Hybrid Moth-Flame Optimization Algorithm and Incremental Conductance for Tracking Maximum Power of Solar PV/Thermoelectric System under Different Conditions

Abstract

For an efficient energy harvesting by the PV/thermoelectric system, the maximum power point tracking (MPPT) principle is targeted, aiming to operate the system close to peak power point. Under a uniform distribution of the solar irradiance, there is only one maximum power point (MPP), which easily can be efficiently determined by any traditional MPPT method, such as the incremental conductance (INC). A different situation will occur for the non-uniform distribution of solar irradiance, where more than one MPP will exist on the power versus voltage plot of the PV/thermoelectric system. The determination of the global MPP cannot be achieved by conventional methods. To deal with this issue the application of soft computing techniques based on optimization algorithms is used. However, MPPT based on optimization algorithms is very tedious and time consuming, especially under normal conditions. To solve this dilemma, this research examines a hybrid MPPT method, consisting of an incremental conductance (INC) approach and a moth-flame optimizer (MFO), referred to as (INC-MFO) procedure, to reach high adaptability at different environmental conditions. In this way, the combination of the two different algorithms facilitates the utilization of the advantages of the two methods, thereby resulting in a faster speed tracking with uniform radiation distribution and a high accuracy in the case of a non-uniform distribution. It is very important to mention that the INC method is used to track the maximum power point under normal conditions, whereas the MFO optimizer is most relevant for the global search under partial shading. The obtained results revealed that the proposed strategy performed best in both of the dynamic and the steady-state conditions at uniform and non-uniform radiation.

Parameter Identification of Optimized Fractional Maximum Power Point Tracking for Thermoelectric Generation Systems Using Manta Ray Foraging Optimization

“…To form a TEG module, some of the p-type and n-type groups are combined in series to raise the DC voltage and, accordingly, the output power. Simultaneously, these groups are thermally in parallel to reduce the thermal resistance [23,24]. The couples are planted between two parallel ceramic plates.…”

“…Figure 2 presents the equivalent-circuit of the TEG unit. The open circuit voltage as a source (V O.C ) is series connected with an internal resistance (R int ) [23,24].…”

An Improved Moth-Flame Optimization Algorithm for Engineering Problems

“…Supposing the algorithm iteration solution is the first k generation of the ith solution for , 0.5,0,0.5 , the objective function value of , 0.5. In the process of the iteration algorithm, the first hypothesis according to Formula (5) is that the , overall updates to (0, -1, 0), resulting in the objective function value of function value is big, so the algorithm will retain the original objective function values and discard the updated value of the target function. However, the value of the updated solution in the first dimension evolves from the previous 0.5 to 0 and and the value of the third dimension evolves from 0.5 to 0, but since the value of the second dimension degrades from 0 to −1, the algorithm will discard the updated solution directly in the evaluation strategy of the full dimension update.…”

“…Satomi Ishiguro [4] proposed a new method to optimize the loading mode of nuclear reactors-a multi-group moth-flame optimization algorithm with predators. Hegazy Rezk [5] studied the hybrid moth-flame optimization algorithm and the power condition of the maximum solar photovoltaic/hot spot system with incremental conductance tracking under different conditions. Mahrous a. Taher [6] used an improved moth-flame optimization algorithm to solve the optimal power flow problem.…”

A robust type‐2 fuzzy logic‐based maximum power point tracking approach for thermoelectric generation systems

“…The fundamental component of a TEG is a thermocouple which comprises a p-type and an n-type pellet couple. 6,7 The Seebeck voltages produced by each pellet are in series. Corresponding to the theorem of maximum power transfer, if the load matches the TEG's internal resistance, then maximum power can be extracted.…”