D. Bejmert

Ultrasaturation Phenomenon in Power Transformers—Myths and Reality

In this paper, the ultrasaturation phenomenon of power transformers during their energization is studied. It is shown that under special conditions, the currents observed after transformer switching on do not contain enough restraining information (e.g., second harmonic), which may lead to protection maloperation. This paper concentrates on a thorough explanation of the problem and possible causes of ultrasaturation. Theoretical investigations are supported and illustrated with simulation studies performed both with MATLAB and electromagnetic transients program-alternative transients program. The outcomes of this research can further be used as hints for substation operation personnel as well as for the development of new protection stabilization criteria, which is not discussed further in this paper.

Investigation Into Islanding Detection With Capacitor Insertion-Based Method

When islanding conditions arise, local distribution companies usually require the disconnection of all distributed generations from the isolated grid. For this purpose, mainly passive anti-islanding protections are used The most common and widely used, because of their low cost, are passive anti-islanding protections (e.g., frequency and voltage based). Unfortunately, passive schemes cannot detect islanding situations under certain system operating conditions, namely, when the active/reactive power imbalance in an island is low. To improve operation and to reduce the nondetection zone of this, the protection schemes capacitor insertion method is proposed. The capacitor bank switching strategy and effectiveness of the proposed method are demonstrated through simulation studies, conducted in the PSCAD/EMTDC software environment and real-time laboratory testing. The results may be useful for utility companies and distributed generators owners to enhance islanding condition identification efficiency.

Transformer Differential Protection with Neural Network Based Inrush Stabilization

Application of artificial neural networks (ANN) for transformer differential protection stabilization against inrush conditions is presented. Three versions of the stabilization scheme are described. The best of them employs three ANNs fed with transformer terminal currents that has proven to be superior over the two other ANN schemes. The final solution combines the classification strengths of neural networks with commonly used second harmonic restraint, thus being a hybrid classification unit. To determine the most suitable ANN topology for the inrush classifier a genetic algorithm was used. The developed optimized neural inrush detection units have been tested with EMTP-ATP generated signals, proving better performance than traditionally used stabilization algorithms.

Current transformer saturation detection with genetically optimized neural networks

Application of the genetic algorithm (GA) for optimization of artificial neural network (ANN) based CT saturation detector is presented. To determine the most suitable ANN topology for the CT state classifier the rules of evolutionary improvement of the characteristics of individuals by concurrence and heredity are used. The proposed genetic optimization principles were implemented in MATLAB programming code. The initial as well as further consecutive network populations were created, trained and graded in a closed loop until the selection criterion was fulfilled. Various aspects of genetic optimization have been studied, including ANN quality assessment, versions of genetic operations etc. The developed optimized neural CT saturation detector has been tested with EMTP-ATP generated signals, proving better performance than traditionally used algorithms and methods.

Analysis of potential use of the SVM technique for transformer protection

Reliable and fast discrimination between internal faults and inrush conditions is still a challenging issue. In this paper an application of Support Vector Machine (SVM) for the transformer differential protection is discussed. To achieve the satisfactory classification strength various input vectors and training parameters were considered. Finally, 16 different versions of SVM classifiers are proposed. The developed SVM based power transformer protection units have been trained and tested with EMTP-ATP generated signals. The operation performance of designed SVM classifiers is compared to standard differential protection with traditional second harmonic stabilization approach. Moreover, potential hardware implementation of the presented SVM classifiers is analyzed.

Enhanced differential protection algorithm for tapped transmission lines

In the paper new algorithm enhancing performance of the current differential protection for tapped transmission lines is presented. In order to find an optimal solution various disturbances that may be the sources of unbalance of differential signals are discussed. The newly developed protection except differential current itself employs also negative-sequence and zero-sequence signals calculated from differential currents. The proposed scheme has been thoroughly tested using signals generated for a power system model prepared in ATP-EMTP software package. The new algorithm proved to be very effective in elimination of the problems observed in tapped lines configurations.