HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
Voltage Stability Improvement by Reactive Power Rescheduling Incorporating P...IJMER
In this paper, reactive power rescheduling is done to keep the voltage stable. Due to system
disturbances the active as well as reactive power flows changes. Generators being always connected to the
system reactive power rescheduling of generators can be effectively done. Therefore it is selected as the
suitable method for voltage control. The voltage and reactive power management is studied from the
generator’s point of view to minimize generator reactive power loss. To reduce the reactive losses
optimization procedure is used. The simulations are done using MATLAB.
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.
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.
HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in...TELKOMNIKA JOURNAL
Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.
Voltage Stability Improvement by Reactive Power Rescheduling Incorporating P...IJMER
In this paper, reactive power rescheduling is done to keep the voltage stable. Due to system
disturbances the active as well as reactive power flows changes. Generators being always connected to the
system reactive power rescheduling of generators can be effectively done. Therefore it is selected as the
suitable method for voltage control. The voltage and reactive power management is studied from the
generator’s point of view to minimize generator reactive power loss. To reduce the reactive losses
optimization procedure is used. The simulations are done using MATLAB.
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.
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.
Convergence Parameter Analysis for Different Metaheuristic Methods Control Co...IJPEDS-IAES
This paper is an extension of our previous work, which discussed the
difficulty in implementing different methods of resistance emulation
techniques on the hardware due to its control constant estimation delay. In
order to get rid of the delay this paper attempts to include the meta-heuristic
methods for the control constants of the controller. To achieve the minimum
Total Harmonic Disturbance (THD) in the AC side of the converter modern
meta-heuristic methods are compared with the traditional methods. The
convergence parameters, which are primary for the earlier estimation of the
control constants, are compared with the measured parameters, tabulated and
tradeoff inference is done among the methods. This kind of implementation
does not need the mathematical model of the system under study for finding
the control constants. The parameters considered for estimation are
population size, maximum number of epochs, and global best solution of the
control constants, best THD value and execution time. MatlabTM /Simulink
based simulation is optimized with the M-file based optimization techniques
like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo
Search Algorithm, Gravity Search Algorithm, Harmony Search Algorithm
and Bat Algorithm.
A cost effective computational design of maximum power point tracking for pho...IJECEIAES
Maximum Power Point Tracking (MPPT) is one of the essential controller operations of any Photo-Voltaic (PV) cell design. Developing an efficient MPPT system includes a significant challenge as there are various forms of uncertainty factors that results in higher degree of fluctuation in current and voltage in PV cell. After reviewing existing system, it has been found that there is no presence of any benchmarked model to ensure a better form of computational model. Hence, this paper presents a novel and very simple design of MPPT without using any form of complex design mechanism nor including any form of frequently used iterative approach. The proposed model is completely focused on developing an algorithm that takes the input of voltage (open circuit), current (short circuit), and max power in order to obtain the peak power to be extracted from the PV cells. The study outcome shows faster response time and better form of analysis of current-voltage-power for given state of PV cells.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
Robust pole placement using firefly algorithmIJECEIAES
In this paper, the new automatic tool that is based on the firefly algorithm whose purpose is optimization of pole location in the control of state feedback has been presented. The aim is satisfying specifications of performance like settling and rise time, steady state as well as overshoot error. Utilization of Firefly algorithm has demonstrated the benefits of controllers based on this kind of time domain over controllers based on the frequency domain like Proportional-Integral Derivative (PID). The presented method is more particular for the multi-input multi-output (MIMO) systems that have substantial state numbers. The simulation results indicated that the proposed method had superior performance in providing solution to the problems that involved stabilization of helicopter under the Rationalized Model of helicopter/ Moreover, it demonstrates the Firefly algorithm effectiveness with regards to, the state observer design and feedback controller and auto-tuning.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality.
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.
A review of techniques in optimal sizing of hybrid renewable energy systemseSAT Journals
Abstract This paper presents a review of techniques usedin recent published works on optimal sizing of hybrid renewable energy sources. Hybridization of renewable energy sources is an emergent promising trend born out of the need to fully utilize and solve problems associated with the reliability of renewable energy resources such as wind and solar. Exploitation of these resources has been instrumental in tackling or mitigating present day energy problems such as price instability for fossil based fuels, global warming and climate change in addition to being seen as way of meeting future demand for power. This paper targets researchers in the renewable energy space and the general public seeking to inform them on trends in methods applied in optimal sizing of hybrid renewable energy sources as well as to provide a scope into what has been done in this field. In reviewing previous works, a two prong approach has been used focusing attention on the sizing methods used in the reviewed works as well as the performance indices used to check quality by these works. In summary there is a clear indication of increased interest in recent years in optimal sizing of hybrid renewable energy resources with metaheuristic approaches such as Genetic Algorithms and Particle Swarm Optimization coming out as very interesting to researchers. It has also been observed that resources being hybridized are those with complementary regimes on specific sites.
Index Terms - Energy storage, hybrid power systems, optimization methods, renewable energy sources, reviews, solar energy, wind energy.
Convergence Parameter Analysis for Different Metaheuristic Methods Control Co...IJPEDS-IAES
This paper is an extension of our previous work, which discussed the
difficulty in implementing different methods of resistance emulation
techniques on the hardware due to its control constant estimation delay. In
order to get rid of the delay this paper attempts to include the meta-heuristic
methods for the control constants of the controller. To achieve the minimum
Total Harmonic Disturbance (THD) in the AC side of the converter modern
meta-heuristic methods are compared with the traditional methods. The
convergence parameters, which are primary for the earlier estimation of the
control constants, are compared with the measured parameters, tabulated and
tradeoff inference is done among the methods. This kind of implementation
does not need the mathematical model of the system under study for finding
the control constants. The parameters considered for estimation are
population size, maximum number of epochs, and global best solution of the
control constants, best THD value and execution time. MatlabTM /Simulink
based simulation is optimized with the M-file based optimization techniques
like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo
Search Algorithm, Gravity Search Algorithm, Harmony Search Algorithm
and Bat Algorithm.
A cost effective computational design of maximum power point tracking for pho...IJECEIAES
Maximum Power Point Tracking (MPPT) is one of the essential controller operations of any Photo-Voltaic (PV) cell design. Developing an efficient MPPT system includes a significant challenge as there are various forms of uncertainty factors that results in higher degree of fluctuation in current and voltage in PV cell. After reviewing existing system, it has been found that there is no presence of any benchmarked model to ensure a better form of computational model. Hence, this paper presents a novel and very simple design of MPPT without using any form of complex design mechanism nor including any form of frequently used iterative approach. The proposed model is completely focused on developing an algorithm that takes the input of voltage (open circuit), current (short circuit), and max power in order to obtain the peak power to be extracted from the PV cells. The study outcome shows faster response time and better form of analysis of current-voltage-power for given state of PV cells.
Sampling-Based Model Predictive Control of PV-Integrated Energy Storage Syste...Power System Operation
This paper proposes a novel control solution designed to solve the local and grid-connected
distributed energy resources (DERs) management problem by developing a generalizable framework capable
of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses
sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts
of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while
minimizing the overall cost. The strategy developed aims to nd the ideal combination of solar, grid, and
energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system.
Both ofine and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario
and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algo-
rithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP),
and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the
current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon
with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when
compared to the other baseline control algorithms.
Robust pole placement using firefly algorithmIJECEIAES
In this paper, the new automatic tool that is based on the firefly algorithm whose purpose is optimization of pole location in the control of state feedback has been presented. The aim is satisfying specifications of performance like settling and rise time, steady state as well as overshoot error. Utilization of Firefly algorithm has demonstrated the benefits of controllers based on this kind of time domain over controllers based on the frequency domain like Proportional-Integral Derivative (PID). The presented method is more particular for the multi-input multi-output (MIMO) systems that have substantial state numbers. The simulation results indicated that the proposed method had superior performance in providing solution to the problems that involved stabilization of helicopter under the Rationalized Model of helicopter/ Moreover, it demonstrates the Firefly algorithm effectiveness with regards to, the state observer design and feedback controller and auto-tuning.
A Comparative study on Different ANN Techniques in Wind Speed Forecasting for...IOSRJEEE
There are several available renewable sources of energy, among which Wind Power is the one which is most uncertain in nature. This is because wind speed changes continuously with time leading to uncertainty in availability of amount of wind power generated. Hence, a short-term forecasting of wind speed will help in prior estimation of wind power generation availability for the grid and economic load dispatch.This paper present a comparative study of a Wind speed forecasting model using Artificial Neural Networks (ANN) with three different learning algorithms. ANN is used because it is a non-linear data driven, adaptive and very powerful tool for forecasting purposes. Here an attempt is made to forecast Wind Speed using ANN with Levenberg-Marquard (LM) algorithm, Scaled Conjugate Gradient (SCG) algorithm and Bayesian Regularization (BR) algorithm and their results are compared based on their convergence speed in training period and their performance in testing period on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE).A 48 hour ahead wind speed is forecasted in this work and it is compared with the measured values using all three algorithms and the best out of the three is selected based on minimum error.
Nano-satellites are key features for sharing the space data and scientific researches. They embed subsystems that are fed from solar panels and batteries. Power generated from these panels is subject to environmental conditions, most important of them are irradiance and temperature. Optimizing the usage of this power versus environmental variations is a primary task. Synchronous DC-DC buck converter is used to control the power transferred from PV panels to the subsystems while maintaining operation at maximal power. In this paper, artificial intelligence techniques: neural networks and adaptive neural fuzzy inference systems (ANFIS) are used to accomplish the tracking task. Simulation and experimental results demonstrate their efficiency, robustness and tracking quality.
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.
A review of techniques in optimal sizing of hybrid renewable energy systemseSAT Journals
Abstract This paper presents a review of techniques usedin recent published works on optimal sizing of hybrid renewable energy sources. Hybridization of renewable energy sources is an emergent promising trend born out of the need to fully utilize and solve problems associated with the reliability of renewable energy resources such as wind and solar. Exploitation of these resources has been instrumental in tackling or mitigating present day energy problems such as price instability for fossil based fuels, global warming and climate change in addition to being seen as way of meeting future demand for power. This paper targets researchers in the renewable energy space and the general public seeking to inform them on trends in methods applied in optimal sizing of hybrid renewable energy sources as well as to provide a scope into what has been done in this field. In reviewing previous works, a two prong approach has been used focusing attention on the sizing methods used in the reviewed works as well as the performance indices used to check quality by these works. In summary there is a clear indication of increased interest in recent years in optimal sizing of hybrid renewable energy resources with metaheuristic approaches such as Genetic Algorithms and Particle Swarm Optimization coming out as very interesting to researchers. It has also been observed that resources being hybridized are those with complementary regimes on specific sites.
Index Terms - Energy storage, hybrid power systems, optimization methods, renewable energy sources, reviews, solar energy, wind energy.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
Abstract: In this paper, an algorithm to solve the optimal unit commitment problem under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique accounting economic dispatch constraints. In the present electric power market, where renewable energy power plants have been included in the system, there is a lot of unpredictability in the demand and generation. This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment with the consideration of various constraints. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The program was developed in MATLAB and the proposed method implemented on IEEE 14 bus test system.
Universal Motors (UM) are normally used for driving portable apparatus such as hand tool machines, vacuum cleaners and most domestic apparatus. The importance of UM is due to its own advantages such as high starting torque, very powerful in relation to its small size, having a variable speed; and lower cost. So, this paper focus on UM speed control under variable loading conditions. A mathematical model for UM is designed. Two controllers are proposed for controlling the motor speed, output rate controller and output reset controller. Ant Colony Optimization (ACO) is proposed for tuning the controller’s parameters due to its impact on solving different optimization problems. It possesses fast convergence, minimum algorithm parameters required, lower consecution time and give optimal results without needing large number of iterations. The results are compared and discussed accurately, which show the proposed tuning technique work well and give optimal results for both controllers.
Optimization of FLC Using PSO – FF Hybrid Algorithm Using DSTATCOM for Powe...YogeshIJTSRD
Electrical power is the most established form of power and the people is greatly dependant on the electric power supply. A life cannot be imagined with no supply of electricity. Most of the industrial and domestic loads require uninterrupted and high standard continuous power supply. The electric power suppliers and consumers are getting more concerned about the quality of electric power that is provided to them and which is consumed by the users. Therefore, retaining the qualitative power is of most important 1 . In this research paper our main aim is to optimize and Analysed the performance of PSO – FF hybrid algorithms 2 for the control of DSTATCOM in power quality improvement 3 and the results are analysed with the help of MATLAB software. Kamal Kumar | Poonam Kumari "Optimization of FLC Using PSO – FF Hybrid Algorithm Using DSTATCOM for Power Quality Improvement" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd44962.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/44962/optimization-of-flc-using-pso- – -ff-hybrid-algorithm-using-dstatcom-for-power-quality-improvement/kamal-kumar
life cycle cost analysis of a solar energy based hybrid power systemINFOGAIN PUBLICATION
The importance of life-cycle cost analysis of an integrated solar power system is explained in this paper. To analyze the energy power and cash flow computations, there exist many commercial types of energy audit softwares like Emat, Optimizer, Homer, Energy gauge, Treat and so on. Among the aforementioned audit softwares, homer software is selected since it consists of several built-in options to perform audit studies. Homer software basically utilizes the concept of finding the total net present cost to represent the life-cycle cost of the total system. This software is vividly used for obtaining the optimized energy audit solutions to integrate several equipments embedding into a single workable system.
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.
Firefly Algorithm to Opmimal Distribution of Reactive Power Compensation Units IJECEIAES
The issue of electric power grid mode of optimization is one of the basic directions in power engineering research. Currently, methods other than classical optimization methods based on various bio-heuristic algorithms are applied. The problems of reactive power optimization in a power grid using bio-heuristic algorithms are considered. These algorithms allow obtaining more efficient solutions as well as taking into account several criteria. The Firefly algorithm is adapted to optimize the placement of reactive power sources as well as to select their values. A key feature of the proposed modification of the Firefly algorithm is the solution for the multi-objective optimization problem. Algorithms based on a bio-heuristic process can find a neighborhood of global extreme, so a local gradient descent in the neighborhood is applied for a more accurate solution of the problem. Comparison of gradient descent, Firefly algorithm and Firefly algorithm with gradient descent is carried out.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.