Multiple Phasor Measurement Units Research Articles

Development and implementation of a MATLAB-based phasor data concentrator for synchrophasor applications

This work presents the development of MatPDC, an IEEE Std. C37.118.2-2011 compliant Phasor Data Concentrator (PDC) implemented in the Matlab environment. MatPDC enables the integration of wide-area monitoring, protection and control system synchrophasor data into the Matlab platform, offering real-time access to synchronized data streams from multiple Phasor Measurement Units (PMUs). The key features of MatPDC include real-time data access, data integrity verification, data aggregation and alignment, compliant data communication, and latency calculation. These features facilitate efficient processing and analysis of synchronized data, reducing complexity and improving latency between data sources and applications. The development of MatPDC addresses the need for accessing and analyzing real-time synchrophasor data within the Matlab environment, providing researchers and engineers with a powerful tool for power system analysis and experimentation. By developing the PDC and synchrophasor applications on the same platform, MatPDC reduces complexity and latency between data sources and applications. It offers real-time access to synchronized data streams, ensuring data integrity through verification mechanisms and aggregating the data into a unified dataset based on time tags. The implementation of MatPDC opens up opportunities for researchers to work with real-time synchrophasor data. It facilitates the development of advanced algorithms, real-time simulations, and the verification of control strategies. The integration and evaluation of MatPDC demonstrate its effectiveness and potential in power system analysis, providing researchers and engineers with a valuable tool for their research and development activities.

Power system event detection and localization—A new approach

Time-synchronized estimates acquired using the Wide Area Measurement System (WAMS) have substantially helped enhance the health of the modern power grid. WAMS data when used in conjunction with appropriate tools can aid in the timely detection of power system events. With the continuous expansion of the WAMS network, identification of events and narrowing down on their location is emerging as a serious challenge for power system operators. Therefore, in this paper an event detection and localization tool is developed which analyzes data obtained from multiple Phasor Measurement Units (PMUs) installed throughout the WAMS network in order to identify the occurrence of an event. The effectiveness of this tool is demonstrated using practical signals from the ISO-NE power system and simulation based signals obtained from a 4-machine 10-bus system. Occurrence of an event is flagged by comparing the wavelet energy and standard deviation values of a PMU signal against a threshold. An event localization algorithm based on the number of PMUs involved in the event detection stage is proposed. Based on the presented algorithm, disturbances are classified as local or wide-spread events. Finally, a method to identify a loss-of-synchronism condition using phase angle difference (PAD) signals obtained across transmission lines is also proposed.

Transmission line parameters estimation in the presence of realistic PMU measurement error models

Proposals have been presented in literature to estimate line parameters and monitor their changes. Synchrophasor measurements from phasor measurement units (PMUs) have appeared as a possible breakthrough for accurate estimation. However, few methods consider a realistic measurement chain including PMUs and instrument transformers and their systematic and random error contributions. This paper proposes an improved method to simultaneously estimate line parameters and systematic measurement errors on multiple network lines. The algorithm is designed to deal with realistic PMU measurement errors and, in particular, with phase-angle errors caused by common time-base errors on multiple PMU channels. The impact of PMU measurement errors is investigated to achieve a comprehensive view of the performance under realistic conditions. The results obtained on an IEEE test network prove the advantages of the proposed method with respect to other recent methods and its robustness in the presence of mismatches in the error model.

Fault Detection Utilizing Convolution Neural Network on Timeseries Synchrophasor Data From Phasor Measurement Units

An end-to-end supervised learning method is proposed for fault detection in the electric grid using Big Data from multiple Phasor Measurement Units (PMUs). The approach consists of preprocessing steps aimed at reducing data noise and dimensionality, followed by utilization of six classification models considered for detecting faults. Three of the models were variants of Convolutional Neural Network (CNN) architectures that consider a single type of measurement (voltage, current or frequency) at all PMUs or all types together also at all PMUs. CNN based models were compared to traditional methods of Logistic Regression (LR), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Evaluation was conducted on two-year data measured by PMUs at 37 locations in a large electric grid. The response variable for classification were extracted from the grid-wide outage event log. Experiments show that CNN-based models outperformed traditional methods on one year out-of-sample outage detection over the entire grid.

Power Systems Dynamic State Estimation With the Two-Step Fault Tolerant Extended Kalman Filtering

Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss of communication links, external disturbances, extraneous noise biases, etc. In order to develop a more resilient and reliable state estimation technique, this manuscript presents a novel two-step fault tolerant extended Kalman filter framework for discrete-time stochastic power systems, under bad data, PMU failures, external disturbances, extraneous noise, and bounded observer-gain perturbation conditions. The failure mechanisms of multiple phasor measurement units are assumed to be independent of each other with various bad data or malfunction rates. The benchmark IEEE standard test systems are utilized as a demonstrative example to carry out computer simulation studies and to examine different estimation algorithms. Experimental results demonstrates that the proposed second-order fault tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter, and the unscented Kalman filter. The proposed two-step fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing dynamic power system state estimation techniques.

A Real-Time Approach for Detection and Correction of False Data in PMU Measurements

Recent studies imply that despite having a high level of security features and accurate algorithms, Phasor Measurement Units (PMUs) are vulnerable to False Data Injection (FDI) in the measurements. These FDIs could be: 1) an intentional FDI Attack (FDIA) or 2) unintentional errors resulting from limited precision, communication medium, noise, etc. Traditional false data detection techniques that use SCADA measurements in conjunction with the PMU measurements are insufficient to detect malicious data in PMU measurements. Thus, a new approach is proposed in this paper, which can detect the presence of false data in the PMU measurements and correct them, without relying on the use of the SCADA measurements. An affine relationship between the sparsely placed PMUs, based on the AC model of the network, is statistically extracted by offline training of a support vector regressor. This relationship is further used for the real-time detection and correction of false data in the PMU measurements. The proposed approach is tested on the IEEE 118 bus system and the Northern Regional Power Grid (NRPG) of India for detecting intentional as well as unintentional false data in single and multiple PMU measurements. Numerical results, thus obtained after testing synthetic and real-world data, are also compared with existing approaches, which justify and prove the efficacy of the proposed method.

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Further, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method.

Spatio-Temporal Characterization of Synchrophasor Data Against Spoofing Attacks in Smart Grids

“Source ID Mix” has emerged as a new type of highly deceiving attack which can alter the source information of synchrophasor data measured by multiple phasor measurement units, thereby paralyzing many wide-area measurement systems applications. To address such sophisticated cyber attacks, we have exploited the spatio-temporal characteristics of synchrophasor data for authenticating measurements' source information. Specifically, the source authentication is performed when the measurements are subjected to three types of spoofing attacks. Some practical difficulties in applying the proposed method on real-time authentication caused by insufficient measurement data have also been solved. Experimental results with real synchrophasor measurements have validated the effectiveness of the proposed method in detecting such complicated data spoofing and improving power systems' cyber security.

A Tool to Characterize Delays and Packet Losses in Power Systems With Synchrophasor Data

This paper describes the implementation of a tool to estimate latencies and data dropouts in communication networks transferring synchrophasor data defined by the C37.118 standard. The tool assigns a time tag to synchrophasor packets at the time it receives them according to a global positioning system clock and with this information is able to determine the time those packets took to reach the tool. The tool is able to connect simultaneously to multiple phasor measurement units (PMUs) sending packets at different reporting rates with different transport protocols such as user datagram protocol or transmission control protocol. The tool is capable of redistributing every packet it receives to a different device while recording the exact time this information is re-sent into the network. The results of measuring delays from a PMU using this tool are presented and compared with those of a conventional network analyzer. The results show that the tool presented in this paper measures delays more accurately and precisely than the conventional network analyzer.

Contingency constrained phasor measurement units placement with n − k redundancy criterion: a robust optimisation approach

This study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n − k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non-linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi-level programming model is solved by its evolution to an equivalent single-level mixed-integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7-bus test network, 118- and 2383-bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases.

Multi-dimensional ringdown modal analysis by filtering

Based on collected data from multiple phasor measurement units (PMUs), this paper investigates and establishes the advantages of Taylor, Kalman, and Fourier filters for extracting relevant modal information of power system responses after a large disturbance takes place. The main strengths of the filters-based approach are speed estimation, ability to track different damping levels and receive multiple measurement channels for their processing by a multi-dimensional ringdown analysis. The proposals are tested under noisy conditions in simulated data and actual measurements, for showcasing the capability of identifying multiple electromechanical modes and for providing global information about modal properties of the power system. Proper results exhibit the performance of the proposition, enabling to establish it into a wide-area measurements system.

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.

A backend framework for the efficient management of power system measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30× speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.

Feed-forward State Estimation of Multi-bus Distribution Network Using Sample Shifting Technique

This article deals with an alternative technique for state estimation (i.e., voltage magnitude and angle δ) of different buses, along with the other power system parameters in a multi-bus system. The multi-bus system is modeled as a sequential segmentation of a 2-bus model in such a way that the remote bus parameters are estimated only from the samples of voltage and current signals with the help of a phasor measurement unit placed at the local bus end. The reactive power flow is computed from the samples using a state-of-the-art sample shifting technique, whereas the active power is estimated from the usual product of the samples and average method. The other remote bus parameters are then evaluated following the system model equations in a feed-forward manner. The estimated parameters are verified with the standard load flow techniques and compared with the results obtained from real experiments. The verification of results proves its utilization for on-line load flow analysis and the reduction of the number of phasor measurement units (PMUs) to one. This development thus eliminates the need of multiple PMUs at the different buses and the associated communication systems from those remote ends.

A Cross-Layer Defense Mechanism Against GPS Spoofing Attacks on PMUs in Smart Grids

Recent investigations have revealed the susceptibility of phasor measurement units (PMUs) to the time synchronization attack by spoofing its global positioning system (GPS). This paper proposes a cross-layer detection mechanism to fight against simultaneous attacks toward multiple PMUs. In the physical layer, we propose a GPS carrier-to-noise ratio (C/No) based spoofing detection technique. We apply the patch-monopole hybrid antenna to two GPS receivers and compute the difference between the standard deviation of each receiver’s C/No. The priori probability of spoofing is calculated from the distributions of the difference. A counter is embedded in the physical layer to identify the most suspicious PMU. In the upper layer, the spoofing attack is considered similarly to the bad data injection toward the power system. A trustworthiness evaluation, which is based on both the physical layer information and power grid measurements, is applied to identify the PMU being attacked. An experiment has been carried to validate the proposed algorithm.

Design phasor data concentrator as adaptive delay buffer for wide-area damping control

Phasor Data Concentrators (PDCs) are employed to aggregate data frames from multiple Phasor Measurement Units (PMUs) with the same time-stamp into data packets. This function is an essential step in the utilization of PMU data in Wide-Area Monitoring and Control (WAMC) system. This is facilitated by assigning a fixed and heuristically decided wait time to the PDC buffer. The processed PMU data packets are sent to WAMC applications or similarly PDCs at higher hierarchies in the WAMC system when either a PDC buffer is full or its wait time has passed. In this paper, two methods to configure adaptive PDC wait time based on recent PMU traffic delay patterns are proposed. The purpose is to reduce the frequency of performing delay compensations in wide-area damping control system. With the adaptive PDC delay buffer in place, the wide-area damping controller only switches its gain once every five seconds, given the studied PMU traffic delay scenarios, instead of the current practice which requires the control gain to be adapted on a per PMU data frame basis, e.g., 50 or 60 times every second. The proposed methods offer a perspective to efficiently utilize the supporting Information and Communication Technology infrastructure with the purpose to simplify the design and implementation of wide-area damping control system.

Spatio-temporal statistical identification methodology applied to wide-area monitoring schemes in power systems

Detection and characterization of the dynamic phenomena that arise when the power system is subjected to a perturbation become a significant problem. Therefore, a great deal of attention has been paid to identify oscillatory activity in interconnected power systems through the use of wide-area monitoring schemes. This paper presents a method for detection of propagation features from wide-area system measurements through its traveling and standing components, exploring the relationship between complex modes and the wave motion. The method consists in a biorthogonal decomposition considered from a statistical perspective which has the potential to be applied for wide-area monitoring and analysis using real-time synchronized measurements recorded from power systems. Although the technique is general, data obtained from global positioning system (GPS)-based multiple phasor measurement units (PMUs) from a real event in power systems are used to examine the potential usefulness of the proposed methodology. Furthermore, the decomposition technique based on optimal persistent patterns (OPPs) for time-varying fields is used to validate the applicability of the method.

A Novel Approach for the Optimal PMU Placement using Binary Integer Programming Technique

Phasor measurement units (PMUs) are considered as a promising tool for future monitoring, protection and control of power systems. In this paper, a novel approach is proposed in order to determine the optimal number and locations of PMUs to make the power system observable. The PMU placement problem is formulated as a binary integer linear programming, in which the binary decision variables (0, 1) determine where to install a PMU, preserving the system set observability, such as full or partial. The proposed approach integrates the impacts of both existing conventionalpower injection/flow measurements (if any) and the possible failure of single or multiple PMU / communication line into the decision strategy of the optimal PMU allocation. The network topology and hence network connectivity matrix remains unaltered for the inclusion of conventional measurements. Program is developed usingMatlabsoftware and is tested on sample seven-bus system and IEEE 14 bus system.

Modeling and Simulation of Wide-Area Communication for Centralized PMU-Based Applications

Phasor-based wide-area monitoring and control (WAMC) systems are becoming a reality with increased research, development, and deployments. Many potential control applications based on these systems are being proposed and researched. These applications are either local applications using data from one or a few phasor measurement units (PMUs) or centralized utilizing data from several PMUs. An aspect of these systems, which is less well researched, is the WAMC system's dependence on high-performance communication systems. This paper presents the results of research performed in order to determine the requirements of transmission system operators on the performance of WAMC systems in general as well as the characteristics of communication delays incurred in centralized systems that utilize multiple PMUs distributed over a large geographic area. This paper presents a summary of requirements from transmission system operators with regards to a specific set of applications and simulations of communication networks with a special focus on centralized applications. The results of the simulations indicate that the configuration of central nodes in centralized WAMC systems needs to be optimized based on the intended WAMC application.

Observability enhancement by optimal PMU placement considering random power system outages

This paper enhances the observability of power networks by taking into consideration random component outages. The architecture of wide-area measurement system (WAMS) is analyzed in order to identify components that would affect the network observability. An iterative framework is devised to calculate a bus index in power networks equipped with phasor measurement units (PMUs) and conventional measurements. The average of bus indices represents a system index which provides an overall insight on the power network observability. The system index is utilized as a criterion to distinguish among multiple optimal PMU placements. Conventional bus injection and line flow measurements and the effect of zero-injection buses are considered in the proposed model. The numerical analyses are carried out for the proposed model and the results are discussed in detail.