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1. Adequacy evaluation of wind power generation systems

Author

Mabel, M. Carolin, Raj, R. Edwin, and Fernandez, E.

Subjects
*WIND power, *SIMULATION methods & models, *DATA analysis, *MONTE Carlo method, *WIND turbines, *ALGORITHMS, *WIND power plants
Abstract

Abstract: This paper attempts to assess the adequacy of wind power generation systems using the data collected from seven wind farms in Muppandal, Tamilnadu (India) with a total capacity of 37 MW. A Monte Carlo model simulation is incorporated in the algorithm to obtain the hourly power output of wind farms, which also takes into account the unavailability of wind turbines. A typical load demand profile is used to examine the chronological hourly wind power generation for each month. The reliability index of LOLE (loss of load expectation) is used to estimate the reliable contribution of wind farm power generation. [ABSTRACT FROM AUTHOR]

Published
2010
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2. Research from Tamil Nadu Has Provided New Study Findings on Autism Spectrum Disorders (An In-Depth Analysis of Autism Spectrum Disorder Using Optimized Deep Recurrent Neural Network).

Subjects
AUTISM spectrum disorders, RECURRENT neural networks, SPECTRUM analysis, FEATURE extraction, COMMUNICATIVE competence, COST control
Abstract

A recent study conducted in Tamil Nadu, India, has focused on autism spectrum disorders (ASD) and their impact on behavior and communication abilities. The study highlights the increasing number of people being diagnosed with ASD and the high medical expenses associated with it. To address these challenges, the researchers propose a novel deep recurrent neural network algorithm for the detection of autism levels. The algorithm utilizes an artificial algae algorithm for feature extraction and an intelligent water droplet algorithm for obtaining optimal weights and biases. The experimental results show promising classification accuracy, sensitivity, and cost reduction. For more information, readers can refer to the research article published in the International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. [Extracted from the article]

Published
2023

3. Effect of Forecast-Based Pricing on Irrigated Agriculture: A Simulation.

Author

Brown, Casey and Rogers, Peter

Subjects
WEATHER forecasting, WATER supply, GROUNDWATER, PRICING, ALGORITHMS
Abstract

Seasonal climate prediction offers potentially useful information for water managers. However, implementing forecast information is challenging due to the probabilistic nature of forecasts and limited demonstrations of usefulness. In this study, an adaptive groundwater pricing model utilizing operational seasonal climate forecasts was evaluated for groundwater management. The price for groundwater in the upcoming season was selected according to an algorithm that incorporates the current groundwater elevation and a prediction of seasonal rainfall. A simulation based on 37 years of rainfall data and operational monsoon forecasts for Tamil Nadu, India, was conducted to assess the effect of forecast-based pricing on societal benefits, groundwater elevation, and farmer income. Results indicate the adaptive pricing model is far more effective at maintaining groundwater elevations and maximizing societal benefits than a static groundwater price. Current groundwater elevation was a more effective input to the pricing algorithm than the forecast of seasonal rainfall when evaluated separately. A comparison of forecast use by water managers to select prices and use by farmers to choose crop patterns found similar societal benefits for each approach. Controlling demand for groundwater through pricing will cause hardship for current groundwater users who currently access groundwater without tariffs. Water managers should consider using tariff revenue to provide drought relief and transitional assistance. [ABSTRACT FROM AUTHOR]

Published
2006
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