Hybrid of Genetic Algorithm and Minimum Spanning Tree method for optimal PMU placements

Abstract The Phasor Measurement Units (PMUs) generate a huge amount of data during the data acquisition process, which provokes congestion in data transmission. The traffic generation can be reduced by placing the PMUs at appropriate locations. To bridge this gap, Wide Area Monitoring Systems (WAMS) data traffic model has been implemented for PMUs. The main contribution of this technique is to satisfy complete observability, WAMS data traffic index and cost installation index by using the Hybrid algorithm of the Genetic Algorithm and Minimum Spanning Tree method (MST). The MST is combined with the Genetic Algorithm to repair the unobserved combinations of chromosomes. The proposed technique is tested with standard IEEE 7, 14, 30, 57, 118 bus power systems. The results show the efficacy of the proposed technique to find optimal locations. Also, the results of the proposed hybrid algorithm exhibit faster data transmission and convergence.

[1]  Julián M. Ortiz,et al.  A comparison between ACO and Dijkstra algorithms for optimal ore concentrate pipeline routing , 2017 .

[2]  Fei Jiang,et al.  Big data issues in smart grid – A review , 2017 .

[3]  Almoataz Y. Abdelaziz,et al.  Power system observability with minimum phasor measurement units placement , 2013 .

[4]  Vedik Basetti,et al.  Optimal PMU placement for power system observability using Taguchi binary bat algorithm , 2017 .

[5]  Sukumar Brahma,et al.  Efficient Compression of PMU Data in WAMS , 2016, IEEE Transactions on Smart Grid.

[6]  M. Geethanjali,et al.  Fault localization for transmission lines with optimal Phasor Measurement Units , 2018, Comput. Electr. Eng..

[7]  Behrooz Safarinejadian,et al.  Contingency constrained phasor measurement units placement with n − k redundancy criterion: a robust optimisation approach , 2017 .

[8]  Fang Zhang,et al.  Application of a Real-Time Data Compression and Adapted Protocol Technique for WAMS , 2015, IEEE Transactions on Power Systems.

[9]  M. Geethanjali,et al.  A new method for combined optimal placement of Phasor and Traditional flow measurement units under contingency of single Phasor measurement unit loss , 2014 .

[10]  Seyed Abbas Taher,et al.  Optimal PMU location in power systems using MICA , 2016 .

[11]  Victor O. K. Li,et al.  Optimal phasor data concentrator installation for traffic reduction in smart grid wide-area monitoring systems , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[12]  Anna Scaglione,et al.  A Framework for Phasor Measurement Placement in Hybrid State Estimation Via Gauss–Newton , 2013, IEEE Transactions on Power Systems.

[13]  Song Tan,et al.  Survey of Security Advances in Smart Grid: A Data Driven Approach , 2017, IEEE Communications Surveys & Tutorials.

[14]  Victor O. K. Li,et al.  Optimal Phasor Data Compression Unit Installation for Wide-Area Measurement Systems—An Integer Linear Programming Approach , 2016, IEEE Transactions on Smart Grid.

[15]  Ebrahim Abiri,et al.  An optimal PMU placement method for power system observability under various contingencies , 2015 .

[16]  Bijay Baran Pal,et al.  A fuzzy goal programming method to solve congestion management problem using genetic algorithm , 2019, Decision Making: Applications in Management and Engineering.

[18]  Raynitchka Tzoneva,et al.  Surrogate-splits ensembles for real-time voltage stability assessment in the presence of missing synchrophasor measurements , 2017 .

[19]  Nikolaos M. Manousakis,et al.  A Weighted Least Squares Algorithm for Optimal PMU Placement , 2013, IEEE Transactions on Power Systems.

[20]  Ebrahim Farjah,et al.  Lyapunov exponent-based optimal PMU placement approach with application to transient stability assessment , 2016 .

[21]  Chanan Singh,et al.  A novel linear framework for Phasor Measurement Unit placement considering the effect of adjacent zero-injection buses , 2019 .