Real Phasor Measurement Units Data Research Articles

Synthetic PMU Data Creation Based on Generative Adversarial Network Under Time-varying Load Conditions

In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations (ODEs) to capture the transient behaviors of the system during voltage oscillation events. A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic equations. The results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales.

Исследование практических аспектов ОМП на ЛЭП сверхвысокого напряжения по векторным измерениям на базе RTDS

Application of Phasor Measurement Unit (PMU) is a modern technology in electric power systems with quite a few possible applications. Remote identification of fault location (FL) at extra-high voltage transmission lines is one of the possible applications of PMU. The number of research works dedicated to this issue is rather limited. Also, there is no in-depth analysis of the PMU digital filter performance in case of short circuit; some factors that affect the FL accuracy are not covered; and the number of methods to identify FL remotely is insufficient. The current research goal is to study all the above-mentioned issues. The research has been conducted using the methods of mathematical modeling of fault-initiated transients represented with a real-time digital simulator RTDS (including the RSCAD software). A total of 16 FL expressions both in Russian and foreign scientific papers have been analyzed. Monte-Carlo method has been used to assess the impact of various factors on the FL accuracy. The PMU functionality presented in the RTDS simulator has been used to obtain required synchrophasors. A typical timespan of faults at extra-high voltage transmission lines has been deduced (it turns out to be at least three cycles of the fundamental frequency component). Dynamic characteristics of both M and P class PMUs have been thoroughly examined. The authors have made a conclusion that PMU-based FL is as accurate as that provided by conventional FL units. The influence of some major factors on the FL results has been investigated as well. PMU-based FL is considered to be a promising application. At the same time, P filter class PMUs are primarily used. One is to avoid getting synchrophasor data of different class PMUs at the line terminals. The obtained results are meant to be introduced to improve the PMU-based FL accuracy, especially in the context of processing real PMU data.

A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations.

Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification

A two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this article. This approach leverages generative adversarial networks (GAN) in data generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize this approach to synthetically create massive eventful PMU data, which would otherwise be difficult to obtain from the real world due to the critical energy infrastructure information (CEII) protection. To illustrate the utility of such synthetic data for subsequent data-driven methods, we specifically demonstrate the application of using synthetic PMU data for event classification by scaling up the real data set. The addition of the synthetic PMU data to a small set of real PMU data is shown to have improved the event classification accuracy by 2 to 5 percent.

Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation

With more data-driven applications introduced in wide-area monitoring systems (WAMS), data quality of phasor measurement units (PMUs) becomes one of the fundamental requirements for ensuring reliable WAMS applications. This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation, which can: 1 identify bad data under both steady states and contingencies; 2 achieve higher accuracy than conventional pre-filtering approaches; 3 reduce iteration burden for linear state estimation; 4 efficiently identify bad data in a parallelizable scheme. The proposed method consists of four key steps: 1 preprocessing filter; 2 online training of short-term deep neural network; 3 offline training of long-term deep neural network; 4 a decision merger. Through delicate design and comprehensive training, the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information. An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method. Multiple test scenarios are applied, which include steady states, three-phase-to-ground faults with (un)successful auto-reclosing, low-frequency oscillation, and low-frequency oscillation with simultaneous three-phase-to-ground faults. The proposed method demonstrates satisfactory performance during both the training session and the testing session.

Real-Time Event Classification in Power System With Renewables Using Kernel Density Estimation and Deep Neural Network

Real-time classification of events facilitates corrective control strategies, supervisory protection schemes, and on-line transient stability assessment of a power system. The synchrophasor-based event classification techniques face challenges like similar responses for different classes of events, i.e., inter-class similarity (ICS), applicability to limited classes of events, and moderate real-time performance for a large power system. In addition, the enhanced ICS effect of increased renewable penetration on events classification needs to be addressed. This paper proposes a kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data. The proposed method uses a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs). That have distinct scale, shape, and orientation for different classes of events. Thereafter, a set of statistical features is derived from PDFs to train a multi-layered deep neural network for event classification. The proposed method is validated for renewables in IEEE-39 bus system and real transmission system of India grid using DIgSILENT/PowerFactory and also on a real phasor measurement unit data for India grid, where it showed better performance for ICS and renewable integration cases.

Classification and Detection of PMU Data Manipulation Attacks Using Transmission Line Parameters

Modern power grids are increasingly relying on real-time data, such as those from phasor measurement units (PMUs), for their control and management operations. Due to its dependence on the Internet for data transfer, the grid is susceptible to a wide range of cyber-attacks. Among these, data manipulation attacks are of particular interest in the context of PMU data, due to their potential for causing widespread damage. In such attacks, the adversary changes the measurements in order to bias the estimate of system states. In this paper, we propose an effective and simple-to-implement mechanism for detecting such attacks. The proposed methodology is based on evaluating the equivalent impedances of transmission lines. Being independent of the conventional bad data detection scheme, it is also able to detect the so called “false data injection attacks.” Extensive simulation results using real PMU data have been provided in order to verify the accuracy of the proposed detector.

GPS signal loss in the wide area monitoring system: Prevalence, impact, and solution

The phasor measurement unit (PMUs), equipped with Global Positioning System (GPS) receivers for precise time synchronization, provides measurements of voltage and current phasors at different nodes of the wide area monitoring system. However, GPS receivers are likely to lose satellite signals due to various unpredictable factors. The prevalence of GPS signal loss (GSL) on PMUs is first investigated using real PMU data. The historical GSL events are extracted from a phasor data concentrator (PDC) and FNET/GridEye server. The correlation between GSL and time, spatial location, solar activity are explored via comprehensive statistical analysis. Furthermore, the impact of GSL on phasor measurement accuracy has been studied via experiments. Finally, several potential solutions to mitigate the impact of GSL on PMUs are discussed and compared.

Design and Implementation of Security Gateway for Synchrophasor Based Real-Time Control and Monitoring in Smart Grid

Synchrophasor technology has numerous applications ranging from simple grid monitoring/visualization to real-time protection and control. Most legacy phasor measurement units (PMUs) and phasor data concentrators (PDCs) deployed in power grids support the IEEE C37.118.2 communication framework, which is highly vulnerable to cyber attacks due to lack of inherent security mechanisms. The IEC 61850-90-5 recently emerged as new communication framework with support for security features but its use in commercial devices is still very limited. The replacement of legacy PMUs/PDCs in power grids is a big challenge due to cost and deployment complexity. The concept of a gateway has recently been proposed in the literature to enable IEEE C37.118.2 compatible PMUs to send data in IEC 61850-90-5 format. However, the published gateway has limited features and also lacks security functionalities. This paper addresses security, interoperability, and integration issues between legacy and state-of-the-art phasor devices through the design of a security gateway. The security gateway is implemented with flexibility in mind and can be used for PMUs as well as PDCs under different configurations. It provides: 1) protocol conversion functionalities (from IEEE C37.118.2 to IEC 61850-90-5 and vice versa) and 2) security functionalities based on IEC recommended group domain of interpretation security mechanism. The security gateway is very compact in size, based on low-power ARM processor and inexpensive to be deployed in power systems. Through detailed experimental evaluation with real PMU data, this paper also validated the suitability of the security gateway for different types of synchrophasor applications with strict latency and data rate requirements.

Supervisory Protection and Automated Event Diagnosis Using PMU Data

This paper presents a new framework for supervisory protection and situational awareness to enhance grid operations and protection using modern wide-area monitoring systems. In contrast to earlier approaches dealing with the combined processing of data from multiple phasor measurement units (PMUs), the proposed approach analyzes only the PMU data with the strongest or the most prominent disturbance signature. The specific contributions of this paper are: a) new criteria for identification of PMU with the strongest signature, b) simplified approach for quick detection of faults, c) early classification of eight other disturbances suitable for near real-time response, d) time-frequency transform-based feature extraction techniques for speedy and reliable classifiers, and e) a promising approach to locate disturbances within narrow geographical constraints. The contributions are verified with exhaustive simulation data from the Western Electricity Coordination Council system model and limited real PMU data.

Modal Analysis of Ambient PMU Measurements Using Orthogonal Wavelet Bases

This paper proposes a new method, called wavelet scale decomposition, for modal analysis of ambient synchrophasor data using orthogonal wavelet bases. Wavelet formulation of the problem enables reliable estimation results even with short data analysis windows that are a few minutes long. Also, in addition to finding the mode frequency and damping ratio of oscillatory modes, the proposed method is able to estimate their mode shapes as well. The method is tested on some archived real phasor measurement unit data sets and on simulations from Kundur two area system. The results show that the proposed method is able to track damping ratio variations in power systems effectively with short analysis windows.

Application of Dynamic State and Parameter Estimation Techniques on Real-World Data

The objective of this paper is to apply two dynamic state and parameter estimation techniques to real-world data. Dynamic state and parameter estimation can estimate equivalent generator internal voltages and rotor angles (dynamic states) during electromechanical dynamics. In addition, parameters such as the transient reactances and inertia can be estimated. A set of real world data recorded by Eastern Interconnection Phasor Measurement Units (PMU) regarding a generator trip event is analyzed in this paper. An estimation algorithm developed by Chow was applied to estimate the states and parameters of a transfer path. An alternative estimation method based on the classic generator model and swing equation is presented in this paper and applied to the real world PMU data with the aid of digital filtering technology. Challenges in real-world data applications are identified in this paper.

Line Outage Detection Using Phasor Angle Measurements

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Although phasor measurement units (PMUs) have become increasingly widespread throughout power networks, the buses monitored by PMUs still constitute a very small percentage of the total number of system buses. Our research explores methods to derive useful information from PMU data in spite of this limited coverage. In particular, we have developed an algorithm which uses known system topology information, together with PMU phasor angle measurements, to detect system line outages. In addition to determining the outaged line, the algorithm also provides an estimate of the pre-outage flow on the outaged line. To demonstrate the effectiveness of our approach, the algorithm is demonstrated using simulated and real PMU data from two systems—a 37-bus study case and the TVA control area. </para>