible.
Fuzzy Logic Powers: The Future of Wind Energy
Fuzzy logic may sound complicated, but it’s simple at its core: it’s a way for machines to make decisions under uncertain conditions. Imagine trying to adjust the speed of a wind turbine, but the wind is constantly shifting. Instead of using rigid rules, fuzzy logic allows the system to adjust based on a range of conditions, smoothly adapting the turbine’s behavior to maintain optimum performance.
In the case of wind turbines, fuzzy adaptive control takes into account various factors, such as wind speed changes and power fluctuations, to decide how to adjust the turbine settings. This dynamic adaptation ensures that the turbine operates at peak efficiency, especially in unstable environments.
dP-P&O Algorithm
The dP-P&O (Perturb and Observe) Maximum Power Point Tracking (MPPT) technique is an improvement in overcoming the classical P&O limitations. These limitations range from steady-state oscillations around the MPP, slow response speed and even wrong direction tracking under rapidly changing atmospheric conditions. It works by slightly changing the operating voltage (fixed-step perturbation) and observing power increases or decreases, then adjusting accordingly to keep the system near the MPP.
The dP-P&O method helps distinguish wind speed variations between controller-induced effects. This is accomplished by adding a new power measurement in the middle of the algorithm's sampling period, when the controller is not taking action.
Proposed Fuzzy Adaptive dP-P&O Algorithm
This algorithm combines the simplicity of the dP-P&O with the adaptability of fuzzy logic, enabling the wind turbine to adjust more quickly and accurately to changing wind conditions.
Our work proposes a scheduling algorithm for the perturbation step size adaptation using a fuzzy inference system. It is based on the successive dP1
and dP2 power variations (Figure 1) to generate a large step size for increasing environmental conditions to guarantee a fast convergence to the MPP. It generates a smaller step size for decreasing conditions to avoid the decreasing drift phenomenon and divergence from the MPP. It also generates a near null step size for the steady-state operating point to minimize steady-state oscillations.

This research work presents the following novelties and main contributions:
1) Modifying the dP-P&O algorithm, initially proposed and used for PV systems, to be implemented in a real time experimental Wind Energy Conversion System.
2) Scheduling the dP-P&O step size using a fuzzy inference system.
3) Using successive power variations (dP1 and dP2) as input variables for the fuzzy adaptation system to define the dP-P&O perturbation step size.
The proposed Fuzzy Adaptive dP-P&O algorithm is presented in Figure 2.

The experimental results comparison is presented in Figures 3, 4 and 5 for the power, voltage and current variations when the dP-P&O and proposed algorithm are used. The proposed controller significantly reduces steady state perturbations with rapid convergence to the MPP under both increasing and decreasing conditions.



The Future of Wind Energy Control
The success of fuzzy adaptive MPPT is just the beginning. As engineers continue to refine this technology, even more advanced control systems could predict and adapt to environmental conditions before they happen. The possibilities are endless, and point to a future where renewable energy will not only be cleaner but smarter.
So, the next time you see wind turbines on the horizon, remember that they’re more than just spinning blades. They feature advanced technology that captures the power of the wind and brings us one step closer to a sustainable energy future.
This fusion of clean energy and cutting-edge technology is driving the global shift toward sustainability, and innovations like fuzzy adaptive controls are key to unlocking wind energy’s full potential.