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


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.


Genetics algorithms Monitors allocation Monte Carlo method Power quality 

List of symbols


Ambiguity vector


Descendants vector


Total number of descendants of the system


Remainder voltage threshold


Fault node


Monitoring cost objective function


Topological coverage quality objective function


Sag coverage objective function


Node under observation


Load vector


Total load in the system


Coverage matrix given a threshold e


Voltage matrix during fault


Transpose of the voltage matrix during fault


Number of nodes in the system


Sample size in MCM


Number of executions of MCM


Population size in the NSGA-II


Monitor installation cost vector


Parent population of generation t in NSGA-II


Child population of generation t in NSGA-II


Auxiliary population of generation I in NSGA-II


Observability vector


Vulnerability vector


Non-monitored sags vector


Weight of load coverage


Weight of descendants coverage


Weight of ambiguity


Allocation vector

\(\sigma _{x}\)

Sample standard deviation in MCM

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

Approximation error of MCM


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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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