A new data driven long-term solar yield analysis model of photovoltaic power plants
- Ray, Biplob, Shah, Rakibuzzaman, Islam, Md Rabiul, Islam, Syed
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
- Full Text:
- Reviewed:
- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
- Full Text:
- Reviewed:
- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.
Forced oscillation in power systems with converter controlled-based resources- a survey with case studies
- Surinkaew, Tossaporn, Emami, Koanoush, Shah, Rakibuzzaman, Islam, Syed, Mithulananthan, N.
- Authors: Surinkaew, Tossaporn , Emami, Koanoush , Shah, Rakibuzzaman , Islam, Syed , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 150911-150924
- Full Text:
- Reviewed:
- Description: In future power systems, conventional synchronous generators will be replaced by converter controlled-based generations (CCGs), i.e., wind and solar generations, and battery energy storage systems. Thus, the paradigm shift in power systems will lead to the inferior system strength and inertia scarcity. Therefore, the problems of forced oscillation (FO) will emerge with new features of the CCGs. The state-of-the-art review in this paper emphasizes previous strategies for FO detection, source identification, and mitigation. Moreover, the effect of FO is investigated in a power system with CCGs. In its conclusion, this paper also highlights important findings and provides suggestions for subsequent research in this important topic of future power systems. © 2013 IEEE.
- Authors: Surinkaew, Tossaporn , Emami, Koanoush , Shah, Rakibuzzaman , Islam, Syed , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 150911-150924
- Full Text:
- Reviewed:
- Description: In future power systems, conventional synchronous generators will be replaced by converter controlled-based generations (CCGs), i.e., wind and solar generations, and battery energy storage systems. Thus, the paradigm shift in power systems will lead to the inferior system strength and inertia scarcity. Therefore, the problems of forced oscillation (FO) will emerge with new features of the CCGs. The state-of-the-art review in this paper emphasizes previous strategies for FO detection, source identification, and mitigation. Moreover, the effect of FO is investigated in a power system with CCGs. In its conclusion, this paper also highlights important findings and provides suggestions for subsequent research in this important topic of future power systems. © 2013 IEEE.
Numerical model of cloud-to-ground lightning for pyroCb thunderstorms
- Barman, Surajit, Shah, Rakibuzzaman, Islam, Syed, Kumar, Apurv
- Authors: Barman, Surajit , Shah, Rakibuzzaman , Islam, Syed , Kumar, Apurv
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 16, no. (2023), p. 8689-8701
- Full Text:
- Reviewed:
- Description: This paper demonstrates a 2-D numerical model to represent two conceptual pyrocumulonimbus (pyroCb) thundercloud structures: i) tilted dipole and ii) tripole structure with enhanced lower positive charge layer, which are hypothesized to explain the occurrence of lightning flashes in pyroCb storms created from severe wildfire events. The presented model considers more realistic thundercloud charge structures to investigate the electrical states and determine surface charge density for identifying potential lightning strike areas on Earth. Simulation results on dipole structure-based pyroCb thunderclouds confirm that the wind-shear extension of its upper positive (UP) charge layer by 2-8 km reduces the electric field and indicates the initiation of negative surface charge density around the earth periphery underneath the anvil cloud. These corresponding lateral extensions have confined the probable striking zone of-CG and +CG lightning within 0-23.5 km and 23.5-30 km in the simulation domain. In contrast, pyroCb thundercloud possessing the tripole structure with enhanced lower positive charge develops a negative electric field at the cloud's bottom part to block the progression of downward negative leader and cause the surface charge density beneath the thundercloud to become negative, which would lead to the formation of +CG flashes. Later, a parametric study is conducted assuming a positive linear correlation between the charge density and aerosol concentration to examine the effect of high aerosol concentration on surface charge density in both pyroCb thunderclouds. The proposed model can be expanded into 3-D to simulate lightning leader movement, aiding wildfire risk management. © 2008-2012 IEEE.
- Authors: Barman, Surajit , Shah, Rakibuzzaman , Islam, Syed , Kumar, Apurv
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 16, no. (2023), p. 8689-8701
- Full Text:
- Reviewed:
- Description: This paper demonstrates a 2-D numerical model to represent two conceptual pyrocumulonimbus (pyroCb) thundercloud structures: i) tilted dipole and ii) tripole structure with enhanced lower positive charge layer, which are hypothesized to explain the occurrence of lightning flashes in pyroCb storms created from severe wildfire events. The presented model considers more realistic thundercloud charge structures to investigate the electrical states and determine surface charge density for identifying potential lightning strike areas on Earth. Simulation results on dipole structure-based pyroCb thunderclouds confirm that the wind-shear extension of its upper positive (UP) charge layer by 2-8 km reduces the electric field and indicates the initiation of negative surface charge density around the earth periphery underneath the anvil cloud. These corresponding lateral extensions have confined the probable striking zone of-CG and +CG lightning within 0-23.5 km and 23.5-30 km in the simulation domain. In contrast, pyroCb thundercloud possessing the tripole structure with enhanced lower positive charge develops a negative electric field at the cloud's bottom part to block the progression of downward negative leader and cause the surface charge density beneath the thundercloud to become negative, which would lead to the formation of +CG flashes. Later, a parametric study is conducted assuming a positive linear correlation between the charge density and aerosol concentration to examine the effect of high aerosol concentration on surface charge density in both pyroCb thunderclouds. The proposed model can be expanded into 3-D to simulate lightning leader movement, aiding wildfire risk management. © 2008-2012 IEEE.
- «
- ‹
- 1
- ›
- »