PMU-based State Estimation Research Articles

Bias Analysis of PMU-Based State Estimation and Its Linear Bayesian Improvement

With the rapidly changing states of modern power systems, real-time state estimation based on phasor measurement units (PMUs) plays an increasingly important role in energy management systems (EMSs). However, due to the complicated distribution of PMU measurement errors, conventional state estimation methods such as the weighted least squares (WLS) estimator suffer from the estimation bias. Consequently, they do not represent the least variance unbiased estimators. To solve the estimation bias, a state estimation method combining the PMU linear measurement model and the linear Bayesian estimation theory is proposed. Specifically, considering the prior information on estimated parameters and the complicated probability distribution of PMU measurement errors, the linear Bayesian theory is applied to PMU-based state estimation to improve the performance of state estimation. To support this method, the correlation between real and imaginary errors is analyzed, and the prior information of estimated parameters is determined by the volatility of system states. When the real states of practical power systems are unavailable, performance indicators for state estimation are proposed based on the estimation residuals. The efficacies of the proposed estimation and evaluation methods are verified using IEEE 14 bus system and a large-scale power grid in China.

Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation

The ever-increasing penetration of renewable energy sources and distributed generation needs developing and deploying more sophisticated and precise control techniques. The paradigm shift from high-rotational inertia towards inverter-connected facilities has made modern power systems subject to strongly non-stationary operating conditions. In this context, phasor measurement units (PMUs) represent a promising solution due to their accuracy and time synchronization. The current paper further develops the concepts recently published proposing performance assessment of PMUs relying on metrics specifically defined to quantify the estimation accuracy, reporting latency, and response time. Our focus is on determining the confidence interval associated with these metrics and thus derive a robust approach for their application in measurement-based network controlling efforts. The configuration of the used simulation model is detailed, and the state estimation results as a function of the selected measurement weighing criteria are presented, concluding as a possible application of the reliability metrics in Weighted Least Squares and Discrete Kalman Filter state estimators, highlighting the performance enhancement as well as the theoretical limits of the proposed approach.

Robust coupled single-port method based on PMU-based state estimation method for voltage stability assessment

Real-time monitoring of voltage stability margin is an essential issue to warrant secure operation of electrical power systems. One of the important tools for real-time monitoring of voltage stability margin is the PMU-based state estimation method. This paper presents a novel PMU-based State Estimation method based on the continuous Levenberg Marquardt (CLM) method and fourth-order Runge–Kutta (RK4) formulation, which is named as PSECLR method. Moreover, based on the proposed PSECLR method, a novel Robust Coupled Single-Port (RCSP) method for real-time monitoring of voltage stability margin in power systems is presented in this paper. To determine the efficiency of the proposed methods, detailed simulation results are presented and analyzedfor IEEE 14-bus, IEEE 30-bus, IEEE 118-bus and IEEE 300-bus power systems.

PMU Based State Estimation Considering Fault Localization

PMU Based State Estimation Considering Fault Localization

PMU-Based State Estimation for Networks Containing LCC-HVDC Links

This letter proposes an equality-constrained weighted least squares (WLS) state estimation model for networks containing LCC-HVDC links. Voltage and current phasor measurements for the AC network are provided by phasor measurement units (PMUs) and DC voltage and current measurements for the DC transmission lines are acquired by appropriate DC sensors. AC/DC coupling equations and zero injections are treated as equality constraints. Simulation results for a 6-bus hybrid AC/DC network are shown to validate the proposed model and algorithm.

A Novel Technique to Detect False Data Injection Attacks on Phasor Measurement Units.

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.

An investigation of the impact of synchrophasors measurement on multi-area state estimation in active distribution grids

ABSTRACT This paper examines the impact of synchrophasors measurement from a number of phasor measurement units (PMUs) on multi-area state estimator (MASE) in the distribution grid (DG). The benefits are largely in the form of improved state estimation (SE) accuracy. An important decision in the implementation of PMU-based SE is the optimal selection of the channel capacity of the PMUs. This decision becomes more important for the distribution systems because of larger number of measurement points as compared to the transmission system and also because of higher cost of PMUs with more measurement channels. Further, critical measurement analysis is important as they indicate the robustness of measurement system. In this paper to compare the robustness of the measurement system and the accuracy of SE, various performance metrics were compared for conventional, infinite channel and PMUs with limited number of channels. Therefore, an important contribution of the present work is the impact analysis of different number of PMU channels on the accuracy of the results of MASE and also on the measurement robustness for distribution networks. The results presented are expected to be useful for the utilities when designing a PMU-based MASE for the distribution grids.

The impact of system nonlinearities in the problem of optimal PMU placement for power system state estimation

In PMU-based state estimation, a linear measurement model with phasors of both state variables and measurements expressed in rectangular coordinates has proven efficiency. The rectangular coordinate formulation is also used in optimal PMU placement problem aimed at providing the power system state estimation with the most informative measurements. In this case, it is assumed that the linearity of the measurement model ensures the optimality of the found placement of PMUs for any steady-state operating condition of the power system. The results presented in this paper show that this is not the case.

Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation

With the rapid advancement of phasor measurement units (PMUs) technology, system operators in different level of power systems have access to new and abundant measurements. Taking into account these measurements in active distribution systems (ADNs), a new algorithm for short-circuit fault detection and identification based on state estimation (SE) is introduced in this paper. In this regard, as the first step, traditional SE process is revised to be compatible with fault conditions. Then, a fault location algorithm (FLA) based on the revised SE (RDSSE) is presented which attends to detect the location of fault after diagnosing faulted zone. For this purpose, current and voltage synchrophasors captured by PMUs as well as pre-fault SE results are used and according to calculated measurement residual indexes, the correct location of fault is diagnosed. The performance of RDSSE and SE based fault location method are tested by applying on an ADN, considering different fault scenarios in the network. The results proved that the proposed method is more accurate and reliable than traditional SE based methods in fault conditions and can precisely determine the real location of fault at lower SE execution times.

Packet-data anomaly detection in PMU-based state estimator using convolutional neural network

With more phasor measurement units (PMU) being deployed by utilities for reliable monitoring of power systems, there grows an increasing risk of vulnerability to various cyber-attacks. This paper is concerned with a class of false data injection attacks (FDIA) which aim to modify PMU measurements resulting in incorrect state estimation solutions. We extract multi-variate time-series signals from PMU data packets aggregated in phasor data concentrators (PDC) corresponding to different events such as line faults and trips, generation and load fluctuations, shunt disconnections and FDIA prior to every cycle of state estimation (SE). A Convolutional Neural Network (CNN) data filter with Nesterov Adam gradient descent and categorical cross entropy loss is proposed to validate the PMU data. This filter extracts inter time-series relationships to classify different power system events by comparing the temporal structure of PMU packet data. The performance of the filter is then compared with (a) deep learning algorithms such as Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) and (b) traditional classifiers such as SVM and ensemble methods. It is seen that the proposed CNN-based filter results in higher classification accuracies among all classifiers. This makes the CNN classifier suitable to serve as an independent data filter to identify falsified data streams targeted to alter SE. In order to verify the accuracy of the proposed filter, a hybrid state estimator (HSE) has been used in this study which obtains measurements from both PMU and traditional meters. All simulations are carried out on IEEE-30 bus and IEEE-118 bus systems.

Model Order Reduction for PMU-Based State Estimation in Distribution Grids

In this paper, an approach to perform model reduction of distribution grids for phasor measurement unit (PMU)-based state estimation (SE) purposes is presented. The method is based on ac load flow equations. An arbitrary number of lines and nodes can be selected and reduced to an equivalent one. The performance of the method is tested using the IEEE34 and IEEE123 test cases. An evaluation of the computational performance of the SE process is also reported. To conclude, the SE process based on the reduced model is tested against uncertainty in the parameters of the system and in the measurements so to evaluate how the performance of the SE is deteriorated by these phenomena.

Pseudo-Dynamic Network Modeling for PMU-Based State Estimation of Hybrid AC/DC Grids

This paper presents a PMU-based state estimation (SE) algorithm that uses a pseudo-dynamic network modeling approach. The pseudo-dynamic network model combines different equations with static network equations. Then it applies the weighted least squares algorithm to solve an over-determined least squares estimation problem. The proposed method can improve SE accuracy during both steady state and transient conditions without increasing the computational burden. In addition, the proposed modeling approach is applied to networks containing both a STATCOM and a voltage source converter-HVdc to demonstrate how to develop and apply a pseudo-dynamic SE model. Case studies aim to illustrate and verify the performance of the proposed method under steady state and transient conditions.

A Co-Simulation Framework for Integrated Planning and Analysis of Wide Area Measurement and Protection Systems

The recent efforts to extend measurement and communication infrastructures in power grids have brought about new potentials for a more efficient and optimized grid operation using the data which become available. Due to this integration, on the other hand, not only the network expansion and planning but also the development of new applications must be carried out under consideration of the interdependencies between the power grid and the underlying communication network. In this work, we present a co-simulation environment and tool chain to enable integrated planning and subsequent performance analysis of a wide area measurement system. In the first part of the paper, we investigate the performance of PMU-based state estimation techniques under several power system and communication network scenarios. In the second part of the paper, furthermore, the presented setup is extended for co-simulative investigations of the machine learning-based fault detection and classification techniques in power systems. The presented results not only validate the developed software solution for similar research studies but also scrutinize the impact of communication network performance on power system state estimation and PMU-based decision-making for fault detection and classification.

A PMU-based state estimator considering classic HVDC links under different control modes

This paper aims to develop a PMU-based state estimation algorithm that considers the presence of classic HVDC links operating under different control modes. Thus, hybrid AC/DC grid state estimation becomes feasible. In this algorithm we assume that DC link measurements can be sampled and reported at the same rate as PMU measurements, so that both AC and DC states can be estimated simultaneously in real-time. The estimation algorithm uses synchrophasors in polar coordinates, which allows angle bias detection and correction. In addition, some practical issues for the proposed state estimator are discussed, including observability and measurement redundancy, measurement noise and weightings, and angle bias correction. Finally, study cases using different power system models are carried out to show the state estimator’s performances during both steady conditions and dynamic changes. Relatively small residuals during steady conditions validate accuracy; results during dynamic changes verify the estimator’s reliability.