The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.
Recurrent Neural Networks (RNNs) can model temporal dependencies in time series well. In this paper we present an interesting application of stacked Gated Recurrent Unit (GRU) based RNN for early prediction of imminent instability in a power system based on normal measurements of power system variables over time. In a power system, disturbances like a fault can result in transient instability which may lead to blackouts. Early prediction of any such contingency can aid the operator to take timely preventive control actions. In recent times some machine learning techniques such as SVMs have been proposed to predict such instability. However, these approaches assume availability of accurate fault information like its occurrence and clearance instants which is impractical. In this paper we propose an Online Monitoring System (OMS), which is a GRU based RNN, that continuously keeps predicting the current status based on past measurements. Through extensive simulations using a standard 118-bus system, the effectiveness of the proposed system is demonstrated. We also show how we can use PCA and predictions from the RNN to identify the most critical generator that leads to transient instability.
The large amount of video content is being transmitted over internet and other channels. With the help of existing multimedia editing tools one can easily change the content of data which lead to lose the authenticity of the information. Thus, it becomes necessary to develop different methods by which the authenticity of the videos can be confirmed. In the past researchers have proposed several methods for authentication of videos. This paper presents an algorithm that is divided in two parts: computing the repeated frames by processing the image pixels to produce a frame-byframe motion energy time and computing the tampering attack and its location with the help of the Support Vector Machine. This helps to predict whether the given video has been tampered or not.
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