Faculty Publications
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Item An over-limit risk assessment of PV integrated power system using probabilistic load flow based on multi-time instant uncertainty modeling(Elsevier Ltd, 2018) Prusty, B.R.; Jena, D.In this paper, the risk assessment of a PV integrated power system is accomplished by computing the over-limit probabilities and the severities of events such as under-voltage, over-voltage, over-load, and thermal over-load. These aspects are computed by performing temperature-augmented probabilistic load flow (TPLF) using Monte Carlo simulation. For TPLF, the historical data for PV generation, ambient temperature, and load power, each collected at twelve specific time instants of a day for the past five years are pre-processed by using three linear regression models for accurate uncertainty modeling. For PV generation data, the developed model is capable of filtering out the annual predictable periodic variation (owing to positioning of the Sun) and decreasing production trend due to ageing effect whereas, for ambient temperature and load power, the corresponding models accurately remove the annual cyclic variations in the data and their growth. The simulations pertaining to the aforesaid risk assessment are performed on a PV integrated New England 39-bus test system. The system over-limit risk indices are calculated for different PV penetrations and input correlations. In addition, the changes in the values of TPLF model parameters on the statistics of the result variables are analyzed. The risk indices so obtained help in executing necessary steps to reduce system risks for reliable operation. © 2017 Elsevier LtdItem Preprocessing of Multi-Time Instant PV Generation Data(Institute of Electrical and Electronics Engineers Inc., 2018) Prusty, B.; Jena, D.For the evaluation of system overlimit risk indices in a PV-integrated power system, PV generation data at specific instants of time (in each day for several years) are required to be collected. Such data have inherent annual periodic variations, which are different at various places. These variations are skewed and/or multimodal, which contributes significantly toward the overall variance of data and is primarily attributable to the Sun's position. This letter proposes a regression model that assumes the observed data as a function of few influencing factors related to the Sun's position and trend in data. Finally, the estimated variations using the developed model are removed from the data to characterize the unpredictable components. © 1969-2012 IEEE.Item A spatiotemporal probabilistic model-based temperature-augmented probabilistic load flow considering PV generations(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Prusty, B.R.; Jena, D.The probabilistic steady-state forecasting of a PV-integrated power system requires a suitable forecasting model capable of accurately characterizing the uncertainties and correlations among multivariate inputs. The critical and foremost difficulties in the development of such a model include the accurate representation of the characterizing features such as complex nonstationary pattern, non-Gaussianity, and spatial and temporal correlations. This paper aims at developing an improved high-dimensional multivariate spatiotemporal model through enhanced preprocessing, transformation techniques, principal component analysis, and a suitable time series model that is capable of accurately modeling the trend in the variance of uncertain inputs. The proposed model is applied to the probabilistic load flow carried out on the modified Indian utility 62-bus transmission system using temperature-augmented system model for an operational planning study. A detailed discussion of various results has indicated the effectiveness of the proposed model in capturing the aforesaid characterizing features of uncertain inputs. © 2019 John Wiley & Sons, Ltd.Item An improved sliding window prediction-based outlier detection and correction for volatile time-series(John Wiley and Sons Ltd, 2021) Ranjan, K.G.; Tripathy, D.S.; Prusty, B.R.; Jena, D.Steady-state forecasting is indispensable for power system planning and operation. A forecasting model for inputs considering their historical record is a preliminary step for such type of studies. Since the historical data quality is decisive in edifice an accurate forecasting model, data preprocessing is essential. Primarily, the quality of raw data is affected by the presence of outliers, and preprocessing refers to outlier detection and correction. In this paper, an effort is made to improve the existing sliding window prediction-based preprocessing method. The recommended reforms are the calculation of appropriate window width and a new outlier correction approach. The proposed method denoted as improved sliding window prediction-based preprocessing is applied to the historical data of PV generation, load power, and the ambient temperature of different time-steps collected from various places in the United States and India. Firstly, the method's efficacy through detailed result analysis demonstrating the proposed preprocessing as a better way than its precursor and k-nearest neighbor approach is presented. Later, the improved out-of-sample forecasting accuracy canonizes the proposed method’s concert compared to both the above techniques and the case without preprocessing. © 2020 John Wiley & Sons Ltd