A Multi-objective Approach for Optimized Monitoring of Voltage Sags in Distribution Systems

  • Savio Mota Carneiro
  • Ricardo de Andrade Lira Rabelo
  • Hermes Manoel Galvao Castelo Branco
Article
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Abstract

Voltage sags are among the most relevant power quality disturbances. Furthermore, they also have high occurrence rates. Their stochastic nature makes monitoring difficult and causes significant losses to power utilities and customers. This paper presents an approach to overcome the problem of allocating power quality monitors. To do so, our approach accounts for topological coverage, unmonitored voltage sags, and the total cost of required equipment. We used NSGA-II to build our approach due to its efficiency in dealing with combinatorial problems. We also used the Monte Carlo simulation method to model the time series in our approach due to the random nature of power quality disturbances. To evaluate our approach, we simulated the IEEE 13-, 34- and 37-bus distribution systems using the DigSILENT Power Factory 15.1 software. The evaluation results show that our approach supported cost reduction associated with the installation of power quality monitors, both in terms of identifying adequate number and position of the performance monitors.

Keywords

Genetics algorithms Monitors allocation Monte Carlo method Power quality 

List of symbols

A(X)

Ambiguity vector

D(X)

Descendants vector

\(D_\mathrm{total}\)

Total number of descendants of the system

e

Remainder voltage threshold

F

Fault node

\(f_{1}(x)\)

Monitoring cost objective function

\(f_{2}(x)\)

Topological coverage quality objective function

\(f_{3}(x)\)

Sag coverage objective function

k

Node under observation

L(X)

Load vector

\(L_\mathrm{total}\)

Total load in the system

\(\mathrm{CM}_{e}\)

Coverage matrix given a threshold e

\(\mathrm{VMDF}\)

Voltage matrix during fault

\(\mathrm{VMDF}^{T}\)

Transpose of the voltage matrix during fault

n

Number of nodes in the system

\(N_\mathrm{sample}\)

Sample size in MCM

\(N_\mathrm{mcm}\)

Number of executions of MCM

\(N_\mathrm{pop}\)

Population size in the NSGA-II

C

Monitor installation cost vector

\(P_{t}\)

Parent population of generation t in NSGA-II

\(Q_{t}\)

Child population of generation t in NSGA-II

\(R_{t}\)

Auxiliary population of generation I in NSGA-II

V

Observability vector

\(\overline{V}\)

Vulnerability vector

U

Non-monitored sags vector

\(w_{1}\)

Weight of load coverage

\(w_{2}\)

Weight of descendants coverage

\(w_{3}\)

Weight of ambiguity

X

Allocation vector

\(\sigma _{x}\)

Sample standard deviation in MCM

\(\sigma _{\overline{x}}\)

Approximation error of MCM

References

  1. Almeida, C., & Kagan, N. (2011). Using genetic algorithms and fuzzy programming to monitor voltage sags and swells. IEEE Intelligent Systems, 26(2), 46–53.CrossRefGoogle Scholar
  2. Bollen, M. H. (2000). Understanding power quality problems (Vol. 3). New York: IEEE press.Google Scholar
  3. Bollen, M. H., & Gu, I. (2006). Signal processing of power quality disturbances (Vol. 30). New York: Wiley.CrossRefGoogle Scholar
  4. Branco, H. M., Oleskovicz, M., Delbem, A. C., Coury, D. V., & Silva, R. P. (2015). Optimized allocation of power quality monitors in transmission systems: A multiobjective approach. International Journal of Electrical Power and Energy Systems, 64, 156–166.CrossRefGoogle Scholar
  5. Cebrian, J. C., Almeida, C. F. M., & Kagan, N. (2010). Genetic algorithms applied for the optimal allocation of power quality monitors in distribution networks. In 14th international conference on harmonics and quality of power (ICHQP) (pp. 1–10).Google Scholar
  6. Conrad, L., Little, K., & Grigg, C. (1991). Predicting and preventing problems associated with remote fault-clearing voltage dips. IEEE Transactions on Industry Applications, 27(1), 167–172.CrossRefGoogle Scholar
  7. Das, J. (2016). Power system analysis: Short-circuit load flow and harmonics. Boca Raton: CRC Press.Google Scholar
  8. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRefGoogle Scholar
  9. Dugan, R., McGranaghan, M., Santoso, S., & Beaty, H. (2012). Electrical power systems quality (3rd ed.). New York: McGraw-Hill Education.Google Scholar
  10. Espinosa-Juárez, E., & Hernandez, A. (2007). A method for voltage sag state estimation in power systems. IEEE Transactions on Power Delivery, 22(4), 2517–2526.CrossRefGoogle Scholar
  11. Espinosa-Juarez, E., Hernandez, A., & Olguin, G. (2009). An approach based on analytical expressions for optimal location of voltage sags monitors. IEEE Transactions on Power Delivery, 24(4), 2034–2042.CrossRefGoogle Scholar
  12. Gupta, G. & Fritz, W. (2016). Power quality monitoring by advanced mathematical tools: A survey. In 1st International conference on power electronics, intelligent control and energy systems (ICPEICES) (pp. 1–5).Google Scholar
  13. Hong, Y. Y., & Chen, Y. Y. (2011). Placement of power quality monitors using enhanced genetic algorithm and wavelet transform. IET Generation, Transmission Distribution, 5(4), 461–466.CrossRefGoogle Scholar
  14. Hopcroft, J. E. (1983). Data structures and algorithms (Vol. 175). Boston, MA: Addison-Wesley.MATHGoogle Scholar
  15. Kempner, T. R., Oleskovicz, M., & Santos, A. Q. (2014). Optimal allocation of monitors by analyzing the vulnerability area against voltage sags. In 16th International conference on harmonics and quality of power (ICHQP) (pp. 536–540).Google Scholar
  16. Kersting, W. (1991). Radial distribution test feeders. IEEE Transactions on Power Systems, 6(3), 975–985.CrossRefGoogle Scholar
  17. Li, W., & Billinton, R. (2013). Reliability assessment of electric power systems using Monte Carlo methods. Berlin: Springer.MATHGoogle Scholar
  18. Liao, H., Liu, Z., Milanovi, J. V., & Woolley, N. C. (2016). Optimisation framework for development of cost-effective monitoring in distribution networks. IET Generation, Transmission Distribution, 10(1), 240–246.CrossRefGoogle Scholar
  19. Mahela, O. P., Shaik, A. G., & Gupta, N. (2015). A critical review of detection and classification of power quality events. Renewable and Sustainable Energy Reviews, 41, 495–505.CrossRefGoogle Scholar
  20. Newman, M. E., Barkema, G. T., & Newman, M. (1999). Monte Carlo methods in statistical physics (Vol. 13). Oxford: Clarendon Press.MATHGoogle Scholar
  21. Nilsson, J. W. (2008). Electric circuits. London: Pearson Education India.MATHGoogle Scholar
  22. Singh, B., Chandra, A., & Al-Haddad, K. (2014). Power quality: Problems and mitigation techniques. New York: Wiley.Google Scholar
  23. Won, D.-J., Chung, I.-Y., Kim, J.-M., Moon, S.-I., Seo, J.-C., & Choe, J.-W. (2006). A new algorithm to locate power-quality event source with improved realization of distributed monitoring scheme. IEEE Transactions on Power Delivery, 21(3), 1641–1647.CrossRefGoogle Scholar
  24. Won, D.-J., & Moon, S.-I. (2008). Optimal number and locations of power quality monitors considering system topology. IEEE Transactions on Power Delivery, 23(1), 288–295.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2018

Authors and Affiliations

  • Savio Mota Carneiro
    • 1
  • Ricardo de Andrade Lira Rabelo
    • 1
  • Hermes Manoel Galvao Castelo Branco
    • 2
  1. 1.Department of Computer SciencesFederal University of PiauiTeresinaBrazil
  2. 2.Department of Electrical EngineeringState University of PiauiTeresinaBrazil

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