Microgrid Protection Coordination Considering Clustering and Metaheuristic Optimization

Javier E. Santos-Ramos, Sergio D. Saldarriaga-Zuluaga, Jesús M. López-Lezama (), Nicolás Muñoz-Galeano and Walter M. Villa-Acevedo
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Javier E. Santos-Ramos: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Sergio D. Saldarriaga-Zuluaga: Facultad de Ingenieria, Departamento de Eléctrica, Institución Universitaria Pascual Bravo, Medellín 050036, Colombia
Jesús M. López-Lezama: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Nicolás Muñoz-Galeano: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia
Walter M. Villa-Acevedo: Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Universidad de Antioquia (UdeA), Medellín 050010, Colombia

Energies, 2023, vol. 17, issue 1, 1-30

Abstract: This paper addresses the protection coordination problem of microgrids combining unsupervised learning techniques, metaheuristic optimization and non-standard characteristics of directional over-current relays (DOCRs). Microgrids may operate under different topologies or operative scenarios. In this case, clustering techniques such as K-means, balanced iterative reducing and clustering using hierarchies (BIRCH), Gaussian mixture, and hierarchical clustering were implemented to classify the operational scenarios of the microgrid. Such scenarios were previously defined according to the type of generation in operation and the topology of the network. Then, four metaheuristic techniques, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and Artificial Bee Colony (ABC) were used to solve the coordination problem of every cluster of operative scenarios. Furthermore, non-standard characteristics of DOCRs were also used. The number of clusters was limited to the maximum number of setting setting groups within commercial DOCRs. In the optimization model, each relay is evaluated based on three optimization variables, namely: time multiplier setting (TMS), the upper limit of the plug setting multiplier (PSM), and the standard characteristic curve (SCC). The effectiveness of the proposed approach is demonstrated through various tests conducted on a benchmark test microgrid.

Keywords: clustering; microgrids; protection coordination; overcurrent relays; unsupervised learning techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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