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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]

To achieve clean energy targets, more renewable generators – mainly wind and photovoltaic (PV) – are being integrated into electricity networks. However, wind and PV are non-synchronous generators to the grid and they traditionally have neither enough inertia response nor governor support to control major frequency excursions under most situations. Currently in South Australia, the combined generation of wind and rooftop PV has nearly reached the level of average demand with minor support through interconnection import. In such a situation it is likely that there may be fewer synchronous machines online for frequency regulation. Under such a scenario, a contingency could produce a more severe frequency excursion, violating in place frequency standards. Further, many distributed PV systems in South Australia may have a default under-frequency protection setting, which could be higher than the under-frequency load shedding (UFLS) threshold. Hence, a moderate generation/import trip may cause a massive secondary PV trip, which may result in an unacceptably low frequency, triggering further load shedding. This will certainly have an adverse impact on network security. A similar issue of secondary PV trip is not restricted to South Australia and may occur in other regions and countries as well. In this paper, a network which loosely represents the Australian National Electricity Market (NEM) is selected and modified to investigate and analyze network frequency response under high non-synchronous machine penetration. This network contains all necessary models for a comprehensive frequency response study, such as synchronous machine, governor, stabilizer and wind generator. The relevant functions such as load frequency relief, PV tripping and load shedding which are essential for frequency response evaluation are established in the approved and well-accepted platform – PSS®E. The results show that low inertia and secondary PV tripping can become serious issues for network frequency regulation and in some situations they can even cause system oscillation and stability concerns. [ABSTRACT FROM AUTHOR]