Neural-network representation of Voltage Stability Indices at the voltage collapse

2011 ◽  
Author(s):  
Guilherme G. Lage ◽  
Ricardo A. S. Fernandes ◽  
Geraldo R. M. da Costa
Keyword(s):  
Neural Network ◽  
Voltage Stability ◽  
Voltage Collapse ◽  
Network Representation ◽  
Stability Indices

scholarly journals Feedforward Artificial Neural Network for Predicting Voltage Stability Indices in Power Systems

2019 ◽  
Vol 8 (3S) ◽  
pp. 47-54
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Voltage Stability ◽  
Test System ◽  
Stability Control ◽  
Main Concern ◽  
Voltage Collapse ◽  
Artificial Neural ◽  
Voltage Stability Control ◽  
Stability Indices

Several electricity failures associated with the voltage stability incident have appeared in a few countries. Nowadays, main concern towards voltage stability control and prediction is no longer crucial, however significant awareness is arising to sustain power system’s stability to conceal recurrence of major blackouts. Numerous types of line voltage stability indices (LVSI) being appointed to validate the weakest lines in IEEE 30-Bus test system. Besides that, LVSI is being forecasted by Feedforward Back Propagation Artificial Neural Network (FFBPNN) in order to recognize the voltage stability in IEEE 30-Bus test system. The calculated indices by using LVSI and forecasted indices by using FFBPNN are realistically applicable to discover the voltage collapse event in the system. The actual output for the VCPI(Power) in line 2-5 is 1.0459, while the predicted VCPI(Power) by using FFBPNN is 1.0459 with 3 seconds training time with 0% error percentage. Generally, the voltage collapse event has been successfully proven based on the capability of VCPI(Power). Therefore, necessary measures are capable to be performed by the power system operators to evade voltage collapse events occurred.

scholarly journals Voltage collapse prediction using artificial neural network

2021 ◽  
Vol 11 (1) ◽  
pp. 124
Author(s):  
Samuel Isaac ◽  
Soyemi Adebola ◽  
Awelewa Ayokunle ◽  
Katende James ◽  
Awosope Claudius
Keyword(s):  
Neural Network ◽  
Voltage Stability ◽  
Stability Index ◽  
Voltage Collapse ◽  
Feed Forward Neural Network ◽  
Voltage Stability Index ◽  
Voltage Instability ◽  
Collapse Prediction ◽  
R Value ◽  
Bus System

Unalleviated voltage instability frequently results in voltage collapse; which is a cause of concern in power system networks across the globe but particularly in developing countries. This study proposed an online voltage collapse prediction model through the application of a machine learning technique and a voltage stability index called the new line stability index (NLSI_1). The approach proposed is based on a multilayer feed-forward neural network whose inputs are the variables of the NLSI_1. The efficacy of the method was validated using the testing on the IEEE 14-bus system and the Nigeria 330-kV, 28-bus National Grid (NNG). The results of the simulations indicate that the proposed approach accurately predicted the voltage stability index with an R-value of 0.9975 with a mean square error (MSE) of 2.182415x10<sup>−5</sup> for the IEEE 14-bus system and an R-value of 0.9989 with an MSE of 1.2527x10<sup>−7</sup> for the NNG 28 bus system. The results presented in this paper agree with those found in the literature.

scholarly journals Voltage Collapse Prediction of IEEE 30-Bus system

2021 ◽  
Vol 28 (1) ◽  
pp. 98-112
Author(s):  
Mohammed Ibrahim ◽  
Abdulsattar Jasim
Keyword(s):  
Power System ◽  
Modal Analysis ◽  
Voltage Stability ◽  
Stability Index ◽  
Maximum Load ◽  
Voltage Collapse ◽  
Analysis Method ◽  
The Stability ◽  
Stability Indices ◽  
Participation Factor

Voltage collapse in the power system occurs as a result of voltage instability, thus which lead to a blackout, and this is a constant concern for network workers and customers alike. In this paper, voltage collapse is studied using two approved methods: the modal analysis method and voltage stability indices. In the modal analysis method, the eigenvalues were calculated for all the load buses, through which it is possible to know the stability of the power system, The participation factor was also calculated for the load buses, which enables us to know the weakest buses in the system. As for the Voltage stability Indices method, two important indices were calculated, which are: Fast Voltage Stability Index (FVSI) and Line stability index (Lmn). These two indices give a good visualization of the stability of the system and the knowledge of the weakest buses, as well as the Maximum load-ability of the load buses. The above mentioned two methods were applied using software code using MATLAB \ R2018a program to the IEEE 30-Bus test system. In the modal analysis, the buses which have the maximum participation factor are 26, 29, and 30 this indicates that they are the weakest in the system. as well as in the voltage stability indices. These buses have the lowest maximum load ability which demonstrates the possibility of using both methods or one of them to study the voltage collapse.

scholarly journals Evaluation for Voltage Stability Indices in Power System Using Artificial Neural Network

2015 ◽  
Vol 118 ◽  
pp. 1127-1136 ◽  
Author(s):  
H.H. Goh ◽  
Q.S. Chua ◽  
S.W. Lee ◽  
B.C. Kok ◽  
K.C. Goh ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Voltage Stability ◽  
Artificial Neural ◽  
Stability Indices

Probabilistic Voltage Stability Assessment Considering Variability of Wind Power

2020 ◽  
Author(s):  
Moumita Sarkar ◽  
Anca Daniela Hansen ◽  
Poul Ejnar Sørensen
Keyword(s):  
Wind Power ◽  
Voltage Stability ◽  
Current System ◽  
Stability Index ◽  
Voltage Collapse ◽  
Assessment Methodology ◽  
Stability Assessment ◽  
Renewable Power ◽  
Probabilistic Distribution ◽  
Stability Indices

Traditional voltage stability assessment methods do not include temporal variation of renewable power generations like wind. This paper proposes a novel methodology for probabilistic voltage stability assessment methodology which can be used in conjunction with any of the existing traditional voltage stability indices. Historical wind power data are used to determine probabilistic distribution of wind power at future instant based on wind power value at current instant. Based on the probabilistic risk of increase and decrease of wind power at future instant, two probabilistic voltage stability indices are computed. The worse case value among the two indices are used as prediction of voltage stability index at future instant, based on current system parameters. Effectiveness of the proposed methodology in predicting proximity of the system voltage collapse is illustrated through case studies and time-series simulations. Results show that proposed methodology predicts more realistic proximity to voltage collapse than traditional stability assessments.<br>

scholarly journals Probabilistic Voltage Stability Assessment Considering Variability of Wind Power

2020 ◽  
Author(s):  
Moumita Sarkar ◽  
Anca Daniela Hansen ◽  
Poul Ejnar Sørensen
Keyword(s):  
Wind Power ◽  
Voltage Stability ◽  
Current System ◽  
Stability Index ◽  
Voltage Collapse ◽  
Assessment Methodology ◽  
Stability Assessment ◽  
Renewable Power ◽  
Probabilistic Distribution ◽  
Stability Indices

Traditional voltage stability assessment methods do not include temporal variation of renewable power generations like wind. This paper proposes a novel methodology for probabilistic voltage stability assessment methodology which can be used in conjunction with any of the existing traditional voltage stability indices. Historical wind power data are used to determine probabilistic distribution of wind power at future instant based on wind power value at current instant. Based on the probabilistic risk of increase and decrease of wind power at future instant, two probabilistic voltage stability indices are computed. The worse case value among the two indices are used as prediction of voltage stability index at future instant, based on current system parameters. Effectiveness of the proposed methodology in predicting proximity of the system voltage collapse is illustrated through case studies and time-series simulations. Results show that proposed methodology predicts more realistic proximity to voltage collapse than traditional stability assessments.<br>

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