Abstract
Thermoelectric generators (TEGs) are equipment for transforming thermal power into electricity via the Seebeck effect. These modules have gained increasing interest in research fields related to sustainable energy. The harvested energy is mostly reliant on the differential temperature between the hot and cold areas of the TEGs. Hence, a reliable maximum power point tracker is necessary to operate TEGs too close to their maximum power point (MPP) under an operational and climate variation. In this paper, an optimized fractional incremental resistance tracker (OF-INRT) is suggested to enhance the output performance of a TEG. The introduced tracker is based on the fractional-order PIλDμ control concepts. The optimal parameters of the OF-INRT are determined using a population-based sine cosine algorithm (SCA). To confirm the optimality of the introduced SCA, experiments were conducted and the results compared with those of particle swarm optimization (PSO) and whale optimization algorithm (WOA) based techniques. The key goal of the suggested OF-INRT is to overcome the two main issues in conventional trackers, i.e., the slow dynamics of traditional incremental resistance trackers (INRT) and the high steady-state fluctuation around the MPP in the prevalent perturb and observe trackers (POTs). The main findings prove the superiority of the OF-INRT in comparison with the INRT and POT, for both dynamic and steady-state responses.
Highlights
In recent decades, thermal energy has become one of the renewable energies abundantly available in several industrial and civil sectors, namely, in powering electronic devices, driving electric vehicles, and in pumping applications [1,2]
In the fractional-order maximum power point tracking (MPPT) techniques, the error signal that defines the incremental conductance (INC)/incremental resistance (INR) of the energy converter is used as an input to the integrator
MPPT approach and remove the steady-state variations of the perturb and observe (P&O) MPPT technique, in this paper, an Optimized Fractional INR Tracker (OF-incremental resistance trackers (INRT)) was proposed based on an sine cosine algorithm (SCA)-tuned PIλ controller to rise the energy harvested from the thermoelectric generator (TEG)
Summary
Thermal energy has become one of the renewable energies abundantly available in several industrial and civil sectors, namely, in powering electronic devices, driving electric vehicles, and in pumping applications [1,2]. The authors of [25,26] introduced an enhanced Fractional Order Fuzzy Logic Controller (FOFLC)-based MPPT method for TEG and PV–TEG hybrid energy devices to efficiently harvest the maximum power. In the fractional-order MPPT techniques, the error signal that defines the INC/INR of the energy converter is used as an input to the integrator The gain of such an integrator is the scaling factor to tune the step size of the MPP tracker. The major contributions for this research work are summarized as: (1) An efficient fractional VSS-INR-based MPPT approach for a TEG module is proposed to deal with the steady-state oscillations and lower accuracy of classical MPPT methods for TEG systems such as the PandO and HC techniques.
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