Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.

Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

This paper presents a new framework of recovering missing synchrophasor measurements (erasures). Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we connect the problem of recovering PMU data erasures with recent advances in low-rank matrix completion methods. Since the existing analysis for matrix completion methods assumes an independent-erasure model that does not capture the correlations in PMU erasures, we propose two models to characterize the temporal and the channel correlations in PMU erasures and provide theoretical guarantees of a matrix completion method in recovering correlated erasures in both models. We also propose an online algorithm that can fill in the missing PMU measurements for real-time applications. Numerical experiments on actual PMU data are conducted to verify the effectiveness of the proposed methods.

Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements

In this paper, we discuss the compression of waveforms obtained from measurements of power system quantities and analyze the reasons why its importance is growing with the advent of smart grid systems. While generation and transmission networks already use a considerable number of automation and measurement devices, a large number of smart monitors and meters are to be deployed in the distribution network to allow broad observability and real-time monitoring. This situation creates new requirements concerning the communication interface, computational intelligence and the ability to process data or signals and also to share information. Therefore, a considerable increase in data exchange and in storage is likely to occur. In this context, one must achieve an efficient use of channel communication bandwidth and a reduced need of storage space for power system data. Here, we review the main compression techniques devised for electric signal waveforms providing an overview of the achievements obtained in the past decades. Additionally, we envision some smart grid scenarios emphasizing open research issues regarding compression of electric signal waveforms. We expect that this paper will contribute to motivate joint research efforts between electrical power system and signal processing communities in the area of signal waveform compression.

The Compression of Electric Signal Waveforms for Smart Grids: State of the Art and Future Trends

Smart meters play vital roles in the aspects of the management and operation of smart grids such as demand response, energy efficiency improvement, and electricity pricing. Massive amounts of data are being collected owing to the popularity of smart meters. Two main issues should be addressed in this context. One is the communication and storage of big data from smart meters at reduced cost. The other is the effective extraction of useful information from this massive dataset. In this paper, the K-SVD sparse representation technique, which includes two phases (dictionary learning and sparse coding), is used to decompose load profiles into linear combinations of several partial usage patterns (PUPs), which allows the smart meter data to be compressed and hidden electricity consumption patterns to be extracted. Then, a linear support vector machine (SVM) based method is used to classify the load profiles into two groups, residential customers and small and medium-sized enterprises (SMEs), based on the extracted patterns. Comprehensive comparisons with the results of k-means clustering, the discrete wavelet transform (DWT), principal component analysis (PCA), and piecewise aggregate approximation (PAA) are conducted on real datasets in Ireland. The results show that our proposed technique outperforms these methods in both compression ratio and classification accuracy.

Sparse and Redundant Representation-Based Smart Meter Data Compression and Pattern Extraction

Despite its wide applications in power grid monitoring, the classic discrete Fourier transform (DFT)-based synchrophasor estimation algorithms suffer from significant errors when the power system operates under off-nominal frequency conditions. This phenomenon is caused by spectral leakage of DFT and becomes even more severe for single-phase synchrophasor estimation. To address this issue, a theory to eliminate the spectral leakage-caused errors is proposed and a Clarke transformation-based DFT synchrophasor estimation algorithm is proposed to implement the theory in this paper. The Clarke transformation constructs a second signal that has exactly 90° phase angle difference from the original single-phase input signal and helps eliminate the estimation errors for a wide frequency range. The proposed algorithm is tested under the conditions required in the phasor measurement unit standard C37.118.1-2011 and C37.118.1a-2014, as well as the harmonic and noise conditions not required in the standard to verify its performance. More importantly, the idea of using Clarke transformation can be used for other DFT-based synchrophasor algorithms in order to achieve higher synchrophasor measurement accuracy under dynamic conditions. An example is presented at last to demonstrate the expandability of the proposed idea.

A Clarke Transformation-Based DFT Phasor and Frequency Algorithm for Wide Frequency Range

This paper presents a novel application of smart meters (SMs) in the high impedance fault (HIF) detection in distribution systems. Detection of HIF on distribution systems poses a significant challenge to the protection engineers. Existing HIF detection methods primarily use measurements available at distribution substations. Lack of availability of measurements near the fault points often makes HIF detection further difficult. The proposed method utilizes the voltage measurements of SMs to address this issue. HIF detection in the presence of nonlinear loads is another challenge. The proposed method detects HIF depending on the amount of even harmonics present in the voltage waveforms measured by SMs. The performance of the proposed method has been evaluated using both PSCAD simulations and experimental set-up. The proposed method has also been implemented on a commercial energy meter to show its viability. The performance of the proposed method has also been evaluated considering HIF, voltage sag-swells, capacitor/load switching, transformer energization, feeder energization, power electronic loads, arc furnace loads, and distributed generators. The proposed method performs satisfactorily in the investigated scenarios. The performance of the proposed method has also been compared with the existing HIF detection methods.

Application of Smart Meters in High Impedance Fault Detection on Distribution Systems

In this paper, areas of power system synchrophasor data communication which can be improved by compressive sampling (CS) theory are identified. CS reduces the network bandwidth requirements of Wide Area Measurement Systems (WAMS). It is shown that CS can reconstruct synchrophasors at higher rates while satisfying the accuracy requirements of IEEE standard C37.118.1-2011. Different steady state and dynamic power system scenarios are considered here using mathematical models of C37.118.1-2011. Synchrophasors of lower reporting rates are exempted from satisfying the accuracy requirements of C37.118.1-2011 during system dynamics. In this work, synchrophasors are accurately reconstructed from above and below Nyquist rates. Missing data often pose challenges to the WAMS applications. It is shown that missing and bad data can be reconstructed satisfactorily using CS. Performance of CS is found to be better than the existing interpolation techniques for WAMS communication.

Application of Compressive Sampling in Synchrophasor Data Communication in WAMS

In this paper, a simple synchrophasor estimation algorithm has been proposed considering power system steady state, dynamic conditions, and protection requirements. The new IEEE Standard C37.118.1-2011 for synchrophasor measurements includes the dynamic performance requirements for the phasor measurement units. It is shown that the proposed method satisfies all the steady state and dynamic requirements of the new IEEE Standard. Presently, synchrophasors are mainly used for wide area monitoring. Increasingly, synchrophasors are being considered for protection applications in smart grids. Protection applications require shorter response times and lower overshoot/undershoot values for estimated phasors during large disturbances. It is shown that the proposed method performs satisfactorily during system faults, which is an important requirement for protection applications. A detailed performance comparison with the existing phasor estimation algorithms has also been presented.

A Simple Synchrophasor Estimation Algorithm Considering IEEE Standard C37.118.1-2011 and Protection Requirements

Smart meters for enhancing protection and monitoring functions in emerging distribution systems

Growing use of digital instruments in smart grids (SG) is resulting in the rapid increase of the measured data volume In future SG, vast amount of data will be generated by smart meters, PMU based WAMS, SCADA and other monitoring devices While research has been done to find suitable compression technologies to store power system disturbance and PMU data; there is lack of research on the storage of SCADA data which is basically steady state operational data of the grid Presently, utilities store some important SCADA data for a limited period and then they either delete them or store them in unreliable manner (CD/DVD etc) The investigations presented here explore the application of Principal Component Analysis based lossy compression technique for archiving the steady state operational data Four important operational data - voltage, line flow, MW and MVAr generation are considered for the study The effectiveness of the proposed method is evaluated considering practical data pertaining to the Southern Regional Grid of India The results illustrate the usefulness of the technique

Sarasij Das

Papers

Application of Smart Meters in High Impedance Fault Detection on Distribution Systems

Application of Compressive Sampling in Synchrophasor Data Communication in WAMS

A Simple Synchrophasor Estimation Algorithm Considering IEEE Standard C37.118.1-2011 and Protection Requirements

Smart meters for enhancing protection and monitoring functions in emerging distribution systems

Principal component analysis based compression scheme for power system steady state operational data