Enhanced P&O MPPT Algorithm based on Fuzzy Logic for PV System: Brief Review and Experimental Implementation

Yıl 2023, Cilt: 7 Sayı: 4, 105 - 116, 31.12.2023

Claude Bertin Nzoundja Fapı , Hyacinthe Tchakounté

Öz

The maximum power point tracking (MPPT) based perturbation and observation (P&O) approach for photovoltaic (PV) devices is first created in this study after a survey of the literature. The MPPT P&O algorithms described in the state of the art fall into three categories: modifications of the fundamental P&O approach, combinations of P&O techniques with more traditional techniques, and combinations with additional smart techniques. The experimental use of an improved P&O strategy based on fuzzy logic (FL) for PV utilization is then suggested. The traditional P&O method is frequently used in solar power generation since it is easy to construct and uses inexpensive technology. However, the trade-off between steady state oscillations and dynamic responsiveness is its fundamental drawback. A FL-based controller block is utilized to offer a variable step in order to address the flaws in current implementations of the classic P&O technique. The results of the experiments demonstrate that the recommended approach’s reaction time is faster than the conventional P&O strategy. The efficiency, the average power, the ripple rate of the power and the response time are respectively 99.6%, 100W, 0.05 and 0.01. These results are interesting regarding the vast majority of similar existing works. Additionally, it is discovered that the stability and power oscillations of the suggested control are virtually completely eradicated. Compared to recommended P&O methods available in the literature, the enhanced P&O control based on FL is precise, straightforward, and enables to optimize more quickly for optimal power point.

Anahtar Kelimeler

P&O, fuzzy Logic, photovoltaic devices, experimental result.

Kaynakça

• [1] K. Nimptsch and P. Tobias. “Body fatness, related biomarkers and cancer risk: an epidemiological perspective.” Hormone molecular biology and clinical investigation 22, no. 2, pp. 39-51, 2015.
• [2] Prospective Studies Collaboration. “Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies.” The Lancet 373, no. 9669, pp. 1083-1096, 2009.
• [3] L. Cominato, G.F. Di Biagio, D. Lellis, R.R. Franco, M.C. Mancini, and M.E. de Melo. “Obesity prevention: strategies and challenges in Latin America.” Current obesity reports 7, pp. 97-104, 2018.
• [4] J.H. Friedman. “Data Mining and Statistics: What's the connection?.” Computing science and statistics, 29(1), pp. 3-9, 1998.
• [5] F.E. Horita, J.P. de Albuquerque, V. Marchezini, and E.M. Mendiondo. “Bridging the gap between decision-making and emerging big data sources: An application of a model-based framework to disaster management in Brazil.” Decision Support Systems, 97, pp. 12-22, 2017.
• [6] S.B. Kotsiantis, I. Zaharakis, and P. Pintelas. “Supervised machine learning: A review of classification techniques.” Emerging artificial intelligence applications in computer engineering, 160(1), pp. 3-24, 2007.
• [7] J. Sun and C.K. Reddy. “Big data analytics for healthcare.” In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1525-1525, 2013..
• [8] K. Srinivas, B.K Rani, and A. Govrdhan. “Applications of data mining techniques in healthcare and prediction of heart attacks.” International Journal on Computer Science and Engineering (IJCSE), 2(02), pp. 250-255, 2010.
• [9] L.N. Borrell and L. Samuel. “Body mass index categories and mortality risk in us adults: the effect of overweight and obesity on advancing death.” Am. J. Public Health 104 (3), 2014.
• [10] T.M. Dugan, S. Mukhopadhyay, A. Carroll, S. Downs. “Machine learning techniques for prediction of early childhood obesity.” Appl. Clin. Inform. 6 (3), 2015.
• [11] K. Jindal, N. Baliyan, and P.S. Rana. “Obesity prediction using ensemble machine learning approaches.” In: Proceedings of the 5th ICACNI, 2, pp. 355–362, 2017.
• [12] B. Singh and H. Tawfik. “Machine learning approach for the early prediction of the risk of overweight and obesity in young people.” In Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part IV 20, pp. 523-535, Springer International Publishing, 2020.
• [13] F. Ferdowsy, K.S.A Rahi, M.I. Jabiullah, and M.T. Habib. “A machine learning approach for obesity risk prediction.” Current Research in Behavioral Sciences, 2, 100053, 2021.
• [14] A.M. Erturan, G. Karaduman, and H. Durmaz. “Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.” Journal of hazardous materials, 455, 131616, 2023.
• [15] E. Frank, M.A. Hall, and I.H. Witten. “The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques".” 4th edn. Morgan Kaufmann, Burlington, 2016.
• [16] S.R. Garner. “Weka: The waikato environment for knowledge analysis.” In Proceedings of the New Zealand computer science research students conference, pp. 57-64, 1995.
• [17] M.Ç .Cengiz. “Obezite cerrahi geçiren bireylerde yağ dokusu kaybı ile demir ve D vitamini düzeyi arasındaki ilişki.” Master's thesis, Biruni Üniversitesi Sağlık Bilimleri Enstitüsü, 2019.
• [18] K.G. Şişman and Ş.K. Anemisi. “Beslenme Örüntüsü ile Kronik İnflamasyon Belirteçleri ve Diyet Tedavisinin Etkinliğinin Belirlenmesi.” Doktora tezi. Ankara: Hacettepe Üniversitesi, 2013.
• [19] I. Damoune, I. Khaldouni, L. Agerd and F. Ajdi. “Obésité: prévalence et profil métabolique chez une population de diabétique type 2.” In Annales d'Endocrinologie, Vol. 75, No. 5-6, pp. 457, Elsevier Masson, 2014.
• [20] M. Valle, R. Martos, F. Gascon, R. Canete, M.A. Zafra and R. Morales. “Low-grade systemic inflammation, hypoadiponectinemia and a high concentration of leptin are present in very young obese children, and correlate with metabolic syndrome.” Diabetes & metabolism, 31(1), pp. 55-62, 2005.
• [21] M. Valle, R. Martos, F. Gascon, R. Canete, M.A. Zafra, and R. Morales. “Low-grade systemic inflammation, hypoadiponectinemia and a high concentration of leptin are present in very young obese children, and correlate with metabolic syndrome.” Diabetes & metabolism, 31(1), pp. 55-62, 2005.
• [22] H. Hüsna. “Santral obezite ve bel/kalça çevresinin dislipidemi ile ilişkisi.” Dünya Beslenme Dergisi , 1 (2), pp. 18-22, 2018.
• [23] M.A. Burza, S. Romeo, A. Kotronen, P.A. Svensson, K. Sjöholm, J.S. Torgerson, and M. Peltonen. “Long-term effect of bariatric surgery on liver enzymes in the Swedish Obese Subjects (SOS) study.” PloS one, 8(3), e60495, 2013.
• [24] J. A. Demirovic, A.B. Pai, and M.P. Pai. “Estimation of creatinine clearance in morbidly obese patients.” American Journal of Health-System Pharmacy, 66(7), pp. 642-648, 2009.
• [25] J. J Rayner, M.A. Peterzan, W.D. Watson, W.T. Clarke, S. Neubauer, C.T. Rodgers, and O.J. Rider. “Myocardial energetics in obesity: enhanced ATP delivery through creatine kinase with blunted stress response.” Circulation, 141(14), pp. 1152-1163, 2020.
• [26] B. Hansel, P.Giral, L.Gambotti, A.Lafourcade, G. Peres, C.Filipecki, D.Kadouch, A.Hartemann, J.M. Oppert, E. Bruckert, and M. Marre. “A fully automated web-based program improves lifestyle habits and HbA1c in patients with type 2 diabetes and abdominal obesity: randomized trial of patient e-coaching nutritional support (the ANODE study).” Journal of medical Internet research, 19(11), pp.e360, 2017.
• [27] O. Pinhas-Hamiel, N. Doron-Panush, B. Reichman, D. Nitzan-Kaluski, S. Shalitin, and L. Geva-Lerner. “Obese children and adolescents: a risk group for low vitamin B12 concentration.” Archives of pediatrics & adolescent medicine, 1;160(9), pp. 933-6, 2006.
• [28] A. Valea, M. Carsote, C. Moldovan, and C. Georgescu. “Chronic autoimmune thyroiditis and obesity.” Archives of the Balkan Medical Union, 53(1), pp. 64-69, 2018.
• [29] A. SÜNER, O. BALAKAN, V. KIDIR. “Association of Thalassemia Minor and Lead Intoxication in a Patient who Applied with Hypochromic Microcytic Anemia.” International Journal of Hematology and Oncology, 32(1), pp. 133-136, 2006.
• [30] N.H. Noğay and G. Köksal. “Çocuklarda metabolik sendromun tedavisinde beslenme yönetimi” Güncel Pediatri, 10(3), pp. 92-97, 2012.
• [31] F. Kelleci Çelik and G. Karaduman. “In silico QSAR modeling to predict the safe use of antibiotics during pregnancy.” Drug and Chemical Toxicology. doi: 10.1080/01480545.2022.2113888, pp. 1-10, 2002.
• [32] G. Karaduman and F. Kelleci Çeli. “2D-Quantitative structure-activity relationship modeling for risk assessment of pharmacotherapy applied during pregnancy.” Journal of Applied Toxicology: JAT, 10.1002/jat.4475. https://doi.org/10.1002/jat.4475, 2023.
• [33] F. Kelleci Çelik and G. Karaduman. “Machine Learning-Based Prediction of Drug-Induced Hepatotoxicity: An OvA-QSTR Approach.” Journal of Chemical Information and Modeling, 63(15), pp. 4602-4614, 2023.
• [34] M. Narasimha Murty, V. Susheela Devi, “Pattern Recognition: An Algorithmic Approach”, Springer Science & Business Media, May 25, 2011.
• [35] K. Sridharan and G. Komarasamy. “Sentiment classification using harmony random forest and harmony gradient boosting machine.” Soft Computing, 24(10), pp. 7451-7458, 2020.
• [36] Z. Wang, F. Chegdani, N. Yalamarti, B. Takabi, B. Tai, M. El Mansori, and S. Bukkapatnam. “Acoustic Emission Characterization of Natural Fiber Reinforced Plastic Composite Machining Using a Random Forest Machine Learning Model.” Journal of Manufacturing Science and Engineering, 142(3), 2020.
• [37] N. Bhargava, G. Sharma, R. Bhargava, and M. Mathuria. “Decision tree analysis on j48 algorithm for data mining.” Proceedings of International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 2013.
• [38] G. Fung and O. L. Mangasarian. “Incremental support vector machine classification.” In Proceedings of the 2002 SIAM International Conference on Data Mining pp. 247- 260, Society for Industrial and Applied Mathematics, 2002.
• [39] S. Li, K. Zhang, Q. Chen, S. Wang, and S. Zhang. “Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm.” IEEE Access, 2020.
• [40] F. C. Pampel. “Logistic Regression: A Primer”, SAGE Publishers, pp. 35-39, May 26, 2000.
• [41] P. Perner. “Machine Learning and Data Mining in Pattern Recognition”, 6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009.
• [42] A. Cutler and G. Zhao. “Pert-perfect random tree ensembles.” Computing Science and Statistics, 33, pp. 490-497, 2001.
• [43] GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9(2), pp. 137-150, 2022.
• [44] R.S.C. Aman. “Disease predictive models for healthcare by using data mining techniques: state of the art.” SSRG Int. J. Eng. Trends Technol, 68, pp. 52-57, 2020.
• [45] A. Muniasamy, V. Muniasamy, and R. Bhatnagar, “Predictive analytics for cardiovascular disease diagnosis using machine learning techniques,” in Advances in Intelligent Systems and Computing, vol. 114, pp. 493–502, 2021.
• [46] J. Majali, R. Niranjan, V. Phatak, O. Tadakhe. “Data Mining Techniques for Diagnosis And Prognosis of Cancer”, Int. Journal of Advanced Research in Computer and Communication Engg., Vol. 4, Issue 3, pp. 613-614, 2015.
• [47] A.P. Sinhaand and J.H. May. “Evaluating and tuning predictive data mining models using receiver operating characteristic curves.” Journal of Management Information Systems, 21(3), pp. 249-280, 2005.
• [48] K. R. Lakshmi, M. Veera Krishna, S.Prem Kumar. “Performance Comparison of Data Mining Techniques for Prediction and Diagnosis of Breast Cancer Disease Survivability”, Asian Journal of Computer Science and Information Technology, Vol. 3, pp. 81 – 87, 2013.
• [49] P. Apostolou and F. Fostira. “Hereditary Breast Cancer: The Era of New Susceptibility Genes”, BioMed Research International Vols. 2013.