Identification of Ferroresonance on Transformer using Wavelet Transformatio

Negara, I Made Yulistya; Hernanda, I Gusti Ngurah Satriyadi; Asfani, Dimas Anton; Fahmi, Daniar; Hakim, Luthfi; Putra, I Gede Arka Prawira

doi:10.1109/ichveps47643.2019.9011098

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Negara et al investigated the influence of special transformer core types on the formation of ferroresonance modes. Through a comprehensive cumulative energy analysis, significant distinctions were determined among normal waves, harmonics, and ferroresonances [38]. Heidary et al recommended the use of ferroresonance for dual function protection element limiting by fault currents [39].…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning

et al. 2023

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Ferroresonance is a non-linear and dangerous resonance phenomenon that can affect power networks and damage electrical equipment. The ferroresonance phenomenon is examined by dividing it into classes, with chaotic ferroresonance being the most dangerous type that causes overvoltage's. Detecting chaotic ferroresonance in a short period of time is of great importance in terms of taking measures and reducing equipment damage. In this study, we explored the application of deep convolutional neural networks (DCNNs) for the identification and classification of chaotic ferroresonance phenomena. Two pre-trained AlexNet models were adapted using transfer learning to perform these tasks. The first model was utilized to identify chaotic ferroresonance, while the second was employed to distinguish between different subtypes of chaotic ferroresonance by dividing voltage curve graphs into different periods and shapes. The training and testing of both DCNN models were conducted using snapshot images extracted from the voltage curves of all phase voltages. The results of the experiments showed high accuracy in both the identification and classification of chaotic ferroresonance phenomena.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning

et al. 2023

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“…If this value is exceeded for 100

, ferroreresonance is detected. Similarly, in [ 22 ], the voltage waveforms are processed with wavelet filters. By identifying the accumulation of energy at different levels and scales of decomposition, the threshold level for ferroresonance detection is set.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Noise-Based Detection of Ferroresonance Events in Isolated Neutral Power Systems with Inductive Voltage Transformers

Martínez

Arroyo

Pigazo

et al. 2022

Sensors

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Power-quality events and operation transients in power systems (PS) with isolated neutral can saturate inductive voltage transformers (IVT), which, when interacting with the overhead and underground cable capacitances, can cause ferroresonance events in the local PS. This abnormal operating mode can partially or totally damage the transformers and switchgears within the affected PS. Distribution system operators (DSO) can minimize these effects by detecting ferroresonance events accurately and fast enough and changing the mode of operation accordingly. Direct detection methods, i.e., based on voltage measurements, are reliable, but the massive deployment of this solution is relatively expensive; i.e., power quality analyzers cost thousands of USD. Alternatively, indirect detection methods are also available, e.g., IVT vibration measurements with accelerometers costing hundreds of USD, but their reliability depends on the installation method used. This manuscript proposes using the acoustic noise caused by magnetostriction forces within the IVT core during ferroresonance events to detect their occurrence. Compared to other indirect methods, electret condenser microphones with preamplifying stage cost less than USD 10 and are less sensitive to the installation procedure. The proposed method is validated experimentally, and its performance compared to IVT vibration measurements one by using the same detection methodology.

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Identification of Ferroresonance on Transformer using Wavelet Transformatio

Cited by 2 publications

References 14 publications

Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning

Diagnosis of Chaotic Ferroresonance Phenomena Using Deep Learning

Acoustic Noise-Based Detection of Ferroresonance Events in Isolated Neutral Power Systems with Inductive Voltage Transformers

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