On-line estimation of bus voltages based on fuzzy logic

Ramaswamy, M.; Nayar, K.R.

doi:10.1016/j.ijepes.2004.04.014

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…FL is often used for VSA studies. The authors of [112] came up with a good fuzzy-based method for estimating online bus voltages during a power outage and expected changes in load. A fuzzy-based model is utilized in this work for each load bus for the possible scenarios and the voltage at each load bus was predicted separately.…”

Section: ) Machine Learning In Synchrophasors For Voltage Stability A...mentioning

confidence: 99%

“…In the suggested method, which starts by using PCA to reduce the size of the training data, and further two optimization techniques are implemented to find the best dimensions for the PMU data and thereby reduce the prediction erros of the security assessment. Traditional Boolean logic FL [111], [112], [113], [114] ANFIS [127], [128] Supervised Learning (Neural Networks) ANN [109], [119] MLP-NN [116], [117] ELM [121] DT [129], [130], [131], [132] Supervised Learning (Deep Learning Neural Network) NNE [120] Supervised Learning (Classification) SVM [135], [136] GA-SVR [137], [138], [139], [140] SVM is a classification-based supervised learning technique used widely in modern PS for classification and regression. In recent times, SVM has become an effective computational method in PS networks due to its wide range of applications to handle big data analysis.…”

Section: Figure 6 Time-response Characteristics Of Vsamentioning

confidence: 99%

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A Review of Machine Learning Approaches in Synchrophasor Technology

Lal

Ramesh

2023

IEEE Access

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Section: ) Machine Learning In Synchrophasors For Voltage Stability A...mentioning

confidence: 99%

Section: Figure 6 Time-response Characteristics Of Vsamentioning

confidence: 99%

A Review of Machine Learning Approaches in Synchrophasor Technology

Lal

Ramesh

2023

IEEE Access

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“…Gerilim kararlılık sınırının tahminini ne kadar doğru ve hızlı bir yöntemle sağlanırsa, sistem operatörü de gerekli kontrol eylemlerini o kadar doğru ve hızlı başlatır [4]. Literatürde güç sistemlerinde gerilim kararlılığı analizi için Karar Ağacı Tekniği [6] ve Bulanık Ağlar [7], Yapay Sinir Ağları (YSA) [8] gibi yapay zekâ tekniklerinin kullandığı yöntemler önerilmiştir. 1989'lardan sonra YSA enerji sistemlerinin birçok problemini çözmek için kullanılmaya başlamıştır [9].…”

Section: Gi̇ri̇ş (Introduction)unclassified

IEEE 14-Baralı Güç Sisteminde Gerilim Kararlılığının Uç Öğrenme Makinesi İle Analizi

Açıkgöz

Poyraz²,

Çöteli³

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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In this study, the voltage stability of the IEEE 14-bus power system has been investigated under the different load conditions with the help of the line stability index values by using extreme learning machine (ELM) algorithms. For this purpose, IEEE 14-bus power system model is built in Matlab environment. The load flow analysis of the model is performed by using Newton-Raphson method. Figure A. IEEE 14-Bus System Purpose: The voltage stability indices in IEEE 14-bus power system are estimated using ELM. With the help of the superior properties of ELM, it is aimed to predict the voltage faults in the system. Theory and Methods: The parameters related to IEEE 14-bus system are obtained for load flow analysis. With the help of these values, the line stability index is calculated. Then, comparison is made using different activation functions. Results: From the obtained regression and estimation curves, it is observed that ELM is predicted the line stability index value with high accuracy. Conclusion: In this study, ELM is presented for stability analysis of IEEE 14-bus power system. To test the validity of the proposed method, simulation studies are realized using different activation functions. According to the realized studies, it has been shown that ELM has very successful results in determination of voltage stability.

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“…The standard load flow methods such as Newton-Raphson (NR) and Gauss-Seidel have already been proved to be suitable for off-line applications (Stott et al, 1987;Wood and Wollenberg, 1984). In the literature, several approaches such as DFs (Lee and Chen, 1992;Singh and Srivastava, 1996), concentric relaxation method (Zaborszky et al, 1980), a method using four layered artificial neural networks (Hsu and Yang, 1993), an approach based on self-organizing hierarchical neural networks (Srivastava et al, 2001), a method based on fuzzy logic (Ramaswamy and Nayar, 2004) and new approaches based on sensitivities and genetic algorithms (Pablo and Peter, 2007;Ozdemir et al, 2005) have been proposed to predict bus voltages for different loading and outage conditions. Ramana et al (2012) and An et al (2006), have proposed approaches based on Thevenin theorem which their application is limited just for steady state voltage stability limit at a given bus due to load apparent power fluctuations and cannot handle voltage estimation problem after system reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

A novel and fast voltage estimation scheme for assessment of power system component outages

Fatemi

Abedi

Vahidi

et al. 2014

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose -The purpose of this paper is to pursue two following main goals: first, theorizing a new concept named as equivalent bus load in order to make a promising simplification over power system analysis. Second, proposing an outstanding fast and simple approach based on introduced concept for voltage estimation after multiple component outages while satisfying required accuracy. Design/methodology/approach -Equivalent load bus theory introduces three transfer matrices that describe power system topology. Mentioned matrices could be calculated simply after system reconfiguration without matrix inversion. Using transfer matrices a large-scale power system can be modeled by a simple two-bus power system from the viewpoint of any desired bus so that load flow calculation leads to same value. The analysis of simplified power system yields to extract a new incremental model based on equivalent bus load theory that will be distinguished as an outstanding fast method for voltage estimation aim. Findings -A deep study for fast voltage estimation aim is dedicated to evaluate proposed method from the accuracy and quickness point of view and the outcomes are compared to a well-known method as Distribution Factors (DF). Results and computational times unveil that presented approach is more accurate and much faster. Originality/value -A novel and new fast voltage estimation method for assessment of power system component outages is introduced. present, he has been with the Department of Electrical Engineering of the Amirkabir University of Technology, where he is now a Professor. His main fields of research are high voltage, electrical insulation, power system transient, lightning protection, and pulse power technology. He has authored and coauthored more than 300 papers and six books on high-voltage engineering and power system. Professor Behrooz Vahidi is the corresponding author and can be contacted at: vahidi@aut.ac.ir Sajjad Abedi received the BS Degree in Electrical engineering from the Isfahan University of Technology (IUT), Isfahan, Iran, in 2008, and the MS Degree (with honors) in Electrical Engineering, majoring in electrical energy management, from the Amirkabir University of Technology (AUT), Tehran, Iran, in 2011. Presently, he is a Graduate Research and Teaching Assistant in Electrical Engineering Department at AUT. He currently leads a number of research projects related to power system planning and economics, stochastic optimization, renewable energy integration, as well as smart grids development and deployment.Dr Hassan Rastegar received the BSc, MSc, and PhD Degrees in Electrical Engineering from the Amirkabir University of Technology, Tehran, Iran, in 1987, 1989, and 1998 Currently, he is an Associate Professor at the Amirkabir University of Technology. He has published many papers in journals and conferences. His research interests include power system control, application of computational intelligence in power systems, simulation and analysis of power systems, and renewable energy.

show abstract

On-line estimation of bus voltages based on fuzzy logic

Cited by 9 publications

References 7 publications

A Review of Machine Learning Approaches in Synchrophasor Technology

A Review of Machine Learning Approaches in Synchrophasor Technology

IEEE 14-Baralı Güç Sisteminde Gerilim Kararlılığının Uç Öğrenme Makinesi İle Analizi

A novel and fast voltage estimation scheme for assessment of power system component outages

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