Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    A detailed formulation of sensitivity matrices for probabilistic load flow assessment considering electro-thermal coupling effect
    (IEEE Computer Society, 2017) Prusty, B.R.; Jena, D.
    In recent times, use of an analytical method (AM) is prevalent in solving probabilistic load flow (PLF) problem for better computational efficiency. AMs are employed to power system models that endure linear relations between the result variables and input random variables via sensitivity matrices. The accuracy of a sensitivity matrix-based PLF model can be improved by considering the effects of environmental conditions on line parameters. Looking out for an opportunity to upgrade existing PLF model to foresee the strength of thermal resistance model, a temperature-augmented model is presented. A detailed mathematical formulation of the aforesaid model is deliberated. The influence of temperature-augmentation on distributions of resistances, temperatures, power flows, and power losses of the temperature dependent branches is studied in detail. Finally, a note on applicability of the proposed model in the assessment of various power system studies is discussed. © 2017 IEEE.
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    Comparison of two data cleaning methods as applied to volatile time-series
    (Institute of Electrical and Electronics Engineers Inc., 2019) Ranjan, K.G.; Prusty, B.R.; Jena, D.
    Out-of-sample forecasting of historically observed time-series inevitably necessitates the application of a suitable data cleaning method to assist improved accuracy of the obtained results. The existing data cleaning methods though work amply with nonvolatile time series; fail when applied to a volatile time-series. In this paper, the suitability of the k-nearest neighbor approach and sliding window prediction approach is tested on a set of nonvolatile and volatile time-series. The performance comparison is carried out considering the historical record of furniture sales data, PV generation, load power, and ambient temperature data of different time-steps collected from various places in the USA. Further, the effect of parameters allied with both the methods on the preprocessing result is also analyzed. Finally, possible reforms are suggested for the appropriate preprocessing of volatile time-series. © 2019 IEEE.
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    Analysis on photovoltaic panel temperature under the influence of solar radiation and ambient temperature
    (Institute of Electrical and Electronics Engineers Inc., 2021) Tripathi, A.K.; Ray, S.; Mangalpady, M.
    The generation of electrical energy from solar energy is one of the most promising utilization of solar energy technology and it can be achieved by the application of solar photovoltaic (PV) panel. In this paper an experimental study has been conducted to examine the effect of solar radiation and ambient temperature on the surface temperature of the solar photovoltaic panel. With the help of experimental measurements, a multi-linear regression model is developed relating the three quantities. The developed model validated with the actual measured values shows good accuracy with small values of root mean square error. During the study, the recordedvalue of maximum panel temperature was 78.50°C for the atmospheric condition which having solar radiation of 1140 W/m2 and ambient temperature of 36°C. The developed relation and subsequent outcomes of the study will help the PV panel designers and manufacturers incomprehending the effects of atmospheric parameters on the temperature of the photovoltaic panel. © 2021 IEEE