scholarly journals SMART ALGORITHM FOR INTERVAL ESTIMATION OF ARC-QUENCHING REACTOR PARAMETERS

2021 ◽  
pp. 64-72
Author(s):  
Yuri A. Dementiy ◽  
Evgeny V. Shornikov ◽  
Kirill P. Nikolaev
Keyword(s):  
Simulation Model ◽  
Fault Location ◽  
Interval Estimation ◽  
Parameters Estimation ◽  
Capacitive Current ◽  
Generalization Ability ◽  
Zero Sequence ◽  
Object Parameters ◽  
Quantile Regression Method ◽  
Estimation Models

The purpose of the arc suppression reactor is to reduce the capacitive current of the network to a safe level where the single-phase earth fault current at the fault location does not exceed five amperes. The current reduced to a permissible level prevents open arcing at the fault location. For proper operation of this device, the arc suppression reactor control automatics needs to adjust the zero-sequence circuit to resonance, which balances the capacitive current of the mains and the inductive current of the reactor. To perform this tuning, it is not necessary to have information about the absolute values of the parameters of the zero-sequence circuit, but by determining them, the automation device is able to solve a wider range of tasks related to network diagnostics and increasing the efficiency of the arc suppression reactor. In this article we consider an approach to solving the problem of parametric identification of arc suppression reactor using the method of interval estimation of object parameters. The information about the operation modes of the arc suppression reactor is obtained by means of a simulation model of the object. Using the observed values, the object parameters are obtained by use of the inverse function to the simulation model. The dependence of the object parameters on the observed parameters is approximated using upper and lower parameter estimation models. The quantile regression method was applied to tune the estimation models. The need to increase the generalization ability of the algorithm is revealed. The method of adjustment of parameters of regularization of learning process to increase generalization ability of algorithm without increase of informativity of data in a training sample is offered. The results of algorithm performance are presented on the example of magnetization branch parameters estimation of arc suppression reactor. The boundaries of the interval of equivalent magnetic core loss resistance and magnetizing inductance are obtained. The limitations of the methods are analyzed, and recommendations for improving the quality of the algorithms are given.

scholarly journals Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1203 ◽  
Author(s):  
Chaichan Pothisarn ◽  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul ◽  
Chaiyan Jettanasen ◽  
Dimas Anton Asfani ◽  
...  
Keyword(s):  
Power System ◽  
Fault Location ◽  
Classification Algorithm ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
System Protection ◽  
Fault Type ◽  
Zero Sequence ◽  
Mother Wavelets

This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

Estimation of Line Zero Sequence Impedance using Real Field Fault Data for Fault Location Application

2019 ◽  
Author(s):  
Marian Dragomir ◽  
Anamaria Iamandi ◽  
Marcel Istrate ◽  
Alin Dragomir ◽  
Dragos Machidon
Keyword(s):  
Fault Location ◽  
Real Field ◽  
Zero Sequence

scholarly journals Detection and Location of Earth Fault in MV Feeders Using Screen Earthing Current Measurements

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1293 ◽  
Author(s):  
Krzysztof Lowczowski ◽  
Jozef Lorenc ◽  
Jozef Zawodniak ◽  
Grzegorz Dombek
Keyword(s):  
Fault Location ◽  
Overhead Lines ◽  
Earth Fault ◽  
Ground Fault ◽  
Detection And Identification ◽  
Zero Sequence ◽  
Network Experiment ◽  
System Configurations ◽  
Zero Sequence Voltage ◽  
Core Current

The paper analyzes the utilization of cable screen currents for earth fault identification and location. Attention is paid on cable and mixed feeders—cable and overhead lines. The principle of operation is based on utilization of 3 criterion values: Ratio of cable screen earthing current and zero sequence cable core current—RF110/15, phase shift between cable screen earthing current and zero sequence cable core current—α and cable screen admittance defined as a ratio of cable screen earthing current and zero sequence voltage—Y0cs. Earth fault location is possible thanks to discovered relation between RF110/15 and α, whereas Y0cs allows for reliable detection of earth faults. Detection and identification are very important because it allows to increase the reliability of supply—reduce downtime and number of consumers affected by the fault. The article presents a phase to ground fault current flow for different power system configurations. At the end solution, which improves location capabilities is proposed. The solution is analyzed in PSCAD software and verified by network experiment.

scholarly journals Study on Load Parameters Estimation during AMT Startup

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Yunxia Li ◽  
Yufeng Lu ◽  
Huilai Sun ◽  
Peng Yan
Keyword(s):  
Least Squares Method ◽  
Identification Problem ◽  
Load Resistance ◽  
Parameters Estimation ◽  
Estimation Methods ◽  
Mechanical Transmission ◽  
Belt Conveyor ◽  
Soft Starter ◽  
Resistance Torque ◽  
Estimation Models

The identification problem of the load parameters is the basis of researching the clutch control strategy and the shift schedule for heavy-duty AMT (Automated Mechanical Transmission). AMT is proposed as the soft starter for the belt conveyor which expands AMT application areas. In order to accomplish the goal of AMT as the soft starter, the load parameters estimation methods including the load resistance torque and the load inertia are firstly studied to accommodate the actual control for AMT startup as the soft starter for belt conveyor. In the paper, the estimation models of the load resistance torque and the load inertia are established during AMT startup for belt conveyor. On the basis of the recursion least squares method, the on-line estimations for the load parameters are recognized according to the test data during AMT startup. The estimation results of these two parameters prove that the load parameters estimation models and the estimation methods during AMT startup are valid.

Parameters Estimation for Chemical Flooding Modeling Using Ensemble-Based Method

2021 ◽  
Author(s):  
Xindan Wang ◽  
Yin Zhang ◽  
Abhijit Dandekar ◽  
Yudou Wang
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
History Matching ◽  
Simulation Models ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Chemical Flooding ◽  
Multiple Model ◽  
Assisted History Matching ◽  
Matching Techniques

Abstract Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.

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