IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power. The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.
Optimal tuning of a wind plant energy production based on improved grey wolf ...journalBEEI
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
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 study of reducing the cost of investment in wind energy based on the cat ...TELKOMNIKA JOURNAL
Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This paper presented the study, development and implementation of the maximum power point of a photovoltaic energy generator adapted by elevator converter and controlled by a maximum power point command. In order to improve photovoltaic system performance and to force the photovoltaic generator to operate at its maximum power point, the idea of the context of this paper deals with the exploitation of the technique of the artificial intelligence mechanism (neural network) certainly based on the three parts of the photovoltaic system (photovoltaic module inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach the optimal operating point regardless of variations in climatic conditions, the use of a neuron network based disturbance and observation algorithm (P&O) is put into service of the system given its reliability, its simplicity and view that at any time it can follow the desired maximum power. The entire system is implemented in the Matlab / Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P&O.
Optimal tuning of a wind plant energy production based on improved grey wolf ...journalBEEI
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
Multi-objective based economic environmental dispatch with stochastic solar-w...IJECEIAES
This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
In this paper, the artificial neural network (ANN) has been utilized for rotating machinery faults detection and classification. First, experiments were performed to measure the lateral vibration signals of laboratory test rigs for rotor-disk-blade when the blades are defective. A rotor-disk-blade system with 6 regular blades and 5 blades with various defects was constructed. Second, the ANN was applied to classify the different x- and y-axis lateral vibrations due to different blade faults. The results based on training and testing with different data samples of the fault types indicate that the ANN is robust and can effectively identify and distinguish different blade faults caused by lateral vibrations in a rotor. As compared to the literature, the present paper presents a novel work of identifying and classifying various rotating blade faults commonly encountered in rotating machines using ANN. Experimental data of lateral vibrations of the rotor-disk-blade system in both x- and y-directions are used for the training and testing of the network.
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 study of reducing the cost of investment in wind energy based on the cat ...TELKOMNIKA JOURNAL
Wind and solar are the most important source of renewable energy for power supply in remote locations involves serious consideration of the reliability of these unconventional energy sources. We apply the cat swarm meta-heuristic optimization method to solve the problem of wind power system design optimization. The electrical power components of the system are characterized by their cost, capacity and reliability. This study seeks to optimize the design of parallel power systems in which multiple choices of generators wind, transformers and lines. Our plan has the advantage of allowing electrical components with different parameters to be customized in electrical power systems. The UMGF method is applied to allow rapid reliability estimation. A computer program is developed for the UMGF application and CS algorithm. An example is provided to explain.
A modified particle swarm optimization algorithm to enhance MPPT in the PV ar...IJECEIAES
Due to the growing demand for electrical power, the researchers are trying to fulfill this demand by considering different ways of renewable energy resource as existing energy resources failed to do so. The solar energy from the sun is freely available, and by using photovoltaic (PV) cell power can be generated. However, it depends on rays fall on the PV cell, climatic condition. Thus, to enhance the efficiency of the photovoltaic (PV) systems, maximum power point tracking (MPPT) of the solar arrays is needed. The output of solar arrays mainly depends on solar irradiance and temperature. The mismatch phenomenon takes place due to partial shade, and it causes to the power output, which brings the incorrect operation of traditional MPP tracker. In this shaded condition, PV array exhibits multiple extreme points. In general, under this scenario, the MPPT approaches fail to judge the MPP, and it leads to low efficiency. The conventional approaches of PSO based algorithms can able to track the MPP under shading condition. However, the optimization process leads to issues in tracking speed. Thus, there a need for an efficient MPPT system which can track MPPT effectively in shaded condition? Hence, the proposed manuscript presents a modified particle swarm optimization (PSO) algorithm is introduced to enhance the tracking speed as well as performance. The outcomes of the proposed system are compared with the traditional PSO system and are found that the tracking speed of MPP, accuracy, and efficiency is improved.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
International Journal of Engineering Research and DevelopmentIJERD Editor
• Electrical, Electronics and Computer Engineering,
• Information Engineering and Technology,
• Mechanical, Industrial and Manufacturing Engineering,
• Automation and Mechatronics Engineering,
• Material and Chemical Engineering,
• Civil and Architecture Engineering,
• Biotechnology and Bio Engineering,
• Environmental Engineering,
• Petroleum and Mining Engineering,
• Marine and Agriculture engineering,
• Aerospace Engineering
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
OPTIMAL TRAJECTORY OF ROBOT MANIPULATOR FOR ENERGY MINIMIZATION WITH QUARTIC ...cscpconf
In this paper, a different way to find the trajectory of the robot manipulators for energyoptimization is presented. In our method, the joint angles of the manipulator are set as quadratic polynomial functions. Then, with them taken into the variational function of energy consumption, Finite Element Modelling is employed to optimize the unknown parameters of the fourth order joint angles
Modelling and Prediction of 150KW PV Array System in Northern India using Art...inventionjournals
In this research work modeling of solar power using feed forward back propagation (FFBP) artificial neural network. In three different ways inputs are given to FFBP to model photovoltaic module power and generated power is approximated .Six month’s data is taken i.e. from August 2015 to January 2016.Generated power is obtained at the end. All three show good modeling performance. However second input method shows the better results
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
modeling and characterization of mems electrostatic energy harvesterINFOGAIN PUBLICATION
In low-power wireless electronic devices, Energy harvesting generators have received increasing research interest in recent years. This paper describes the design and analysis of electrostatic transduction based MEMS energy harvester. Due to the benefit of a folded beam configuration that can be displace large dimensions and compliant in desired direction and stiffer in orthogonal direction. Since, large displacement of proof mass of energy harvester renders the enhance performance. Hence, the folded beam configuration of harvest has been modeled and designed and optimized the dimension of geometry. FEM simulations using COMSOL were conducted to evaluate the natural frequency and mode shape of the system and compared results with that of analytically calculated values. Spice circuit of harvester has been modeled and performed simulation to evaluate the output power of the harvester in LTSPICE. Parameter analysis was conducted to determine the optimal load and optimal output power.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping To...journalBEEI
This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ksand damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.
SIMULATION OF A MATHEMATICAL MODEL OF A WIND TURBINE Mellah Hacene
Abstract
This paper presents a mathematical model of a wind turbine and its simulation. This is one of the main resources available to
the island system (Grid-Off system).
Keywords: wind turbine, island system, Grid-Off system, renewable energy source.
1 Introduction to wind turbine
A wind turbine is basically a converter, or in other words a device that transforms one type of energy into
another. In this case, it is the transformation of mechanical energy into electrical energy.
The source of mechanical energy is the flow (flow) of air, which acts on the turbine blades. The blades are
located on a shaft which is coupled to a permanent magnet (magnet). The magnets are a rotating part, which is
named the rotor. The stator consists of a coil (coils) of wound copper conductor. Due to the changing magnetic
field (PM - permanent magnets), an electrical voltage is induced at the terminals (terminals) of the coil / coils. In
essence, it is a synchronous generator, since the variable electric field is coupled (synchronized) with the speed
of the changing and magnetic fields. [1-5]
A schematic block diagram of a wind turbine as a synchronous generator is shown in Fig. 1.
Economic and Emission Dispatch using Whale Optimization Algorithm (WOA) IJECEIAES
This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Artificial Neural Network Applied to Estimate the Power Output of BIPV SystemsIOSRjournaljce
This paper presents an artificial neural network (ANN) model to estimate the power generated by integrated photovoltaic systems in buildings - BIPVS. The model has as primordial variables, the solar radiation and the ambient temperature of the site of installation of the photovoltaic generator and integrates secondary variables such as the zenith solar angle and the azimuth solar angle. The artificial neural network consists of three layers of operation that allows to adapt to the behavior of the environmental and electrical variables of the photovoltaic generator to create output variables of electrical power through daily profiles. The neural network was implemented in the software Matlbab™ and it was validated using the actual data of monitoring of a 6 kW BIPV system installed at Universidad de Bogotá Jorge Tadeo Lozano, in Bogotá, Colombia. The results indicate a correlation coefficient of 98% on the output power of the BIPV system between the artificial neural network and the performance data of the solar photovoltaic plant. These results show the reliability of the model for PV systems operating in different climatic conditions and different generation capacities.
Hybrid neural networks in cyber physical system interface control systemsjournalBEEI
The calculation and results of simulation of the magnetic control system for the spacecraft momentum are presented in the paper. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilization by resetting the total momentum with the aid of the system under review. The outcome of a comparative analysis of resource efficiency and energy efficiency are demonstrated in the implementation of the proposed hardware models of controllers on FPGA. The strengths and weaknesses of the programming models are shown. The developed models will allow to be modified and perform more complex operations in the future.
A modified particle swarm optimization algorithm to enhance MPPT in the PV ar...IJECEIAES
Due to the growing demand for electrical power, the researchers are trying to fulfill this demand by considering different ways of renewable energy resource as existing energy resources failed to do so. The solar energy from the sun is freely available, and by using photovoltaic (PV) cell power can be generated. However, it depends on rays fall on the PV cell, climatic condition. Thus, to enhance the efficiency of the photovoltaic (PV) systems, maximum power point tracking (MPPT) of the solar arrays is needed. The output of solar arrays mainly depends on solar irradiance and temperature. The mismatch phenomenon takes place due to partial shade, and it causes to the power output, which brings the incorrect operation of traditional MPP tracker. In this shaded condition, PV array exhibits multiple extreme points. In general, under this scenario, the MPPT approaches fail to judge the MPP, and it leads to low efficiency. The conventional approaches of PSO based algorithms can able to track the MPP under shading condition. However, the optimization process leads to issues in tracking speed. Thus, there a need for an efficient MPPT system which can track MPPT effectively in shaded condition? Hence, the proposed manuscript presents a modified particle swarm optimization (PSO) algorithm is introduced to enhance the tracking speed as well as performance. The outcomes of the proposed system are compared with the traditional PSO system and are found that the tracking speed of MPP, accuracy, and efficiency is improved.
Optimal unit commitment of a power plant using particle swarm optimization ap...IJECEIAES
Economic load dispatch among generating units is very important for any power plant. In this work, the economic load dispatch was made at Egbin Thermal Power plant supplying a total load of 600MW using six generating units. In carrying out this study, transmission losses were assumed to be included into the load supplied. Also, three different combinations in the form of 6, 5- and 4-units commitment were considered. In each case, the total load was optimally dispatched between committed generating units using Particle Swarm Optimization (PSO). Similarly, the generation cost for each generating unit was determined. For case 1, the six generators were committed and the generation cost is 2,100,685.069$/h. For case 2, five generators were committed and the generation cost is 2,520,861.947$/h. For case 3, four generators were committed and the generation cost is 3,150,621.685$/h. From all considered cases, it was found that, the minimum generation cost was achieved when all six generating units were committed and a total of 420,178.878$/h was saved.
International Journal of Engineering Research and DevelopmentIJERD Editor
• Electrical, Electronics and Computer Engineering,
• Information Engineering and Technology,
• Mechanical, Industrial and Manufacturing Engineering,
• Automation and Mechatronics Engineering,
• Material and Chemical Engineering,
• Civil and Architecture Engineering,
• Biotechnology and Bio Engineering,
• Environmental Engineering,
• Petroleum and Mining Engineering,
• Marine and Agriculture engineering,
• Aerospace Engineering
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
OPTIMAL TRAJECTORY OF ROBOT MANIPULATOR FOR ENERGY MINIMIZATION WITH QUARTIC ...cscpconf
In this paper, a different way to find the trajectory of the robot manipulators for energyoptimization is presented. In our method, the joint angles of the manipulator are set as quadratic polynomial functions. Then, with them taken into the variational function of energy consumption, Finite Element Modelling is employed to optimize the unknown parameters of the fourth order joint angles
Modelling and Prediction of 150KW PV Array System in Northern India using Art...inventionjournals
In this research work modeling of solar power using feed forward back propagation (FFBP) artificial neural network. In three different ways inputs are given to FFBP to model photovoltaic module power and generated power is approximated .Six month’s data is taken i.e. from August 2015 to January 2016.Generated power is obtained at the end. All three show good modeling performance. However second input method shows the better results
Performance comparison of distributed generation installation arrangement in ...journalBEEI
Placing Distributed Generation (DG) into a power network should be planned wisely. In this paper, the comparison of having different installation arrangement of real-power DGs in transmission system for loss control is presented. Immune-brainstorm-evolutionary programme (IBSEP) was chosen as the optimization technique. It is found that optimizing fixed-size DGs locations gives the highest loss reduction percentage. Apart from that, scattered small-sized DGs throughout a network minimizes transmission loss more than allocating one biger-sized DG at a location.
modeling and characterization of mems electrostatic energy harvesterINFOGAIN PUBLICATION
In low-power wireless electronic devices, Energy harvesting generators have received increasing research interest in recent years. This paper describes the design and analysis of electrostatic transduction based MEMS energy harvester. Due to the benefit of a folded beam configuration that can be displace large dimensions and compliant in desired direction and stiffer in orthogonal direction. Since, large displacement of proof mass of energy harvester renders the enhance performance. Hence, the folded beam configuration of harvest has been modeled and designed and optimized the dimension of geometry. FEM simulations using COMSOL were conducted to evaluate the natural frequency and mode shape of the system and compared results with that of analytically calculated values. Spice circuit of harvester has been modeled and performed simulation to evaluate the output power of the harvester in LTSPICE. Parameter analysis was conducted to determine the optimal load and optimal output power.
This paper analyses the optimal power system planning with DGs used as real and reactive power compensator. Recently planning of DG placement reactive power compensation are the major problems in distribution system. As the requirement in the power is more the DG placement becomes important. When planned to make the DG placement, cost analysis becomes as a major concern. And if the DGs operate as reactive power compensator it is most helpful in power quality maintenance. So, this paper deals with the optimal power system planning with renewable DGs which can be used as a reactive power compensators. The problem is formulated and solved using popular meta-heuristic techniques called cuckoo search algorithm (CSA) and particle swarm optimization (PSO). the comparative results are presented.
Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping To...journalBEEI
This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ksand damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems.
SIMULATION OF A MATHEMATICAL MODEL OF A WIND TURBINE Mellah Hacene
Abstract
This paper presents a mathematical model of a wind turbine and its simulation. This is one of the main resources available to
the island system (Grid-Off system).
Keywords: wind turbine, island system, Grid-Off system, renewable energy source.
1 Introduction to wind turbine
A wind turbine is basically a converter, or in other words a device that transforms one type of energy into
another. In this case, it is the transformation of mechanical energy into electrical energy.
The source of mechanical energy is the flow (flow) of air, which acts on the turbine blades. The blades are
located on a shaft which is coupled to a permanent magnet (magnet). The magnets are a rotating part, which is
named the rotor. The stator consists of a coil (coils) of wound copper conductor. Due to the changing magnetic
field (PM - permanent magnets), an electrical voltage is induced at the terminals (terminals) of the coil / coils. In
essence, it is a synchronous generator, since the variable electric field is coupled (synchronized) with the speed
of the changing and magnetic fields. [1-5]
A schematic block diagram of a wind turbine as a synchronous generator is shown in Fig. 1.
Economic and Emission Dispatch using Whale Optimization Algorithm (WOA) IJECEIAES
This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Artificial Neural Network Applied to Estimate the Power Output of BIPV SystemsIOSRjournaljce
This paper presents an artificial neural network (ANN) model to estimate the power generated by integrated photovoltaic systems in buildings - BIPVS. The model has as primordial variables, the solar radiation and the ambient temperature of the site of installation of the photovoltaic generator and integrates secondary variables such as the zenith solar angle and the azimuth solar angle. The artificial neural network consists of three layers of operation that allows to adapt to the behavior of the environmental and electrical variables of the photovoltaic generator to create output variables of electrical power through daily profiles. The neural network was implemented in the software Matlbab™ and it was validated using the actual data of monitoring of a 6 kW BIPV system installed at Universidad de Bogotá Jorge Tadeo Lozano, in Bogotá, Colombia. The results indicate a correlation coefficient of 98% on the output power of the BIPV system between the artificial neural network and the performance data of the solar photovoltaic plant. These results show the reliability of the model for PV systems operating in different climatic conditions and different generation capacities.
Hybrid neural networks in cyber physical system interface control systemsjournalBEEI
The calculation and results of simulation of the magnetic control system for the spacecraft momentum are presented in the paper. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilization by resetting the total momentum with the aid of the system under review. The outcome of a comparative analysis of resource efficiency and energy efficiency are demonstrated in the implementation of the proposed hardware models of controllers on FPGA. The strengths and weaknesses of the programming models are shown. The developed models will allow to be modified and perform more complex operations in the future.
Study of Boron Based Superconductivity and Effect of High Temperature Cuprate...IOSR Journals
This paper illustrates the main normal and Boron superconducting state temperature properties of magnesium diboride, a substance known since early 1950's, but lately graded to be superconductive at a remarkably high critical temperature Tc=40K for a binary synthesis. What makes MgB2 so special? Its high Tc, simple crystal construction, large coherence lengths, high serious current densities and fields, lucidity of surface boundaries to current promises that MgB2 will be a good material for both large scale applications and electronic devices. Throughout the last seven month, MgB2 has been fabricated in various shape, bulk, single crystals, thin films, ribbons and wires. The largest critical current densities >10MA/cm2 and critical fields 40T are achieved for thin films. The anisotropy attribution inferred from upper critical field measurements is still to be resolved, a wide range of values being reported, γ = 1.2 ÷ 9. Also there is no consensus about the existence of a single anisotropic or double energy cavity. One central issue is whether or not MgB2 represents a new class of superconductors, being the tip of an iceberg that waits to be discovered. Until now MgB2 holds the record of the highest Tc among simple binary synthesis. However, the discovery of superconductivity in MgB2 revived the interest in non-oxides and initiated a search for superconductivity in related materials, several synthesis being already announced to become superconductive: TaB2, BeB2.75, C-S composites, and the elemental B under pressure.
Uncompressed Image Steganography using BPCS: Survey and AnalysisIOSR Journals
Abstract: Steganography is the art and science of hide secret information in some carrier data without leaving
any apparent evidence of data alternation. In the past, people use hidden tattoos, invisible ink or punching on
papers to convey stenographic data. Now, information is first hide in digital image, text, video and audio. This
paper discusses existing BPCS (Bit Plane Complexity Segmentation) steganography techniques and presences
of some modification. BPCS technique makes use of the characteristics of the human visible system. BPCS
scheme allows for large capacity of embedded secret data and is highly customized. This algorithm offers higher
hiding capacity due to that it exploits the variance of complex regions in each bit plane. In contrast, the BPCS
algorithm provided a much more effective method for obtaining a 50% capacity since visual attacks did not
suffice for detection.
Keywords: BPCS, Data security, Information hiding, Steganography, Stego image
The fuel cell is currently considered as one of the most promising technologies for future energy
demand. Solid oxide fuel cells (SOFCs) have several advantages including flexibility of fuel used and relatively
inexpensive materials due to high temperature operation. SOFCs operate easily in the single-chamber mode
due to the simplified, compact, sealing-free cell structure. An artificial neural network (ANN) can be used as a
black-box tool to simulate systems without solving the physical equations merely by utilizing available
experimental data. In this study, the ANN is used for modelling a singular cell behavior. The error
backpropagation algorithm was used for an ANN training procedure. Experiments of a planar button solid
oxide fuel cell were used to train and verify the networks. The fuel cell system is fed by methane and oxygen. The
cathode is LSCF6482, the anode is GDC-Ni, the electrolyte is LDM and the operating pressure is 1 atm. The
ANN based SOFC model has the following input parameters: current density, temperature; and the cell voltage
is predicted by the model. Obtained results show that the ANN can be successfully used for modelling the single
chamber solid oxide fuel cell without knowledge of numerous physical, chemical, and electrochemical factors.
Iris Publishers- Journal of Engineering Sciences | Performance and Design Opt...IrisPublishers
The aim of this work is to optimize the design and performance of solar powered γ Stirling engine based on genetic algorithm (GA). A second-order mathematical model which includes thermal losses coupled with genetic algorithm GA has been developed and used to find the best values for different design variables. The physical geometry of the γ Stirling engine has been used as an objective variable in the genetic algorithm GA to determine the optimal parameters. The design geometry of the heat exchanger was considered to be the objective variable. The heater slots height, heater effective length, cooler slots height, cooler effective length, re-generator foil unrolled length and re-generator effective length are assumed to be the objective variables. Also, three different types of working fluids have been used in the model simulation to investigate the effect of the different working fluid on the engine performance. The comparison between the results obtained from the simulation by using the original parameters and the results from the optimized parameters when the engine was powered by solar energy; the higher temperature was 923 K applied to the working fluid when the air, helium, and hydrogen were used as working fluid. The engine power increases from 140.58 watts to 228.54 watts, and it is enhanced by approximately 50%, when the heating temperature is 923 K and the air is used as working fluid. The result showed that the working temperature is one of the most important parameters; because the output power increases by increasing of the hot side temperature.
One-dimensional Lumped-Circuit for Transient Thermal Study of an Induction El...IJECEIAES
Electrical machines lifetime and performances could be improved when along the design process both electromagnetic and thermal behaviors are taken into account. Moreover, real time information about the device thermal state is necessary to an appropriate control with minimized losses. Models based on lumped parameter thermal circuits are: generic, rapid, accurate and qualified as a convenient solution for power systems. The purpose of the present paper is to validate a simulation platform intended for the prediction of the thermal state of an induction motor covering all operation regimes. To do so, in steady state, the proposed model is validated using finite element calculation and experimental records. Then, in an overload situation, obtained temperatures are compared to finite element’s ones. It has been found that, in both regimes, simulation results are with closed proximity to finite element’s ones and experimental records.
MODELING AND OPTIMIZATION OF PIEZOELECTRIC ENERGY HARVESTING adeij1
In this paper, the modeling, optimization and simulation results of the piezoelectric energy harvesting using bond graph approach are presented. Firstly, a lightweight equivalent model derived from the bond graph is proposed. It’s a comprehensive model, which is suitable for piezoelectric seismic energy harvester investigation and power optimization. The optimal charge impedance for both the resistive load and complex load are given and analysed. Finally a bond graph approach is proposed to allow optimization of the extracted energy while keeping simplicity and standalone capability. The proposed model does not rely on any inductor and is constructed with a simple switch. The power harvested is more than twice the conventional technique one on a wide band of resistive load. The bond graph model is valid close to the analysed mode centre frequency and delivers results compared to experimental and analytical data. Furthermore, we also show that the harvester can be electrically tuned to match the excitation frequency. This makes it possible to maximize the power output for both linear and non-linear loads.
This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Energy Consumption Saving in Embedded Microprocessors Using Hardware Accelera...TELKOMNIKA JOURNAL
This paper deals with the reduction of power consumption in embedded microprocessors.
Computing power and energy efficiency are becoming the main challenges for embedded system
applications. This is, in particular, the caseof wearable systems. When the power supply is provided by
batteries, an important requirement for these systems is the long service life. This work investigates a
method for the reduction of microprocessor energy consumption, based on the use of hardware
accelerators. Their use allows to reduce the execution time and to decrease the clock frequency, so
reducing the power consumption. In order to provide experimental results, authors analyze a case of study
in the field of wearable devices for the processing of ECG signals. The experimental results show that the
use of hardware accelerator significantly reduces the power consumption.
Coal-Fired Boiler Fault Prediction using Artificial Neural Networks IJECEIAES
Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.
Harmonic and Modal Finite Element Modeling of Piezo-Electric Micro Harvesterresearchinventy
In recent years, vibration energy harvesters have drawn more attention in the world. Energy harvesting (also known as power harvesting or energy scavenging) is the process by which energy is derived from external sources (e.g. solar power, thermal energy, wind energy, salinity gradients, and kinetic energy), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks. Energy harvesting devices converting ambient energy into electrical energy have attracted much interest in both the military and commercial sectors. Some systems convert motion, such as that of ocean waves, into electricity to be used by oceanographic monitoring sensors for autonomous operation. This work is concerned with, harmonic and modal modeling of piezoelectric micro harvester using finite element software (Ansys). The effect of Seismic mass on the voltage output of piezoelectric micro harvester is monitored. The developed finite element model is exposed to harmonic fluctuation on different masses to compare different cases. The results also show the dependency of the piezoelectric material on the operating frequency.
Exergy Assessment of Photovoltaic Thermal with V-groove Collector Using Theor...TELKOMNIKA JOURNAL
The solution of the environmental problems because of fuel fossil is to use new and renewable
energy. There are many studies about energy analysis of solar collector with v-groove but exergy analysis
of photovoltaic thermal system with v-groove is still less especially by theoretical study. Photovoltaic
thermal with v-groove collector has been conducted the exergy analysis by theoretical assessment. The
matrix inversion methods were used to analyze the energy balance equation. The theoretical assessment
was conducted under the solar intensity of 385 W/m2, 575 W/m2, and 875 W/m2 and mass flow rate
between 0.01 and 0.05 kg/s. The maximum exergy efficiency and exergy of PVT system with v -groove
collector were 17.80% and 86.32 Watt at the solar intensity of 875 W/m2.
We present this work by two steps. In the first one, the new structure proposed of the FP-HEMTs device (Field plate High Electron Mobility Transistor) with a T-gate on an 4H-SIC substrate to optimize these electrical performances, multiple field-plates were used with aluminum oxide to split the single electric field peak into several smaller peaks, and as passivation works to reduce scaling leakage current. In the next, we include a modeling of a simulation in the Tcad-Silvaco Software for realizing the study of the influence of negative voltage applied to gate T-shaped in OFF state time and high power with ambient temperature, the performance differences between the 3FP and the SFP devices are discussed in detail.
The use of ekf to estimate the transient thermal behaviour of induction motor...Mellah Hacene
In this paper, a survey is conducted to examine the problem of estimating the states and parameters of an asynchronous machine when some of these measures are not available or the estimation approach is the best solution. The modeling is based on the theory of power dissipation; heat transfer and the rate of temperature increase the stator and the rotor, taking into account the effect of speed on trade. The first purpose of this article is displayed the effect of variable losses depending on the load and constant losses on the thermal behavior of asynchronous motor. According to the sensor’s problems and the obtaining of the thermal information about the rotor, the second goal is the use of a sensorless method like the use of the EKF (extended Kalman filter), some simulation results are given and commented.
Power Estimation for Wearable Piezoelectric Energy HarvesterTELKOMNIKA JOURNAL
The aim of this research work is to estimate the amount of electricity produced to power up wearable devices using a piezoelectric actuator, as an alternative to external power supply. A prototype of the device has been designed to continuously rotate a piezoelectric actuator mounted on a cantilever beam. A MATLAB® simulation was done to predict the amount of power harvested from human kinetic energy. Further simulation was conducted using COMSOL Multiphysics® to model a cantilever beam with piezoelectric layer. With the base excitation and the presence of tip mass at the beam, the natural frequencies and mode shapes have been analyzed to improve the amount of energy harvested. In this work, it was estimated that a maximum amount of power that could be generated is 250 μW with up to 5.5V DC output. The outcome from this research works will aid in optimising the design of the energy harvester. This research work provides optimistic possibility in harvesting sufficient energy required for wearable devices.
COMPUTATIONAL STUDY OF COOLING OF PV SOLAR PANEL USING FINNED HEAT PIPE TECHN...IAEME Publication
Various solar energy technologies exist and they have different application techniques in the generation of electrical power. The widespread use of photovoltaic (PV) modules in such
technologies has been relatively high costs and low efficiencies. The efficiency of PV panel decreases as the operating temperature increases. This is due to reflection from the top surface, absorption of heat by the parts other than the cell, absorption of heat from the other portion of the spectrum.
Design of spark ignition engine speed control using bat algorithm IJECEIAES
The most common problem in spark ignition engine is how to increase the speed performance. Commonly researchers used traditional mathematical approaches for designing speed controller of spark ignition engine. However, this solution may not be sufficient. Hence, it is important to design the speed controller using smart methods. This paper proposes a method for designing speed controller of a spark ignition engine using the bat algorithm (BA). The simulation is carried out using the MATLAB/SIMULINK environment. Time domain simulation is carried out to investigate the efficacy of the proposed method. From the simulation results, it is found that by designing speed controller of spark ignition engine using PI based bat algorithm, the speed performance of spark ignition engine can be enhanced both in no load condition and load condition compared to conventional PI controler.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
"Impact of front-end architecture on development cost", Viktor Turskyi
L1303038388
1. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 13, Issue 3 Ver. III (May- Jun. 2016), PP 83-88
www.iosrjournals.org
DOI: 10.9790/1684-1303038388 www.iosrjournals.org 83 | Page
Forecasting of the thermal lag type of Solar Stirling Engine
output power performance, using Neural Networks
1
Mojtaba Alborzi, 2
Faramarz Sarhadi, 3
Fatimah Sobh Namayan
1
Student of Master education in Mechanical Engineering, the Mechanical Engineering Department, Sistan and
Baluchistan University
2
Assistant professor of Mechanical Engineering Department, Sistan and Baluchistan University
3
Ph.D. Student of Mechanical Engineering, the Mechanical Engineering Department, Sistan and Baluchistan
University
Abstract: In this article the simulation and forecasting of solar sterling engine performance has been done,
using the Artificial Neural Networks (ANN). The analytical data of measured previous references have been
utilized for training the neural network. Input parameters contain the angular velocity, temperature, heat
resistance, course length, piston diameter, tank volume, heat buffer, gas tank volume and the output parameter
is the maximum output power. Utilized training algorithm has been the Levenberg-Marquardt algorithm that is
a kind of Recurrent/Feedback networks. Multi neural networks with various neurons have been utilized in
hidden layer for simulation and then the best network has been chosen for prediction, surveying their
performance. Applied performance evaluation method was the Mean Square Error (MSE) and regression
analysis. Using this method, the system performance can be assessed in different situations in a very short time,
without needing to solve the complex govern equations.
Keywords: Solar Thermal lag Sterling Engine, Artificial Neural Networks, Output power performance
forecasting.
I. Introduction
Nowadays the energy plays a crucial role in improving and development of human societies. Running
out the fossil fuels, their pollution properties and environmental problems caused the researcher’s attention to
the other kind of reproducible energies such as solar energy. The broadest source of energy in the universe is the
solar energy.The amount of energy, comes from the sun during an hour, is more than whole energy, consumed
by the Earth residents during a year. Among the other countries, Iran has a high rank in energy receiving from
the sun. The amount of solar radiation in Iran is about the 1800 to 2200 kwh/m2 during a year that is more than
the world average. The Stirling engine is a kind of ideas, has attracted the attention of interested during the
recent years. Low pollution, low sound and vibration noise, low fuel consumption, using of multiphase fuels
(solid, liquid, gas, animal fuels and solar energy as the best of them) and recently the consumption of nuclear
fuel are the advantages, determines the good business prospects in research about the stirling engines. In the
past, the huge attempt has been done in modelling and simulation of α, β, γ and thermal lag of solar stirling
engines. In performed studies the numerical and analytical methods have been used for simulation but the
Artificial Neural Networks have not been utilized in solar stirling engine simulation so far. Therefore the
Artificial Neural Networks will be used in this research for the simulation of stirling engine output power
performance.Cheng and Yang in 2012 have simulated numerically the thermodynamic behavior of stirling
engine thermal lag upon to the improved model theory. The geometric effects and parameters such as heating
and cooling of temperature, the volume of tank, thermal resistance, course length and the amount of cylinder
inner diameter have been surveyed in output power and thermal efficiency (Cheng and Yang, 2012). Altamirano
et al. in 2013 presented two model of control volume for thermal lag engine (Altamirano et al, 2013). Using the
artificial neural networks for the thermal lag type of solar Stirling engine output power performance forecasting
is the differences of this article comparing to the other studies.
The Artificial Neural Networks and Their Applications
Artificial Neural Networks are one of the important branches of Artificial Intelligence that consist of
related nonlinear parts, called Neuron. Unlike the previous simulation models, the neural networks are made by
the lab or analytic data and work in black box procedure that means there is no information about their
performance (Xie et al, 2009).After the neural network training, an especial input gives an especial output.
Generally the lots of these pairs are used to train the neural network that called supervised training (Kia,
1387).Generally the network consist of an input, hidden and an output layer. The information is saved in
connected weights and the network training is the changing of connected weights, using the new data. Figure 1
2. Forecasting of the thermal lag type of Solar Stirling Engine output power performance, using ….
DOI: 10.9790/1684-1303038388 www.iosrjournals.org 84 | Page
and 2 show the performance of a simple neuron in neural network and the performance of a multilayer neural
network respectively (Kalogirou, 2001).
Fig1: the performance way in a neuron of neural network.
A neural network can forecast the similar problems after the training (Kalogirou, 2001).
Fig2. Schematic diagram of a multilayer neural network.
Assessment of simulation performance by the neural network is done via the reggression analysis of
experimental data and network outputs. The creteria that are used in network performance determination are as
below (Xie et al, 2009):
(1)
2
( )
2 11
2
1
N
a pi iiR N
aii
(2)1 2
( )
1
N
RMSE a pi iiN
(3)
100
1
RMSE
COV N
aii
Where the N is the number of data and a and p are the real and forecasted data respectively. The lesser COV and
RMSE in addition to R2
closer to 1 show the better performance of network.
Parameter selection and Neural Network construction
The input parameters are angular velocity, temperature, thermal resistance, course length, piston
diameter, the volume of thermal buffer tank, the volume of gas tank. The output parameter is the maximum
output power. The constructed schematic of network is shown in figure 3.
3. Forecasting of the thermal lag type of Solar Stirling Engine output power performance, using ….
DOI: 10.9790/1684-1303038388 www.iosrjournals.org 85 | Page
Fig3. The neural network schematic for performance prediction.
The number of neurons in hidden layer has been assessed in various situation and the best forecasting
situation has been utilized for the prediction of solar stirling engine performance. The analytic data of previous
references have been used in network training.
Validation
In figure 4 the temperature of gas in simulated situation by the neural networks was compared by the
gas temperature in analytic data.The amount of error in 800, 1000 and 1200 K° are 4, 1.5 and 2.5 %
respectively. Also in figure 5 the volume of simulated gas tank was compared by the volume of gas tank for
analytic data. The error amount of gas tank volume equal to 30, 10 and 5 cm3 is 2.24, 3 and 4 % respectively.
Fig4. The comparison of predicted gas temperature by the neural network to gas temperature of analytic data.
4. Forecasting of the thermal lag type of Solar Stirling Engine output power performance, using ….
DOI: 10.9790/1684-1303038388 www.iosrjournals.org 86 | Page
Fig5. The comparison of predicted gas tank volume by the neural network to the gas tank volume of analytic
data
II. Results
The best trained neural network structure has been shown in figure 6, considering the least RMSE and the
maximum R2.
Fig6. The designed neural network structure.
The neural network toolbox of Matlab software has been used for network training. The 572 data were used for
this purpose. Figure 7 shows the regression analysis for training, validation and test of networks. In figure 8 the
changes of RMSE during the training process has been shown and also the figure 9 reveals the error gradient
changes, μ and validation error.
Fig7. Regression analysis for training, validation and test of data network.
5. Forecasting of the thermal lag type of Solar Stirling Engine output power performance, using ….
DOI: 10.9790/1684-1303038388 www.iosrjournals.org 87 | Page
Fig8. The RMSE changes during the training process.
Fig9. The variation of error gradient, 𝜇 and the validation error.
In figure 10 the thermal resistance of 1, 0.5 and 0.75 K°/watt has been shown. Using the neural network the
amount of output power in various angular velocities was obtained at 0.75 thermal resistance.Figure 11 shows
the predicted gas tank volumeby the neural network. The output power in different angular velocities in 20 cm3
was obtained by the neural network.
Fig10. Changes of predicted thermal resistance by the neural network
6. Forecasting of the thermal lag type of Solar Stirling Engine output power performance, using ….
DOI: 10.9790/1684-1303038388 www.iosrjournals.org 88 | Page
Fig 11. Changes of predicted gas tank volume by the neural network.
III. Conclusion
In present research, an artificial neural network was applied successfully for simulation of performance
of solar thermal lag Stirling engine shaft power. The sigmoidal functions were used for the network unit’s
connection. The network was trained by the experimental data. The input parameters were the angular velocity,
temperature, thermal resistance, course length, piston diameter, the volume of thermal buffer tank, the volume
of gas tank. The results were assessed by the correlation coefficient, RMSE and COV coefficients that showed
the high accuracy in prediction process of stirling engine, using the limited experimental data. The overall result
of this research shows that, using of this method is suitable in engine performance prediction and this method
can be used instead of time consuming and high cost experimental works and also the complex numerical
calculations for the study of solar thermal lag Stirling engine performance.
List of Symbols
NThe number of data
R2
Correlation Coefficient
COVCoefficient of variation
RMSEMean Square Error
TTemperature
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