Nowadays, the gridcomputing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find chea...
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ISBN:
(数字)9798331523657
ISBN:
(纸本)9798331523664
Nowadays, the gridcomputing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.
There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine t...
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ISBN:
(数字)9798350355611
ISBN:
(纸本)9798350355628
There have been several developments in renewable resources, standby sources of energy, and storage technologies. Because renewable sources are inconsistent, the best method to ensure supply continuity is to combine them with traditional energy to serve mini-grids or the isolated loads that are not linked to the main grid. In hybrid power system, many different energy sources are blended. Every time, effective and efficient Energy Management strategies (EMS) are necessary when more than a source of energy is employed to supply a given load. This strategy directs the energy flow via the supply chain. This is necessary not just for standalone hybrid systems, but for hybrid power system that are linked to the main network. The intelligent control of hybrid MG has been the subject of a large number of serious research projects. A comprehensive assessment of the approaches used by a number of writers in their research work on applying energy management approaches is conducted. standalone hybrid energy systems and the hybrid systems those are linked to the electrical grids are examples of these solutions. The aim of this paper is to give summary of various energy management schemes utilized in sustainable hybrid systems. Brief study is conducted for a number of freestanding and grid-linked hybrid system designs in order to make sure and find that which energy management technique that works well and will be applied for one renewable source is not always the best for another.
The integration of Renewable Energy Sources (RES) into the grid for reliable and stable operation is witnessing significant growth. Solar photovoltaic (PV) systems are key contributors to enhancing the grid's RES ...
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ISBN:
(数字)9798331542108
ISBN:
(纸本)9798331542115
The integration of Renewable Energy Sources (RES) into the grid for reliable and stable operation is witnessing significant growth. Solar photovoltaic (PV) systems are key contributors to enhancing the grid's RES capacity. The Solar PV installed capacity improved by 30 % in 2023. Challenges arise in partially shaded conditions, where some parts of the solar PV array may be obstructed, leading to suboptimal performance of the entire array. Achieving optimal performance in the connected solar PV arrays is essential, typically accomplished by configuring arrays in parallel and/or series to operate at the Maximum Power Point (MPP). This paper explores the application of various metaheuristic methods, including Particle Swarm Optimization, Bald Eagle Search, Manta Ray Foraging Optimization, and Gorilla Troop Optimizer. The study considers complete cloud movement across a 9X9 solar PV array. The movement of the clouds is categorized into 9 different stages of partial shading. The switching of the solar panels is employed to maximize output power under shaded conditions. The results are categorized into dynamic and static reconfiguration. MATLAB is employed for the implementation of these metaheuristic techniques.
Microinverters are typically integrated with individual solar photovoltaic (PV) modules to enhance system performance. This paper presents the design and evaluation of an interleaved flyback DC-DC converter for microi...
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ISBN:
(数字)9798331542108
ISBN:
(纸本)9798331542115
Microinverters are typically integrated with individual solar photovoltaic (PV) modules to enhance system performance. This paper presents the design and evaluation of an interleaved flyback DC-DC converter for microinverters in a methodical approach. The flyback converter is selected for its advantages, including a reduced component count and a simplified architecture. Additionally, it provides electrical isolation, meeting grid code requirements, and offers high voltage gain, making it suitable for a wide range of input voltages. The time-interleaved operation of the DC-DC flyback stage, utilizing multiple parallel switching phases, effectively reduces ripple current, minimizes component stress, and improves power density. An active clamp circuit is incorporated to recover leakage energy. The operational principles, including all modes of operation, are thoroughly explained. The complete design is presented with relevant mathematical formulations and validated through software simulations.
Big Data Progressive Sampling requires initial and final data bounding values to generate optimal number of samples in order to train any learning algorithm. Any learning algorithm can be trained for minimal hypothesi...
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In response to the intermittent power generated by the Wind Energy System (WES) and the Battery Energy storage Systems (BESS) acting as a supplementary source in parallel with WES, the work proposes to determine the o...
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ISBN:
(数字)9798331542108
ISBN:
(纸本)9798331542115
In response to the intermittent power generated by the Wind Energy System (WES) and the Battery Energy storage Systems (BESS) acting as a supplementary source in parallel with WES, the work proposes to determine the optimal size of BESS in kWh. The study uses wind speed data from the WES in Gujarat, India, to determine the forecasting errors. A realistic energy cycle generation targeting forecasting errors is developed using the Normal Distribution Curve. The optimum size in kWh of BESS for supplying the deficit forecasting power is determined, and it is found that the size is not enough to recover the investment cost even in a lifetime by selling the discharged power from the BESS at the Day Ahead Market (DAM) prices of Indian Energy Exchange at each time block. The study then investigates the profitable option through BESS by feeding power to the grid more than the forecasting error at favorable DAM prices and determine the kWh size of the BESS. An iterative technique is proposed for the maximum profitable discharged power. In the investigation process, an upgrade technique is also proposed to upgrade the initial size of the BESS in case of state of Charge (SoC) limit violations. The results are compared with the Genetic Algorithms (GAs) to justify the effectiveness of the proposed technique.
Microgrid is a confined energy system that integrates renewable sources, energy storage and advanced control system. This paper discusses about the management of Energy storage System which is important to ideal use o...
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We are aware about the role of machine learning in engineering domain increasing day by day to ease the life of human being. Now a day's population, globalization and industrialization are increasing which lead to...
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ISBN:
(数字)9798350355611
ISBN:
(纸本)9798350355628
We are aware about the role of machine learning in engineering domain increasing day by day to ease the life of human being. Now a day's population, globalization and industrialization are increasing which lead to increasing electrical consumption and due to this challenges for existing energy infrastructure increased. Entire world looking for the cheap, smooth, easily available and reliable energy resources to fulfill this challenges in efficient way at local level so energy generation and consumption will match to each other. To minimize carbon and other contaminant emissions, there is combination of renewable and clean distributed generation energy is increasing continuously. But integrating these energy into the grid may increase in electrical demand consumption enhance instability. With the help of Electrical load forecasting; we can reduce the grid instability and enhances the strength of power quality. Electrical energy forecasting by referring a historical data modelling plays a major role in base, peak electrical energy consumption, generation balancing; but forecasting an accurate electrical energy data is very tough work due to variation and nonlinearity in load nature. Machine Learning has different forecasting methods based on the previous data set. In this study a dataset is gathered from Industrial Feeder in Nasik rural district, Maharashtra state, India. The aim of this real time case study is to design a model to forecast the electrical energy consumption by using different regression techniques like LR, SVM, LstM, KNN, Bi-layered NN etc. A best fitted model using KNN is identified based on the evaluation parameters values.
Cloud computing is a technology that encompasses several computers interconnected via the Internet or distributedcomputing over a network. A substantial database, services, applications, software, and resources are e...
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ISBN:
(数字)9798331523657
ISBN:
(纸本)9798331523664
Cloud computing is a technology that encompasses several computers interconnected via the Internet or distributedcomputing over a network. A substantial database, services, applications, software, and resources are essential components of this technology. It may execute programs or applications on several interconnected computers simultaneously, enabling users to access apps and resources via a web browser or web service over the Internet at any time and from any location. Despite the numerous advantages of cloud computing, including substantial data storage, dynamic virtualization of resources, and cost efficiency, it concurrently presents several hazards associated with malicious code that impact cloud services. The Trojan horse is a very perilous form of malicious programming that may disrupt cloud computing services and compromise harmful services, applications, or virtual infrastructures within the cloud framework. The sophistication and cunning of contemporary Trojan horse attacks render them more challenging to identify than in the past. Currently, there is no definitive method to classify the Trojan horse in cloud computing. The main contribution of this research is a novel classification of Trojan horses inside a cloud computing environment by applying tests in a cloud control laboratory and using dynamic analysis to monitor the infection of each sample.
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