Frequency and fundamental signal measurement algorithms for distributed control and protection applications

Increasing penetration of distributed generation within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise cost, algorithms for the measurement of AC voltage and current waveforms can be implemented on a single microcontroller, which also carries out other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although analogue to digital converters (ADCs) can be oversampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality, which may arise within grid-connected or islanded (e.g. emergency, battlefield or marine) power system scenarios. This study presents a 'Clarke-FLL hybrid' architecture, which combines a three-phase Clarke transformation measurement with a frequency-locked loop (FLL). This hybrid contains suitable algorithms for the measurement of frequency, amplitude and phase within dynamic three-phase AC power systems. The Clarke-FLL hybrid is shown to be robust and accurate, with harmonic content up to and above 28% total harmonic distortion (THD), and with the major algorithms executing at only 500 samples per second. This is achieved by careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal through low-sample-rate Fourier analysis to sub-harmonic ripple removal. Platform-independent algorithms for three-phase nodal power flow analysis are benchmarked on three processors, including the Infineon TC1796 microcontroller, on which only 10% of the 2000 mus frame time is required, leaving the remainder free for other algorithms

Auditory Spectrum-Based Pitched Instrument Onset Detection

In this paper, a method for onset detection of music signals using auditory spectra is proposed. The auditory spectrogram provides a time-frequency representation that employs a sound processing model resembling the human auditory system. Recent work on onset detection employs DFT-based features describing spectral energy and phase differences, as well as pitch-based features. These features are often combined for maximizing detection performance. Here, the spectral flux and phase slope features are derived in the auditory framework and a novel fundamental frequency estimation algorithm based on auditory spectra is introduced. An onset detection algorithm is proposed, which processes and combines the aforementioned features at the decision level. Experiments are conducted on a dataset covering 11 pitched instrument types, consisting of 1829 onsets in total. Results indicate that auditory representations outperform various state-of-the-art approaches, with the onset detection algorithm reaching an F-measure of 82.6%

Multiple-F0 estimation of piano sounds exploiting spectral structure and temporal evolution

This paper proposes a system for multiple fundamental frequency estimation of piano sounds using pitch candidate selection rules which employ spectral structure and temporal evolution. As a time-frequency representation, the Resonator Time-Frequency Image of the input signal is employed, a noise suppression model is used, and a spectral whitening procedure is performed. In addition, a spectral flux-based onset detector is employed in order to select the steady-state region of the produced sound. In the multiple-F0 estimation stage, tuning and inharmonicity parameters are extracted and a pitch salience function is proposed. Pitch presence tests are performed utilizing information from the spectral structure of pitch candidates, aiming to suppress errors occurring at multiples and sub-multiples of the true pitches. A novel feature for the estimation of harmonically related pitches is proposed, based on the common amplitude modulation assumption. Experiments are performed on the MAPS database using 8784 piano samples of classical, jazz, and random chords with polyphony levels between 1 and 6. The proposed system is computationally inexpensive, being able to perform multiple-F0 estimation experiments in realtime. Experimental results indicate that the proposed system outperforms state-of-the-art approaches for the aforementioned task in a statistically significant manner. Index Terms: multiple-F0 estimation, resonator timefrequency image, common amplitude modulatio

Adaptive Vectorial Filter for Grid Synchronization of Power Converters Under Unbalanced and/or Distorted Grid Conditions

This paper presents a new synchronization scheme for detecting multiple positive-/negative-sequence frequency harmonics in three-phase systems for grid-connected power converters. The proposed technique is called MAVF-FLL because it is based on the use of multiple adaptive vectorial filters (AVFs) working together inside a harmonic decoupling network, resting on a frequency-locked loop (FLL) which makes the system frequency adaptive. The method uses the vectorial properties of the three-phase input signal in the αβ reference frame in order to obtain the different harmonic components. The MAVF-FLL is fully designed and analyzed, addressing the tuning procedure in order to obtain the desired and predefined performance. The proposed algorithm is evaluated by both simulation and experimental results, demonstrating its ability to perform as required for detecting different harmonic components under a highly unbalanced and distorted input grid voltage