In this paper

a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed

which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method

a scheme for power quality disturbances detection and classification is developed

which involves three steps: denoising

feature extraction and morphological clustering. First

various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation

so the original signal can be denoised. Next

the fundamental wave of the denoised signal is suppressed by Hadamard matrix; thus

disturbances can be extracted. Finally

the Walsh power spectrum of the waveform extracted in the previous step is calculated

and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances

and the Walsh-MM method is less affected by noise and only involves simple calculation

which has a potential to be implemented in hardware and more suitable for real-time application.