Detection of Turn-to-Turn Faults in the Stator and Rotor of Synchronous Machines During Startup

Afrandideh, Saeed; Haghjoo, Farhad; Cruz, Sérgio M. A.; Milasi, Mostafa Eshaghi

doi:10.1109/tie.2020.3003626

Cited by 15 publications

(7 citation statements)

References 40 publications

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“…where i a , i b , i c are the three-phase stator currents, V a , V b and V c are the stator voltages and λ a , λ b and λ c are MFL. MFL includes two types of flux: (1) The air-gap MFL and (2) The armature leakage flux [27]. In this paper, E φ as the back EMF caused by the air-gap MFL in each phase of the stator is expressed as follows ( 5):…”

Section: Magnetic Flux As the Primary Criterion For Lof Protectionmentioning

confidence: 99%

“…There are several methods for frequency estimation for sinusoidal signals [27,28]. This paper utilises one of the simplest ones, namely zero crossing (ZC) detection.…”

Section: Frequency Estimation Of V and E φmentioning

confidence: 99%

“…The three‐phase stator voltages are obtained by using the following equations:

\frac{d}{dt}\left[\begin{array}{@{}c@{}}{\lambda }_{a}\\ {\lambda }_{b}\\ {\lambda }_{c}\end{array}\right]=\left[\begin{array}{@{}c@{}}{V}_{a}\\ {V}_{b}\\ {V}_{c}\end{array}\right]+{R}_{s}\left[\begin{array}{@{}c@{}}{i}_{a}\\ {i}_{b}\\ {i}_{c}\end{array}\right]

where i a , i b , i c are the three‐phase stator currents, V a , V b and V c are the stator voltages and λ a , λ b and λ c are MFL. MFL includes two types of flux: (1) The air‐gap MFL and (2) The armature leakage flux [27]. In this paper, E φ as the back EMF caused by the air‐gap MFL in each phase of the stator is expressed as follows ():

\left[\begin{array}{@{}c@{}}{E}_{\varphi }^{a}\\ {E}_{\varphi }^{b}\\ {E}_{\varphi }^{c}\end{array}\right]=\left[\begin{array}{@{}c@{}}{V}_{a}\\ {V}_{b}\\ {V}_{c}\end{array}\right]+{R}_{s}\left[\begin{array}{@{}c@{}}{i}_{a}\\ {i}_{b}\\ {i}_{c}\end{array}\right]+j{X}_{l}\left[\begin{array}{@{}c@{}}{i}_{a}\\ {i}_{b}\\ {i}_{c}\end{array}\right]

where

{E}_{\varphi }^{a}

{E}_{\varphi }^{b}

and

E_{φ}^{c} ...

…”

Section: The Proposed Techniquementioning

confidence: 99%

See 2 more Smart Citations

A new air gap flux‐based technique to detect loss of field in synchronous generators

Haseli,

Haghjoo,

Hasani

2024

IET Electric Power Appl

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A new technique is proposed for loss of field (LOF) detection in synchronous generators (SGs). The proposed scheme employs the variations of SG angular velocity ∆ωφ and load angle ∆δφ to reach this goal, while the mentioned parameters are estimated through air gap flux estimation. The proposed scheme performance is evaluated using realistic simulation case studies based on IEEE C. 37. 102 standard compared to the conventional impedance‐based one. The obtained results show that the presented scheme excels the conventional scheme from speed and security viewpoints. Moreover, the estimation procedure of ∆ωφ and ∆δφ is experimentally validated by using a 10 kVA SG and the results demonstrate that the proposed technique can be easily set and implemented.

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Section: Magnetic Flux As the Primary Criterion For Lof Protectionmentioning

confidence: 99%

“…There are several methods for frequency estimation for sinusoidal signals [27,28]. This paper utilises one of the simplest ones, namely zero crossing (ZC) detection.…”

Section: Frequency Estimation Of V and E φmentioning

confidence: 99%

“…The three‐phase stator voltages are obtained by using the following equations:

\frac{d}{dt}\left[\begin{array}{@{}c@{}}{\lambda }_{a}\\ {\lambda }_{b}\\ {\lambda }_{c}\end{array}\right]=\left[\begin{array}{@{}c@{}}{V}_{a}\\ {V}_{b}\\ {V}_{c}\end{array}\right]+{R}_{s}\left[\begin{array}{@{}c@{}}{i}_{a}\\ {i}_{b}\\ {i}_{c}\end{array}\right]

\left[\begin{array}{@{}c@{}}{E}_{\varphi }^{a}\\ {E}_{\varphi }^{b}\\ {E}_{\varphi }^{c}\end{array}\right]=\left[\begin{array}{@{}c@{}}{V}_{a}\\ {V}_{b}\\ {V}_{c}\end{array}\right]+{R}_{s}\left[\begin{array}{@{}c@{}}{i}_{a}\\ {i}_{b}\\ {i}_{c}\end{array}\right]+j{X}_{l}\left[\begin{array}{@{}c@{}}{i}_{a}\\ {i}_{b}\\ {i}_{c}\end{array}\right]

where

{E}_{\varphi }^{a}

{E}_{\varphi }^{b}

and

E_{φ}^{c} ...

…”

Section: The Proposed Techniquementioning

confidence: 99%

See 1 more Smart Citation

A new air gap flux‐based technique to detect loss of field in synchronous generators

Haseli,

Haghjoo,

Hasani

2024

IET Electric Power Appl

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“…The digital current signal is applied to the frequency estimation module and fundamental DFT algorithm. The frequency estimation module measures the fundamental frequency to adapt the sampling rate and avoid off‐nominal frequency error in phasor estimation [31, 32]. The parameter M in (3) is set to

N/2

, N , and 2 N for the HCDFT, OCDFT, and TCDFT, respectively.…”

Section: Proposed Algorithmsmentioning

confidence: 99%

Two modified DFT‐based algorithms for fundamental phasor estimation

Afrandideh

Arabshahi

Fazeli

2022

IET Generation Trans & Dist

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The discrete Fourier transform (DFT) is a powerful phasor estimation algorithm conventionally used in digital relaying applications. The DFT-based phasor estimation is usually accompanied by oscillatory error due to the presence of the decaying DC (DDC) components in fault current signals. Accordingly, two modified DFT-based phasor estimation algorithms are proposed in this paper. The proposed algorithms accurately estimate the exact fundamental phasor by extracting the DDC components using successive outputs of the conventional DFT algorithm. The performance of the proposed algorithms is validated by the mathematically generated and PSCAD-simulated signals and is compared with previous algorithms. The results demonstrate that the proposed algorithms in this paper are more robust than other ones. Moreover, they are applicable in the DFT algorithm with different data windows. INTRODUCTIONThe fundamental phasor estimation plays an important role in detecting, locating, and discriminating faults in power systems. There are a number of conventional phasor estimation algorithms, that include: discrete Fourier transform (DFT), least-square error (LSE), discrete wavelet transform (DWT), Kalman filters, discrete Hartley transform (DHT), and artificial neural networks (ANNs) [1][2][3][4]. The discrete Fourier transform (DFT) is a powerful phasor estimation algorithm, widely used in digital relaying applications due to its simplicity, accuracy, and speed. The DFT algorithm has a high capability to estimate the fundamental phasor of periodic sinusoidal signals. However, its performance is extremely influenced by non-periodic signals such as decaying DC components (DDCs), which are created in the current and voltage signals during fault conditions. This issue causes an undesired oscillating error in the estimated phasor, especially phase angle. Although this error decays after a certain period of time (depending on the time constant of the DDC component), it extremely reduces the speed and accuracy of decision-making in relays. The DDC component has two parameters, the initial value and time constant, which depend on fault inception angle and the value of pathway X ∕R ratio, respectively. Because the source impedance and line X ∕R ratio are so close to each other, the initial value ofThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Another proposed intrusive approach is to track the air gap rotating magnetic field for detection of stator and rotor turn-toturn faults [5]- [6], where spectral components are used for classification. For the rotor turn-to-turn fault, an invasive lowvoltage AC source is suggested to be applied over the rotor field winding in offline conditions [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Parametric Condition Monitoring of Medium-Power Generators with Brushless Exciters under Mechanical Faults

Gyftakis

Platero

Nøland

2023

IEEE Trans. on Ind. Applicat.

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The interest in reliable condition monitoring of small-to-medium-power generator sets (GenSets) has increased in recent years due to the multiple benefits of prompt and decisive fault detection. This leads to the minimization of catastrophic failures and significantly improves the interconnected load's power reliability. In this paper, the case of mechanical faults is examined and particularly the eccentricity caused by a cocked bearing. The main electrical machine under investigation is a salient pole wound-field synchronous machine (WFSM) with a brushless exciter (BE), which is a very common topology in GenSets. The work is based purely on experimental testing, while a plethora of methods has been assessed based on their accuracy to detect and identify the fault, both stationary and dynamic. Finally, the paper proposes a new and non-intrusive method based on the monitoring of the stray leakage flux and its analysis while the generator experiences a deaccelerating transient, and the role of the exciters's geometrical properties in this new methodology is revealed.

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Detection of Turn-to-Turn Faults in the Stator and Rotor of Synchronous Machines During Startup

Cited by 15 publications

References 40 publications

A new air gap flux‐based technique to detect loss of field in synchronous generators

A new air gap flux‐based technique to detect loss of field in synchronous generators

Two modified DFT‐based algorithms for fundamental phasor estimation

Multi-Parametric Condition Monitoring of Medium-Power Generators with Brushless Exciters under Mechanical Faults

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