This paper presents evaluations among the most usual maximum power point tracking (MPPT) techniques, doing meaningful comparisons with respect to the amount of energy extracted from the photovoltaic (PV) panel [tracking factor (TF)] in relation to the available power, PV voltage ripple, dynamic response, and use of sensors. Using MatLab/Simulink and dSPACE platforms, a digitally controlled boost dc-dc converter was implemented and connected to an Agilent Solar Array E4350B simulator in order to verify the analytical procedures. The main experimental results are presented for conventional MPPT algorithms and improved MPPT algorithms named IC based on proportional-integral (PI) and perturb and observe based on PI. Moreover, the dynamic response and the TF are also evaluated using a user-friendly interface, which is capable of online program power profiles and computes the TF. Finally, a typical daily insulation is used in order to verify the experimental results for the main PV MPPT methods.

Evaluation of the Main MPPT Techniques for Photovoltaic Applications

Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of the model parameters. Bayes theorem states that the posterior probability, p ( H | Y ˜ ) of a hypothesis, H is proportional to the product of the prior probability, p(H) of this hypothesis and the likelihood, L ( H | Y ˜ ) of the same hypothesis given the new observations, Y ˜ , or p ( H | Y ˜ ) ∝ p ( H ) L ( H | Y ˜ ) . In science and engineering, H often constitutes some numerical model, ℱ(x) which summarizes, in algebraic and differential equations, state variables and fluxes, all knowledge of the system of interest, and the unknown parameter values, x are subject to inference using the data Y ˜ . Unfortunately, for complex system models the posterior distribution is often high dimensional and analytically intractable, and sampling methods are required to approximate the target. In this paper I review the basic theory of Markov chain Monte Carlo (MCMC) simulation and introduce a MATLAB toolbox of the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm developed by Vrugt et al. (2008a, 2009a) and used for Bayesian inference in fields ranging from physics, chemistry and engineering, to ecology, hydrology, and geophysics. This MATLAB toolbox provides scientists and engineers with an arsenal of options and utilities to solve posterior sampling problems involving (among others) bimodality, high-dimensionality, summary statistics, bounded parameter spaces, dynamic simulation models, formal/informal likelihood functions (GLUE), diagnostic model evaluation, data assimilation, Bayesian model averaging, distributed computation, and informative/noninformative prior distributions. The DREAM toolbox supports parallel computing and includes tools for convergence analysis of the sampled chain trajectories and post-processing of the results. Seven different case studies illustrate the main capabilities and functionalities of the MATLAB toolbox.

/pdf/markov-chain-monte-carlo-simulation-using-the-dream-software-2k57zrst2y.pdf

Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

First Year – Fall ......................... .............................................................................................. Credits MAT 210 ..................................... Calculus I ............................................................................. 4.0 ENG 110...................................... Expository Writing ............................................................. 3.0 CHE 210 ...................................... Chemistry I .......................................................................... 4.0 HUM 100 OR SOC 101 OR PSY 101 ........................................................................................ 6.0 Subtotal ...................................... .............................................................................................. 17

Chemical Engineering Scienceについて

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Afsar%20-%20Clustering%20in%20Sensor%20Networks%20-%202014.pdf

Clustering in sensor networks

Thank you very much for reading elements of chemical reaction engineering. As you may know, people have search numerous times for their favorite books like this elements of chemical reaction engineering, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some harmful bugs inside their desktop computer. elements of chemical reaction engineering is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the elements of chemical reaction engineering is universally compatible with any devices to read.

Elements Of Chemical Reaction Engineering

Traffic flow control optimization in the traffic light systems is studied for improvement in this paper. Traffic light systems are built to control the traffic flows at the intersections to ensure the fluency of traffic flow within the traffic network. The increasing traffic flows that cannot be supported by the current traffic light systems cause lengthen of queue length at the intersection. The effect of queue length, green time, cycle time and amber time in the traffic system is observed and studied through simulations. Longer green time will pass through more vehicles, but it will increase the cycle time at the same time which causes more vehicles to accumulate at the intersection during the waiting time. Genetic algorithm is introduced in this paper for the optimization of the traffic flow control as its ability to find the optimized solution in its self tuning process. Genetic algorithm taking current queue length as its input then it will output the optimized green time for the intersection. The result of Genetic algorithm is further improved with the introduced of the incoming traffic flow during red time of each phase.

Optimization of Traffic Flow within an Urban Traffic Light Intersection with Genetic Algorithm

This paper presents fuzzy based perturb and observe maximum power point tracking in solar panel. The solar system is modelled and analysed in MATLAB/SIMULINK. The photovoltaic panel has an optimal operating voltage where the PV panel can produce maximum power at this particular point. Due to the nonlinearity of the voltage-current characteristic in solar panel, it is difficult to determine analytically the maximum power operating voltage that varies with the change of solar irradiance and cell temperature. Maximum power point tracking (MPPT) is implemented to identify the maximum power operating point, subsequently regulate the solar panel to operate at that particular operating voltage for maximum power gaining. Perturb and observe (P&O) MPPT and fuzzy based optimized P&O MPPT are developed and the performances of both controllers are examined at variable solar irradiances at different temperatures. Simulation results show that fuzzy based P&O MPPT has better performance where it can facilitate the solar panel to produce a more stable power.

/pdf/fuzzy-logic-based-mppt-for-photovoltaic-modules-influenced-3cqxua8qgz.pdf

Fuzzy Logic Based MPPT for Photovoltaic Modules Influenced by Solar Irradiation and Cell Temperature

Evaluation for Voltage Stability Indices in Power System Using Artificial Neural Network

Traffic congestion in the urban area occurs more frequent than the past due to rapidly increasing on road vehicle usage rates. It could seriously hinder the development of urban area if a well management system has not being established. These scenarios necessitate the development of advance traffic management systems to increase the performance of signalized intersection. Traffic signal timing management (TSTM) system which comprise of genetic algorithm based optimization is proposed. Using a proper TSTM system, network traffic flow can be improved with considerably less cost than other infrastructural improvements. The proposed genetic algorithm based optimization approach allows signal timing parameters such as offset, cycle time, green split and phase sequence to be optimized with objective of minimum delay and better traffic fluency. The proposed GATSTM system has the ability to handle and manage the dynamic changes of the traffic networks condition by calibrating the system parameters accordingly.

Multiple intersections traffic signal timing optimization with genetic algorithm

Solar photovoltaic (PV) array which is exposed to the uniform solar irradiance shows the non-linear P-V characteristic. Nevertheless, the P-V characteristic becomes more complex with multiple maximum power point (MPP) when the array is operated under partially shaded condition. Conventional maximum power point tracking (MPPT) approach which is designed to track the MPP will be trapped at the local MPP. Therefore, one of the challenges is to continuously track the absolute MPP while the environmental factors such as solar irradiation, PV temperature and partial shaded condition are rapidly changing. This paper presents a novel method of parallel tracking function to assist the on-line fuzzy logic perturb and observe (P&O) MPPT to continuously search beyond the trapped MPP operating voltage point. The information of PV array such as the operating voltage and the corresponding generation current are stored in the database. The characteristics of the shaded array can be estimated via simulation and the absolute MPP is identified via the tracking function. The simulation results show that the enhanced fuzzy logic P&O MPPT is capable of tracking the real absolute MPP during the partially shaded condition.

https://msclab.files.wordpress.com/2011/01/maximum-power-point-tracking-for-pv-array-under-partially-shaded-conditions.pdf

Kenneth Tze Kin Teo

Papers

Optimization of Traffic Flow within an Urban Traffic Light Intersection with Genetic Algorithm

Fuzzy Logic Based MPPT for Photovoltaic Modules Influenced by Solar Irradiation and Cell Temperature

Evaluation for Voltage Stability Indices in Power System Using Artificial Neural Network

Multiple intersections traffic signal timing optimization with genetic algorithm

Maximum Power Point Tracking for PV Array Under Partially Shaded Conditions