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Raksha Ramakrishna
Arizona State University
6Publications
1H-index
2Citations
Publications 6
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#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 1
#2Anna Scaglione (ASU: Arizona State University)H-Index: 45
While the graph theoretic properties pertaining to the electrical grid are well known, the field of graph signal processing offers new insights and understanding about the measurements from the electrical grid. In this paper we establish that voltage measurements are the result of a low-rank excitation to a low-pass graph filter. Then, we illustrate the identification of community structure in the electrical grid since the excitations are low-rank in nature. Proposed algorithm for community dete...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 1
#2Anna Scaglione (ASU: Arizona State University)H-Index: 45
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 4 authors...
In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV...
#1Nikhil Ravi (ASU: Arizona State University)
#2Raksha Ramakrishna (ASU: Arizona State University)H-Index: 1
Last.Anna Scaglione (ASU: Arizona State University)H-Index: 45
view all 4 authors...
In this paper, we explore the application of system identification techniques to the inference of a model that characterizes crowd dynamics, inspired by the social force model proposed by Helbing and Molnar. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Previous work using IoT sensors to uncover social interactions is not ...
Apr 1, 2018 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 1
#2Andrey Bernstein (NREL: National Renewable Energy Laboratory)H-Index: 7
Last.Anna Scaglione (ASU: Arizona State University)H-Index: 45
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In this paper, a mathematical relationship between temperature and solar irradiance is established in order to reduce the sample space and provide joint probabilistic forecasts. These forecasts can then be used for the purpose of stochastic optimization in power systems. A Volterra system type of model is derived to characterize the dependence of temperature on solar irradiance. A dataset from NOAA weather station in California is used to validate the fit of the model. Using the model, probabili...
In this paper, a stochastic model with regime switching is developed for solar photo-voltaic (PV) power in order to provide short-term probabilistic forecasts. The proposed model for solar PV power is physics inspired and explicitly incorporates the stochasticity due to clouds using different parameters addressing the attenuation in power.Based on the statistical behavior of parameters, a simple regime-switching process between the three classes of sunny, overcast and partly cloudy is proposed. ...
Nov 1, 2016 in ASILOMAR (Asilomar Conference on Signals, Systems and Computers)
#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 1
#2Anna Scaglione (ASU: Arizona State University)H-Index: 45
In this paper we propose a new stochastic model for solar Photo-Voltaic (PV) power that explicitly models the effect of cloud coverage as an attenuation of the two components that make up the deterministic solar irradiation pattern. Relying on compressive sensing methods we are able to fit a set of solar PV power data from California with the components of this stochastic model and extract the parameters of such a process thus effectively capturing the variability of solar power production. One ...
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