The Experts below are selected from a list of 38469 Experts worldwide ranked by ideXlab platform
Shijaw Chen - One of the best experts on this subject based on the ideXlab platform.
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photovoltaic energy conversion System Fault Detection using fractional order color relation classifier in microdistribution Systems
IEEE Transactions on Smart Grid, 2017Co-Authors: Chaolin Kuo, Jianliung Chen, Shijaw Chen, Chihcheng Kao, Herterng Yau, Chiahung LinAbstract:In this paper, we propose a photovoltaic (PV) energy conversion System (PVECS) Fault Detection scheme using a fractional-order color relation classifier in microdistribution Systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded Faults, mismatch Faults, bridged Faults between two PV panels, and open-circuit Faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution System in the event of a Fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from Fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for Fault identification. Its Detection rates exceeded 88.23% for six events.
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photovoltaic energy conversion System Fault Detection using fractional order color relation classifier in microdistribution Systems
IEEE Transactions on Smart Grid, 2017Co-Authors: Jianliung Chen, Shijaw ChenAbstract:In this paper, we propose a photovoltaic (PV) energy conversion System (PVECS) Fault Detection scheme using a fractional-order color relation classifier in microdistribution Systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded Faults, mismatch Faults, bridged Faults between two PV panels, and open-circuit Faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution System in the event of a Fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from Fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for Fault identification. Its Detection rates exceeded 88.23% for six events.
Jianliung Chen - One of the best experts on this subject based on the ideXlab platform.
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photovoltaic energy conversion System Fault Detection using fractional order color relation classifier in microdistribution Systems
IEEE Transactions on Smart Grid, 2017Co-Authors: Chaolin Kuo, Jianliung Chen, Shijaw Chen, Chihcheng Kao, Herterng Yau, Chiahung LinAbstract:In this paper, we propose a photovoltaic (PV) energy conversion System (PVECS) Fault Detection scheme using a fractional-order color relation classifier in microdistribution Systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded Faults, mismatch Faults, bridged Faults between two PV panels, and open-circuit Faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution System in the event of a Fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from Fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for Fault identification. Its Detection rates exceeded 88.23% for six events.
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photovoltaic energy conversion System Fault Detection using fractional order color relation classifier in microdistribution Systems
IEEE Transactions on Smart Grid, 2017Co-Authors: Jianliung Chen, Shijaw ChenAbstract:In this paper, we propose a photovoltaic (PV) energy conversion System (PVECS) Fault Detection scheme using a fractional-order color relation classifier in microdistribution Systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded Faults, mismatch Faults, bridged Faults between two PV panels, and open-circuit Faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution System in the event of a Fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from Fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for Fault identification. Its Detection rates exceeded 88.23% for six events.
Chiahung Lin - One of the best experts on this subject based on the ideXlab platform.
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photovoltaic energy conversion System Fault Detection using fractional order color relation classifier in microdistribution Systems
IEEE Transactions on Smart Grid, 2017Co-Authors: Chaolin Kuo, Jianliung Chen, Shijaw Chen, Chihcheng Kao, Herterng Yau, Chiahung LinAbstract:In this paper, we propose a photovoltaic (PV) energy conversion System (PVECS) Fault Detection scheme using a fractional-order color relation classifier in microdistribution Systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded Faults, mismatch Faults, bridged Faults between two PV panels, and open-circuit Faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution System in the event of a Fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from Fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for Fault identification. Its Detection rates exceeded 88.23% for six events.
Ravi S. Srinivasan - One of the best experts on this subject based on the ideXlab platform.
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A novel ensemble learning approach to support building energy use prediction
Energy and Buildings, 2018Co-Authors: Zeyu Wang, Yueren Wang, Ravi S. SrinivasanAbstract:Broadly speaking, building energy use prediction can be classified into two categories based on modeling approaches namely engineering and Artificial Intelligence (AI). While engineering approach requires solving physical equations representing the thermal performance of Systems and components that constitute the buildings, the AI-based approach uses historical data to predict future performance. Although engineering approach estimates energy use with greater accuracy, it falls short in the overall complexity of model building and simulation in which detailed data that represent the building geometry, Systems, configurations, and occupant schedule is needed. Whereas, the AI-based approach offers a rapid prediction of building energy use and, if appropriately trained and tested, may be used for quick and efficient decision-making of energy use reduction. Nevertheless, for robust integration with and to improve automated building Systems management and intelligence, the need for consistent, stable, and higher prediction accuracy cannot be understated. To alleviate the instability issue, and to improve prediction accuracy, we have exploited and tested an ensemble learning technique, ‘Ensemble Bagging Trees’ (EBT), using data obtained from meteorological Systems and building-level occupancy and meters.Results showed that the proposed EBT model predicted hourly electricity demand of the test building with improved accuracy of Mean Absolute Prediction Error that ranged from 2.97% to 4.63%. Additionally, results showed that proposed variable selection method could reduce the computation time of EBT by 38–41% without sacrificing the prediction accuracy. The proposed ensemble learning model that exemplifies improved prediction accuracy over other AI techniques can be used for real-time applications such as System Fault Detection and diagnosis.
Chaolin Kuo - One of the best experts on this subject based on the ideXlab platform.
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photovoltaic energy conversion System Fault Detection using fractional order color relation classifier in microdistribution Systems
IEEE Transactions on Smart Grid, 2017Co-Authors: Chaolin Kuo, Jianliung Chen, Shijaw Chen, Chihcheng Kao, Herterng Yau, Chiahung LinAbstract:In this paper, we propose a photovoltaic (PV) energy conversion System (PVECS) Fault Detection scheme using a fractional-order color relation classifier in microdistribution Systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded Faults, mismatch Faults, bridged Faults between two PV panels, and open-circuit Faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution System in the event of a Fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from Fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for Fault identification. Its Detection rates exceeded 88.23% for six events.
