Multiscale PMU Data Compression via Density-Based WAMS Clustering Analysis

During the life span of an instrument transformers (IT), non-catastrophic and typically undetected problems may degrades the overall performance of the instrumentation system. Synchrophasor provides high resolution synchronized data compared to the Supervisory Control and Data Acquisition (SCADA) systems. In the proposed non-intrusive method, time-series Phasor Measurement Units (PMUs) data are processed using three step approach for real time condition monitoring of IT at the control center. Bad data are filtered or flagged using MLE based ensemble method before applying density-based spatial clustering of applications with noise (DBSCAN) to detect clusters, changing over time within each time window. In the third step, a decision logic diagram is developed using additional data and system information to categorize the observed anomalies into different types of malfunction, failure, event or degradation. The proposed method has been validated using IEEE test system modeled in the Real Time Digital Simulator (RTDS) interfaced with industrial hardware PMU device. Detecting equipment failures at an early stage will result in improved system reliability and reduced maintenance cost. Several test cases demonstrate superiority of the proposed method for detecting incipient failures in the IT, and provides early warning mechanism for preventing full blown faults resulting in major impact on the power grid.

This paper concerns with the production of synthetic phasor measurement unit (PMU) data for research and education purposes. Due to the confidentiality of real PMU data and no public access to the real power systems infrastructure information, the lack of credible realistic data becomes a growing concern. Instead of constructing synthetic power grids and then producing synthetic PMU measurement data by time simulations, we propose a model-free approach to directly generate synthetic PMU data. we train the generative adversarial network (GAN) with real PMU data, which can be used to generate synthetic PMU data capturing the system dynamic behaviors. To validate the sequential generation by GAN to mimic PMU data, we theoretically analyze GAN's capacity of learning system dynamics. Further by evaluating the synthetic PMU data by a proposed quantitative method, we verify GAN's potential to synthesize realistic samples and meanwhile realize that GAN model in this paper still has room to improve. Moreover it is the first time that such generative model is applied to synthesize PMU data.

Synchronized phasor measurement unit (PMU) data contain rich information about power systems, hence the quality of estimates is of utmost concern. The existing methodologies for estimation rely on certain assumptions regarding the error in the measurement data. This paper revisits a key assumption specifically the Gaussian character of the error. A quantification of the PMU error yields its nature and statistical properties including its dependence on various sections of the PMU instrumentation channel (supposedly the major source of error in the PMU data). The non Gaussian nature of the error is asserted using various null hypotheses tests and a novel Gaussian mixture model based clustering technique is proposed to characterize and relate the errors present in PMU measurement data to the saturation in current transformer, cable length and the PMU burden. The proposed approach is tested using both real and synthetic PMU datasets. The ultimate goal of the paper is towards creating a PMU error emulator for testing and research of data analytic algorithms focused on crucial WAMS based applications.

This study proposes a deep learning-based fault detection method for intelligent distribution networks using Phasor Measurement Unit (PMU) data. With the increasing development of intelligent distribution systems, the need for fast and accurate fault detection systems is crucial to improve the reliability and resilience of the power grid. Utilizing PMU data, which provides real-time information on voltage, current, and frequency, enables more precise and rapid fault detection. In this study, we developed a deep learning model that uses Long Short-Term Memory (LSTM) to sequentially process PMU data and detect faults such as short circuits, phase faults, and line outages. The model was trained on a PMU dataset covering a wide range of normal and fault conditions in the distribution network. Evaluation results show that the proposed model is capable of detecting faults with >98% accuracy and has a faster detection time compared to traditional detection methods. This approach also demonstrates the ability to identify fault types with a high degree of reliability and reduces the risk of system failure due to detection delays. By using deep learning methods, this study contributes to improving the reliability of intelligent distribution systems and provides a basis for the application of PMU technology in more efficient and automated distribution network monitoring and maintenance.

In this paper, a wavelet packet decomposition (WPD) based technique is proposed for denoising and compression of the power system data in the smart grid (SG). WPD, which is an expansion of the wavelet decomposition (WD) tree algorithm to a full binary tree, offers better signal representation than WD by finding the best tree from a number of bases of the WPD. Thus, for data denoising and compression, WPD provides satisfactory preservation of feature integrity and removal of redundancy to achieve better noise reduction and higher compression with controlled degradation in data fidelity. The proposed data compression and denoising tasks are performed through selection of a suitable wavelet function, decomposition the signal up to the optimum level, determination of the best tree representation, application of the threshold to the coefficients, and reconstruction of the signal. The proposed method is evaluated using a set of simulated and real phasor measurement unit (PMU) data. Comparative results are presented with the wavelet decomposition (WD).

Introduction of Synchronized phasor measurement unit (PMU) in power system is a solution for monitoring system wide disturbances. PMUs provide precise information about a system’s phasors such as magnitude and phase angle of sine waves of the system. PMU data is a valuable source when determining the post-mortem of a fault, or a system disturbance. It can be unveiled what time the system began to falter and exactly what instabilities the system experienced. Knowing the cause of such instabilities will allow enhancement of the current system protection system. This will help in minimizing risk, disruptions or total system collapse. PMUs are synchronized via global positioning system (GPS). For wide area monitoring (WAM), synchronization of the PMUs allows for interconnected systems to be monitored simultaneously, giving real time records. This paper explores the impact of PMU in modern power system, deployment strategies of PMU network around Texas Tech University, importance of PMU data collected from the network, correlation of events by data analysis and focus on how the time stamped information is valuable for grid stability. The ongoing smart architecture of grid will increase dependency and importance on PMU based network in future.

In this paper, a new disturbance detection method is proposed based on principal component analysis (PCA). The PCA-based method exploits the low-rank characteristics of synchrophasor data reported by multiple phasor measurement units (PMUs). The principal component (PC) scores of the low-rank data sets are used to propose a disturbance indicator. Moreover, an analytical disturbance detection threshold is designed that helps classify different events based on their severity. Finally, a PCA-based data compression technique is developed to reduce the archival requirements while preserving critical PMU data. Numerical simulations confirm that the proposed disturbance detection method efficiently detects a wide range of disturbances and is robust against limited low-quality and false PMU data.

A PMU data recovering method based on preferred selection strategy

Chapter 6 - Application of the phasor measurement unit for protecting unintentional islanding of the distribution system

Density-Based Spatial Clustering of Application with Noise (DBSCAN) is a well-known, unsupervised machine learning tool for clustering. DBSCAN creates clusters based on dense regions while marking points that lie alone in low-density regions as outliers. In DBSCAN, the density is detected via core points which are quite sensitive to input parameters: ϵ is radius of the neighborhood and MinPts is minimum number of points constraint within ϵ radius. In contrast to crisp core point definition, intuitionistic fuzzy core point definition makes DBSCAN algorithm capable to detect different patterns of density by two different combinations of input parameters. In this study, a DBSCAN extension is proposed based on this idea: IFDBSCAN. The proposed extension is then illustrated by a computational experiment on a synthetic dataset. Results show that, IFDBSCAN can establish fine-tuned clusters relatively to classical DBSCAN and provide users more insight on the estimation of input parameters.

This paper studies the fundamental dimensionality of synchrophasor data, and proposes an online application for early event detection using the reduced dimensionality. First, the dimensionality of the phasor measurement unit (PMU) data under both normal and abnormal conditions is analyzed. This suggests an extremely low underlying dimensionality despite the large number of the raw measurements. An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed. Theoretical justification for the algorithm is provided using linear dynamical system theory. Numerical simulations using both synthetic and realistic PMU data are conducted to validate the proposed algorithm.

PurposeRecent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection.Methods2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson’s correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms using Fβ score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4.ResultsThe PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p < 0.0001). The Fβ scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 ± 0.3 s).ConclusionAF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms – and parameters – should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country’s economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability. Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover

The widespread implementation of Phasor Measurement Units (PMUs) on power systems has facilitated the development of accurate and fast dynamic state estimators (DSE), which helps to reliably achieve the real-time monitoring and control of the power systems dynamics. Dynamic state estimators are used by the investigators to estimate and identify the state variables and other parameters associated with the nonlinear dynamic models within the power systems, by using the measurements which are mainly provided by the PMUs. This paper primarily tries to present an innovative and robust dynamic state estimation approach when the PMU data are missing due to various reasons. The highly efficient Unscented Kalman Filter (UKF) algorithm is used to formulate the DSE. The simulation results on IEEE 14 bus system shows that the proposed algorithm remains robust and produce accurate estimates during the missing of PMU data coming from generator buses.

The excitation system is an important system of power plants, which has an effective role in power system dynamic and stability. Consequently, developing an approach for the excitation system's model and parameter estimation is a necessary research goal. In real situations, it is often needed to identify and estimate unknown parameters of the excitation system by field recorded signals. The recorded signal can be internal from the distributed control system (DCS) or external from the phasor measurement unit (PMU). In this article, a novel method based on cubature Kalman filter and data mining is proposed to identify the parameters of a standard type excitation system. Firstly, the best available signals for parameters estimation are chosen. Secondly, a method is proposed to parameters estimation of excitation systems efficiently, when both the DCS and PMU with different sample rates are employed to record the measurement data. Data mining is performed at intervals that DCS data is missing (or unavailable), while PMU data is available. Experimental data are used for validation of the proposed approach.