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Maximum power point tracking algorithms of a piezoelectric energy harvester under forced excitation
This paper deals with the power production from a piezoelectric biodynamic harvesting scheme via the local step optimization point of the algorithm. In addition, the sensitivity and error of the maximum power point tracking (MPPT) algorithm are determined in this work due to its non-linear extrapola...
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creator | Touairi, Souad El Bahloul, El Mehdi Adar, Mustapha Mabrouki, Mustapha |
description | This paper deals with the power production from a piezoelectric biodynamic harvesting scheme via the local step optimization point of the algorithm. In addition, the sensitivity and error of the maximum power point tracking (MPPT) algorithm are determined in this work due to its non-linear extrapolation approach in order to provide a real-time adaptation of the circuit impulses and to obtain matching of the target load impedance. The presented method provides a non-linear extrapolation model for the combined harvester design based on an artificial neural network (ANN). An analytical model of harvestable electrical power for piezoelectric vibration energy generation is proposed. Furthermore, steering stability is enhanced by a strong active front steering and an active differential drive control. The applicability of this approach outperformed the conventional method of observing perturbations under various operational requirements. |
doi_str_mv | 10.1063/5.0171779 |
format | conference_proceeding |
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In addition, the sensitivity and error of the maximum power point tracking (MPPT) algorithm are determined in this work due to its non-linear extrapolation approach in order to provide a real-time adaptation of the circuit impulses and to obtain matching of the target load impedance. The presented method provides a non-linear extrapolation model for the combined harvester design based on an artificial neural network (ANN). An analytical model of harvestable electrical power for piezoelectric vibration energy generation is proposed. Furthermore, steering stability is enhanced by a strong active front steering and an active differential drive control. 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In addition, the sensitivity and error of the maximum power point tracking (MPPT) algorithm are determined in this work due to its non-linear extrapolation approach in order to provide a real-time adaptation of the circuit impulses and to obtain matching of the target load impedance. The presented method provides a non-linear extrapolation model for the combined harvester design based on an artificial neural network (ANN). An analytical model of harvestable electrical power for piezoelectric vibration energy generation is proposed. Furthermore, steering stability is enhanced by a strong active front steering and an active differential drive control. The applicability of this approach outperformed the conventional method of observing perturbations under various operational requirements.</description><subject>Active control</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Circuits</subject><subject>Energy harvesting</subject><subject>Extrapolation</subject><subject>Mathematical models</subject><subject>Maximum power tracking</subject><subject>Optimization</subject><subject>Perturbation</subject><subject>Piezoelectricity</subject><subject>Steering</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNo1kD9PwzAUxC0EEqUw8A0ssSGlPNuJHY-o4p9UxNKBzbIdu3Vp4uAk0PLpCWpZ7pZ3904_hK4JzAhwdlfMgAgihDxBE1IUJBOc8FM0AZB5RnP2fo4uum4DQKUQ5QTZV70L9VDjNn67NGpoetwnbT9Cs8J6u4op9Ou6w9FjjdvgfqLbOtunYLFrXFrt8VqnL9f1Y3poqlF9TNZV2O1s6HUfYnOJzrzedu7q6FO0fHxYzp-zxdvTy_x-kbWSQ6ZJRSmAkN5bbRzNS2O8yQvjOQhWONCcGwnelLll1GpKKK2k4Z6xklbWsCm6OdS2KX4O4yK1iUNqxo-KloJBAeVIaIpuD1fd_zzVplDrtFcE1B9DVagjQ_YLsvtlpg</recordid><startdate>20231005</startdate><enddate>20231005</enddate><creator>Touairi, Souad</creator><creator>El Bahloul, El Mehdi</creator><creator>Adar, Mustapha</creator><creator>Mabrouki, Mustapha</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20231005</creationdate><title>Maximum power point tracking algorithms of a piezoelectric energy harvester under forced excitation</title><author>Touairi, Souad ; El Bahloul, El Mehdi ; Adar, Mustapha ; Mabrouki, Mustapha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p960-a1d220079ffcabe248bbfb45bf60735e0a66b90fb84c32ca2122d9b6f3382dcb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Active control</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Circuits</topic><topic>Energy harvesting</topic><topic>Extrapolation</topic><topic>Mathematical models</topic><topic>Maximum power tracking</topic><topic>Optimization</topic><topic>Perturbation</topic><topic>Piezoelectricity</topic><topic>Steering</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Touairi, Souad</creatorcontrib><creatorcontrib>El Bahloul, El Mehdi</creatorcontrib><creatorcontrib>Adar, Mustapha</creatorcontrib><creatorcontrib>Mabrouki, Mustapha</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Touairi, Souad</au><au>El Bahloul, El Mehdi</au><au>Adar, Mustapha</au><au>Mabrouki, Mustapha</au><au>Belkassmi, Youssef</au><au>Maimouni, Lahoucine El</au><au>Ait-Taleb, Thami</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Maximum power point tracking algorithms of a piezoelectric energy harvester under forced excitation</atitle><btitle>AIP conference proceedings</btitle><date>2023-10-05</date><risdate>2023</risdate><volume>2761</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>This paper deals with the power production from a piezoelectric biodynamic harvesting scheme via the local step optimization point of the algorithm. In addition, the sensitivity and error of the maximum power point tracking (MPPT) algorithm are determined in this work due to its non-linear extrapolation approach in order to provide a real-time adaptation of the circuit impulses and to obtain matching of the target load impedance. The presented method provides a non-linear extrapolation model for the combined harvester design based on an artificial neural network (ANN). An analytical model of harvestable electrical power for piezoelectric vibration energy generation is proposed. Furthermore, steering stability is enhanced by a strong active front steering and an active differential drive control. The applicability of this approach outperformed the conventional method of observing perturbations under various operational requirements.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0171779</doi><tpages>10</tpages></addata></record> |
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source | American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list) |
subjects | Active control Algorithms Artificial neural networks Circuits Energy harvesting Extrapolation Mathematical models Maximum power tracking Optimization Perturbation Piezoelectricity Steering |
title | Maximum power point tracking algorithms of a piezoelectric energy harvester under forced excitation |
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