Three‐phase resistive capacitor switching transient limiter for mitigating power capacitor switching transients

Ghanbari, Teymoor; Farjah, Ebrahim; Naseri, Farshid

doi:10.1049/iet-gtd.2015.0525

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…Accordingly, these matrices are selected as follows: where I ∈ ℝ 3×3 is the identity matrix. Assuming the model and observation (measurement) noises are applied to the state-space model in an additive form [21][22][23][24], G ∈ ℝ 6×6 and M ∈ ℝ 3×3 matrices can also be written as follows:…”

Section: Formulation Of the Ekfmentioning

confidence: 99%

“…Accordingly, these matrices are selected as follows:

\begin{matrix} true \\ right Q & = & left d i a g false(0.05, 0.05, 0.04, 0.04, 0.06, 0.06 false), \\ right S & = & left 0.03 \times I, \end{matrix}

where

I \in R^{3 \times 3}

is the identity matrix. Assuming the model and observation (measurement) noises are applied to the state‐space model in an additive form [21–24],

G \in R^{6 \times 6}

and

M \in R^{3 \times 3}

matrices can also be written as follows:

G = \frac{\partial \dot{x}}{\partial ω} = (\begin{matrix} \frac{\partial {\overset{λ}{true}}_{1}}{\partial ω_{1}} & \dots & \frac{\partial {\overset{λ}{true}}_{1}}{\partial ω_{6}} \\ ⋮ & ⋱ & ⋮ \\ \frac{\partial {\overset{λ}{true}}_{6}}{\partial ω_{1}} & \dots & \frac{\partial {\overset{λ}{true}}_{6}}{\partial ω_{6}} \end{matrix}) = I,

M = \frac{\partial y}{\partial υ} = (\begin{matrix} \frac{\partial i_{1}}{\partial υ_{1}} & \dots & \frac{\partial i_{1}}{\partial υ_{3}} \\ ⋮ & ⋱ & ⋮ \\ \frac{\partial i_{3}}{\partial υ_{1}} & \dots & \frac{\partial i_{3}}{\partial υ_{3}} \end{matrix}) = I .

…”

Section: Model Derivation and Ekf Formulationmentioning

confidence: 99%

See 1 more Smart Citation

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

Kazemi

Naseri

Yazdi

et al. 2021

IET Science Measure & Tech

Self Cite

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In this paper, a hybrid approach for effective diagnosis of power transformers is proposed. In the proposed method, the extended Kalman filter is used for the estimation of three‐phase currents in the primary windings of the transformer. Three residual signals are defined as the differences between the measured and estimated three‐phase currents. When the transformer is healthy, the EKF perfectly estimates the primary currents and hence, the residual signals are nearly zero. However, when the transformer is faulty, the EKF cannot suitably estimate the currents due to the large model mismatch resulting from the transformer internal faults. Consequently, large residual signals are generated, which are used as the key signatures for discriminating the internal faults from energisation conditions. Besides, the proposed method uses the entries of the covariance matrix of the estimation error to locate and classify the internal faults. For these purposes, support vector machine classifiers are used. The effectiveness of the proposed method is demonstrated by a large number of simulation test cases obtained using PSCAD/EMTDC software. Also, hardware‐in‐the‐loop experiments are conducted using dSPACE1104 development platform and real‐time feasibility of the proposed method is authenticated.

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Section: Formulation Of the Ekfmentioning

confidence: 99%

“…Accordingly, these matrices are selected as follows:

\begin{matrix} true \\ right Q & = & left d i a g false(0.05, 0.05, 0.04, 0.04, 0.06, 0.06 false), \\ right S & = & left 0.03 \times I, \end{matrix}

where

I \in R^{3 \times 3}

is the identity matrix. Assuming the model and observation (measurement) noises are applied to the state‐space model in an additive form [21–24],

G \in R^{6 \times 6}

and

M \in R^{3 \times 3}

matrices can also be written as follows:

G = \frac{\partial \dot{x}}{\partial ω} = (\begin{matrix} \frac{\partial {\overset{λ}{true}}_{1}}{\partial ω_{1}} & \dots & \frac{\partial {\overset{λ}{true}}_{1}}{\partial ω_{6}} \\ ⋮ & ⋱ & ⋮ \\ \frac{\partial {\overset{λ}{true}}_{6}}{\partial ω_{1}} & \dots & \frac{\partial {\overset{λ}{true}}_{6}}{\partial ω_{6}} \end{matrix}) = I,

M = \frac{\partial y}{\partial υ} = (\begin{matrix} \frac{\partial i_{1}}{\partial υ_{1}} & \dots & \frac{\partial i_{1}}{\partial υ_{3}} \\ ⋮ & ⋱ & ⋮ \\ \frac{\partial i_{3}}{\partial υ_{1}} & \dots & \frac{\partial i_{3}}{\partial υ_{3}} \end{matrix}) = I .

…”

Section: Model Derivation and Ekf Formulationmentioning

confidence: 99%

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

Kazemi

Naseri

Yazdi

et al. 2021

IET Science Measure & Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…It can significantly affect the network health compared with load switching [18]. Although capacitor switching transients can be mitigated by limiters in the operational context [19], it is worthwhile to monitor capacitor switching to ensure it is functioning correctly. Therefore, for anomaly detection, capacitor switching should be taken into account for fault type, but load switching should be treated as a normal event.…”

Section: A Pq Transientsmentioning

confidence: 99%

A Sequential Bayesian Approach to Online Power Quality Anomaly Segmentation

Stephen

McArthur

2021

IEEE Trans. Ind. Inf.

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Increased observability on power distribution networks can reveal signs of incipient faults which can develop into costly and unexpected plant failures. While low-cost sensing and communications infrastructure is facilitating this, it is also highlighting the complex nature of fault signals, a challenge which entails precisely extracting anomalous regions from continuous data streams before classifying the underlying fault signature. Doing this incorrectly will result in capture of uninformative data. Extraction processes can be confounded by operational noise on the network including harmonics produced by embedded generation. In this paper, an online model is proposed. Our Bayesian Changepoint Power Quality anomaly Segmentation allows automated segmentation of anomalies from continuous current waveforms, irrespective of noise. Demonstration of the effectiveness of the proposed technique is carried out with operational field data as well as a challenging simulated network, highlighting the ability to accommodate noise from typical network penetration levels of power electronic devices.

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Simulation analysis of the switching of $$230\hbox { kV}$$ 230 kV substation shunt capacitor banks with a 6% series reactor for limiting transient inrush currents and oscillation overvoltage

et al. 2017

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Three‐phase resistive capacitor switching transient limiter for mitigating power capacitor switching transients

Cited by 11 publications

References 17 publications

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers

A Sequential Bayesian Approach to Online Power Quality Anomaly Segmentation

Simulation analysis of the switching of $$230\hbox { kV}$$ 230 kV substation shunt capacitor banks with a 6% series reactor for limiting transient inrush currents and oscillation overvoltage

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