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.
Online monitoring of rotary machines, like induction motors, can effectively diagnosis electrical and mechanical faults. The origin of most recurrent faults in rotary machines is in the components: bearings, stator, rotor and others. Different methodologies based on current and vibration monitoring have been proposed using FFT and wavelet analysis for preventive monitoring of induction motors resulting in countless techniques for diagnosing specific faults, arising the necessity for a generalized technique that allows multiple fault detection. This work presents a novel methodology and its FPGA implementation for multiple fault online detection analyzing the current and vibration signals of an induction motor combining FFT and wavelet processing during its startup transient and steady state, precisely performing the identification of different faults like misalignment, unbalance, outerrace bearing defects and broken bars. The results obtained using the proposed methodology show its effectiveness providing a precise diagnosis of the induction motor condition.
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