Optimal Siting of Distributed Generators in a Distribution Network using Arti...IJECEIAES
Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.
Optimal Siting of Distributed Generators in a Distribution Network using Arti...IJECEIAES
Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
Application of the Least Square Support Vector Machine for point-to-point for...IJECEIAES
In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability.
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.
Network Reconfiguration of Distribution System for Loss Reduction Using GWO A...IJECEIAES
This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Cluster Computing Environment for On - line Static Security Assessment of lar...IDES Editor
The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.
Economical and Reliable Expansion Alternative of Composite Power System under...IJECEIAES
The paper intends to select the most economical and reliable expansion alternative of a composite power system to meet the expected future load growth. In order to reduce time computational quantity, a heuristic algorithm is adopted for composite power system reliability evaluation is proposed. The proposed algorithm is based on Monte-Carlo simulation method. The reliability indices are estimated for system base case and for the case of adding peaking generation units. The least cost reserve margin for the addition of five 20MW generating units sequentially is determined. Using the proposed algorithm an increment comparison approach used to illustrate the effect of the added units on the interruption and on the annual net gain costs. A flow chart introduced to explain the basic methodology to have an adequate assessment of a power system using Monte Carlo Simulation. The IEEE RTS (24-bus, 38-line) and The Jordanian Electrical Power System (46bus and 92-line) were examined to illustrate how to make decisions in power system planning and expansions.
Short term load forecasting system based on support vector kernel methodsijcsit
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
VOLTAGE PROFILE IMPROVEMENT AND LINE LOSSES REDUCTION USING DG USING GSA AND ...Journal For Research
In recent years, the power industry has experienced significant changes on the power distribution systems primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. The distribution power system is generally designed for radial power flow, but with the introduction of DG, power flow becomes bidirectional. Therefore this thesis focuses on testing various indices and using effective techniques for the optimal placement and sizing of the DG unit by minimizing power losses and voltage deviation. A 14-bus radial distribution system has been taken as the test system. The feasibility of the work lies on the fast execution of the programs as it would be equipped with the real time operation of the distribution system and it is seen that execution of the DG placement is quite fast and feasible with the optimization techniques used in this work.
An Application of Genetic Programming for Power System Planning and OperationIDES Editor
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.
Single core configurations of saturated core fault current limiter performanc...IJECEIAES
Economic growth with industrialization and urbanization lead to an extensive increase in power demand. It forced the utilities to add power generating facilities to cause the necessary demand-generation balance. The bulk power generating stations, mostly interconnected, with the penetration of distributed generation result in an enormous rise in the fault level of power networks. It necessitates for electrical utilities to control the fault current so that the existing switchgear can continue its services without upgradation or replacement for reliable supply. The deployment of fault current limiter (FCL) at the distribution and transmission networks has been under investigation as a potential solution to the problem. A saturated core fault current limiter (SCFCL) technology is a smart, scalable, efficient, reliable, and commercially viable option to manage fault levels in existing and future MV/HV supply systems. This paper presents the comparative performance analysis of two single-core SCFCL topologies impressed with different core saturations. It has demonstrated that the single AC winding configuration needs more bias power for affecting the same current limiting performance with an acceptable steady-state voltage drop contribution. The fault state impedance has a transient nature, and the optimum bias selection is a critical design parameter in realizing the SCFCL applications.
Multi-objective optimal placement of distributed generations for dynamic loadsIJECEIAES
Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High R/X ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.
Big Data Framework for Predictive Risk Assessment of Weather Impacts on Elect...Power System Operation
Loss of electric power leads to major economic, social, and environmental impacts. It is estimated that the Annual economic impacts from weather-related electric grid outages in the U.S. result in as high as $150 billion. Due to the high level of environmental exposure of the electric utility overhead infrastructure, the most dominant cause of electricity outages is weather impact. More than 70% of electric power outages are caused by weather, either directly (e.g., lightning strikes to the equipment, trees coming in contact with lines under high wind speeds), or indirectly due to weather-caused increases in equipment deterioration rates or overloading (e.g. insulation deterioration, line overloading due to high temperature causing high demand). This paper illustrates how the impact of severe weather can be significantly reduced, and in some cases even eliminated, by accurate prediction of where faults may occur and what equipment may be vulnerable. With this predicted assessment of network vulnerabilities and expected exposure, adequate mitigation approaches can be deployed.
To solve the problem, variety of approaches have been deployed but none seem to be addressing the problem comprehensively. We are introducing a predictive approach that uses Big Data analytics based on machine learning using variety of utility measurements and data not coming from utility infrastructure, such as weather, lightning, vegetation, and geographical data, which also comes in great volumes, is necessary. The goal of this paper is to provide a comprehensive description of the use of Big Data to assess weather impacts on utility assets. In the study reported in this paper a unified data framework that enables collection and spatiotemporal correlation of variety of data sets is developed. Different prediction algorithms based on linear and logistic regression are used. The spatial and temporal dependencies between components and events in the smart grid are leveraged for the high accuracy of the prediction algorithms, and its capability to deal with missing and bad data. The study approach is tested on following applications related to weather impacts on electric networks: 1) Outage prediction in Transmission, 2) Transmission Line Insulation Coordination, 3) Distribution Vegetation Management, 4) Distribution Transformer Outage Prediction, and 5) Solar Generation Forecast. The algorithms shows high accuracy of prediction for all applications of interest.
PVPF tool: an automated web application for real-time photovoltaic power fore...IJECEIAES
In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records’ time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.
Application of the Least Square Support Vector Machine for point-to-point for...IJECEIAES
In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability.
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.
Network Reconfiguration of Distribution System for Loss Reduction Using GWO A...IJECEIAES
This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.
Optimal design of adaptive power scheduling using modified ant colony optimi...IJECEIAES
For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.
Cluster Computing Environment for On - line Static Security Assessment of lar...IDES Editor
The increased size of modern power systems
demand faster and accurate means for the security assessment,
so that the decisions for reliable and secure operation planning
could be drawn in a systematic manner. Large computational
overhead is the major impediment in preventing the power
system security assessment (PSSA) from on-line use. To
mitigate this problem, this paper proposes, a cluster computing
based architecture for power system static security assessment,
utilizing the tools in the open source domain. A variant of the
master/slave pattern is used for deploying the cluster of
workstations (COW), which act as the computational engine
for the on-line PSSA. The security assessment is performed
utilizing the developed composite security index that can
accurately differentiate the secure and non-secure cases and
has been defined as a function of bus voltage and line flow
limit violations. Due to the inherent parallel structure of
security assessment algorithm and to exploit the potential of
distributed computing, domain decomposition is employed for
parallelizing the sequential algorithm. Extensive
experimentations were carried out on IEEE 57 bus and IEEE
145-bus 50 machine standard test systems for demonstrating
the validity of the proposed architecture.
Economical and Reliable Expansion Alternative of Composite Power System under...IJECEIAES
The paper intends to select the most economical and reliable expansion alternative of a composite power system to meet the expected future load growth. In order to reduce time computational quantity, a heuristic algorithm is adopted for composite power system reliability evaluation is proposed. The proposed algorithm is based on Monte-Carlo simulation method. The reliability indices are estimated for system base case and for the case of adding peaking generation units. The least cost reserve margin for the addition of five 20MW generating units sequentially is determined. Using the proposed algorithm an increment comparison approach used to illustrate the effect of the added units on the interruption and on the annual net gain costs. A flow chart introduced to explain the basic methodology to have an adequate assessment of a power system using Monte Carlo Simulation. The IEEE RTS (24-bus, 38-line) and The Jordanian Electrical Power System (46bus and 92-line) were examined to illustrate how to make decisions in power system planning and expansions.
Short term load forecasting system based on support vector kernel methodsijcsit
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
VOLTAGE PROFILE IMPROVEMENT AND LINE LOSSES REDUCTION USING DG USING GSA AND ...Journal For Research
In recent years, the power industry has experienced significant changes on the power distribution systems primarily due to the implementation of smart-grid technology and the incremental implementation of distributed generation. Distributed Generation (DG) is simply defined as the decentralization of power plants by placing smaller generating units closer to the point of consumption, traditionally ten mega-watts or smaller. The distribution power system is generally designed for radial power flow, but with the introduction of DG, power flow becomes bidirectional. Therefore this thesis focuses on testing various indices and using effective techniques for the optimal placement and sizing of the DG unit by minimizing power losses and voltage deviation. A 14-bus radial distribution system has been taken as the test system. The feasibility of the work lies on the fast execution of the programs as it would be equipped with the real time operation of the distribution system and it is seen that execution of the DG placement is quite fast and feasible with the optimization techniques used in this work.
An Application of Genetic Programming for Power System Planning and OperationIDES Editor
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.
Single core configurations of saturated core fault current limiter performanc...IJECEIAES
Economic growth with industrialization and urbanization lead to an extensive increase in power demand. It forced the utilities to add power generating facilities to cause the necessary demand-generation balance. The bulk power generating stations, mostly interconnected, with the penetration of distributed generation result in an enormous rise in the fault level of power networks. It necessitates for electrical utilities to control the fault current so that the existing switchgear can continue its services without upgradation or replacement for reliable supply. The deployment of fault current limiter (FCL) at the distribution and transmission networks has been under investigation as a potential solution to the problem. A saturated core fault current limiter (SCFCL) technology is a smart, scalable, efficient, reliable, and commercially viable option to manage fault levels in existing and future MV/HV supply systems. This paper presents the comparative performance analysis of two single-core SCFCL topologies impressed with different core saturations. It has demonstrated that the single AC winding configuration needs more bias power for affecting the same current limiting performance with an acceptable steady-state voltage drop contribution. The fault state impedance has a transient nature, and the optimum bias selection is a critical design parameter in realizing the SCFCL applications.
Multi-objective optimal placement of distributed generations for dynamic loadsIJECEIAES
Large amount of active power losses and low voltage profile are the two major issues concerning the integration of distributed generations with existing power system networks. High R/X ratio and long distance of radial network further aggravates the issues. Optimal placement of distributed generators can address these issues significantly by alleviating active power losses and ameliorating voltage profile in a cost effective manner. In this research, multi-objective optimal placement problem is decomposed into minimization of total active power losses, maximization of bus voltage profile enhancement and minimization of total generation cost of a power system network for static and dynamic load characteristics. Optimum utilization factor for installed generators and available loads is scaled by the analysis of yearly load-demand curve of a network. The developed algorithm of N-bus system is implemented in IEEE-14 bus standard test system to demonstrate the efficacy of the proposed method in different loading conditions.
Big Data Framework for Predictive Risk Assessment of Weather Impacts on Elect...Power System Operation
Loss of electric power leads to major economic, social, and environmental impacts. It is estimated that the Annual economic impacts from weather-related electric grid outages in the U.S. result in as high as $150 billion. Due to the high level of environmental exposure of the electric utility overhead infrastructure, the most dominant cause of electricity outages is weather impact. More than 70% of electric power outages are caused by weather, either directly (e.g., lightning strikes to the equipment, trees coming in contact with lines under high wind speeds), or indirectly due to weather-caused increases in equipment deterioration rates or overloading (e.g. insulation deterioration, line overloading due to high temperature causing high demand). This paper illustrates how the impact of severe weather can be significantly reduced, and in some cases even eliminated, by accurate prediction of where faults may occur and what equipment may be vulnerable. With this predicted assessment of network vulnerabilities and expected exposure, adequate mitigation approaches can be deployed.
To solve the problem, variety of approaches have been deployed but none seem to be addressing the problem comprehensively. We are introducing a predictive approach that uses Big Data analytics based on machine learning using variety of utility measurements and data not coming from utility infrastructure, such as weather, lightning, vegetation, and geographical data, which also comes in great volumes, is necessary. The goal of this paper is to provide a comprehensive description of the use of Big Data to assess weather impacts on utility assets. In the study reported in this paper a unified data framework that enables collection and spatiotemporal correlation of variety of data sets is developed. Different prediction algorithms based on linear and logistic regression are used. The spatial and temporal dependencies between components and events in the smart grid are leveraged for the high accuracy of the prediction algorithms, and its capability to deal with missing and bad data. The study approach is tested on following applications related to weather impacts on electric networks: 1) Outage prediction in Transmission, 2) Transmission Line Insulation Coordination, 3) Distribution Vegetation Management, 4) Distribution Transformer Outage Prediction, and 5) Solar Generation Forecast. The algorithms shows high accuracy of prediction for all applications of interest.
Impacto de la crisis en el modelo de gestión de activosibaiurra
Artículo de Verónica Ruiz e Ibai Urra
"Impacto de la crisis en el modelo de gestión de activos"
Los autores sostienen la instauración de un nuevo paradigma económico
fi nanciero donde la liquidez y el riesgo de crédito van a
ser un elemento central.
Publicado en el 1ª Trimestre del año 2009
Bullet Express & Logistics on globaalset kiirkulleri ja projektipõhiseid logistilisi erilahendusi
pakkuv ettevõte. Teostame rahvusvahelist kaubavedu ja eriteenuseid üle maailma
ning meie fookus on suunatud tarnekindlusele. Meie teenused on kättesaadavad
ööpäevaringselt erinevate lennu-,maantee- ja meretranspordi lahendustena.
Study on the performance indicators for smart grids: a comprehensive reviewTELKOMNIKA JOURNAL
This paper presents a detailed review on performance indicators for smart grid (SG) such as voltage stability enhancement, reliability evaluation, vulnerability assessment, Supervisory Control and Data Acquisition (SCADA) and communication systems. Smart grids reliability assessment can be performed by analytically or by simulation. Analytical method utilizes the load point assessment techniques, whereas the simulation technique uses the Monte Carlo simulation (MCS) technique. The reliability index evaluations will consider the presence or absence of energy storage elements using the simulation technologies such as MCS, and the analytical methods such as systems average interruption frequency index (SAIFI), and other load point indices. This paper also presents the difference between SCADA and substation automation, and the fact that substation automation, though it uses the basic concepts of SCADA, is far more advanced in nature.
Short term residential load forecasting using long short-term memory recurre...IJECEIAES
Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Contingency plans based on N - 1 and N - 2 contingencies are already very much used by utilities . Artificial intelligent methods are new trends for analysing the contingency scenario along with state of art congestion management. This gives extra backup and b oost to reliable operation under contingent scenario of power system. This paper envisages the summary of all those efforts. This paper will help utilities to put more thinking in terms of recent developments in fast and intelligent computing methods. The paper highlights classical research and modern trends in contingency analysis such as hybrid artificial intelligent methods. Steady state stability assessment of a power system pursues a twofold objective:first to appraise the system's capability to withs tand major contingencies,and second to suggest remedial actions,i.e. means to enhance this capability,whenever needed. The first objective is the concern of analysis,the second is a matter of control.
Risk assessment of power system transient instability incorporating renewabl...IJECEIAES
Transient stability affected by renewable energy sources integration due to reductions of system inertia and uncertainties associated with the expected generation. The ability to manage relation between the available big data and transient stability assessment (TSA) enables fast and accurate monitoring of TSA to prepare the required actions for secure operation. This work aims to build a predictive model using Gaussian process regression for online TSA utilizing selected features. The critical fault clearing time (CCT) is used as TSA index. The selected features map the system dynamics to reduce the burden of data collection and the computation time. The required data were collected offline from power flow calculations at different operating conditions. Therefore, CCT was calculated using electromagnetic transient simulation at each operating point by applying self-clearance three phase short circuit at prespecified locations. The features selection was implemented using the neighborhood component analysis, the Minimum Redundancy Maximum Relevance algorithm, and K-means clustering algorithm. The vulnerability of selected features tends to result great variation on the best features from the three methods. Hybrid collection of the best common features was used to enhance the TSA by refining the final selected features. The proposed model was investigated over 66-bus system.
Medium term load demand forecast of Kano zone using neural network algorithmsTELKOMNIKA JOURNAL
Electricity load forecasting refers to projection of future load requirements of an area or region or country through appropriate use of historical load data. One of several challenges faced by the Nigerian power distribution sectors is the overloaded power distribution network which leads to poor voltage distribution and frequent power outages. Accurate load demand forecasting is a key in addressing this challenge. This paper presents a comparison of generalized regression neural network (GRNN), feed-forward neural network (FFNN) and radial basis function neural network for medium term load demand estimation. Experimental data from Kano electricity distribution company (KEDCO) were used in validating the models. The simulation results indicated that the neural network models yielded promising results having achieved a mean absolute percentage error (MAPE) of less than 10% in all the considered scenarios. The generalization capability of FFNN is slightly better than that of RBFNN and GRNN model. The models could serve as a valuable and promising tool for the forecasting of the load demand.
Neural computing is now one of the most promising technologies in all fields of engineering,
resulting in the development of a number of Artificial Neural Networks (ANN). Double circuit transmission lines
are being employed in the distribution of power to consumers and have become more widespread than single
transmission line, as they increase the electric power transmission capacity and the reliability of an electrical
system. Losses along transmission lines occur due to faults. Possible faults on the transmission line were
predicted using Artificial Neutral Network. In this work, the simulation of fault on a 132kV double circuit
transmission lines using MATLAB was undertaken. Parameters considered during the simulation were the input
of the network which is the fault current value at each fault location while the output of the network is the fault
location. The efficiency of the neural network was tested and verified. This approach provided satisfactory
results with accuracy of 95% or higher.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
INTELLIGENT ELECTRICAL MULTI OUTLETS CONTROLLED AND ACTIVATED BY A DATA MININ...ijscai
In the proposed paper are discussed results of an industry project concerning energy management in building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets placed into a building and having defined priorities. The priority rules are grouped into two level: the first level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm, together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three for three outlets and able to control load matching with defined thresholds. The goal of the paper is to provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the electrical network in a building. Finally in the paper are analyzed the correlation between global active power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction and the correlation analyses provide information about load balancing, possible electrical faults and energy cost optimization.
Intelligent Electrical Multi Outlets Controlled and Activated by a Data Minin...IJSCAI Journal
In the proposed paper are discussed results of an industry project concerning energy management in
building. Specifically the work analyses the improvement of electrical outlets controlled and activated by a
logic unit and a data mining engine. The engine executes a Long Short-Terms Memory (LSTM) neural
network algorithm able to control, to activate and to disable electrical loads connected to multiple outlets
placed into a building and having defined priorities. The priority rules are grouped into two level: the first
level is related to the outlet, the second one concerns the loads connected to a single outlet. This algorithm,
together with the prediction processing of the logic unit connected to all the outlets, is suitable for alerting
management for cases of threshold overcoming. In this direction is proposed a flow chart applied on three
for three outlets and able to control load matching with defined thresholds. The goal of the paper is to
provide the reading keys of the data mining outputs useful for the energy management and diagnostic of the
electrical network in a building. Finally in the paper are analyzed the correlation between global active
power, global reactive power and energy absorption of loads of the three intelligent outlet. The prediction
and the correlation analyses provide information about load balancing, possible electrical faults and energy
cost optimization.
Optimization scheme for intelligent master controller with collaboratives ene...IAESIJAI
This paper explores the use of deep learning to optimize the performance of a peer-to-peer energy system with an intelligent master controller. The goal addresses inefficiencies caused by energy seasonality by predicting hourly power consumption through a deep learning algorithm. The intelligent master controller was designed to manage the collaborative energy system, and the deep learning technique was employed as an optimization scheme to forecast power system performance for more efficient utilization. The deep learning algorithm was trained using dataset from American electric power, where consumer load data serves as input, and forecasted power serves as output. The forecasted power was then used as input to the intelligent master controller, which determines suitable power supply for generation and storage based on the predicted demand. The experiment results show promising accuracy with a root mean square error (RMSE) of 0.1819 for hourly energy consumption averaged over a year, 0.2419 for hourly energy consumption averaged over a month, 0.0662 for hourly energy consumption averaged per day, and 0.0217 for hourly energy consumption. These findings demonstrate that the system is well-trained and capable of accurately predicting the energy required by the intelligent master controller, thus enhancing the overall performance of the peer-to-peer energy system.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Development of methods for managing energy consumption and energy efficiency...IJECEIAES
The work aims to analyze and examine renewable energy sources (RES) to develop interconnected energy efficiency and energy consumption management system by integrating the software-defined machine-tomachine (M2M) communication. The article’s objectives include analysis of using RES as alternative raw materials for electricity production, the study of intelligent technologies for integrating RES into monitoring and control systems, research of devices and methods for monitoring energy production and consumption, analysis of sensor application for automation of control systems in the energy sector, a study of data transmission and information processing rates. The study results showed that the data transfer rate was delayed by 6 seconds to process 1,000 MB of information. It has been proven that wind energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy. It is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not fully provide energy to a large city, on the basis of which it is necessary to additionally use other energy sources. Three different types of power generation facilities were examined and compared. Wind farms were found to have the highest potential for electricity generation, amounting to 1,600-1,700 kW.
The International Journal of Engineering and Science (The IJES)theijes
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.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability
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Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
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zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
N020698101
1. International Journal of Engineering Inventions
e-ISSN: 2278-7461, p-ISSN: 2319-6491
Volume 2, Issue 6 (April 2013) PP: 98-101
www.ijeijournal.com Page | 98
Electrical Energy Management and Load Forecasting in a
Smart Grid
Eisa Bashier M. Tayeb1*
, A. Taifour Ali2
, Ahmed A. Emam3
1,2
School of Electrical and Nuclear Engineering; College of Engineering;
Sudan University of Science &Technology; SUDAN
3
Karary University, College of Engineering, SUDAN
Abstract: Artificial Neural Networks (ANN) has been applied to many fields in recent years. Among them, the
neural networks with Back Propagation algorithm appear to be most popular and have been widely used in
applications such as forecasting and classification problems. This paper presents a study of short-term load
forecasting using Artificial Neural Networks (ANNs) and applied it to the Sudan National Electric Company
NEC. Neuroshell2 software was used to provide back-propagation neural networks. ANN model used to forecast
the load with the performance error as a measure characteristic. The error obtained by comparing the
forecasted load data with actual load data.
Keywords: Demand Forecasting, Energy Management, Generation Dispatch, Neural Networks, Smart Grid,
I. INTRODUCTION
The Smart energy generation as a concept can be defined as the matching electricity production with
demand using multiple identical generators which can start, stop and operate efficiently at chosen load,
independently of the others, making them suitable for base load and peak power generation. Electricity
produced and delivered to customers through generation, transmission and distribution systems. Reliable electric
power systems serve customer loads without interruptions in supply voltage. Generation facilities must produce
enough power to meet customer demand. Matching supply and demand called load balancing and is essential for
a stable and reliable supply of electricity. Operators of power transmission systems are charged with the
balancing task, matching the power output of all the generators to the load of their electrical grid. The load
balancing task has become much more challenging as increasingly intermittent and variable generation
resources such as renewable energy when we need to add it to the grid through the smart grid concept.
One element of the smart grid that related to the efficient production of electricity has to do with
condition monitoring and assessment [1]. In condition monitoring and assessment, sensors and communications
are used to monitor plant performance and to correlate that performance to historic data, theoretical models and
comparable plant performance. The concept of the expanded use of sensor, communications and computational
ability is part of the smart grid concept. The improvement in the power delivery system (electric transmission
and distribution) through the use of smart grid technologies can provide significant opportunities to improve
energy efficiency in the electric power.
Neural networks are often used for statistical analysis and data modeling, in which their role is
perceived as an alternative to standard nonlinear regression or cluster analysis techniques. Thus, they are
typically used in problems that may be couched in terms of classification, or forecasting. Some examples
include image and speech recognition, textual character recognition, and domains of human expertise such as
medical diagnosis, geological survey for oil and mining, and financial market indicator prediction [2-8].
The introduction of the paper should explain the nature of the problem, previous work, purpose, and the
contribution of the paper. The contents of each section may be provided to understand easily about the paper.
II. GENERATION DISPATCH AND DEMAND FORECAST
The Demand Forecast and the Generation Expansion Plan form the basic input data for planning the activity.
The indicated levels of demand and generation are important to consider the extreme power transfer cases to
ensure that the infrastructure is adequate to accommodate any credible operational scenario within the studied
cases.
a. Generation Dispatch and Scheduling
As power resources become more distributed, systems more conducive to demand-response, and
generation more intermittent, efficient and robust system operation will depend critically on the ability of new
dispatch methods to provide a better predictive, forward-looking and holistic view of system conditions and
generation patterns. Automatic Generation Control (AGC), Economic Dispatch (ED) and Reserve Monitoring
(RM) are among techniques used today for the generation dispatch and scheduling.
2. Electrical Energy Management and Load Forecasting in A Smart Grid
www.ijeijournal.com Page | 99
The AGC is related to Area Control Error (ACE) which defines as a combination of the deviation of
frequency from nominal, and the difference between the actual flow out of an area and the scheduled flow.
Ideally the ACE should always be zero and because the load is constantly changing, each utility must constantly
change its generation to chase the ACE. The Major objectives of AGC is to regulate the system frequency to a
specified nominal value, maintain the net interchange power across the boundaries of the operation area at the
scheduled value and to adjust each unit's generation at the most economic level. Automatic generation control
(AGC) is used to automatically change generation to keep the ACE close to zero.
b. Demand Forecast
Currently the demand or load forecasting is become very essential for reliable power system operations
and market system operations. It determines the amount of system load against which real-time dispatch and
day-ahead scheduling functions need to balance in different time horizon.
The Demand forecasting technique typically used three different time frames:-
1. Short-Term load forecast (STLF):- Next 60-120 minutes by 5-minute increments.
2. Mid-Term load forecast (MTLF): Next n days (n can be any value from 3-31), in intervals of one hour or less
(e.g., 60, 30, 20, 15 minute intervals).
3. Long-Term load forecast (LTLF): Next n years (n can be any value from 2-10), broken into one month
increments. The LTLF forecast is provided for three scenarios (pessimistic growth, expected growth, and
optimistic growth). Demand forecasting play an increasingly important role in the restructured electricity market
and it is challenge for smart grid environment due to its impacts on market prices and market participants.
In general, demand forecasting is a challenging subject in view of complicated features of load and effective
data gathering [9]. With Demand Response being one of the few near-term options for large-scale reduction of
greenhouse gases, and fits strategically with the drive toward clean energy technology such as wind and solar,
advanced demand forecasting should effectively take the demand response features/characteristics and the
uncertainty of intermittent renewable generation into account. Many load forecasting techniques including
extrapolations, auto regressive model, similar day methods, fuzzy logic, and artificial neural networks have been
used.
III. DESIGN OF NEURAL NETWORK FOR DAY LOAD FORECASTING
Neural networks are applied widely for solving different problems which in general are difficult to
solve by humans or conventional computational algorithms. In order to design a neural network for addressing
the one day load forecasting problem, several different training data and training time are studied. As a pre-
processing step the training and the testing data generated from the Load Dispatch Centre of National Electrical
Corporation (NEC) Sudan, base from years 2008 and 2009 are used for training and implemented in the Neural
Network (see Appendix :).
Fig 1 Neural Network Architecture for Load Forecasting
Selecting the right size of the network training data is not only important for obtaining good results but
also significantly impacts the generalization and representational capabilities of the trained network. The Neural
Network architecture used is shown in Fig 1; which has one layer for the inputs, two hidden layer and one
output layer. A back-propagation neural network is used for learning the neural. The network has one output to
determine the load value at specific time during the day.
Input Layer (Hours)
1
2
1
3
24
Two
Hidden
Layers
Load
forecasting
n Days
3. Electrical Energy Management and Load Forecasting in A Smart Grid
www.ijeijournal.com Page | 100
Fig 2 One Day Load Forecasting and Error
In order to determine the size of training data, error and performance of network are considered as two
main measures factors. Day load data (Performance and Error plots) shown in Fig 2 is used to train the network.
5 and 10days load data are implemented. The performance of the selected training data size and errors plots
associated with this architecture are given in Fig 3&4. The ten days load data gives the best result of minimum
error as shown in Fig 3.
Fig 3 Mid-Term load forecast (5Days Load Forecasting)
Fig 4 Mid-Term load forecast (10Days Loads Forecasting)
-200
-100
0
100
200
300
400
500
600
700
800
0 40 80 120 160 200 240
MW
Hours
Network
Actual
Error
4. Electrical Energy Management and Load Forecasting in A Smart Grid
www.ijeijournal.com Page | 101
IV. CONCLUSION
Neural networks provide a reliable and an attractive approach for the load forecasting and it was able to predict
the nonlinear relation exist between the historical data. The results obtained demonstrate that in general the
performance of the back-propagation neural network (BP) architecture was highly satisfactory in producing the
expected load.
REFERENCES
[1] Clark W. Gelling “Smart Grid: Enabling Energy Efficiency and Demand Response” 2009 Fairmont press, Inc.
[2] Xun Liang, “Impacts of Internet Stock News on Stock Markets Based on Neural Networks” Springer-Verlag Berlin Heidelberg ;
LNCS 3497, 2005, pp. 897–903,
[3] Harrald, P. G., Kamstra, M. “Evolving Artificial Neural Networks to Combine Financial Forecasts. IEEE Trans on Evolutionary
Computation, 1, 1997, pp 40-52.
[4] Liang, X, Xia, S. “Methods of Training and Constructing Multilayer Perceptrons with Arbitrary Pattern Sets” Int Journal of Neural
Systems, 6 (1995) 233-247.
[5] Mohamad Adnan Al-Alaoui, Lina Al-Kanj, Jimmy Azar, and Elias Yaacoub “Speech Recognition using Artificial Neural Networks
and Hidden Markov Models” IEEE Multidisplinary Engineering Education Magazine, Vol. 3, No. 3, 2008, pp 77-86.
[6] Joe Tebelskis “Speech Recognition using Neural Networks” PhD thesis, School of Computer Science Carnegie Mellon University,
1995.
[7] Mahesh P.Gaikwad “ Self Medical Diagnosis Using Artificial Neural Network” Int.J.Computer Technology & Applications,Vol 3
(6), pp 2006-2013.
[8] Dolly Gupta, Gour Sundar Mitra Thakur, Abhishek “Detection of Gallbladder Stone Using Learning Vector Quantization Neural
Network” International Journal of Computer Science and Information Technologies, Vol. 3 (3), 2012, pp 3934-3937.
[9] G.A. Adepoju, S.O.A. Ogunjuyigbe, and K.O. Alawode “Application of Neural Network to Load Forecasting in Nigerian Electrical
Power System” Volume 8. Number 1. May 2007, pp 68-72.
Appendix: Online Forecasting Load Value Sudan NEC Khartoum Area