Simulated Phasor Measurement Units Research Articles

An Improved Interpolated DFT-Based Parameter Identification for Sub-/Super-Synchronous Oscillations With Synchrophasors

The sub-synchronous resonance oscillations (SSO) caused by considerable renewable energy generations and power electronic devices seriously affect power system stability. Synchrophasor-based SSO parameter identification can effectively monitor SSOs. Aiming at the fundamental and frequency-coupled sub-/super-synchronous components that appear simultaneously in SSOs, an improved parameter identification algorithm is proposed in this paper based on the original interpolation Discrete Fourier Transform (DFT) and Hann-window. As the complement to the original algorithm, the improvements proposed in this paper additionally focus on the influence between the positive-/negative-frequency parts of the sub-/super-synchronous and fundamental components. Thus, the frequency, amplitude, and phase of the fundamental, sub-synchronous, and super-synchronous components can be accurately obtained by the proposed algorithm. This algorithm is compared to the original interpolated DFT and Prony analysis with both the simulated Phasor Measurement Unit (PMU) data and PMU data recorded during an actual SSO incident to verify its correctness and effectiveness. In the improved algorithm, the additional consideration for the super-synchronous component and the additional identified parameters significantly enhance the practical value of the interpolated DFT algorithm.

Auto-Starting Semisupervised-Learning-Based Identification of Synchrophasor Data Anomalies

In Internet of Things (IoT)-enabled modern power grids, advanced IoT devices, e.g., synchronous phasor measurement units (PMUs), have been widely deployed to closely monitor the grids’ states and dynamics. In practice, however, PMU measurements are often contaminated with anomalous (low-quality) data, e.g., data spikes, unchanged data, data losses/dropouts, and high-level data errors. To ensure the reliability of various PMU data-based applications, it is imperative to efficiently implement PMU data anomaly identification (PDAI). Focusing on performing online PDAI in a cost-effective way, this article develops an intelligent data-driven PDAI approach for practical power grids. Given the defect that the majority of the existing data-driven PDAI efforts necessitate costly domain expertise-based data annotation to start offline learning, the PDAI approach in this article is realized by designing an auto-starting semisupervised learning (SSL) scheme that automatically starts to learn from totally unlabeled PMU data. First, on the basis of the inherent spatial–temporal correlations in regional PMU measurements, sequential PMU data acquired from a specific power grid are characterized in a discriminative manner by profiling their spatial–temporal nearest neighbors (STNNs). With the exploration of the discriminability of STNN profiles, part of the obviously anomalous/normal data is reliably labeled on the basis of a statistical prior knowledge-based rule. Such an STNN-based preprocessing technique for partial data labeling enables the desirable auto-starting functionality of the whole SSL scheme. Then, taking both labeled and unlabeled data as inputs, a recurrent SSL machine for PDAI is efficiently built in two steps, i.e., unsupervised pretraining and supervised fine-tuning. Numerical test results with simulated PMU data from the Nordic test system and actual PMU data from two practical power grids illustrate the excellent PDAI performances of the proposed approach during the online application.

A Real-Time Synchrophasor Data Compression Method Using Singular Value Decomposition

The proliferation of phasor measurement units (PMUs) presents new challenges in archiving and processing large amounts of synchrophasor data which necessitates advanced data compression methods. This paper proposes a singular value decomposition (SVD)-based method for compression of synchrophasor data, including magnitude, phase-angle, and complex phasor. The proposed method includes a dimensionality evaluation and reduction technique and a real-time progressive partitioning algorithm. The proposed dimensionality reduction technique employs the measurement uncertainty of PMUs and introduces a threshold criterion on the signal-to-noise ratio (SNR) of SVD modes. Singular modes with high SNR are retained, and those dominated by measurement error are discarded to achieve a high compression ratio (CR) while preserving the critical information with adequate accuracy. The proposed progressive partitioning separates the data corresponding to normal and disturbance conditions by monitoring the dimensionality variations in real-time. The partitions containing the data of similar dimensionality are separately compressed to further improve the accuracy and CR. The performance of the proposed method is evaluated and benchmarked against state-of-the-art methods using both field and simulated PMU data. The results show that the proposed method provides high CR while accurately preserving the critical information of events and disturbances.

A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering

Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations.

Impacts of swinging door lossy compression of synchrophasor data

As the number of operational Phasor Measurement Units (PMUs) is increasing, compression is becoming crucial for efficiently transferring and storing enormous amount of synchrophasor data continuously measured from a power system. Some utilities in the U.S.have already adopted a nonlinear compression technique, called Swinging Door Algorithm (SDA), assuming compressed data meets technical and regulatory requirements without resulting in any degradation of the measurements. This paper studies impacts of this compression algorithm on field measured and simulated PMU data, and presents results from spectral analyses of raw and decompressed measurements. The results show blind application of the SDA compression alters frequency content in the measurements and introduces false harmonics and aliasing. These can lead to false conclusions being drawn from the measurements about the power system condition.

Optimal PMU Placement using Binary Particle Swarm and Artificial Bee Colony with Channel Limitations and Redundancy.

Phasor Measurement Unit (PMU) being expensive and to be placed optimally, a meta-heuristic approach of Binary particle swarm optimization (BPSO) and Binary artificial bee colony optimization (BABC) is made for the optimal allocation of PMU in a power system. The PMU locations resulted are served by basic system conditions like network configuration, critical generators, and loads. The pattern of locations on including Zero-Injection Buess (ZIB) is also discussed. The redundancy in case of PMU loss is coined so as to obtain a complete observability of the power system. the channel limitations of device is also taken into consideration for better results in real-time systems. Optimal PMU locations for IEEE 30-bus and 14-bus systems with channel limits are compared with all above considerations. The number of PMU locations is reduced as channel limits increases. The simulated PMU locations are decreased with improved observability by Binary Artificial Bee Colony Optimization as compared to Binary Particle Swarm Optimization.

A Novel On-Line Substation Instrument Transformer Health Monitoring System Using Synchrophasor Data

Substation asset health has always been one of the utmost concerns for utilities. The substation secondary assets such as current transformers, potential transformers, and coupling capacitor voltage transformers play vital roles for system situational awareness and grid operations. This paper proposes a novel on-line substation instrument transformer health monitoring system (SITHMS), which merely uses time-synchronized and high-resolution phasor measurement unit (PMU) data. The SITHMS can provide early warning and alert utility personnel to diagnose the equipment malfunction and take proactive actions for asset management to prevent equipment failure. The core component of the SITHMS is a model-based three-phase substation-level linear state estimator, which features a built-in largest normalized residual (LNR) module that can detect anomalies in PMU data by using substation model and configuration, so as to probe if there is malfunction or potential failure of these secondary equipment. In addition, we present the system's implementation architecture and testing environment setup. Case studies with the PSCAD simulated data and field PMU data from actual substations at American Electric Power (AEP) demonstrate the effectiveness of the proposed system.

Ensemble-Based Algorithm for Synchrophasor Data Anomaly Detection

With the advent of phasor measurement units (PMUs), high resolution synchronized phasor measurements enables real time system monitoring and control. PMUs transmit data to local controllers in substations and phasor data concentrators for system wide monitoring and control application in the control center. They provide real-time phasor data for critical power system applications such as remedial action schemes, oscillation detection, and state estimation. The quality of phasor data from PMUs is critical for smart grid applications. Several methods are developed to detect anomalies in time series data, tailored for PMU data analysis. Nevertheless, applying stand alone methods (with fixed parameters) takes great tuning effort, and does not always achieve high accuracy. In this paper, we adopt an unsupervised ensemble learning approach to develop fast, scalable bad data/event detection for PMU data. The ensemble method invokes a set of base detectors to generate anomaly scores of the PMU data, and makes decisions by aggregating the scores from each detector. We develop two algorithms: 1) a learning algorithm that trains the ensemble model and 2) an online algorithm that infers the anomaly scores with the ensemble model over PMU data streams. The proposed method provides flag for data anomalies and triggers further analysis to differentiate between events and bad data. Using both simulated and real-world PMU data, we show that our ensemble model can be efficiently trained, can achieve high accuracy in detecting diversified errors/events, and outperforms its counterparts that use single standalone detection method.

Validation of a PMU‐based fault location identification method for smart distribution network with photovoltaics using real‐time data

A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS). The method can accurately identify the FL in a distribution network in the presence of distributed generation (DG). This method is based on state estimation (SE) algorithm which uses real-time data from simulated phasor measurement units (PMUs), placed in the distribution network. SE needs the fault currents of the generators and voltage measurements of an optimal number of nodes to perform the FL algorithm. The method was validated using the IEEE 37 node test feeder with DGs. PMUs are placed on the real-time model of the system. The real-time model was implemented on a DRTS which streams phasor data over the Internet using C37.118 protocol. OpenPDC is used to collect real-time PMU data coming from the DRTS. Microsoft SQL is used as a database management server to store data coming from OpenPDC. In the last step of the FL process, data stored in OpenPDC is fed into a FL identification algorithm to locate the fault. Both balanced and unbalanced fault types are applied to different nodes and an accurate estimation of the FL (over 90% of the cases) is achieved.

Performance Assessment of Communication Network in WAMS

Wide Area Measurement Systems (WAMS) based on Synchronized Measurement Technology (SMT) is getting considerable attention worldwide from the power system engineers in last few years. The fast and synchronized measurements facilitate the real time Wide Area Monitoring and Control (WAMC) of the power system. The backbone of WAMS is high performance communication infrastructure. Network designer has vast variety of architecture, protocol and communication medium. The medium should be selected in such a manner that it provides guaranteed bandwidth for the utilities. In this paper, we have simulated Phasor Measurement Unit (PMUs) data traffic in OPNET, and then created scenarios with different routing protocols. The performance of WAMS has also been analyzed in presence of varying background traffic and main link failure. Simulation results have been pursued in such a way that under worst situation the ‘end to end delay’ is within an acceptable range.

Line Outage Detection Using Phasor Angle Measurements

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Although phasor measurement units (PMUs) have become increasingly widespread throughout power networks, the buses monitored by PMUs still constitute a very small percentage of the total number of system buses. Our research explores methods to derive useful information from PMU data in spite of this limited coverage. In particular, we have developed an algorithm which uses known system topology information, together with PMU phasor angle measurements, to detect system line outages. In addition to determining the outaged line, the algorithm also provides an estimate of the pre-outage flow on the outaged line. To demonstrate the effectiveness of our approach, the algorithm is demonstrated using simulated and real PMU data from two systems—a 37-bus study case and the TVA control area. </para>