The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropy of such decompositions is analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.

The difficulties facing electric utility companies to satisfy on-site customer power demand aroused an increasing interest in small distributed generators (DG). Also the environmental concerns, the technology evolution and the need to integrate renewable energy resources into distribution system gave way to the construction of (DG) using alternative energy sources. The presence of such new generators creates new operating conditions for the interconnected system. The aim of the paper is to study the impacts of faults on stability in systems with (DG) and their satiability during faults. In this paper, the impact of different types of faults at various locations on a distribution system with and without the presence of (DG) is studied. The IEEE 13 node distribution test feeder is used as a model for the study together with synchronous generator (DG). The simulation of the distribution system and the (DG) is performed using PSCAD software. A comparison between fault currents with and without the insertion of the DG is carried. The effect of such faults on the stability of the used DG is considered taking into account the control actions expected for the system to remain within the stability limit. A tuned PID controller is used to enhance the DG transients due to different load changes and faults. The results of simulation are discussed and the scope of the future work is highlighted.

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Smart grid technology has gained much consideration recently to make use of intelligent control in the automatic fault-detection and self-healing of electric networks. This ensures a reliable electricity supply and an efficient operation of the distribution system against disasters with minimum human interaction. In this paper, a fully decentralized multi-agent system (MAS) algorithm, for self-healing in smart distribution systems, is proposed. The novelty of the proposed algorithm, compared to related work, is its ability to combine the zone and feeder agents, specified for system self-healing, with micro-grid agents. This enables the system to successfully achieve functions of fault locating and isolation along with service-restoration using expert rules while considering both operational constraint and load priorities. Meanwhile, managing the power flow and controlling the distributed generator (DG) contribution, in the considered network, is a bonus merit for the proposed algorithm. Hence, system self-healing as well as strengthening energy security and resiliency are guaranteed. The proposed algorithm is tested on a 22 kV radial distribution system through several case-studies with/without a DG wind-energy source. The employed agents are implemented in the Java Agent Developing Framework (JADE) environment to communicate and make decisions. Power system simulation and calculations are carried out in MATLAB to validate the agents’ decisions.