تعداد نشریات | 31 |

تعداد شمارهها | 698 |

تعداد مقالات | 6,840 |

تعداد مشاهده مقاله | 11,121,775 |

تعداد دریافت فایل اصل مقاله | 7,502,258 |

## Fuzzy Logic Inherited machine learning based Maximum Power Point Tracker for Cost-Optimized Grid Connected Hybrid Renewable systems | ||

Iranian Journal of Fuzzy Systems | ||

دوره 21، شماره 1، فروردین و اردیبهشت 2024، صفحه 103-128 اصل مقاله (1.33 M) | ||

نوع مقاله: Original Manuscript | ||

نویسندگان | ||

Sampathraja Natarajan^{*} ^{1}؛ Ashok Kumar Loganathan^{2}
| ||

^{1}Department of Electrical and Electronics Engineering, PSG College of Technology,Coimbatore,Tamil Nadu,India - 641 004 | ||

^{2}Professor, Department of Electrical and Electronics Engineering PSG College of Technology, Coimbatore 641 004 | ||

چکیده | ||

In this article, Multitrial vector-based differential evolution algorithm (MTDE) is proposed as energy and cost management controller under Time of Use (TOU) tariff in grid associated domestic PV-wind power system tied with battery storages. To enrich the energy efficiency of a proposed power system, two optimisation algorithms are proposed in the scheduling operation. TOU billing is a cost- reflective power pricing strategy that has been found as an effective way to reduce peak energy consumption in the residential segment everywhere the world, mainly in industrialised nations. In the optimisation maximizing the cost benefit of the household energy is taken as the objective and the dispatching ratio of electricity sold to the grid and used locally is treated as the optimisation variable. Using MATLAB, the performance of proposed MTDE in the aspect of daily cost benefit and revenue growth rate are presented with the comparative analysis of gravitational search algorithm and conventional self-made for self-consumed and rest for sale (SFC&RFS) mode -based energy management controller. In comparison to the SFC&RFS mode, the GSA-based cost optimization offers a 21.46% increase in revenue growth, while it is improved to 38.7% using proposed MTDE algorithm. The paper also addresses the significance of fuzzy logic based maximum power point tracking (MPPT) of solar PV in enhancing the energy management of the proposed system. The prototype of fuzzy logic MPPT for 10 W solar panel is presented and the tracked maximum power is visualized using Thingspeak IoT cloud server. The tracking speed of the MPPT can be increased by introducing machine learning algorithms at the cost of memory and complexity. In this article, the linear regression-based machine learning algorithm is implemented in the hardware prototype by utilizing the dataset aggregated from fuzzy MPPT and hence the proposed MPPT inherits the characteristics of the fuzzy MPPT with increased tracking speed. Around 75 % data are used for training, 25% of data are used to test the model and it is observed that the root mean square error (rmse) is 5.1334 and mean square error is 26.3521 and the model is utilized as MPPT for the real-time inputs. | ||

کلیدواژهها | ||

Solar PV system؛ Wind energy؛ Time of Use tariff؛ Energy and Cost Optimization؛ Fuzzy Logic MPPT for Solar PV؛ Internet of Things؛ Linear regression based machine learning | ||

مراجع | ||

[1] U. Akram, M. Khalid, S. Shafiq, An innovative hybrid wind-solar and battery-supercapacitor microgrid system- development and optimization, IEEE Access, 5 (2017), 25897-25912. [2] L. Ashok Kumar, A. Alexander, Power electronic converters for solar photovoltaic systems, Academic Press, Elsevier, 2020. [3] M. A. E. S. Badr, Modeling, simulation and optimization of wind farms and hybrid systems, IntechOpen, (2020), 145-162. [4] R. Banos, F. Manzano-Agugliaro, F. G. Montoya, C. Gil, A. Alcayde, J. G´omez, Optimization methods applied to renewable and sustainable energy: A review, Renewable and Sustainable Energy Reviews, 15(4) (2011), 1753-1766. [5] F. Cucchiella, P. Rosa, End-of-Life of used photovoltaic modules: A nancial analysis, Renewable and Sustainable Energy Reviews, 47 (2015), 552-561. [6] I. Cviti´c, D. Perakovi´c, M. Peri˘sa, B. Gupta, Ensemble machine learning approach for classi cation of IoT devices in smart home, International Journal of Machine Learning and Cybernetics, 12(11) (2021), 3179-202. [7] M. Deveci, D. Pamucar, I. Gokasar, M. K¨oppen, B. B. Gupta, Personal mobility in metaverse with autonomous vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model, IEEE Transactions on Intelligent Transportation Systems, (2022), 1-10. [8] A. Ghodousian, F. S. Yousef, Linear optimization problem subjected to fuzzy relational equations and fuzzy con- straints, Iranian Journal of Fuzzy Systems, 20(2) (2023), 1-20. [9] A. R. Gollou, N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets, Journal of Intelligent and Fuzzy Systems, 32(6) (2017), 4031-4045. [10] D. G´omez-Lorente, F. Aznar, O. Rabaza, MPPT algorithm based on multiple linear regression model for solar PV systems, 20th International Conference on Renewable Energies and Power Quality, 30 (2022), 100-105. [11] M. Grace, C. Sunetra, Optimal energy management of a grid-tied solar PV-battery microgrid: A reinforcement learning approach, Energies, 14(9) (2021), 2700. [12] M. Hamian, A. Darvishan, M. Hosseinzadeh, M. J. Lariche, N. Ghadimi, A. Nouri, A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm, Engineering Applications of Artificial Intelligence, 72 (2018), 203-212. [13] N. Hemalatha, R. Seyezhai, Implementation of fuzzy MPPT controller for PV-based three-phase modi ed capacitor- assisted extended boost q-ZSI, Applied Nanoscience, 13(3) (2023), 1971-1979. [14] M. Hosseini Firouz, N. Ghadimi, Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods, Complexity, 21(6) (2016), 70-88. [15] R. Inglesi-Lotz, J. N. Blignaut, Estimating the price elasticity of demand for electricity by sector in South Africa, South African Journal of Economic and Management Sciences, 14(4) (2011), 449-465. [16] I. Kouatli, Fuzziness control of fuzzimetric sets, IEEE International Conference on Fuzzy Systems, (2019), 1-5.
[17] Y. Liu, W. Wang, N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level con- sumers, Energy, 139 (2017), 18-30. [18] F. Mirzapour, M. Lakzaei, G. Varamini, M. Teimourian, N. Hadimi, A new prediction model of battery and wind- solar output in hybrid power system, Journal of Ambient Intelligence and Humanized Computing, 10(1) (2019), 77-87. [19] M. H. Nadimi-Shahraki, S. Taghian, S. Mirjalili, H. Faris, MTDE: An e ective multi-trial vector-based di erential evolution algorithm and its applications for engineering design problems, Applied Soft Computing Journal, 97 (2020), 106761. [20] K. Rafeeq Ahmed, K. Farrukh Sayeed, K. Logavani, T. J. Catherine, R. Shimpy, R. Mahesh Singh, R. Thandaiah Prabu, B. Bala Subramanian, Adane Kassa, Maximum power point tracking of PV grids using deep learning, International Journal of Photoenergy, (2022). DOI:10.1155/2022/1123251. [21] M. M. Rahman, S. Hettiwatte, G. M. Shafiullah, A. Arefi, An analysis of the time of use electricity price in the residential sector of Bangladesh, Energy Strategy Reviews, 18 (2017), 183-198. [22] E. Rashedi, H. Nezamabadi-pour, S. Saryazdi, GSA: A gravitational search algorithm, Information Sciences, 179(13) (2009), 2232-2248. [23] N. Sampathraja, L. Ashok Kumar, Analysis of energy management controller in grid-connected PV wind power system coupled with battery using whale optimisation algorithm, Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 46(1) (2022), 77-90. [24] J. R. San Crist´obal, A goal programming model for the optimal mix and location of renewable energy plants in the north of Spain, Renewable and Sustainable Energy Reviews, 16 (2012), 4461-4464. [25] M. R. Setayandeh, A. R. Babaei, A novel method for multi-objective design optimization based on fuzzy systems, Iranian Journal of Fuzzy Systems, 18(5) (2021), 181-198. [26] S. Sumathi, S. Rajappa, L. Ashok Kumar, S. Surekha, Advanced decision sciences based on deep learning and ensemble learning algorithms, Nova Science Publishers, 2021. [27] R. K. Swain, N. C. Sahu, P. K. Hota, Gravitational search algorithm for optimal economic dispatch, Procedia Technology, 6 (2012), 411-419. [28] S. Zhang, Y. Tang, Optimal schedule of grid-connected residential PV generation systems with battery storages under time-of-use and step tari s, Journal of Energy Storage, 23 (2019), 175-182. | ||

آمار تعداد مشاهده مقاله: 437 تعداد دریافت فایل اصل مقاله: 642 |