Reactualization of a Modified Single Machine to Infinite Bus Model to Multimachine System Steady State Stability Analysis Studies Using Losses Network Concepts and Radial Basis Function Neural Network (RBFNN)

Rusilawati, Rusilawati; Soeprijanto, Adi; Wibowo, Rony Seto

doi:10.15866/iremos.v10i2.11207

Cited by 5 publications

(13 citation statements)

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“…Simulation is carried out on the IEEE 4 bus with two generators system is shown in Figure 1. The procedure to convert a multimachine system into an M-SMIB system and determining the maximum generation (Pmax and Qmax) of each generator is explained in subsection 2.2 [11]. The generator 1 maximum generation limit at the 500 MW loading value are 97.892 1 MW and 164.771 9 MVar, and the generator two maximum generation limit are 302.194 8 MW and 269.116 6 MVar.…”

Section: Resultsmentioning

confidence: 99%

“…Minimum limit in under-excitation conditions. A simple method for converting a multimachine system into an M-SMIB system and determining the maximum generation (Pmax and Qmax) of each generator on the IEEE 4 bus and 2 generators system is explained in reference [11]. The multi-machine system must be converted to a Modified Single Machine to Infinite Bus (M-SMIB) system.…”

Section: Generator Capability Curvementioning

confidence: 99%

“…The multimachine system shown in Figure 1 is converted to equivalent impedance and equivalent load as shown in Figure 2. The procedure for converting a multimachine system into an M-SMIB system and determining the maximum generation (Pmax and Qmax) of each generator can be explained as follows [11]: i. Prepare a loading value data, running the power flow program.…”

Section: Generator Capability Curvementioning

confidence: 99%

“…In practice, the generator can reach its maximum loading limit before reaching the maximum limit on the capability curve provided by the generator manufacturer [10]. This might occur because of the load location is far from the generator or the varying of loading value so that the maximum generation limit is also smaller than the value given on the generator capability curve of the manufacturer [11].…”

Section: Introductionmentioning

confidence: 99%

“…The multimachine system is converted into an M-SMIB system with an equivalent impedance and an equivalent load by determining the contribution of each generator to a loading value, and then the maximum generation limit of each generator unit can be determined [11][12][13][14]. After the maximum generation limit for each generator unit is known, a capability curve can be formed.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Generator Capability Curve using Modified-Single Machine to Infinite Bus (M-SMIB) System Approach

Rusilawati¹,

Sulistiawati

Yorino

2020

E3S Web Conf.

Self Cite

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The capability curve for each generator unit is usually provided by the generator manufacturer. But in practice, the generator can reach its maximum generation limit before reaching the maximum limit on the generator capability curve provided by the generator manufacturer. This might occur because of the load location is far from the generator or the varying of the loading value so that the maximum generation limit is smaller than the value given on the generator capability curve of the manufacturer. In this paper, the generator capability curve is determined using the Modified Single Machine to Infinite Bus (M-SMIB) system approach to determine the maximum generation limit every time there is a change in loading or change in the load location. After the maximum generation limit of each unit generator is known, the generator capability curve that is always in accordance with the real time situation can be formed. Thus, the operation limit of each generator can be recognized, determine the appropriate protection system setting and can prevent the electric power system disturbance. This method will be applied to generator units in the four bus IEEE system with two generators.

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Section: Resultsmentioning

confidence: 99%

Section: Generator Capability Curvementioning

confidence: 99%

Section: Generator Capability Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determination of Generator Capability Curve using Modified-Single Machine to Infinite Bus (M-SMIB) System Approach

Rusilawati¹,

Sulistiawati

Yorino

2020

E3S Web Conf.

Self Cite

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Control of the technological process of the waveguide paths induction soldering in the framework of Industry 4.0

Milov

Bukhtoyarov

Кукарцев

et al. 2019

J. Phys.: Conf. Ser.

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The control of the induction soldering technological process is significantly complicated by the presence in the heating zone of physical phenomena that make it difficult, and sometimes completely impossible, to measure the temperature in the heating zone using non-contact temperature sensors. At the same time, the use of contact sensors is also difficult in the production of spacecraft’s elements due to the high requirements for the quality of the product surface, as well as the high time required for the installation of contact sensors and the human factor. This paper presents the concept of a cyber-physical system for controlling the technological process of induction soldering of spacecraft waveguide paths in the framework of Industry 4.0, which is actively developed at the enterprises of the rocket-space complex. As a part of this concept implementation, it is proposed to develop mathematical models for each element of the waveguide assembly separately, and for the entire assembly of the waveguide path. The use of mathematical models developed within the framework of this concept will make it possible to improve the quality of the induction heating process control using adaptive and intelligent methods.

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An Adaptive Vector Median Filtering Approach to Enhance the Prediction Efficiency of Signal Power Loss

Ebhota¹,

Srivastava²

2020

jcm

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This research work designed and implemented an adaptive Artificial Neural Network (ANN) model using Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) models built on a Vector Median Filter (VMF) for pre-processing of the dataset. Normalized dataset is denoised using VMF and trained with both MLP- and RBF-ANN models. The proposed model has been developed from measurement data collected from two transmitter locations of non-line-of-sight and line-of-sight operating at the 1900MHz frequency band from LTE cellular network over distances of 1800m 1400m respectively. For non-line-of-sight site-1, VMF-MLP gives a correlation coefficient of 0.9600 compared to 0.9490 for VMF-RBF with a Bayesian regularization training algorithm. The VMF-MLP has 2.1380, 1.5000, and 1.4510 for root mean squared error, mean absolute error, and standard deviation compared to 2.3550, 1.5370, and 1.5610 for VMF-RBF network, respectively. The same trend was seen for line-of-sight in site-2 where correlation coefficient for VMF-MLP is 0.9900 and for VMF-RBF is 0.9840. The VMF-MLP has root mean squared error, mean absolute error, and standard deviation as 2.0670, 1.4900, and 1.3180, respectively, compared to VMF-RBF as 2.3470, 1.9010, and 1.3760, respectively. The predictions of these measurement data have been analyzed in this research work.

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Reactualization of a Modified Single Machine to Infinite Bus Model to Multimachine System Steady State Stability Analysis Studies Using Losses Network Concepts and Radial Basis Function Neural Network (RBFNN)

Cited by 5 publications

References 0 publications

Determination of Generator Capability Curve using Modified-Single Machine to Infinite Bus (M-SMIB) System Approach

Determination of Generator Capability Curve using Modified-Single Machine to Infinite Bus (M-SMIB) System Approach

Control of the technological process of the waveguide paths induction soldering in the framework of Industry 4.0

An Adaptive Vector Median Filtering Approach to Enhance the Prediction Efficiency of Signal Power Loss

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