Statistical method for identification of weak nodes in power system based on voltage magnitude deviation

Abstract Identification of weak buses in an electric power system is important for operational and planning purposes. In this paper, a framework that uses the variance of the magnitude of voltage at load buses when reactive power of all loads are varied concurrently is used to identify a set of weak buses. In the framework, reactive power for all load buses are generated concurrently and independently based on the probability distribution function of load demands at each node. Three IEEE test systems are used to test the framework in Pypower in conjunction with Python programming language. The result shows that weak buses can be identified with the framework. It was observed that the first three set of most critical nodes in a network can be identified within a variation of 1% of reactive power at load nodes in case of ordinary power flow (pf), while the same set of nodes can be identified with at least 5% of variation of reactive power at nodes based on optimal power flow (opf). One area of application of this work could be in the use of the variance of voltage magnitude measurements to quickly identify a subset of weak nodes in a network.

[1]  Adisa A. Jimoh,et al.  Voltage stability assessment and identification of important nodes in power transmission network through network response structural characteristics , 2017 .

[2]  Da Lei,et al.  Power system weak bus identification based on voltage distribution characteristic , 2017, 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2).

[3]  Yang Wang,et al.  A new node voltage stability index based on local voltage phasors , 2009 .

[4]  Liangzhong Yao,et al.  Multi-criteria integrated voltage stability index for weak buses identification , 2009, 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific.

[5]  Fangxing Li,et al.  Hybrid voltage stability assessment (VSA) for N−1 contingency , 2015 .

[6]  G. J. Berg,et al.  Identifying electrically weak and strong segments of a power system from a voltage stability viewpoint , 1990 .

[7]  Vishal Kumar,et al.  Genetic algorithm based support vector machine for on-line voltage stability monitoring , 2015 .

[8]  G. A. Mahmoud.  Voltage stability analysis of radial distribution networks using catastrophe theory , 2012 .

[9]  P. R. Bijwe,et al.  A new index for voltage stability monitoring and enhancement , 1998 .

[10]  D. Devaraj,et al.  On-line voltage stability assessment using radial basis function network model with reduced input features , 2011 .

[11]  Ismail Musirin,et al.  Application of electricity tracing theory and hybrid ant colony algorithm for ranking bus priority in power system , 2012 .

[12]  Debapriya Das,et al.  A novel fuzzy index for steady state voltage stability analysis and identification of critical busbars , 2002 .

[13]  Gerard Olivar,et al.  A Simplified Voltage Stability Index (SVSI) , 2014 .

[14]  Huaguang Zhang,et al.  Critical Nodes Identification of Complex Power Systems Based on Electric Cactus Structure , 2020, IEEE Systems Journal.

[15]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[16]  Adedayo Ademola Yusuff.  Voltage stability index based on standard deviation-mean ratio for identification of weak nodes , 2017, 2017 IEEE AFRICON.

[17]  Yuanyuan Sun,et al.  Weak Link Identification in Active Distribution Network Based on Probabilistic Load Flow , 2019, 2019 IEEE Sustainable Power and Energy Conference (iSPEC).

[18]  Wanxing Sheng,et al.  Critical node identification in active distribution network using resilience and risk theory , 2020, IET Generation, Transmission & Distribution.

[19]  D. Thukaram,et al.  A new and fast technique for voltage stability analysis of a grid network using system voltage space , 1998 .

[20]  G. J. Berg,et al.  Probabilistic voltage stability indexes , 1991 .