A spatiotemporal probabilistic model‐based temperature‐augmented probabilistic load flow considering PV generations

Prusty, B Rajanarayan; Jena, Debashisha

doi:10.1002/2050-7038.2819

Cited by 27 publications

(8 citation statements)

References 22 publications

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“…Cases 2, 3, 4, 5, 6, 8, and 9 of Table 1 are used for this comparison. The fast Fourier transform‐based algorithm proposed in Reference 21 identifies a sensible candidate set of frequency components in the data to correct outliers. As ISWP mainly focuses on the window width selection and outlier correction, these two aspects of ISWP are compared with those of SWP.…”

Section: Results Analysesmentioning

confidence: 99%

“…Suppose x i is an outlier detected on the basis of its predicted value

x_{i}^{'}

, the best way to correct would be to replace x i with the average of

x_{i}^{'}

and the seasonally periodic values of x i . A practical algorithm, as proposed in Reference 21, is capable of identifying a sensible candidate set of frequency components, which is applied to the raw data after updating the missing values. A fast Fourier transform helps to identify the dominant frequency terms ( f d ) which represent the seasonal patterns in the time‐series.…”

Section: Iswp‐based Outlier Detection and Correctionmentioning

confidence: 99%

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An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Ranjan

Tripathy

Prusty

et al. 2020

Int J Numerical Modelling

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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.

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Section: Results Analysesmentioning

confidence: 99%

“…Suppose x i is an outlier detected on the basis of its predicted value

x_{i}^{'}

, the best way to correct would be to replace x i with the average of

x_{i}^{'}

Section: Iswp‐based Outlier Detection and Correctionmentioning

confidence: 99%

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Ranjan

Tripathy

Prusty

et al. 2020

Int J Numerical Modelling

Self Cite

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“…The seasonalities in ambient temperature time‐series are often additive; to obtain the different seasonality orders common in any 2 years, the time‐series is split into different yearly datasets, and FFT is applied. Optimal frequencies are chosen by using a penalizing strategy 32 . Thus, the MLR model is given as,

Y = \sum_{m = 1}^{t_{0}} a_{m} N^{m} + \sum_{r = 1}^{s} b_{r} \sin ((), \frac{2 πrN}{365}) + c_{r} \cos ((), \frac{2 πrN}{365}) + ε .

…”

Section: Proposed Forecasting Modelmentioning

confidence: 99%

Quantile regression averaging‐based probabilistic forecasting of daily ambient temperature

Tripathy

Prusty

2020

Int J Numerical Modelling

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The inclusion of conductor temperature variations for numerous power system planning and operational studies has long been recognized in the literature. The conductor temperature is majorly affected by environmental factors such as the ambient temperature. An efficient forecasting technique for forecasting ambient temperature is the need of the hour to prevent unexpected hazards in power systems and other areas caused due to temperature variations. Numerous researches have proposed different point forecasting and probabilistic forecasting models for the forecasting of ambient temperature. The probabilistic forecasting of ambient temperature provides complete information about future uncertainties, therefore quantifying the effects of daily temperature variations. A forecast combination approach, such as the quantile regression averaging, has never been utilized in this context. The selection of suitable point forecasters that complement each other's effects by characterizing different aspects of the ambient temperature data for averaging and the use of frequency components that explain the daily and periodic seasonal variations of ambient temperature to construct a forecasting model are the significant features of this paper. The proposed model is used with four varieties, and each is compared with the others. It is found that the variant using all the complementary individual point forecasters performs better in making probabilistic forecasts than the other options as well as better than the popular quantile k-nearest neighbors, quantile egression forests, and basic quantile regression as inferred from the quantile score, Winkler score, and reliability plots.

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“…This correlation is usually modeled using rank correlation using Nataf transformation to represent the non‐Gaussianity of the involved PDFs. Therefore, spatial rank correlation is used in a variety of applications such as the evaluation of uncertainty influence using Borgonovo method, frequency stability, small‐signal stability, addressing the over‐voltage issues with and without the voltage control algorithm, optimal power flow, and spatiotemporal correlations for temperature‐augmented probabilistic load flow . Hence, the effect of correlation on work related to risk assessment seems to be ignored as in Reference .…”

Section: Introductionmentioning

confidence: 99%

New risk analysis considering spatial correlations among connected PVs for bidirectional distribution feeders

Al‐Saadi

Živanović

Al-Sarawi

2020

Int Trans Electr Energ Syst

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Summary The distribution networks are in a transition stage from being “passive” (consuming energy and typically with unidirectional power flows) into “active” (consuming/producing energy with bidirectional power flows). This transition has exposed the networks to stochastically behaving risks such as violation of the prescribed limits of power quality or overloading the network elements. The current paper presents a new risk analysis to quantify the risks of violating operational constraints due to a large number of small‐size Photovoltaics (PVs). Risk's likelihood and severity are estimated based on the relative frequency of the number and the relative frequency of the accumulative depth of a violation, respectively. This article has two objectives. The first is to determine the hosting capacity of the targeted network. The second is to address the effect of spatial correlation on risk quantification, specifically the effect of perfectly positive correlation (PEC) to the effect of partially positive correlation (PAC). Modelling PAC through spatial rank correlation is established utilizing the Nataf transformation due to the non‐Gaussianity of the probability densities of PV uncertainties. The approach is implemented on two distribution networks: IEEE 13 bus test distribution feeder and a large distribution network with multiple zones situated in South Australia. The approach is location‐specific and time‐varying. Hosting capacity is determined with the results of PEC showed an overestimation of the problem in comparison with the more realistic simulation under PAC. The analysis outcomes can help distribution network operators in managing and regulating the growing risks of high PV penetration.

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A spatiotemporal probabilistic model‐based temperature‐augmented probabilistic load flow considering PV generations

Cited by 27 publications

References 22 publications

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

An improved sliding window prediction‐based outlier detection and correction for volatile time‐series

Quantile regression averaging‐based probabilistic forecasting of daily ambient temperature

New risk analysis considering spatial correlations among connected PVs for bidirectional distribution feeders

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