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Optimization of fuzzy entropy on solar panel’s motor

MUHAMMAD IKHWAN, MARWAN RAMLI, MARDLIJAH MARDLIJAH

Abstract


Renewable energy has been in great demand by the public, even some countries have set regulations for substitution and transition from fossil energy to renewable energy. This study aims to modify the fuzzy control system with a metaheuristic method, namely fuzzy entropy. The entropy value of the fuzzy set in the previous stage becomes the basis for calculating the foot of uncertainty in the new fuzzy set. This process makes the entropy method parallel to other optimization methods that have been carried out on fuzzy control systems. The results obtained indicate that the modified fuzzy control system successfully controls the angle and angular velocity of the solar panel. The error value shown is very small and the time to reach stability is below 5 s. This is a rapid development of several previous studies. The modified system has no overshoot and steady state error below 1%. Based on these results, entropy research can be developed again by changing the fuzzy set to a more complex form.


Keywords


entropy, fuzzy logic controller, sun altitude, solar tracker, dc motor

References


Ballabel, A.; Mahfouz, A.; Salem, F.A. 2013. Design and Performance of Solar Tracking Photo-Voltaic System. International Journal of Control, Automation and Systems. 1(2) 49-55.

Mardlijah; Rinanto, N.; Soemarsono, A.R. 2017. Type 2 Fuzzy Sliding Mode Control (T2FSMC) Controller on Solar Panel Prototype using The Most Representative Parameters. Journal of Theoretical and Applied Information Technology. 95(20) 5562-5570.

Mardlijah, M.; Zuhri, Z. 2018. Solar Panel Control System Using an Intelligent Control: T2FSMC and Firefly Algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control). 16(6) 2988.

Tomson, T. 2008. Discrete Two-Positional Tracking of Solar Collectors. Renewable Energy. 33 400-405.

Abadi, I.; Musyafa, A.; Soeprijanto, A. 2015. Design and implementation of active two axes solar tracking system using particle swarm optimization based fuzzy logic controller. International Review on Modelling and Simulations. 8(6) 640–652.

Oliva, D.; Abd Elaziz, M.; Hinojosa, S. 2019. Metaheuristic Algorithms for Image Segmentation: Theory and Applications (Switzerland: Springer Nature).

Baculi, J.; Ayoubi, M. A. 2017. Fuzzy attitude control of solar sail via linear matrix inequalities. Acta Astronautica. 138 233–241.

Rani, P.; Singh, O.; Pandey, S. 2018. An Analysis on Arduino based Single Axis Sola Tracker. 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering. 1–5.

Ikhwan, M.; Mardlijah; Imron, C. 2018. Model predictive control on dual axis solar tracker using Matlab/Simulink simulation. 2018 International Conference on Information and Communications Technology (ICOIACT). 784–788.

Sawant, A.; Bondre, D.; Joshi, A.; Tambavekar, P.; Deshmukh, A. 2019. Design and analysis of automated dual axis solar tracker based on light sensors. Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud). 454–459.

Ganesh, N.J.; Maniprakash, S, Chandrasekaran, L.; Srinivasan, S.M.; Srinivasan, A.R. 2011. Design and Development of A Sun Tracking Mechanism Using the Direct SMA Actuation. Transactions-American Society of Mechanical Engineers Journal of Mechanical Design. 133 1-14.

Ikhwan, M Mardlijah; Imron, C.; Safitri, W. D.; Rusdiana, S. 2020. Solar panel angle position correction using light sensor and fuzzy logic control. 3rd Smart Cities Symposium (SCS 2020). 646-650.

Ikhwan, M.; Rizal, S.; Ramli, M.; Muchlisin, Z.A.; Mardlijah. 2019. Wind disturbance elimination on dual axis solar tracker using fuzzy logic control. IOP Conf. Ser.: Eart Environ. Sci. 364 012038.

Ishikawa, A.; Mieno, H. 1979. The fuzzy entropy concept and its application. Fuzzy Sets and Systems. 2 113–123.

Yu, H.; Zhi, X.; Fan, J. 2015. Neurocomputing Image segmentation based on weak fuzzy partition entropy. Neurocomputing. 168 994-1010.

Cánovas, J. S. 2016. On fuzzy entropy and topological entropy of fuzzy extensions of dynamical systems. Fuzzy Sets and Systems. 1 1–16.

Yan, K.; Zeng, F. 2018. Conditional fuzzy entropy of fuzzy dynamical systems. Fuzzy Sets and Systems. 1 1–15.


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DOI: 10.24815/jn.v22i1.23187

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