Milan Biswal

Characterizing and quantifying noise in PMU data

Data recorded by Phasor Measurement Units (PMUs) contains noise. This paper characterizes and quantifies this noise for voltage, current and frequency data recorded at three different voltage levels. The probability distribution of the measurement noise and its typical power are identified. The PMU noise quantification can help in generation of experimental PMU data in close conformity with field PMU data, bad data removal, missing data prediction, and effective design of statistical filters for noise rejection.

Supervisory Protection and Automated Event Diagnosis Using PMU Data

This paper presents a new framework for supervisory protection and situational awareness to enhance grid operations and protection using modern wide-area monitoring systems. In contrast to earlier approaches dealing with the combined processing of data from multiple phasor measurement units (PMUs), the proposed approach analyzes only the PMU data with the strongest or the most prominent disturbance signature. The specific contributions of this paper are: (a) new criteria for identification of PMU with the strongest signature, (b) simplified approach for quick detection of faults, (c) early classification of eight other disturbances suitable for near real-time response, (d) time-frequency transform-based feature extraction techniques for speedy and reliable classifiers, and (e) a promising approach to locate disturbances within narrow geographical constraints. The contributions are verified with exhaustive simulation data from the Western Electricity Coordination Council system model and limited real PMU data.

Efficient Compression of PMU Data in WAMS

Widespread placement and high data sampling rate of current generation of phasor measurement units (PMUs) in wide area monitoring systems result in huge amount of data to be analyzed and stored, making efficient storage of such data a priority. This paper presents a generalized compression technique that utilizes the inherent correlation within PMU data by exploiting both spatial and temporal redundancies. A two stage compression algorithm is proposed using principal component analysis in the first stage and discrete cosine transform in the second. Since compression parameters need to be adjusted to compress critical disturbance information with high fidelity, an automated but simple statistical change detection technique is proposed to identify disturbance data. Extensive verifications are performed using field data, as well as simulated data to establish generality and superior performance of the method.

Signal features for classification of power system disturbances using PMU data

Event identification is one among numerous applications being researched for PMU data. This application is intended to increase visualization of power system events, as well as for protection and control, including verification of relay operation to detect any misoperations. This paper uses data from field as well as from simulation to test a large variety of features using two well-known classifiers on a common dataset to find the most suitable features for disturbance data recorded by PMUs. The approach also uses data from only one PMU instead of data from multiple PMUs used by researchers so far, thus significantly reducing the data to be processed. It is shown that simple observation-based features capturing shape and statistics of disturbance waveforms work better than some well-known features derived from domain transformations. Classification accuracy and speed achieved with these features are shown to be satisfactory and suitable for the intended applications.

iCenS: An information-centric smart grid network architecture

Smart grid technologies will equip the electrical grid of the future with two-way information flow between grid entities and consumers. This bidirectional information flow facilitates improved grid monitoring, control automation, energy efficiency, and sustainability. Several smart grid networking architectures have been proposed recently. However, the majority of these are restricted to subdomains such as home area networks or substation networks, or are not scalable. There is a need for an overarching and inclusive communication architecture which accounts for all smart grid communication scenarios. In this paper, we propose iCenS, a holistic smart grid networking architecture. We identify various communication scenarios, elaborate on the suitability of iCenS, and discuss how it can be used to solve smart grid networking challenges. We also present simulation results demonstrating the scalability of our design and its effectiveness in serving various types of smart grid traffic.

Efferent-induced alterations in distortion and reflection otoacoustic emissions in children

The medial olivocochlear efferent fibers control outer hair cell responses and inhibit the cochlear-amplifier gain. Measuring efferent function is both theoretically and clinically relevant. In humans, medial efferent inhibition can be assayed via otoacoustic emissions (OAEs). OAEs arise by two fundamentally different mechanisms-nonlinear distortion and coherent reflection. Distortion and reflection emissions are typically applied in isolation for studying the efferent inhibition. Such an approach inadvertently assumes that efferent-induced shifts in distortion and reflection emissions provide redundant information. In this study, efferent-induced shifts in distortion and reflection emissions (click-evoked and stimulus frequency OAEs) were measured in the same subjects-5- to 10-yr-old children. Consistent with the OAE generation theory, efferent-induced shifts in distortion and reflection emissions did not correlate, whereas the two reflection emission shifts correlated. This suggests that using either OAE types provides fragmented information on efferent inhibition and highlights the need to use both distortion and reflection emissions for describing efferent effects.

Comparison of time-frequency methods for analyzing stimulus frequency otoacoustic emissions

Stimulus frequency otoacoustic emissions (SFOAEs) can have multiple time varying components, including multiple internal reflections. It is, therefore, necessary to study SFOAEs using techniques that can represent their time-frequency behavior. Although various time-frequency schemes can be applied to identify and filter SFOAE components, their accuracy for SFOAE analysis has not been investigated. The relative performance of these methods is important for accurate characterization of SFOAEs that may, in turn, enhance the understanding of SFOAE generation. This study using in silico experiments examined the performance of three linear (short-time Fourier transform, continuous wavelet transform, Stockwell transform) and two nonlinear (empirical mode decomposition and synchrosqueezed wavelet transform) time-frequency approaches for SFOAE analysis. Their performances in terms of phase-gradient delay estimation, frequency specificity, and spectral component extraction are compared, and the relative merits and limitations of each method are discussed. Overall, this paper provides a comparative analysis of various time-frequency methods useful for otoacoustic emission applications.

Detection of fault using local measurements at inverter interfaced distributed energy resources

High proliferation of Inverter Interfaced Distributed Energy Resources (IIDERs) into the electric distribution grid results in failure of the traditional overcurrent based fault detection at the point of interconnection (POI) of IIDERs. This paper focuses on other strategies of detection, namely, 1) using the transient content at the inverter output, 2) using zero sequence current magnitude at the high side of interconnecting transformer, and 3) using voltage drop at the POI. These strategies are tested by simulating a 100 kW three-phase Voltage-sourced Inverter connected to the IEEE 13-node distribution feeder in PSCAD environment. Performance of the selected strategies to various faults in the system is documented and evaluated.