Detailed analysis of grid connected and islanded operation modes based on P/U and Q/f droop characteristics

This paper presents a thorough control structure of the distributed generators inside the microgrid during both grid-connected and islanded operation modes. These control structures of the DGs voltage source inverters are implemented in synchronous reference frame (SRF) and controlled using linear PI controllers. By implementing the control structures, the desired real and reactive power can be efficiently transferred to the local loads and the utility load by the microgrid generating units. A modified droop control technique is introduced to facilitate the microgrid performance during both modes of operation. The active and reactive power sharing of the load demand between the utility grid and the microgrid can be performed by this drop control technique during the islanded mode. The system performance during intentional islanding event and utility load increase is investigated. The effectiveness of the offered control structures is confirmed through simulation results during both modes of operation.

End-to-End Deep Graph Convolutional Neural Network Approach for Intentional Islanding in Power Systems Considering Load-Generation Balance

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.

Multi-Objective Evolutionary Algorithm for Service Restoration in the Presence of Distributed Generation

Distributed Generators (DGs) have been used to improve quality and reliability of service in Distribution Systems (DSs). They can be used to reduce faults impact on System Average Interruption Duration Index by allowing the minimization of healthy out-ofservice (OFS) loads after the occurrence of permanent faults. IEEE also encourages power supply companies and customers to restore OFS loads by intentional islanding. This paper proposes a modification in recently proposed Multi-Objective Evolutionary Algorithm (MOEA) in subpopulation tables to combine intentional islanding of DGs with network reconfiguration to maximize restoration of OFS loads. The idea is to force intentional islanding whenever OFS heathy areas can be fully supplied by DGs. Simulation results (with a DS presented in the literature) have demonstrated the reliability of the MOEA new version to deal with service restoration problem in the presence of DGs.