Probability Distribution Analysis Research Articles

Poisson probability distribution analysis of Makurdi and Abeokuta rainfalls

Early information for sustainable utilization of water resources through poisson probability distribution of rainfall is an important regulatory measure for flood control and water security management. As a follow-up to our previous studies on distributions, this paper reports statistical goodness-of-fit evaluations of selected rainfall data. It is the utilization of the maximum likelihood method (MLM) for the poisson probability distribution (PPD)of selected rainfall data. The numerically estimated constant of the density of PPD was estimated by the MLM, and Microsoft Excel Solver (MES). These estimated constants were used to compute probabilities of poisson distributions. The computed probabilities using the constants obtained were evaluated statistically (analysis of variance, (ANOVA), relative error, model of' selection criterion (MSC), Coefficient of Determination (CD) and Correlation coefficient (R). The study established that the poisson probability distribution’s parameter (p) was the average of the logarithm to base 10 of rainfall using the MLM and MES estimators. The constants were found to be 0.665 and 0.535 for Makurdi, 0.695 and 0.478 for Abeokuta using MLM and MES, respectively. The relative errors were found to be 0.479 and 0.743, and 1.141 and 1.509 for Makurdi and Abeokuta using MLM and MES, respectively. The correlation coefficient for Makurdi and Abeokuta using MLM and MES were found to be 0.876 and 0.800, and 0.269 and 0.341, respectively. It was concluded that the MLM constant was better than MES based on the values of MSC, CD, relative error and R. MLM predicted Weibull probability of rainfall intensity better than MES. Utilization of PPD in the estimation of rainfall intensity will help in the prediction of rainfall for agriculture in attaining sustainable goal 2 (zero hunger), regulatory measures for flood control and water security management. There is a need to evaluate MLM and other probability distributions to further assist in attaining sustainable development goals.

On the analysis of power law distribution in software component sizes

AbstractComponent‐based software development (CBSD) is an active area of research. Ascertaining the quality of components is important for overall software quality assurance in CBSD. One of the important metrics for measuring defects, analyzability, efforts, and cost in CBSD is component size. The paper presents an analytical model based on maximization of Tsallis entropy to obtain closed form expression for component size distribution (maximum Tsallis entropy component size distribution, MTECSD) in steady state. It is found that the component size distribution follows power law asymptotically. A procedure based on generalized Jensen–Shannon measure is developed to estimate model parameters. A detailed analysis of many popular probability distributions along with MTECSD is carried out on many diverse real data sets of component‐based softwares. The analysis reveals that lognormal and MTECSD distributions fit well to component sizes in many software conforming the presence of power law behavior. The software whose component size distributions are described by MTECSD are in equilibrium implying that new defects in these software systems occur occasionally. Power law behavior in component sizes also imply high variation leading to difficulty in software analyzability. The precise knowledge of component size distribution also provides an alternative method to compute efforts and cost estimates by modified COCOMO model.

Wavelet transform for medium-range streamflows projections in national interconnected system

This work aims to analyze the variability of average annual streamflow time series of the SIN (Brazil) and create a projection model of future streamflow scenarios from 3 to 10 years using wavelet transform. The streamflow time series were used divided into two periods: 1931 to 2005 and 2006 to 2017, for calibration and verification, respectively. The annual series was standardized, and by the wavelet transform, it was decomposed into two bands plus the residue for each Base Posts (BP) for later reconstruction. Then an autoregressive model per band and residue was made. The projection was obtained by adding the autoregressive models. For performance evaluation, a qualitative analysis of the cumulative probability distribution of the projected years and the likelihood were made. The model identified the probability distribution function of the projected years and obtained likelihood greater than 1 in most of the SIN regions, indicating that this methodology can capture the medium-range variability.

A novel methodology for identifying appliance usage patterns in buildings based on auto-correlation and probability distribution analysis

In today’s society, a current concern is to mitigate the risks of global climate change. Throughout the years there have been several initiatives to achieve more sustainable energy distribution in buildings. In this work, a new methodology is proposed for identifying appliance consumption patterns in buildings. It consists of, at first, conducting a seasonality analysis based on the Auto-Correlation Function for detecting the different appliance use patterns that arise in a given time window. Then, it is conducted a Probability Distribution Analysis based on the auto-correlation results and the calculation of an informative measure to select the prevailing consumption pattern. The methodology enables to distinguish between different use patterns for a given appliance for each building at specific time intervals, e.g., the seasons of the year. For the purpose of illustration, the methodology is applied to consumption data of four appliances selected from a domestic energy consumption dataset (REFIT) over one year. The results provide several insights on how a given appliance use evolves throughout the seasons for each household, and also highlighting use similarity for different appliances across the seasons. These results would be, otherwise, hidden away, and would require an individual analysis of consumption patterns of each appliance. Consequently, the methodology provides a consistent mechanism to identify different user profiles.

Property Rights Freedom and Innovation: Eponymous Skills in Women's Gymnastics

Prior research uses the collapse of Soviet-style communism in 1991 as a de facto experimental framework within which to examine the impact of prospective benefits on the motivation of athletes to succeed in the Olympic Games. Prior to the collapse, successful Soviet Bloc Olympians were provided extraordinary living conditions and lifestyles. These rewards evaporated with the demise of the Soviet Union in 1991, subsequently resulting in relatively poorer Olympic performances of Soviet Bloc athletes. The current study extends earlier work by investigating the impact of appropriability on the supply of innovation by examining the frequency of eponymous skills in women's gymnastics before and during the transition to a new market-based economic order. Our central hypothesis is that following the dissolution the communist governments of the Soviet Bloc and its satellites, the supply of innovation in the form of eponymous skills in women's gymnastics from these countries has fallen. Frequency distributions of eponymous skills in women's gymnastics both prior to and after the dissolution of the aforementioned communist regimes support this hypothesis, as do results from goodness-of-fit tests and stochastic dominance analysis of joint probability distributions.

Presence of Maximal Characteristic Time in Photoluminescence Blinking of MAPbI3 Perovskite

AbstractPhotoluminescence (PL) blinking is a common phenomenon in nanostructured semiconductors associated with charge trapping and defect dynamics. PL blinking kinetics exhibit very broadly distributed timescales. The traditionally employed analysis of probability distribution of ON and OFF events suffers from ambiguities in their determination in complex PL traces making its suitability questionable. Here, the statistically correct power spectral density (PSD) estimation method applicable for fluctuations of any complexity is employed. PSDs of the blinking traces of submicrometer MAPbI3 crystals at high frequencies follow power law with excitation power density dependent parameters. However, at frequencies less than 0.3 Hz, the majority of the PSDs saturate revealing the presence of a maximal characteristic timescale of blinking in the range of 0.5–10 s independently of the excitation power density. Super‐resolution optical microscopy shows the characteristic timescale to be an inherent material property independent of polycrystallinity. Thus, for the first time the maximum timescale of the multiscale blinking behavior of nanoparticles is observed demonstrating that the power law statistics are not universal for semiconductors. It is proposed that the viscoelasticity of metal‐halide perovskites can limit the maximum timescale for the PL fluctuations by limiting the memory of preceded deformations/re‐arrangements of the crystal lattice.

Influence of Hydrostatic Pressure on the Pitting Corrosion Behavior of API X80 Steel

The effect of hydrostatic pressure on the metastable and stable pitting corrosion of API X80 steel was investigated in a simulated deep-sea environment. Cumulative probability distribution analysis of metastable pitting events revealed that hydrostatic pressure promoted the occurrence of metastable pitting corrosion and the transformation of metastable pitting corrosion to stable pitting corrosion. According to the results of scanning Kelvin probe force microscopy tests, pitting corrosion of the test steel originated at the interface between inclusions and the substrate. Combined with finite element analysis, the mechanism by which hydrostatic pressure promoted the occurrence and propagation of stable pitting corrosion of test steel in the simulated deep-sea environment was explained.

Assessment of wind-induced fragility of transmission towersunder quasi-static wind load

Overhead power transmission line systems consisting of long-span conductors and high-rise towers are wind-sensitive structures featured with significant structural nonlinearity and fragility under wind hazards. To assess wind-induced structural fragility of a transmission tower, a novel efficient quasi-static approach, which is based on the analytical probability distribution of extreme wind effect in frequency domain and the probabilistic wind-resistant capacity, is developed in the present study. The 90-degree wind direction (perpendicular to conductors), which is always the worst scenario, is considered in this paper. The structural nonlinearity and failure modes are captured using a nonlinear static push-over analysis method, which simulates the failure process of the tower structure with random initial geometric defects. Wind-resistance performance of the tower is quantified based on the principle of energy equivalence. Damage of the tower is classified into three levels including slight damage, severe damage, and collapse. The tower fragility curve, which predicts damage of the tower as a function of wind speed, is presented and discussed.

Analysis of Probability Distribution due to Aspiration Level in Fuzzy Matrix Game

In this paper we studied zero-sum matrix game. These games have been represented in form of fuzzy linear programming problems which consists the aspiration level and tolerance level. The aspiration levels are determined on the basis of payoff matrix instead of using probability distribution over the strategies. For a particular tolerance level, the aspiration level is studied and the result are illustrated graphically.

Abstract P242: The Role Of Kappa Opioid Receptors In The Development Of Hypertension And Kidney Injury In Sprague-Dawley Rats

The extensive use of opioid-based pain management strongly correlates with poor cardiovascular and cardiorenal outcomes. Our recent studies suggest that treatment with kappa opioid receptor (KOR) agonist BRL 52537 leads to the progression of chronic kidney disease (CKD) and aggravation of salt-sensitive hypertension. We hypothesize that stimulation of KORs leads to blood pressure elevation, albuminuria, and kidney damage in healthy Sprague-Dawley (SD) rats. To characterize the effect of the KOR agonist BRL 52537 on the development of blood pressure and kidney function in vivo , SD rats were treated with a daily i.v. bolus infusion of BRL 52537 or a corresponding vehicle. To test the contribution of KOR stimulation on calcium homeostasis in podocytes, BRL 52537 was used on freshly isolated glomeruli from SD rats. Single-channel analysis was applied to assess the effect of KORs stimulation on TRPC6 channel activity in the human immortalized podocytes. Chronic treatment with BRL 52537 leads to increased mean arterial pressure (88±1 vs 101±4 mmHg, vehicle vs treated, p<0.05), podocyte basal calcium (90±12 vs 216±16 a.u., vehicle vs treated, p<0.05), and GFB impairment in SD rats which is reflected by a transient increase in albumin excretion (Alb/cre ratio 0.35±0.1 vs 0.72±0.2, vehicle vs treated, p<0.05). Cumulative probability distribution analysis of the glomerular injury score revealed a rightward shift toward a high glomerular injury score in the group treated with BRL 52537 (p<0.05). Angiotensin II level was higher in a BRL-treated group (156±17 vs 232±59 pmol, vehicle vs treated, p=0.065); however, it did not reach a statistical difference. Acute application of BRL 52537 resulted in sustained calcium response (0.23±0.01 a.u., Fluo4/FuraRed, maximum calcium response) in freshly isolated glomeruli from SD rats. Furthermore, patch-clamp experiments in human immortalized podocytes (cell-attached configuration) revealed that BRL 52537 activated TRPC6 channels. Taken together, these data support the hypothesis that administration of opioids in SD rats leads to activation of the KOR/TRPC6 pathway, which in turn led to glomerular filtration barrier impairment, increased glomerular damage, and blood pressure elevation.

A search for satellite galaxies of nearby star-forming galaxies with resolved stars in LBT-SONG

ABSTRACT We present results from a resolved stellar population search for dwarf satellite galaxies of six nearby (D < 5 Mpc), sub-Milky Way mass hosts using deep (m ∼ 27 mag) optical imaging from the Large Binocular Telescope. We perform image simulations to quantify our detection efficiency for dwarfs over a large range in luminosity and size, and develop a fast catalogue-based emulator that includes a treatment of unresolved photometric blending. We discover no new dwarf satellites, but we recover two previously known dwarfs (DDO 113 and LV J1228+4358) with MV < −12 that lie in our survey volume. We preview a new theoretical framework to predict satellite luminosity functions using analytical probability distribution functions and apply it to our sample, finding that we predict one fewer classical dwarf and one more faint dwarf (MV ∼ −7.5) than we find in our observational sample (i.e. the observational sample is slightly top-heavy). However, the overall number of dwarfs in the observational sample (2) is in good agreement with the theoretical expectations. Interestingly, DDO 113 shows signs of environmental quenching and LV J1228+4358 is tidally disrupting, suggesting that low-mass hosts may affect their satellites more severely than previously believed.

Spatial distribution of inselbergs in Igbajo district of Western Nigeria

Inselbergs’ spatial distribution is the primary focus of this study carried out in Igbajo District of Western Nigeria. Additionally, a common hypothesis that spatial distribution of inselbergs is informed by the differing modes of evolution of the landforms was also examined. Inselbergs are defined as points on the topographic sheet and satellite terrain elevation data and are thereafter subjected to point pattern analysis. Two point pattern analyses: Poisson Probability Distribution (PPD) and Nearest Neighbour Analysis (NNA) are employed to determine the pattern of inselberg distribution over space. Additional analysis focused on the relationship between regional river drainage and inselbergs’ spatial distribution. Findings from the study confirmed that inselberg form a clustered distribution. The PPD results suggest that the observed distribution of inselbergs has a statistically significant difference from the expected pattern generated by the random process (c2 test statistic = 49.82 > =12.59 (α= 0.05) and 16.81 (α= 0.01). Similarly, the NNA with a R < 1 (Nearest Neighbour Ratio, R = 0.92) and p-value = -2.52, also indicated a clustered distribution. Analysis of river drainage pattern similarly revealed a cluster of high rising landforms situated in the centre of the regional watershed. It is concluded that inselberg distribution in the study area is clustered although the observed pattern does not necessarily validate or invalidate the alternative evolutionary theories.

Analysis of Probability Distribution of Muzzle Velocity for Chrome Plated Barrel

To confirm the change of muzzle velocity and the most suitable probability distribution model of the 155 mm K9 howitzer barrel with chrome plating and changed rifling. Using a statistical program, the muzzle velocity were plotted on a normal distribution, a 2-parameter and 3-parameter Weibull distribution on a probability paper. Also, statistical parameters were estimated and muzzle velocity fitness test and probability of K676 charge were plotted. In both the chrome-plated with standard rifling and changed rifling for K9 barrel, the 2-parameter and 3-parameter Weibull distribution were skewed to the left compared to the normal distribution. It was confirmed that the muzzle velocity of the K9 barrel with chromium-plated is suitable for the normal distribution and 3-parameter Weibull distribution model.

Design and analysis of statistical probability distribution and non-parametric trend analysis for reference evapotranspiration.

Accurate estimates of reference evapotranspiration are critical for water-resource management strategies such as irrigation scheduling and operation. Therefore, knowledge of events such as spatial and temporal reference evapotranspiration (ETo) and their related principle of statistical probability theory plays a vital role in amplifying sustainable irrigation planning. Spatiotemporal statistical probability distribution and its associated trends have not yet has explored in Pakistan. In this study, we have two objectives: (1) to determine the most appropriate statistical probability distribution that better describes ETo on mean monthly and seasons wise estimates for the design of irrigation system in Khyber Pakhtunkhwa, and (2) to check the trends in ETo on a monthly, seasonal, and annual basis. To check the ETo trends, we used the modified version of the Mann-Kendall and Sen Slope. We used Bayesian Kriging for spatial interpolation and propose a practical approach to the design and study of statistical probability distributions for the irrigation system and water supplies management. Also, the scheme preeminent explains ETo, on a monthly and seasonal basis. The statistical distribution that showed the best fit ETo result occupying 58.33% and 25% performance for the design of irrigation scheme in the entire study region on the monthly level was Johnson SB and Generalized Pareto, respectively. However, according to the Anderson-Darling (AD) and Kolmogorov–Smirnov (KS) goodness of fit measure, seasonal ETo estimates were preferably suited to the Burr, Johnson SB & Generalized Extreme Value. More research work must be conduct to assess the significance of this study to other fields. In conclusion, these findings might be helpful for water resource management and policymaker in future operations.

Structure-based framework for the design and risk assessment of hydraulic structures, with application to offline flood detention basins

The design of hydraulic structures for water control is based on the principle of accepting that structures might fail to fulfill their intended purpose as consequence of extreme, yet rare events producing damages. Traditionally, structures are sized to resist to a “design event”, i.e. a hyetograph or hydrograph associated to a fixed probability of occurrence. This implicitly assumes that the probability of structure failure equals that of the hydrological load, although unjustified under general conditions. Indeed, structure failure also depends on the failure mechanism – which is a characteristic of the specific structure and problem at hand – ruling the interaction between the hydrological load and the structure itself. To overcome the limitations of the hydrological design event approach, we provide here a general, consistent, “structure-based” risk framework, for design or risk assessment. The framework combines the multivariate statistical distribution of the hydrological process and the hydraulic response of the structure. Remarkably, it reduces the potential complexity of the problem to the statistical analysis of the univariate probability distribution of the damage, i.e. the consequence of structure failure. The approach is exemplified for an offline reservoir for flood mitigation, by adopting both a simplified routing model and a Monte Carlo procedure based on a detailed hydrodynamic simulation. Results demonstrate the potential of the framework, regardless of the accuracy of the modeling approach adopted for its implementation. Specifically, the framework allows disentangling the roles of the different design parameters involved, by providing clear indications for practitioners.

Some applications of the GRAN1 to analyze two-dimensional continuous probability distributions

The article considers the use of GRAN1 software in the analysis of two-dimensional continuous probability distributions. The learning process can be provided by freely distributable software, through the use of cloud technologies, in particular software from the GRAN package. However, the use of any technology in the educational process, including modern information and communication technologies, as well as the content of education, must be pedagogically balanced, which will avoid any negative consequences for the formation of the future member of society, his mental and physical development. Attention is drawn to the fact that children are increasingly looking for entertainment, using mobile devices, sharing new products with their peers and spending time together, playing a variety of online games, and parents cannot control this process. At the same time, teachers conduct lessons using mobile devices to draw attention to the subject, they are teaching. In the process of using computer technology, students can form a special type of thinking, the so-called “clip consciousness”, which leads to fragmentary thinking and the irreversibility of changes in consciousness. Therefore, the problem of excessive use of mobile devices by children requires constant analysis and coverage. The article discusses some examples of using the GRAN1 software to solve problems in probability theory. Examples of approximate calculation of the value of the double integral are given. Tables of values of the corresponding integrals are described. The use of modern information and communication technologies during mathematics lessons is considered.

Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia

Abstract The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g., a Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANNs), the masked autoregressive flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional Markov chain Monte Carlo method, and MAF obtains better training results for a small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat ΛCDM model. For SNe Ia, we use fitted light-curve parameters to find constraints on H 0, Ω m , and ΩΛ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use a Gaussian process in the procedure of OHD simulation.

OH, PAH, and sooting imaging in piloted liquid-spray flames of diesel and diesel surrogate

Comparison of the flame structure, polycyclic aromatic hydrocarbons (PAH), and soot formation characteristics of commercial diesel and a diesel surrogate is investigated using planar laser-induced fluorescence (PLIF) of OH and PAH at 283 nm, and laser-induced incandescence (LII) of soot at 1064 nm. The diesel and diesel surrogate flames present a similar overall appearance, with a “dark core” region of fine droplets exiting the injector and a light blue outer flame near the base. Hot soot regions appear downstream, indicated by bright yellowish flame luminosity. Comparison of simultaneously acquired single-laser-shot PLIF images of OH and PAH indicates that smaller PAH reside mostly in the central regime in the ~20–60% height of the flame, sandwiched between the flame fronts. In contrast, larger PAH and soot particles are formed further downstream. Soot-LII signals are present only in the upper ~40% height of the flame for both the fuels. The distribution of mean PAH-PLIF and soot-LII signals along the flame height is also found to be similar for both fuels, except diesel is shown to produce approximately 1.5× more soot than the diesel surrogate. Based on the analysis of probability distributions, the maximum OH-PLIF signals in both fuels have similar radial and axial spatial distributions, while maximum PAH-PLIF signals occur at a higher axial location for the diesel surrogate. However, for the diesel surrogate, maximum soot-LII signals are frequently appeared at lower heights. Therefore, exploration of more appropriate diesel surrogates may be necessary to closely mimic the PAH and soot formation behaviors of diesel fuel. The present study shows that the combined optical diagnostic techniques and the statistical analysis can be used for studying commercial diesel and diesel surrogates, and thus establish a robust framework for detailed studies of chemical kinetics, combustion dynamics, and soot formation in liquid hydrocarbon spray combustion.

Statistical analysis of flow rate for the design optimisation of a proposed CO2 pipeline network

Coal power plants produce about 38% of the electricity around the world and are a major source of CO2. The CO2 emitted by these plants has to be captured and transported to a storage site as part of the CO2 mitigation strategy defined by Carbon Capture and Storage (CCS). The most economical method for long-term onshore CO2 transport is by pipeline, and it is recognised that the CO2 flow rate assumed for pipeline design has an impact on transport costs. Therefore, it is important to investigate the impact of the variability of flow rate in a CO2 pipeline network on its design and economics. CO2 flow rate is a strong function of power plant load. The latter is affected by seasonal and diurnal changes, rarer events such as a global recession, variations in coal prices and climate change, which can impact the demand. Besides plant load, the instantaneous CO2 flow rate is also a function of capture rate and emission intensity. The combination of all these factors makes power plant load behaviour complex and random, making it a candidate for stochastic treatment. Several studies have been conducted for CO2 pipeline optimisation but none of these investigate the randomness in behaviour of CO2 flow rate. Therefore, this paper presents a stochastic analysis of the flow rates used for the optimisation of the design of a proposed CO2 pipeline network. The CO2 sources considered for this analysis are the black coal-fired power plants located in NSW, Australia. These plants use bituminous coal as fuel, which has a high HHV/LHV, i.e. the amount of heat released by unit mass or volume of fuel (initially at 25 °C) once it is combusted and the products have returned to a temperature of 25 °C. Therefore, these power plants are good candidates for CCS, having high mitigation potential. The analysis in this work is applicable to the expected range of CO2 flow rates around the world, as the CO2 sources have a rated capacity ranging from 1,320 MW to 3,000 MW, equivalent to CO2 flow rates of 1 to 20 Mtpa. The load data is investigated for a period of two consecutive years, spaced over 5-minute intervals, to observe the trends in the flow behaviour such as seasonality and diurnal patterns. For real cases, the CO2 flow is expected to change with time as the power plant load varies, which necessitates a dynamic flow analysis. However, if the variation in load is gradual, the dynamic state can also be treated as quasi-steady. Therefore, this analysis is conducted assuming quasi-steady state conditions. The approach used involves fitting multi-modal probability models to the distribution of flow rate of CO2 captured from the power plant. Three distributions types are evaluated; Normal, Logistic-normal and Gamma, which are used to model the cumulative probability distribution of the flow in terms of time. The load data are compared using goodness of fit measures to observe their conformance with analytical probability distribution behaviour. The probability distribution with the best goodness of fit to the actual plant data is then chosen for predicting the percentage of time the flow rate takes a specific range of values, and this information is used in the design of the CO2 pipeline network. The probability distribution of flow rates is then used to determine the probability distribution of the operating costs of the pipeline network. Depending on the flow rate chosen for pipeline design, the pipeline may be over or under designed over a given time period and this impacts the annual operating costs. For example, if the average flow rate is chosen as the design flow rate, then the pipeline will be under designed for the times when flow rate is higher, and vice versa. During the time when operating flow rate exceeds the design flow rate, the operating costs will be higher than estimated. However, there is a trade-off between the operating costs and the time for which the pipeline is under-utilized (over-designed) or under-designed. This analysis helps to identify the conditions under which is it more suitable to under or over design the pipeline, given the probability distribution.

Solving combined economic emission dispatch problem in wind integrated power systems

<p>A meta-heuristic based optimization method for solving combined economic emission dispatch (CEED) problem for the power system with thermal and wind energy generating units is proposed in this paper. Wind energy is environmentally friendly and abundantly available, but the intermittency and variability of wind power affects the system operation. Therefore, the system operator (SO) must aware of wind forecast uncertainty and dispatch the wind power accordingly. Here, the CEED problem is solved by including the nonlinear characteristics of thermal generators, and the stochastic behavior of wind generators. The stochastic nature of wind generators is handled by using probability distribution analysis. The purpose of this CEED problem is to optimize fuel cost and emission levels simultaneously. The proposed problem is changed into a single objective optimization problem by using weighted sum approach. The proposed problem is solved by using particle swarm optimization (PSO) algorithm. The feasibility of proposed methodology is demonstrated on six generator power system, and the obtained results using the PSO approach are compared with results obtained from genetic algorithm (GA) and enhanced genetic algorithms (EGA).</p>