PMU data analysis in smart grid using WPD

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 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.

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.

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.

The development and study of Smart Grid technologies rely heavily on high-fidelity data from Phasor Measurement Units (PMUs). However, the scarcity of real-world PMU data due to privacy, security, and variability issues poses significant challenges to researchers, developers, and related industries. To address these challenges, this article introduces the bases for a digital metrology framework, focusing on a newly designed and developed synthetic PMU data generator, that is both metrologically accurate and easy to adapt to various grid configurations for data generation from point-on-wave (PoW) data. This initial phase for a Smart Grid research framework aligns with Open Science principles, ensuring that the generated data are Findable, Accessible, Interoperable, and Reusable (FAIR). By embracing these principles, the generated synthetic data not only facilitate collaboration for Smart Grid research but also ensure their easy integration into existing Smart Grid simulation environments. Additionally, the proposed digital metrology framework for Smart Grid research will provide a robust platform for simulating real-world scenarios, such as grid stability, fault detection, and optimization. Through this open science approach, future digital metrology frameworks can support the acceleration of research and development, overcoming current limitations, e.g., lack of significant amounts of real-world scenarios by PMU data. This article also presents an initial case study for situational awareness and control systems, demonstrating the potential for future Smart Grid research framework and its direct real-world impact. All research outcomes are provided to highlight future opportunities for reusability and collaborations by a novel approach for research on sensor network metrology.

In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in the data for reconstructing missing measurements and detecting anomalies. Our focus is on processing an incessant stream of large-scale data such as synchronized phasor measurements in the power grid, which is challenging due to computational complexity, memory requirement, and missing and corrupt observations. In order to mitigate these issues, a low-rank representation (LRR) model-based subspace clustering problem is formulated that can handle missing measurements and sparse outliers in the data. Then, an efficient online algorithm is derived based on stochastic approximation. The convergence property of the algorithm is established. Strategies to maintain a representative yet compact dictionary for capturing the subspace structure are also proposed. The developed method is tested on both simulated and real phasor measurement unit (PMU) data to verify the effectiveness and is shown to significantly outperform existing algorithms based on simple low-rank structure of data.

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.

Optimal phasor measurement unit placement for power system observability using teaching–learning based optimization

The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise.

Increasingly, smart grid solutions use wireless communications as a core component for moving information and, thus, facilitate more intelligent decision making. This article considers a synchrophasor system implemented on a distribution feeder simultaneously using two separate wireless communication technologies to transmit synchrophasor measurement data. This system was implemented to demonstrate advanced distributed-generation control and how it impacts grid support on the broader electrical feeder. For purposes of performance evaluation, both a wireless serial communications solution and a third-generation (3G) cellular solution were implemented. Phasor measurement unit (PMU ) data, which are streamed without interruption up to 60 messages/s, offer a means to continuously evaluate the two communications systems; performance data measured and archived over a one-week period provided detailed comparison information. Using this information, this article recommends which wireless technology may be better suited for various utility applications.

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

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.

This paper presents a new framework of identifying cyber data attacks on synchrophasor measurements. We focus on detecting “unobservable” cyber data attacks that cannot be detected by any existing detection method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the unobservable cyber attack identification problem as a matrix decomposition problem where the observed data matrix is the sum of a low-rank matrix plus a linear projection of a column-sparse matrix. We propose a convex-optimization-based decomposition method and provide its theoretical guarantee in the attack identification. Numerical experiments on actual PMU data and synthetic data are conducted to verify the effectiveness of the proposed method.

<p>The most of the new protective schemes are based on a communication channel, which cannot be guaranteed in practice. However, during blackouts or cascading failures in the power grid, as system conditions change significantly and rapidly, more information exchanges may be required by the control centers and substations. In other words, the communication channels are operating with high load and therefore become more vulnerable when the power grid is in contingent conditions. Thus, relying on the communication channel for decision making may not be the optimal solution for protective relays, although it might be beneficial to have information exchange. In this article, a novel protective logic is proposed based on phasor measurement units (PMUs) data for optimal coordination of overcurrent relays. PMUs measure the positive sequence voltage at two substations separated by hundreds of miles which are synchronized precisely with the aid of a GPS satellite system. The precise time-tags are attached with samples, and this information is exchanged over communication channels and collected by control centers and/or substations. By extracting the relevant information from these measurements, phasor information can be obtained at any node where PMUs are installed in the power grid. This can be used to do more accurate state estimation, control, and protection. In these relays, besides current and voltage, phasor information has become an important measurement in decision making. The proposed method is tested on IEEE 8-bus standard network.</p>