A MPPT based on optimized FLC using manta ray foraging optimization algorithm for thermo-electric generation systems

The output power from thermoelectric generator (TEG) system is mainly dependent on the differential temperature between the hot side and cold side of TEG in addition to the load demand. Therefore, maximum power point tracking (MPPT) control is highly needed to continuously track the optimal operating point with changing the operating condition. In this research paper, a MPPT method based on optimized fuzzy logic control (FLC) is proposed. The proposed method utilizes the freedom and flexibilities from FLC systems to develop accurate and fast-tracking controller of maximum power point for TEG applications. The parameters of the optimized FLC have been identified using recent manta ray foraging optimization (MRFO) algorithm. During the proposed optimization process, the gains of the membership functions are used as decision variables, whereas the integral of the error is used as a cost function. Different scenarios of changing the difference temperature are used to prove the reliability of the optimized FLC. The obtained results by using the optimized FLC are compared with conventional FLC and the hill-climbing methods. The main findings confirm that the proposed design using combined features of MRFO and FLC presents a promising solution for MPPT in TEG systems. The proposed optimized FLC method achieves superior performance through minimizing the fluctuations in the output power at the various studied scenarios. Moreover, continuous tracking of the maximum power from TEG is achieved by the proposed optimized FLC method at different hot side and cold side temperatures in addition to output load variations.