Analytical approach to estimate mechanical parameters in induction machine using transient response parameters

Saxena, Nitin Kumar; Kumar, Ashwani

doi:10.1002/etep.2751

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…A 4 kW, 0.9 lagging power factor, 50 Hz, 4 pole, 4% slip, 400 V asynchronous machine is used for developing the SCIG model in MATLAB. Electrical and mechanical parameters are evaluated with the help of Saxena and Kumar 33 as required for defining the IG model. Constant negative torque with speed above synchronous speed is required to operate it as a generator.…”

Section: Resultsmentioning

confidence: 99%

“…A 4 kW, 0.9 lagging power factor, 400 V, 50 Hz induction machine with 2 pole‐pairs is chosen in this study (Appendix A). The equivalent mechanical and electrical parameters for the machine are estimated as in previous studies 33,34 . For speed above synchronous speed, negative torque operates machine as a generator.…”

Section: Modelling Details For Microgrid Componentsmentioning

confidence: 99%

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Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM

Saxena¹,

Gao

Sharma

et al. 2022

Circuit Theory & Apps

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This paper presents the genetic algorithm (GA) and particle swarm optimization (PSO) based frequency regulation for a wind‐based microgrid (MG) using reactive power balance loop. MG, operating from squirrel cage induction generator (SCIG), is employed for exporting the electrical power from wind turbines, and it needs reactive power which may be imported from the grid. Additional reactive power is also required from the grid for the load, directly coupled with such a distributed generator (DG) plant. However, guidelines issued by electric authorities encourage MGs to arrange their own reactive power because such reactive power procurement is defined as a local area problem for power system studies. Despite the higher cost of compensation, static synchronous compensator (STATCOM) is a fast‐acting FACTs device for attending to these reactive power mismatches. Reactive power control can be achieved by controlling reactive current through the STATCOM. This can be achieved with modification in current controller scheme of STATCOM. STATCOM current controller is designed with reactive power load balance for the proposed microgrid in this paper. Further, gain values of the PI controller, required in the STATCOM model, are selected first with classical methods. In this classical method, iterative procedures which are based on integral square error (ISE), integral absolute error (IAE), and integral square of time error (ISTE) criteria are developed using MATLAB programs. System performances are further investigated with GA and PSO based control techniques and their acceptability over classical methods is diagnosed. Results in terms of converter frequency deviation show how the frequency remains under the operating boundaries as allowed by IEEE standards 1159:1995 and 1250:2011 for integrating renewable‐based microgrid with grid. Real and reactive power management and load current total harmonic distortions verify the STATCOM performance in MG. The results are further validated with the help of recent papers in which frequency regulation is investigated for almost similar power system models. The compendium for this work is as following: (i) modelling of wind generator‐based microgrid using MATLAB simulink library, (ii) designing of STATCOM current controller with PI controller, (iii) gain constants estimation using classical, GA and PSO algorithm through a developed m codes and their interfacing with proposed simulink model, (v) dynamic frequency responses for proposed grid connected microgrid during starting and load perturbations, (vi) verification of system performance with the help of obtained real and reactive power management between STATCOM and grid, and (vii) validation of results with available literature.

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Section: Resultsmentioning

confidence: 99%

Section: Modelling Details For Microgrid Componentsmentioning

confidence: 99%

Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM

Saxena¹,

Gao

Sharma

et al. 2022

Circuit Theory & Apps

Self Cite

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“…The induction motor specifications, provided by the manufacturer date as represented in Table 2, are used to find according to the following steps [29]; Equivalent circuit parameters for induction motor are obtained by using initial reference frame [30].…”

Section: B Modelling For 5 Th Order Induction Motor Modelmentioning

confidence: 99%

Reactive Power Cost Optimization Acquiring the Combined Properties of Static and Aggregate Dynamic Load as Composite Load Model

Saxena,

Kumar,

Gebreyohans

2020

IJEAT

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In electrical systems especially isolated wind and diesel based hybrid systems, voltage control has been achieved in several works from adequate supply of reactive power using STATCOM/SVC as dynamic compensators. However, in most of the existing works, studies are performed only with static loads which do not have any influence of previous system conditions. In such available studies, the two major concerns were (i) cost based studies, and (ii) control techniques of dynamic compensation. The electrical systems are always influenced by the unrealistic load modelling in such systems which is almost untouched in available works and therefore motivate the authors to work on it. Realistic loads are of composite nature that composes the blend of characteristics that acquire static as well as dynamic nature of the load. In present work, the authors are interested to investigate the lower cost of reactive power with composite load for best proportion of dynamic and static compensations. The study is done in MATLAB software with 10% step increment in reactive power demand and input wind power to analyse compensation effect for extreme voltage deviation of pu at load end as recommended by several energy policies and standard IEC 60038.

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“…The structure of BP neural network includes an input layer, an output layer, and a hidden layer. The number of nodes in the input layer and the output layer is determined according to the actual problem, while the number of nodes in the hidden layer needs to be determined by Equation 8. a n m N    (8) Where, is a constant between 1 and 10, m and n are the number of input layer nodes and the number of output nodes, respectively.…”

Section: ) Formulate the Neural Network Structurementioning

confidence: 99%

“…A large number of experimental studies show that the mechanical parameters of the deep buried surrounding rock show significant attenuation characteristics with the increase of strain from the rheological attenuation stage to the acceleration stage [7][8][9][10][11]. However, the existing models for predicting the long-term stability of surrounding rocks all take mechanical parameters as constants for calculations and their models can only be applied to environments where the depth of surrounding rocks is relatively shallow.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

et al. 2019

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A large number of experimental studies show that the mechanical parameters of deep buried surrounding rock show significant attenuation characteristics with the increase of strain from the rheological acceleration stage to the attenuation stage. However, the existing numerical models all take mechanical parameters as constants when describing the rheological behavior of surrounding rocks, which can only be applied to the stability analysis of the shallowly buried tunnel. Therefore, this work proceeding from the actual project, improved the sandstone rheological constitutive model and optimized the algorithm of parameter inversion, and put forward a long-term stability analysis model that can accurately reflect the rheological characteristics of surrounding rocks under the complex geological condition including high stress induced by great depth and high seepage pressure. In the process, a three-dimensional nonlinear rheological damage model was established based on Burgers rheological model by introducing damage factors into the derivation of the sandstone rheological constitutive model to accurately describe the rheological behaviors of the deep buried tunnel. And BP (Back Propagation) neural network optimized by the multi-descendant genetic algorithm is used to invert the mechanical parameters in the model, which improves the efficiency and precision of parameter inversion. Finally, the rheological equation was written by using parametric programming language and incorporated into the general finite element software ANSYS to simulate the rheological behavior of the tunnel rock mass at runtime. The results of the model analysis are in good agreement with the monitoring data in the later stage. The research results can provide a reference for the stability analysis of similar projects.

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Analytical approach to estimate mechanical parameters in induction machine using transient response parameters

Cited by 7 publications

References 20 publications

Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM

Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM

Reactive Power Cost Optimization Acquiring the Combined Properties of Static and Aggregate Dynamic Load as Composite Load Model

Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

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