Online Robust Subspace Clustering With Application to Power Grid Monitoring

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.

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).

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 provides initial justification for adopting a distributed architecture for phasor measurement unit. The characteristics of synchronized phasor measurement is studied in detail. The benefits and short comings of phasor measurements are highlighted from the point of view of their different applications. The need for extension of phasor measurement unit functionality is brought out through analysis of the application and synchronized phasor measurement characteristic compatibility. The concept of phasor measurement unit is extended to a distributed system with several small functions working independently to provide a complete functionality of a phasor measurement unit. The proposed substation automation works on the framework of IEC 61850 process bus.

As the node power is controllable, the traditional dynamic security region (DSR) is defined on power injection space. The phasor measurement unit (PMU) can measure and transfer the state variables (phase angles) of power system synchronously. How to make full use of the synchronized phasor measurement (SPM) in the security monitoring, protection and control of power system is a challenging research task. In this paper, we presented the concept of practical dynamic security region (PDSR) in phase angle space. A study on PDSR of power systems shows that critical points satisfying transient stability in angle space can still be described by one or a few critical hyper- planes. With the PDSR in the node angle space, a concise expression to calculate the transient stability margin of a current operation point in each direction in node angle space is suggested. Besides, this paper suggested an approach to acquire the PDSR in low-dimensional angle space based on dynamic equivalence of transient stability and a method to select appropriate locations for partially equipping PMU, which provide an innovative way of making use of the PMU information to carry out dynamic security monitoring of power systems.

A PMU data recovering method based on preferred selection strategy

The PMU (Phasor Measurement Unit) is used for accurate measurement of synchronous phasor of voltages and currents. Based on synchronous phasor continuous measurement at the different location of distribution network is possible prevent fault which can cause blackout. Synchronous phasor measurement is described in IEEE C37.118-2005 standard. The IEEE C37.118-2005 is primarily focused on communication with the PMU and precision of the measurement at steady state. PMU manufacturers declare compliance with IEEE C37.1182005, which doesn't mean correct behaviour of the PMU for dynamic changes in the power network. The new standard for the PMU is currently developing. This standard should contain both the issues included in IEEE C37.118-2005 and description of PMU testing in connection with dynamic changes in power network. This article deals with the implementation of tests for testing the dynamic properties of PMU. These tests are conducted by tester, which was designed and implemented based on virtual instrumentation. The results of several tests of commercial PMUs are presented in the article. (4 pages)

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.

04/02797 Effects of unbalances and harmonics on optimalcapacitor placement in distribution system: Ghose, T. and Goswami, S. K. Electric Power Systems Research, 2004,68, (2), 167–173

This paper is concerned about optimal placement of synchronized phasor measurements that can monitor voltage and current phasors along network branches. Earlier investigations on placement of phasor measurement units (PMUs) have assumed that a PMU could be placed at a bus and would provide bus voltage phasor as well as current phasors along all branches incident to the bus. This study considers those PMUs which are designed to monitor a single branch by measuring the voltage and current phasors at one end of the monitored branch. It then determines the optimal location of such PMUs in order to make the entire network observable. The paper also addresses the reliability of the resulting measurement design by considering loss or failure of PMUs as well as contingencies involving line or transformer outages. Developed placement strategies are illustrated using IEEE test systems.

<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 introduces a novel approach for power system fault diagnosis based on synchronized phasor measurements during the fault. The synchronized measurements are obtained in real time from Phasor Measurement Units (PMUs) and compared with offline thresholds determined by decision trees (DTs) to diagnose the fault. The DTs have already been trained offline using detailed power system analysis for different fault cases. While the traditional methods for fault diagnosis use the status of protective relays (PRs) and circuit breakers (CBs) to infer the fault section in the power system, the proposed method uses the available signals following the fault and thus can be trusted even in case of protection system failure. Therefore, fault diagnosis can be achieved successfully especially in blackout situations, where many CBs and and/or PRs malfunction. Simulation results on the IEEE 118-bus test system show that the proposed method is highly accurate in wide area fault diagnosis in spite of incomplete system observability.

In uses, such as stability surveillance of an electric power system, the synchronous voltage current phasor measurement using GPS satellite base timing keeping technology is demanded. In the past, the synchronous phasor measurement technique of a voltage current based in DFT conversion theory was proposed, and it was established as an IEC standard[1]. In the actual electric power system, the frequency of voltage in each place is delicately different, and it is always changing. Since the sampling data in the time window of fixed width cannot take in the integer cycle of a voltage waveform and does not satisfy the signal periodicity conditions of Fourier transform, a spectrum leakage phenomenon occurs. By this, the accuracy of the synchronous phasor measurement by single DFT-BIN frequency falls. This paper proposes the method of realizing recursively DFT conversion with a window function [RWDFT] which can reduce the influence of system voltage frequency change. Moreover, the method of calculating Phasor of true signal frequency is proposed by compounding three or more DFT conversion factors. In simulation verification, the synchronous phasor measuring method which applied the above two methods has checked that a highly precise Phasor measurement result was obtained in system voltage frequency change.

This paper presents a real-time voltage stability assessment in distribution networks (DNs) capable of separating the effects of the transmission and distribution network by using synchronized phasor measurements. The method aims to assist transmission and distribution system operators to quantify the need of their services in different parts of the DN in order to provide adequate voltage support to their specific stakeholders. The method uses data from phasor measurement units (PMUs) to estimate models for both DN and transmission network (TN). A Thevenin equivalent model is estimated for the TN and a T-model for the DN. By applying the superposition theorem on these two models, the contribution of each network to the overall system voltage stability can be distinguished at a specific bus (equipped with a PMU). The method has been validated by a hardware-in-the-loop setup which consists an OPAL-RT real-time simulator and three PMUs.