Generator Parameter Identification Using Time Series Model and PMU Measurements

With the deployment of advanced metering infrastructure (AMI), an avalanche of new energy-use information became available. Better understanding of the actual power consumption patterns of customers is critical for improving load forecasting and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a fresh perspective to the way load forecasting is performed, ranging from very short-term load forecasting to long-term load forecasting at the system level, regional level, feeder level, or even down to the consumer level. This paper addresses the efforts involved in improving the system level intraday load forecasting by applying clustering to identify groups of customers with similar load consumption patterns from smart meters prior to performing load forecasting.

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.

Electrical power system monitoring, protection, operation, and control schemes are undergoing significant changes toward the next generation fully automated, resilient, and self-healing grids. At present, there still exists a lack of available user-friendly tools for the synchronized measurement technology supported application design. This paper presents a synchro-measurement application development framework (SADF) to promote a simplified design and thorough validation of synchro-measurement (IEEE Standard C37.118.2-2011) supported user-defined applications under realistic conditions. The proposed SADF supports online receiving of a phasor measurement unit (PMU) or phasor data concentrator (PDC) provided data stream and enables simultaneous use of processed machine-readable synchro-measurements in advanced user-defined applications. This paper fills the scientific gap between the IEEE Standard C37.118.2-2011 specifications and its implementation by proposing a novel robust communication technique and efficient synchro-measurement data parsing methodology. As a proof of concept, the proposed SADF is implemented as a novel open-source MATLAB library. Combining this library with MATLAB’s signal processing and visualization functions allows mastering the design and validation of complex wide-area monitoring, protection, and control applications, as well as PMU/PDC performance and compliance verification. Finally, this paper verifies the proposed library against the standard specifications, assesses its interoperability and performance via a cyber-physical simulation platform, and presents online voltage magnitude monitoring as an example application.

As the primary heat source in the district heating systems (DHSs), combined heat and power (CHP) units are playing an increasingly significant role in electricity generation. Considering that, this paper innovatively proposes an integral generation expansion planning (GEP) framework, which enables competition to occur among conventional power plants (CPPs) of the GENCOs and CHP units of the DHSCOs. The proposed framework is based on decentralized decision-making which involves an iterative process for simulating the interactions of ISO, GENCOs, and DHSCOs. The combined heat and power dispatch model is reconstructed to determine the LMPs to offer incentives for market participants' investments. The fuzzy load duration curve (FLDC) method is proposed to model the electricity load that would influence the LMPs. In each FLDC state, uncertain load is represented by a fuzzy number. Investment decision-making models of the GENCOs and DHSCOs are formulated as a mixed integer linear programming with fuzzy objectives (MILPFO). A technique is proposed to solve the MILPOF models with the consideration of the risk appetite of the market participants. The examples illustrate the benefits of integrated planning of electric power system and DHSs.

The ability to meet the demand for electricity in an electrical power system depends not only on subsystems of generation, transmission and distribution, but also the ability to supply the primary energy sources such as natural gas, oil and coal, since the effects of a contingency in some of the subsystems, including supply networks can propagate and affect the operation of the system in terms of availability and price. Traditionally, the operation of electrical power systems has been developed by omitting supply energy aspects and the use of financial instruments as part of a trade policy. In this work, a comprehensive model, that includes fuels transportation networks, fuel storage and units' maintenance program, able to solve large-scale systems, under a centralized decision-making is developed. The objective is to assess the operation and acquisition of take-or-pay contracts, contracts for differences, and Swaps contracts in the economic operation of electrical power systems. Additionally, generating units CO2 emission constraints are relaxed through the transference of emissions bonuses.

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.

There have been quite a few attempts to compute synchronous generator parameters based on voltage and current synchrophasors taken under power system transients. However, we have not seen any publications with thorough analysis as to how soon the phasor measurement unit reacts to disturbance conditions, which components of the transient are filtered out and which are passed through, as well as what the total vector error is. The goal of this research is to determine all of these characteristics of a phasor measurement unit when playing back transient oscillograms for a stator short circuit obtained through mathematical modeling. The transient oscillograms have been derived via both a full Park-Gorev system of flux linkage equations as well as the MATLAB/Simulink Synchronous Machine block. Physical modeling was then conducted via a real-time digital simulator (RTDS) along with a dedicated phasor measurement unit ENIP-2 (PMU), and the stator current phasors were recorded. Our analysis has shown that both RTDS and ENIP-2 (PMU) almost entirely filter out the exponentially decaying DC component of the fault current while closely following the periodical signal envelope. The total vector error has been estimated to become below 1–2 % after around 0,02–0,03 s into the fault when selecting the “P” class filters according to IEEE C37.118. We have come to a conclusion that synchrophasor measurements under power system disturbances could be utilized for estimating the synchronous, transient, and subtransient generator parameters. The selected synchronous machine model in the form of flux linkage equations is correct, as the obtained transient oscillograms are exactly the same as those produced by Simulink. “P” class phasor measurements can be recommended for representing transients in the stator circuit of a synchronous generator. The results of this investigation are meant to be employed for synchronous machine parameter estimation based on phasors sourced from RTDS and, hopefully, from phasor measurement units installed at power plants.

Reliability evaluation of electric power systems has traditionally been an integral part of planning and operation of electric power systems. The changes in the electric utility industry create a need for more realistic techniques for power system reliability evaluation. In this paper, we present a reliability analysis algorithm for radially operated, electrical distribution systems. The algorithm takes into account equipment performance history and converges in a matter of seconds for systems containing a number of components. The study presented here evaluates improvement in reliability evaluation scheme over the conventional process. A new reliability evaluation approach using Monte Carlo method is proposed to assess distribution system reliability. This exploration makes possible the comparison of alternative system designs to discover systems yielding desired reliability properties utilizing the reliability indices for load points and the overall system that have been computed . Basic features, algorithms and a variety of case studies are presented in this paper to illustrate the capabilities of the proposed method and its suitability for educational and research purposes.

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

In recent years, Japan has rapidly extended its use of electricity obtained via photovoltaic (PV) generation. When the proportion of PV in the generation mix was small, its unpredictable output fluctuations had little effect on the supply and demand operation. However, as the role of PV in grid supply grows, predicting these fluctuations becomes increasingly necessary to ensure stable and economical operation of the electric power system. In this study, we discuss the current approaches of estimating and forecasting the PV output. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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.

Electrical Engineering in JapanVolume 109, Issue 6 p. 17-27 Article On-line P-Q decoupled preventive control considering the operating cost Hiroyuki Kita, Hiroyuki Kita Faculty of Engineering, Hokkaido University Hiroyuki Kita obtained a Master's degree in 1988 in Electrical Engineering from the Engineering Research Division of Hokkaido University. He is presently in the doctoral program at the University. He is engaged in research on analysis and control of electric power systems.Search for more papers by this authorEiichi Tanaka, Eiichi Tanaka Faculty of Engineering, Hokkaido University Eiichi Tanaka obtained a Master's degree in 1977 in Electrical Engineering from the Engineering Research Division of Hokkaido University and in April of the same year became a Research Associate in the Department of Electrical Engineering of the Faculty of Engineering. He is engaged mainly in research on the operation and control of electric power systems. He is a member of the Society of Instrument and Control Engineers.Search for more papers by this authorJun Hasegawa, Jun Hasegawa Faculty of Engineering, Hokkaido University Jun Hasegawa obtained a Dr. of Eng. degree in 1971 in Electrical Engineering from the Engineering Research Division of Hokkaido University, and in April of the same year became a Lecturer in the Department of Electrical Engineering of the Faculty of Engineering. In 1985, he became a Professor at Hokkaido Univ. He is engaged mainly in research on the operation, planning, control, analysis of electric power systems, and energy storage systems. He is a member of IEEE, etc.Search for more papers by this author Hiroyuki Kita, Hiroyuki Kita Faculty of Engineering, Hokkaido University Hiroyuki Kita obtained a Master's degree in 1988 in Electrical Engineering from the Engineering Research Division of Hokkaido University. He is presently in the doctoral program at the University. He is engaged in research on analysis and control of electric power systems.Search for more papers by this authorEiichi Tanaka, Eiichi Tanaka Faculty of Engineering, Hokkaido University Eiichi Tanaka obtained a Master's degree in 1977 in Electrical Engineering from the Engineering Research Division of Hokkaido University and in April of the same year became a Research Associate in the Department of Electrical Engineering of the Faculty of Engineering. He is engaged mainly in research on the operation and control of electric power systems. He is a member of the Society of Instrument and Control Engineers.Search for more papers by this authorJun Hasegawa, Jun Hasegawa Faculty of Engineering, Hokkaido University Jun Hasegawa obtained a Dr. of Eng. degree in 1971 in Electrical Engineering from the Engineering Research Division of Hokkaido University, and in April of the same year became a Lecturer in the Department of Electrical Engineering of the Faculty of Engineering. In 1985, he became a Professor at Hokkaido Univ. He is engaged mainly in research on the operation, planning, control, analysis of electric power systems, and energy storage systems. He is a member of IEEE, etc.Search for more papers by this author First published: November/December 1989 https://doi.org/10.1002/eej.4391090603 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume109, Issue6November/December 1989Pages 17-27 RelatedInformation