Large-scale power systems state estimation using PMU and SCADA data

Saadabadi, Hamideh; Dehghani, Maryam

doi:10.1109/iraniancee.2016.7585649

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…Using multi-source data integration technology and a public information model of distribution network grid based on data integration can improve the 10kV distribution network. Fault repair rate, shorten the time to restore power supply, and effectively improve the accuracy of fault diagnosis of distribution network; [7] completed the integration of multi-source real-time data such as SCADA, PMU and safety and stability control system, which can accurately simulate the dynamic characteristics of wind farms; In [8], the switch state data of SCADA, the continuous time data of fault recorder and WAMS are integrated to diagnose the power failure of multiple components, the method of data fusion can improve the accuracy of diagnosis; [9] fused Micro-PMU and SCADA data, and proposed a distribution network state estimation algorithm based on the least squares method of dynamic variable weights to achieve high-precision state estimation, and the effectiveness of the algorithm was verified by tests; [10] proposed a hybrid dynamic estimation algorithm for PMU and SCADA measurements, and experimental studies show that the hybrid method can improve the estimation to some extent. In [11], SCADA data with slow sampling rate and PMU with high sampling rate are fused into the dynamic state estimator of the power system to realize the dynamic tracking of the power system, and the test results prove the availability; [12] adopted a fusion method to make PMU fill in the missing SCADA data, and established a multi-time scale data set for multi-time scale state estimation.…”

Section: Introductionmentioning

confidence: 99%

Power grid fault diagnosis method based on alarm information and PMU fusion

liu¹,

Zheng²,

Zhang³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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With the rapid development of smart grid technology, more and more attention has been paid to the stability of the power grid. When the power grid fails, the dispatch center will receive various types of data, including the PMU data collected by the PMU (Power Management Unit) device used by the Wide Area Monitoring System (WAMS) and the data collected by the Supervisory Control System for Data Acquisition and Control (SCADA). Alarm information data, they describe the same grid fault from different dimensions. At present, the power grid fault diagnosis based on PMU data and alarm information texts has been fully studied, but the information source is single, and it is difficult to ensure the accuracy of the diagnosis results when complex faults and multiple faults occur in the power grid. This paper starts with the information source of power grid fault diagnosis and develops a power grid fault diagnosis method that integrates the dual data sources of PMU and alarm information: 1. effectively connect alarm message text and PMU based on fault events. 2. complete the fusion of PMU and alarm information to improve the scope and accuracy of power grid fault diagnosis. Finally, the simulation fault data is used to test. The experimental results show that the accuracy of fault diagnosis of dual data source fusion is higher than that of single data.

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Section: Introductionmentioning

confidence: 99%

Power grid fault diagnosis method based on alarm information and PMU fusion

liu¹,

Zheng²,

Zhang³

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

View full text Add to dashboard Cite

show abstract

“…Kalman Filter (KF) is a well‐known tool to estimate the states when the model parameters are uncertain or the noise level is considerable [24]. Due to non‐linearity of the synchronous generator model, this algorithm is used in the form of Extended Kalman Filter (EKF) [25] or Unscented Kalman Filter (UKF) [26]. In [27] synchronous generator parameters including inertia constant, damping factor and mechanical input power are estimated using EKF algorithm.…”

Section: Introductionmentioning

confidence: 99%

A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

Hosseini

Afsharnia

2021

IET Generation Trans & Dist

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In this paper a novel method is proposed to estimate the rotor speed of synchronous generators in steady state and dynamic conditions using phasor measurement unit's data. This method uses the synchronous generator model to find the relationship between generator rotor speed and frequencies at different buses of the power system. The principles of the proposed method are first presented through a simple network consisting a generator connected to a transmission line. Afterward the general formulation is developed. To demonstrate the effectiveness of our method, IEEE 9-bus and IEEE 39-bus power systems are used as the benchmark systems. Different tests are carried out under three conditions including, normal condition, presence of measurement errors and uncertainty of the power system parameters. As well, the estimation results are compared with the results of other estimation methods. The results show the high accuracy of the proposed method in different conditions.

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“…In reference [8], the author mainly considered the impact of PMU data on the accuracy of state estimation, but did not mention the data difference between different systems. In reference [9], the author considered the fusion of PMU data and SCADA data, and used a hybrid state estimation algorithm to solve it, but for the problem of inconsistent time sections, no effective solution was proposed.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of Distribution Network Considering Data Compatibility

Wu¹,

Li²

2020

EPE

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Considering that the measurement devices of the distribution network are becoming more and more abundant, on the basis of the traditional Supervisory Control And Data Acquisition (SCADA) measurement system, Phasor measurement unit (PMU) devices are also gradually applied to the distribution network. So when estimating the state of the distribution network, the above two devices need to be used. However, because the data of different measurement systems are different, it is necessary to balance this difference so that the data of different systems can be compatible to achieve the purpose of effective utilization of the estimated power distribution state. To this end, this paper starts with three aspects of data accuracy of the two measurement systems, data time section and data refresh frequency to eliminate the differences between system data, and then considers the actual situation of the three-phase asymmetry of the distribution network. The three-phase state estimation equations are constructed by the branch current method, and finally the state estimation results are solved by the weighted least square method.

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Large-scale power systems state estimation using PMU and SCADA data

Cited by 9 publications

References 11 publications

Power grid fault diagnosis method based on alarm information and PMU fusion

Power grid fault diagnosis method based on alarm information and PMU fusion

A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

State Estimation of Distribution Network Considering Data Compatibility

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