# Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
## 1 Introduction  
Modern societies are highly dependent on the supply of electrical energy for their daily functioning. The per capita energy consumption has steadily grown, due to the increasing of industrialization and world population; so, fossil fuel and conventional energy resources might not be sufficient for meeting humanity's energy needs in the medium term [1]; in other words, the traditional electrical network will not be enough to meet the growing energy demand. For overcoming this issue, the massive implementation of renewable energy resources and efficient energy storage systems have been thought as the future of power generation [2, 3]. Microgrids play a key role in this solution, since they facilitate the integration of renewable energy resources in distribution systems through distributed generation (DG) [4]; nevertheless, such integration brings new issues regarding their control and operation [5]. The fact that microgrids may exhibit different operational modes that are associated with the presence of DG units has become one of the main challenges in their protection coordination [6]. DG units generate bi-directional power flows and variable short-circuit levels that modify the traditional behavior of distribution networks. According to [7, 8], protection systems in microgrids must face two remarkable challenges: the first one corresponds to their inherent dynamic behavior, due to intermittent loads and generators, and the second one is related to their operating characteristics (grid connected or islanded). These two aspects tangle the problem of microgrid protection coordination; therefore, finding a protection scheme that guarantees speed, selectivity, and reliability has become one of the most challenging tasks in planning microgrid operation [9]. Directional over-current relays (DOCRs) are widely used in distribution networks (DNs) and they are recognized as the main protection device due to their simplicity and low implementation cost [10]. Nevertheless, non-radial flexible systems such as microgrids complicate the setting of DOCRs [11; 12; 13]. Recently, researchers have focused their efforts on the implementation of optimization techniques for reducing the protection times of DOCRs, guaranteeing selectivity and reliability resorting to non-standard characteristics [14; 15] and machine learning techniques [16; 17; 18]. In [14], an alternative approach to conventional protection coordination modeling was proposed. This is achieved by adding a new constraint to the coordination problem that takes into account the Plug Setting Multiplier (PSM), which is the ratio of the fault current that is seen by the relay to the pickup current. The tripping characteristic of over-current relays is limited by the PSM value. The approach of [14] takes the limitation of conventional IEC tripping characteristics used nowadays in numerical relays into consideration. The authors in [10] proposed a setting group-based adaptive protection scheme to take different configurations of a microgrid that result from the connection and disconnection of DG into account. The work proposes different setting groups, limiting its number according to the capabilities of commercial relays; additionally, a new method based on integer linear programming and particle swarm optimization (PSO) is used to specify the adjustments of DOCRs. A similar approach to the one that was proposed in [10] is implemented in [16] and [17] while using unsupervised machine learning techniques. In [16], the authors exploit the facility to save multiple setting groups within digital DOCRs to adapt the setting of the relays to the current topology of the network. Because the number of setting groups is much lower than that of possible network topologies, a K-means algorithm is used to classify topologies into a number of clusters equal to the number of setting groups. Based on this work, the authors in [17] proposed a technique for DOCRs coordination while using the Self-Organizing Map (SOM) clustering algorithm.