Relevant bibliographies by topics / Mppt; neural network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Mppt; neural network'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Mppt; neural network"

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Khanam, Jobeda J., and Simon Y. Foo. "Modeling of a photovoltaic array in MATLAB simulink and maximum power point tracking using neural network." Electrical & Electronic Technology Open Access Journal 2, no. 2 (2018): 40–46. http://dx.doi.org/10.15406/eetoaj.2018.02.00019.

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In this paper, we present our work on Maximum Power Point Tracking (MPPT) using neural network. The MATLAB/Simulink is used to establish a model of photovoltaic array. The Simulink model is tested with different temperature and irradiation and resultant I-V and P-V characteristics proved the validation of Simulink model of PV array. We collected a set of data from the Simulink model of PV array after simulated under a range of irradiation and temperature. The data collected from the system is used to train the neural network. When we tested the neural network with different irradiance and temp
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Harrag, Abdelghani, Hamza Bahri, and Sabir Messalti. "Steady state oscillations reduction using neural network IC-based variable step Size MPPT." Journal of Renewable Energies 19, no. 3 (2023): 487–95. http://dx.doi.org/10.54966/jreen.v19i3.588.

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This paper deals with the development of neural network IC-based variable step size MPPT controller. The proposed neural network MPPT controller is firstly, developed in offline mode required for testing different set of neural network parameters and architectures, and used secondly in the online mode to track the output power of the PV system composed of Solarex MSX 60W PV module fed by a DC-DC boost converter drived using the proposed ANN MPPT controller. The proposed neural network MPPT controller is tested and validated using Matlab/Simulink environments. Simulation results and analysis ar
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Chang, Shuhao, Qiancheng Wang, Haihua Hu, Zijian Ding, and Hansen Guo. "An NNwC MPPT-Based Energy Supply Solution for Sensor Nodes in Buildings and Its Feasibility Study." Energies 12, no. 1 (2018): 101. http://dx.doi.org/10.3390/en12010101.

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Sensors for data collecting are vital in the development of IoT and intelligent systems. High power consuming current and voltage monitors are indispensable in conducting maximum power point tracking (MPPT) in traditional PV energy wireless sensor nodes. This paper presents a sensor node system based on Neural Network MPPT with cloud method (NNwC) which utilizes information sharing process that is specific to sensor networks. NNwC uses a few sample sensor nodes to collect environmental parameter data such as light intensity (L) and temperature (T) to build the MPPT regression model by Neural N
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Wen, Liong Han, and Mohd Rezal Mohamed. "Softplus function trained artificial neural network based maximum power point tracking." International Journal of Power Electronics and Drive Systems (IJPEDS) 16, no. 2 (2025): 1174. https://doi.org/10.11591/ijpeds.v16.i2.pp1174-1183.

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To optimize the electrical output of a photovoltaic system, maximum power point tracking (MPPT) methods are commonly employed. These techniques work by operating the photovoltaic system at its maximum power point (MPP), which varies based on environmental factors like solar irradiance and ambient temperature, thereby ensuring optimal power transfer between the photovoltaic system and the load. In this paper, an artificial neural network (ANN) is selected as an MPPT technique. The main contribution of the work is to introduce a softplus function trained artificial neural network-based maximum p
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BENCHIKH, Salma, Tarik JAROU, Mohamed Khalifa BOUTAHIR, Elmehdi NASRI, and Roa ELAMRANI. "Improving Photovoltaic System Performance with Artificial Neural Network Control." Data and Metadata 2 (December 30, 2023): 144. http://dx.doi.org/10.56294/dm2023144.

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Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels
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Myint, Thuzar, and Hnin Moh Moh Aung Cho. "Artificial Neural Network for Solar Photovoltaic System Modeling and Simulation." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2110–14. https://doi.org/10.5281/zenodo.3591117.

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This paper presented neural network based maximum power point tracking on the design of photovoltaic power input to a DC DC boot converter to the load. Simulink model of photovoltaic array tested the neural network with different temperature and irradiance for maximum power point of a photovoltaic system. DC DC boot converter is used in load when an average output voltage is stable required which can be lower than the input voltage. At the end, the different temperature and irradiance of the data collected from the photovoltaic array system is used to train the neutral network and output effic
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Subramanian, Balakumar, Samuel Kefale, Mesfin Godato, Yalisho Girma, and Zerihun Zegeye. "Design and simulation of zeta solar charge controller with artificial neural network MPPT." Multidisciplinary Science Journal 7, no. 2 (2024): 2025079. http://dx.doi.org/10.31893/multiscience.2025079.

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Fossil fuel are non-renewable energy sources with constrained reserves. This advocates renewable energy as a viable alternative for electricity generation. PV is a renewable energy source that harnesses solar energy. The current issue with PV is its low efficiency and elevated cost. Photovoltaic control devices and Maximum Power Point Tracking (MPPT) mimic human neural networks in processing multiple conditions and providing solutions from current reference data to optimise photovoltaic power. This technique is known as Artifical neural network (ANN). This method uses a zeta converter to adjus
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Khalin, Khalin, Sutedjo Sutedjo, and Dimas Okky Anggriawan. "Identifikasi Gangguan Open Circuit Dan Short Circuit Pada Instalasi Photovoltaic Array Dengan MPPT Berbasis Artificial Neural Network." ENERGI & KELISTRIKAN 14, no. 1 (2022): 34–44. http://dx.doi.org/10.33322/energi.v14i1.1554.

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In the field of photovoltaic, the last few years have been very hotly discussed and researched as a new renewable source to produce electricity that cannot be exhausted. In the development effort there must be some problems arising from the existence of a new system. As with open circuit and short circuit interference. Therefore, The Identification of Open Circuit and Short Circuit Interference in Photovoltaic Array Installation with MPPT Based Artificial Neural Network is present to solve the problem. For identification of the location of the disruption is carried out on each photovoltaic str
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HABIBI, MUHAMMAD NIZAR, DIMAS NUR PRAKOSO, NOVIE AYUB WINDARKO, and ANANG TJAHJONO. "Perbaikan MPPT Incremental Conductance menggunakan ANN pada Berbayang Sebagian dengan Hubungan Paralel." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 8, no. 3 (2020): 546. http://dx.doi.org/10.26760/elkomika.v8i3.546.

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ABSTRAKAlgoritma IncrementaL Conductance (IC) adalah algoritma yang bisa diimplementasikan pada sistem Maximum Power Point Tracking (MPPT) untuk mendapatkan daya maksimum dari panel surya. Akan tetapi algoritma MPPT IC tidak bisa bekerja dikondisi berbayang sebagian, karena menimbulkan daya maksimum lebih dari satu. Artificial Neural Network (ANN) bisa mengidentifikasi kurva karakteristik pada kondisi berbayang sebagian dan dapat mengetahui posisi daya maksimum yang sebenarnya. Masukan dari ANN merupakan nilai arus hubung singkat serta tegangan buka dari panel surya, dan keluaran dari ANN adal
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Allahyari, S. A., Nasser Taheri, M. Zadehbagheri, and Z. Rahimkhani. "A Novel Adaptive Neural MPPT Algorithm for Photovoltaic System." International Journal of Automotive and Mechanical Engineering 15, no. 3 (2018): 5421–34. http://dx.doi.org/10.15282/ijame.15.3.2018.2.0417.

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This paper presents a novel adaptive neural network (ANN) for maximum power point tracking (MPPT) in photovoltaic (PV) systems under variable working conditions. The ANN-based MPPT model includes two separate NNs for PV system identification and control. NNs are trained by using of a novel back propagation algorithm in pre/post control phases. Because of online optimal performance of NNs, the proposed method, not only overcome the common drawbacks of the conventional MPPT methods, but also gives a simple and a robust MPPT scheme. Simulation results, which carried on MATLAB, show that proposed
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Dissertations / Theses on the topic "Mppt; neural network"

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Sahnoun, Mohamed Aymen. "Contribution à la modélisation et au contrôle de trajectoire de Trackers photovoltaïques à haute concentration (HCPV)." Thesis, Paris, ENSAM, 2015. http://www.theses.fr/2015ENAM0043/document.

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Dans une optique de maximisation de la production et de réduction des coûts d’installation, de maintenance et d’entretien des trackers solaires, qui permettent d’orienter les modules photovoltaïques à haute concentration (HCPV), ces travaux de thèse se focalisent sur l’amélioration de la précision et la réduction du coût de la stratégie de génération de la trajectoire du tracker. Dans un premier temps, un simulateur de tracker HCPV est développé offrant une étude de l’influence de la performance du suivi du soleil sur la production des modules HCPV, permettant ainsi une étude et une comparaiso
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Courdouan, Elie. "Développement d'un module BMS multi-sources harvesting." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0633.

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Avec le développement des applications mobiles (téléphonie, IoT, domotique, …), les systèmes embarqués ont montré une croissance exponentielle ces dernières années. Or la principale caractéristique de ces nouveaux systèmes est la combinaison d’une puissance de calcul importante avec une grande autonomie de fonctionnement. Malheureusement, ces caractéristiques étant diamétralement opposées, les concepteurs de systèmes se heurtent à un dilemme leur imposant de limiter la puissance embarquée. Afin de pallier ce problème d'autonomie, de plus en plus d'architectures se tournent vers la mise en plac
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Abderrahmane, Elhor. "MPPT technique based on neural network for photovoltaic system." Master's thesis, 2020. http://hdl.handle.net/10198/24500.

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Mestrado de dupla diplomação com a Superior School of Applied Sciences of Tlemcen<br>The using of an efficient MPPT (Maximum Power Point Tracking) algorithm influences a lot in the global efficiency of the PV system. This thesis presents a detailed study based on simulation of different MPPT algorithms with their features using two systems (off-grid and on-grid). The off-grid system contains a PV array connected to a boost converter and a resistive load. On the off-grid system a simulation is presented using MATLAB/SIMULINK platform with several MPPT algorithms. The simulat
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Hung, Yu-Hsiang, and 洪裕翔. "A Novel MPPT Control Design for Wind-Turbine Generation Systems Using Neural Network Compensator." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/58494809556533022533.

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碩士<br>明新科技大學<br>電機工程研究所<br>101<br>This thesis presents a novel maximum-power-point-tracking (MPPT) scheme in wind-turbine generation systems using neural network compensator. The proposed method is based on the slope of the wind-turbine mechanical power versus rotation speed is equal to zero on the maximumpowerpointsto avoid the oscillation problem and effect of uncertain parameters. In addition, the characteristics of the wind-turbine rotation speed is determined by the wind speed and air density conditions, the technologies of changing the location of the maximum power point must be develope
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KAUSHIK, HARSH. "PERFORMANCE ANALYSIS OF NEURAL NETWORK BASED MPPT CONTROLLER FOR SOLAR PV SYSTEM WITH CONVENTIONAL AND CASCADED BOOST CONVERTERS." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19280.

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Among all renewable energy sources, solar photovoltaic (PV) represents a very important and reliable energy source. However, the output of the PV module is limited. The system performance in renewable energy sources is improved using DC - DC converters. Boost converters are used if output voltage higher than the PV module is desired. If further higher voltage step-up ratio is required by the solar PV system for which the performance of traditional boost converter declines, then cascaded boost converter configurations are employed. Also, besides using cascaded converters for voltage
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Yao, Chia-Shiang, and 姚家翔. "Maximum Power Point Tracking (MPPT) of Small Wind Power Generators Based on Radial Basis Function Neural Network (RBFNN) and Particle Swarm Optimization (PSO)." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/39615240755897221449.

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碩士<br>中原大學<br>電機工程研究所<br>100<br>This thesis proposes a maximum power point tracking (MPPT) to control wind generators with unsteady wind speed and load, because wind generators are nonlinear and the maximum power point would change under such circumstances. First, two radial basis function neural network models are proposed, one for estimating wind power and the other for estimating power, and then particle swarm optimization is used to obtain their best neuron distribution. In that, the network for estimating wind speed operates with inputting the rotational rate and the output power of the w
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Houng, Yi-ming, and 黃意明. "Neural Networks Based MPPT for Wind Power Generation." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/71789319958659574097.

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碩士<br>中原大學<br>電機工程研究所<br>94<br>The wind-turbine generation system (WTGS) exhibits a nonlinear characteristic and thus its maximum power point varies with changing atmospheric conditions. In order to operate the WTGS at maximum power points under different wind speeds and to avoid using anemometer in practical applications, the thesis adopts neural network base maximum power points tracking (MPPT) control theory in the WTGS. 　　In the thesis, load characteristic models of the WTGS under different wind speeds are first built up for design of control rules and feasibility studies of the proposed M
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Book chapters on the topic "Mppt; neural network"

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Mohammed, Benzaouia, Hajji Bekkay, Rabhi Abdelhamid, and Benzaouia Soufyane. "Experimental Assessment of MPPT Based on a Neural Network Controller." In Artificial Intelligence and Smart Environment. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26254-8_58.

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Patil, Sangita Bapu, and L. M. Waghmare. "Intelligently Trained Elman Neural Network-Based MPPT for Photovoltaic Systems." In ICT Analysis and Applications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5655-2_67.

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Dahmane, Kaoutar, El-Mahfoud Boulaoutaq, Brahim Bouachrine, Belkasem Imodane, and Mohamed Ajaamoum. "Neural Network MPPT Control of an On-Grid Wind Energy System." In Proceedings of Ninth International Congress on Information and Communication Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3299-9_22.

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Dangi, Pushpendra, Suresh Kr Gawre, and Amit Ojha. "Dynamic Performance Analysis of Neural Network Based MPPT Under Varying Climatic Condition." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0193-5_14.

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Karthik, Ramireddy, Harshit Harsh, Y. V. Pavan Kumar, D. John Pradeep, Ch Pradeep Reddy, and Ramani Kannan. "Modelling of Neural Network-based MPPT Controller for Wind Turbine Energy System." In Control and Measurement Applications for Smart Grid. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7664-2_35.

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Hussaian Basha, CH, Srikanth Velpula, P. Ashwini Kumari, et al. "Design of Neural Network Fed MPPT Controller for Enhancing the Efficiency of Wind Power Network." In Proceedings of International Conference on Computational Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3526-6_44.

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Hermassi, Mahdi, Saber Krim, Youssef Kraiem, and Mohamed Ali Hajjaji. "Artificial Neural Network-Based MPPT Controller for Variable-Speed Wind Energy Conversion System." In Soft Computing in Renewable Energy Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003462460-7.

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Bouchetob, E., and B. Nadji. "WBG Devices Efficiency and Application in PV System-Based Neural Network MPPT Controller." In Springer Proceedings in Energy. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-2777-7_37.

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Harrag, Abdelghani. "New Neural Network Single Sensor Variable Step Size MPPT for PEM Fuel Cell Power System." In Springer Proceedings in Energy. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6595-3_46.

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Murali, M., CH Hussaian Basha, Shaik Rafi Kiran, and K. Amaresh. "Design and Analysis of Neural Network-Based MPPT Technique for Solar Power-Based Electric Vehicle Application." In Advances in Sustainability Science and Technology. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4321-7_44.

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Conference papers on the topic "Mppt; neural network"

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A, Venkadesan, Sri Raghu Charan M, Dinesh Kumar M, Senthamizh Selvan S, and Sedhuraman K. "Cascade Neural Network Based MPPT Controller for Thermoelectric Generator." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627779.

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Chouiekh, Sihame, Yassamine Zoubaa, Ayoub EL Bakri, and Ismail Boumhidi. "Neural Network-Observer-Based MPPT Control for Variable Speed Wind Turbine." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756344.

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Yessef, Mourad, Yassine Seghrouchni, Youness Hakam, Mohamed Tabaa, and Mohamed Benslimane. "Enhancing Photovoltaic System Performance: An Artificial Neural Network-Based MPPT Control." In 2025 7th Global Power, Energy and Communication Conference (GPECOM). IEEE, 2025. https://doi.org/10.1109/gpecom65896.2025.11061983.

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Kumar, R. Tharwin, V. Balaji, K. Sakthidhasan, S. Gomathi, R. Sreedhar, and N. Janaki. "High Speed Neural Network MPPT Algorithm For DFIG Based Wind Energy Conversion System." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673119.

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Pandey, Nirmal Kumar, Sakshi Sharma, Ankur Kumar Gupta, Priyanka Sharma, Rupendra Kumar Pachauri, and Shashikant. "Neural Network-Enhanced MPPT for Hybrid PV-Wind-Battery DC Micro-grid System." In 2024 Conference on Renewable Energy Technologies and Modern Communications Systems: Future and Challenges. IEEE, 2024. https://doi.org/10.1109/ieeeconf63577.2024.10881910.

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Haque, Md Enamul, and Nur Mohammad. "Performance Analysis of Levenberg-Marquardt algorithm-based Neural Network MPPT of a Solar PV System." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013780.

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Bin Lokman Fikry, Ahmad Saufi, Shuria Saaidin, Norakmar Sulaiman, Suhaili Beeran Kutty, and Murizah Kassim. "Neural Network (NN), Perturb and Observe (PO), and Hybrid NN-PO for MPPT Controller in PV System." In 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE, 2024. http://dx.doi.org/10.1109/i2cacis61270.2024.10649624.

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Prananda, Sandra July, Feby Agung Pamuji, and Heri Suryoatmojo. "Design and Analysis of Reccurent Neural Network (RNN) MPPT Controller for Hybrid Solar and Wind Energy System." In 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10667937.

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Kumar, V. Rattan, T. C. Sameer, and S. Prakash. "Intelligent MPPT Controller for PV Fed Grid-Tied EV Charging with Power Quality Improvement using Neural Network Controller." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10672823.

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Rajab, Z., M. Elrashid, M. Elhashane, et al. "Artificial Neural Network-Based Inverter Control and MPPT for Enhanced Performance of Hybrid Microgrids With PV and Battery Storage." In 2025 15th International Renewable Energy Congress (IREC). IEEE, 2025. https://doi.org/10.1109/irec64614.2025.10926813.

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