A power transformer protection with recurrent ANN saturation correction

Abstract

Current transformers (CTs) are present in ElectricPower Systems for protection and measurement purposes and they are susceptible to the saturation phenomenon. This paper presents an alternative approach to the correction of distorted waveforms caused by CT saturation. The method uses Recurrent Artificial Neural Networks (ANN) algorithms. As an example of an application, a complete protection system for a power transformer based on the deferential logic has been utilized. The EMTP-ATP software has been chosen as the computational tool to simulate the electrical system in order to generate data to train and test the ANNs. Many ANN architectures were trained and tested. Encouraging results related to the application of the new method are presented.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

“…A summary of the application of ANN in DGA-based transformer fault diagnosis is presented in Table 6. [42,122,123,128,129] knowledge discovery-based neural network [43] knowledge extraction-based neural network [44] fuzzy reasoning-based neural network [45] MLP neural network-based decision [46] BP neural network [103] recurrent ANN [104] DL based ANN [105] hybrid ANN and EPS [106] GRNN [40,107] combined with mathematical morphology [108] combined GA multi-layer feedforward network [120,135] combined with competitive learning theory [121] WNN and FWNN [67,[124][125][126][127] EDA-ANN [131] combined with FAHP [134] …”

“…Therefore, when the development of EPS in transformer fault diagnosis using DGA meets with some technical obstacles, the research and application of ANN is developing rapidly, especially the new AI techniques, such as improved probabilistic neural network [41], self-adaptive radial basis function (RBF) neural network [42], knowledge discovery-based neural network [43], knowledge extraction-based neural network [44], fuzzy reasoning-based neural network [45], MLP neural network-based decision [46], back propagation (BP) neural network [103], recurrent ANN [104], deep learning (DL) based ANN [105], hybrid ANN and EPS [106], and generalized regression neural network (GRNN) [40,107]. Besides, the combination of ANN and mathematical morphology has been applied for the transformer fault diagnosis [108].…”

Dissolved Gas Analysis Principle Based Intelligent Approaches to Fault Diagnosis and Decision Making of Large Oil-Immersed Power Transformers: A Survey

“…As reviewed in chapter 2, it is essential to combine the EPS with other AI techniques so that the EPS can play a better role in transformer fault diagnosis based on DGA. Therefore, when the development of EPS in transformer fault diagnosis using DGA meets with some technical obstacles, the research and application of ANN is developing rapidly, especially the new AI techniques, such as improved probabilistic neural network [41], self-adaptive radial basis function (RBF) neural network [42], knowledge discovery-based neural network [43], knowledge extraction-based neural network [44], fuzzy reasoning-based neural network [45], MLP neural network-based decision [46], back propagation (BP) neural network [103], recurrent ANN [104], deep learning (DL) based ANN [105], hybrid ANN and EPS [106], and generalized regression neural network (GRNN) [40,107]. Besides, the combination of ANN and mathematical morphology has been applied for the transformer fault diagnosis [108].…”

Phasor Measurement Unit Test Under Interference Conditions

“…High currents that exceed the normal operation capability, such as during a fault, can cause the core to saturate and produce a highly distorted signal [31]. This can lead to mis-operation of relays and other equipment that uses these signals, such as preventing tripping of equipment at the correct time that results in equipment damage [32].…”