scholarly journals ANALYSIS OF ELECTRICAL LOAD SCHEDULES OF 10/0.4 KV TRANSFORMER SUBSTATIONS OF RESIDENTIAL AREAS FOR THE IDENTIFICATION OF STATIONARITY PLOTS

2021 ◽  
pp. 96-101
Author(s):  
Iryna Shcherbak ◽  
Yuliia Kovalova ◽  
Volodymyr Korobka
Keyword(s):  
Autocorrelation Function ◽  
Mathematical Expectation ◽  
Reactive Power ◽  
Least Squares Method ◽  
Electrical Load ◽  
Residential Areas ◽  
Distribution Law ◽  
Random Load ◽  
Constant Variance ◽  
Student’S T

It is proposed on the electrical load graphs of transformer substations 10/0.4 kV in residential areas to allocate the stationarity areas for further modelling of load schedules and the implementation of controlling influences on the modes of consumers-regulators in order to align the overall graph of the electrical load. The relevance and complexity of the problem under consideration is caused by the fact that the load variation of transformer substations 10/0.4 kV in residential areas occurs randomly. This is due to the significant number, nomenclature and diversity of types of connected consumers, as well as the lack of deterministic connections between consumers of electricity, in addition, the random load function in the daily interval is non-stationary. In this regard, there was a need to develop the stages of selecting the areas of stationarity on the electrical load graphs of transformer substations 10/0.4 kV of residential areas. A measurement of the load graphs of 10/0,4 kV transformer substations is carried out, according to the results of which the distribution law of active and reactive power measurements is investigated. After confirming the hypothesis of normal distribution law, parametric tests are performed. Fisher's F-criterion is used to confirm the hypothesis of a constant variance, and Student's t-criterion is used to confirm the hypothesis of a constant mathematical expectation. The next stage, based on constancy of the variance and mathematical expectation, is the determination of autocorrelation coefficients of the studied random function and plotting of the autocorrelation function. To approximate the function the autocorrelation coefficients are determined by the least squares method and the autocorrelation function attenuation analysis is performed. The implementation of the defined stages allows to identify the areas of stationarity on the load graphs of transformer substations 10/0.4 kV. For a reliable description of the process of changing the load of transformer substations 10/0.4 kV the use of probabilistic-statistical method of modelling is justified that takes into account the stochastic nature of the load changes on the selected areas of stationarity.

scholarly journals Allocation of steady-state areas of total electrical load schedules of 10/0.4 kv transformer substations located in residential areas

2021 ◽  
pp. 11-19
Author(s):  
Oksana Dovgalyuk ◽  
Iryna Shcherbak ◽  
Yuliia Kovalova ◽  
Volodymyr Korobka
Keyword(s):  
Autocorrelation Function ◽  
Reactive Power ◽  
Distribution Networks ◽  
Voltage Regulation ◽  
Electrical Network ◽  
Electrical Load ◽  
Residential Areas ◽  
Total Load ◽  
Load Variation ◽  
Wide Range

Improving the efficiency of regulating the parameters of distribution network modes is an actual task for the energy sector, requiring a detailed analysis of the nature of the total electrical load graphs of 10/0.4 kV transformer substations. The complexity of the problem in consideration is due to the fact that the load variation of transformer substations 10/0.4 kV in residential areas occurs randomly, due to the significant number, wide range and probabilistic nature of the operating modes of connected consumers, and the lack of deterministic links between consumers of electricity. The investigated function of total load of transformer substations 10/0.4 kV on a daily interval is non-stationary, in this connection there was a necessity of allocation of stationarity areas for adjusting the process of regulation of parameters of electrical network modes. In order to solve this problem, total load graphs for 10/0.4 kV transformer substations located in a residential area were constructed based on experimental measurements of active and reactive loads. The use of these dependencies made it possible to analyse the distribution law for the active and reactive power of the total load of 10/0.4 kV transformer substations, which change in time is stochastic characterised. The use of probabilistic-statistical modelling was justified to reliably describe the load variation process of 10/0.4 kV transformer substations. The hypothesis of a normal law distribution of the functions under consideration was confirmed and parametric tests were performed. Fisher's F-criterion was used to confirm the hypothesis of variance constancy, and Student's t-criterion was used to confirm the hypothesis of mathematical expectation constancy. Using the fact of constancy of dispersions and expectations of investigated mode parameters, autocorrelation coefficients of investigated random functions were determined and autocorrelation function graphs were plotted. In order to approximate the functions under investigation, the autocorrelation function coefficients were determined using the least-squares method, and an analysis of the attenuation of the autocorrelation function was carried out. The calculations carried out have highlighted the areas of stationarity on the total load curves of the 10/0.4 kV transformer substations. These stationarity plots can be used for further modelling of load graphs and the formation of control actions to adjust the load of consumer regulators in order to equalise the overall electrical load graph, as well as for voltage regulation facilities, which will contribute to the required modes of operation of the electricity distribution networks.

Electrical Energy Saving Based on Production Process Optimization for Steel Enterprise

2011 ◽  
Vol 383-390 ◽  
pp. 4475-4481
Author(s):  
Hai Nan Zhu ◽  
Jia Chuan Shi ◽  
Li Ping Liang
Keyword(s):  
Energy Saving ◽  
Reactive Power ◽  
Electrical Energy ◽  
Utilization Efficiency ◽  
Iron And Steel Industry ◽  
Production Plan ◽  
Electrical Load ◽  
Practical Application ◽  
Steel Rolling ◽  
Iron And Steel

The iron and steel industry annually consumes about 10% of electricity in China, while the utilization efficiency is relatively low. A two-step energy-saving method for steel enterprise is proposed in this paper. The production plan is regulated to minimize the energy utilized in steel rolling. The electrical load is forecasted according to the optimized steel-rolling plan and the reactive power is compensated to minimize the active power loss and voltage fluctuation. Practical application in Jigang Group Co., Ltd shows the efficiency of the proposed method.

Extract Characteristic Parameters of FID Signal Combining Autocorrelation Function with the Least Absolute Value

2012 ◽  
Vol 239-240 ◽  
pp. 1108-1112
Author(s):  
Hui Cui Hao ◽  
Jun Lin ◽  
Bao Feng Tian ◽  
Qi Wan
Keyword(s):  
Autocorrelation Function ◽  
Least Squares Method ◽  
Measurement Data ◽  
Characteristic Parameters ◽  
Absolute Value ◽  
Nonlinear Fitting ◽  
Least Absolute Value ◽  
Function Fitting ◽  
Parameters Extraction ◽  
Fitting Method

FID signal is the envelope of magnetic resonance signal, and the extraction accuracy of characteristic parameters directly influence the accuracy of the hydrogeologic parameters of the inverse interpretation. In order to improve the accuracy of characteristic parameters extraction, made simulation and study combined the autocorrelation function fitting with the least absolute value nonlinear fitting method in different SNR and different noise in this paper. The simulation results showed that, the characteristic parameters fitting error using this method was smaller than that using linear, nonlinear fitting method or the autocorrelation function with the least squares method, within 7% in lower SNR. The field measurement data and inversion results verified the method validity.

The laws of distribution of some parameters when organizing the movement of trains

2021 ◽  
pp. 27-39
Author(s):  
M. V. Fufacheva ◽  
Keyword(s):  
Coefficient Of Variation ◽  
Least Squares Method ◽  
Erlang Distribution ◽  
Key Factors ◽  
Distribution Law ◽  
Statistical Parameters ◽  
Railway Stations ◽  
Coefficients Of Variation ◽  
Kolmogorov Criterion ◽  
Laws Of Distribution

The article discusses the key factors for the organization of train traffic. The analysis of statistical parameters of the intervals of departure of freight trains from railway stations and the length of the freight train flow is carried out. Based on these train schedules, graphical dependences of the intervals of departure of trains from separate points on the volume of train traffic and the distribution of train lengths in even and odd directions are constructed. Polynomial dependencies with coefficients of variation are derived. The curve of the second-order Erlang distribution function for the departure intervals is constructed. It is determined that the average departure intensity in one hour is equal to three trains, and the average departure interval lies between the 25th and 35th minutes with a probability of 0.793. There is a correlation between the coefficient of variation (mean square deviation) of the departure interval and its average daily deviation. The power function of the analytical ratio of the coefficient of variation from the average interval of departure of freight trains from the technical stations of the Krasnoyarsk railway during the day is determined using the least squares method. The degrees of correspondence of theoretical and statistical distributions of the number of cars in trains are studied using the Kolmogorov criterion with the calculation of the probability value and confirmation that the distribution of the number of cars in trains obeys the binomial law. Using the Pearson agreement criterion, it is proved that the frequency of departure of freight trains from railway stations obeys the exponential distribution law.

scholarly journals Prediction of the electrical load of the power system using neural networks

2019 ◽  
Vol 124 ◽  
pp. 05026 ◽  
Author(s):  
D.I. Nabiullin ◽  
R.N. Balobanov
Keyword(s):  
Neural Network ◽  
Reactive Power ◽  
Forecast Accuracy ◽  
Cost Effective ◽  
Correct Selection ◽  
Group Method ◽  
Electrical Load ◽  
Network Training ◽  
Load Schedule ◽  
Electrical Loads

Prediction of the electrical load schedule of an electrical system is an important aspect for determining electrical loads, which ensures the correct selection and cost-effective operation of reactive power compensation devices and voltage control devices, as well as relay protection and automation. This article discusses methods for predicting electrical load using an artificial neural network. The problems of choosing the optimal architecture and algorithm of neural network training are considered. The methods of the best forecast accuracy are determined. A genetic algorithm based on the group method of data handling was chosen as the main calculation.

scholarly journals Fryze reactive power of trams in effective stochastic recuperation processes

2019 ◽  
Vol 294 ◽  
pp. 01006
Author(s):  
Oleh Bondar ◽  
Mikola Kostin ◽  
Andrei Mukha ◽  
Olha Sheikina ◽  
Svitlana Levytska
Keyword(s):  
Data Processing ◽  
Transport System ◽  
Direct Current ◽  
Mathematical Expectation ◽  
Statistical Data ◽  
Reactive Power ◽  
Current System ◽  
Numerical Calculations ◽  
Regenerative Braking ◽  
Electric Transport

Urban electric transport system, particularly tram systems, is not a direct current system not only in traction mode but in regenerative modes as both voltage on a collector and regenerative current are stochastic abruptly variable processes. The above- mentioned facts determine availability of Fryze’s reactive power in this system that flows from a railway substation to trams, leads to incidental losses of energy and significantly reduces its quality. So evaluation of power effectiveness of the system in electrical trams operation is impossible without determining the level of reactive power in this system. We have analytical expression of reactive power by Fryze. Numerical calculations for trams type T3D and T4D in regenerative braking modes are done. Probabilistic statistical data processing operation of reactive power expressions is done. It is determined that reactive power changes in the limit of 10…100 kilo-volt ampere reactive with mathematical expectation – 37,0 kilo-volt ampere reactive. Statistical allocation of random power values are different. Numerical calculations of incidental losses, energy of recuperation are done and they range supplementary – 20% from total losses. It is stated that coefficient of reactive power of system route of trams is exceeding permissible value 0,25.

Study of Local Ice Loads Measured at Norströmsgund Lighthouse

2017 ◽  
Author(s):  
Petr Zvyagin ◽  
Gesa Ziemer
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Stationary Process ◽  
The Other ◽  
Stationary Time Series ◽  
Distribution Law ◽  
Local Loads ◽  
Simultaneous Record ◽  
Key Factor ◽  
Ice Loads

It is believed that ice loading can be a stationary process at least sometimes during the state of continuous brittle crushing. Confidence in the distribution law, stationarity in time, and autocorrelation function of local ice loads is the key factor for assessment of such loads and their successful simulation. Good understanding of the load process on the level of a single transducer record can be helpful in future analysis and simulation of loads on wider contact areas. In this paper local loads, simultaneously measured by two middle subpanels at the Norströmsgrund lighthouse in March 2001, are studied. Stationary time series of lognormal origin of 50 seconds duration are extracted from both of the subpanel records. From the studied data, stationarity was not observed simultaneously at different subpanels. The correlation of one stationary subpanel record with simultaneous record of the other subpanel found to be weak. A simple function with good fit to the observed autocorrelation curve of stationary load fragments is suggested. The findings are compared with parameters obtained for local loads in previous studies. A transition from autocorrelation function for raw lognormal data to autocorrelation function of logarithmic normal data is performed.

scholarly journals MODELING AND OPTIMIZATION OF PARALLEL INFORMATION SEARCHING IN FILES

2014 ◽  
pp. 24-32
Author(s):  
Volodymyr Lisovets ◽  
Hryhoriy Tsehelyk
Keyword(s):  
Probability Distribution ◽  
Mathematical Expectation ◽  
Optimal Strategies ◽  
External Memory ◽  
Distribution Law ◽  
Information Searching ◽  
Parallel Method ◽  
Searching Method ◽  
Made In ◽  
Multiprocessing System

In this article the m-parallel method of sequential field searching and two variants of m-parallel block field searching method are offered. These methods are oriented to be used in multiprocessing system for information searching in files of database. We research the effectiveness of these methods for different probability distribution law of field access. The mathematical expectation of number of parallel comparisons necessary for field searching in files is taken as a criterion of effectiveness. The effectiveness of the methods is compared and analyzed. The best of offered methods is founded for every considered probability distribution. Optimal strategies of field searching in sequenced files stored in external memory of multiprocessing system are made. In this case the mathematical expectation of total time needed for field searching in files is taken as a criterion of effectiveness.

scholarly journals The Comparison of Some Methods in Analysis of Linear Regression Using R Software

2019 ◽  
Vol 3 (3) ◽  
pp. 22
Author(s):  
Ilir Palla
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Least Squares Method ◽  
Random Errors ◽  
R Software ◽  
Bootstrap Methods ◽  
Constant Variance ◽  
R Program ◽  
Regression Parameters ◽  
Comparison Results

This article contains the OLS method, WLS method and bootstrap methods to estimate coefficients of linear regression and their standard deviation. If regression holds random errors with constant variance and if those errors are independent normally distributed we can use least squares method, which is accurate for drawing inferences with these assumptions. If the errors are heteroscedastic, meaning that their variance depends from explanatory variable, or have different weights, we can’t use least squares method because this method cannot be safe for accurate results. If we know weights for each error, we can use weight least squares method. In this article we have also described bootstrap methods to evaluate regression parameters. The bootstrap methods improved quantile estimation. We simulated errors with non constant variances in a linear regression using R program and comparison results. Using this software we have found confidence interval, estimated coefficients, plots and results for any case.

scholarly journals Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach

2022 ◽  
Vol 19 (2) ◽  
pp. 738
Author(s):  
Abdelgader Alamrouni ◽  
Fidan Aslanova ◽  
Sagiru Mati ◽  
Hamza Sabo Maccido ◽  
Afaf. A. Jibril ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Short Term Memory ◽  
Least Squares Method ◽  
Arima Model ◽  
Information Criterion ◽  
Generalized Least Squares ◽  
Performance Criteria ◽  
Regional Modeling ◽  
Estimation Model

Reliable modeling of novel commutative cases of COVID-19 (CCC) is essential for determining hospitalization needs and providing the benchmark for health-related policies. The current study proposes multi-regional modeling of CCC cases for the first scenario using autoregressive integrated moving average (ARIMA) based on automatic routines (AUTOARIMA), ARIMA with maximum likelihood (ARIMAML), and ARIMA with generalized least squares method (ARIMAGLS) and ensembled (ARIMAML-ARIMAGLS). Subsequently, different deep learning (DL) models viz: long short-term memory (LSTM), random forest (RF), and ensemble learning (EML) were applied to the second scenario to predict the effect of forest knowledge (FK) during the COVID-19 pandemic. For this purpose, augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, autocorrelation function (ACF), partial autocorrelation function (PACF), Schwarz information criterion (SIC), and residual diagnostics were considered in determining the best ARIMA model for cumulative COVID-19 cases (CCC) across multi-region countries. Seven different performance criteria were used to evaluate the accuracy of the models. The obtained results justified both types of ARIMA model, with ARIMAGLS and ensemble ARIMA demonstrating superiority to the other models. Among the DL models analyzed, LSTM-M1 emerged as the best and most reliable estimation model, with both RF and LSTM attaining more than 80% prediction accuracy. While the EML of the DL proved merit with 96% accuracy. The outcomes of the two scenarios indicate the superiority of ARIMA time series and DL models in further decision making for FK.

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