作者：
Sifi, NejmeddineTouayar, OualidUniv Carthage
Natl Inst Appl Sci & Technol Res Lab Mat Measurements & Applicat INSATDept Genie Phys & InstrumentatCtr Urbain N BP 676 Tunis 1080 Tunisia
A technique to estimate the parameters of an equivalent electrical model of a pyroelectric sensor prototype, based on measurements and an optimization algorithm, is presented. The dynamic behavior predicted by the equ...
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A technique to estimate the parameters of an equivalent electrical model of a pyroelectric sensor prototype, based on measurements and an optimization algorithm, is presented. The dynamic behavior predicted by the equivalent electrical model is compared with measurements, and the effect of some geometrical mismatches is investigated as well. The optimization algorithm is a judicious combination of two search methods: random (a uniform statistical distribution) and deterministic (a conjugate gradient method). The reliability of the optimization algorithm to estimate the parameters values of the equivalent electrical model is also studied and discussed. The sensor equivalent electrical model can be used as a quick and intuitive analysis tool to allow the simulation of the device in a system-level design environment.
The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sou...
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The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal
In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also em...
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In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also emphasizing the environmental impact. In order to measure this impact, the key parameter is the CO 2 emission in the atmosphere. The most powerful mean to satisfy this compromise between economic benefits and emission decrease is represented by the concept of Smart Grid. A Smart Grid implies a joint participation between information network and electric grid. In order to acquire the data from the electric grid, transmit them through the IT network, compute and translate them into commands to the plant devices, an ‘intelligent brain’ is necessary. In order to embed a small local network in the larger VPP a delocalized intelligent device is necessary, able to interface with the Smart Grid. An optimization algorithm performs this function of intelligent delocalized brain by setting different set-points for the energy devices on field. In this paper a purposefully developed optimization algorithm is described, with the aim of optimizing the operations of an existent trigeneration plant managing both RES and fossil energy sources. The plant analysed is a real plant located in central Italy, provided by several generators (PV, CHP, absorption chiller, electric chiller, gas boiler and a wind turbine). The results are yielded by a MATLAB/Simulink simulation tool, where all plant devices are characterized by datasheet information and on-field measurements. The benefits evaluation of the algorithm optimized management is obtained by embedding inside Simulink the optimization logic and executing it during the simulation runtime. The performance is compared with conventional thermal led management operations simulated in the same platform. The comparison is mainly based on economic costs but also considers CO 2 emissions and primary energy consumption. The analysis t
作者：
Li, JunzhiTan, YingPeking Univ
Sch Artificial Intelligence Key Lab Machine Percept MOE Beijing 100871 Peoples R China
Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of h...
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ISBN:
(纸本)9783031096778;9783031096761
Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of heuristics. However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms. In this paper, the metric of information utilization ratio (IUR) is defined, which is the ratio of the utilized information quantity over the acquired information quantity in the search process. The IUR proves to be well-defined. Several examples of typical heuristic algorithms are given to demonstrate the procedure of calculating the IUR. Empirical evidences on the correlation between the IUR and the performance of a heuristic are also provided. The IUR can be an index of how sophisticated an algorithm is designed and guide the invention of new heuristics and the improvement of existing ones.
optimization of the intelligent student educational intelligent cloud architecture based on computer topic generation algorithm is studied in the paper. There is no conflict between reverse proxy mode and packet filte...
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ISBN:
(数字)9781665408370
ISBN:
(纸本)9781665408387;9781665408370
optimization of the intelligent student educational intelligent cloud architecture based on computer topic generation algorithm is studied in the paper. There is no conflict between reverse proxy mode and packet filtering mode or ordinary proxy mode. These two modes can be used in firewall devices at the same time. The reverse proxy mode is used when the external network accesses the internal network. The forward proxy or packet filtering mode is used to reject other external access. Hence, for the designed model, this will be used as the cloud basis architecture. According to the regulatory cloud architecture design, its network can be divided into front-end service network and back-end resource high-speed synchronization network. This type of layered model is used to implement the intelligent student educational smart cloud architecture based on computer topic generation algorithm. Through the testing and implementation, the overall performance is presented.
To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimizat...
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To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.
The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coeffi...
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The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for optimization algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
Electric tractors driven by dual motors have become a critical research topic in the field of pure electric tractor *** a key component of the transmission system,the optimization of the internal parameters of the pow...
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Electric tractors driven by dual motors have become a critical research topic in the field of pure electric tractor *** a key component of the transmission system,the optimization of the internal parameters of the power-coupled transmission gearbox has a crucial impact on the power transmission of the whole *** work combines the characteristics of low speed and high torque during tractor operation,and adopts the transmission form of double motor input and double planetary group coupling output to design the transmission structure of the ***,this paper proposes a dynamic optimization method of the transmission system based on the Improved Deep Deterministic Policy Gradient(IDDPG)algorithm,which realizes the optimization of the gear ratio of the transmission system by constructing a virtual prototype and hardware-in-the-loop simulation *** transport mode,the optimized gear ratios shorten the acceleration time of the tractor from 0-20 km/h by 13.6%and increase the motor efficiency by 10%;in rotary mode,the acceleration performance is improved by 28.5%and the motor efficiency is increased by 5%.The study shows that the proposed method is significantly better than the traditional static design and provides a new technical path for the intelligent optimization of the electric tractor drive train,while promoting the efficient and sustainable development of agricultural machinery.
Recently, discontinuous polymer flooding has been proposed and successfully applied in some offshore oilfields. The performance of discontinuous polymer flooding depends on various operational parameters, such as inje...
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Recently, discontinuous polymer flooding has been proposed and successfully applied in some offshore oilfields. The performance of discontinuous polymer flooding depends on various operational parameters, such as injection timing, polymer concentrations, and crosslinker concentrations of four types of chemical slugs. Because the number of the operational parameters are large and they are nonlinearly related, the traditional reservoir numerical simulation might not simultaneously obtain the optimal results of these operational parameters. In this study, to simulate the discontinuous polymer flooding processes, a simulation model was built using a commercial reservoir simulator (CMG STARS), in which the mechanisms of the four types of chemical slugs were considered, such as polymer viscosification, adsorption, and degradation. Then, a PSO-ICA algorithm was developed by using the PSO algorithm to improve the exploration ability of the ICA algorithm. The codes were written with MATLAB and linked to CMG STARS to perform optimization processes. Finally, the PSO-ICA algorithm was compared with the ICA and PSO algorithms on benchmark functions to verify its reliability and applied to optimize a discontinuous polymer flooding process in a typical offshore oilfield in Bohai Bay, China. The results showed that the developed PSO-ICA algorithm had lower iteration numbers, higher optimization accuracy, and faster convergence rate than these of PSO and ICA, indicating that it was an effective method for optimizing the operational parameters of discontinuous polymer flooding processes. Compared to the continuous polymer flooding, the discontinuous polymer flooding had a higher oil production rate, a lower water cut, and a lower residual oil saturation. The net present value of the optimal scheme of discontinuous polymer flooding reached 7.49 x 108 $, which is an increase of 6% over that of the scheme of continuous polymer flooding. More research including selecting more reasonable
A self-adaptive algorithm is proposed in this study to optimize optical parameters, such as correlated color temperature (CCT), color rendering index (CRI), and synthesized spectra, in a trichromatic white light-emitt...
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ISBN:
(数字)9781510652095
ISBN:
(纸本)9781510652095;9781510652088
A self-adaptive algorithm is proposed in this study to optimize optical parameters, such as correlated color temperature (CCT), color rendering index (CRI), and synthesized spectra, in a trichromatic white light-emitting diode (WLED) system. The key contributions of this algorithm include automatically finding target CCT values in different seasons and time periods, and optimizing the CRI values simultaneously under various CCT values. Comparing with the conventionally used algorithm for spectrum optimization, the realization of target values and running time of the proposed algorithm behaves excellent, which are significant in realization of real-time and automatic control in intelligent lighting.
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