P1121340294
•Download as PPT, PDF•
0 likes•332 views
Unit Commitment Using a Hybrid Differential Evolution with Triangular Distribution Factor for Adaptive Crossover N. Malla Reddy* K. Ramesh Reddy** and N. V. Ramana***
Report
Share
Report
Share
Recommended
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Resume
Siva Sankar G is seeking a challenging position as an electrical/control engineer or instrumentation engineer. He has a M.Tech in Instrumentation and Control Systems from Jawaharlal Nehru Technological University, Kakinada with expertise in power systems, control systems, MATLAB Simulink. He has work experience in electrical machines and control systems. His academic projects involve renewable energy systems and direct torque control of induction motors using artificial intelligence techniques. He is proficient in English, Telugu, C, C++ and familiar with software like MATLAB Simulink, LABVIEW.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITY
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
A review on optimal placement and sizing of custom power devices/FACTS device...
A review on optimal placement and sizing of custom power devices/FACTS device...International Journal of Power Electronics and Drive Systems
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.35
This paper proposes a novel packet forwarding scheme for wireless sensor networks that aims to improve energy efficiency and reliability. The scheme uses a packet splitting algorithm based on the Chinese Remainder Theorem that requires only simple modular division operations, keeping computational complexity low. An analytical model is presented for estimating the energy savings of the approach. Several practical considerations for unreliable channels, topology changes and MAC overhead are also discussed. Evaluation results show the proposed algorithm outperforms traditional methods in terms of power saving, simplicity and balancing energy consumption across all nodes in the network.
Macro microestructuras
This document reviews the use of wavelet transforms to analyze power quality issues. It discusses how continuous wavelet transforms allow construction of time-frequency representations and how discrete wavelet transforms use discrete signal values in the time domain. The document also examines the advantages and disadvantages of using oscillatory technology devices to investigate the effects of power quality through different electric power tools.
Presentation
This document presents models to quantify sustainability, cost, and dependability of data center infrastructures. It aims to provide integrated evaluation of these factors to support sustainable data center planning with high availability. The models include an energy flow model, stochastic Petri nets, and reliability block diagrams to analyze availability, downtime, and other metrics. A case study applies the methodology and algorithms to evaluate different configurations. The models are implemented in an evaluation environment to analyze scenarios and optimize design tradeoffs between dependability, sustainability impact, and cost.
29 16109 paper 098 ijeecs(edit)
Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.
Recommended
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Resume
Siva Sankar G is seeking a challenging position as an electrical/control engineer or instrumentation engineer. He has a M.Tech in Instrumentation and Control Systems from Jawaharlal Nehru Technological University, Kakinada with expertise in power systems, control systems, MATLAB Simulink. He has work experience in electrical machines and control systems. His academic projects involve renewable energy systems and direct torque control of induction motors using artificial intelligence techniques. He is proficient in English, Telugu, C, C++ and familiar with software like MATLAB Simulink, LABVIEW.
DCT AND DFT BASED BIOMETRIC RECOGNITION AND MULTIMODAL BIOMETRIC SECURITY
This Research paper discusses the study and analysis conducted during this research on various techniques in biometric domain. A close glance on biometric enhancement techniques and their limitations are presented in this research paper. This process would enable researcher to understand the research contributions in the area of DCT and DFT based recognition and security, locate some crucial limitations of these notable research. This paper having summary about the different research papers that applicable to our topic of research which mentioned above. Biometric Recognition and security is a most important subject of research in this area of image processing.
A review on optimal placement and sizing of custom power devices/FACTS device...
A review on optimal placement and sizing of custom power devices/FACTS device...International Journal of Power Electronics and Drive Systems
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.35
This paper proposes a novel packet forwarding scheme for wireless sensor networks that aims to improve energy efficiency and reliability. The scheme uses a packet splitting algorithm based on the Chinese Remainder Theorem that requires only simple modular division operations, keeping computational complexity low. An analytical model is presented for estimating the energy savings of the approach. Several practical considerations for unreliable channels, topology changes and MAC overhead are also discussed. Evaluation results show the proposed algorithm outperforms traditional methods in terms of power saving, simplicity and balancing energy consumption across all nodes in the network.
Macro microestructuras
This document reviews the use of wavelet transforms to analyze power quality issues. It discusses how continuous wavelet transforms allow construction of time-frequency representations and how discrete wavelet transforms use discrete signal values in the time domain. The document also examines the advantages and disadvantages of using oscillatory technology devices to investigate the effects of power quality through different electric power tools.
Presentation
This document presents models to quantify sustainability, cost, and dependability of data center infrastructures. It aims to provide integrated evaluation of these factors to support sustainable data center planning with high availability. The models include an energy flow model, stochastic Petri nets, and reliability block diagrams to analyze availability, downtime, and other metrics. A case study applies the methodology and algorithms to evaluate different configurations. The models are implemented in an evaluation environment to analyze scenarios and optimize design tradeoffs between dependability, sustainability impact, and cost.
29 16109 paper 098 ijeecs(edit)
Nowadays, the location and sizing of distributed generation (DG) units in power system network are crucial to be at optimal as it will affect the power system operation in terms of stability and security. In this paper, a new technique termed as Immune Log-Normal Evolutionary Programming (ILNEP) is applied to find the optimal location and size of distributed generation units in power system network. Voltage stability is considered in solving this problem. The proposed technique has been tested on the IEEE 26 bus Reliability Test System to find the optimal location and size of distributed generation in transmission network. In order to study the performance of ILNEP technique in solving DG Installation problem, the results produced by ILNEP were compared with other meta-heuristic techniques like evolutionary programming (EP) and artificial immune system (AIS). It is found that the proposed technique gives better solution in term of lower total system loss compared to the other two techniques.
Lect01
The document discusses the finite element method (FEM), a numerical technique used to solve complex engineering problems. FEM was originally developed for aerospace engineering but is now widely used in civil, mechanical, and electrical engineering. It works by dividing a structure into finite elements and obtaining an approximate solution by analyzing the properties of individual elements and their interaction. The key principles of FEM include using computational methods to solve boundary value problems representing physical structures, with field variables governed by differential equations and boundary conditions specified on the field boundaries.
Y4502158163
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.
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
PERFORMANCE ASSESSMENT OF ANFIS APPLIED TO FAULT DIAGNOSIS OF POWER TRANSFORMER
Continuous monitoring of Power transformer is very much essential during its operation. Incipient faults inside the tank and winding insulation needs careful attention. Traditional ratio methods and Duval triangle can be employed to diagnose the incipient faults. Many times correct diagnosis due to the
borderline problems and the existence of multiple faults may not be possible. Artificial intelligence (AI) techniques could be the best solution to handle the non linearity and complexity in the input data. In the proposed work, adaptive neuro fuzzy inference system (ANFIS), is utilized to deal with 9 incipient fault conditions including healthy condition of power transformer with sufficient DGA transformer oil samples. Comparison of the diagnosis performance of both the methods of ANFIS and the feasibility pertaining to the problem is presented. Diagnosis error in classifying the oil samples and the network structure are the main considerations of the present study.
Stem warm up week 1a fall 2016 - day 1- 8-15-16 -
Energy levels are fixed paths or orbits that surround the nucleus where electrons are found. One of the largest CTSO's in Georgia is called TSA, and it stands for Technology Student Association. A work breakdown structure, in project management and systems engineering, is a deliverable-oriented decomposition of a project into smaller components. The property shown in a(b + c) = ab + ac is the distributive property.
poster
This document summarizes a student project analyzing the performance of renewable powered and cooperative energy harvesting networks. The project aims to set up an ideal renewable energy field model, analyze the transmission probabilities of networks in this field and cooperative networks, and characterize network-level performance metrics. It reproduces results from two previous works on renewable energy field modeling and analyzing large-scale cooperative wireless networks powered by energy harvesting. Through MATLAB simulations, the project analyzes how changing the energy field density and shape parameter impact the energy field and transmission probabilities, providing insight into overcoming renewable energy randomness.
An Application of Genetic Programming for Power System Planning and Operation
This work incorporates the identification of model
in functional form using curve fitting and genetic programming
technique which can forecast present and future load
requirement. Approximating an unknown function with
sample data is an important practical problem. In order to
forecast an unknown function using a finite set of sample
data, a function is constructed to fit sample data points. This
process is called curve fitting. There are several methods of
curve fitting. Interpolation is a special case of curve fitting
where an exact fit of the existing data points is expected.
Once a model is generated, acceptability of the model must be
tested. There are several measures to test the goodness of a
model. Sum of absolute difference, mean absolute error, mean
absolute percentage error, sum of squares due to error (SSE),
mean squared error and root mean squared errors can be used
to evaluate models. Minimizing the squares of vertical distance
of the points in a curve (SSE) is one of the most widely used
method .Two of the methods has been presented namely Curve
fitting technique & Genetic Programming and they have been
compared based on (SSE)sum of squares due to error.
Artificial Intelligence Applications For Building Energy Management Applicati...
Artificial Intelligence Applications For Building Energy Management Applicati...University of the Highlands and Islands
This document summarizes a PhD candidate's research using artificial intelligence to automate building energy management. The research aims to integrate a building energy management system with micro-generation systems on a single platform to achieve zero energy building status. The candidate is developing a cost function controller that considers energy costs, thermal comfort, and energy production. Preliminary MATLAB modeling shows a neural network model can accurately predict and optimize a simulated heat plant to track temperature references. Further work is needed to optimize plant tracking and include integral or derivative controls. Overall, the research aims to make buildings more adaptive and energy-productive entities.Kcc201728apr2017 170828235330
This document evaluates different deep learning algorithms and data preprocessing techniques for demand power prediction. It finds that a recurrent neural network model achieves the best prediction performance. All algorithms show improved accuracy when trained on preprocessed data that balances the dimension of power load and weather feature data, rather than raw data of varying dimensions. Further research into prediction using extreme learning machine algorithms is suggested.
Static & Dynamic Systems |Solved problems|
Systems can be classified as either static or dynamic based on how their outputs change over time in response to inputs. Static systems always produce the same output for a given input, regardless of time, while dynamic systems have outputs that change with time even if the inputs remain constant. This document discusses the classification of systems as static or dynamic and provides examples of problems involving each type.
Alternating optimization algorithms for power adjustment and receive filter d...
Alternating optimization algorithms for power adjustment and receive filter d...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in VijayangarA1802050102
This document compares different data-driven modeling techniques for reservoir inflow analysis, specifically artificial neural networks (ANN) and M5 model trees. Traditional hydrological methods for inflow analysis were complex, time-consuming, and required extensive data collection. Data-driven techniques provide simpler alternatives by using attributes like direct rainfall-runoff data from surrounding rain gauge stations. The document outlines how ANNs and M5 model trees work, and finds that M5 model trees performed more accurately than ANNs for this reservoir inflow prediction task, as the model setting is easier, training is faster, and results are in a linear equation format.
Neural prosthesis final
This document discusses numerical investigations of the electric field distribution induced in the brain by transcranial magnetic stimulation (TMS). It presents formulations and computations of the magnetic field using finite element methods and compares different coil configurations. The results show that an optimized iron core shape can increase both the magnitude and localization of the induced electric field in the brain, improving the energy efficiency of coupling electromagnetic stimulation to brain tissue.
Complex system
This explains the general algorithmic flow which goes into developing a Neural Network ensemble hybridized with evolutionary optimization schemes which are targeted in optimizing more than one cost function.
Pattern recognition system based on support vector machines
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
Comparative Study of Transmission Line And Cavity Model of Rectangular Micros...
Comparative Study of Transmission Line And Cavity Model of Rectangular Micros...Conferenceproceedings
This document is an abstract from the 3rd International Scientific Conference on Applied Sciences and Engineering that took place in Bangkok, Thailand in July 2015. The abstract describes a comparative study carried out to estimate the performance of transmission line and cavity models of rectangular microstrip antennas mounted on six different dielectric substrate materials with variable substrate heights. An artificial intelligence technique called Adaptive Neuro-Fuzzy Inference System was used to optimize the length and width of the antennas. The simulation results predicted that the cavity model performs better than the transmission line model for thick substrates, while the transmission line model is better for low dielectric constants and thin substrates.IRJET- Use of Artificial Neural Network in Construction Management
This document discusses the use of artificial neural networks (ANNs) in construction management. It provides an overview of ANNs and their advantages over traditional methods for dealing with uncertainties in construction processes. The document then reviews several applications of ANNs in construction management, including predicting construction costs, safe work behavior, safety risks, building valuations, construction productivity, and labor productivity. It finds that ANNs have been effectively used for prediction and decision-making in the construction field. The review concludes that ANNs provide best results compared to conventional methods for solving complex civil engineering problems.
AI IEEE
This paper presents a study using an artificial neural network (ANN) for load forecasting in the smart grid. Specifically, it uses a backpropagation network to forecast electricity load in Ontario, Canada based on weather and other input data. The paper describes collecting hourly load and weather data over two years, normalizing the data, creating a three-layer backpropagation network with different numbers of neurons, training the network using two algorithms, and testing the network on a separate data set to analyze forecast accuracy. The results show the ANN approach is able to accurately forecast electricity load based on the input factors.
Syamsir Abduh-Grounding abduh-2004
This document presents a model for evaluating the performance of grounding systems for primary distribution substations and associated underground cable networks. The model encompasses the dangerous voltages, transferred potentials, and ground fault current distribution caused by using underground cables during operations. It also includes the conductive and magnetic coupling between elements of the grounding system and the nonlinearity of cable sheath impedances.
Unit Commitment
The document proposes using semi-definite programming (SDP) to solve the security constrained unit commitment (SCUC) problem in power systems operations. SDP formulates the SCUC problem as an SDP problem that can minimize an objective function while handling constraints to provide a physically feasible and secure solution. The document describes the SCUC problem, limitations of conventional approaches, how SDP can solve SCUC while meeting operational constraints and security, and provides a case study example.
Economic operation of Power systems by Unit commitment
This document discusses unit commitment in power systems. Unit commitment aims to schedule generating units to meet forecasted load at minimum cost while maintaining reliability. It considers startup costs, operating costs, and shutdown costs over a daily load cycle. Dynamic programming is used to solve the unit commitment problem by evaluating combinations of generating units at each time interval and carrying minimum costs backward from the final interval to find the overall lowest-cost solution. The objective is to determine the optimal set of units to operate at each time period to supply predicted load economically.
More Related Content
What's hot
Lect01
The document discusses the finite element method (FEM), a numerical technique used to solve complex engineering problems. FEM was originally developed for aerospace engineering but is now widely used in civil, mechanical, and electrical engineering. It works by dividing a structure into finite elements and obtaining an approximate solution by analyzing the properties of individual elements and their interaction. The key principles of FEM include using computational methods to solve boundary value problems representing physical structures, with field variables governed by differential equations and boundary conditions specified on the field boundaries.
Y4502158163
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.
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting (STLF) can predict load from several minutes to week plays
the vital role to address challenges such as optimal generation, economic scheduling, dispatching and
contingency analysis. This paper uses Multi-Layer Perceptron (MLP) Artificial Neural Network
(ANN) technique to perform STFL but long training time and convergence issues caused by bias,
variance and less generalization ability, unable this algorithm to accurately predict future loads. This
issue can be resolved by various methods of Bootstraps Aggregating (Bagging) (like disjoint
partitions, small bags, replica small bags and disjoint bags) which helps in reducing variance and
increasing generalization ability of ANN. Moreover, it results in reducing error in the learning process
of ANN. Disjoint partition proves to be the most accurate Bagging method and combining outputs of
this method by taking mean improves the overall performance. This method of combining several
predictors known as Ensemble Artificial Neural Network (EANN) outperform the ANN and Bagging
method by further increasing the generalization ability and STLF accuracy.
The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
PERFORMANCE ASSESSMENT OF ANFIS APPLIED TO FAULT DIAGNOSIS OF POWER TRANSFORMER
Continuous monitoring of Power transformer is very much essential during its operation. Incipient faults inside the tank and winding insulation needs careful attention. Traditional ratio methods and Duval triangle can be employed to diagnose the incipient faults. Many times correct diagnosis due to the
borderline problems and the existence of multiple faults may not be possible. Artificial intelligence (AI) techniques could be the best solution to handle the non linearity and complexity in the input data. In the proposed work, adaptive neuro fuzzy inference system (ANFIS), is utilized to deal with 9 incipient fault conditions including healthy condition of power transformer with sufficient DGA transformer oil samples. Comparison of the diagnosis performance of both the methods of ANFIS and the feasibility pertaining to the problem is presented. Diagnosis error in classifying the oil samples and the network structure are the main considerations of the present study.
Stem warm up week 1a fall 2016 - day 1- 8-15-16 -
Energy levels are fixed paths or orbits that surround the nucleus where electrons are found. One of the largest CTSO's in Georgia is called TSA, and it stands for Technology Student Association. A work breakdown structure, in project management and systems engineering, is a deliverable-oriented decomposition of a project into smaller components. The property shown in a(b + c) = ab + ac is the distributive property.
poster
This document summarizes a student project analyzing the performance of renewable powered and cooperative energy harvesting networks. The project aims to set up an ideal renewable energy field model, analyze the transmission probabilities of networks in this field and cooperative networks, and characterize network-level performance metrics. It reproduces results from two previous works on renewable energy field modeling and analyzing large-scale cooperative wireless networks powered by energy harvesting. Through MATLAB simulations, the project analyzes how changing the energy field density and shape parameter impact the energy field and transmission probabilities, providing insight into overcoming renewable energy randomness.
An Application of Genetic Programming for Power System Planning and Operation
This work incorporates the identification of model
in functional form using curve fitting and genetic programming
technique which can forecast present and future load
requirement. Approximating an unknown function with
sample data is an important practical problem. In order to
forecast an unknown function using a finite set of sample
data, a function is constructed to fit sample data points. This
process is called curve fitting. There are several methods of
curve fitting. Interpolation is a special case of curve fitting
where an exact fit of the existing data points is expected.
Once a model is generated, acceptability of the model must be
tested. There are several measures to test the goodness of a
model. Sum of absolute difference, mean absolute error, mean
absolute percentage error, sum of squares due to error (SSE),
mean squared error and root mean squared errors can be used
to evaluate models. Minimizing the squares of vertical distance
of the points in a curve (SSE) is one of the most widely used
method .Two of the methods has been presented namely Curve
fitting technique & Genetic Programming and they have been
compared based on (SSE)sum of squares due to error.
Artificial Intelligence Applications For Building Energy Management Applicati...
Artificial Intelligence Applications For Building Energy Management Applicati...University of the Highlands and Islands
This document summarizes a PhD candidate's research using artificial intelligence to automate building energy management. The research aims to integrate a building energy management system with micro-generation systems on a single platform to achieve zero energy building status. The candidate is developing a cost function controller that considers energy costs, thermal comfort, and energy production. Preliminary MATLAB modeling shows a neural network model can accurately predict and optimize a simulated heat plant to track temperature references. Further work is needed to optimize plant tracking and include integral or derivative controls. Overall, the research aims to make buildings more adaptive and energy-productive entities.Kcc201728apr2017 170828235330
This document evaluates different deep learning algorithms and data preprocessing techniques for demand power prediction. It finds that a recurrent neural network model achieves the best prediction performance. All algorithms show improved accuracy when trained on preprocessed data that balances the dimension of power load and weather feature data, rather than raw data of varying dimensions. Further research into prediction using extreme learning machine algorithms is suggested.
Static & Dynamic Systems |Solved problems|
Systems can be classified as either static or dynamic based on how their outputs change over time in response to inputs. Static systems always produce the same output for a given input, regardless of time, while dynamic systems have outputs that change with time even if the inputs remain constant. This document discusses the classification of systems as static or dynamic and provides examples of problems involving each type.
Alternating optimization algorithms for power adjustment and receive filter d...
Alternating optimization algorithms for power adjustment and receive filter d...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in VijayangarA1802050102
This document compares different data-driven modeling techniques for reservoir inflow analysis, specifically artificial neural networks (ANN) and M5 model trees. Traditional hydrological methods for inflow analysis were complex, time-consuming, and required extensive data collection. Data-driven techniques provide simpler alternatives by using attributes like direct rainfall-runoff data from surrounding rain gauge stations. The document outlines how ANNs and M5 model trees work, and finds that M5 model trees performed more accurately than ANNs for this reservoir inflow prediction task, as the model setting is easier, training is faster, and results are in a linear equation format.
Neural prosthesis final
This document discusses numerical investigations of the electric field distribution induced in the brain by transcranial magnetic stimulation (TMS). It presents formulations and computations of the magnetic field using finite element methods and compares different coil configurations. The results show that an optimized iron core shape can increase both the magnitude and localization of the induced electric field in the brain, improving the energy efficiency of coupling electromagnetic stimulation to brain tissue.
Complex system
This explains the general algorithmic flow which goes into developing a Neural Network ensemble hybridized with evolutionary optimization schemes which are targeted in optimizing more than one cost function.
Pattern recognition system based on support vector machines
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
Comparative Study of Transmission Line And Cavity Model of Rectangular Micros...
Comparative Study of Transmission Line And Cavity Model of Rectangular Micros...Conferenceproceedings
This document is an abstract from the 3rd International Scientific Conference on Applied Sciences and Engineering that took place in Bangkok, Thailand in July 2015. The abstract describes a comparative study carried out to estimate the performance of transmission line and cavity models of rectangular microstrip antennas mounted on six different dielectric substrate materials with variable substrate heights. An artificial intelligence technique called Adaptive Neuro-Fuzzy Inference System was used to optimize the length and width of the antennas. The simulation results predicted that the cavity model performs better than the transmission line model for thick substrates, while the transmission line model is better for low dielectric constants and thin substrates.IRJET- Use of Artificial Neural Network in Construction Management
This document discusses the use of artificial neural networks (ANNs) in construction management. It provides an overview of ANNs and their advantages over traditional methods for dealing with uncertainties in construction processes. The document then reviews several applications of ANNs in construction management, including predicting construction costs, safe work behavior, safety risks, building valuations, construction productivity, and labor productivity. It finds that ANNs have been effectively used for prediction and decision-making in the construction field. The review concludes that ANNs provide best results compared to conventional methods for solving complex civil engineering problems.
AI IEEE
This paper presents a study using an artificial neural network (ANN) for load forecasting in the smart grid. Specifically, it uses a backpropagation network to forecast electricity load in Ontario, Canada based on weather and other input data. The paper describes collecting hourly load and weather data over two years, normalizing the data, creating a three-layer backpropagation network with different numbers of neurons, training the network using two algorithms, and testing the network on a separate data set to analyze forecast accuracy. The results show the ANN approach is able to accurately forecast electricity load based on the input factors.
Syamsir Abduh-Grounding abduh-2004
This document presents a model for evaluating the performance of grounding systems for primary distribution substations and associated underground cable networks. The model encompasses the dangerous voltages, transferred potentials, and ground fault current distribution caused by using underground cables during operations. It also includes the conductive and magnetic coupling between elements of the grounding system and the nonlinearity of cable sheath impedances.
What's hot (20)
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
Short Term Load Forecasting Using Bootstrap Aggregating Based Ensemble Artifi...
The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering and Science (The IJES)
PERFORMANCE ASSESSMENT OF ANFIS APPLIED TO FAULT DIAGNOSIS OF POWER TRANSFORMER
PERFORMANCE ASSESSMENT OF ANFIS APPLIED TO FAULT DIAGNOSIS OF POWER TRANSFORMER
An Application of Genetic Programming for Power System Planning and Operation
An Application of Genetic Programming for Power System Planning and Operation
Artificial Intelligence Applications For Building Energy Management Applicati...
Artificial Intelligence Applications For Building Energy Management Applicati...
Alternating optimization algorithms for power adjustment and receive filter d...
Alternating optimization algorithms for power adjustment and receive filter d...
Pattern recognition system based on support vector machines
Pattern recognition system based on support vector machines
Comparative Study of Transmission Line And Cavity Model of Rectangular Micros...
Comparative Study of Transmission Line And Cavity Model of Rectangular Micros...
IRJET- Use of Artificial Neural Network in Construction Management
IRJET- Use of Artificial Neural Network in Construction Management
Viewers also liked
Unit Commitment
The document proposes using semi-definite programming (SDP) to solve the security constrained unit commitment (SCUC) problem in power systems operations. SDP formulates the SCUC problem as an SDP problem that can minimize an objective function while handling constraints to provide a physically feasible and secure solution. The document describes the SCUC problem, limitations of conventional approaches, how SDP can solve SCUC while meeting operational constraints and security, and provides a case study example.
Economic operation of Power systems by Unit commitment
This document discusses unit commitment in power systems. Unit commitment aims to schedule generating units to meet forecasted load at minimum cost while maintaining reliability. It considers startup costs, operating costs, and shutdown costs over a daily load cycle. Dynamic programming is used to solve the unit commitment problem by evaluating combinations of generating units at each time interval and carrying minimum costs backward from the final interval to find the overall lowest-cost solution. The objective is to determine the optimal set of units to operate at each time period to supply predicted load economically.
Presentation on Smart Grid
A presentation project on Smart Grid -an intelligent electricity delivery system for Information System course
The selena's
The document discusses key elements of art in photography including closed form, open form, linear perspective, atmospheric perspective, scale, lighting, and texture. It provides an example of how scale is demonstrated in a photo of dominos getting smaller in the background to show depth through linear perspective.
PIMA News Letter
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
P1111223206
The document presents a cascaded plus state feedback optimal controller design for a three phase induction motor. It begins with an overview of induction motors, electric drives, linearization, and optimal control techniques. It then describes the proposed controller which cascades a pre-defined controller with the plant and uses an LQR controller. Simulation results show the cascaded controller improves dynamic and steady state performance and better rejects disturbances compared to a regular controller. The conclusion is that the cascaded plus state feedback controller provides better control of the induction motor.
The element of art
This presentation discusses various artistic techniques including closed form, open fork, scales, line perspective, atmospheric perspective, lighting, and texture. It begins exploring these techniques and concludes with an end to the presentation.
Open source software
Diese Präsentation habe ich in einem Kurs für PowerPoint erstellt (ich musste einen Kurs besuchen, dieser war der beste im Angebot *langweil*)
Dengue fever algorithm and charts
The document contains 15 pages and encloses 4 forms and 6 monitoring charts related to screening, identifying, and managing patients with dengue fever and dengue hemorrhagic fever. Form I provides a screening form for dengue patients. Form II and III provide algorithms for suspecting dengue fever and identifying dengue hemorrhagic fever. Form IV provides an algorithm for fluid management in the critical phase of dengue treatment.
Market based cash balance plans
This document provides an overview of market-based cash balance plans, including:
- A brief history of interest credit options for cash balance plans under different regulations and notices.
- Examples of how cash balance accounts accumulate pay credits and interest credits over time.
- Seven key issues that can arise for market-based cash balance plans, such as investment risk, nondiscrimination testing, lump sum restrictions, and administrative challenges. Suggested approaches are provided to help address these issues.
- Contact information for the actuarial consulting firm that authored the document.
P1121133746
This document discusses language engineering and introduces a framework for building flexible and adaptable processing chains using combinatory logic. It proposes:
1) Using typed modules that can be composed together in processing chains using combinators like B, C, and Φ to ensure coherence.
2) Implementing a prototype called SATIM that allows engineers to select modules, build processing chains, and test them before deploying as autonomous software applications.
3) Aiming to address needs for coherence, flexibility, and easy communication between programs through replacing modules as long as inputs and outputs are compatible.
P1161208119
This document discusses nonlinear regression analysis and comparing different statistical techniques for linear and nonlinear models.
It introduces nonlinear regression, where observational data is modeled by a nonlinear combination of parameters and independent variables. Some nonlinear problems can be transformed into linear models through suitable transformations.
The document then applies various statistical techniques - least squares estimation, Fisher's F-test, and Student's t-test - to linear and exponential nonlinear regression models. It compares the results of these techniques on sample images of different sizes, finding that Fisher's F-test for nonlinear models is more accurate on smaller areas, while the accuracy decreases for larger sample sizes. Overall, the nonlinear t-test performs better than the linear t-test for extracting lesions,
Quality story august
Four quality stories were generated in August from markets in SGE, Jeddah, Lahore and SELV. Two stories came from HHP and two from CTV. The document calls for increased participation from markets in North Africa (SEMRC, Cairo, Tunis and Algiers) and Tehran and Tel Aviv. It provides details on four quality stories from August, including publications and dates.
P1151418327
The document proposes a new model called the Simple Connected Pattern Array Grammar (SCPAG) that is capable of generating and recognizing complex connected patterns in an image neighborhood. SCPAG aims to address the difficulty of representing all possible connected patterns on even a small 3x3 neighborhood using existing methods. The paper introduces SCPAG as a way to efficiently generate and recognize patterns by properly describing the pattern set with a uniquely parsable array grammar.
Oficina drupal - Temas Drupal (Theming)
The document discusses Drupal themes and templates. It explains that themes define regions, stylesheets, and scripts in their .info file. Themes can have sub-themes and are built upon core modules and contrib modules. The document outlines how template files, preprocess functions, and theme override functions work. It also lists some popular community and commercial themes as well as modules that help with theming.
Quality stories - september
1. A report summarizes the quality stories generated in September across several Samsung markets including Lahore, Jeddah, and SELV.
2. A total of 7 quality stories were scored in September, with 6 generated by HHP and 4 about the Galaxy S smartphone.
3. The report calls for increased participation in quality stories from markets like North Africa, Tehran, and Tel Aviv.
APD Presentation by Prof.Javed Akram
This document discusses peptic ulcer disease and provides information on diagnosing and treating ulcers. It defines a peptic ulcer and notes that they occur in the stomach or duodenum. Common misconceptions about ulcers are described. Diagnosis of ulcers involves physical exam, imaging like barium swallow, and endoscopy. Treatment focuses on eradicating Helicobacter pylori infection if present using antibiotic therapy. Other treatment options discussed include sucralfate, H2 receptor antagonists, and proton pump inhibitors. Risk factors and complications of ulcers are also outlined.
Final layout plans
The document discusses changes made to magazine layout plans. The masthead was moved to the top to allow for easier image layering. Story text was scattered around and wrapped models to highlight the main image without crowding. Small changes were made but generic conventions were kept. The contents page was difficult to spread across pages so a single page was used, removing some content but adding an editor's column as a music magazine convention. Three students' double page layout designs are then listed.
Photos 110208145628-phpapp02
This document discusses copyrights for photos and music. It states that all copyrights to photos and music belong to the original authors. The document is signed by "Loly".
Viewers also liked (20)
Economic operation of Power systems by Unit commitment
Economic operation of Power systems by Unit commitment
Similar to P1121340294
MOHAMMED OVAIZ_Resume -Industry
A. Mohammed Ovaiz is seeking a career in the power industry where he can learn about emerging technologies and take on challenging roles. He has 6 years of teaching experience and 3 years of experience in power sector operations and maintenance. He holds an M.E. in Power Electronics and Drives with high marks and a B.E. in Electrical and Electronics Engineering also with high marks. He has expertise in various power-related fields and has published papers in international journals and conferences.
Resume
Siva Sankar G is seeking a challenging position as an electrical/control engineer or instrumentation engineer. He has a M.Tech in Instrumentation and Control Systems from Jawaharlal Nehru Technological University, Kakinada with expertise in power systems, control systems, MATLAB Simulink. He has work experience in electrical machines and control systems with good communication and problem solving skills. His academic projects involved renewable energy systems and improving induction motor control using artificial intelligence techniques.
CV_SR_IND1
Swakshar Ray has over 15 years of experience in electrical engineering. He currently works as a scientist at ABB focusing on HVDC and FACTS systems. Previously he held research positions at GE and ABB researching topics like wide area control, power system modeling, and energy storage. He holds a PhD in electrical engineering from the University of Missouri with a focus on intelligent wide area control.
Optimal state estimation techniques for accurate measurements in internet of...
This document presents techniques for optimal state estimation and forecasting in Internet of Things (IoT) enabled microgrids using deep neural networks (DNNs). It discusses using Kalman filters and variants as preprocessors to handle raw and missing sensor data. A formulated DNN approach is described to enable accurate component and system-level state estimation and forecasting. Experiments on the IEEE 118-bus system use real load data to test state estimation and forecasting. The research aims to develop novel DNN algorithms for power systems under dynamic conditions and time dependencies.
resume
Kavin Keerthana is seeking a challenging career in electrical engineering, particularly in power electronics, renewable systems, and machines. She has work experience modeling and simulating a PEM fuel cell with grid-connected inverter at BHEL. She has also worked on projects involving stability analysis of a photovoltaic system, short term load forecasting using evolutionary ELM, and traffic signal control using a PIC microcontroller. Kavin holds an M.E. in power electronics and drives and a B.E. in electrical and electronics engineering.
Optimal placement of facts devices to reduce power system losses using evolu...
The rapid and enormous growths of the power electronics industries have made the flexible ac transmission system (FACTS) devices efficient and viable for utility application to increase power system operation controllability as well as flexibility. This research work presents the application of an evolutionary algorithm namely differential evolution (DE) approach to optimize the location and size of three main types of FACTS devices in order to minimize the power system losses as well as improving the network voltage profile. The utilized system has been reactively loaded beginning from the base to 150% and the system performance is analyzed with and without FACTS devices in order to confirm its importance within the power system. Thyristor controlled series capacitor (TCSC), unified power flow controller (UPFC) and static var compensator (SVC) are used in this research work to monitor the active and reactive power of the carried out system. The adopted algorithm has been examined on IEEE 30-bus test system. The obtained research findings are given with appropriate discussion and considered as quite encouraging that will be valuable in electrical grid restructuring.
Upfc supplementary-controller-design-using-real-coded-genetic-algorithm-for-d...
This document summarizes a research paper that proposes using a Real-Coded Genetic Algorithm to design Unified Power Flow Controller (UPFC) damping controllers. The goal is to damp low frequency oscillations in power systems. The paper models a single-machine infinite-bus power system installed with a UPFC. It linearizes the system equations and formulates the controller design as an optimization problem to minimize oscillations. Simulation results comparing the proposed RCGA approach to conventional tuning are presented to demonstrate its effectiveness and robustness in damping power system oscillations.
Comparative study of methods for optimal reactive power dispatch
Reactive power dispatch plays a main role in order to provide good facility secure and economic operation
in the power system. In a power system optimal reactive power dispatch is supported to improve the voltage
profile, to reduce losses, to improve voltage stability, to reduce cost etc. This paper presents a brief literature survey of reactive power dispatch and also discusses a comparative study of conventional and evolutionary computation techniques applied for reactive power dispatch. The paper is useful for researchers for further research and study so that it can apply in the various areas of power system
Power system transient stability margin estimation using artificial neural ne...
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.
Resume of Swaminathan - Master’s in Electrical Engineering, Power Systems
Masters in Electrical Engineering professional seeking challenging roles in the Energy and Utilities Industry
Gz3612511256
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.
Presentation on FAULT CLASSIFICATION.pptx
FAULT CLASSIFICATION AND LOCATION IN SERIES COMPENSATED LINE DURING POWER SWING.
NIT JAMSHEDPUR (M.TECH)
Load_Forecastinglfviuguuyihonrekgdbgr.pptx
piogufdtr6sdt7f8yg9uh0ijo[kpojpinbiuvuctyrd67tf86g798h0jpo[mnl;kb jvhgcuxtr7d5f687g98h0ipon;k b jlkvhcutd76f8guobikn;
Seminar.pptx
The document summarizes a seminar presentation on machine learning. It includes an introduction to machine learning, common machine learning algorithms, and the general machine learning process. It also discusses different types of learning approaches such as supervised, unsupervised, reinforced, and semi-supervised learning. Literature on applications of machine learning in various domains like healthcare, robotics, and power systems is also reviewed.
A Review on Prediction of Compressive Strength and Slump by Using Different M...
The document reviews different machine learning techniques for predicting the compressive strength and slump of concrete, including artificial neural networks, genetic algorithms, and hybrid algorithms. It finds that artificial neural networks trained with the Levenberg-Marquardt algorithm can predict compressive strength with over 95% accuracy. For slump prediction, federated learning achieves the best results in terms of correlation coefficient, root mean square error, and mean absolute error. A hybrid approach combining biogeography-based optimization and multilayer perceptron neural networks most accurately predicts slope stability. In general, machine learning methods show potential for effectively predicting concrete properties.
Indranil saaki resume
This document appears to be a resume for someone named Indranil Saaki. It includes sections about their name, address, career objective, educational background with degrees and percentages, assets, and dissertations completed. It also lists workshops attended and papers published. The document is written in multiple languages and contains some characters that are unreadable, so it is difficult to summarize the key details.
Indranil resume
This document appears to be a resume for someone named Indranil Saaki. It includes sections about their name, address, career objective, educational background with degrees and percentages, assets, and dissertations completed. It also lists workshops attended and papers published. The document is written in multiple languages and contains some symbols, so it is difficult to fully understand.
Fast and accurate primary user detection with machine learning techniques for...
Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with orthogonal frequency division multiplexing and energy detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...
This document presents a novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm for a smart grid integrating solar, wind, and grid power sources. The proposed ANFIS controller is used to improve the steady-state and transient response of the hybrid power system. Fuzzy logic maximum power point tracking algorithms are used to extract maximum power from solar photovoltaic panels and a permanent magnet synchronous generator is used for wind power generation. Back-to-back voltage source converters operated by the ANFIS controller are used to maximize both renewable power generations. Simulation results under different operating conditions and nonlinear faults show the proposed ANFIS control algorithm improves the overall system performance.
Similar to P1121340294 (20)
Handwritten_Equation_Project_PDF.pdf handwritten equation
Handwritten_Equation_Project_PDF.pdf handwritten equation
Optimal state estimation techniques for accurate measurements in internet of...
Optimal state estimation techniques for accurate measurements in internet of...
Optimal placement of facts devices to reduce power system losses using evolu...
Optimal placement of facts devices to reduce power system losses using evolu...
Upfc supplementary-controller-design-using-real-coded-genetic-algorithm-for-d...
Upfc supplementary-controller-design-using-real-coded-genetic-algorithm-for-d...
Comparative study of methods for optimal reactive power dispatch
Comparative study of methods for optimal reactive power dispatch
Power system transient stability margin estimation using artificial neural ne...
Power system transient stability margin estimation using artificial neural ne...
Resume of Swaminathan - Master’s in Electrical Engineering, Power Systems
Resume of Swaminathan - Master’s in Electrical Engineering, Power Systems
A Review on Prediction of Compressive Strength and Slump by Using Different M...
A Review on Prediction of Compressive Strength and Slump by Using Different M...
Fast and accurate primary user detection with machine learning techniques for...
Fast and accurate primary user detection with machine learning techniques for...
A novel efficient adaptive-neuro fuzzy inference system control based smart ...
A novel efficient adaptive-neuro fuzzy inference system control based smart ...
More from Ashraf Aboshosha
P1151351311
Secure Image Encryption using Two Dimensional Logistic Map
* Gangadhar Tiwari1, Debashis Nandi2, Abhishek Kumar3, Madhusudhan Mishra4 1, 2Department of Information Technology, NIT Durgapur (W.B.), India 3Department of Electronics and Electrical Engineering, NITAP, (A.P.), India 4Department of Electronics and Communication Engineering, NERIST, (A.P.), India
P1151345302
Non-Invertible Wavelet Domain Watermarking using Hash Function
*Gangadhar Tiwari1, Debashis Nandi 2, Madhusudhan Mishra3
1,2 IT Department, NIT, Durgapur-713209, West Bengal, India,
3ECE Department, NERIST, Nirjuli-791109, Arunachal Pradesh, India,
P1121352313
Converting UML class diagram with anti-pattern problems to verified code based on Event-B
Eman K. Elsayed
Mathematical and computer science Dep., Faculty of Science,
Al-Azhar University, Cairo, Egypt
P1121340296
Approach to Seismic Signal Discrimination based on Takagi-Sugeno Fuzzy Inference System
E. H. Ait Laasri, E. Akhouayri, D. Agliz, A. Atmani Electronic, Signal processing and Physical Modelling Laboratory, Physics’ Department, Faculty of Sciences, Ibn Zohr University, B.P. 8106, Agadir, Morocco
P1121327289
Intelligent e-assessment: ontological model for personalizing assessment activities
Rafaela Blanca Silva-López1, Iris Iddaly Méndez-Gurrola1, Victor Germán Sánchez Arias2
1 Universidad Autónoma Metropolitana, Unidad Azcapotzalco.
Av. San Pablo 180, Col. Reynosa Tamaulipas, Del. Azcapotzalco, México, D.F.
2 Universidad Nacional Autónoma de México
Circuito Escolar Ciudad Universitaria, 04510 México, D.F.
P1151442348
Visual Perception Oriented CBIR envisaged through Fractals and Presence Score
Suhas Rautmare, Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
P1151442347
Measuring Sub Pixel Erratic Shift in Egyptsat-1 Aliased Images: proposed method
1M.A. Fkirin, 1S.M. Badway, 2A.K. Helmy, 2S.A. Mohamed
1Department of Industrial Electronic Engineering and Control, Faculty of Electronic Engineering,
Menoufia University, Menoufia, Egypt.
2Division of Data Reception Analysis and Receiving Station Affairs, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt.
P1151439345
The State of the Art of Video Summarization for Mobile Devices:
Review Article
Hesham Farouk *, Kamal ElDahshan**, Amr Abozeid **
* Computers and Systems Dept., Electronics Research Institute, Cairo, Egypt.
** Dept. of Mathematics, Computer Science Division,
Faculty of Science, Al-Azhar University, Cairo, Egypt.
P1151424332
Overwriting Grammar Model to Represent 2D Image Patterns
1Vishnu Murthy. G, 2Vakulabharanam Vijaya Kumar
1,2Anurag Group of Institutions, Hyderabad, AP,India.
P1151423331
Texture Classification Based on Binary Cross Diagonal Shape Descriptor Texture Matrix (BCDSDTM)
1P.Kiran Kumar Reddy, 2Vakulabharanam Vijaya Kumar, 3B.Eswar Reddy
1RGMCET, Nandyal, AP, India, 2Anurag Group of Institutions, Hyderabad, AP, India
3JNTUA College of Engineering, India.
P1151420328
Improved Iris Verification System
Basma M.Almezgagi, M. A. Wahby Shalaby, Hesham N. Elmahdy Faculty of Computers and Information, Cairo University, Egypt.
P1151404314
Bench Marking Higuchi Fractal for CBIR
A. Suhas Rautmare, B. Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
P1111351312
The document discusses the performance of Flexible AC Transmission System (FACTS) devices on voltage stability in a deregulated power system. It focuses on enhancing voltage stability using FACTS controllers like Static Var Compensator (SVC) and Thyristor-controlled series capacitor (TCSC). The optimal location of FACTS devices is determined using sensitivity methods. The effectiveness of the proposed method is demonstrated on a modified IEEE-9 bus system using PowerWorld simulator 8.0.
P1111444352
This document summarizes a numerical study of automatically controlling and stabilizing the pulse repetition rate of passively Q-switched laser systems. The researchers propose a technique called automatic pulsed pumping that detects generated Q-switched pulses and alters the pumping power accordingly. By simulating a diode-pumped Yb:YAG laser with Cr4+:YAG as a saturable absorber using rate equations, the technique showed good control over pulse repetition rate with high stability, allowing for both high rates like pulsed pumping and stability like continuous pumping.
P1111440346
The document analyzes and compares the movement of metallic particles in SF6/N2, SF6/CO2, and SF6/Air gas mixtures used in gas insulated substations. It finds that SF6 has a nonlinear breakdown strength affected by metallic particles, and is also a greenhouse gas. Alternative gas mixtures are needed. The paper presents results on particle movement in these three gas mixtures as potential SF6 alternatives with good dielectric properties.
P1111431335
This paper discusses suppressing transformer inrush current when connecting to a PWM voltage source converter. It compares this technique to using PI control of DC voltage. The PWM converter acts as a resistor for source current, eliminating inrush. Inrush suppression principles and varying magnetizing current with control gains are examined. Both methods were developed and simulated in MATLAB/SIMULINK. The paper aims to confirm the validity of using a PWM converter to suppress transformer inrush current when connecting to the source.
P1111410320
This document summarizes a paper that investigates the robustness of linear time-invariant state observers when state and measurement variations occur. The paper applies observer theory to an internal combustion engine model and analyzes how state and sensor disturbances affect observer error dynamics and performance robustness. It was authored by S.O. Omekanda from Oakland University, T. Perkins from Oakland University, and M.A. Zohdy from Oakland University.
P1111410321
The document summarizes a two-branch parallel RC circuit Simulink model of a lithium polymer battery. The model parameters depend on temperature and state of charge, which are represented using 2D lookup tables. User-defined functions calculate open circuit voltage and internal resistance. A dedicated block autonomously detects charge/discharge transitions and resets the current integrator. Comparisons to experimental data show maximum errors of 3% for dynamic responses and 5% for static discharging responses.
P1111350310
Effect of Multi FACTS Devices On the Enhancement of
Available Transfer Capability in a Deregulated Power System
P1111348307
An Efficient Novel Reduced Order Observer for State Estimation of an Internal Combustion Engine Model
More from Ashraf Aboshosha (20)
Recently uploaded
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Recycled Concrete Aggregate in Construction Part III
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Recycled Concrete Aggregate in Construction Part II
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology towards Sustainable Steelmaking
Modelagem de um CSTR com reação endotermica.pdf
Modelagem em função de transferencia. CSTR não-linear.
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting. Casting-Defect-inSlab continuous casting
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
For International shipping and maritime laws all wood must contain the ISPM 15 Stamp. Here is how and why.
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
A review on techniques and modelling methodologies used for checking electrom...
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Recently uploaded (20)
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Recycled Concrete Aggregate in Construction Part III
Recycled Concrete Aggregate in Construction Part III
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
Recycled Concrete Aggregate in Construction Part II
Recycled Concrete Aggregate in Construction Part II
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
Iron and Steel Technology Roadmap - Towards more sustainable steelmaking.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
A review on techniques and modelling methodologies used for checking electrom...
A review on techniques and modelling methodologies used for checking electrom...
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
P1121340294
- 1. Unit Commitment Using a Hybrid Differential Evolution with Triangular Distribution Factor for Adaptive Crossover N. Malla Reddy* K. Ramesh Reddy** and N. V. Ramana*** 1www.icgst.com http://www.icgst.com/paper.aspx?pid=P1121340294
- 2. In the present day power scenario Unit Commitment (UC) is one of the complex challenging tasks for power system operators. UC is a nonlinear, non-convex, large scale, mixed integer problem. To mitigate this complex problem in this paper a hybrid Differential Evolution with local search technique and an adaptive Crossover using triangular distribution factor (DE-TCR) is presented. The salient features of the proposed DE-TCR are: An intelligent chromosome representation is used which is independent of number of units present in UC problem thereby reducing the length of chromosome. It is able to interlink the cross over probability in conjunction with the non-separable and decision variable dependency of UC problems. Local search using Sequential Quadratic Programming, which has proved in improving the performance of the classical DE algorithm. Initially, the proposed DE-TCR is used to determine an optimal generation schedule for each hourly demand. Later, SQP is utilized to find the optimal dispatch strategy to minimize the fuel cost. The effectiveness of the proposed algorithm is tested on standard 4 units, 8 hour and 10 units, 24 hour UC systems. Results demonstrate that the proposed algorithm can perform better and produce global optimal solutions compared to that of other reported methods. 2www.icgst.com http://www.icgst.com/paper.aspx?pid=P1121340294 Unit Commitment Using a Hybrid Differential Evolution with Triangular Distribution Factor for Adaptive Crossover Abstract
- 3. 3www.icgst.com N. Venkata Ramana has received M. Tech from, S.V.University, India in 1991 and Ph.D. in Electrical Engineering from Jawaharlal Nehru Technological University (J.N.T.U), India in Jan’ 2005. His main research interest includes Power System Modeling and Control.He is currently Professor at J.N.T.U. College of Engineering, Jagityal, Karimnagar District, A.P., India J.N.T.U. http://www.jntuh.ac.in/new/
- 4. 4www.icgst.com K.Ramesh Reddy has received M.Tech from REC Warangal, India in 1989 and Ph.D in Electrical Engineering from Sri Venkateswara University, India in 2004. His main research includes Power system modeling and control, Power quality. He is currently Dean and Head of EEE at G.Narayanamma Institute of Technology and Science, Hyderabad, India G.Narayanamma Institute of Technology and Science http://www.gnits.ac.in/
- 5. 5www.icgst.com N.Malla Reddy has received B.Tech from Sri Venkateswara University, Tirupathi in 1999 and M.Tech from J.N.T.U, Hyderabad in 2005 and pursuing Ph.D in electrical engineering from J.N.T.U, Hyderabad. His main research includes power system operation and control. He is presently working as Associate Professor of EEE Department, G.Narayanamma Institute of Technology and Science, Hyderabad, India. G.Narayanamma Institute of Technology and Science http://www.gnits.ac.in/
- 6. 6
- 7. 6