Phasor Measurement Units Measurement Data Research Articles

AM-HNN based real-time voltage estimation method of distribution network using joint PMU and AMI data

A large amount of distributed energy access increases the risk of voltage fluctuation and off-limit voltage. Accurate voltage estimation is more and more important to ensure the safety of distribution networks. With the introduction of phasor measurement unit (PMU), multi-source measuring data with different time scales and measuring precision exist in distribution networks. How to fully integrate multi-source measurement data to achieve fast and accurate state estimation of distribution networks is an important prerequisite for efficient operation decision making. In this paper, a distribution network voltage state estimation method based on AM-HNN model is proposed. The attentional mechanism (AM) is introduced to mine the influence of PMU measurement data on voltage state estimation, and the feature weight is modified adaptively. The highway neural network (HNN) was used to fit the mapping relationship between the injected power of grid nodes and the voltage of key nodes. Taking the distribution network data of different scenes as an example, the superiority of the proposed optimal measurement model is verified by comparing with various voltage estimation methods.

Event Analysis in Transmission Systems Using Spatial Temporal Graph Encoder Decoder (STGED)

Phasor Measurement Units (PMUs) are located at different geographic positions in the transmission system, generating measurement data that can be analyzed to monitor and control the power grid. One utilization for such measurement data is monitoring based on machine learning based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">event analysis</i> in the transmission system, which includes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">event detection, localization, and classification</i> . The approach developed in this work exploits the latent spatial and temporal features of PMU measurement data for event analysis. (1) For event detection, this research proposes a novel unsupervised <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Spatial Temporal Graph Encoder Decoder” (STGED)</i> deep learning model that concurrently/jointly exploits the spatial and temporal features of PMU measurements. STGED further supports downstream unsupervised event localization and classification. (2) For event localization, events are localized by estimating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbulence and Proximity statistical scores</i> over predicted measurements/output from STGED. (3) For event classification, an unsupervised algorithm is developed with a classification scoring metric that leverages physics informed rules based on the fundamentals of power system. The proposed approach is evaluated on the IEEE test systems and other benchmark systems for diversified event scenarios. Additionally, performance of the developed approach has been compared with other techniques, and validated using real-world industry data. Experimental results show that the proposed approach outperforms other existing techniques for event analysis.

Identification of topology changes in power grids via reduced admittance matrix

Frequent changes in power grid topology bring risks to the stable operation of power systems. It is essential to identify changes in the power grid topology quickly and accurately. This paper presents a novel method named network reduction-based topology change identification (NR-TCI) algorithm to identify topology changes in multi-machine power systems. The proposed algorithm can quickly identify power grid topology changes using only phasor measurement unit (PMU) data sampled during the system’s transient process. The NR-TCI algorithm uses the network order reduction method to reduce the order of a bus admittance matrix and then uses PMU measurement data to estimate the reduced admittance matrix by least square method. Finally, the reduced admittance matrix is adopted to find topological information, and the Sherman–Morrison formula is utilized to identify the topology changes. The effectiveness of the proposed NR-TCI algorithm is verified with a case study of a 3 machine 9 bus system in Matlab. In addition, the influence of PMU sampling frequency on the effectiveness of the proposed algorithm is also studied.

하이브리드 측정데이터를 활용한 상태추정에 관한 연구

As the economy develops and power demand increases, the need for stable power system operation is also increasing. State estimation, one of the important functions of EMS(energy management system), estimates the state of the power system which is essential for reliable power system operation. State estimation has traditionally been performed using measurement data, obtained from SCADA(supervisory control acquisition system). PMU(phasor measurement unit), which appeared in the early 1990s, improved state estimation by providing more accurate synchronized data than SCADA measurement data. However, due to various problems, the PMU cannot be implemented at all buses in the power system, and for this problem, a study on a hybrid state estimation technique using both SCADA and PMU measurement data is required. This paper proposes a new technique for hybrid state estimation using SCADA and PMU measurement data. The proposed technique can improve the conventional state estimator and reduce the influence of bad data. Tests are done in the IEEE test system and the KEPCO power system.

Improved SVD-based data compression method for synchronous phasor measurement in distribution networks

The integration of phasor measurement units (PMUs) greatly improves the operation monitoring level of distribution networks. However, high sampling rates in PMUs generate huge volumes of measurement data, which creates heavy transmission and storage burdens in information and communication systems. In this paper, an improved singular value decomposition (SVD)-based data compression method for PMU measurements in distribution networks is proposed. First, a lossless phase angle conversion method is proposed, which converts the discontinuous phase angle data of PMU into continuous data sequence to enhance the compression performance. Then, a PMU data compression method is proposed based on SVD, and the compression capability is further enhanced by a lossless compression that utilizes the orthogonal property of the two sub-matrices generated by SVD. Moreover, an error control strategy is designed to dynamically optimizes the scale of transmitted data according to the accuracy requirement of different applications in distribution networks. Finally, case studies are performed using real PMU measurement data from a pilot project in China to validate the compression performance and advantages of the proposed method.

Statistical Characterization of PMU Error for Robust WAMS Based Analytics

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.

Distributed State Estimation for Distribution Network with Phasor Measurement Units Information

As the distribution network structure becomes increasingly complex, this paper proposes a distributed state estimation for distribution network with phasor measurement units (PMUs) information to improve the performance of state estimation. This proposed method takes the PMU installation locations as alternative points of the partition, and takes the scale of sub-zone, the number of real-time measurement, and the DG configuration position as the partition criteria, and then conducts distributed state estimation. During the state estimation, the root bus voltage and branch current are used as state variables to reserve the advantage of measurement conversion technology, and the traditional measurement data and PMU measurement data are converted into corresponding currents, which simplifies the Jacobian matrix, thereby reducing iterations and improving the calculation speed of state estimation. Finally, the IEEE 69-bus radial distribution network is used to verify the effectiveness of the proposed method.

Operational Reliability Assessment of an Interconnected Power System Based on an Online Updating External Network Equivalent Model with Boundary PMU

Information between interconnected power systems is difficult to share in real time, due to trade secrets and technical limitations. The regional power grid cannot timely detect the impact of changes in the operation mode of the external power grid on the regional reliability, due to faults, load fluctuations, power generation plan adjustments, and other reasons. How to evaluate the reliability of a regional power system under the conditions of information isolation is a difficult problem for the security of interconnected power systems. Aiming at this problem, an operational reliability evaluation method for an interconnected power system is proposed herein, which does not depend on external network information directly, but only uses boundary phasor measurement unit (PMU) measurement data and internal network information. A static equivalent model with sensitivity consistency was used to simplify the external network to ensure the accuracy of the reliability calculation of interconnected power systems. The boundary PMU measurement data were used to update the external network equivalent model online. The algorithm flow of the operation reliability assessment for the interconnected power grid is given. The results of an example based on the IEEE-RTS-96 test system show that the proposed method can track the equivalent parameters of the external network without depending on the actual topological information, and calculate the reliability index of the internal network accurately.

Fault Detection in Wide Area Monitoring Systems via PMU Optimal Placement

The Phasor measurement units (PMUs) have become more and more attractive in power engineering as they can provide synchronized measurements of real-time voltage and currents phasors. The objective of this work is twofold: first, the optimal placement of PMUs is done in the standardized IEEE systems. Next, fault location is determined based on the measurements collected from these PMUs. The simulations are carried out using MATLAB SIMULINK. The results show that it is possible to exploit the PMU measurement data to locate and hence cure the faults in the power system.

Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data

Synchrophasor technology, also known as wide-area monitoring system technology, utilizes phasor measurement unit (PMU) to monitor real-time system data, which can provide unique insights into the operation of a power grid. In this paper, a rule-based data-driven analytics method for wide-area fault detection in a power system using synchrophasor data is proposed. As a data-driven approach, this method relies on rules created using PMU measurement data, and does not require knowledge of the power system's topology and model. It can detect fault location (bus and line) and fault type for a particular fault event. Three common types of short circuit faults in a power grid, single-line-to-ground, line-to-line, and three-phase faults, can be identified using the proposed method. Fault thresholds used in rules are determined based on theoretical values and recorded PMU data during fault events in Bonneville power administration (BPA)'s large power grid. The proposed method is validated by comparing with the recorded field data for fault events provided by BPA. It is found that it can effectively detect most faults with a great accuracy. It has been developed into a software program, and can be readily used by utility companies.