# Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
## 1 Introduction
In [18], the protection settings and network topology of a microgrid are modified through a rule-based adaptive protection scheme to enhance network reliability. The protection coordination is achieved by means of a machine-learning methodology that is based on a hybrid artificial neural network and a support vector machine model. In [19], the authors proposed a protection coordination index to measure the impact of DG on DOCRs coordination. The index is determined by a two-phase non-linear programming optimization problem that considers variations of the maximum DG penetration level with changes in the protection coordination time interval. Authors in [20] proposed a methodology for coordinating DOCRs that considers topology changes due to single outages of lines or generators. The optimal relay settings when considering the N-1 security criterion are achieved through the solution of a mixed integer nonlinear programming problem. In [21], a multi-objective swarm optimization algorithm combined with a fuzzy decision-making tool is developed for the coordination of DOCRs in microgrids. The proposed approach aims to overcome the limitations of single-objective optimization algorithms that are applied to minimize the operation time of DOCRs coordination. In [22], the authors proposed an online adaptive coordination protection scheme for DOCRs using intelligent electronic devices and a communication channel to obtain real-time information to update the configuration of the relays. This paper proposes a new protection coordination approach of DOCRs in microgrids that integrates and complements the characteristics of the models that are presented in [10; 14; 16; 17]. The proposed coordination model is applicable to adaptive protection schemes that use different settings groups and considers the current capabilities and limitations of DOCRs. The coordination problem is solved by a genetic algorithm, while several machine learning techniques carry out the clustering of operational scenarios. The contributions of this work are twofold: (1) it adopts the constraint that was proposed in [14] regarding the PSM while taking into account the limitation of conventional IEC tripping characteristics currently used in numerical relays, and (2) it limits the number of clusters to that of the setting groups according to the capabilities of commercially available relays, as proposed in [10; 16; 17]. These two features are available in normal operation of commercial relays; nonetheless, they are traditionally not considered in the optimization models of DOCRs. It is a fact that the number of relay setting groups is far less than the possible operating scenarios of a microgrid. Therefore, the proposed approach adopts machine learning techniques to intelligently group the operation scenarios according to the number of setting groups available in the relay. Additionally, a comparison of the performance of different machine learning techniques, which includes K-means and SOM techniques that were adopted in [16; 17] is presented. Table 1 shows the features of related research works in the field, evidencing the knowlege dgap in the existing literature that is filled by the proposed approach. It is worth mentioning that these features are available in commercial relays, but are not considered in traditional protection coordination schemes of DOCRs. It is also a fact that the number of relay setting groups is far less than the possible operating scenarios of a microgrid. Therefore, the proposed approach adopts machine learning techniques to intelligently group operation scenarios, according to the number of setting groups available in the relay. Apart from the aforementioned contributions, this paper compares the performance of several Machine Learning techniques, as detailed in the results section. To summarize, a methodology is proposed for the coordination of DOCRs that jointly uses the K-means, self-Organizing Map, and hierarchical techniques to improve the operating times of the coordination scheme while guaranteeing network security.