Optimal Multi-Objective Hybrid Measurement Placement Using NSGA-II

Basetti Vedik*, Ashwani Kumar Chandel**
* Research Scholar, Department ot Electrical Engineering, NIT-Hamirpur, H.P, India.
** Professor, Department of Electrical Engineering, NIT-Hamirpur, H.P, India.
Periodicity:August - October'2014
DOI : https://doi.org/10.26634/jps.2.3.3033

Abstract

In this paper, non-dominated sorting genetic algorithm-II(NSGA-II) is proposed for optimal placement of hybrid, i.e. PMUs and conventional meters. The hybrid measurement placement problem has been solved by simultaneously optimizing the two conflicting objectives, viz, Maximizing the measurement redundancy and the other, minimizing the installation cost ensuring full network observability. The Pareto optimal solutions are obtained using the non-dominated sorting and crowding distance criterion. Subsequently, a decision making procedure based on VIKOR method is employed to determine the best conciliation solution from the set of Pareto-optimal front. The effectiveness and flexibility of the proposed algorithm has been tested on IEEE 14-bus, IEEE-30 bus, and IEEE 57-bus systems respectively

Keywords

Observability, Optimization, Phasor Measurement Unit, Power System, Redundancy, State Estimation.

How to Cite this Article?

Vedik, B., and Chandel, A. K. (2014). Optimal Multi-Objective Hybrid Measurement Placement Using Nsga-II. i-manager’s Journal on Power Systems Engineering, 2(3), 28-43. https://doi.org/10.26634/jps.2.3.3033

References

[1]. Abbasy, N. H., & Ismail, H. M. (2009). A unified approach for the optimal PMU location for power system state estimation. Power Systems, IEEE Transactions, Vol.24, No.2, pp.806-813.
[2]. Ahmadi, A., Alinejad-Beromi, Y., & Moradi, M. (2011). Optimal PMU placement for power system observability using binary particle swarm optimization and considering measurement redundancy. Expert Systems with Applications, Vol.38, No.6, pp.7263-7269.
[3]. Aminifar, F., Lucas, C., Khodaei, A., & Fotuhi- Firuzabad, M. (2009). Optimal placement of phasor measurement units using immunity genetic algorithm. Power Delivery, IEEE Transactions, Vol.24, No.3, pp.1014- 1020.
[4]. Aminifar, F., Khodaei, A., Fotuhi-Firuzabad, M., & Shahidehpour, M. (2010). Contingency-constrained PMU placement in power networks. Power Systems, IEEE Transactions, Vol.25, No.1, pp.516-523.
[5]. Baldwin, T. L., Mili, L., BoisenJr, M. B., & Adapa, R. (1993). Power system observability with minimal phasor measurement placement. Power Systems, IEEE Transactions, Vol.8, No.2, pp.707-715.
[6]. Basu, M. (2008). Dynamic economic emission dispatch using nondominated sorting genetic algorithm- II. International Journal of Electrical Power & Energy Systems, Vol.30, No.2, pp.140-149.
[7]. Chakrabarti, S., & Kyriakides, E. (2008). Optimal placement of phasor measurement units for power system observability. Power Systems, IEEE Transactions, Vol.23, No.3, pp.1433-1440.
[8]. Chakrabarti, S., Venayagamoorthy, G. K., & Kyriakides, E. (2008, December). PMU placement for power system observability using binary particle swarm optimization. In Power Engineering Conference, 2008. AUPEC'08. Australasian Universities. pp. 1-5). IEEE.
[9]. Chakrabarti, S., Kyriakides, E., &Eliades, D. G. (2009). Placement of synchronized measurements for power system observability. Power Delivery, IEEE Transactions, Vol.24, No.1, pp.12-19.
[10]. Chang, C. L. (2010). A modified VIKOR method for multiple criteria analysis. Environmental monitoring and assessment, Vol.168, pp.1-4, pp.339-344.
[11]. Christie, R. (1999). Power System Test Archive, Aug.
[12]. Chunhua, P., & Xuesong, X. (2008, April). A hybrid algorithm based on immune BPSO and N-1 principle for PMU multi-objective optimization placement. In Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference, pp. 610-614, IEEE.
[13]. de Almeida, M. C., Garcia, A. V., & Asada, E. N. (2012). Regularized least squares power system state estimation. Power Systems, IEEE Transactions, Vol.27, No.1, pp.290-297.
[14]. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions, Vol.6, No.2, pp.182-197.
[15]. Do CouttoFilho, M. B., da Silva, A. L., Cantera, J. C., & da Silva, R. A. (1989, May). Information debugging for real-time power systems monitoring. In IEE Proceedings C (Generation, Transmission and Distribution), Vol. 136, No. 3, pp. 145-152. IET Digital Library.
[16]. Enshaee, A., Hooshmand, R. A., & Fesharaki, F. H. (2012). A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies. Electric Power Systems Research, Vol.89, pp.1-10.
[17]. Firouzjah, K. G., Sheikholeslami, A., & Barforoushi, T. (2012). Multi-objective allocation of measuring system based on binary particle swarm optimization. Frontiers of Electrical and Electronic Engineering, Vol.7, No.4, pp.399-415.
[18]. Gou, B. (2008). Optimal placement of PMUs by integer linear programming. IEEE Transactions on power systems, Vol.23, No.3, pp.1525-1526.
[19]. Hajian, M., Ranjbar, A. M., Amraee, T., & Shirani, A. R. (2007, November). Optimal placement of phasor measurement units: particle swarm optimization approach. In Intelligent Systems Applications to Power Systems, 2007. ISAP 2007. International Conference, pp. 1-6. IEEE.
[20]. Jamuna, K., & Swarup, K. S. (2012). Multi-objective biogeography based optimization for optimal PMU placement. Applied Soft Computing, Vol.12, No.5, pp.1503-1510.
[21]. Kavasseri, R., & Srinivasan, S. K. (2011). Joint placement of phasor and power flow measurements for observability of power systems. Power Systems, IEEE Transactions, Vol.26, No.4, pp.1929-1936.
[22]. Khiabani, V., Yadav, O. P., & Kavasseri, R. (2011). Reliability-based placement of phasor measurement units in power systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X11417959.
[23]. Khiabani, V., Kavasseri, R., & Farahmand, K. (2012). A Reliability Based Multi-Objective Formulation for Optimal PMU Placement. International Review on Modelling & Simulations, Vol.5, No.4.
[24]. Khiabani, V., Erdem, E., Farahmand, K., & Nygard, K. (2013, August). Genetic algorithm for instrument placement in smart grid. In Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress, pp. 214-219, IEEE.
[25]. Korkali, M., & Abur, A. (2009, July). Placement of PMUs with channel limits. In Power & Energy Society General Meeting, 2009.PES'09. IEEE, pp. 1-4, IEEE.
[26]. Koutsoukis, N. C., Manousakis, N. M., Georgilakis, P. S., & Korres, G. N. (2013). Numerical observability method for optimal phasor measurement units placement using recursive Tabu search method. Generation, Transmission & Distribution, IET, Vol.7, No.4, pp.347-356.
[27]. Li, D. H., Cao, Y. J., Jiang, Q. Y., & Zhan, Z. B. (2005). Optimal Placement of Phasor Measurement Unit Based on Multi-Objective Evolutionary Algorithm [J].Power System Technology, Vol.22, 013.
[28]. Mahaei, S. M., & Hagh, M. T. (2012). Minimizing the number of PMUs and their optimal placement in power systems. Electric Power Systems Research, Vol.83, No.1, pp.66-72.
[29]. Mahari, A., & Seyedi, H. (2013). Optimal PMU placement for power system observability using BICA, considering measurement redundancy. Electric Power Systems Research, Vol.103, pp.78-85.
[30]. Manousakis, N. M., Korres, G. N., & Georgilakis, P. S. (2012). Taxonomy of PMU placement methodologies. Power Systems, IEEE Transactions, 27, No.2, pp.1070- 1077.
[31]. Manousakis, N. M., &Korres, G. N. (2013). A Weighted Least Squares Algorithm for Optimal PMU Placement. Power Systems, IEEE Transactions, Vol.28, No.3, pp.3499-3500.
[32]. Marin, F. J., Garcia-Lagos, F., Joya, G., & Sandoval, F. (2003). Genetic algorithms for optimal placement of phasor measurement units in electrical networks. Electronics Letters, Vol.39, No.19, pp.1403-1405.
[33]. Mazhari, S. M., Monsef, H., Lesani, H., & Fereidunian, A. (2013). A multi-objective PMU placement method considering measurement redundancy and observability value under contingencies. IEEE Transactions on Power Systems, Vol.28, No.3 pp.2136-2146.
[34]. Milosevic, B., & Begovic, M. (2003). Nondominated sorting genetic algorithm for optimal phasor measurement placement. Power Systems, IEEE Transactions, Vol.18, No.1, pp.69-75.
[35]. Nuqui, R. F., & Phadke, A. G. (2005). Phasor measurement unit placement techniques for complete and incomplete observability. Power Delivery, IEEE Transactions, Vol.20, No.4, pp.2381-2388.
[36]. Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, Vol.156, No.2, pp.445-455.
[37]. Peng, J., Sun, Y., & Wang, H. F. (2006). Optimal PMU placement for full network observability using Tabu search algorithm. International Journal of Electrical Power & Energy Systems, Vol.28, No.4, pp.223-231.
[38]. Peng, C. H., Sun, H. J., & Guo, J. F. (2009). Nondominated sorting differential evolution algorithm for multi-objective optimal PMU placement. Control Theory & Applications, Vol.10, No.007.
[39]. Peng, C., Sun, H., & Guo, J. (2010). Multi-objective optimal PMU placement using a non-dominated sorting differential evolution algorithm. International Journal of Electrical Power & Energy Systems, Vol.32, No.8, pp.886- 892.
[40]. Phadke, A. G., & Thorp, J. S. (2008). Synchronized phasor measurements and their applications. Springer.
[41]. Shahraeini, M., Ghazizadeh, M. S., & Javidi, M. H. (2012). Co-optimal placement of measurement devices and their related communication infrastructure in wide area measurement systems. Smart Grid, IEEE Transactions, Vol.3, No.2, pp.684-691.
[42]. Sodhi, R., Srivastava, S. C., & Singh, S. N. (2010). Optimal PMU placement method for complete topological and numerical observability of power system. Electric Power Systems Research, Vol.80, No.9, pp.1154- 1159.
[43]. Sodhi, R., Srivastava, S. C., & Singh, S. N. (2011). Multi-criteria decision-making approach for multistage optimal placement of phasor measurement units. Generation, Transmission & Distribution, IET, Vol.5, No.2, pp.181-190.
[44]. Vedik, B., & Chandel, A. K. (2013, March). Optimal placement of PMUs using differential evolution. In Intelligent Systems and Signal Processing (ISSP), 2013 International Conference, pp. 17-22,. IEEE.
[45]. Xingang, W., Qian, A., Weihua, X., & Peng, H. (2009). Multi-objective optimal energy management of microgrid with distributed generation. Power System Protection and Control, Vol.37, No.20, pp.79-83.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.