The study investigates the applicability of linear
regression and ANN models for estimating weekly reference
evapotranspiration (ET0) at Tirupati, Nellore, Rajahmundry,
Anakapalli and Rajendranagar regions of Andhra Pradesh.
The climatic parameters influencing ET0 were identified
through multiple and partial correlation analysis. The
sunshine, temperature, wind velocity and relative humidity
mostly influenced the study area in the weekly ET0 estimation.
Linear regression models in terms of the climatic parameters
influencing the regions and, optimal neural network
architectures considering these climatic parameters as inputs
were developed. The models’ performance was evaluated with
respect to ET0 estimated by FAO-56 Penman-Monteith method.
The linear regression models showed a satisfactory
performance in the weekly ET0 estimation in the regions
selected for the present study. The ANN (4,4,1) models,
however, consistently showed a slightly improved performance
over linear regression models.
Robust Evolutionary Approach to Mitigate Low Frequency Oscillation in a Multi...IDES Editor
This paper proposes a new optimization algorithm
known as Modified Shuffled Frog Leaping Algorithm (MSFLA)
for optimal designing of PSSs controller. The design problem
of the proposed controller is formulated as an optimization
problem and MSFLA is employed to search for optimal
controller parameters. An eigenvalue based objective function
reflecting the combination of damping factor and damping
ratio is optimized for different operating conditions. The
proposed approach is applied to optimal design of multimachine
power system stabilizers. Three different power
systems, A Single Machine Infinite Bus (SMIB), four-machine
of Kundur and ten-machine New England systems are
considered. The obtained results are evaluated and compared
with other results obtained by Genetic Algorithm (GA).
Eigenvalue analysis and nonlinear system simulations assure
the effectiveness and robustness of the proposed controller in
providing good damping characteristic to system oscillations
and enhancing the system dynamic stability under different
operating conditions and disturbances.
Cross-layer Design of an Asymmetric Loadpower Control Protocol in Ad hoc Netw...IDES Editor
Cross-layer design is important in wireless ad hoc
network and the power control methods. Power control is the
intelligent selection of transmit power in a communication to
achieve the better performance within the system. Cross-layer
is used to sharing the information between the layers. CLD
using LOADPOWER (LOADPOW) control protocol is reduce
the overall end-end delay in transmission power. So many
power control schemes are dealt in network layer but this
work Power control protocol was done in MAC layer and it
plays a vital role. A MAC approach to power control only does
a local optimization whereas network layer is capable of a
global optimization. Simulation was done in NS-2 simulator
with the performance metrics as throughput, and energy
consumption and end-end delay. The key concept is to improve
the throughput, saves energy by sending all the packets with
optimal transmit power according to the network load,
transmission power was given, when the network load is low,
higher transmission power gives lower end-end delay and viceversa.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
Power loss reduction, improvement of voltage profile, system reliability and system security are the important objectives that motivated researchers to use custom power devices/FACTS devices in power systems. The existing power quality problems such as power losses, voltage instability, voltage profile problem, load ability issues, energy losses, reliability problems etc. are caused due to continuous load growth and outage of components. The significant qualities of custom power devices /FACTS devices such as power loss reduction, improvement of voltage profile, system reliability and system security have motivated researchers in this area and to implement these devices in power system. The optimal placement and sizing of these devices are determined based on economical viability, required quality, reliability and availability. In published literatures, different algorithms are implemented for optimal placement of these devices based on different conditions. In this paper, the published literatures on this field are comprehensively reviewed and elaborate comparison of various algorithms is compared. The inference of this extensive comparative analysis is presented. In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. The combination of these two methods are also implemented and presented.
Robust Evolutionary Approach to Mitigate Low Frequency Oscillation in a Multi...IDES Editor
This paper proposes a new optimization algorithm
known as Modified Shuffled Frog Leaping Algorithm (MSFLA)
for optimal designing of PSSs controller. The design problem
of the proposed controller is formulated as an optimization
problem and MSFLA is employed to search for optimal
controller parameters. An eigenvalue based objective function
reflecting the combination of damping factor and damping
ratio is optimized for different operating conditions. The
proposed approach is applied to optimal design of multimachine
power system stabilizers. Three different power
systems, A Single Machine Infinite Bus (SMIB), four-machine
of Kundur and ten-machine New England systems are
considered. The obtained results are evaluated and compared
with other results obtained by Genetic Algorithm (GA).
Eigenvalue analysis and nonlinear system simulations assure
the effectiveness and robustness of the proposed controller in
providing good damping characteristic to system oscillations
and enhancing the system dynamic stability under different
operating conditions and disturbances.
Cross-layer Design of an Asymmetric Loadpower Control Protocol in Ad hoc Netw...IDES Editor
Cross-layer design is important in wireless ad hoc
network and the power control methods. Power control is the
intelligent selection of transmit power in a communication to
achieve the better performance within the system. Cross-layer
is used to sharing the information between the layers. CLD
using LOADPOWER (LOADPOW) control protocol is reduce
the overall end-end delay in transmission power. So many
power control schemes are dealt in network layer but this
work Power control protocol was done in MAC layer and it
plays a vital role. A MAC approach to power control only does
a local optimization whereas network layer is capable of a
global optimization. Simulation was done in NS-2 simulator
with the performance metrics as throughput, and energy
consumption and end-end delay. The key concept is to improve
the throughput, saves energy by sending all the packets with
optimal transmit power according to the network load,
transmission power was given, when the network load is low,
higher transmission power gives lower end-end delay and viceversa.
Prediction of Extreme Wind Speed Using Artificial Neural Network ApproachScientific Review SR
Prediction of an accurate wind speed of wind farms is necessary because of the intermittent nature
of wind for any region. Number of methods such as persistence, physical, statistical, spatial correlation, artificial
intelligence network and hybrid are generally available for prediction of wind speed. In this paper, ANN based
methods viz., Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used. The
performance of the networks applied for prediction of wind speed is evaluated by model performance indicators
viz., Correlation Coefficient (CC), Model Efficiency (MEF) and Mean Absolute Percentage Error (MAPE).
Meteorological parameters such as maximum and minimum temperature, air pressure, solar radiation and
altitude are considered as input units for MLP and RBF networks to predict the extreme wind speed at Delhi.
The study shows the values of CC, MEF and MAPE between the observed and predicted wind speed (using
MLP) are computed as 0.992, 95.4% and 4.3% respectively while training the network data. For RBF network,
the values of CC, MEF and MAPE are computed as 0.992, 95.9% and 3.0% respectively. The model
performance analysis indicates the RBF is better suited network among two different networks studied for
prediction of extreme wind speed at Delhi.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
The accurate prediction of solar irradiation has been
a leading problem for better energy scheduling approach.
Hence in this paper, an Artificial neural network based solar
irradiance is proposed for five days duration the data is
obtained from National Renewable Energy Laboratory, USA
and the simulation were performed using MATLAB 2013. It
was found that the neural model was able to predict the solar
irradiance with a mean square error of 0.0355.
Power loss reduction, improvement of voltage profile, system reliability and system security are the important objectives that motivated researchers to use custom power devices/FACTS devices in power systems. The existing power quality problems such as power losses, voltage instability, voltage profile problem, load ability issues, energy losses, reliability problems etc. are caused due to continuous load growth and outage of components. The significant qualities of custom power devices /FACTS devices such as power loss reduction, improvement of voltage profile, system reliability and system security have motivated researchers in this area and to implement these devices in power system. The optimal placement and sizing of these devices are determined based on economical viability, required quality, reliability and availability. In published literatures, different algorithms are implemented for optimal placement of these devices based on different conditions. In this paper, the published literatures on this field are comprehensively reviewed and elaborate comparison of various algorithms is compared. The inference of this extensive comparative analysis is presented. In this research, Meta heuristic methods and sensitive index methods are used for determining the optimal location and sizing of custom power devices/FACTS devices. The combination of these two methods are also implemented and presented.
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Power System State Estimation - A ReviewIDES Editor
The aim of this article is to provide a comprehensive
survey on power system state estimation techniques. The
algorithms used for finding the system states under both static
and dynamic state estimations are discussed in brief. The
authors are opinion that the scope of pursuing research in the
area of state estimation with PMU and SCADA measurements
is the state of the art and timely.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping To...journalBEEI
This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ksand damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.
Optimal Siting of Distributed Generators in a Distribution Network using Arti...IJECEIAES
Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Study and Development of an Energy Saving Mechanical SystemIDES Editor
A new energy-saving mechanical system with
automatically controlled air valves has been proposed by
investigator and the preliminary model setup has been tested.
The testing results indicated the proper function of this
energy-saving mechanical system. This mechanical system
model has been simulated and analyzed by the computational
aided engineering solution. The major advantages of this
mechanical system include: simple and compact in design,
higher efficiency in mechanical functioning, quiet in
manufacturing operation, less energy losses due to less
frictional forces in this free piston-cylinder setup, selfadjustable
in operational parameter to improve the system
performance, and etc.
Simulative study of cause-effect interdependencies in tool logisticsIDES Editor
Today, the forging industry is facing new challenges.
The day-to-day business is characterized by fluctuating order
quantities and the production of high numbers of variants.
Under this condition the tool logistics is gaining more
importance in order to minimize production downtimes
through guaranteeing high tool availability with minimal tool
costs. This paper presents an approach for a synchronisation
of tool supply processes to production requirements by
evaluation of the cause-effect interdependencies from tool life
quantity and tool stock level on production performance and
tool utilization. Therefore the developed simulation model and
its usage for an extensive simulation study is specified in this
paper. It presents the findings from the cause-effect analysis
and is subdivided into four sections. In the first section the tool
logistic is described by a typical tool loop in massive forging
industries which is confirmed by a survey in 27 German forging
companies. Elements of a tool loop and the influencing factors
are specified. From this, the objectives of tool logistics (e. g.
high tool availability and low tool stock level) are deduced in
the second section. For the quantification of objectives in tool
logistics basic key indicators are defined and described
mathematically. In the third section an ideal tool loop is defined
and associated key indicators, such as “Maximum production
output rate per tool” are calculated. The modular structure of
the simulation model and the experimental design for the
simulation study is described in section four. The simulation
study is carried out in order to describe cause-effect
interdependencies between influencing factors and objectives
(e. g. tool availability) in tool logistics. Therefore a one by one
factor study gives significant value ranges for fully factorial
experiments. This is used to analyze the effects of interactions
between influencing factors on the tool logistics objectives.
Finally the basic conclusions from the experimental simulation
study are described as well as the ongoing research which is
facing an approximation function, that enables to calculate tool
operating points.
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Optimum capacity allocation of distributed generation units using parallel ps...eSAT Journals
Abstract This paper proposes the application of Parallel Particle Swarm Optimization (PPSO) technique to find the optimal sizing of multiple DG(Distributed Generation) units in the radial distribution network by reduction in real power losses and enhancement in voltage profile. Message passing interface (MPI) is used for the parallelization of PSO. The initial population of PSO algorithm has been divided between the processors at run time. The proposed technique is tested on standard 123-bus test system and the obtained results show that the simulation time is significantly reduced and is concluded that parallelization helps in enhancing the performance of basic PSO. The procedure has been implemented in an environment in which OpenDSS (Open Distribution System Simulator) is driven from MATLAB. An adaptive weight particle swarm optimization algorithm has been developed in MATLAB , parallelization is achieved using MATLABMPI and the unbalanced three-phase distribution load flow (DLF) has been performed using Electric Power Research Institute’s (EPRI) open source tool OpenDSS. Index Terms: Distributed Generation, Message Passing Interface, Optimal Placement, Parallel Particle Swarm Optimisation
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Power System State Estimation - A ReviewIDES Editor
The aim of this article is to provide a comprehensive
survey on power system state estimation techniques. The
algorithms used for finding the system states under both static
and dynamic state estimations are discussed in brief. The
authors are opinion that the scope of pursuing research in the
area of state estimation with PMU and SCADA measurements
is the state of the art and timely.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping To...journalBEEI
This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ksand damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.
Optimal Siting of Distributed Generators in a Distribution Network using Arti...IJECEIAES
Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Study and Development of an Energy Saving Mechanical SystemIDES Editor
A new energy-saving mechanical system with
automatically controlled air valves has been proposed by
investigator and the preliminary model setup has been tested.
The testing results indicated the proper function of this
energy-saving mechanical system. This mechanical system
model has been simulated and analyzed by the computational
aided engineering solution. The major advantages of this
mechanical system include: simple and compact in design,
higher efficiency in mechanical functioning, quiet in
manufacturing operation, less energy losses due to less
frictional forces in this free piston-cylinder setup, selfadjustable
in operational parameter to improve the system
performance, and etc.
Simulative study of cause-effect interdependencies in tool logisticsIDES Editor
Today, the forging industry is facing new challenges.
The day-to-day business is characterized by fluctuating order
quantities and the production of high numbers of variants.
Under this condition the tool logistics is gaining more
importance in order to minimize production downtimes
through guaranteeing high tool availability with minimal tool
costs. This paper presents an approach for a synchronisation
of tool supply processes to production requirements by
evaluation of the cause-effect interdependencies from tool life
quantity and tool stock level on production performance and
tool utilization. Therefore the developed simulation model and
its usage for an extensive simulation study is specified in this
paper. It presents the findings from the cause-effect analysis
and is subdivided into four sections. In the first section the tool
logistic is described by a typical tool loop in massive forging
industries which is confirmed by a survey in 27 German forging
companies. Elements of a tool loop and the influencing factors
are specified. From this, the objectives of tool logistics (e. g.
high tool availability and low tool stock level) are deduced in
the second section. For the quantification of objectives in tool
logistics basic key indicators are defined and described
mathematically. In the third section an ideal tool loop is defined
and associated key indicators, such as “Maximum production
output rate per tool” are calculated. The modular structure of
the simulation model and the experimental design for the
simulation study is described in section four. The simulation
study is carried out in order to describe cause-effect
interdependencies between influencing factors and objectives
(e. g. tool availability) in tool logistics. Therefore a one by one
factor study gives significant value ranges for fully factorial
experiments. This is used to analyze the effects of interactions
between influencing factors on the tool logistics objectives.
Finally the basic conclusions from the experimental simulation
study are described as well as the ongoing research which is
facing an approximation function, that enables to calculate tool
operating points.
Geographic Information Systems Based Quantity Takeoffs in Buildings ConstructionIDES Editor
Paper presents a Geographic Information System
(GIS) based quantity takeoffs methodology, which is helpful
in increasing the productivity of quantity estimator by reducing
the manual work in quantity takeoffs. Proposed methodology
also reduces the missing or duplication of various items of
work by visualizing each components corresponding to the
items in space. Several scripts developed within ArcView3.2
were used to extract the necessary dimensions from the
drawings and to perform various calculations for quantity
takeoffs. Accurate Bill of Quantities (BOQ) may be generated
on the basis of dimensions of various data themes in GIS.
Environment Protection of Reserved areas – A Study of Gulf of Mannar (GOM) Bi...IDES Editor
Protected areas in India comprise of Sanctuaries,
National parks and Biosphere Reserves. The program of
Biosphere Reserve was initiated under “The man and
Biosphere (MAB) “program by UNESCO in 1971. Biosphere
Reserves are areas of terrestrial and coastal or marine
ecosystem, or a combination thereof, which are internationally
recognized for promoting and demonstrating a balanced
relationship between people and nature. This paper focuses
on environmental planning of Gulf of Mannar Biosphere
Reserve which extends from Rameswaram Island to Tuticorin
with 130 species of corals. The study provides guideline for
sustainable use of resources, activities to be permitted along
coastal stretch, coastal highways, and mangroves, reclamation
of saline and alkaline soil, coastal pollution, sedimentation,
avifauna, settlements, tourism and recommended vegetation.
It also provides guideline for the improvement of the relation
between people and their environment globally.
Overbank Flow Condition in a River SectionIDES Editor
When the flows in natural or man made channel
sections exceed the main channel depth, the adjoining
floodplains become inundated and carry part of the river
discharge. Due to different hydraulic conditions prevailing in
the river and floodplain of a compound channel, the mean
velocity in the main channel and in the floodplain are different.
This leads to the transfer of momentum between the main
channel water and that of the floodplain making the flow
structure more complex. Results of some experiments
concerning the overbank flow distribution in a compound
channel are presented. Flow sharing in river channels is
strongly dependant on the interaction between flow in the
main channel and that in the floodplain. The influence of the
geometry on velocity and flow distribution and different
functional relationships are obtained. Dimensionless
parameters are used to form equations representing the over
bank flow sharing in the subsections. The equations agree
well with experimental discharge data and other published
data. Using the proposed method, the error between the
measured and calculated discharge distribution for the a
compound sections is found to be the minimum when compared
with that using other investigators.
Urban Air Quality Modelling and Simulation: A Case Study of Kolhapur (M.S.), ...IDES Editor
As a consequence of urbanization a phenomenal
surge has been observed in the vehicular population in India,
giving rise to elevated levels of traffic related pollutants like
carbon monoxide, nitrogen oxides, hydrocarbons, and
particulates in Indian urban centers. These pollutants can
have both acute and chronic effects on human health. Thus
air quality management needs immediate attention. Air
quality models simulate the physical and chemical processes
occurring in the atmosphere to estimate the atmospheric
pollutant concentration. A variety of air quality models are
available ranging from simple empirical models to complex
Computational Fluid Dynamic (CFD) models. Air quality
models can be a valuable tool in pollution forecasting, air
quality management, traffic management and urban planning.
This paper evaluates the performance of widely used Danish
Operational Street Pollution Model (OSPM) under Indian
traffic conditions. Comparison between predicted and observed
concentrations was performed using both quantitative and
statistical methods. OSPM was found to perform exceedingly
well for the prediction of particulates whereas NO2 predictions
were poorly predicted.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EVALUATION OF REFERENCE EVAPOTRANSPIRATION ESTIMATION METHODS AND DEVELOPMENT...IAEME Publication
This study is an attempt to find best alternative method to estimate reference evapotranspiration (ETo) for the Nagarjuna Sagar Reservoir Project [NSRP], command area located at Andhra Pradesh, India. When input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices.
Neural networks have gained a great deal of importance in the area of soft computing and are widely used in making predictions. The work presented in this paper is about the development of Artificial Neural Network (ANN) based models for the prediction of sugarcane yield in India. The ANN models have been experimented using different partitions of training patterns and different combinations of ANN parameters.
Experiments have also been conducted for different number of neurons in hidden layer and the algorithms for ANN training. For this work, data has been obtained from the website of Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. In this work, the experiments have been conducted for 2160 different ANN models. The least Root Mean Square Error (RMSE) value that could be achieved on
test data was 4.03%. This has been achieved when the data was partitioned in such a way that there were 10% records in the test data, 10 neurons in hidden layer, learning rate was 0.001, the error goal was set to 0.01 and traincgb algorithm in MATLAB was used for ANN training.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Overview of soft intelligent computing technique for supercritical fluid extr...IJAAS Team
Optimization of Supercritical Fluid Extraction process with mathematical modeling is essential for industrial applications. The response surface methodology (RSM) has been proven to be a useful and effective statistical method for studying the relationships between measured responses and independent factors. Recently there are growing interest in applying smart system or artificial technique to model and simulate a chemical process and also to predict, compute, classify and optimize as well as for process control. This system works by generalizing the experimental result and the process behavior and finally predict and estimate the problem. This smart system is a major assistance in the development of process from laboratory to pilot or industrial. The main advantage of intelligent systems is that the predictions can be performed easily, fast, and accurate way, which physical models unable to do. This paper shares several works that have been utilizing intelligent systems for modeling and simulating the supercritical fluid extraction process.
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
Reactive Power Planning is a major concern in the
operation and control of power systems This paper compares
the effectiveness of Evolutionary Programming (EP) and
New Improved Differential Evolution (NIMDE) to solve
Reactive Power Planning (RPP) problem incorporating
FACTS Controllers like Static VAR Compensator (SVC),
Thyristor Controlled Series Capacitor (TCSC) and Unified
power flow controller (UPFC) considering voltage stability.
With help of Fast Voltage Stability Index (FVSI), the critical
lines and buses are identified to install the FACTS controllers.
The optimal settings of the control variables of the generator
voltages,transformer tap settings and allocation and parameter
settings of the SVC,TCSC,UPFC are considered for reactive
power planning. The test and Validation of the proposed
algorithm are conducted on IEEE 30–bus system and 72-bus
Indian system.Simulation results shows that the UPFC gives
better results than SVC and TCSC and the FACTS controllers
reduce the system losses.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This paper is an attempt to base on auctions which
presents a frame work for the secure multi-party decision
protocols. In addition to the implementations which are very
light weighted, the main focus is on synchronizing security
features for avoiding agreements manipulations and reducing
the user traffic. Through this paper one can understand that
this different auction protocols on top of the frame work can
be collaborated using mobile devices. This paper present the
negotiation between auctioneer and the proffered and this
negotiation shows that multiparty security is far better than
the existing system.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
The Cloud based services provide much efficient
and seamless ways for data sharing across the cloud. The fact
that the data owners no longer possess data makes it very
difficult to assure data confidentiality and to enable secure
data sharing in the cloud. Despite of all its advantages this
will remain a major limitation that acts as a barrier to the
wider deployment of cloud based services. One of the possible
ways for ensuring trust in this aspect is the introduction of
accountability feature in the cloud computing scenario. The
Cloud framework requires promotion of distributed
accountability for such dynamic environment[1]. In some
works, there‘s an accountable framework suggested to ensure
distributed accountability for data sharing by the generation
of only a log of data access, but without any embedded feedback
mechanism for owner permission towards data
protection[2].The proposed system is an enhanced client
accountability framework which provides an additional client
side verification for each access towards enhanced security of
data. The integrity of content of data which resides in the
cloud service provider is also maintained by secured
outsourcing. Besides, the authentication of JAR(Java Archive)
files are done to ensure file protection and to maintain a safer
environment for data sharing. The analysis of various
functionalities of the framework depicts both the
accountability and security feature in an efficient manner.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
in cloud computing data storage is a significant issue
because the entire data reside over a set of interconnected
resource pools that enables the data to be accessed through
virtual machines. It moves the application software’s and
databases to the large data centers where the management of
data is actually done. As the resource pools are situated over
various corners of the world, the management of data and
services may not be fully trustworthy. So, there are various
issues that need to be addressed with respect to the
management of data, service of data, privacy of data, security
of data etc. But the privacy and security of data is highly
challenging. To ensure privacy and security of data-at-rest in
cloud computing, we have proposed an effective and a novel
approach to ensure data security in cloud computing by means
of hiding data within images following is the concept of
steganography. The main objective of this paper is to prevent
data access from cloud data storage centers by unauthorized
users. This scheme perfectly stores data at cloud data storage
centers and retrieves data from it when it is needed.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Mental Stress Evaluation using an Adaptive ModelIDES Editor
Chronic stress can have serious physiological and
psychological impact on an individual’s health. Wearable
sensor systems can enable physicians to monitor physiological
variables and observe the impact of stress over long periods of
time. To correlate an individual’s physiological measures with
their perception of psychological stress, it is essential that
the stress monitoring system accounts for individual
differences in self-reporting. Self-reporting of stress is highly
subjective as it is dependent on an individual’s perception of
stress and thus prone to errors. In addition, subjects can tailor
their answers to present their behavior more favorably. In
this paper we present an adaptive model which allows recorded
stress scores and physiological variables to be tuned to remove
biases in self-reported scores. The model takes an individual’s
physiological and psychological responses into account and
adapts to the user’s variations. Using our adaptive model,
physiological data is mapped efficiently to perceived stress
levels with 90% accuracy.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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