Browsing by Author "Nguyen, N."

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    Correlation of wind speed and wind turbine reliability in system adequacy assessment
    (2018) Nguyen, N.; Almasabi, S.; Mitra, J.; Shenoy, B.B.
    This paper proposes a new method to evaluate the reliability of a power system in the presence of wind generation, considering the negative correlation of wind turbine reliability and wind speed. Although wind power integration supports the power system by increasing generation, its intermittent nature is a matter of concern. As the integration of wind power systems steadily increases, the reliability of such integrated systems needs re-evaluation. Besides the relationship between wind speed and wind power output, the relationship between wind speed and wind turbine failure rate also has an impact on reliability of a wind farm and needs to be given due consideration. The method proposed in this paper to evaluate system reliability is implemented using the sequential Monte Carlo simulation. The implementation is tested on the IEEE RTS-79 system with relevant modifications. The effectiveness of the proposed method is proved by comparing system reliability indexes with and without considering the impacts of correlation between wind turbine reliability and wind speed. � 2018 IEEE.
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    Correlation of wind speed and wind turbine reliability in system adequacy assessment
    (Institute of Electrical and Electronics Engineers Inc., 2018) Nguyen, N.; Almasabi, S.; Mitra, J.; Shenoy, B.B.
    This paper proposes a new method to evaluate the reliability of a power system in the presence of wind generation, considering the negative correlation of wind turbine reliability and wind speed. Although wind power integration supports the power system by increasing generation, its intermittent nature is a matter of concern. As the integration of wind power systems steadily increases, the reliability of such integrated systems needs re-evaluation. Besides the relationship between wind speed and wind power output, the relationship between wind speed and wind turbine failure rate also has an impact on reliability of a wind farm and needs to be given due consideration. The method proposed in this paper to evaluate system reliability is implemented using the sequential Monte Carlo simulation. The implementation is tested on the IEEE RTS-79 system with relevant modifications. The effectiveness of the proposed method is proved by comparing system reliability indexes with and without considering the impacts of correlation between wind turbine reliability and wind speed. © 2018 IEEE.
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    Groundwater level modeling using Augmented Artificial Ecosystem Optimization
    (Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.
    Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, Augmented Artificial Ecosystem Optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel Augmented version of Artificial Ecosystem Optimization and traditional MLP network. In AAEO, Levy-flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989–2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling–Gupta efficiency, normalized Nash–Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods. © 2022 Elsevier B.V.