State estimation of the power system using robust estimator

Khan, Zahid; Razali, Radzuan; Daud, Hanita; Nor, Nursyarizal Mohd; Firuzabad, Mahmud Fotuhi

doi:10.1063/1.4968167

Cited by 3 publications

(1 citation statement)

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“…Traditionally, power system state estimation is realized based on supervisory control and data acquisition system (SCADA) (Abur and Exposito, 2004;Zhu and Giannakis, 2014;Khan et al, 2016;Rosli et al, 2014). SCADA can provide measurements of voltage magnitude, current magnitude, active power and reactive power in a low sampling rate, i.e.…”

Section: Introductionmentioning

confidence: 99%

An extended Kalman particle filter for power system dynamic state estimation

Wang

2018

COMPEL

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Purpose The purpose of this paper is to propose an extended Kalman particle filter (EPF) approach for dynamic state estimation of synchronous machine using the phasor measurement unit’s measurements. Design/methodology/approach EPF combines the extended Kalman filter (EKF) with the particle filter (PF) to accurately estimate the dynamic states of synchronous machine. EKF is used to make particles of PF transfer to the likelihood distribution from the previous distribution. Therefore, the sample impoverishment in the implementation of PF is able to be avoided. Findings The proposed method is capable of estimating the dynamic states of synchronous machine with high accuracy. The real-time capability of this method is also acceptable. Practical implications The effectiveness of the proposed approach is tested on IEEE 30-bus system. Originality/value Introducing EKF into PF, EPF is proposed to estimate the dynamic states of synchronous machine. The accuracy of a dynamic state estimation is increased.

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Section: Introductionmentioning

confidence: 99%

An extended Kalman particle filter for power system dynamic state estimation

Wang

2018

COMPEL

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Power system state estimation using teaching learning-based optimization algorithm

Salkuti

2020

TELKOMNIKA

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The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional optimization algorithms is that TLBO gives global optimum solution for the present problem. To show the suitability of TLBO for solving SE problem, IEEE 14 bus test system has been selected in this work. The results obtained with TLBO are also compared with conventional weighted least square (WLS) technique and evolutionary based particle swarm optimization (PSO) technique.

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Evaluating the Steady-state Performance of the Synthetic Coefficient of Variation Chart

Chew¹,

Yeong²,

Talib³

et al. 2021

JST

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The synthetic coefficient of variation (CV) chart is attractive to practitioners as it allows for a second point to fall outside the control limits before deciding whether the process is out-of-control. The existing synthetic CV chart is designed with a head-start feature, which shows an advantage under the zero-state assumption where shifts happen immediately after process monitoring has started. However, this assumption may not be valid as shifts may happen quite some time after process monitoring has started. This is called the steady-state condition. This paper evaluates the performance of the chart under the steady-state condition. It is shown that the steady-state out-of-control average run length (ARL1) is substantially larger than the zero-state ARL1, hence larger number of samples are needed to detect the out-of-control condition. From the comparison with other CV charts, the steady-state synthetic CV chart does not show better performance, especially for small sample sizes and shift sizes. Hence, the synthetic CV chart is not recommended to be adopted under the steady-state condition, and its good performance is only applicable under the zero-state assumption. The results of this paper enable practitioners to be aware that the performance of the synthetic CV chart may be inferior under actual application (when shifts do not happen at the beginning of process monitoring) compared to its zero-state performance.

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State estimation of the power system using robust estimator

Cited by 3 publications

References 15 publications

An extended Kalman particle filter for power system dynamic state estimation

An extended Kalman particle filter for power system dynamic state estimation

Power system state estimation using teaching learning-based optimization algorithm

Evaluating the Steady-state Performance of the Synthetic Coefficient of Variation Chart

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