Microgrids (MG) treat local energy supply issues effectively and from a point of view of the distribution grid, may be a power supply or virtual load. Despite holding a myriad of benefits, MGs also bear a set of challenges, including a higher fault rate. Currently, many articles focus on control techniques; however, little has been written about the techniques of control, hierarchical control, and fault-tolerant control (FTC) applied to MGs, which is the motive of this bibliographic revision on control systems. A brief comparison of the different approaches in the field of present-day research is carried out primarily addressing hierarchical control and fault tolerance. The objective of this investigation is to attract the interest of researchers to the field of control and fault tolerance applied to MGs, such as: modeling, testbed, benchmark systems, control and hierarchical control strategies, fault diagnosis and FTC.

The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods have shown very promising results mainly because they can deal with uncertain data and the variability of models better than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location in water distribution networks. This approach is based on a zone division of the network, and it only requires data from a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than 2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure sensors.