On tracing flicker sources and classification of voltage disturbances
Doktorsavhandling, 2007

Developments in measurement technology, communication and data storage have resulted in measurement systems that produce large amount of data. Together with the long existing need for characterizing the performance of the power system this has ressaulted in demand for automatic and efficient information-extraction methods. The objective of the research work presented in this thesis was therefore to develop new robust methods that extract additional information from voltage and current measurements in power systems. This work has contributed to two specific areas of interest. The first part of the work has been the development of a measurement method that gives information how voltage flicker propagates (with respect to a monitoring point) and how to trace a flicker source. As part of this work the quantity of flicker power has been defined and integrated in a perceptionally relevant measurement method. The method has been validated by theoretical analysis, by simulations, and by two field tests (at low-voltage and at 130-kV level) with results that matched the theory. The conclusion of this part of the work is that flicker power can be used for efficient tracing of a flicker source and to determine how flicker propagates. The second part of the work has been the development of a voltage disturbance classification system based on the statistical learning theory-based Support Vector Machine method. The classification system shows always high classification accuracy when training data and test data originate from the same source. High classification accuracy is also obtained when training data originate from one power network and test data from another. The classification system shows, however, lower performance when training data is synthetic and test data originate from real power networks. It was concluded that it is possible to develop a classification system based on the Support Vector Machine method with “global settings” that can be used at any location without the need to retrain. The conclusion is that the proposed classification system works well and shows sufficiently high classification accuracy when trained on data that originate from real disturbances. However, more research activities are needed in order to generate synthetic data that have statistical characteristics close enough to real disturbances to replace actual recordings as training data.

power transmission and distribution

digital signal processing

voltagedips

power system disturbance

power quality monitoring

light flicker

Support Vector Machines

HC1, Hörsalsvägen 14, Chalmers
Opponent: Professor Alain Robert, Laboratoire d'Electrotechnique et d'Instrumentation, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Författare

Peter G.V. Axelberg

Chalmers, Signaler och system

Ämneskategorier

Elektroteknik och elektronik

Annan elektroteknik och elektronik

ISBN

978-91-7291-936-5

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 2617

HC1, Hörsalsvägen 14, Chalmers

Opponent: Professor Alain Robert, Laboratoire d'Electrotechnique et d'Instrumentation, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Mer information

Skapat

2017-10-08