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Dynamic voltage restorer (DVR) is usually employed to mitigate sag/swell in supply voltages so that load voltage is regulated at nominal value. This paper proposes artificial neural network (ANN) based reference voltage generation (RVG) scheme for the control of 3-phase DVR. ANN replaces the traditional control of DVR, which involves abc-dq0 and dq0-abc transformations, estimation of d-q axes voltage errors and proportional-integral (PI) controllers along with their tuning. In proposed control scheme, the feedforward ANN utilizes present and previous samples of supply voltage and peak magnitude of load voltage for RVG, which when impressed across the injection transformer results in sag/swell mitigation. It is important to note that the proposed scheme is free from transformations and controller tuning. The performance of 3-phase DVR with the proposed ANN based RVG scheme results in standard IEEE-519 compliant operation with load voltage regulated at nominal value even under sag/swell in supply voltage. This is verified through MATLAB/SIMULINK based simulaiton studies.

Insular grids are fragile owing to lower inertia and the absence of interconnection with other grids. With the increasing penetration of non-dispatchable renewable energy sources, the vulnerability of such insular grids increases further. The government of India has proposed several projects to improve the photovoltaic systems (PV) penetration in the Andaman and Nicobar Islands' grid. This paper investigates joint stochastic scheduling of energy and reserve generation for insular grids fed from diesel and gas-based generators, PV, and battery energy storage systems (BESS). The proposed stochastic scheduling model considers a wide range of probabilistic forecast scenarios instead of a deterministic model that assumes a single-point forecast. Hence, it provides an optimal solution that is technically feasible for a wide range of PV power forecast scenarios. The striking feature of the model developed in this work is the inclusion of stochastic constraints that represent (i) the coordination between PV and BESS, (ii) reserve constraints, (iii) battery charging/discharging limit constraints, and (iv) non-anticipatory constraints that ensure technical viability of scheduling decisions. The proposed model is validated on the dataset for South Andaman Island. Results reveal the applicability and feasibility of the proposed stochastic dispatch model for different generation mix scenarios. ; peer-reviewed

Abstract
Building more homes and amenities in the wildland-urban interface (wui) is not a sustainable practice as it is associated with a greater risk of wildfire, social vulnerability, and ecological damage. Yet, the issue of whether or how to regulate the expansion of the wui remains contentious and largely unresolved in understanding sustainable development. There are fewer studies that explore how wildfire risks are compounded by social vulnerability of people who reside in the fire prone wui. Additionally, much of the extant research is focused on the national or regional level management of ecosystems and forest fires, with a clear lack of focus on local level dynamics. To fill these gaps, our analysis outlines the preliminary steps to identify social vulnerability, ecological damage, and wildfire risk in the wui fire hazard zones of the highest severity type. Utilizing gis mapping, wildfire risk, and census data on social vulnerability, our analysis reveals patterns of the wui expansion in the San Francisco Bay Area from 1990 to 2010 and provides policy recommendations from a sustainable development perspective to address social vulnerability, wildfire risk, and ecological concerns over the wui.