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Comparison of control strategies for thermoelectric generator emulator Ayop, Razman; Tan, Chee Wei; Ayob, Shahrin Md; Daud, Mohd Zaki; Jamian, Jasrul Jamani; Nordin, Norjulia Mohamad
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp2094-2106

Abstract

Thermoelectric generator (TEG) can directly convert heat energy into electrical energy. It improves the power efficiency of the energy generation system by converting the power loss in the form of heat produced during the generation process into additional electrical energy. The TEG emulator (TEGE) is a power converter that produces a similar current-voltage characteristic as the TEG. It is a valuable device used to develop and test the TEG-based energy generation system. Nonetheless, the research on the TEGE is still in the early stage. This paper proposed a proper, low-cost, and high-efficient TEGE design using the buck converter. The contribution of the paper covers the TEG model in the form of an array, the buck converter design tailored to the TEGE, and 4 new control strategies proposed for the TEGE. The control strategies are the direct referencing method (DRM), perturb and observed (PnO) method, resistance comparison method (RCM), and resistance feedback method (RFM). The conventional proportional-integral controller is used to maintain a smooth operation during transient and steady-state periods. The results show the merits or demerits for each proposed control strategy based on the accuracy, transient response, stability, overshoot, and efficiency.
Determination of biomass energy potential based on regional characteristics using adaptive clustering method Alvianingsih, Ginas; Hashim, Haslenda; Jamian, Jasrul Jamani; Senen, Adri
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp46-55

Abstract

Determining the energy potential of biomass is the first step in selecting the most suitable and efficient energy conversion technology based on regional characteristics. The approach to estimating and determining biomass potential generally uses geospatial technology related to collecting and processing data about mapping an area. Unfortunately, this method is inadequate for simulating the interaction between variables, nor can it provide accurate predictions for the biomass supply chain. As a result, the results obtained from this method tend to be biased and macro, particularly in regions experiencing rapid land-use development. In this paper, the author has developed a clustering methodology with a fuzzy c-means (FCM) algorithm to determine biomass energy potential based on regional characteristics to produce data clusters with high accuracy. Grouping the characteristics of clustering-based areas involves grouping physical or abstract objects into classes or similar objects.