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This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive voltage and frequency control method for inverter-based distributed generations (DGs) in a multi-microgrid (MMG) structure using distributed cooperative control and adaptive neural networks (ANN). First, model-based controllers are designed using the Lyapunov theory and dynamics of the inverter-based DGs. ANNs are then utilized to approximate these dynamics, resulting in an intelligent controller, which does not require a priori information about DG parameters. Also, the proposed controllers do not require the use of voltage and current proportional-integral controllers normally found in the literature. The effectiveness of the proposed controllers are verified through simulations under different scenarios on an MMG test system. Using Lyapunov analysis, it is proved that the tracking error and the neural network weights are uniformly ultimately bounded, which results in achieving superior dynamic voltage and frequency regulation. [ABSTRACT FROM AUTHOR]