Analysis of Bad Data in Power System State Estimation Under Non-Gaussian Measurement Noise

Martínez-Parrales, R.; Fuerte‐Esquivel, Claudio R.; Alcaide-Moreno, Boris A.

doi:10.1016/j.epsr.2020.106424

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…RCPF2 implementation: a time-varying, multi-dimensional scale factor needs to be introduced into (10). Thus, Equation (10) can be modified as follows [34]:…”

Section: Designing the Importance Density Functionmentioning

confidence: 99%

“…In practice, measurement data obey non-Gaussian distribution. At present, the common method to solve this problem is by providing a comprehensive analysis of the quality of estimated states using both the weighted least squares method and the largest normalized residual test [10]. In most cases, the pseudo-measured statistical also cannot be approximated by a include the following.…”

Section: Introductionmentioning

confidence: 99%

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A novel distribution system state estimator based on robust cubature particle filter used for non‐gaussian noise and bad data scenarios

Shi

Liu

Hang

2021

IET Generation Trans & Dist

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State estimation is the core function of distribution management system, and its estimation accuracy directly affects the operation of other advanced application functions of distribution management system. However, the access of a large number of distributed generation brings non‐Gaussian noise and bad data problems, which affect the accuracy of state estimation. Therefore, based on the conventional cubature particle filter (CPF), two new state estimation operators, robust cubature filter 1 (RCPF1) and robust cubature filter 2 (RCPF2), are proposed by introducing the Huber's M‐estimation theory into the CPF algorithm and integrating a time‐varying, multi‐dimensional scale factor with the CPF, respectively. The simulation results based on IEEE33 bus system showed that: (1) RCPF has better estimation accuracy and robustness than conventional cubature particle filter and cubature Kalman filter; (2) RCPF2 has better estimation performance than RCPF1 in the case of non‐Gaussian noise and bad data.

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“…RCPF2 implementation: a time-varying, multi-dimensional scale factor needs to be introduced into (10). Thus, Equation (10) can be modified as follows [34]:…”

Section: Designing the Importance Density Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel distribution system state estimator based on robust cubature particle filter used for non‐gaussian noise and bad data scenarios

Shi

Liu

Hang

2021

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…According to the works in [ 29 , 30 ], the DG outputs obey non-Gaussian distribution or Gaussian mixture models. Moreover, the distribution of real-time measurement error is not always Gaussian especially due to system errors and quantization errors of acquisition equipment [ 9 , 10 ].…”

Section: Proposed Ise Approachmentioning

confidence: 99%

“…With the widespread application of distributed generation (DG) and electric vehicle (EV) in the distribution systems, the DSSE would contain more significant uncertainties caused by the stochastic power outputs of DG and the plug-in EV [ 5 , 6 , 7 ]. In addition, the effects of other uncertainties generated by the inaccurate line parameters and the non-Gaussian measurement noise statistics can also be non-negligible in DSSE [ 8 , 9 , 10 ]. Many researchers propose the interval state estimation (ISE) methods to model and analyze distribution systems with multiple uncertainties [ 8 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties

Chen

Ren

Amaratunga

2021

Sensors

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Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO.

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“…It should be noted that, if an incorrect distribution is assumed for PMU measurement errors, the results obtained for a power system application might lead to incorrect actions [15]. Indeed, the estimation of the state of the power grid can deviate significantly from the actual operation due to an incorrect measurement error model [16]. Nowadays, in the scientific literature, different papers suggest using a non-Gaussian model for PMU errors in specific applications based on synchrophasor measurements.…”

Section: Introductionmentioning

confidence: 99%

Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

Castello

Muscas

Pegoraro

2022

IEEE Open J. Instrum. Meas.

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Different power system applications based on synchrophasors measured in different nodes of the electric grid require information about the statistical distribution of the errors introduced by the Phasor Measurement Units (PMUs). The performance of these applications can be significantly affected by possible incorrect assumptions. The Gaussian distribution has been historically assumed in most of the approaches, but some more recent studies suggest the possibility of considering different distributions for a more accurate modeling of the actual situation. In this paper, proper statistical tools applied to the results achieved through a high performance experimental test system are proposed to assess the statistical distribution of PMU errors under controlled steady-state conditions, thus providing a basis for defining suitable models to be used in the specific applications.

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Analysis of Bad Data in Power System State Estimation Under Non-Gaussian Measurement Noise

Cited by 19 publications

References 12 publications

A novel distribution system state estimator based on robust cubature particle filter used for non‐gaussian noise and bad data scenarios

A novel distribution system state estimator based on robust cubature particle filter used for non‐gaussian noise and bad data scenarios

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties

Statistical Behavior of PMU Measurement Errors: An Experimental Characterization

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