Artificial Intelligence Techniques Applications for Power Disturbances Classification

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K.Manimala ; Dr.K.Selvi ; R.Ahila (2008)
  • Related identifiers: doi: 10.5281/zenodo.1055187, doi: 10.5281/zenodo.1055188
  • Subject: probabilistic neural network | back propagation network | radial basis function support vector machine | power quality
    acm: ComputingMethodologies_PATTERNRECOGNITION
    arxiv: Computer Science::Neural and Evolutionary Computation

Artificial Intelligence (AI) methods are increasingly being used for problem solving. This paper concerns using AI-type learning machines for power quality problem, which is a problem of general interest to power system to provide quality power to all appliances. Electrical power of good quality is essential for proper operation of electronic equipments such as computers and PLCs. Malfunction of such equipment may lead to loss of production or disruption of critical services resulting in huge financial and other losses. It is therefore necessary that critical loads be supplied with electricity of acceptable quality. Recognition of the presence of any disturbance and classifying any existing disturbance into a particular type is the first step in combating the problem. In this work two classes of AI methods for Power quality data mining are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs in two critical respects: SVMs train and run an order of magnitude faster; and SVMs give higher classification accuracy.
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