Imprecise Adequacy Assessment of Generating System Incorporating Wind Power Based on Interval Probability

2013 ◽  
Vol 860-863 ◽  
pp. 2083-2087
Author(s):  
Xian Jun Qi ◽  
Jia Yi Shi ◽  
Xiang Tian Peng
Keyword(s):  
Wind Power ◽  
Output Power ◽  
Cumulative Distribution Function ◽  
Cumulative Distribution ◽  
Empirical Cumulative Distribution Function ◽  
Interval Probability ◽  
Generating Capacity ◽  
Adequacy Assessment ◽  
Generating System

Probability box (P-box) and interval probability (IP) were used to express both variability and imprecision of wind speed and output power of WTGs. The p-box of WTG's output power was constructed by empirical cumulative distribution function and K.S. confidence limits. The discrete IP distribution of WTG's output power was elicited from the p-box. The optimization model of imprecise generating capacity adequacy assessment incorporating wind power was established and solved by genetic algorithm (GA). Case study on RBTSW system shows the rationality of presented method.

scholarly journals Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Emission Characteristics ◽  
Goodness Of Fit Test ◽  
Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

scholarly journals Small-Scale Fading Analysis of the Vehicular-to-Vehicular Channel inside Tunnels

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Susana Loredo ◽  
Adrián del Castillo ◽  
Herman Fernández ◽  
Vicent M. Rodrigo-Peñarrocha ◽  
Juan Reig ◽  
...  
Keyword(s):  
Communication Systems ◽  
Cumulative Distribution Function ◽  
Road Traffic ◽  
Cumulative Distribution ◽  
Small Scale ◽  
Channel Measurements ◽  
Empirical Cumulative Distribution Function ◽  
Rice Distribution ◽  
V2v Communication ◽  
Traffic Conditions

We present a small-scale fading analysis of the vehicular-to-vehicular (V2V) propagation channel at 5.9 GHz when both the transmitter (Tx) and the receiver (Rx) vehicles are inside a tunnel and are driving in the same direction. This analysis is based on channel measurements carried out at different tunnels under real road traffic conditions. The Rice distribution has been adopted to fit the empirical cumulative distribution function (CDF). A comparison of theKfactor values inside and outside the tunnels shows differences in the small-scale fading behavior, with theKvalues derived from the measurements being lower inside the tunnels. Since there are so far few published results for these confined environments, the results obtained can be useful for the deployment of V2V communication systems inside tunnels.

scholarly journals A New Approach to Assess Wind Potential

2021 ◽  
Keyword(s):  
Distribution Function ◽  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Wind Power ◽  
Cumulative Distribution Function ◽  
Maximum Likelihood Method ◽  
New Method ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Wind Distribution

<p>Weibull Cumulative Distribution Function (C.D.F.) has been employed to assess and compare wind potentials of two wind stations Europlatform and Stavenisse of The Netherland. Weibull distribution has been used for accurate estimation of wind energy potential for a long time. The Weibull distribution with two parameters is suitable for modeling wind data if wind distribution is unimodal. Whereas wind distribution is generally unimodal, random weather changes can make the distribution bimodal. It is always desirable to find a method that accurately represents actual statistical data. Some well-known statistical methods are Method of Moment (MoM), Linear Least Square Method (LLSM), Maximum Likelihood Method (M.L.M.), Modified Maximum Likelihood Method (MMLM), Energy Pattern Factor Method (EPFM), and Empirical Method (E.M.), etc. All these methods employ Probability Density Function (PDF) of Weibull distribution, except LLSM, which uses Cumulative Distribution Function (C.D.F.). In this communication, we are presenting a newly proposed method of evaluating Weibull parameters. Unlike most methods, this new method employs a cumulative distribution function. A MATLAB® GUI-based simulation is developed to estimate Weibull parameters using the C.D.F. approach. It is found that the Mean Square Error (M.S.E.) is the lowest when using the new method. The new method, therefore, estimates wind power density with reasonable accuracy. Wind Power (W.P.) is estimated by considering four different Wind Turbine (W.T.) models for two sites, and maximum W.P. is found using Evance R9000.</p>

scholarly journals Have You Seen the Standard Deviation?

2019 ◽  
Vol 3 ◽  
pp. 1-10
Author(s):  
Jyotirmoy Sarkar ◽  
Mamunur Rashid
Keyword(s):  
Standard Deviation ◽  
Cumulative Distribution Function ◽  
Geometric Method ◽  
Cumulative Distribution ◽  
Mean Deviation ◽  
Similar Data ◽  
Tabular Data ◽  
Empirical Cumulative Distribution Function ◽  
Quadratic Transformation ◽  
The Mean

Background: Sarkar and Rashid (2016a) introduced a geometric way to visualize the mean based on either the empirical cumulative distribution function of raw data, or the cumulative histogram of tabular data. Objective: Here, we extend the geometric method to visualize measures of spread such as the mean deviation, the root mean squared deviation and the standard deviation of similar data. Materials and Methods: We utilized elementary high school geometric method and the graph of a quadratic transformation. Results: We obtain concrete depictions of various measures of spread. Conclusion: We anticipate such visualizations will help readers understand, distinguish and remember these concepts.

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