Search results for: optimization of power LED

Authors: Li Long, Thomas Ortlepp

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A theoretical model for the optimization of thermopile sensor performance is developed for thermoelectric-based infrared radiation detection. It is shown that the performance of polycrystalline silicon film thermopile sensor can be optimized according to the thermoelectric quality factor, sensor layer structure factor, and sensor layout geometrical form factor. Based on the properties of electrons, phonons, grain boundaries, and their interactions, the thermoelectric quality factor of polycrystalline silicon is analyzed with the relaxation time approximation of the Boltzmann transport equation. The model includes the effect of grain structure, grain boundary trap properties, and doping concentration. The layer structure factor is analyzed with respect to the infrared absorption coefficient. The optimization of layout design is characterized by the form factor, which is calculated for different sensor designs. A double-layer polycrystalline silicon thermopile infrared sensor on a suspended membrane has been designed and fabricated with a CMOS-compatible process. The theoretical approach is confirmed by measurement results.

Keywords: polycrystalline silicon, relaxation time approximation, specific detectivity, thermal conductivity, thermopile infrared sensor

Procedia PDF Downloads 130

Authors: Xiaoming Jiang, Jinqiao Shi, Qingfeng Tan, Wentao Zhang, Xuebin Wang, Muqian Chen

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Nowadays, big companies such as Google, Microsoft, which have adequate proxy servers, have perfectly implemented their web crawlers for a certain website in parallel. But due to lack of expensive proxy servers, it is still a puzzle for researchers to crawl large amounts of information from a single website in parallel. In this case, it is a good choice for researchers to use free public proxy servers which are crawled from the Internet. In order to improve efficiency of web crawler, the following two issues should be considered primarily: (1) Tasks may fail owing to the instability of free proxy servers; (2) A proxy server will be blocked if it visits a single website frequently. In this paper, we propose Proxisch, an optimization approach of large-scale unstable proxy servers scheduling, which allow anyone with extremely low cost to run a web crawler efficiently. Proxisch is designed to work efficiently by making maximum use of reliable proxy servers. To solve second problem, it establishes a frequency control mechanism which can ensure the visiting frequency of any chosen proxy server below the website’s limit. The results show that our approach performs better than the other scheduling algorithms.

Keywords: proxy server, priority queue, optimization algorithm, distributed web crawling

Procedia PDF Downloads 210

Authors: Sabir Messalti, Ahmed Gherbi, Ahmed Bouchlaghem

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This paper presents comparative study for power systems transient stability improvement using three FACTS devices: the SVC(Static Var Compensator), the Thyristor Control Breaking Resistor (TCBR) and superconducting fault current limiter (SFCL)The transient stability is assessed by the criterion of relative rotor angles. Critical Clearing Time (CCT) is used as an index for evaluated transient stability. The present study is tested on the WSCC3 nine-bus system in the case of three-phase short circuit fault on one transmission line.

Keywords: SVC, TCBR, SFCL, power systems transient stability improvement

Procedia PDF Downloads 644

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: KumarShashvat, ArshpreetKaur, RajeshKumar, Raman Chadha

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Wireless sensor networks have principal characteristic of having restricted energy and with limitation that energy of the nodes cannot be replenished. To increase the lifetime in this scenario WSN route for data transmission is opted such that utilization of energy along the selected route is negligible. For this energy efficient network, dandy infrastructure is needed because it impinges the network lifespan. Clustering is a technique in which nodes are grouped into disjoints and non–overlapping sets. In this technique data is collected at the cluster head. In this paper, Adaptive-PSO algorithm is proposed which forms energy aware clusters by minimizing the cost of locating the cluster head. The main concern is of the suitability of the swarms by adjusting the learning parameters of PSO. Particle Swarm Optimization converges quickly at the beginning stage of the search but during the course of time, it becomes stable and may be trapped in local optima. In suggested network model swarms are given the intelligence of the spiders which makes them capable enough to avoid earlier convergence and also help them to escape from the local optima. Comparison analysis with traditional PSO shows that new algorithm considerably enhances the performance where multi-dimensional functions are taken into consideration.

Keywords: Particle Swarm Optimization, adaptive – PSO, comparison between PSO and A-PSO, energy efficient clustering

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Authors: Ankur Sachan

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The ability to make quick yet intelligent and value-added decisions to develop new fields has always been of great significance. In situations where the capital expenses and subsurface risk are high, carefully analyzing the inherent uncertainties in the reservoir and how they impact the predicted hydrocarbon accumulation and production becomes a daunting task. The problem is compounded in offshore environments, especially in the presence of heavy oils and disconnected sands where the margin for error is small. Uncertainty refers to the degree to which the data set may be in error or stray from the predicted values. To understand and quantify the uncertainties in reservoir model is important when estimating the reserves. Uncertainty parameters can be geophysical, geological, petrophysical etc. Identification of these parameters is necessary to carry out the uncertainty analysis. With so many uncertainties working at different scales, it becomes essential to have a consistent and efficient way of incorporating them into our analysis. Ranking the uncertainties based on their impact on reserves helps to prioritize/ guide future data gathering and uncertainty reduction efforts. Assigning probabilistic ranges to key uncertainties also enables the computation of probabilistic reserves. With this in mind, this paper, with the help the uncertainty and optimization process in petrel RE shows how the most influential uncertainties can be determined efficiently and how much impact so they have on the reservoir model thus helping in determining a cost effective and accurate model of the reservoir.

Keywords: uncertainty, reservoir model, parameters, optimization analysis

Procedia PDF Downloads 632

Authors: Chien-Kuo Chang, Yu-Hsiang Lin, Yi-Yun Tang, Min-Chiu Wu

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With the trend of technological advancement, the harm caused by power outages is substantial, mostly due to problems in the power grid. This highlights the necessity for further improvement in the reliability of the power system. In the power system, gas-insulated switches (GIS) and power cables play a crucial role. Long-term operation under high voltage can cause insulation materials in the equipment to crack, potentially leading to partial discharges. If these partial discharges (PD) can be analyzed, preventative maintenance and replacement of equipment can be carried out, there by improving the reliability of the power grid. This research will diagnose defects by identifying three different defects in GIS and three different defects in straight cable joints, for a total of six types of defects. The partial discharge data measured will be converted through phase analysis diagrams and pulse sequence analysis. Discharge features will be extracted using convolutional image processing, and three different deep learning models, CNN, ResNet18, and MobileNet, will be used for training and evaluation. Class Activation Mapping will be utilized to interpret the black-box problem of deep learning models, with each model achieving an accuracy rate of over 95%. Lastly, the overall model performance will be enhanced through an ensemble learning voting method.

Keywords: partial discharge, gas-insulated switches, straight cable joint, defect identification, deep learning, ensemble learning

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Authors: Sukant Mehra, Parth Gupta, Varun Arora, Sarvoday Singh, Amit Kohli

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The study was emphasised on dry sliding wear behavior of AISI 1042 steel. Dry sliding wear tests were performed using pin-on-disk apparatus under normal loads of 5, 7.5 and 10 kgf and at speeds 600, 750 and 900 rpm. Response surface methodology (RSM) was utilized for finding optimal values of process parameter and experiment was based on rotatable, central composite design (CCD). It was found that the wear followed linear pattern with the load and rpm. The obtained optimal process parameters have been predicted and verified by confirmation experiments.

Keywords: central composite design (CCD), optimization, response surface methodology (RSM), wear

Procedia PDF Downloads 571

Authors: Phuong Nguyen

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Along with the ongoing energy transition, the electrical power system is getting more vulnerable with the increasing penetration of renewable energy sources (RES). By replacing a large amount of fossil fuel-based power plants with RES, the rotating mass of the power grid is decreasing drastically, which has been reported by a number of system operators. This leads to a huge challenge for operators to secure the operation of their grids in all-time horizon ranges, from sub-seconds to minutes and even hours. There is a need to revise the grid capabilities in dealing with transient (angle) stability and voltage dynamics. While the traditional approaches relied on deterministic scenarios (worst-case scenarios), there is also a need to cover a whole range of probabilities regarding a wide range of uncertainties coming from massive RES units. To contribute to handle these issues, this paper aims to focus on developing a new analytical approach for transient stability.

Keywords: transient stability, uncertainties, renewable energy sources, analytical approach

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Authors: Srushti Bhatt, M. B. Kiran

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Two-sided assembly line balancing problem is yet to be addressed simply to compete for the global market for manufacturers. The task assigned in an ordered sequence to get optimum performance of the system is known as assembly line balancing problem mainly classified as single and two sided. It is very challenging in manufacturing industries to balance two-sided assembly line, wherein the set of sequential workstations the task operations are performed in two sides of the line. The conflicting major objective in two-sided assembly line balancing problem is either to maximize /minimize the performance parameters. The present study emphases on combining different evolutionary algorithm; ant colony, Tabu search and petri net method; and compares their results of an algorithm for solving two-sided assembly line balancing problem. The concept of multi objective optimization of performance parameters is now a day adopted to make a decision involving more than one objective function to be simultaneously optimized. The optimum result can be expected among the selected methods using multi-objective optimization. The performance parameters considered in the present study are a number of workstation, slickness and smoothness index. The simulation of the assembly line balancing problem provides optimal results of classical and practical problems.

Keywords: Ant colony, petri net, tabu search, two sided ALBP

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Authors: Mehdi Ganjkhani, Sobhan Badakhshan, Seyedvahid Hosseini

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Power plants exploit striking proportion of underground water consumption. Correspondingly, natural gas-fired power plants need less water than the other conventional power plants. Therefore, shifting unit commitment planning toward these power plants would help to save water consumption. This paper discusses the impacts of water consumption limitation on natural gas consumption and vice versa as a short-term water consumption management solution. To do so, conventional unit commitment problem is extended by adding water consumption and natural gas constraints to the previous constrains. The paper presents the impact of water saving on natural gas demands as well as natural gas shortage on water demand. Correspondingly, the additional cost of electricity production according to the aforementioned constraints is evaluated. Finally, a test system is applied to investigate potentials and impacts of water saving and natural gas shortage. Different scenarios are conducted and the results are presented. The results of the study illustrate that in order to use less water for power production it needs to use more natural gas. Meanwhile, natural gas shortage causes to utilize more amount of water in aggregate.

Keywords: electric energy generation system, underground water sources, unit commitment, water consumption saving, natural gas

Procedia PDF Downloads 187

Authors: Ibrahim Ellithy, Mauricio Esguerra, , Rewanth Radhakrishnan

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In this study, the effects of hydrostatic stress on the magnetic properties of MnZn ferrite rings of different power grades, were measured and analyzed in terms of the magneto-mechanical effect on core losses was modeled via the Hodgdon-Esguerra hysteresis model. The results show excellent agreement with the model and a correlation between the permeability drop and the core loss increase in dependence of the material grade properties. These results emphasize the vulnerabilities of MnZn ferrites when subjected to mechanical perturbations, especially in real-world scenarios like under-road embedding for WPT.

Keywords: hydrostatic stress, power ferrites, core losses, wireless power transfer

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Authors: Alireza Nikbakht Shahbazi, Emadeddin Shirali

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Since optimization issues of water resources are complicated due to the variety of decision making criteria and objective functions, it is sometimes impossible to resolve them through regular optimization methods or, it is time or money consuming. Therefore, the use of modern tools and methods is inevitable in resolving such problems. An accurate and essential utilization policy has to be determined in order to use natural resources such as water reservoirs optimally. Water reservoir programming studies aim to determine the final cultivated land area based on predefined agricultural models and water requirements. Dam utilization rule curve is also provided in such studies. The basic information applied in water reservoir programming studies generally include meteorological, hydrological, agricultural and water reservoir related data, and the geometric characteristics of the reservoir. The system of Dez dam water resources was simulated applying the basic information in order to determine the capability of its reservoir to provide the objectives of the performed plan. As a meta-exploratory method, genetic algorithm was applied in order to provide utilization rule curves (intersecting the reservoir volume). MATLAB software was used in order to resolve the foresaid model. Rule curves were firstly obtained through genetic algorithm. Then the significance of using rule curves and the decrease in decision making variables in the system was determined through system simulation and comparing the results with optimization results (Standard Operating Procedure). One of the most essential issues in optimization of a complicated water resource system is the increasing number of variables. Therefore a lot of time is required to find an optimum answer and in some cases, no desirable result is obtained. In this research, intersecting the reservoir volume has been applied as a modern model in order to reduce the number of variables. Water reservoir programming studies has been performed based on basic information, general hypotheses and standards and applying monthly simulation technique for a statistical period of 30 years. Results indicated that application of rule curve prevents the extreme shortages and decrease the monthly shortages.

Keywords: optimization, rule curve, genetic algorithm method, Dez dam reservoir

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Authors: Brahim Ferdi, Samira Dib

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The unified power quality conditioner (UPQC), a combination of shunt and series active power filter, is one of the best solutions towards the mitigation of voltage and current harmonics problems in distribution power system. PI controller is very common in the control of UPQC. However, one disadvantage of this conventional controller is the difficulty in tuning its gains (Kp and Ki). To overcome this problem, an adaptive fuzzy logic PI controller is proposed. The controller is composed of fuzzy controller and PI controller. According to the error and error rate of the control system and fuzzy control rules, the fuzzy controller can online adjust the two gains of the PI controller to get better performance of UPQC. Simulations using MATLAB/SIMULINK are carried out to verify the performance of the proposed controller. The results show that the proposed controller has fast dynamic response and high accuracy of tracking the current and voltage references.

Keywords: adaptive fuzzy PI controller, current harmonics, PI controller, voltage harmonics, UPQC

Procedia PDF Downloads 552

Authors: Suyong Kim

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Recently because of the rise in the price of rare earth magnet, interest of non-rare earth or less-rare earth motor is growing. Especially to achieve the high power density, Spoke-Type BLDC (Brushless Permanent Magnet) Motor with ferrite permanent magnet are spotlighted. But Spoke-Type Ferrite BLDC Motor has much of magnetic flux leakage in the direction of rotor shaft. In order to solve this problem, there are two conventional ways. But conventional ways bring the increases of product cost or the decreases of the power density. Therefore, this paper proposes new Spoke-Type BLDC Rotor shape that has the advantages of both conventional methods. The new shape is consists of a one-piece core. The inside and the outside of the rotor are open alternately. So it can take reduced production cost and high power density.

Keywords: motor, BLDC, spoke, ferrite

Procedia PDF Downloads 568

Authors: Amruta Rout, Golak Bihari Mahanta, Gunji Bala Murali, Bibhuti Bhusan Biswal, B. B. V. L. Deepak

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The use of industrial robots for performing welding operation is one of the chief sign of contemporary welding in these days. The weld joint parameter and weld process parameter modeling is one of the most crucial aspects of robotic welding. As weld process parameters affect the weld joint parameters differently, a multi-objective optimization technique has to be utilized to obtain optimal setting of weld process parameter. In this paper, a hybrid optimization technique, i.e., Principal Component Analysis (PCA) combined with fuzzy logic has been proposed to get optimal setting of weld process parameters like wire feed rate, welding current. Gas flow rate, welding speed and nozzle tip to plate distance. The weld joint parameters considered for optimization are the depth of penetration, yield strength, and ultimate strength. PCA is a very efficient multi-objective technique for converting the correlated and dependent parameters into uncorrelated and independent variables like the weld joint parameters. Also in this approach, no need for checking the correlation among responses as no individual weight has been assigned to responses. Fuzzy Inference Engine can efficiently consider these aspects into an internal hierarchy of it thereby overcoming various limitations of existing optimization approaches. At last Taguchi method is used to get the optimal setting of weld process parameters. Therefore, it has been concluded the hybrid technique has its own advantages which can be used for quality improvement in industrial applications.

Keywords: robotic arc welding, weld process parameters, weld joint parameters, principal component analysis, fuzzy logic, Taguchi method

Procedia PDF Downloads 177

Authors: Qudratullah Muradi

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Geothermal energy is a renewable energy source that can be found in abundance on our planet. Only a small fraction of it is currently converted to electrical power, though in recent years installed geothermal capacity has increased considerably all over the world. In this paper, we assumed a model for designing of Enhanced Geothermal System, EGS. We used computer modeling group, CMG reservoir simulation software to create the typical Hot Dry Rock, HDR reservoir. In this research two wells, one injection of cold water and one production of hot water are included in the model. There are some hydraulic fractures created by the mentioned software. And cold water is injected in order to produce energy from the reservoir. The result of injecting cold water to the reservoir and extracting geothermal energy is defined by some graphs at the end of this research. The production of energy is quantified in a period of 10 years.

Keywords: geothermal energy, EGS, HDR, hydraulic fracturing

Procedia PDF Downloads 191

Authors: Wen-Bao Qiao, Jean-Charles Créput

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To accelerate the solution for large scale traveling salesman problems (TSP), a parallel 2-opt local search algorithm with simple implementation based on Graphics Processing Unit (GPU) is presented and tested in this paper. The parallel scheme is based on technique of data decomposition by dynamically assigning multiple K processors on the integral tour to treat K edges’ 2-opt local optimization simultaneously on independent sub-tours, where K can be user-defined or have a function relationship with input size N. We implement this algorithm with doubly linked list on GPU. The implementation only requires O(N) memory. We compare this parallel 2-opt local optimization against sequential exhaustive 2-opt search along integral tour on TSP instances from TSPLIB with more than 10000 cities.

Keywords: parallel 2-opt, double links, large scale TSP, GPU

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Authors: Pushpendra S. Bharti, S. Maheshwari

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Electric discharge machining (EDM) is one of the most widely used non-conventional manufacturing process to shape difficult-to-cut materials. The process yield, in terms of material removal rate, surface roughness and tool wear rate, of EDM may considerably be improved by selecting the optimal combination(s) of process parameters. This paper employs Multi-response signal-to-noise (MRSN) ratio technique to find the optimal combination(s) of the process parameters during EDM of Inconel 718. Three cases v.i.z. high cutting efficiency, high surface finish, and normal machining have been taken and the optimal combinations of input parameters have been obtained for each case. Analysis of variance (ANOVA) has been employed to find the dominant parameter(s) in all three cases. The experimental verification of the obtained results has also been made. MRSN ratio technique found to be a simple and effective multi-objective optimization technique.

Keywords: electric discharge machining, material removal rate, surface roughness, too wear rate, multi-response signal-to-noise ratio, multi response signal-to-noise ratio, optimization

Procedia PDF Downloads 350

Authors: S. M. Javad Davoodi, Mojtaba Shourian

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Groundwater table close to the ground surface causes major problems in construction and mining operation. One of the methods to control groundwater in such cases is using pumping wells. These pumping wells remove excess water from the site project and lower the water table to a desirable value. Although the efficiency of this method is acceptable, it needs high expenses to apply. It means even small improvement in a design of pumping wells can lead to substantial cost savings. In order to minimize the total cost in the method of pumping wells, a simulation-optimization approach is applied. The proposed model integrates MODFLOW as the simulation model with Firefly as the optimization algorithm. In fact, MODFLOW computes the drawdown due to pumping in an aquifer and the Firefly algorithm defines the optimum value of design parameters which are numbers, pumping rates and layout of the designing wells. The developed Firefly-MODFLOW model is applied to minimize the cost of the dewatering project for the ancient mosque of Kerman city in Iran. Repetitive runs of the Firefly-MODFLOW model indicates that drilling two wells with the total rate of pumping 5503 m3/day is the result of the minimization problem. Results show that implementing the proposed solution leads to at least 1.5 m drawdown in the aquifer beneath mosque region. Also, the subsidence due to groundwater depletion is less than 80 mm. Sensitivity analyses indicate that desirable groundwater depletion has an enormous impact on total cost of the project. Besides, in a hypothetical aquifer decreasing the hydraulic conductivity contributes to decrease in total water extraction for dewatering.

Keywords: groundwater dewatering, pumping wells, simulation-optimization, MODFLOW, firefly algorithm

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Authors: G. V. Siva Krishna Rao, B. Srinivasa Rao

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Electrical energy is vital in every aspect of day-to-day life. Keen interest is taken on all possible sources of energy from which it can be generated and this led to the encouragement of generating electrical power using renewable energy resources such as solar, tidal waves and wind energy. Due to the increasing interest on renewable sources in recent times, the studies on integration of distributed generation to the power grid have rapidly increased. Distributed Generation (DG) is a promising solution to many power system problems such as voltage regulation, power loss and reduction in operational cost, etc. To reduce production cost, it is important to minimize the losses by determining the location and size of local generators to be placed in the radial distribution systems. In this paper, reduction of production cost by optimal size of DG unit operated at optimal power factor is dealt. The optimal size of the DG unit is calculated analytically using approximate reasoning suitable nodes and DG placement to minimize production cost with minimum loss is determined by fuzzy technique. Total Cost of Power generation is compared with and without DG unit for 1 year duration. The suggested method is programmed under MATLAB software and is tested on IEEE 33 bus system and the results are presented.

Keywords: distributed generation, operational cost, exact loss formula, optimum size, optimum location

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Authors: Nazanin Naderi, Milton Smith

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Since worldwide demand for renewable energy is increasing rapidly because of the climate problem and the limitation of fossil fuels, technologies of alternative energy sources have been developed and the electric power network now includes renewable energy resources such as wind energy. Because of the huge advantages that wind energy has, like reduction in natural gas use, price pressure, emissions of greenhouse gases and other atmospheric pollutants, electric sector water consumption and many other contributions to the nation’s economy like job creation it has got too much attention these days from different parts of the world especially in the United States which is trying to provide 20% of the nation’s energy from wind by 2030. This study is trying to evaluate the effect of splitting wind farms on power output. We are trying to find if we can get more output by installing wind turbines in different sites rather than installing all wind turbines in one site. Five potential sites in Texas have been selected as a case study and two years wind data has been gathered for these sites. Wind data are analyzed and effect of correlation between sites on power output has been evaluated. Standard deviation and autocorrelation effect has also been considered for this study. The paper has been organized as follows: After the introduction the second section gives a brief overview of wind analysis. The third section addresses the case study and evaluates correlation between sites, auto correlation of sites and standard deviation of power output. In section four we describe the results.

Keywords: auto correlation, correlation between sites, splitting wind farms, power output, standard deviation

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Authors: Zakarya Kourdi, Mohammed Khaouani, Benyounes Bouazza, Ahlam Guen-Bouazza, Amine Boursali

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In this paper, we have evaluated the thermal effect for high electron mobility transistors (HEMTs) heterostructure InAlN/GaN with a gate length 30nm high-performance. It also shows the analysis and simulated these devices, and how can be used in different application. The simulator Tcad-Silvaco software has used for predictive results good for the DC, AC and RF characteristic, Devices offered max drain current 0.67A; transconductance is 720 mS/mm the unilateral power gain of 180 dB. A cutoff frequency of 385 GHz, and max frequency 810 GHz These results confirm the feasibility of using HEMTs with InAlN/GaN in high power amplifiers, as well as thermal places.

Keywords: HEMT, Thermal Effect, Silvaco, InAlN/GaN

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Authors: Y. Long, L. Liu, K. V. Branin

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One challenge faced by procurement decision-maker during the acquisition process is how to compare similar products from different suppliers and allocate orders among different products or services. This work focuses on allocating orders among multiple suppliers considering rebate. The objective function is to minimize the total acquisition cost including purchasing cost and rebate benefit. Rebate benefit is complex and difficult to estimate at the ordering step. Rebate rules vary for different suppliers and usually change over time. In this work, we developed a system to collect the rebate policies, standardized the rebate policies and developed two-stage optimization models for ordering allocation. Rebate policy with multi-tiers is considered in modeling. The discontinuous cost function of rebate benefit is formulated for different scenarios. A piecewise linear function is used to approximate the discontinuous cost function of rebate benefit. And a Mixed Integer Programing (MIP) model is built for order allocation problem with multi-tier rebate. A case study is presented and it shows that our optimization model can reduce the total acquisition cost by considering rebate rules.

Keywords: discontinuous cost function, mixed integer programming, optimization, procurement, rebate

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Authors: Hisham M. Soliman, Hassan Yousef

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This manuscript presents new results on design saturated power system stabilizers (PSS) to assign system poles within a desired region for achieving good dynamic performance. The regional pole placement is accomplished against model uncertainties caused by different load conditions. The design is based on a sufficient condition in the form of linear matrix inequalities (LMI) which forces the saturated nonlinear controller to lie within the linear zone. The controller effectiveness is demonstrated on a single machine infinite bus system.

Keywords: power system stabilizer, saturated control, robust control, regional pole placement, linear matrix inequality (LMI)

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Authors: Hedi Dghim, Ahmed El-Naggar, Istvan Erlich

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Power electronic converters are being introduced in low voltage (LV) grids at an increasingly rapid rate due to the growing adoption of power electronic-based home appliances in residential grid. Photovoltaic (PV) systems are considered one of the potential installed renewable energy sources in distribution power systems. This trend has led to high distortion in the supply voltage which consequently produces harmonic currents in the network and causes an inherent voltage unbalance. In order to investigate the effect of harmonic distortions, a case study of a typical LV grid configuration with high penetration of 3-phase and 1-phase rooftop mounted PV from southern Germany was first considered. Electromagnetic transient (EMT) simulations were then carried out under the MATLAB/Simulink environment which contain detailed models for power electronic-based loads, ohmic-based loads as well as 1- and 3-phase PV. Note that, the switching patterns of the power electronic circuits were considered in this study. Measurements were eventually performed to analyze the distortion levels when PV operating under different solar irradiance. The characteristics of the load-side harmonic impedances were analyzed, and their harmonic contributions were evaluated for different distortion levels. The effect of the high penetration of PV on the harmonic distortion of both positive and negative sequences was also investigated. The simulation results are presented based on case studies. The current distortion levels are in agreement with relevant standards, otherwise the Total Harmonic Distortion (THD) increases under low PV power generation due to its inverse relation with the fundamental current.

Keywords: harmonic distortion analysis, power quality, PV systems, residential distribution system

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Authors: Azim Khodadadi, Sahar Sadaat Vakili, Ebrahim Babaei

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This study presents a numerical method to optimize the failure rate and repair time of a typical radial distribution system. Failure rate and repair time are effective parameters in customer and energy based indices of reliability. Decrease of these parameters improves reliability indices. Thus, system stability will be boost. The penalty functions indirectly reflect the cost of investment which spent to improve these indices. Constraints on customer and energy based indices, i.e. SAIFI, SAIDI, CAIDI and AENS have been considered by using a new method which reduces optimization algorithm controlling parameters. Imperialist Competitive Algorithm (ICA) used as main optimization technique and particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) has been applied for further investigation. These algorithms have been implemented on a test system by MATLAB. Obtained results have been compared with each other. The optimized values of repair time and failure rate are much lower than current values which this achievement reduced investment cost and also ICA gives better answer than the other used algorithms.

Keywords: imperialist competitive algorithm, failure rate, repair time, radial distribution network

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Authors: Deepak Kumar, Jahangeer, Brijesh Kumar Yadav, Shashi Mathur

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In-situ remediation is a technique which can remediate either surface or groundwater at the site of contamination. In the present study, simulation optimization approach has been used to develop management strategy for remediating LNAPL (Light Non-Aqueous Phase Liquid) contaminated aquifers. Benzene, toluene, ethyl benzene and xylene are the main component of LNAPL contaminant. Collectively, these contaminants are known as BTEX. In in-situ bioremediation process, a set of injection and extraction wells are installed. Injection wells supply oxygen and other nutrient which convert BTEX into carbon dioxide and water with the help of indigenous soil bacteria. On the other hand, extraction wells check the movement of plume along downstream. In this study, optimal design of the system has been done using PSO (Particle Swarm Optimization) algorithm. A comprehensive management strategy for pumping of injection and extraction wells has been done to attain a maximum allowable concentration of 5 ppm and 4.5 ppm. The management strategy comprises determination of pumping rates, the total pumping volume and the total running cost incurred for each potential injection and extraction well. The results indicate a high pumping rate for injection wells during the initial management period since it facilitates the availability of oxygen and other nutrients necessary for biodegradation, however it is low during the third year on account of sufficient oxygen availability. This is because the contaminant is assumed to have biodegraded by the end of the third year when the concentration drops to a permissible level.

Keywords: groundwater, in-situ bioremediation, light non-aqueous phase liquid, BTEX, particle swarm optimization

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Authors: Dimeji Abe, Nonso Okoye, Gideon Ikpimi, Prince Idemudia

Abstract:

Multi-reservoir optimization operation has been a critical issue for river basin management. Water, as a scarce resource, is in high demand and the problems associated with the reservoir as its storage facility are enormous. The complexity in balancing the supply and demand of this prime resource has created the need to examine the best way to solve the problem using optimization techniques. The objective of this study is to evaluate the performance of the multi-objective meta-heuristic algorithm for the operation of Gariep Dam for satisfying ecological flow requirements. This study uses an evolutionary algorithm called backtrack search algorithm (BSA) to determine the best way to optimise the dam operations of hydropower production, flood control, and water supply without affecting the environmental flow requirement for the survival of aquatic bodies and sustain life downstream of the dam. To achieve this objective, the operations of the dam that corresponds to different tradeoffs between the objectives are optimized. The results indicate the best model from the algorithm that satisfies all the objectives without any constraint violation. It is expected that hydropower generation will be improved and more water will be available for ecological flow requirements with the use of the algorithm. This algorithm also provides farmers with more irrigation water as well to improve their business.

Keywords: BSA evolutionary algorithm, metaheuristics, optimization, river basin management

Procedia PDF Downloads 241

Authors: Cha’o-Kuang Chen, Ching-Chang Cho

Abstract:

This paper investigates the natural convection heat transfer performance in a complex-wavy-wall cavity filled with power-law fluid. In performing the simulations, the continuity, Cauchy momentum and energy equations are solved subject to the Boussinesq approximation using a finite volume method. The simulations focus specifically on the effects of the flow behavior index in the power-law model and the Rayleigh number on the flow streamlines, isothermal contours and mean Nusselt number within the cavity. The results show that pseudoplastic fluids have a better heat transfer performance than Newtonian or dilatant fluids. Moreover, it is shown that for Rayleigh numbers greater than Ra=103, the mean Nusselt number has a significantly increase as the flow behavior index is decreased.

Keywords: non-Newtonian fluid, power-law fluid, natural convection, heat transfer enhancement, cavity, wavy wall

Procedia PDF Downloads 258