A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stab...A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.展开更多
The ascorbic acid(AA)is a biomarker that can be used to detect the symptoms of severe disorders such as scurvy,Parkinson’s,Alzheimer’s,and cardiovascular diseases.In this work,a simple and effective sensor model is ...The ascorbic acid(AA)is a biomarker that can be used to detect the symptoms of severe disorders such as scurvy,Parkinson’s,Alzheimer’s,and cardiovascular diseases.In this work,a simple and effective sensor model is developed to diagnose the presence of AA samples.To develop the sensor,a tapered single-mode optical fiber has been used with the well-known phenomenon of localized surface plasmon resonance(LSPR).For LSPR,the tapered region is immobilized with synthesized gold nanoparticles(AuNPs)and zinc oxide nanoparticles(ZnO-NPs)whose absorbance peak wavelengths appear at 519nm and 370nm,respectively.On the basis of nanoparticles(NPs)configurations,two different biosensor probes are developed.In the first one,the sensing region is immobilized with AuNPs and named Probe I.In the second probe,the immobilized layer of AuNPs is further coated with a layer of ZnO-NPs,and a resultant probe is termed as Probe II.The characterizations of synthesized AuNPs and developed fiber probes are done by the ultraviolet-visible(UV-vis)spectrophotometer,high-resolution transmission electron microscope(HR-TEM),atomic force microscopy(AFM),and scanning electron microscope(SEM).To enhance the selectivity,a sensing region of probes is functionalized with ascorbate oxidase enzyme that oxidizes the AA in the presence of oxygen.The response of developed sensor probes is authenticated by sensing the samples of AA in the range from 500 nM to 1 mM,which covers the range of AA found in human bodies,i.e.,40μM-120μM.The performance analysis of the developed sensor probes has been done in terms of their stability,reproducibility,reusability,and selectivity.To observe the stability of AA,a pH-test has also been done that results in a better solubility of AA molecules in phosphate-buffered saline(PBS)solution.展开更多
文摘A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.
基金This work was supported by the National Key Research&Development Program of China(Grant No.2016YFB0402105)the Belt and Road Special Project approved by Shandong Province for the Introduction of Foreign Experts in 2018,Double-Hundred Talent Plan of Shandong Province,Liaocheng University,China(Grant Nos.31805180301 and 31805180326)Science and Engineering Research Board(SERB),India(Grant No.TAR/2018/000051).
文摘The ascorbic acid(AA)is a biomarker that can be used to detect the symptoms of severe disorders such as scurvy,Parkinson’s,Alzheimer’s,and cardiovascular diseases.In this work,a simple and effective sensor model is developed to diagnose the presence of AA samples.To develop the sensor,a tapered single-mode optical fiber has been used with the well-known phenomenon of localized surface plasmon resonance(LSPR).For LSPR,the tapered region is immobilized with synthesized gold nanoparticles(AuNPs)and zinc oxide nanoparticles(ZnO-NPs)whose absorbance peak wavelengths appear at 519nm and 370nm,respectively.On the basis of nanoparticles(NPs)configurations,two different biosensor probes are developed.In the first one,the sensing region is immobilized with AuNPs and named Probe I.In the second probe,the immobilized layer of AuNPs is further coated with a layer of ZnO-NPs,and a resultant probe is termed as Probe II.The characterizations of synthesized AuNPs and developed fiber probes are done by the ultraviolet-visible(UV-vis)spectrophotometer,high-resolution transmission electron microscope(HR-TEM),atomic force microscopy(AFM),and scanning electron microscope(SEM).To enhance the selectivity,a sensing region of probes is functionalized with ascorbate oxidase enzyme that oxidizes the AA in the presence of oxygen.The response of developed sensor probes is authenticated by sensing the samples of AA in the range from 500 nM to 1 mM,which covers the range of AA found in human bodies,i.e.,40μM-120μM.The performance analysis of the developed sensor probes has been done in terms of their stability,reproducibility,reusability,and selectivity.To observe the stability of AA,a pH-test has also been done that results in a better solubility of AA molecules in phosphate-buffered saline(PBS)solution.
