Frequency invariant transformation of periodic signals (FIT-PS) for signal representation in NILM

It is the aim of non-intrusive load monitoring (NILM) to determine the individual energy consumption of multiple devices on the basis of the total energy consumption. The energy consumption of a single device is measured at a central point without the need of individual measuring instruments on the devices themselves. We present a new method for signal separation, decomposition of the signal into individual states and feature extraction. Frequency invariant transformation of periodic signals (FIT-PS) is based on a signal diagram similar to the concept of trajectories [1]-[3] utilizing the periodicity of voltage and current, and their correlation. A major advantage of this approach is the dimensional expansion of the signal. Thereby smallest changes cif the current can be identified easily and the associated change of state can be detected. This approach breaks down the current signal into its individual periods using the voltage as a reference signal for the determination of trigger points. Thereby, the phase information between current and voltage is maintained and is inherently part of the new signal representation. The efficiency of the new signal representation and signal separation is demonstrated by the example of an event detection algorithm. For testing the BLUED dataset [4] was used reaching a sensitivity in event detection of 99.31%.