The high altitude Antarctic sites of Dome A and the South Pole offer intriguing locations for future large scale optical astronomical Observatories. The Gattini project was created to measure the optical sky brightness, large area cloud cover and aurora of the winter-time sky above such high altitude Antarctic sites. The Gattini-DomeA camera was installed on the PLATO instrument module as part of the Chinese-led traverse to the highest point on the Antarctic plateau in January 2008. This single automated wide field camera contains a suite of Bessel photometric filters (B, V, R) and a long-pass red filter for the detection and monitoring of OH emission. We have in hand one complete winter-time dataset (2009) from the camera that was recently returned in April 2010.The Gattini-South Pole UV camera is a wide-field optical camera that in 2011 will measure for the first time the UV properties of the winter-time sky above the South Pole dark sector. This unique dataset will consist of frequent images taken in both broadband U and B filters in addition to high resolution (R~5000) long slit spectroscopy over a narrow bandwidth of the central field. The camera is a proof of concept for the 2m-class Antarctic Cosmic Web Imager telescope, a dedicated experiment to directly detect and map the redshifted lyman alpha fluorescence or Cosmic Web emission we believe possible due to the unique geographical qualities of the site.We present the current status of both projects.
Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and load in distribution networks (DNs), is challenging due to the lack of real-time communications. Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks (ANNs). To this end, we first use an offline, centralized data-driven conservative convex approximation of chance-constrained optimal power flow (CVaR-OPF) in which conditional value-at-risk (CVaR) is used to compute reactive power setpoints of PV inverter, taking into account PV and load uncertainties in DNs. Following that, an artificial neural network (ANN) controller is trained for each PV inverter to emulate the optimal behavior of the centralized control setpoints of PV inverter in a decentralized fashion. Additionally, the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs (local controllers) developed using model-based learning (regressionbased controller), optimization (affine feedback controller), and case-based learning (mapping) approaches. Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.
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