In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conven...In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conventional power sources (CPS) alone unable to control the frequency deviations caused by the aforementioned disturbances. Therefore, it is essential to modify the structure of LFC, to handle the disturbances caused by the RES and load. With regards to the above problem, this work proposes a novel coordinated LFC strategy with modified control signal to have Plug-in Hybrid Electric Vehicles (PHEVs) for frequency stability enhancement of the Japanese power system. Where, the coordinated control strategy is based on the PID controller, which is optimally tuned by the recently developed JAYA Algorithm (JA). Numerous simulations are performed with the proposed methodology and, the results have confirmed the effectiveness of a proposed approach over some recent and well-known techniques in literature. Furthermore, simulation results reveal that the proposed coordinated approach significantly minimizing the frequency deviations compared to the JAYA optimized LFC without PHEVs & with PHEVs but no coordination.展开更多
In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a plant.The recognition of agricultural plant diseases by utilizing the image processing...In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a plant.The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products.In this paper,Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed.For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal,bacterial blight,brown spot,sheath rot and blast diseases.In pre-processing,for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part.For the segmentation of diseased portion,normal portion and background a clustering method is used.Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm(DNN_JOA).In order to precise the stability of this approach a feedback loop is generated in the post processing step.The experimental results are evaluated and compared with ANN,DAE and DNN.The proposed method achieved high accuracy of 98.9%for the blast affected,95.78%for the bacterial blight,92%for the sheath rot,94%for the brown spot and 90.57%for the normal leaf image.展开更多
Numerical simulation of concrete-faced rockfill dams(CFRDs)considering the spatial variability of rockfill has become a popular research topic in recent years.In order to determine uncertain rockfill properties effici...Numerical simulation of concrete-faced rockfill dams(CFRDs)considering the spatial variability of rockfill has become a popular research topic in recent years.In order to determine uncertain rockfill properties efficiently and reliably,this study developed an uncertainty inversion analysis method for rockfill material parameters using the stacking ensemble strategy and Jaya optimizer.The comprehensive implementation process of the proposed model was described with an illustrative CFRD example.First,the surrogate model method using the stacking ensemble algorithm was used to conduct the Monte Carlo stochastic finite element calculations with reduced computational cost and improved accuracy.Afterwards,the Jaya algorithm was used to inversely calculate the combination of the coefficient of variation of rockfill material parameters.This optimizer obtained higher accuracy and more significant uncertainty reduction than traditional optimizers.Overall,the developed model effectively identified the random parameters of rockfill materials.This study provided scientific references for uncertainty analysis of CFRDs.In addition,the proposed method can be applied to other similar engineering structures.展开更多
文摘In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conventional power sources (CPS) alone unable to control the frequency deviations caused by the aforementioned disturbances. Therefore, it is essential to modify the structure of LFC, to handle the disturbances caused by the RES and load. With regards to the above problem, this work proposes a novel coordinated LFC strategy with modified control signal to have Plug-in Hybrid Electric Vehicles (PHEVs) for frequency stability enhancement of the Japanese power system. Where, the coordinated control strategy is based on the PID controller, which is optimally tuned by the recently developed JAYA Algorithm (JA). Numerous simulations are performed with the proposed methodology and, the results have confirmed the effectiveness of a proposed approach over some recent and well-known techniques in literature. Furthermore, simulation results reveal that the proposed coordinated approach significantly minimizing the frequency deviations compared to the JAYA optimized LFC without PHEVs & with PHEVs but no coordination.
文摘In the agriculture field,one of the recent research topics is recognition and classification of diseases from the leaf images of a plant.The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products.In this paper,Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed.For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal,bacterial blight,brown spot,sheath rot and blast diseases.In pre-processing,for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non-diseased part.For the segmentation of diseased portion,normal portion and background a clustering method is used.Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm(DNN_JOA).In order to precise the stability of this approach a feedback loop is generated in the post processing step.The experimental results are evaluated and compared with ANN,DAE and DNN.The proposed method achieved high accuracy of 98.9%for the blast affected,95.78%for the bacterial blight,92%for the sheath rot,94%for the brown spot and 90.57%for the normal leaf image.
基金supported by the National Natural Science Foundation of China(Grants No.51879185 and 52179139)the Open Fund of the Hubei Key Laboratory of Construction and Management in Hydropower Engineering(Grant No.2020KSD06).
文摘Numerical simulation of concrete-faced rockfill dams(CFRDs)considering the spatial variability of rockfill has become a popular research topic in recent years.In order to determine uncertain rockfill properties efficiently and reliably,this study developed an uncertainty inversion analysis method for rockfill material parameters using the stacking ensemble strategy and Jaya optimizer.The comprehensive implementation process of the proposed model was described with an illustrative CFRD example.First,the surrogate model method using the stacking ensemble algorithm was used to conduct the Monte Carlo stochastic finite element calculations with reduced computational cost and improved accuracy.Afterwards,the Jaya algorithm was used to inversely calculate the combination of the coefficient of variation of rockfill material parameters.This optimizer obtained higher accuracy and more significant uncertainty reduction than traditional optimizers.Overall,the developed model effectively identified the random parameters of rockfill materials.This study provided scientific references for uncertainty analysis of CFRDs.In addition,the proposed method can be applied to other similar engineering structures.
