The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
T. Inoue - One of the best experts on this subject based on the ideXlab platform.
-
a control method to charge series connected ultraelectric double layer capacitors suitable for photovoltaic generation systems combining mppt control method
IEEE Transactions on Industrial Electronics, 2007Co-Authors: Nobuyoshi Mutoh, T. InoueAbstract:A control method is described to charge series-connected ultraelectric double-layer capacitors (ultra-EDLCs) suitable for photovoltaic generation systems in combination with a maximum power point tracking (MPPT) control method. The EDLC charge control method allows the maximum power acquired by the MPPT control to be quickly charged into series-connected ultra-EDLCs no matter how the weather conditions may change. In the MPPT control, the output current of the solar arrays is controlled so that the output power converges on the maximum power in the Prediction Line previously determined based on the Linearity between the maximum output power and the optimization current. The proportionality coefficient of the Prediction Line is automatically corrected using the hill-climbing method when the panel temperature of the solar arrays is changed. The EDLC charge control is performed with the three charge modes, i.e., the constant current charge mode, constant power charge mode, and the constant voltage charge mode while supervising the maximum voltage and allowable temperature of each series-connected EDLC. Effectiveness of the methods is verified by simulations and experiments
-
a controlling method for charging photovoltaic generation power obtained by a mppt control method to series connected ultraelectric double layer capacitors
IEEE Industry Applications Society Annual Meeting, 2004Co-Authors: Nobuyoshi Mutoh, T. InoueAbstract:A photovoltaic (PV) generation system which can efficiently acquire the electric energy irrespective of the weather is described. The PV generation system is characterized by two controllers, namely, a maximum power point tracking (MPPT) controller which makes it possible to take the maximum power which solar arrays can generate at any time irrespective of the weather, and a controller which quickly charges the maximum power obtained by the MPPT control to series connected ultraelectric double layer capacitors (ultraEDLCs) while maintaining the electric capacity which solar arrays can generate at that time. The MPPT controller controls the output current of the solar arrays so that the output power converges on the maximum power in the Prediction Line previously determined based on the Linearity between the maximum output power and the optimal current. The MPPT controller is characterized by having the ability to enlarge the controllable range of the output power to lower solar radiation in comparison with the conventional hill-climbing method. The charging controller controls the current and voltage so as to be charged at around the power generated at that time while supervising the maximum voltage and allowable temperature of each series connected EDLC, The combined control method makes it possible to charge with the maximum power generation from solar arrays into ultraEDLCs. Effectiveness of the methods is verified by simulations and experiments.
Paul Annus - One of the best experts on this subject based on the ideXlab platform.
-
Dual-Source Linear Energy Prediction (Line-P) Model in the Context of WSNs
Sensors, 2017Co-Authors: Faisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul AnnusAbstract:Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy Prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “Line-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; Line-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based Predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where Line-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.
-
Dual-Source Linear Energy Prediction (Line-P) Model in the Context of WSNs
Sensors, 2017Co-Authors: Faisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul AnnusAbstract:Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy Prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “Line-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; Line-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based Predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where Line-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.
Nobuyoshi Mutoh - One of the best experts on this subject based on the ideXlab platform.
-
a control method to charge series connected ultraelectric double layer capacitors suitable for photovoltaic generation systems combining mppt control method
IEEE Transactions on Industrial Electronics, 2007Co-Authors: Nobuyoshi Mutoh, T. InoueAbstract:A control method is described to charge series-connected ultraelectric double-layer capacitors (ultra-EDLCs) suitable for photovoltaic generation systems in combination with a maximum power point tracking (MPPT) control method. The EDLC charge control method allows the maximum power acquired by the MPPT control to be quickly charged into series-connected ultra-EDLCs no matter how the weather conditions may change. In the MPPT control, the output current of the solar arrays is controlled so that the output power converges on the maximum power in the Prediction Line previously determined based on the Linearity between the maximum output power and the optimization current. The proportionality coefficient of the Prediction Line is automatically corrected using the hill-climbing method when the panel temperature of the solar arrays is changed. The EDLC charge control is performed with the three charge modes, i.e., the constant current charge mode, constant power charge mode, and the constant voltage charge mode while supervising the maximum voltage and allowable temperature of each series-connected EDLC. Effectiveness of the methods is verified by simulations and experiments
-
a controlling method for charging photovoltaic generation power obtained by a mppt control method to series connected ultraelectric double layer capacitors
IEEE Industry Applications Society Annual Meeting, 2004Co-Authors: Nobuyoshi Mutoh, T. InoueAbstract:A photovoltaic (PV) generation system which can efficiently acquire the electric energy irrespective of the weather is described. The PV generation system is characterized by two controllers, namely, a maximum power point tracking (MPPT) controller which makes it possible to take the maximum power which solar arrays can generate at any time irrespective of the weather, and a controller which quickly charges the maximum power obtained by the MPPT control to series connected ultraelectric double layer capacitors (ultraEDLCs) while maintaining the electric capacity which solar arrays can generate at that time. The MPPT controller controls the output current of the solar arrays so that the output power converges on the maximum power in the Prediction Line previously determined based on the Linearity between the maximum output power and the optimal current. The MPPT controller is characterized by having the ability to enlarge the controllable range of the output power to lower solar radiation in comparison with the conventional hill-climbing method. The charging controller controls the current and voltage so as to be charged at around the power generated at that time while supervising the maximum voltage and allowable temperature of each series connected EDLC, The combined control method makes it possible to charge with the maximum power generation from solar arrays into ultraEDLCs. Effectiveness of the methods is verified by simulations and experiments.
Faisal Ahmed - One of the best experts on this subject based on the ideXlab platform.
-
Dual-Source Linear Energy Prediction (Line-P) Model in the Context of WSNs
Sensors, 2017Co-Authors: Faisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul AnnusAbstract:Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy Prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “Line-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; Line-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based Predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where Line-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.
-
Dual-Source Linear Energy Prediction (Line-P) Model in the Context of WSNs
Sensors, 2017Co-Authors: Faisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul AnnusAbstract:Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy Prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “Line-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; Line-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based Predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where Line-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.
Gert Tamberg - One of the best experts on this subject based on the ideXlab platform.
-
Dual-Source Linear Energy Prediction (Line-P) Model in the Context of WSNs
Sensors, 2017Co-Authors: Faisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul AnnusAbstract:Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy Prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “Line-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; Line-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based Predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where Line-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.
-
Dual-Source Linear Energy Prediction (Line-P) Model in the Context of WSNs
Sensors, 2017Co-Authors: Faisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul AnnusAbstract:Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy Prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “Line-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; Line-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based Predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where Line-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.