Wavelet feature vectors for neural network based harmonics load recognition

Power quality embraces problems caused by harmonics, over or under-voltages, or supply discontinuities. Harmonics are caused by all sorts of non-linear loads. In order to fully understand the problems, an effective means of identifying sources of power harmonics is important. In this paper, the authors make use of new developments in wavelets so that each type of current waveform polluted with power harmonics can well be represented by a normalised energy vector consisting of five elements. Furthermore, a mixture of harmonics load can also be represented by a corresponding vector. This paper describes the mathematics and algorithms for arriving at the vectors, forming a strong foundation for real-time harmonics signature recognition, in particular, useful to the re-structuring of the whole electric power industry. The system performs exceptionally well with the aid of an artificial neural network.