Statistical method for on-line voltage collapse proximity estimation

This paper is aimed to propose a reliable method for estimating the voltage collapse proximity through a model obtained using statistical techniques. In the model building process a database is required, therefore the Voltage Collapse Proximity Index (VCPI) is used to obtain previous readings for different contingencies and loading conditions. This analytical proposal could be combined with existing equipment in the power system control centers for future on-line applications. The proposed method is applied to the IEEE 14-bus test system and the 190-bus Mexican equivalent. Results indicate that the proposed strategy is a reliable choice.

[1]  Jen-Hao Teng,et al.  Power system vulnerability assessment considering Energy Storage Systems , 2013, 2013 IEEE 10th International Conference on Power Electronics and Drive Systems (PEDS).

[2]  Jose M. Yusta,et al.  Grid vulnerability analysis based on scale-free graphs versus power flow models , 2013 .

[3]  Michael H. Kutner Applied Linear Statistical Models , 1974 .

[4]  M. S. Sachdev,et al.  Technique for online prediction of voltage collapse , 2004 .

[5]  Jean-Charles Noyer,et al.  Formulating Robust Linear Regression Estimation as a One-Class LDA Criterion: Discriminative Hat Matrix , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[6]  M. Pandit,et al.  Voltage stability based contingency ranking using distributed computing environment , 2013, 2013 International Conference on Power, Energy and Control (ICPEC).

[7]  Jose M. Yusta,et al.  Distribution power flow method based on a real quasi-symmetric matrix , 2013 .

[8]  Rachid Cherkaoui,et al.  Preventive reactive power management for improving voltage stability margin , 2013 .

[9]  D. Trudnowski,et al.  A stepwise regression method for estimating dominant electromechanical modes , 2012, 2012 IEEE Power and Energy Society General Meeting.

[10]  Saugata S. Biswas,et al.  Development and real time implementation of a synchrophasor based fast voltage stability monitoring algorithm with consideration of load models , 2013, 2013 IEEE Industry Applications Society Annual Meeting.

[11]  Cristian Bovo,et al.  Online fuzzy voltage collapse risk quantification , 2009 .

[12]  Lucian Toma,et al.  On-line power systems voltage stability monitoring using artificial neural networks , 2015, 2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE).

[13]  Li Xu,et al.  Real-Time Diagnosis of Network Anomaly Based on Statistical Traffic Analysis , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[14]  Kjetil Uhlen,et al.  Online voltage stability monitoring based on PMU measurements and system topology , 2013, 2013 3rd International Conference on Electric Power and Energy Conversion Systems.

[15]  Marianna Vaiman,et al.  Implementation of ROSE for on-line voltage stability analysis at ISO New England , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[16]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[17]  Shen Yin,et al.  A modified partial robust M-regression to improve prediction performance for data with outliers , 2013, 2013 IEEE International Symposium on Industrial Electronics.

[18]  Juan Carlos Viera,et al.  Online SOC Estimation of Li-FePO4 Batteries through a New Fuzzy Rule-Based Recursive Filter with Feedback of the Heat Flow Rate , 2014, 2014 IEEE Vehicle Power and Propulsion Conference (VPPC).

[19]  A.M.L. da Silva,et al.  On-line voltage stability monitoring , 2000 .

[20]  P. Kessel,et al.  Estimating the Voltage Stability of a Power System , 1986, IEEE Power Engineering Review.

[21]  Pascal Maussion,et al.  Electrical Aging of the Insulation of Low-Voltage Machines: Model Definition and Test With the Design of Experiments , 2013, IEEE Transactions on Industrial Electronics.

[22]  Antonio J. Conejo,et al.  Robust WLS estimator using reweighting techniques for electric energy systems , 2013 .

[23]  Sambarta Dasgupta,et al.  Real-time monitoring of short-term voltage stability using PMU data , 2014 .

[24]  Hua Li,et al.  On-line voltage stability monitoring of large power systems , 2011, 2011 IEEE Power and Energy Society General Meeting.

[25]  Kai Sun,et al.  An adaptive three-bus power system equivalent for estimating voltage stability margin from synchronized phasor measurements , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[26]  S. Perez-Londono,et al.  Effects of doubly fed wind generators on voltage stability of power systems , 2012, 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA).

[27]  Enrico Zio,et al.  Vulnerability of Smart Grids With Variable Generation and Consumption: A System of Systems Perspective , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[28]  Khadija Ben-Kilani,et al.  Structural Analysis of Voltage Stability in Power Systems Integrating Wind Power , 2013, IEEE Transactions on Power Systems.

[29]  Venkataramana Ajjarapu,et al.  Real-Time Monitoring of Short-Term Voltage Stability Using PMU Data , 2013, IEEE Transactions on Power Systems.

[30]  F. A. Althowibi,et al.  On-line voltage collapse indicator for power systems , 2010, 2010 IEEE International Conference on Power and Energy.

[31]  Worawat Nakawiro,et al.  Voltage Stability Assessment and Control of Power Systems using Computational Intelligence , 2011 .

[32]  D. Devaraj,et al.  On-line voltage stability assessment using least squares support vector machine with reduced input features , 2014, 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT).

[33]  Venkataramana Ajjarapu,et al.  An approach for real time voltage stability margin control via reactive power reserve sensitivities , 2013, IEEE Transactions on Power Systems.

[34]  Tao Xu,et al.  Electrical Power and Energy Systems , 2015 .