Power-quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. This paper presents a novel system based on neural networks for the classification of electrical disturbances in real time. In addition, an electrical pattern generator has been developed in order to generate common disturbances which can be found in the electrical grid. The classifier obtained excellent results (for both test patterns and field tests) thanks in part to the use of this generator as a training tool for the neural networks. The neural system is integrated on a software tool for a PC with hardware connected for signal acquisition. The tool makes it possible to monitor the acquired signal and the disturbances detected by the system.

The Endesa Company is the main power utility in Spain. One of the main concerns of power distribution companies is energy loss, both technical and non-technical. A non-technical loss (NTL) in power utilities is defined as any consumed energy or service that is not billed by some type of anomaly. The NTL reduction in Endesa is based on the detection and inspection of the customers that have null consumption during a certain period. The problem with this methodology is the low rate of success of these inspections. This paper presents a framework and methodology, developed as two coordinated modules, that improves this type of inspection. The first module is based on a customer filtering based on text mining and a complementary artificial neural network. The second module, developed from a data mining process, contains a Classification & Regression tree and a Self-Organizing Map neural network. With these modules, the success of the inspections is multiplied by 3. The proposed framework was developed as part of a collaboration project with Endesa.