A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid
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Isaiah G. Adebayo
und
Yanxia Sun
Abstract
Modern power systems are increasingly becoming more complex and thus become vulnerable to voltage collapse due to constant increase in load demand and introduction of new operation enhancement technologies. In this study, an approach which is based on network structural properties of a power system is proposed for the identification of critical nodes that are liable to voltage instability. The proposed Network Structurally Based Closeness Centrality (NSBCC) is formulated based on the admittance matrix between the interconnection of load to load nodes in a power system. The vertex (node) that has the highest value of NSBCC is taken as the critical node of the system. To demonstrate the significance of the concept formulated, the comparative analysis of the proposed NSBCC with the conventional techniques such as Electrical Closeness Centrality (ECC), Closeness Voltage Centrality (CVC) and Modal Analysis is performed. The effectiveness of all the approaches presented is tested on both IEEE 30 bus and the Southern Indian 10-bus power systems. Results of simulation obtained show that the proposed NSBCC could serve as valuable tool for rapid real time voltage stability assessment in a power system.
Funding source: National Research Foundation
Award Identifier / Grant number: 112108
Award Identifier / Grant number: 112142
Funding source: National Research Foundation
Award Identifier / Grant number: 95687
Funding source: Eskom Tertiary Education Support Programme
Funding source: University of Johannesburg
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This research is supported partially by South African National Research Foundation Grants (No. 112108 and 112142), and South African National Research Foundation Incentive Grant (No. 95687), Eskom Tertiary Education Support Programme, Research grant from URC of University of Johannesburg.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
Line data: 10 bus Southern Indian test system.
| From | To | R (pu) | X (pu) | BC/2(pu) | Line limit (MVA) |
|---|---|---|---|---|---|
| 1 | 5 | 0.00272 | 0.02872 | 1.51829 | 500 |
| 1 | 4 | 0.00569 | 0.06008 | 0.79414 | 500 |
| 1 | 2 | 0.00477 | 0.05103 | 0.72673 | 500 |
| 2 | 10 | 0.00676 | 0.09429 | 0.75003 | 500 |
| 10 | 6 | 0.00546 | 0.06794 | 0.88836 | 500 |
| 7 | 9 | 0.00289 | 0.03603 | 0.46222 | 500 |
| 3 | 9 | 0.00145 | 0.01802 | 0.93968 | 500 |
| 7 | 4 | 0.00589 | 0.05995 | 0.7841 | 500 |
| 7 | 5 | 0.0043 | 0.0477 | 0.637 | 500 |
| 5 | 8 | 0.00388 | 0.04834 | 0.6547 | 500 |
| 3 | 8 | 0.00297 | 0.03706 | 0.47543 | 500 |
| 6 | 7 | 0.0004 | 0.004 | 0.15 | 500 |
Line data for the IEEE 30 bus test system.
| S/N | From bus | To bus | R(p.u) | X(p.u) | 1/2B | Tap ratio |
|---|---|---|---|---|---|---|
| 1 | 1 | 2 | 0.0192 | 0.0575 | 0.0264 | 1 |
| 2 | 1 | 3 | 0.0452 | 0.1652 | 0.0204 | 1 |
| 3 | 2 | 4 | 0.0570 | 0.1737 | 0.0184 | 1 |
| 4 | 3 | 4 | 0.0132 | 0.0379 | 0.0042 | 1 |
| 5 | 2 | 5 | 0.0472 | 0.1983 | 0.0209 | 1 |
| 6 | 2 | 6 | 0.0581 | 0.1763 | 0.0187 | 1 |
| 7 | 4 | 6 | 0.0119 | 0.0414 | 0.0045 | 1 |
| 8 | 5 | 7 | 0.0460 | 0.1160 | 0.0102 | 1 |
| 9 | 6 | 7 | 0.0267 | 0.0820 | 0.0085 | 1 |
| 10 | 6 | 8 | 0.0120 | 0.0420 | 0.0045 | 1 |
| 11 | 6 | 9 | 0.0 | 0.2080 | 0.0 | 0.978 |
| 12 | 6 | 10 | 0.0 | 0.5560 | 0.0 | 0.969 |
| 13 | 9 | 11 | 0.0 | 0.2080 | 0.0 | 1 |
| 14 | 9 | 10 | 0.0 | 0.1100 | 0.0 | 1 |
| 15 | 4 | 12 | 0.0 | 0.2560 | 0.0 | 0.932 |
| 16 | 12 | 13 | 0.0 | 0.1400 | 0.0 | 1 |
| 17 | 12 | 14 | 0.1231 | 0.2559 | 0.0 | 1 |
| 18 | 12 | 15 | 0.0662 | 0.1304 | 0.0 | 1 |
| 19 | 12 | 16 | 0.0945 | 0.1987 | 0.0 | 1 |
| 20 | 14 | 15 | 0.2210 | 0.1997 | 0.0 | 1 |
| 21 | 16 | 17 | 0.0824 | 0.1923 | 0.0 | 1 |
| 22 | 15 | 18 | 0.1073 | 0.2185 | 0.0 | 1 |
| 23 | 18 | 19 | 0.0639 | 0.1292 | 0.0 | 1 |
| 24 | 19 | 20 | 0.0340 | 0.0680 | 0.0 | 1 |
| 25 | 10 | 20 | 0.0936 | 0.2090 | 0.0 | 1 |
| 26 | 10 | 17 | 0.0324 | 0.0845 | 0.0 | 1 |
| 27 | 10 | 21 | 0.0348 | 0.0749 | 0.0 | 1 |
| 28 | 10 | 22 | 0.0727 | 0.1499 | 0.0 | 1 |
| 29 | 21 | 23 | 0.0116 | 0.0236 | 0.0 | 1 |
| 30 | 15 | 23 | 0.1000 | 0.2020 | 0.0 | 1 |
| 31 | 22 | 24 | 0.1150 | 0.1790 | 0.0 | 1 |
| 32 | 23 | 24 | 0.1320 | 0.2700 | 0.0 | 1 |
| 33 | 24 | 25 | 0.1885 | 0.3292 | 0.0 | 1 |
| 34 | 25 | 26 | 0.2544 | 0.3800 | 0.0 | 1 |
| 35 | 25 | 27 | 0.1093 | 0.2087 | 0.0 | 1 |
| 36 | 28 | 27 | 0.0 | 0.3960 | 0.0 | 0.968 |
| 37 | 27 | 29 | 0.2198 | 0.4153 | 0.0 | 1 |
| 38 | 27 | 30 | 0.3202 | 0.6027 | 0.0 | 1 |
| 39 | 29 | 30 | 0.2399 | 0.4533 | 0.0 | 1 |
| 40 | 8 | 28 | 0.0636 | 0.2000 | 0.0214 | 1 |
| 41 | 6 | 28 | 0.0169 | 0.0599 | 0.065 | 1 |
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Review
- Power quality problem and key improvement technology for regional power grids
- Research Articles
- Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles
- Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems
- Experimental control of photovoltaic system using neuro – Kalman filter maximum power point tracking (MPPT) technique
- Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid
- Influence of inter-turn short circuit on the performance of 10 kV, 1000 kW induction motor
- Detection of coherent groups using measured signals, in an inter-area mode, for creating controlled islands to protect the power system from blackout
- Multi-objective optimization of optimal capacitor allocation in radial distribution systems
- A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid
- Dynamic Simulation of Eastern Regional Grid of India using Power System Simulator for Engineering PSS®E
- Optimal total harmonic distortion minimization in multilevel inverter using improved whale optimization algorithm
- A cost effective accumulator management system for electric vehicles
Artikel in diesem Heft
- Review
- Power quality problem and key improvement technology for regional power grids
- Research Articles
- Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles
- Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems
- Experimental control of photovoltaic system using neuro – Kalman filter maximum power point tracking (MPPT) technique
- Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid
- Influence of inter-turn short circuit on the performance of 10 kV, 1000 kW induction motor
- Detection of coherent groups using measured signals, in an inter-area mode, for creating controlled islands to protect the power system from blackout
- Multi-objective optimization of optimal capacitor allocation in radial distribution systems
- A novel approach of closeness centrality measure for voltage stability analysis in an electric power grid
- Dynamic Simulation of Eastern Regional Grid of India using Power System Simulator for Engineering PSS®E
- Optimal total harmonic distortion minimization in multilevel inverter using improved whale optimization algorithm
- A cost effective accumulator management system for electric vehicles
