Benefits of harmonic analysis method for smart metering

With wider deployments of smart metering, smart grids and distributed generation, power quality monitoring has become quite significant. Harmonic analysis of current and voltage signals allows several key power quality indicators. For example, an energy meter with harmonic analysis functions can characterise the state of the load or supply, enabling predictive maintenance or system optimisation.

The presence of harmonics is an ever increasing concern for providers and consumers of energy alike. Excessive harmonic currents can lead to overheating of power transformers and reactive power compensators and neutral conductors. False tripping of protective relays can also be caused by harmonic currents.

Harmonic voltage and currents can also interfere with sensitive equipment operating in proximity to large harmonic generators. Traditionally, developers would use a digital signal processor to implement some version of the Fourier algorithm or bandpass filtering to perform harmonic analysis.

This article presents a new approach, titled Adaptive Real Time Monitoring (ARTM), and compares it against other possible methods: FFT algorithms and bandpass filtering. ARTM will be featured in the next generation Analog Devices products for energy applications.

Fourier based methods
In energy metering or power quality monitoring systems, when the harmonic analysis is performed, phase currents and voltages are simultaneously sampled and then processed to compute the following power quality measurements on the fundamental and harmonic components: active, reactive and apparent powers, rms values, power factors, and harmonic distortions. Fast Fourier Transform (FFT) analysis comes immediately to mind. The procedure requires the following steps as in figure 1.

One must determine the period of the fundamental component. This can be a time consuming process that is typically realised by low pass filtering the phase voltages to isolate the fundamental and measuring the time between consecutive zero crossings. Any error in determining the period propagates to the error in amplitude and phase of the harmonics.

The sampling frequency must be modified to obtain 2N samples per period. This implies using analogue to digital converters that allow variable sampling frequencies. Then one must acquire 2N samples corresponding to one or more periods. The last step is to execute the FFT algorithm. Samples taken across multiple periods increase the accuracy of the computations. But this means a heavier burden on the DSP and a slower overall response.