scholarly journals An Arc Furnace as a Source of Voltage Disturbances—A Statistical Evaluation of Propagation in the Supply Network

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1076 ◽  
Author(s):  
Ryszard Klempka
Keyword(s):  
Statistical Analysis ◽  
Power System ◽  
Firefly Algorithm ◽  
Statistical Evaluation ◽  
Probability Distributions ◽  
Supply Network ◽  
Arc Furnace ◽  
Large Source ◽  
Basic Parameters ◽  
Modified Firefly Algorithm

This article presents the results of measuring Pst indicators at three points of a power system supplying a large source of voltage disturbances—an arc furnace. Measurements were made at three voltage levels: 30, 110, and 400 kV. Recorded values of Pst at each point were subjected to statistical analysis, the probability distributions were adjusted to their histograms, and the nature of changes in the basic parameters of these distributions with the distance from the source of disturbances was indicated. The adjustments of the distributions were made using a modified firefly algorithm.

scholarly journals Unit commitment based on modified firefly algorithm

2020 ◽  
Vol 53 (3-4) ◽  
pp. 320-327 ◽  
Author(s):  
Balasim M Hussein ◽  
Aqeel S Jaber
Keyword(s):  
Power System ◽  
Firefly Algorithm ◽  
Unit Commitment ◽  
Second Step ◽  
Priority List ◽  
Load Demand ◽  
List Method ◽  
The Cost ◽  
Commitment Problems ◽  
Modified Firefly Algorithm

Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.

scholarly journals Statistical Analysis and Machine Learning Prediction of Fog-Caused Low-Visibility Events at A-8 Motor-Road in Spain

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 679
Author(s):  
Sara Cornejo-Bueno ◽  
David Casillas-Pérez ◽  
Laura Cornejo-Bueno ◽  
Mihaela I. Chidean ◽  
Antonio J. Caamaño ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Probability Distributions ◽  
Pearson Correlation ◽  
Likelihood Method ◽  
Neural Network Approach ◽  
Event Duration ◽  
Full Characterization ◽  
Low Visibility ◽  
Learning Machine ◽  
The Impact

This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8, within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road.

scholarly journals Feature Selection Models Based on Hybrid Firefly Algorithm with Mutation Operator for Network Intrusion Detection

2021 ◽  
Vol 14 (1) ◽  
pp. 192-202
Author(s):  
Karrar Alwan ◽  
◽  
Ahmed AbuEl-Atta ◽  
Hala Zayed ◽  
◽  
...  
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Firefly Algorithm ◽  
Detection System ◽  
Binary Classification ◽  
Feature Reduction ◽  
Detection Accuracy ◽  
Multi Classification ◽  
Modified Firefly Algorithm

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NPhard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.

The ellipsoidal form of clasts with practical applications to fabric and size analyses of fluvial gravels

1980 ◽  
Vol 17 (12) ◽  
pp. 1725-1739 ◽  
Author(s):  
Emlyn H. Koster ◽  
Brian R. Rust ◽  
Don J. Gendzwill
Keyword(s):  
Statistical Analysis ◽  
Flow Direction ◽  
Probability Distributions ◽  
Size Analysis ◽  
Shape Variation ◽  
Practical Applications ◽  
Flow Interactions ◽  
Typical Range ◽  
Plane Area ◽  
Key Parameter

The widespread assumption that most water-worn gravel clasts approximate ellipsoids is confirmed by a statistical analysis of available data. The analysis demonstrates a Gaussian distribution of V/Ve ratios, centred on unit ratio, where V is clast volume and Ve the volume of a symmetric ellipsoid with equivalent triaxial dimensions. For internally isotropic and unbroken clasts, ellipsoidal form evolves as the rounding due to abrasion reaches its final stages. There appears to be no other major control on the tendency towards ellipsoidal geometry. The ellipsoidal tendency assists the interpretation of fluvial gravel deposits, which depends greatly on accurate description of clast size and fabric.Firstly, it facilitates calculation of Ap, the plane area projected upstream by clasts, a key parameter in bed–flow interactions such as preferred fabric. Formulae are derived to calculate Ap for ellipsoidal clasts with any configuration relative to flow direction. Viewing fabric in terms of the Ap variable supports and explains earlier conclusions concerning the controls on variability of imbrication angle.Secondly, an investigation of the relative merits of six size measures as descriptors of areal trends and predictors of nominal diameter, dn, concludes that (abc)1/3(the formula for dn of an ellipsoid) is superior. Other measures, namely, a, b, c, (a + c)/2, and (a + b + c)/3, are all subject to error in proportion to the degree of shape variation. Also, since downstream fining is typically accompanied by a changing proportion of oblate, bladed, prolate, and equant forms, dn is subject to inconsistent levels of under- or overestimation. The commonly used b dimension is endorsed as an acceptable predictor of dn, but a severely overestimates dn and should be abandoned. Information on errors in size analysis is presented as nomograms in the form of contoured c/b versus b/a plots and as probability distributions based on the typical range of shape variation in fluvial gravel.

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