In this paper, an automatic algorithm based an unsupervised neural network for an on-line diagnostics of three-phase induction motor stator fault is presented. This algorithm uses the alfa-beta stator currents as input variables. Then, a fully automatic unsupervised method is applied in which a Hebbian-based unsupervised neural network is used to extract the principal components of the stator current data. These main directions are used to decide where the fault occurs and a relationship between the current components is calculated to verify the severity of the fault. One of the characteristics of this method, given its unsupervised nature, is that it does not need a prior identification of the system. The proposed methodology has been experimentally tested on a 1kW induction motor. The obtained experimental results show the effectiveness of the proposed method

The microgrid vision contains several aspects, and a commonly admitted one is a portion of grid with its own means of production and energy flow controls. Photovoltaic (PV) generation is geographically the most distributed means of electricity production. In this sense, the integration of PVs in microgrids seems natural. The intermittency of PV generation can be compensated not only by using energy storage technologies but also by demand-side management and exchanges with other power networks: the main grid and surrounding microgrids. Many aspects still have to be investigated in the fields of power electronics, information communications technologies (ICTs), protections, and power quality (PQ) issues, to make this association a reality.

The use of sensors and actuators as a form of controlling cyber-physical systems in resource networks has been integrated and referred to as the Internet of Things (IoT). However, the connectivity of many stand-alone IoT systems through the Internet introduces numerous cybersecurity challenges as sensitive information is prone to be exposed to malicious users. This paper focuses on the improvement of IoT cybersecurity from an ontological analysis, proposing appropriate security services adapted to the threats. The authors propose an ontology-based cybersecurity framework using knowledge reasoning for IoT, composed of two approaches: (1) design time, which provides a dynamic method to build security services through the application of a model-driven methodology considering the existing enterprise processes; and (2) run time, which involves monitoring the IoT environment, classifying threats and vulnerabilities, and actuating in the environment ensuring the correct adaptation of the existing services. Two validation approaches demonstrate the feasibility of our concept. This entails an ontology assessment and a case study with an industrial implementation.

This paper presents a three-phase multilevel quasi-Z-source inverter (qZSI) topology operating in normal and fault-tolerant operation mode. This structure is composed by two symmetrical quasi-Z-source networks and a three-phase T-type inverter. Besides the intrinsic advantages of multilevel voltage source inverters, the proposed structure is also characterized by their semiconductor fault tolerance capability. This feature is only obtained through changes on the modulation scheme after the semiconductor fault and does not require additional extra-phase legs or collective switching states. In certain fault types, the reduction of the output power capacity will be compensated by the boost characteristic of the qZSI. The fault-tolerant behavior of the proposed topology is demonstrated by several simulation results of the converter in normal and fault condition. To validate the characteristics of this multilevel qZSI, an experimental prototype was also built to experimentally confirm the results.