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Most of the Power Quality disturbances such as unbalances, sags, swells and harmonic distortion are intimately related to power consumption variations. In this manner, with the aim of identifying electrical problems causing PQ disturbances, a suitable knowledge of these power consumption profiles is required. These profiles can be obtained by either a continuous monitoring or by using some tools capable of representing the behavior of the power demand. This paper presents a comparison between two analytical tools, one is an artificial intelligence approach by means of neural networks, and the other one uses statistical techniques such as time series analysis. These techniques not only can represent power consumption profiles, but also may predict them allowing the customer to make a suitable planning of the electrical facilities.

This paper presents a comparative analysis between the voltage interruption effects (higher and shorter than one minute) in the value of Pst indicator given by the flicker meter instrument. The Colombian regulator has proposed a modification in the flickermeter to disregard the interruption effects, in this paper an analysis related to this topic is presented. In order to simulate the effect of voltage interruptions, a model of the flicker meter has been developed in Matlab according to the IEC 61000-4-15 standard. Random quantities of interruptions with random duration have been simulated in fixed time intervals to study their impact in Pst value.