Phasor Measurement Research Articles

Physical-and-mathematical Simulation of Impedance-based Fault Location Using Synchronized Phasor Measurements

Physical-and-mathematical Simulation of Impedance-based Fault Location Using Synchronized Phasor Measurements

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.

A data-driven method for SSCI identification using synchrophasor measurements

In recent years, subsynchronous control interaction (SSCI) frequently occurred due to the rapid development of inverter based resources. Online monitoring and identifying SSCI are thus of great importance for the safe operation of power systems. This paper proposes a data-driven method for SSCI identification using synchrophasors generated by phasor measurement units (PMUs). The challenges of this work mainly result from two factors: 1) SSCI may simultaneously involve a sub-synchronous oscillation (Sub-SO) mode and a super-synchronous oscillation (Sup-SO) mode. The spectra of these two modes would be aliased due to the limited reporting rate of PMUs. 2) The reporting filter in PMUs significantly weakens the oscillation signal as the frequency of SSCI is generally far away from the nominal frequency. To tackle these challenges, the influence of Sub-SO and Sup-SO on spectra of synchrophasors is analyzed first. It is revealed that the positive and negative spectra are linearly correlated with Sub-SO and Sup-SO modes. Several features are then extracted via the analysis of the spectra which can reflect the SSCI features and a data-driven identification method is further proposed through a fast reduced kernel extreme learning machine (RKELM) model. Compared with the traditional classification techniques, the RKELM algorithm based on kernel function has stronger generalization performance and higher accuracy while ensuring shorter training time. This significantly improves the accuracy and efficiency of SSCI identification, especially under noise conditions. Both simulations and field tests demonstrate the effectiveness and usefulness of the proposed method.

Design of a Wide-Area Power System Stabilizer resilient to permanent communication failures using bio-inspired algorithms

The operation of power systems requires a set of requirements and studies to ensure the continuous and safe supply of electricity to consumer centers. In small-signal stability studies, low-frequency oscillation modes need to be evaluated as they can compromise the operation of power systems if they are not properly damped. Many structures and control devices have been proposed to mitigate these oscillation modes and the Wide-Area Power System Stabilizers (WAPSSs) have proven to be effective in improving the damping rates of these modes as they use remote system signals from Phasor Measurement Units (PMUs). However, cyber-attacks and communication failures can affect WAPSS communication channels and affect its operation in the power system. This article proposes a method based on a multiobjective optimization model for WAPSS design that is robust to the permanent loss of one WAPSS communication channel. Bio-Inspired Algorithms are applied and compared to solve the optimization problem. A set of case studies were conducted and discussed for the IEEE 68-bus system through modal analysis and time domain simulations. The achieved results showed the need for a fault-robust damping controller and the WAPSS-type controller was able to guarantee good dynamic performance regardless of permanent communication failures. Furthermore, different bio-inspired algorithms provided different results for the closed-loop control system when applied to the proposed optimization model.

Comprehensive fault reporting for three-terminal transmission line using adaptive estimation of line parameters

In this study, the post-fault synchronized phasors acquired from the local phasor measurement units (PMUs) are used to provide comprehensive fault reporting in Three terminal transmission line system (TTTL). The reporting data incorporates the classification, section identification, and localization of the fault based on the post-fault measurements of positive, negative, and zero sequence quantities. For accurate reporting, a proposed algorithm computes transmission line parameters, on hourly basis, to account for fluctuations in characteristics caused by temperature influences arising from either the loading factor or the climatic vagaries. Upon fault occurrence, the algorithm firstly determines fault classification and faulty section. Thereafter, localization of symmetrical faults is achieved via the positive sequence quantities to solve the positive sequence fault loop equation while that of asymmetrical faults is obtained through the negative sequence quantities to solve the negative sequence fault loop equation. The fault localization speed of the reporting proposed algorithm is attributed to the fact that synchronized data are obtained after one and half cycles from the fault onset. PSCAD/EMTDC platform is dedicated for time-domain investigations, while the proposed algorithm is carried out in MATLAB. The proposed algorithm was tested against 1537 fault characteristics at different locations along the TTTL, including varying fault resistances, loading conditions, and network configurations, in addition to assessing its sensitivity to changes in the TTTL parameters. These tests confirmed the algorithm’s reliability to be used with confidence in a variety of scenarios as the only 13 tests were observed with an error of estimation greater than 1%.

A Deep-Learning-Based Solution for Securing the Power Grid Against Load Altering Threats by IoT-Enabled Devices

The growing integration of high-wattage Internet-of-Things (IoT)-enabled electrical appliances at the consumer end has created a new attack surface that an adversary can exploit to disrupt power grid operations. Specifically, dynamic load-altering attacks (D-LAAs), accomplished by an abrupt or strategic manipulation of a large number of consumer appliances in a botnet-type attack, have been recognized as major threats that can potentially destabilize power grid control loops. This paper introduces a novel approach based a multi-output network (two-dimensional convolutional neural networks classifier and reconstruction decoder)-called “2DR-CNN”-to detect and localize D-LAAs with high resolution. To achieve this, we leverage the frequency and phase angle data of the generator buses monitored by phasor measurement units (PMUs) installed in the power grid. To verify the effectiveness of the proposed method, simulations are conducted on IEEE 14-and 39-bus systems. The performance of the 2DR-CNN method is compared against several benchmark machine learning-based approaches. The results confirm that the proposed method outperforms other techniques in detection and localizing D-LAAs with high resolution in a number of practical scenarios, including PMU measurement noises and missing measurements.

Study on a comprehensive indicator and online classification of early warning of low frequency oscillation in power system

With the development of smart grid technology in China, the study of low frequency oscillation early warning has become an important element of power system stability research. Aiming at the problems of single warning indicator with poor accuracy and lack of online warning system in the early warning strategy of low frequency oscillation in power system, a comprehensive low frequency oscillation early warning algorithm based on the evaluation method of kernel matrix hierarchical analysis of fuzzy comprehensive is proposed. Firstly, this method screens out the original data collected by Phasor Measurement Unit (PMU), and extracts the key data volume to form multiple early warning indicators, and generates the trapezoidal fuzzy number judgment matrix. Secondly, by calculating the kernel matrix and consistency test, it is determined whether or not the generated fuzzy matrix is reasonable and calculate the relative weights of the evaluation indicators. The affiliation function is constructed for each early warning indicator. A comprehensive indicator is calculated for multiple indicators, and the reasons of low frequency oscillation in power system are classified by the Random Forest (RF) algorithm. Then, the low-frequency oscillation system is constructed, and the algorithm convergence is proved by the Lyapunov indirect method. The algorithm can automatically determine whether low frequency oscillation occurs or not and classify different types of low frequency oscillations online. Finally, the correctness and validity of this paper’s method is verified by a New England 10-machine 39-node system. Compared with the traditional Analytic Hierarchy Process (AHP) method, the proposed method in this paper greatly improves the accuracy of early warning and meets the need of online warning.

Ways of Using Phasor Measurements in Power System State Estimation

A review of publications on using data from phasor measurement units (PMU) in electric grid state estimation (SE) algorithms is presented. Ways of including phasor measurement data in the SE problem formulation are considered based on the weighted least squares method. The advantages and drawbacks of each of the presented approaches are analyzed. Much attention is paid to the prospects of simultaneously estimating telemetric information and PMU data, and performing SE based solely on the PMU data. The computational difficulties and other obstacles that arise in using phasor measurements in performing SE are analyzed, and various ways of solving existing problems in this field are presented. It is pointed out that the accuracy of data obtained from PMU devices differs essentially from that of data obtained from classic measurement instruments. This circumstance imposes certain specific features on selecting the weighting factors for phasor measurement data in solving the SE problem. The existing approaches to determining the weighting factors for phasor measurement data, in particular, by calculating them based on available statistical data, are reviewed.

A Hierarchical Multi-Stage Coordination of Inverters for Voltage Control in Active Distribution Networks with a µPMU-PDC

This paper proposes a hierarchical multi-stage approach based on a distributed level phasor measurement unit (µPMU) at local controllers and a phasor data concentrator (PDC) at the central control unit to restore system voltage when it exceeds the limits recommended by the IEEE 1547-2018 standard. The proposed algorithm does not need an accurate system model or employ optimization solutions. Therefore, it has less implementation complexity and would be popular among distribution network service providers (DNSPs) and distribution network operators (DNOs) as it does not suffer from cost and computational complexity limitations. A PMU-PDC-based communication platform has been developed as a more efficient alternative to the supervisory control and data acquisition (SCADA) system, and provides superior characteristics, including a higher sample rate, higher data resolutions, and faster communication. The proposed coordinated algorithm aims to postpone power generation curtailment in distributed energy resources (DERs) with overvoltage problems (local DERs) by incorporating all the DERs that are not subjected to voltage violation (remote DERs) by absorbing their maximum reactive power capacity. If the system voltage has not recovered after absorbing all of the reactive power capacity of all the DERs, a reduced active power curtailment proposed by the algorithm is then applied to the system to control the voltage. The proposed strategy has been simulated in MATLAB and applied to IEEE 13-bus and IEEE 33-bus radial distribution benchmark systems to validate the performance of the system, in terms of its ability to coordinate voltage control and the accuracy of the PMU-PDC-based communication interface.

Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization

Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.

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.

Decoupling of voltage magnitude and phase in genetic algorithms for smart transmission network state estimation

A large amount of renewable energy resources are integrated into electricity transmission networks. For efficient operation, transmission networks should be smart, in which it is necessary to have the function of estimating the state of the power system. This function determines bus voltage magnitude and phase, which are used for monitoring, operating, and controlling transmission networks. Transmission network state estimation is developed to estimate bus voltage magnitude and phase by many methods, which include genetic algorithms. Published articles have not mentioned in detail how to apply genetic algorithms to estimate the state of large transmission networks, and the application is limited to small networks. Therefore, this work seeks to estimate the state of large transmission networks by genetic algorithms. Strong decoupling was observed between active power and the voltage phase, as well as between reactive power and voltage magnitude, while a weak coupling, between active power and voltage magnitude, as well as between reactive power and voltage phase; voltage magnitude has a value of around 1.0 p.u, and the voltage phase has a value of around 0.0. This paper proposes a novel approach to genetic algorithms for estimating the state of smart transmission networks. In this novel approach, voltage magnitude and phase are separately estimated at each iteration of genetic algorithms. The approach is validated on IEEE 14-, 30-, and 118-bus networks. Results show that our approach can use phasor measurement unit data to estimate the state of large smart transmission networks by genetic algorithms.

Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach

In an attempt to provide reliable power distribution, smart grids integrate monitoring, communication, and control technologies for better energy consumption and management. As a result of such cyberphysical links, smart grids become vulnerable to cyberattacks, highlighting the significance of detecting and monitoring such attacks to uphold their security and dependability. Accordingly, the use of phasor measurement units (PMUs) enables real-time monitoring and control, providing informed-decisions data and making it possible to sense abnormal behavior indicative of cyberattacks. Similar to the ways it dominates other fields, deep learning has brought a lot of interest to the realm of cybersecurity. A common formulation for this issue is learning under data complexity, unavailability, and drift connected to increasing cardinality, imbalance brought on by data scarcity, and fast change in data characteristics, respectively. To address these challenges, this paper suggests a deep learning monitoring method based on robust feature engineering, using PMU data with greater accuracy, even within the presence of cyberattacks. The model is initially investigated using condition monitoring data to identify various disturbances in smart grids free from adversarial attacks. Then, a minimally disruptive experiment using adversarial attack injection with various reality-imitating techniques is conducted, inadvertently damaging the original data and using it to retrain the deep network, boosting its resistance to manipulations. Compared to previous studies, the proposed method demonstrated promising results and better accuracy, making it a potential option for smart grid condition monitoring. The full set of experimental scenarios performed in this study is available online.

A non-PMU-based WAN protection scheme for swing detection and stability enhancement in power systems

This paper presents a scheme for power swing detection and stability assessment for multi-machine power systems in addition to a brief survey on that topic. The scheme adopts smart grid perception. It is an asynchronous scheme that uses the rate of change of the angle of the generator bus voltage instead of synchronously estimating the absolute rotor angle value for stability assessment. Therefore, it does not require phasor measurement units (PMUs). Instead, it uses commercially available intelligent electronic devices (IEDs). It introduces a new application of the multicast Generic Object Oriented Substation Event (GOOSE) protocol or its routable version (R-GOOSE) to reflect the disturbance in the power grid on the transmission pattern of data packets over the grid data network. In this scheme, the angle change information is transmitted in the form of GOOSE packets simultaneously from the IEDs that readily measure the angles of the affected generators. On the other hand, the detecting device identifies the stability status of each affected generator. The proposed scheme is validated by simulating disturbances on the IEEE 39-bus system. The results show that the scheme can discriminate between stable and unstable swings. In addition, the loss of synchronism of the unstable generator is predicted ahead of its occurrence by enough time to take an appropriate action.

A Multiobjective Intelligent Decision-Making Method for Multistage Placement of PMU in Power Grid Enterprises

The wide area measurement system (WAMS) based on synchronous phasor measurement technology plays an increasingly important role in dynamic monitoring and wide area protection of modern power systems. If the phasor measurement unit (PMU) is placed on all buses of the power system, the voltage and branch current of all buses can be directly observed. However, due to the high placement cost of PMU and its ability to measure the voltage phasor of the installed bus and the current of the associated branch, it is unrealistic and unnecessary to install PMU on all buses of the system. This paper discusses the incomplete observability under single PMU loss (N-1) contingencies and its effect on PMUs placement. An improved two-archive algorithm is proposed to solve the five-objective placement optimization model. In addition, a fuzzy decision-making method combining subjective and objective is proposed to help power grid enterprises select the most appropriate solution. The proposed method is tested on several IEEE bus systems and Polish 2383-bus system, and the test results verify its effectiveness.

IEC 61850-9-2 based module for state estimation in co-simulated power grids

<p><span lang="EN-US">This paper presents a research context on the virtualization of phasor measurement units (PMUs) and real-time power grids simulation with state estimation. In this research, real-time simulation is introduced to use powerful features for validating state estimation solutions with PMUs. Virtual and online measurement equipment are reviewed in this manuscript to develop an innovative integration of the OpenPMU incorporated with a real-time simulation power grid and additional virtualized PMUs. The implementation of the platform has useful features within the infrastructure that allows the user to reproduce a detailed modeled power grid with simulation software. The use of real-time simulation tools brings several possibilities for improving testing and prototype assessment with higher precision in different applications. In this case, 2 tests power systems are evaluated by realistic integration of IEC61850-9-2 data utilization to observe the performance of a customized state estimation approach. The study implements a versatile methodology for commissioning OpenPMU devices, interacting simultaneously with additional virtual PMUs within the same simulation through sampled values (SV) to validate the measurement frames and assess the estimation with the generated data. Finally, the proposed work identifies the potential of virtualizing PMUs and the features of the OpenPMU applied to state estimation in conjunction with real-time simulation data.</span></p>

Distributed denial-of-service attack detection for smart grid wide area measurement system: A hybrid machine learning technique

Smart grid networks face several cyber-attacks, where distributed denial-of-service (DDoS) attacks distract the grid network. The synchrophasor technique protects the wide-area measurement system (WAMS) from the complex problem and addresses different issues in a grid. The DDoS attack detection strategy is complicated due to attack complexities, vendor specifications, and communication standard protocols. Attacker target phasor measurement unit (PMU) data on the phasor data concentrator (PDC) database in WAMS. However, during the cyber-attack, the framework ensures the end application uses the normal PDC datastream. The proposed attack detection technique efficiently verifies PMU-generated data in WAMS. However, numerous machine learning algorithms are used to detect DDoS attacks, but the best detection model is still given open choices. The motivation of this study: (a) which machine learning algorithm will be suitable for DDoS attack detection and (b) what would be the accuracy of training algorithms. This study presents a machine learning-based hybrid technique that achieves 83.23% accuracy. Python compiler is used to execute the proposed model, and the result shows that the proposed detection approach efficiently improves the DDoS attack detection accuracy.

An Agent-Based Distributed State Estimation Approach for Interconnected Power Systems

Phasor measurement units (PMUs) are becoming more popular in power systems and most of the distributed state estimation methods are based on the Gaussian measurement noise assumption. However, the PMU measurement noise has been demonstrated to be non-Gaussian. This article presents a distributed multiarea state estimation approach based on the quadratic and exponential functions for power systems to deal with the non-Gaussian measurement noise efficiently. The matrix splitting technique is utilized to implement a robust estimator in a fully distributed scheme. Based on the local measurements and the limited information exchanged between neighboring areas, each local area only estimates its local states and the computation time needed is further reduced. The effective and robust performance of the proposed distributed multiarea state estimation approach is demonstrated using the IEEE 30-, 118-, and 300-bus systems.

Deep-Learning Based Power System Events Detection Technology Using Spatio-Temporal and Frequency Information

Power system events such as load disconnection (LD), generator trip (GT) and short circuit fault, affect the stability of power grids. It is significant for grid operators to acquire the classifications and locations of events quickly and accurately, which can help prevent blackouts and economic losses. Utilizing phasor measurement unit (PMU) data, this paper proposes a two-layer algorithm that monitors the power system state and identifies types and locations of events. In the first layer, with Gramian Angular Field (GAF) and Short-Time Fourier Transform (STFT), PMUs data are mapped to 2D images containing frequency information of the events, and then frequency features are extracted to classify the events by Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM). Based on the results of the first layer, the second layer further determines the locations of the events. Graph Attention Network (GAT) is used to extract spatial information in the power system topology, which is insensitive of event types. CNN can enhance the feature gap between different events, and LSTM can extract temporal information from the PMU data. With the extracted temporal-spatial information, the second layer can produce the locations of events with high accuracy. The proposed two-layer algorithm is tested in the IEEE Standard New England 39 Test System. Compared to benchmark models, the proposed method has higher event identification and location estimation accuracy and stronger noise resistance.

The ECCANDE Project: Design, Field Implementation, and Operation of a Special Protection Scheme Based on Synchronized Phasor Measurements

This paper presents the ECCANDE project, one of the first Special Protection Schemes (SPS) conceived to rely on the structure of a Wide-Area Monitoring, Protection, and Control System (WAMPACS) and currently operating in a real, large-sized power system. This project was developed and implemented to enable the interconnection between the Argentinean and Paraguayan bulk power transmission systems, which involves and directly affects two of the largest hydropower plants in the world (the Itaipu and Yacyreta power plants). Building this WAMPACS-based SPS took more than a decade of work performed by multiple teams, involving modeling, planning studies, design, manufacturing, real-time hardware-in-the-loop (HIL) simulation for equipment testing, implementation, commissioning, field tests and evaluation of daily operation. The paper describes how system stability problems (detected during the initial studies for the interconnection) made it clear that the ECCANDE SPS had to be a WAMPACS-based one, and how its architecture and logic functions were conceived to deal with these problems. The results of HIL real-time simulations and a main field test show that the ECCANDE SPS enabled the interconnection, successfully protecting against the loss of synchronism between the Itaipu and Yacyreta power plants before the system separation.