Phasor measurement units (PMUs) are fundamental tools in the applications of modern power systems, where synchronized phasor estimations are needed. The accuracy and dynamic performance requirements for phasor, frequency, and rate of change of frequency (ROCOF) estimations are established in the IEEE Std. C37.118.1-2011 along with the IEEE Std. C37.118.1a-2014, where two PMU performances are suggested: P class filters for applications requiring fast response and M class filters for applications requiring high rejection to aliased signals. In this paper, a methodology to design new phasor estimators that satisfy the P class and M class requirements in PMUs is presented. The proposed methodology is based on finite impulse response filters, brick-wall filters, and complex filter design concepts, where frequency range, time performance, harmonic rejection and out-of-band interference requirements are considered in its design. A comparative analysis using the reference model given by the IEEE Std. C37.118.1 is presented. The results show the effectiveness of the phasor estimators under steady-state and dynamic conditions according to the PMU standard, making them suitable tools for applications in power systems.
Over the past few years, power quality (PQ) monitoring has become of paramount importance for utilities and users since poor PQ generates negative consequences. In monitoring, fast detection and accurate classification of PQ disturbances (PQDs) are desirable features. In this work, a new method to detect and classify PQDs is proposed. The proposal takes advantage of the low computational resources of both a phasor measurement unit (PMU)-based signal processing scheme and the homogeneity approach. To classify the PQDs, if–then–else rules are used. To validate and test the proposal, synthetic and real signals of sags, swells, interruptions, notching, spikes, harmonics, and oscillatory transients are considered. For the generation of real signals, a PQD generator based on a power inverter is used. In the proposed method, the PMU information is directly used to classify sags, swells, and interruptions, whereas the homogeneity index is used to distinguish among the remaining PQDs. Results show that the proposal is an effective and suitable tool for PQ monitoring.
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