Phasor Measurement Unit Data Research Articles

Structural Tensor Learning for Event Identification With Limited Labels

The increasing uncertainty of distributed energy resources promotes the risks of transient events for power systems. To capture event dynamics, Phasor Measurement Unit (PMU) data is widely utilized due to its high resolutions. Notably, Machine Learning (ML) methods can process PMU data with feature learning techniques to identify events. However, existing ML-based methods face the following challenges due to salient characteristics from both the measurements and the labels: (1) PMU streams have a large size with redundancy and correlations across temporal, spatial, and measurement type dimensions. Nevertheless, existing work cannot effectively uncover the structural correlations to remove redundancy and learn useful features. (2) The number of event labels is limited, but most models focus on learning with labeled data, suffering risks of non-robustness to different system conditions. To overcome the above issues, we propose an approach called Kernelized Tensor Decomposition and Classification with Semi-supervision (KTDC-Se). Firstly, we show that the key is to tensorize data storage, filter information via decomposition, and learn discriminative features via classification. This leads to an efficient exploration of structural correlations via high-dimensional tensors. Secondly, the proposed KTDC-Se can incorporate rich unlabeled data to seek decomposed tensors, invariant to varying operational conditions. Thirdly, we make KTDC-Se a joint model of decomposition and classification so that there are no biased selections of the two steps. Finally, to boost the model accuracy, we add kernels for non-linear feature learning. We demonstrate the KTDC-Se superiority over the state-of-the-art methods for event identification using PMU data.

Precise Recovery of Corrupted Synchrophasors Based on Autoregressive Bayesian Low-Rank Factorization and Adaptive K-Medoids Clustering

Phasor measurement unit (PMU) data quality problems, such as data loss and modification caused by communication contingencies and cyber attacks, threaten the reliability and stability of modern power systems. In this paper, autoregressive Bayesian low-rank matrix/Hankel tensor factorization (BLMF/BLTF) algorithms are proposed to recover corrupted synchrophasor measurements by leveraging the latent electrical-temporal correlations between data points. To mitigate the impact of consecutive data anomalies on recovery, adaptive k-Medoids clustering based on dynamic time wrapping (DTW) distance and Canopy clustering is introduced to preprocess raw data. The effectiveness of the proposed algorithms in recovering missing or modified synchrophasor measurements is demonstrated through comprehensive case studies. Compared with previous works, the proposed algorithms rely on simpler prior hypotheses and can achieve higher recovery precision. Rank reduction and low-cost rolling prediction techniques make the proposed algorithms suitable not only for processing offline data blocks but also for online real-time recovery of PMU data streams.

A Tuning-free Fault Detection Method for Distribution Network Based on PMU Data and Random Matrix Theory

The increasing penetration of renewable energy sources has significantly altered the fault characteristics of distribution networks with distributed generators (DGs). The fault current is limited as the emerging solar plants normally connect to the grid via inverters. And the operating patterns of the system have become more diverse and complex. The traditional pre-tuned protection and fault detection methods are facing substantial challenges. This paper proposes a centralized fault detection scheme based on the random matrix theory (RMT) that utilizes synchronous phasor data from the distribution-level phasor measurement unit (PMU). Firstly, data normalization and resampling techniques construct the operating state matrix (OSM). Then the mean spectral radius (MSR) is calculated and used for fault detection without the complex tuning according to the operation state of the system. Finally, the faulty section is determined by comparing the features from different sub-group of the PMUs according to the topology partition. A multi-feeder distribution network is modeled in PSCAD and used to verify the proposed method.

Key Transmission Section Search Based on Graph Theory and PMU Data for Vulnerable Line Identification in Power System

Failure of vulnerable lines in the power system often results in tidal shifts, and triggering chain failures and their corresponding transmission sections are concentrated manifestations of the weak links in the power system. It is very important to identify the vulnerable lines and search the transmission section to prevent the chain faults as well as to analyze the stability of the power system. Aiming at the problems of inaccurate search of vulnerable lines, difficulties adapting to the complex and changing power system as well as wrong selection and omission of transmission section search in the existing references, this paper proposes an algorithm for searching vulnerable lines and their key transmission sections based on the graph theory and PMU (phasor measurement unit) data. First, the method combines with the graph theory and PMU data to construct the grid topology map. Second, the comprehensive indicators for screening vulnerable lines are proposed by fully considering the network topology and line capacity, which combines with power exchange efficiency and energy fluctuation probability. Third, the distance matrix in the Floyd algorithm is transformed into a unit group that can store more elements, which reduces the traversal times of the algorithm and improves computational efficiency. The fast localization of transmission cross sections associated with vulnerable lines is realized. Finally, the critical transmission cross sections are screened according to the line outage distribution factor and line safety margin. The IEEE 39-bus system is selected for simulation experiments, and the simulation results show that the key transmission section search method proposed in this paper can better adapt to the variable power grid and is faster and more accurate than the other common method.

A novel multi-task learning method for evaluating short-term voltage stability with incomplete PMU measurements

Methods for short-term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data have become quite abundant. However, most of them are challenged to deal with the commonly encountered incomplete PMU data. Existing models for STVS assessment of incomplete PMU data mostly have the disadvantages of high computational complexity and large impact from the level of data missing. This paper proposes a multi-task learning method that performs stability assessment and missing data completion in parallel. The Transformer Encoder is used as the shared feature extractor, a GRU structure is used to output complete PMU data sequences, and a BP neural network is used to output stability assessment results. The loss function used for the missing completion task considers the accuracy in both the shape and time domains. A comparison with relevant models shows that the model proposed in this paper achieves the best performance in all indicators and is minimally affected by the observation windows and the level of data loss, demonstrating good robustness.

Damping Undammed Low Frequency Oscillations in Power Systems: An ANN-Based Approach Using Pre-Disturbance Data

The paper examines undammed low frequency oscillations (LFOs) that can lead to system collapse, citing the Jordan power network incident on May 21, 2021. Traditional model-based methods for studying LFOs' small-signal stability have limitations. To address this, an online damping controller based on an artificial neural network (ANN) is proposed. Unlike existing ANN-based methods relying on offline controllers, this novel approach utilizes pre-disturbance data from phasor measurement units (PMUs) to dampen oscillations effectively. The paper addresses challenges of partially observable systems in online eigenvalue prediction using ANN. MATLAB is used to implement a feedforward ANN system trained on PMU data. The study involves a three-area test system with various operational scenarios, training the ANN across 406 scenarios to predict eigenvalues and damp LFOs.

A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data

Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. To address this issue, various feature selection methods are implemented to choose the best subset of features. Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. Our results indicate that the proposed framework is promising for identifying the two types of events.

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

AbstractThe growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐learning GPS‐Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short‐term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14‐bus and IEEE 39‐bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy‐implementable DLGSC algorithm's satisfactory real‐time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs.

Real-time coordinated control of voltage regulation devices in a high PV penetrated weak distribution network

In the weak distribution network, on-load tap-changer (OLTC) needs to operate frequently to regulate the voltage fluctuations. Substantial solar photovoltaic (SPV) penetration affects OLTC life, which leads to increased maintenance cost. On the other hand, battery energy storage systems (BESS) are the most appropriate solution. However, frequent charging/discharging affects the BESS's life and make it uneconomic. Authors have proposed a control to efficiently utilize the existing voltage regulation devices without new communication channels. The proposed algorithm utilizing a real-time phasor-measurement unit (PMU) data to coordinate BESS, OLTC, and SPV inverters for the optimal control. In addition, for further improvement, the bidirectional state of charge (SOC) regulation method is also proposed. It further optimizes the existing solution to bring back SOC faster to the reference level to make it available for coming contingencies. Quantitatively, BESS processed energy and OLTC tapping operation have been reduced by 81.9% and 52.9%, respectively.

A robust method for transmission line sequence parameter estimation using synchronized phasor measurements

We present a positive and zero sequence line parameter estimation method, that is robust to systematic errors in the instrument transformers, especially when they are within the specified tolerance as per standards. Using Monte Carlo simulations, it is shown that the proposed approach is robust and accurate for all operating conditions, specifically for short length and lightly loaded transmission lines. We also validate the proposed approach on 400 kV and 765 kV transmission lines using actual field phasor measurement unit (PMU) data. Further, estimation of zero sequence line parameter values is somewhat tricky because not enough unbalance exists during the normal operation of the power grid. Therefore, we quantify the minimum percentage unbalance needed in currents to determine zero sequence line parameter values within 1% tolerance. We also present the line length and line loading effects in estimating zero sequence line parameter values using simulations. Simulation and field PMU data results on 765 kV and 400 kV lines of different lengths and loading levels show that the proposed method estimates accurate positive and zero sequence line parameter values.

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.

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.

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.

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.

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.

Wide area measurement systems based Power System State Estimation using a Robust Linear-Weighted Least Square method

The accuracy and robustness of Power System State Estimation (PSSE) are paramount for monitoring and surveillance of Energy Management Systems (EMS). To enhance the PSSE accuracy, phasor measurement units (PMUs) are widely used in Wide-area measurement systems (WAMS). The essential aspects of enhancing PSSE accuracy are systems observability and redundancy. These can be accomplished through an optimal PMU placement strategy. Iterative-based algorithms for estimating power system states using PMU and traditional Supervisory Control and Data Acquisition (SCADA) measurements face challenges such as non-linearity and lack of convergence. In this paper, a Robust Linear Weighted Least Square (RLWLS) estimator is proposed by combining conventional SCADA data with phasor measurements. Further, The RLWLS state estimator overcomes the issues mentioned above. Extensive simulations on various test systems, including traditional IEEE 14, 30, 57, and IEEE-118 bus systems and the real-time Indian Northern Region Power Grid (INRPG)-246 bus system in India, have shown to prove the resilience of this technique. Finally, compared to the prior PSSE approaches, the RLWLS methodology significantly improves the efficacy and performance of the state estimate solution.

Robust Event Classification Using Imperfect Real-World PMU Data

This paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

Event Detection, Classification and Localization in an Active Distribution Grid Using Data-Driven System Identification, Weighted Voting and Graph

Event detection, classification, and localization (ED-C-L) in an active distribution network are challenging given system dynamics but critical for taking the control actions to mitigate the impact on the system operation. In this work, a new set of algorithms are developed for ED-C-L including, a) prony based dynamic window and event detection, b) data-driven system identification (DD-SI) using Koopman Mode analysis (KMA), physics-rule, and weighted-vote based assembling technique for event classification, and c) weighted graph and KMA based localization using distribution phasor measurement unit (D-PMU) data. DD-SI is utilized to construct a transient energy matrix (TEM) and capture the system dynamics using the temporal and spatial signature of events without using a labeled dataset. The proposed technique considers system dynamics for ED-C-L to achieve enhanced accuracy, compared to most of the existing data-driven techniques. A lab-scale real-time testbed is developed to generate D-PMU data utilizing a 24 bus distribution system simulated in the OPAL-RT/Hypersim for evaluating the performance of the proposed algorithm, and measured using selected metrics. Case scenarios consider utility-scale solar photovoltaic (PV) and battery energy storage system (BESS) integration at dispersed locations to validate proposed algorithms under dynamically changing operational scenarios. Further, the developed approach is applied on DPMU data from real distribution systems with DERs to showcase the superiority of the proposed approach to field systems.

Dynamic Security Assessment of Small-Signal Stability for Power Grids Using Windowed Online Gaussian Process

The online small-signal stability assessment of electrical power grids is typically a challenging problem due to uncertainties and parameter variations of power system dynamics as well as the incurred high computational complexity. This paper proposes a novel theoretical framework for dynamic small-signal stability assessment of power grids by estimating the region of attraction (ROA) for operating states in real time. By analyzing the latest sampling data of power grids in a fixed time window, an up-to-date training set is constructed with the aid of converse Lyapunov function, which enables us to develop an online learning approach based on Gaussian Process (GP) to assess the stability level of power grids. As a result, an iteration algorithm is designed to update the assessment parameters by learning the input-output pairs in the training set. Theoretical analysis is conducted to ensure the existence of converse Lyapunov function for differential-algebraic system that serves to describe power system dynamics, as well as to estimate the region of attraction for operating states with a given confidence level. In particular, a practical method is proposed to leverage time series of phasor measurement unit (PMU) measurements including voltage/current magnitude, phase and frequency (i.e., PMU data) of real power grids for validating the online GP approach. Moreover, validations are taken to substantiate the proposed approach by using PMU data of real smart-grid infrastructure and IEEE test cases. The proposed assessment approach contributes to situational awareness of human operators in the control station, thereby taking proactive remedial actions prior to emergencies. Note to Practitioners—This paper was motivated by the problem of online assessment of power systems security but it also applies to other industrial control systems that have stable state trajectories. Existing approaches to security assessment of power systems generally focus on the adoption of various machine-learning algorithms by treating power grid as a “black box,” which ignores the intrinsic characteristics of power systems and thus restricts the inference performance. This paper proposes a new approach using limited sampling data and system dynamics to construct a domain of stability for power grids, which can reflect the evolution of security zones and provide more accurate predictions. In this work, we mathematically characterize the domain of stability for a practical power grid by analyzing and learning its state trajectories. Then we show how the proposed approach can be efficiently implemented online, which can timely alert human operators to abnormalities. Preliminary validations suggest that this approach is feasible and effective. In future research, we will incorporate it into an energy management system and test it in other industrial processes.

Weighted ensemble learning for real‐time short‐term voltage stability assessment with phasor measurements data

AbstractVoltage stability assessment based on machine learning has become an important challenge in power systems. This paper presents real‐time short‐term voltage stability (STVS) assessment based on phasor measurement unit (PMU) data and machine learning (ML). The database is created through time series of measurement data to involve system time‐temporal and dynamics. Then multiple operating states of the power system are classified through the calculation of the Lyapunov exponent and dynamic voltage index according to the database. This paper presents a weighted combination of random forest (RF) and LightGBM (LGBM) classifiers to train a time‐series database. One of the main advantages of this paper is using the gradient concept in data preprocessing, which has enhanced performance metrics and reduced the defect of data noise. Also, hyperparameter optimization is conducted to improve machine performance. Studies on the IEEE 118bus and a real local grid (RLG) demonstrate that the proposed method improves the performance metrics such as accuracy and F1‐score. Also, this approach is robust against PMU data noise and topology changes in the network.