scholarly journals Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 617 ◽  
Author(s):  
Gyul Lee ◽  
Do-In Kim ◽  
Seon Kim ◽  
Yong-June Shin
Keyword(s):  
Power Systems ◽  
Dimensionality Reduction ◽  
Data Compression ◽  
Clustering Analysis ◽  
Spatial Clustering ◽  
Significant Risk ◽  
Reduction Process ◽  
Principal Component ◽  
Wide Area ◽  
Measurement Unit

This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

Spatial clustering analysis of green economy based on knowledge graph

2021 ◽  
pp. 1-10
Author(s):  
Shiyuan Zhou ◽  
Xiaoqin Yang ◽  
Qianli Chang
Keyword(s):  
Spatial Data ◽  
Clustering Analysis ◽  
Spatial Clustering ◽  
Evaluation Model ◽  
Principal Component ◽  
Green Economy ◽  
Membership Degree ◽  
Two Dimensional ◽  
Dimensional Graph ◽  
Improved Algorithm

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

Strategies for EELS Data Analysis. Introducing UMAP and HDBSCAN for Dimensionality Reduction and Clustering

2021 ◽  
pp. 1-14
Author(s):  
Javier Blanco-Portals ◽  
Francesca Peiró ◽  
Sònia Estradé
Keyword(s):  
Dimensionality Reduction ◽  
Clustering Analysis ◽  
Manganese Oxides ◽  
Spatial Clustering ◽  
State Of The Art ◽  
Core Loss ◽  
Nonnegative Matrix ◽  
Case Scenario ◽  
Electron Energy Loss ◽  
Iron And Manganese

Hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and uniform manifold approximation and projection (UMAP), two new state-of-the-art algorithms for clustering analysis, and dimensionality reduction, respectively, are proposed for the segmentation of core-loss electron energy loss spectroscopy (EELS) spectrum images. The performances of UMAP and HDBSCAN are systematically compared to the other clustering analysis approaches used in EELS in the literature using a known synthetic dataset. Better results are found for these new approaches. Furthermore, UMAP and HDBSCAN are showcased in a real experimental dataset from a core–shell nanoparticle of iron and manganese oxides, as well as the triple combination nonnegative matrix factorization–UMAP–HDBSCAN. The results obtained indicate how the complementary use of different combinations may be beneficial in a real-case scenario to attain a complete picture, as different algorithms highlight different aspects of the dataset studied.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Makarand Ballal ◽  
Amit Kulkarni ◽  
Hiralal Suryawanshi
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Real Time ◽  
Principal Component ◽  
Singular Values ◽  
Area Measurement ◽  
Measurement Unit ◽  
Time Data ◽  
Transmission Grid ◽  
Phasor Measurement

AbstractThe advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.

An Overview of Wide Area Measurement System and Its Application in Modern Power Systems

2019 ◽  
pp. 289-307
Author(s):  
H. H. Alhelou
Keyword(s):  
Power Systems ◽  
Power System ◽  
General System ◽  
Area Measurement ◽  
Wide Area ◽  
Measurement Unit ◽  
Monitoring And Control ◽  
Wide Area Measurement System ◽  
Power System Monitoring ◽  
Recent Developments

In this chapter, wide area measurement systems (WAMS), which are one of the cornerstones in modern power systems, are overviewed. The WAMS has great applications in power system monitoring, operation, control, and protection systems. In the modern power systems, WAMS is adopted as a base for the modern monitoring and control techniques. Therefore, an introduction of WAMS is firstly provided. Then, phasor measurement unit (PMU), which is the base of WAMS, is described. Afterward, the most recent developments in power system estimation, stability, and security techniques, which are based on WAMS, are introduced. Later, general system setup for WAMS-based under-frequency load shedding (UFLS) is provided. Finally, the required communications infrastructures are comprehensively discussed.

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