Coordinator: Paul Wright

Digitisers and transducers can produce sample measurements with excellent accuracy; however, this performance is meaningless unless the sampled data is assembled in a well-formulated manner, not unduly degraded by noise, and correctly processed by validated algorithms.   

At the time the project started, the state-of-the-art for accurate waveform analysis algorithms mainly covered sinusoidal, steady state waveforms and a limited set of smoothly fluctuating harmonic waveforms.  Measurements are generally carried out in the laboratory using synchronous sampling schemes; although some asynchronous schemes do exist, these are only accurate for a limited number of steady state signals and often depend on a significant amount of prior knowledge about the signals under analysis. Noise is often a limiting factor for measurements especially in the field environment; noise reduction techniques are applied widely in industrial settings but had not been applied in AC metrology within the context of an uncertainty framework.  

Algorithms were developed in this project to traceably measure the range of complex, non-stationary waveforms defined by power quality standards. Further, as measurements are required on the electricity grid, a noisy environment where the sampling frequency cannot be readily synchronized due to the frequency variation of the supply, the development of asynchronous sampling techniques and noise reduction algorithms with an associated uncertainty analysis was required. 

Significant contributions to the development of accurate sampling techniques and analysis algorithms were made through this project. 

For more information, see  the project webpage >>