Ayman El-hag | American University of Sharjah (original) (raw)
Papers by Ayman El-hag
In this paper, a corona ring design optimization for a 400kV non-ceramic insulator was investigat... more In this paper, a corona ring design optimization for a 400kV non-ceramic insulator was investigated. Two parameters were changed during the study which are: the ring diameter (R) and the diameter of the ring tube (r). In this study, with the aid of a developed mathematical model the maximum electric field curves were recreated and an estimated electric field equation in terms of r and R while fixing H was found. Finally, using the estimated equation, a non-linear optimization technique was applied. The solution for this optimization problem gives the optimal dimensions of r and R as well as the ratio of R/r which will reduce the electric field value most effectively for a given constant cost.
2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS), 2015
IEEE Transactions on Dielectrics and Electrical Insulation, 2015
![Research paper thumbnail of Detection of dry-band arcing using time series modeling [silicone rubber insulator arcing]](https://attachments.academia-assets.com/44923745/thumbnails/1.jpg)
](In this work detection of the beginning of dry-band arcing has been investigated using time serie... more In this work detection of the beginning of dry-band arcing has been investigated using time series modeling. Silicone rubber insulators were tested in saltfog under different voltage and conductivity levels. The autocorrelation function was calculated for both the fundamental and third harmonic components of leakage current (LC). It has been observed that distinct differences exist in the behaviour of both the fundamental and that of the thud harmonic components of the U3 during the early aging period (EM). Although the fundamental component of the LC begins to grow immediately after starting the test, the third harmonic requires a much longer period of time to begin. Dry band arcing is highly correlated with distortion in the LC and hence to its third harmonic component. But it has been observed that the level of the fundamental component of LC at which the third harmonic component started to increase is different from one case to another. Hence in this work, time series modeling is used to quantify the difference between the fundamental and the third harmonic components of the U3 during the EAF'. As such, it is more appropriate to use the autocorrelation function of the thud harmonic component of LC as an indication of dry-band arcing rather than a simple threshold value.
2009 Ieee Electrical Insulation Conference, 2009
In this paper different configurations of artificial neural networks are applied to predict vario... more In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm is implemented. Subsequently, a cascade of these neural networks is deemed to be more promising. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden node combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 83.9% for breakdown voltage, 94.6% for interfacial tension, 56.4% for water content, and 75.4% for oil acidity predictions were obtained by the cascade of neural networks.
2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), 2014
ABSTRACT Furan content in transformer oil is highly correlated with the transformer insulation pa... more ABSTRACT Furan content in transformer oil is highly correlated with the transformer insulation paper aging. In this paper, the ranges of furan content in power transformer is predicted using measurements of transformer oil tests like breakdown voltage, acidity and water content. Machine learning approach is adopted, and maintenance data collected from 90 transformers are used. A maximum of 67% recognition rate was achieved using Decision Tree classifier. The major challenge of the used data is the relatively low number of available samples in certain furan intervals. Two solutions have been proposed to overcome this imbalanced classification problem, namely, using an over-sampling technique and balancing data distributions by reducing the number of intervals to be predicted to three instead of five intervals. The recognition rate has improved to reach 80%.
IEEE Transactions on Dielectrics and Electrical Insulation, 2000
In this paper artificial neural networks have been constructed to predict different transformers ... more In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output; the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.
In this paper different configurations of artificial neural networks are applied to predict vario... more In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm is implemented. Subsequently, a cascade of these neural networks is deemed to be more promising. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden node combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 83.9% for breakdown voltage, 94.6% for interfacial tension, 56.4% for water content, and 75.4% for oil acidity predictions were obtained by the cascade of neural networks.
2011 1st Middle East Conference on Biomedical Engineering, 2011
Nanosecond electroporation has a range of applications including gene therapy and treatment of me... more Nanosecond electroporation has a range of applications including gene therapy and treatment of melanoma tumors. On applying a nanosecond high voltage pulse, potential differences are generated across the membranes of the internal organelles resulting in its electroporation. This paper investigates the effect of nanosecond high voltage pulses simulated on a biological cell placed in a conductive medium (water). The effect
... 6. Acknowledgement: I would like to thank Eng. Salem Alharthi, Asset Performance Department M... more ... 6. Acknowledgement: I would like to thank Eng. Salem Alharthi, Asset Performance Department Manager, Asset Performance Dept. ... References: [1] M. Amin and M. Salman, Aging of Polymeric Insulators (An Overview). 2006 Advanced Study Center. Rev.Adv.Mater.Sci. Vol. ...
IEEE Transactions on Power Delivery, 2000
... Harmonics Aiman Hassan Al-Haj and Ibrahim El-Amin AbstractThe harmonic components of the no-... more ... Harmonics Aiman Hassan Al-Haj and Ibrahim El-Amin AbstractThe harmonic components of the no-load current are influenced by many factors. These factors are the flux density, the degree of saturation and the core stacking technique. ...
Electric Power Systems Research, 2008
The paper presents a neural network based prediction technique for the leakage current (LC) of no... more The paper presents a neural network based prediction technique for the leakage current (LC) of non-ceramic insulators during salt-fog test. Nearly 50 distribution class silicone rubber (SIR) insulators with three different voltage classes have been tested in a salt-fog chamber, where the LC has been continuously recorded for at least 100h. A boundary for early aging period is defined by
IEEE Transactions on Power Delivery, 2005
In this work, time series modeling is used to detect the beginning of dry-band arcing on the surf... more In this work, time series modeling is used to detect the beginning of dry-band arcing on the surface of silicone rubber insulators during salt-fog test. The validity of this approach was verified on a one-shed sample insulator in salt-fog test.
Electric Power Systems Research, 2007
ABSTRACT In this work both detection of the beginning of dry-band arcing and correlating the aver... more ABSTRACT In this work both detection of the beginning of dry-band arcing and correlating the average value of leakage current (LC) with non-ceramic insulator surface damage have been investigated. Silicone rubber insulators were tested in salt-fog under different voltage and conductivity levels. The autocorrelation function (ACF) was calculated for both the fundamental and third harmonic components of LC during the early aging period (EAP). It has been observed that distinct differences exist in the behavior of both the fundamental and that of the third harmonic components of the LC during EAP. Although the fundamental component of the LC begins to grow immediately after starting the test, the third harmonic requires a much longer period of time to begin. Dry-band arcing is highly correlated with distortion in the LC and hence to its third harmonic component. But it has been observed that the level of the fundamental component of LC at which the third harmonic component started to increase is different from one case to another. As such, it is more appropriate to use the ACF of the third harmonic component of LC as an indication of dry-band arcing rather than a simple threshold value. Moreover, the average value of LC during late aging period (LAP) was correlated with the damage of non-ceramic insulators. It has been found that the average level of both the fundamental and third harmonic component of LC is well correlated with the different degrees of damage of non-ceramic insulators’ surface.
In this paper, a corona ring design optimization for a 400kV non-ceramic insulator was investigat... more In this paper, a corona ring design optimization for a 400kV non-ceramic insulator was investigated. Two parameters were changed during the study which are: the ring diameter (R) and the diameter of the ring tube (r). In this study, with the aid of a developed mathematical model the maximum electric field curves were recreated and an estimated electric field equation in terms of r and R while fixing H was found. Finally, using the estimated equation, a non-linear optimization technique was applied. The solution for this optimization problem gives the optimal dimensions of r and R as well as the ratio of R/r which will reduce the electric field value most effectively for a given constant cost.
2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS), 2015
IEEE Transactions on Dielectrics and Electrical Insulation, 2015
In this work detection of the beginning of dry-band arcing has been investigated using time serie... more In this work detection of the beginning of dry-band arcing has been investigated using time series modeling. Silicone rubber insulators were tested in saltfog under different voltage and conductivity levels. The autocorrelation function was calculated for both the fundamental and third harmonic components of leakage current (LC). It has been observed that distinct differences exist in the behaviour of both the fundamental and that of the thud harmonic components of the U3 during the early aging period (EM). Although the fundamental component of the LC begins to grow immediately after starting the test, the third harmonic requires a much longer period of time to begin. Dry band arcing is highly correlated with distortion in the LC and hence to its third harmonic component. But it has been observed that the level of the fundamental component of LC at which the third harmonic component started to increase is different from one case to another. Hence in this work, time series modeling is used to quantify the difference between the fundamental and the third harmonic components of the U3 during the EAF'. As such, it is more appropriate to use the autocorrelation function of the thud harmonic component of LC as an indication of dry-band arcing rather than a simple threshold value.
2009 Ieee Electrical Insulation Conference, 2009
In this paper different configurations of artificial neural networks are applied to predict vario... more In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm is implemented. Subsequently, a cascade of these neural networks is deemed to be more promising. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden node combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 83.9% for breakdown voltage, 94.6% for interfacial tension, 56.4% for water content, and 75.4% for oil acidity predictions were obtained by the cascade of neural networks.
2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), 2014
ABSTRACT Furan content in transformer oil is highly correlated with the transformer insulation pa... more ABSTRACT Furan content in transformer oil is highly correlated with the transformer insulation paper aging. In this paper, the ranges of furan content in power transformer is predicted using measurements of transformer oil tests like breakdown voltage, acidity and water content. Machine learning approach is adopted, and maintenance data collected from 90 transformers are used. A maximum of 67% recognition rate was achieved using Decision Tree classifier. The major challenge of the used data is the relatively low number of available samples in certain furan intervals. Two solutions have been proposed to overcome this imbalanced classification problem, namely, using an over-sampling technique and balancing data distributions by reducing the number of intervals to be predicted to three instead of five intervals. The recognition rate has improved to reach 80%.
IEEE Transactions on Dielectrics and Electrical Insulation, 2000
In this paper artificial neural networks have been constructed to predict different transformers ... more In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output; the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.
In this paper different configurations of artificial neural networks are applied to predict vario... more In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm is implemented. Subsequently, a cascade of these neural networks is deemed to be more promising. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden node combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 83.9% for breakdown voltage, 94.6% for interfacial tension, 56.4% for water content, and 75.4% for oil acidity predictions were obtained by the cascade of neural networks.
2011 1st Middle East Conference on Biomedical Engineering, 2011
Nanosecond electroporation has a range of applications including gene therapy and treatment of me... more Nanosecond electroporation has a range of applications including gene therapy and treatment of melanoma tumors. On applying a nanosecond high voltage pulse, potential differences are generated across the membranes of the internal organelles resulting in its electroporation. This paper investigates the effect of nanosecond high voltage pulses simulated on a biological cell placed in a conductive medium (water). The effect
... 6. Acknowledgement: I would like to thank Eng. Salem Alharthi, Asset Performance Department M... more ... 6. Acknowledgement: I would like to thank Eng. Salem Alharthi, Asset Performance Department Manager, Asset Performance Dept. ... References: [1] M. Amin and M. Salman, Aging of Polymeric Insulators (An Overview). 2006 Advanced Study Center. Rev.Adv.Mater.Sci. Vol. ...
IEEE Transactions on Power Delivery, 2000
... Harmonics Aiman Hassan Al-Haj and Ibrahim El-Amin AbstractThe harmonic components of the no-... more ... Harmonics Aiman Hassan Al-Haj and Ibrahim El-Amin AbstractThe harmonic components of the no-load current are influenced by many factors. These factors are the flux density, the degree of saturation and the core stacking technique. ...
Electric Power Systems Research, 2008
The paper presents a neural network based prediction technique for the leakage current (LC) of no... more The paper presents a neural network based prediction technique for the leakage current (LC) of non-ceramic insulators during salt-fog test. Nearly 50 distribution class silicone rubber (SIR) insulators with three different voltage classes have been tested in a salt-fog chamber, where the LC has been continuously recorded for at least 100h. A boundary for early aging period is defined by
IEEE Transactions on Power Delivery, 2005
In this work, time series modeling is used to detect the beginning of dry-band arcing on the surf... more In this work, time series modeling is used to detect the beginning of dry-band arcing on the surface of silicone rubber insulators during salt-fog test. The validity of this approach was verified on a one-shed sample insulator in salt-fog test.
Electric Power Systems Research, 2007
ABSTRACT In this work both detection of the beginning of dry-band arcing and correlating the aver... more ABSTRACT In this work both detection of the beginning of dry-band arcing and correlating the average value of leakage current (LC) with non-ceramic insulator surface damage have been investigated. Silicone rubber insulators were tested in salt-fog under different voltage and conductivity levels. The autocorrelation function (ACF) was calculated for both the fundamental and third harmonic components of LC during the early aging period (EAP). It has been observed that distinct differences exist in the behavior of both the fundamental and that of the third harmonic components of the LC during EAP. Although the fundamental component of the LC begins to grow immediately after starting the test, the third harmonic requires a much longer period of time to begin. Dry-band arcing is highly correlated with distortion in the LC and hence to its third harmonic component. But it has been observed that the level of the fundamental component of LC at which the third harmonic component started to increase is different from one case to another. As such, it is more appropriate to use the ACF of the third harmonic component of LC as an indication of dry-band arcing rather than a simple threshold value. Moreover, the average value of LC during late aging period (LAP) was correlated with the damage of non-ceramic insulators. It has been found that the average level of both the fundamental and third harmonic component of LC is well correlated with the different degrees of damage of non-ceramic insulators’ surface.