Microgrid Protection Coordination Considering Clustering and Metaheuristic Optimization

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...

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Bibliographic Details
Published in:Energies (Basel) 2024-01, Vol.17 (1), p.210
Main Authors: Santos-Ramos, Javier E., Saldarriaga-Zuluaga, Sergio D., López-Lezama, Jesús M., Muñoz-Galeano, Nicolás, Villa-Acevedo, Walter M.
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Analysis
clustering
Democratization
Emission standards
Empowerment
Energy industry
Energy resources
Energy transition
Genetic algorithms
Industrial plant emissions
Linear programming
Machine learning
Mathematical optimization
microgrids
Optimization techniques
overcurrent relays
Power
protection coordination
unsupervised learning techniques
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17010210