Robust Fractional MPPT-Based Moth-Flame Optimization Algorithm for Thermoelectric Generation Applications

Depending on the temperature difference between hot and cold sides of thermoelectric generator (TEG), output performance TEG can be produced. This means that it is necessary to force a based robust maximum power point tracking (MPPT) operate close its MPP at any given or load. In this paper, an improved fractional MPPT (IFMPPT) proposed in order increase amount energy harvested from TEGs. According suggested method, control used. A moth-flame optimizer (MFO) was used determine IFMPPT’s optimal parameters. comparison results obtained by MFO made with those particle swarm optimizer, genetic algorithm, gray wolf seagull optimization tunicate algorithm demonstrate MFO’s superiority. primary objective enhance dynamic responses exclude steady-state oscillations. Consequently, incremental resistance perturb observe are compared strategy’s performance. It revealed IFMPPT provides superior both conditions when traditional methods.