Power system dynamic state estimation considering correlation of measurement error from PMU and SCADA

Lu, Ziang; Wei, Zhinong; Sun, Yonghui

doi:10.1002/cpe.4726

Cited by 3 publications

(4 citation statements)

References 41 publications

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“…In practice, available measurements may not be independent, as they may be derived from the same set of raw measurements [24]. For example, voltage and current phasors measured by PMUs and power measurements from SCADA may come from the same voltage transformer (VT) and current transformer (CT) via different algorithms.…”

Section: ) Correlations Between Measurementsmentioning

confidence: 99%

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A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

Lin¹,

Abur²,

Gómez-Expósito³

et al. 2024

IEEE Trans. Smart Grid

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State estimation (SE) is indispensable for the situational awareness of power systems. Conventional SE is fed by measurements collected from the supervisory control and data acquisition (SCADA) system. In recent years, available data sources have been greatly enriched with the deployment of phasor measurement units (PMUs), advanced metering infrastructure (AMI), intelligent electronic devices (IEDs), etc. The integration of multiple data sources provides unprecedented opportunities for enhancing the performance of SE, but also presents major challenges to resolve, including optimal multi-type-sensor co-placement, multiple reporting rates and asynchronization, diverse types of measured quantities, correlations between measurements, integration of online and historical data sources, and system and measurement uncertainties. This paper outlines the state of the art and research opportunities in this area by providing a comprehensive literature review and extensive discussions. It starts by presenting the motivations and challenges, followed by a summary of existing data sources for SE in power systems. Subsequently, for both transmission system (static and dynamic) and distribution system SE, existing methods are systematically reviewed and categorized based on the addressed challenges. Interesting attempts of using novel measurements in SE are also studied. Finally, the paper concludes by providing a detailed discussion on the remaining research gaps and future research directions to be explored.

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Section: ) Correlations Between Measurementsmentioning

confidence: 99%

“…In [24], a novel correlated extended Kalman filter (CEKF) is developed by accounting for the correlations of SCADA and PMU measurement errors. The point estimation method is used to calculate the modified covariance matrix for the proposed CEKF.…”

Section: Handling Correlations Between Measurementsmentioning

confidence: 99%

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

Lin¹,

Abur²,

Gómez-Expósito³

et al. 2024

IEEE Trans. Smart Grid

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“…As it was highlighted at the beginning of this section, the range of SE methods published in the literature is broad. Besides conventional solutions, large emphasis has been placed on the use of artificial intelligence: fuzzy logic [74,75], artificial neural network [76][77][78][79], particle swarm optimization [80,81], evolutionary algorithms [82], interior point optimization and brain storm optimization [83], biogeography based optimization [84], firefly algorithm [85], Kalman-filters [73,[86][87][88][89][90] and advanced techniques (forecastaided SE [50,73], multi-area SE [14,63,67,68,[91][92][93], and event triggered approaches [94]).…”

Section: Application Constraints Of the Algorithmsmentioning

confidence: 99%

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

et al. 2021

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Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.

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“…In addition to UT, some methods based on the Kalman filter were used to solve measurement correlations, including the Kalman filter (to predict the error covariance matrix) [10,11], and the point estimation method based on an extended Kalman filter [12]. In addition, the combination of untracked transform and Kalman filter has been used to detect bad data with measurement correlations [13].…”

Section: Introductionmentioning

confidence: 99%

Interval State Estimation of Electricity-Gas Systems Considering Measurement Correlations

Huang,

Feng

2024

Energies

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The popularization of electricity-gas systems leads to increasing demand for state management of systems. However, the existence of neglected measurement correlations brings uncertainties to the electricity-gas systems state estimation. In this paper, an interval state estimation method that considers measurement correlations existing in the electricity-gas systems is presented. We derive the linear measurement model for the electricity-gas systems through Taylor series expansion and estimate the measurement variance-covariance matrix with measurement correlations. The system parameter matrix and the measurement variance-covariance matrix containing measurement correlations are combined into an interval, and the interval state matrix considering measurement correlations is constructed. Then, the linear equations for the state estimation interval considering measurement correlations are established based on the measurement containing correlations and interval state matrix; as a result, the electricity-gas system state estimation model containing measurement correlations is established. In addition, a method for determining the range of state estimation intervals is proposed. Numerical tests on an integrated electricity-gas system comprising a 10-node natural gas network and IEEE 30-bus system indicate that the proposed approach has more advantages over the UT+KO approach in computation accuracy and computation efficiency.

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Power system dynamic state estimation considering correlation of measurement error from PMU and SCADA

Cited by 3 publications

References 41 publications

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond

The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review

Interval State Estimation of Electricity-Gas Systems Considering Measurement Correlations

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