An Optimal Scenario Reduction Method for Stochastic Power System Problems

2021 ◽  
Author(s):  
Alvaro Gonzalez-Castellanos ◽  
David Pozo
Keyword(s):  
Power System ◽  
Reduction Method ◽  
Scenario Reduction

scholarly journals Intelligent alarm data noise reduction for power systems based on singular value decomposition

2021 ◽  
Vol 2108 (1) ◽  
pp. 012074
Author(s):  
Chen Chen ◽  
Hongren Man ◽  
Xiu Liu
Keyword(s):  
Data Structure ◽  
Singular Value Decomposition ◽  
Power System ◽  
Noise Reduction ◽  
Reduction Method ◽  
Singular Value ◽  
Data Noise ◽  
Noise Data ◽  
Value Decomposition ◽  
Noise Statistics

Abstract The noise types of power system intelligent alarm data are complex. When reducing the intelligent alarm data, the profile noise statistics of the noise data are large, resulting in the actual noise reduction value is too small. To solve this problem, a power system intelligent alarm data noise reduction method based on singular value decomposition is designed. The selected normalized decomposition matrix iteratively processes the original matrix, the singular value decomposes the power system alarm data, sets an estimation quantity within the paradigm of the alarm data, controls the noise profile noise statistics, characterizes the noise alarm data structure, uses the SC algorithm to process the cluster basis vectors in the noise data structure, and constructs a repeated iterative convergence process to realize intelligent data noise reduction processing. The original alarm data within a known power system is used as test data, the power system alarm window is set, and the power system alarm data singular values are circled. The data mining-based alarm data noise reduction method, the regularized filter-based alarm data noise reduction method and the designed data noise reduction method are applied to the noise reduction process, and the results show that the designed data noise reduction method has the largest noise value and the best noise reduction effect.

scholarly journals A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4262 ◽  
Author(s):  
Sitong Lv ◽  
Jianguo Li ◽  
Yongxin Guo ◽  
Zhong Shi
Keyword(s):  
Cluster Analysis ◽  
Distributed Generation ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Optimal Allocation ◽  
Reduction Method ◽  
Scene Analysis ◽  
Analysis Method ◽  
Scenario Reduction ◽  
Cluster Analysis Method

In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of DG access to the power grid, the multi-scenario analysis method commonly used in DG optimal allocation method is studied in this paper. In order to solve the problems that may arise from using large-scale scenes in the planning of DG considering uncertainties by using multi-scene analysis method, the cluster analysis method suitable for large-scale scene reduction in scene reduction method is introduced firstly, and then an improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the reduction method is verified. Finally, the method mentioned in this paper is compared with other commonly used methods through IEEE-33 node system.

Multi-stage scenario generation by the combined moment matching and scenario reduction method

2014 ◽  
Vol 42 (5) ◽  
pp. 374-377 ◽  
Author(s):  
Uladzimir Rubasheuski ◽  
Johan Oppen ◽  
David L. Woodruff
Keyword(s):  
Reduction Method ◽  
Scenario Generation ◽  
Moment Matching ◽  
Scenario Reduction ◽  
Multi Stage

A Novel Scenario Reduction Method by 3D-Outputs Clustering for Condition-Based Maintenance Optimization

2017 ◽  
Vol 24 (04) ◽  
pp. 1750018
Author(s):  
Xinbo Qian ◽  
Qiuhua Tang ◽  
Bo Tao
Keyword(s):  
Computational Complexity ◽  
Reduction Method ◽  
Probability Distributions ◽  
Condition Based Maintenance ◽  
Maintenance Optimization ◽  
Scenario Reduction ◽  
Output Performance ◽  
Probabilistic Distribution ◽  
Small Set ◽  
Uncertainty Parameters

Condition-based maintenance (CBM) optimization involves considering inherent uncertainties and external uncertainties. Since computational complexity increases exponentially with the number of degradation uncertainties and stages, scenario reduction aims to select small set of typical scenarios which can maintain the probability distributions of outputs of possible scenarios. A novel scenario reduction method, 3D-outputs-clustering scenario reduction (3DOCS), is presented by considering the impacts of uncertainty parameters on the output performance for CBM optimization which have been overlooked. Since the output performance for CBM is much more essential than the inputs, the proposed scenario reduction method reduces degradation scenarios by [Formula: see text]-means clustering of the multiple outputs of degradations scenarios for CBM. It minimizes the probabilistic distribution distances of outputs between original and selected scenarios. Case studies show that 3DOCS has advantages as a smaller distance of output performance of selected scenarios compared to that of initial scenarios.

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