Supplying load demand and achieving a balance in distribution network considering the lowest operation cost is a significant concerns of electricity distribution companies, particularly in rural or private power netwo...
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ISBN:
(纸本)9798350337938
Supplying load demand and achieving a balance in distribution network considering the lowest operation cost is a significant concerns of electricity distribution companies, particularly in rural or private power networks with in the single or multi microgrid system. To address the electrical load demand in multi zone-microgrid with a focus on minimizing operation cost, a Chromosome matrix [n*m] can be utilized;where "m" represents the number of generation units and "n" denotes the consumption units. A geneticalgorithm has been employed to determine the optimal distribution of generation among units in the most economically efficient manner for multi-microgrid consumers. This paper presents an accurate solution for microgrids using a modified genetic algorithm, which takes into account the specific number of population members and the number of iterations across three scenarios. The modified genetic algorithm model calculates the best response by considering violation in different modes, including the island operation of microgrids in various zones, the operation of a standalone multimicrogrid system, and and connection of the multi-microgrid system to the grid. The proposed model is implemented on an IEEE 33 standard bus and determines the lowest and most precise microgrid operation cost for each mode in three cases.
This study presents an integrated framework for optimizing combined cooling, heating, and power (CCHP) systems coupled with thermal energy storage (TES), aiming to enhance operational efficiency, cost-effectiveness, a...
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This study presents an integrated framework for optimizing combined cooling, heating, and power (CCHP) systems coupled with thermal energy storage (TES), aiming to enhance operational efficiency, cost-effectiveness, and environmental sustainability. The model incorporates real-time optimization strategies, demand flexibility mechanisms such as load shifting, and dynamic adjustments to the performance of grid-sourced electricity and power generation units (PGUs). Key methodological innovations include the formulation of refined energy flow equations, the deployment of demand-side management (DSM) for improved utilization of available energy, and the coordination of cooling and heating demands under variable environmental conditions. To address the complexity of the optimization task, a modified genetic algorithm (MGA) is proposed, incorporating chaotic initialization, variable population sizing, and adaptive local search techniques, thereby improving solution accuracy and convergence dynamics. The efficacy of the proposed approach is validated through simulation results, which demonstrate notable improvements in energy efficiency, cost savings, and reductions in carbon emissions. The model also accounts for uncertainties associated with load forecasting, operational performance, and environmental factors, making it a robust and adaptable solution for optimizing CCHP systems. Performance comparisons indicate that MGA consistently outperforms particle swarm optimization (PSO) and the secretary bird optimization algorithm (SBOA), achieving a 2.2 % and 1.65 % reduction in the mean objective function value (MOFV), respectively. Additionally, MGA yields faster results-14.94 % quicker than PSO and 20.43 % faster than SBOA. In Scenario 2, operational costs were reduced by 0.41 %, from 1234 to 1229 cents, highlighting MGA's superior optimization capability and computational efficiency.
The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deploymen...
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The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deployment of IoT devices has enabled the provision of constant environmental feedback, thereby facilitating the automated adaptation of associated systems. This has brought about a fundamental transformation in the way contemporary society functions. The security of emerging technologies such as IoT has become a significant challenge due to the added complexities, misconfigurations, and conflicts between modern and legacy systems. This challenge has a notable impact on the reliability and accessibility of existing infrastructure. Edge computing (EC) is a collaborative computing system that brings data processing and analysis closer to the edge of the network, where the data is generated, rather than in a centralized cloud environment. The utilization of the IoT has become more prevalent in both everyday life and the manufacturing sector, with a particular emphasis on critical infrastructure. The IoT is presently being utilized across diverse domains, including but not limited to industrial, agricultural, healthcare, and logistical sectors. The security of IoT networks has implications for the safety of individuals, the security of the nation, and economic development. Notwithstanding, conventional intrusion detection techniques that rely on centralized cloud-based systems that have been suggested in previous studies for IoT network security are insufficient to meet the requirements for data confidentiality, network capacity, and prompt responsiveness. In addition, the integration of IoT applications into smart devices has been shown to augment their functionalities. However, it is important to note that this integration also brings about potential security vulnerabilities. Furthermore, a significant number of contemporary IoT devices exhibit restricted security
A super-lift mechanism has made tremendous progress in DC/DC conversion technology. In comparison to the asymmetrical form of MLI, the novel Asymmetric Multilevel Inverter (AMLI) technology proposes a minimized number...
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A super-lift mechanism has made tremendous progress in DC/DC conversion technology. In comparison to the asymmetrical form of MLI, the novel Asymmetric Multilevel Inverter (AMLI) technology proposes a minimized number of components. The Fuzzy-PI (Proportional integral) and modified genetic algorithm (MGA) utilizes to minimize the harmonic content considerably using a variety of modulation index and firing angle values in open-loop and closed-loop control. This architecture for designing single-phase 7-level AMLI with an intelligent algorithm proposed for Renewable Energy (RE) applications. This circuit uses a single MOSFET switch with less switching stress and a single DC source. The effectiveness of the proposed MGA optimization eliminates the lower-order harmonics. MGA and Fuzzy-PI based Distributed Power Flow Intelligent Control (DPFIC) algorithms are applied with multilevel structures while maintaining the fundamental frequency for both MATLAB platform and hardware implementation. During this analysis, the losses is also find to investigate the influence of modulation index and output power factor on inverter efficiency. Simulations and experimental findings confirm the proposed inverter capacity to create high-quality multilayer output voltage. However, the proposed closed loop simulation circuit gives 0.47% minimum THD level, and 10.4% in experimental results.
The need for energy is at an increasing pace and it cannot be easily fulfilled by the normal energy systems which affect the environment in various ways. To overcome this problem, a hybrid power system (HPS) is used n...
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The damping enhancement effect of the inerter system means that its energy dissipation efficiency can be improved with respect to the traditional dampers. Energy dissipation efficiency have been considered as the opti...
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The damping enhancement effect of the inerter system means that its energy dissipation efficiency can be improved with respect to the traditional dampers. Energy dissipation efficiency have been considered as the optimal design principle of the inerter system, however, the solution for optimized key parameters is difficult because of the special mechanical behavior of the inerter. A modified float-point encoding geneticalgorithm is proposed in this study to realize the optimal design of the inerter system with maximized energy dissipation efficiency effectively and robustly. A novel and simple crossover strategy termed differential crossover is proposed and applied in the classical geneticalgorithm to optimize the inerter system more effectively. The differential crossover strategy means that a new individual is generated based on the difference between two randomly selected individuals in the population. The mathematical expression for the optimization problem of the inerter system corresponding to the maximum energy dissipation efficiency design principle is established. Following the performance-oriented design concept, performance demand is taken as the constrained condition of the optimization problem. Case design confirms that the modified genetic algorithm can successfully solve the optimization problem of the inerter system and perform a better solving ability over the original geneticalgorithms.
Arbitrary waveform generator (AWG) can generate various excitation signals flexibly and is widely used in the automatic testing system (ATS). With the continuous evolution of electronic science and technology, higher ...
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ISBN:
(纸本)9781728154008
Arbitrary waveform generator (AWG) can generate various excitation signals flexibly and is widely used in the automatic testing system (ATS). With the continuous evolution of electronic science and technology, higher demand for the AWG's sampling rate and output bandwidth has been put forward. Frequency-interleaved Digital-to-analog converter (FI-DAC) can improve those parameters quite effectively. However, the phase deviation between sub-band paths in the FI-DAC will cause a severe error in the overlapping band when the sub-band signals are combined. Therefore, we set up the error model for the FI-DAC overlapping band, analyzed the impact of the phase deviation of the output signal, and proposed a phase compensation method based on all-pass filter. The all-pass filter coefficient solution for phase compensation is a non-linear least square (NLS) problem and is usually solved using meta-heuristics. Yet the traditional geneticalgorithm (GA) has a slow convergence speed, and the particle swarm optimization (PSO) tends to fall into local optimal when solving for high-order filter coefficient. Hence, we analyzed the parametric characteristics of all-pass filter and proposed a modified GA (MGA) to solve filter coefficients, compensate for the phase deviation between the sub-bands, and guarantee the quality of the final synthesized signals. The experiment result shows that, under the same number of iterations, the root mean square (RMS) error of the traditional GA is 0.1736 rad, PSO is 0.7725 rad, while the error of our MGA is only 0.0387rad, which is significantly better than the conventional method.
The location information of mobile target can be mapped between physical space and signal space, which is one of the key technologies for real-time tracking and multi-machine cooperation. Benefiting from the distribut...
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The location information of mobile target can be mapped between physical space and signal space, which is one of the key technologies for real-time tracking and multi-machine cooperation. Benefiting from the distributed perception and ubiquitous communication capabilities, wireless sensor networks (WSNs) can be used for target real-time positioning indoor and outdoor. However, various sensor noises and environmental interferences bring uncertainty to target positioning, resulting in inconsistent positioning performance. Hence, this paper proposes a coarse to accurate noise-tolerant positioning evaluation for mobile target based on modified genetic algorithm. Considering the uncertainty of various measurements in WSNs, the preliminary positioning results are estimated by total least squares. Taking the preliminary results as the initial search values, the modified genetic algorithm is used to refine the target positioning accuracy with use of adaptively adjusted crossover probability and improved mutation operation. In addition, the theoretical accuracy model is derived by the Cramer-Rao Lower Bound (CRLB) as a benchmark. Comprehensive experiments are conducted through the simulation and testing platform. The experimental results of the proposed algorithm have a 0.25 m average error and 0.152 error variance. The result trends of simulation are consistent with the result of experimental platform, which can validate the superior accuracy of the proposed algorithm compared with relevant positioning algorithms.
The problem of localization in under water sensor nodes has led to proposal of many techniques over the past few decades that depend primarily on Time of Arrival and Time Difference of Arrival. While these techniques ...
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The problem of localization in under water sensor nodes has led to proposal of many techniques over the past few decades that depend primarily on Time of Arrival and Time Difference of Arrival. While these techniques are intuitively very appealing and easy to deploy, accurate node localization in dynamic under water environment has remained elusive. Sensor nodes deployed underwater tend to move from their original positions due to water currents and hence their exact positions at a given moment of time are not known with precision. Due to inherent drawbacks of radio signal propagation in underwater environment, localization of sensor nodes depends on acoustic signals. In this paper, we propose a Doppler shift based localization followed by a geneticalgorithm based optimization technique that improves accuracy in localizing unknown nodes in underwater sensor networks. The proposed technique envisages sink nodes playing a pivotal role in taking over a bulk of the computational load on account of being comparatively more accessible and serviceable as compared to any other nodes in the network that are deployed underwater. The algorithm relies on observed frequency shifts (Doppler shift) of sound waves compared to actual, that happen when source and observer are mobile as they do in a marine environment. While Doppler shift determines the approximate location of an unknown sensor node, geneticalgorithm minimizes the error in localization. Our proposed methodology has much lower localization error as compared to existing protocols.
To fend off the ossification of Internet architecture, virtual network embedding has been propounded as one of the most important techniques to address this issue. Virtual network embedding is a process that consists ...
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To fend off the ossification of Internet architecture, virtual network embedding has been propounded as one of the most important techniques to address this issue. Virtual network embedding is a process that consists of two stages including node mapping stage and link mapping stage, the aim of node mapping stage is to map the virtual nodes from virtual network requests (VNRs) onto the substrate nodes meanwhile satisfying the CPU capacity constraints on nodes, the goal of link mapping stage is to map the virtual links from VNRs onto the substrate paths while satisfying the bandwidth resource constraints on links. This paper proposed a virtual network embedding algorithm based on modified genetic algorithm, improved the classical geneticalgorithm from three aspects: population initialization strategy, improved mutation operation and improvement operation, took advantage of the selection operation, crossover operation, mutation operation, feasibility checking operation, and utilized the fitness function to choose the best chromosome. Simulation results indicated that our proposed method has significantly increased the acceptance ratio of VNRs and the long-term average revenue of Infrastructures (InPs) compared with other two state-of-the-art algorithms.
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