Detection and Classification of Power Quality Disturbances

What is it about?

The accurate detection and classification of power quality (PQ) disturbances in power systems is a key step to determine the causes of these events before any proper countermeasure could be taken. This paper presents a new algorithm for detection and classification of PQ disturbances based on combination of double resolution S-transform (DRST) and directed acyclic graph support vector machines (DAG-SVMs).

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Why is it important?

With the increasing number of electronic and power-electronic equipment components, nonlinear loads, unbalanced power systems and solid-state switching devices used in industrial and public sectors, as well as due to the growing demands for improved power quality (PQ), the quality of electricity supplies has gradually become an important issue for electric utilities and its customers. The existence of PQ disturbances greatly affects the safe and economical operations of electric power systems. Analysis and study the problems existing in power systems is particularly important for the improvement of PQ. The exact detection and classification of PQ disturbances is a top priority in this direction and can also support PQ evaluation.