2021
DOI: 10.1186/s41601-021-00192-0
|View full text |Cite
|
Sign up to set email alerts
|

Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method

Abstract: The impact of wind power forecast errors (WPFEs) on power system reliability can be quantified by a sensitivity model, which helps to determine the importance of different wind farms. However, the unknown distribution and correlation of WPFEs make it difficult to calculate the reliability sensitivity. The existing univariate non-standard third-order polynomial normal transformation (NSTPNT) expresses the reliability sensitivity of WPFEs by a normal random variable with explicit distribution, and is not suitabl… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
11
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 19 publications
(11 citation statements)
references
References 40 publications
0
11
0
Order By: Relevance
“…In statistical analysis of high-dimensional data, when the amount of data is large enough, the data as a whole will show certain random statistical characteristics after corresponding processing, such as the single ring theorem and the M-P law (Ling et al, 2018;Jain et al, 2019;Deepa et al, 2020;Xiong et al, 2020;Yang et al, 2020b;Li et al, 2021b;Li et al, 2021c;Yang et al, 2021c;Li et al, 2021d;Ye et al, 2021;Dong and Li, 2021;Liu et al, 2021;Mousavizadeh et al, 2021;Ouyang and Xu, 2021;Zhu et al, 2021). In the statistical analysis of highdimensional power data, the corresponding linear eigenvalue statistics (LES) are constructed, such as MSR, high-order moment, etc., which can effectively represent the state of the distribution network.…”
Section: Distribution Network State Estimation Methods Based On Fpt Data Pre-processingmentioning
confidence: 99%
“…In statistical analysis of high-dimensional data, when the amount of data is large enough, the data as a whole will show certain random statistical characteristics after corresponding processing, such as the single ring theorem and the M-P law (Ling et al, 2018;Jain et al, 2019;Deepa et al, 2020;Xiong et al, 2020;Yang et al, 2020b;Li et al, 2021b;Li et al, 2021c;Yang et al, 2021c;Li et al, 2021d;Ye et al, 2021;Dong and Li, 2021;Liu et al, 2021;Mousavizadeh et al, 2021;Ouyang and Xu, 2021;Zhu et al, 2021). In the statistical analysis of highdimensional power data, the corresponding linear eigenvalue statistics (LES) are constructed, such as MSR, high-order moment, etc., which can effectively represent the state of the distribution network.…”
Section: Distribution Network State Estimation Methods Based On Fpt Data Pre-processingmentioning
confidence: 99%
“…The tertiary reserve is determined as the capacity of the largest generation unit plus 2% of the expected load on the considered period. As a result, about 10% of revenue from energy production is lost due to forecast errors [36].…”
Section: B Empirical Methodsmentioning
confidence: 99%
“…Load and short-term PV variability can be assumed to follow a normal distribution, whereas in the case of wind, other distributions were assumed to be more accurate [22] [23], such as Weibull and Gamma as in [24] [10], and Laplace as in [16]. While wind forecast errors were represented by a variety of nonparametric distributions [25] [26], and parametric distributions, such as β [27] [28], Laplace [16], Lévy α-stable [29] [30], Cauchy [31] [32], hyperbolic [33], Weibull [34] [35], and mix distribution model [36]. The authors of [14] used normally distributed probability functions for load, wind, and PV forecast errors to simplify the aggregation of the individual probability density functions.…”
Section: Introductionmentioning
confidence: 99%
“…Hence, the learning rate and ability of the network can be greatly increased to generalize. This algorithm has been employed in a number of prediction areas [15][16][17][18]. However, because of random initialization of the ELM's input weights and implicit layer bias, its prediction stability is relatively poor at a massive data scale.…”
Section: Introductionmentioning
confidence: 99%