Linear state estimation and bad data detection for power systems with RTU and PMU measurements

Jovičić, Aleksandar; Hug, Gabriela

doi:10.1049/iet-gtd.2020.0487

Cited by 33 publications

(23 citation statements)

References 26 publications

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“…Fortunately, the advent of advanced monitoring technologies, such as D-PMU and AMI, provides new possibilities for refined parameter estimation in distribution networks. Indeed, the D-PMUs have been employed for event detection [5], state estimation [6], topology detection [7], and islanding detection [8] in the distribution networks. Likewise, the extensive deployment of smart meters in the distribution networks offers intelligent monitoring and control for various applications, such as observability enhancement [9], energy management [10], line outage identification [11], topology and parameter estimation [12,13], and distribution system state estimation [14,15].…”

Section: Motivation and Incitementmentioning

confidence: 99%

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Xiao

Xie

Fang

et al. 2021

IET Generation Trans & Dist

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This paper proposes a novel distribution line parameter estimation method, driven by the probabilistic data fusion of the distributed phasor measurement unit (D-PMU) and the advanced measurement infrastructure. The synchronized and high-precision D-PMU is utilized to tackle the challenge risen by the a-synchronization of smart meters. Correspondingly, a time-alignment algorithm is proposed to obtain the time-synchronous error (TSE) dataset for the up-stream smart meter. The non-parametric estimation method is performed then to evaluate the probabilistic density curve of TSE. Furthermore, TSE data of down-stream smart meters are generated by implementing the acceptance-rejection process based on the obtained probabilistic density curve. Leveraging the generated TSE dataset, a new time-shifted D-PMU curve is probabilistically aligned or fused with the down-stream advanced measurement infrastructure curves. According to the complete voltage drop model, the line parameter estimation of resistance and reactance is formulated as a quadratic programming problem and solved by Optimal Toolbox in MATLAB by conducting multi-run Monte-Carlo simulations under various scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed methodology.

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Section: Motivation and Incitementmentioning

confidence: 99%

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Xiao

Xie

Fang

et al. 2021

IET Generation Trans & Dist

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“…However, in some cases, removal of some critical measurements may result the system to become unobservable. Therefore, Abur and Exposito [37] proposed to correct the faulty measurements by equation 8, which is used widely afterwards, such as [38].…”

Section: A Fdi Identification Based On Wls Estimatormentioning

confidence: 99%

“…WLS does not provide the FDI estimation, yet the proposed method by equation (8) enables estimation of the FDI values in WLS method, where the rBDD is only used for the detection of FDI existence. The FDI estimation performance of the different estimators under question are evaluated in this subsection, which are the CBF, mFDBF and method of [38]. Figure 3 shows the performance of the three estimators for estimation of the FDI values.…”

Section: B Simultaneous Biases Estimationmentioning

confidence: 99%

“…Figure 3 shows the performance of the three estimators for estimation of the FDI values. It can be seen that the performance of method of [38] is relatively reliable for estimation of single FDI values, whereas it has large errors for the double FDIs and has divergent estimation of more than 4 simultaneous FDIs. It can be seen that the CBF has a very good performance for estimation of the single and multiple FDIs before a change occurs in the FDI values.…”

Section: B Simultaneous Biases Estimationmentioning

confidence: 99%

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Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Langeroudi

Abdelaziz

2021

IEEE Access

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In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed. INDEX TERMS Distribution system online monitoring, false data injection, Kalman filter, state estimation NOMENCLATURE Acronyms CBF Constant bias filter DS Distribution system FDI False data injection KF Kalman filter mFDBF Modified failure-detector in bias filter PDF Probability distribution function rBDD Residual-based bad data detector SCADA Supervisory control and data acquisition SE State estimation WLS Weighted least squares Indices a, b, c Three phases (superscript) i, j, s DS node index (subscript) k Discrete time index (subscript) T Matrix transpose (superscript) Sets φ Set of nodes with power measurement ψ Set of nodes with voltage measurement Variables , Voltage magnitude and angle at node i phase p , Active and reactive power at node i phase p b Bias vector z Measurement vector x State vector ̂ State estimates at time k r Residual vector ω Measurement noise Functions and matrices h(x) Measurement function Hk Measurement function Jacobian G Estimation gain matrix R, Q Measurements and states covariance matrix

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“…Algorithm for estimating the state variables based on a limited number of PMU as well as determining the optimal PMU placement was proposed [5][6][7]. To enhance the performance of state estimation using various measurements based on the remote terminal unit (RTU) and PMU, the robust and fast algorithm with the linear weighted least square (WLS) technique and the architecture based on a multistage scheme is proposed [8][9][10]. The second function is identifying the topology and measurement errors of devices using practical heuristic methods.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Cho

Choi

2021

Energies

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This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

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Linear state estimation and bad data detection for power systems with RTU and PMU measurements

Cited by 33 publications

References 26 publications

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

Methodology for Implementing the State Estimation in Renewable Energy Management Systems

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