A recent study from Ankara Yıldırım Beyazıt University [39.9°N, 32.9°E] has introduced a new way to maximise energy output from solar panels, inspired by the hunting strategy of wolves. By using an approach called Grey Wolf Optimisation (GWO) — a mathematical technique modelled on how wolves track their prey — the researchers have developed a system that makes solar power more efficient, stable, and reliable.
Solar panels don’t always produce their maximum possible power because of changing sunlight conditions, shading, and fluctuations in temperature. To get the best performance, engineers use Maximum Power Point Tracking (MPPT) algorithms to constantly adjust the electrical settings of a solar panel. The study found that combining GWO with another MPPT method — Modified Incremental Conductance (M_INC) — created a system that significantly improved solar efficiency, reduced energy losses, and minimised unwanted fluctuations.
Why Do Solar Panels Need Smart Tracking?
Solar panels work best when they operate at a specific point — the Maximum Power Point (MPP) — where they generate the most energy possible for the given sunlight conditions. The problem is that this point constantly shifts as clouds move, seasons change, and temperature fluctuates.
Traditional MPPT methods, like Incremental Conductance (INC) and Perturb & Observe (P&O), try to track the MPP, but they can struggle when sunlight is inconsistent or when multiple panels are affected differently by shading. The researchers aimed to create a smarter, more adaptive system — and that’s where the Grey Wolf Optimisation technique comes in.
What is Grey Wolf Optimisation?
Grey Wolf Optimisation (GWO) is a technique that mimics the way grey wolves hunt in packs. In the wild, wolves don’t attack their prey randomly — they work together, with the alpha wolf leading, while beta, delta, and omega wolves assist by encircling and tracking the prey before attacking at the perfect moment.
Applied to solar power, each “wolf” represents a potential setting for the solar panel’s operation, and the algorithm constantly adjusts its position to zero in on the optimal power output, just like wolves closing in on their target.
The Study’s Breakthrough: Hybrid GWO + M_INC Algorithm
The research team tested six different MPPT methods, including traditional ones like P&O and INC, as well as GWO alone and a new hybrid system that combined GWO with Modified Incremental Conductance (M_INC). The results were striking:
- The hybrid GWO + M_INC method had the best overall performance, balancing speed, accuracy, and stability.
- It reduced energy fluctuations (power oscillations), meaning smoother and more reliable power output.
- It achieved an efficiency of 99.71%, higher than all the other methods.
- It minimised electrical interference (Total Harmonic Distortion, or THD) to just 2.31%, improving overall power quality.
Why This Matters for Solar Power
This study highlights how artificial intelligence and nature-inspired algorithms can enhance renewable energy technologies. Smarter MPPT tracking means:
- More electricity generated from the same solar panels, increasing their value.
- Better performance in cities, cloudy regions, and off-grid locations, where sunlight conditions vary throughout the day.
- Lower costs and longer panel lifespan, as energy fluctuations put less strain on solar equipment.
By looking to nature for inspiration, researchers are finding ways to make solar energy as efficient and adaptable as possible, helping accelerate the transition to a cleaner energy future.
Source
Novel Hybrid MPPT Based on Modified Incremental Conductance-Grey Wolf Optimisation for Grid-Connected PV Systems, International Journal of Energy Studies, 2025-03-10
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