Phasor Measurement Units Research Articles

Identification and suppression of low-frequency oscillations using PMU measurements based power system model in smart grid

Low-frequency oscillations (LFO) are inherent to large interconnected power systems. Timely detection and mitigation of these oscillations is essential to maintain reliable power system operation. This paper presents a methodology to identify and mitigate low-frequency oscillations ( forced and inter-area) using a wide area monitoring system (WAMS) based power system model utilizing phasor measurement units (PMUs). These models accurately identify the behavior and location of generators contributing to low-frequency oscillations in real-time and hence can efficiently improve the performance of WADC to mitigate them. The proportional resonant power system stabilizer (PR-PSS) is utilized to suppress these LFOs, as determined from the Wide Area Power System Model. The damping structure based on PR-PSS with measurements from WAMS effectively suppresses both forced and inter-area oscillation modes.

Adaptive Generator Tripping Scheme based on Deep Learning as Real Time Control Action for Transient Stability

Transient stability has typically received attention in the literature, focusing on its assessment and control under limited operational scenarios and contingencies. This often results in persistent transient stability issues, leaving the system vulnerable to imminent collapses. In this regard, this work aims to develop an adaptable tripping scheme based on the power system dynamics following a major disturbance to prevent grid blackouts due to transient stability loss. The proposed methodology takes advantage of data analysis tools based on deep learning and Phasor Measurement Units (PMUs) technologies. In this approach, the methodology involves generating a database of both operational scenarios and n-1 contingencies, labeling critical generators to be tripped to mitigate transient instability, and training a hybrid deep neural network RCNN (recurrent convolutional neural network) that constitutes the core of the tripping scheme. Following the application of the methodology in a controlled simulation environment, the RCNN model demonstrated strong performance, as it not only mitigated transient instability through minimal tripping of generation plants with an effectiveness of 92.4% but also showed potential for real-time application, as the control action accounts for latencies inherent in real-time operation.

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data

Bulk Low-Inertia Power Systems Adaptive Fault Type Classification Method Based on Machine Learning and Phasor Measurement Units Data

Optimal µPMU Placement Considering Node Importance and Multiple Deployed Monitoring Devices in Distribution Networks

The placement of micro phasor measurement units in an active distribution network can improve the monitoring performance of the system. However, the price and placement cost of micro phasor measurement units are high, so existing research mostly focuses on solving the problem of achieving global observability with the minimum number of micro phasor measurement units. However, the problems of frequent topology change in the distribution network and the increase in the number of faults that will be caused by the high proportion of distributed energy resources connected to the system may lead to the above placement scheme losing the observability of the system. The balance of maintaining the number of micro phasor measurement units placed while ensuring the monitoring stability of the system remains a crucial challenge. To address this issue, a micro phasor measurement unit placement method that considers the observability of multiple monitoring devices and node importance is proposed in this paper, which, on the one hand, fully considers the impact of the observability of existing monitoring devices on the micro phasor measurement units placement scheme and reduces the number of micro phasor measurement units that are used to achieve global observability, and, on the other hand, gives priority to placing the micro phasor measurement units at important nodes in the distribution network that are closely related to observation stability, improving the observation performance of the system. The superiority of the proposed μPMU method is verified in standard systems such as IEEE-33, IEEE-34, and P&G69, in which the number of required μPMUs is reduced by 20%, and the observation performance of the system is improved by 30% in special cases.

Empowering grid intelligence: a comprehensive review on optimal placement and diverse applications of synchronized phasor measurement units in modern power systems

Empowering grid intelligence: a comprehensive review on optimal placement and diverse applications of synchronized phasor measurement units in modern power systems

The use of WAM Systems for Determining the Ampacity of Overhead Power Lines

This study expands upon previous research by focusing on determining the dynamic thermal rating (DLR) of a specific overhead transmission line. It utilizes real-time data from WAMS (Wide Area Measurement Systems), specifically using PMUs (Phasor Measurement Units). The paper presents a comprehensive overview of thermal evaluations, which are categorized into dynamic (including direct, indirect, transient, and steady state) and static (conventional) rating. Furthermore, the article examines standards that outline principles, technologies, and algorithms for estimating conductor temperature.

Linear Quadratic Gaussian Integral Control for Secondary Voltage Regulation

In this paper, the voltage regulation in power systems is addressed from the perspective of the modern paradigm of control logic supported by phasor measurement units. The information available from measurements is used to better adapt the regulation actions to the actual operation point of the system. The use of the online measurement data allows for identifying the sensitivity matrix and for improving the regulation performances with respect to the fast load variations that increasingly affect modern power systems. With the aim of estimating the sensitivity matrices, a preliminary action is necessary to reconstruct the phases of the network voltages, which are assumed not to be provided by the phasor measurement units. This allows for obtaining a model-free adaptive control method. It is then shown how the regulation problem can be formulated in terms of a linear quadratic Gaussian problem, properly considering the load modeling in terms of the stochastic Ornstein–Uhlenbeck process. This control strategy has the advantage of avoiding dangerous oscillations of power flows, as demonstrated through the results of some simulations on a classical test network. Particularly, the advantage of the proposed approach is shown in the presence of different levels of load disturbances.

An Efficient Algorithm for Power Dominating Set

AbstractThe problem Power Dominating Set (PDS) is motivated by the placement of phasor measurement units to monitor electrical networks. It asks for a minimum set of vertices in a graph that observes all remaining vertices by exhaustively applying two observation rules. Our contribution is twofold. First, we determine the parameterized complexity of PDS by proving it is W[P]-complete when parameterized with respect to the solution size. We note that it was only known to be W[2]-hard before. Our second and main contribution is a new algorithm for PDS that efficiently solves practical instances. Our algorithm consists of two complementary parts. The first is a set of reduction rules for PDS that can also be used in conjunction with previously existing algorithms. The second is an algorithm for solving the remaining kernel based on the implicit hitting set approach. Our evaluation on a set of power grid instances from the literature shows that our solver outperforms previous state-of-the-art solvers for PDS by more than one order of magnitude on average. Furthermore, our algorithm can solve previously unsolved instances of continental scale within a few minutes.

An evaluation of methods for detecting false data injection attacks in the smart grid

With the introduction of new Information and Communication Technology (ICT) to Electrical Power Systems (EPSs) there is an increased potential and impact of cyber-attacks. Phasor Measurement Units (PMUs) enable very fine granular measurements to allow situational awareness in smart grids. But false data injection attacks, which manipulate measurement data, can trigger wrong decisions and cause critical situations in the grid. In this paper, we analyze four different false data injection attacks on PMU measurements and investigate different methods to detect such attacks. Classical bad data detection methods are not sufficient to detect stealthy attacks. We therefore propose to complement detection by additional methods. For this we analyze the detection performance of four very different detection methods: (a) the classical adaptive bad data detection approach based on the residuals of linear Kalman Filters, (b) a simple threshold based on the Median Average Deviation (MAD), (c) a distribution-based approach using the Kullback-Leibler Divergence (KLD), and (d) the cumulative sum (CUSUM) as a representative of a change point detection method. We show that each method has advantages and disadvantages and that multiple methods should be used together to prevent that attackers can circumvent detection.

A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD

In power systems, the application of wind power generation equipment and power electronic devices leads to an increased frequency of broadband oscillation events, and the detection of oscillation information becomes extremely difficult, due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of a power system, this paper presents a new recognition method of broadband oscillation information, which combines compressed sensing (CS) technology and an ensemble empirical mode decomposition (EEMD) algorithm to solve the problem of wideband oscillation recognition. Firstly, the broadband oscillation signal data collected by the phasor measuring unit (PMU) is compressed and sampled by a Gaussian random matrix in the substation, then the low-dimensional data obtained is uploaded to the main station. Secondly, in the main station, the subspace pursuit (SP) algorithm is used to reconstruct the low-dimensional signal; the broadband oscillation signal is recovered without losing the main features of the signal. Finally, we use the EEMD algorithm to decompose the reconstructed signal; the intrinsic mode function (IMF) components containing wideband oscillation information are screened by the energy coefficient, and the wideband oscillation information is identified.

An Online Estimation Method for the Equivalent Inertia Time Constant of Power Equipment Based on Node Power Flow Equations

As renewable energy integration scales up, power systems increasingly depend on sources interfaced through power electronic converters, which lack rotating mass and substantially diminish system inertia. This reduction in inertia, coupled with the complex and diverse control strategies governing power electronics, presents significant challenges in accurately assessing the equivalent inertia levels within modern power systems. This paper introduces an online method for estimating the inertia time constant of power nodes, grounded in the node power flow equation, to address these challenges. The approach begins by deriving the rotor motion equation for synchronous generators and defining the inertia time constant of power nodes through an analysis of the power flow equations. Real-time frequency and voltage phasor data are collected from system nodes using phasor measurement units. The frequency state of the power equipment is then characterized using a divider formula, and the equivalent reactance between the power equipment and the node is further derived through the node power flow equation. This enables the real-time estimation of the equivalent inertia time constant for power nodes within the system. The effectiveness of the proposed method is demonstrated through simulations on the WSCC9 system, confirming its applicability for real-time system analysis.

A Novel IoT-Based Controlled Islanding Strategy for Enhanced Power System Stability and Resilience

Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability.

An adaptive assessment method of power system transient stability considering PMU data loss

AbstractTransient stability assessment (TSA) plays an important role in ensuring the reliable operation of power systems. With the popularity of phasor measurement units (PMUs), data‐driven TSA methods have been widely concerned. However, the performance of TSA model may deteriorate when data loss occurs due to PMU failure. This paper proposes an adaptive assessment method for transient stability of power systems considering PMU data loss. First, considering the importance of temporal features, a collection of PMU clusters is constructed to minimize the failure risk and maintain full observability of the whole buses of the grid. Secondly, a weighted integrated assessment model based on PMU clusters is constructed by using an improved eXplainable Convolutional neural network for Multivariate time series classification (XCM) as a TSA classifier. The model can make full use of time series information to carry out adaptive TSA and maintain the robustness of the assessment performance even when PMU failure occurs. Finally, it is verified in a modified IEEE 39‐bus system with wind power and solar power. The effect of the proposed method shows high accuracy and strong anti‐noise interference ability in case of data loss.

A Robust ALOHA and Sequential clustering-based mode estimator for low frequency oscillations in power system using synchrophasors

Accurate estimation of low frequency modes in power system are very much important for improving small signal stability. The parametric model parameters estimator known as Total least square estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) works effectively even in noisy conditions. However, this model parameter estimator requires prior information about numbers of modes of the signal. There are different Model order (MO) estimation techniques discussed in the recent past, which consider the significant eigenvalues of auto-correlation matrix (ACM) for its estimation. As the eigenvalues of ACM highly affected by bad measurements and Outliers, making these techniques inefficient and harder to automate in real time. So, to overcome aforementioned limitations, this paper proposes a robust mode estimation technique that can precisely detect the signal low frequency modes even in the presence of high variance noise and outliers. So, in this proposed work, an annihilating filter-based low-rank Hankel matrix (ALOHA) technique is implemented to obtain the rank deficient Hankel matrix to nullify the existence of noise and outliers in Phasor measurement unit (PMU) signal, thereafter approximated low rank Hankel matrix is considered for estimation of signals dominant modes. Wherein a sequential clustering technique (Sequential K-Mean++) is implemented for detection of numbers of prominent low frequency modes by segregating eigenvalues of ACM into two opponents i.e the signal and noise subspace. Thereafter the estimated MO is considered for estimation of modes through TLS-ESPRIT. The robustness of the proposed technique is validated by scheduling comparative study with recently developed techniques for synthetic signal, two area data, real PMU data of Western Electricity Coordinating Council (WECC) and oscillatory power data obtained from Western System Coordinating Council (WSCC) 9 bus system and IEEE68 bus system simulated through Real Time Digital Simulator (RTDS).

Fault simulation in Phasor Measurement Units: A study on system reaction time in a 5G network environment

Fault simulation in Phasor Measurement Units: A study on system reaction time in a 5G network environment

VNN-DM: a vector neural network-based detection model for time synchronization attacks in park-level energy internet

Micro phasor measurement units (µPMUs) provide high-precision voltage and current phasor data, allowing real-time state estimation and fault detection, which are critical for the stability and reliability of modern power systems. However, their reliance on accurate time synchronization makes them vulnerable to time synchronization attacks (TSAs), which can disrupt grid monitoring and control by corrupting µPMU data. Addressing these vulnerabilities is essential to ensure the secure and resilient operation of smart grids and energy internet technologies. To address these challenges, intelligent detection methods are essential. Therefore, this paper proposes a µPMU measurement data TSA detection model based on vector neural networks (VNNs). This model initially employs a vector neural network to process raw data, effectively extracting and analyzing temporal features. During the same time, a capsule network is employed to classify these temporal features. On this basis, a reconstruction network is used to verify the representational capacity of the model. Simulations based on µPMU measurement data demonstrate that the model exhibits excellent detection capacity in various performance metrics, underscoring its precision and robustness.

Graph topology-constrained BILP for optimal PMU placements

In the monitoring and control of power systems, the deployment of Phasor Measurement Units (PMUs) is of paramount importance, yet their high installation costs have limited their widespread application. To address this issue, this paper proposes an innovative method based on graph topology constraints, known as the Graph Topology-constrained Binary Integer Linear Programming (GT-BILP) approach, aimed at achieving the optimal configuration of PMUs. This method not only takes into account cost-effectiveness but also ensures the complete observability of the system. By quantifying the contribution of each node and bus to the system's observability within the network, a novel contribution matrix is constructed. This matrix integrates the dynamic characteristics of network topology and information flow, providing precise decision support for the deployment of PMUs. By combining the matrix with the network's planning model, the GT-BILP model is obtained, which can effectively solve the PMU layout requirements for large power grids. For the special case of zero-injection buses (ZIBs), the Topology Transformation algorithm has been improved, significantly enhancing the solution efficiency and quality. Through simulation experiments on multiple IEEE standard test systems, the effectiveness and superiority of the proposed GT-BILP model have been verified. This research is of significant importance for improving the monitoring and control capabilities of power systems and provides a new perspective for technological advancement in this field.

Evaluating synchrophasor-enabled angle-based differential protection in distribution networks

The inclusion of Distributed Energy Resources (DERs) affects the protection requirements and the functional constraints of the modern distribution networks (MDNs). With continued research on the primary protection of MDNs, this paper evaluates an angle-based differential protection scheme utilizing synchrophasors from distribution-level phasor measurement units (D-PMUs). The suggested scheme is based on computing the Absolute Superimposed Differential Angle (ASDA) of positive sequence current between two ends of a distribution line. The ASDA concept is explained by considering internal and external fault conditions. The initial investigations are performed on a modified real-distribution network in the Dubai utility, assuming rotating or non-rotating DER. Thereafter, the scheme is thoroughly examined in a seven-node MDN modelled in Real-Time Simulator, exploring various scenarios such as different fault types, fault resistance and line length variations, switching transients, varying DER penetration levels and types, and different network operation modes. Additionally, the analysis of CT saturation effect, measurement noise and response time of the proposed scheme has been carried out. A real-time laboratory testbed is also developed utilizing physical PMUs and a real-time simulator to verify the performance of suggested scheme in a practical environment. Furthermore, a quantitative and qualitative evaluation of the proposed strategy is presented to demonstrate the strategy’s reliability and efficacy. Our findings demonstrate that the suggested approach reliably detects short-circuit faults in a broad spectrum of network conditions.

Validation of Electromechanical Transient Model for Large-Scale Renewable Power Plants Based on a Fast-Responding Generator Method

The requirements for accurate models of renewable energy power plants are urgent for power system operation analysis. Most existing model research in this area is for wind turbine and photovoltaic (PV) power generation units; a rare renewable power plant model validation mainly adopts the single-machine infinite-bus system. The single equivalent machine method is always used, and the interactions between the power plant and the grid are ignored. The voltage at the interface bus is treated as constant, although this is not consistent with its actual characteristics. The phase shifter method of hybrid dynamic simulation has been applied in the model validation of wind farms. However, this method is heavily dependent on phasor measurement units (PMU) data, resulting in a limited application scope, and it is difficult to realize the model error location step by step. In this paper, the fast-responding generator method is used for renewable power plant model validation. The complete scheme comprising model validation, error localization, parameter sensitivity analysis, and parameter correction is proposed. Model validation is conducted based on measured records from a large-scale PV power plant in northwest China. The comparison of simulated and measured data verifies the feasibility and accuracy of the proposed scheme. Compared to the conventional model validation method, the maximum deviation of the active power simulation values obtained by the method proposed in this paper is only 38.8% of that of the conventional method, and the overall simulation curve fits the actual measured values significantly better.

Optimizing Phasor Measurement Units Placement in Power Networks Using Betweenness Centrality

Optimizing Phasor Measurement Units Placement in Power Networks Using Betweenness Centrality