Optimized Maximum Power Point Tracking for Hybrid PV-TEG Systems Using an Improved Water Cycle Algorithm

Hybridizing a photovoltaic (PV) system with a thermoelectric generator (TEG) enhances overall energy density and input utilization. PV efficiency decreases, whereas TEG efficiency improves with increasing temperatures, making them complementary for boosting system efficiency. However, controlling the hybrid PV-TEG system is challenging due to the difficulty of maintaining operation at the maximum power point (MPP) under partial shading conditions (PSC), non-uniform temperature distribution (NUTD) and varying environmental conditions. PSC in PV arrays arises from uneven irradiance, while NUTD occurs within TEG arrays due to uneven temperature. Both issues cause multiple local maxima on the power-voltage curve, often trapping conventional maximum power point tracking (MPPT) methods in local maxima and suboptimal energy harvesting. To address this, an Improved Water Cycle Algorithm (IWCA) has been implemented for MPPT in series connected hybrid PV-TEG systems. An improvement to the conventional WCA algorithm’s mathematical model enhances exploitation and accelerates particle convergence. This is achieved by dynamically adjusting the stream movement equations, boosting performance without increasing computational complexity. Its performance is compared with the conventional WCA (CWCA), Particle Swarm Optimization (PSO), Dragon Fly Optimization (DFO), Flying Squirrel Search Optimization (FSSO), Grey Wolf Optimization (GWO), and the classical Perturb and Observe (P&O) techniques. Results indicate that IWCA successfully achieved the global maxima under all operating scenarios, with an average efficiency of 99.41%, tracked and settled at the global maxima within 80 to 120 ms. The algorithm exhibits high MPPT stability, maintaining consistent performance across varying conditions while avoiding power oscillations once achieve steady state. Furthermore, the results demonstrate that IWCA outperforms other techniques in speed, accuracy, and efficiency, demonstrating its capability to reliably track and operate at the global maxima of the hybrid system’s power-voltage curve.