A PMU data recovering method based on preferred selection strategy

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.

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.

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.

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.

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.

<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>

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.

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

This paper presents a new solution for reconstructing missing data in power system measurements. An Enhanced Denoising Autoencoder (EDAE) is proposed to reconstruct the missing data through the input vector space reconstruction based on the neighbor values correlation and Long Short-Term Memory (LSTM) networks. The proposed LSTM-EDAE is able to remove the noise, extract principle features of the dataset, and reconstruct the missing information for new inputs. The paper shows that the utilization of neighbor correlation can perform better in missing data reconstruction. Trained with LSTM networks, the EDAE is more effective in coping with big data in power systems and obtains a better performance than the neural network in conventional Denoising Autoencoder. A random data sequence and the simulated Phasor Measurement Unit (PMU) data of power system are utilized to verify the effectiveness of the proposed LSTM-EDAE.

This paper proposes a regularized robust recursive least squares (R3LS) method for on-line estimation of power-system electromechanical modes based on synchronized phasor measurement unit (PMU) data. The proposed method utilizes an autoregressive moving average exogenous (ARMAX) model to account for typical measurement data, which includes low-level pseudo-random probing, ambient, and ringdown data.ƒn A robust objective function is utilized to reduce the negative influence from non-typical data, which include outliers and missing data. A dy-namic regularization method is introduced to help include a priori knowledge about the system and reduce the influence of under-determined problems. Based on a 17-machine simulation model, it is shown through the Monte-Carlo method that the proposed R3LS method can estimate and track electromechanical modes by effectively using combined typical and non-typical measurement data.

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.

We introduce a new method for online Phasor Measurement Unit (PMU) missing value replacement. Our approach allows us to decompose PMU event responses into a non-dynamic component (denoted the participation factor) that can be inferred directly from the past and a dynamic component that can be inferred directly from all other PMUs (denoted the event strength). When missing values occur, we can use these two components, which do not rely on the missing index, to estimate the correct value. The method is extremely fast and can easily be used for online applications. Furthermore, extensive testing on real power system event data reveals that our approach achieves state-of-the-art performance in terms of Mean Absolute Percent Errors (MAPEs) for PMU data dropped during event periods. The method also yields an interpretable and simplified view of events for further analysis and applications. The method relies only on PMU data and does not take outside information such as network topology.

The characteristics of oscillation modes, such as interarea, regional, and subsynchronous modes, can vary during a power system fault, which can cause switching and control actions in the power system. Transient data of the modal response due to such a fault can be acquired through phasor measurement units (PMUs). When the transient data have a long duration, it is desirable to perform modal identification separately on each segment of the transient data, so as to reflect the varying characteristics of oscillation modes. A conventional discrete Fourier transform (DFT)-based method for parametric modal identification cannot be efficiently applied to such a segment dataset. In this paper, a DFT-based method with an exponential window function is proposed to identify oscillation modes from each segment of transient data in PMUs. This window function allows the application of the least squares method (LSM) for modal identification in the same manner as the conventional method. The accuracy of identification of the proposed method is compared with those of the conventional method and a Prony method through synthetic data of transient responses. Its feasibility is also verified by identifying real-world oscillation modes from transient data in PMUs.