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Optimal PMUs placement considering ZIBs and single line and PMUs outages

1 Electrical Engineering Dept., Faculty of Engineering, Suez Canal University, 41522, Ismailia, Egypt
2 Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt
3 Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
4 Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran

Special Issues: Advances and Technologies in Smart Power Systems Operation, control, protection and Security

Phasor measurement unit (PMU) is among the most important measurement devices in modern power systems. It measures the voltage and current phasors which have both magnitude and phase angle using a common timing reference. These measurements are utilized in real time applications of electrical power systems. The main drawback of PMUs is its high cost so if PMUs are used to collect or send online real data from a system to a data centre or a decision maker, the used number of PMUs should be minimum with full observability of system measurements. This paper proposes a flower pollination algorithm (FPA) to find the optimal number and locations of PMUs in power systems. The optimization objectives of the work are the minimization of PMUs number, the achievement of complete observability of power system states, and the maximization of measurement redundancy. Existence of zero-injection buses in the system decreases the number of PMUs that is required to fulfil the complete observability of system states. Furthermore, additional constraints for remaining system full observable following failure of single PMU and single line outage are also included to increase the system reliability. The performance and efficiency of the proposed FPA is tested on IEEE standard networks such as 14-bus, 30-bus, 57-bus and 118-bus and new England 39-bus network. The obtained simulation results approve the ability of the proposed optimization method to find the optimal allocation of PMUs in different cases with fulfilling complete observability and reliability of electric power systems. The superiority of FPA is also verified by comparing its results of other optimization algorithms.
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1. Schweitzer EO, Whitehead DE (2009) Real world synchrophasors solutions. 62nd Annual Conference for Protective Relay Engineers, 536-547.

2. Lu Z, Xu Z, Shi Z, et al. (2000) State estimation of voltage phasors based on part of voltage and current phasors measurement. Autom Electr Power Syst 24: 42-44.

3. Kavasseri R, Srinivasa SK (2011) Joint placement of phasor and conventional power flow measurements for fault observability of power systems. IET Gener, Transm Distrib 5: 1019-1024.    

4. Dalali M, Karegar HK (2016) Optimal PMU placement for full observability of the power network with maximum redundancy using modified binary cuckoo optimization algorithm. IET Gener, Transm Distrib 10: 2817-2824.    

5. Rather ZH, Chen Z, Thøgersen P, et al. (2015) Realistic Approach for Phasor Measurement Unit Placement: Consideration of Practical Hidden Costs. IEEE Trans Power Delivery 30: 3-15.    

6. Abdelaziz AY, Ibrahim AM, Salem RH (2013) Power system observability with minimum phasor measurement units placement. Int J Eng, Sci Technol, 5: 1-18.

7. Chakrabarti S, Kyriakides E (2008) Optimal placement of phasor measurement units for power system observability. IEEE Trans Power Syst 23: 1433-1440.    

8. Chakrabarti S, Kyriakides E, Eliades D (2009) Placement of synchronized measurements for power system observability. IEEE Trans Power Delivery 24: 12-19.

9. Gou JB (2008) Generalized integer linear programming formulation for optimal PMU placement IEEE Trans Power Syst 23: 1099-1104.

10. Dua D, Dambhare S, Gajbhiye R, et al. (2006) Optimal multistage scheduling of PMU placement: An ILP approach. IEEE Trans Power Delivery 23: 1812-1820.

11. Abbasy NH, Ismail HM (2009) A unified approach for the optimal PMU location for power system state estimation. IEEE Trans Power Syst 24: 806-813.

12. Gou B, Kavasseri RG (2014) Unified PMU placement for observability and bad data detection in state estimation. IEEE Trans Power Syst 29: 2573-2580.    

13. Babu R, Bhattacharyya B (2018) Strategic placements of PMUs for power network observability considering redundancy measurement. Measurement 134: 606-623.

14. Razavi SE, Falaghi H, Singh C, et al. (2018) A novel linear framework for Phasor Measurement Unit placement considering the effect of adjacent zero-injection buses. Measurement 133: 532-540.

15. Müller HH, Castro CA (2016) Genetic algorithm-based phasor measurement unit placement method considering observability and security criteria. IET Gener, Transm Distrib 10: 270-280.    

16. El-Sehiemy RA, Abdel Aleem SHE, Abdelaziz AY, et al. (2017) A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions. Resour-Effic Technol 3: 542-549.

17. Koutsoukis NC, Manousakis NM, Georgilakis PS, et al. (2013) Numerical observability method for optimal phasor measurement units placement using recursive Tabu search method. IET Gener, Transm Distrib 7: 347-356.    

18. Sodhi R, Srivastava SC, Singh SN (2011) Multi-criteria decision making approach for multistage optimal placement of phasor measurement units. IET Gener, Transm Distrib 5: 181-190.    

19. Saleh AA, Adail AS, Wadoud AA (2017) Optimal phasor measurement units placement for full observability of power system using improved particle swarm optimization. IET Gener, Transm Distrib 11: 1794-1800.    

20. Korres GN, Manousakis NM, Xygkis TC, et al. (2015) Optimal phasor measurement unit placement for numerical observability in the presence of conventional measurements using semidefinite programming. IET Gener, Transm Distrib 9: 2427-2436.    

21. Ram JP, Rajasekar N (2017) A novel flower pollination based global maximum power point method for solar maximum power point tracking'. IEEE Trans power Electron. 32: 8486-8499.    

22. Yang XS (2012) Flower pollination algorithm for global optimization. Unconv comput nat Comput, 240-249.

23. Yang XS, Karamanoglu M, He X (2013) Multi-objective Flower Algorithm for Optimization. Proc Comput Sci 18: 861-868.    

24. Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm, 'a novel approach for multiobjective optimization. Eng Optim 46: 1222-1237.    

25. Yang XS (2014) Nature-inspired Optimization Algorithms, Elsevier.

26. Christie R (1999) Power System Test Archive. Available from: http://www.ee.washington.edu/research/pstca.

27. Billakanti S, Venkaiah C (2014) An effective binary integer linear programmed approach for optimal placement of PMUs in power systems. IEEE International Conference on Smart Electric Grid (ISEG), 1-8.

28. Rahman NHA, Zobaa AF (2016) Optimal PMU placement using topology transformation method in power systems. J Adv Res 7: 625-634.    

29. Xu B, Abur A (2004) Observability analysis and measurement placement for systems with PMUs. IEEE PES Power Systems Conference and Exposition, 1472-1475.

30. Ahmadim A, Alinejad-beromi Y, Moradi M (2011) Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Expert Syst Appl 38: 7263-7269.    

31. Mohammadi-Ivatloo B, Hosseini SH (2008) Optimal PMU placement for power system observability considering secondary voltage control. IEEE Canadian Conference on Electrical and Computer Engineering, 365-368.

32. Al-Mohammed AH, Abido MA, Mansour MM (2011) Optimal placement of synchronized phasor measurement units based on differential evolution algorithm. IEEE PES Conference on Innovative Smart Grid Technologies-Middle East, 1-9.

33. Rather ZH, Liu C, Chen Z, et al. (2013) Optimal PMU placement by improved particle swarm optimization. IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 1-6.

34. Saha BK, Sinha AK, Pradhan AK (2012) An optimal PMU placement technique for power system observability. Int J Electr Power Energy Syst 42: 71-77.    

35. Nadia HA, Zobaa AF (2017) Integrated Mutation Strategy with Modified Binary PSO Algorithm for Optimal PMUs Placement. IEEE Trans Ind Inf 13: 3124-3133.    

36. Manousakis NM, Korres GN (2017) Optimal allocation of PMUs in the presence of conventional measurements considering contingencies. IEEE Trans Power Delivery, 1-8.

37. Basetti V, Chandel AK (2017) Optimal PMU placement for power system observability using Taguchi binary bat algorithm. Measurement 95: 8-20.    

38. Aminifar F, Khodaei A, Fotuhi-Firuzabad M, et al. (2010) Contingency-constrained PMU placement in power networks. IEEE Trans Power Syst 25: 516-523.    

39. Hajian M, Ranjbar AM, Amraee T, et al. (2011) Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm. Electr Power Energy Syst 33: 28-34.    

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