Limited Phasor Measurement Units Research Articles

Robust Fault Location Method for Transmission Lines Using PMUs

The random noise and abnormally greater numbers may affect the accuracy of traditional transmission line fault location. A robust fault location method for transmission lines based on PMUs (Phasor Measurement Unit) is proposed in this paper. The node voltage equations based on voltage fault components are adopted to establish the quadratic nonlinear overdetermined equations of fault location, only using limited PMUs adjacent to the faulted line. In order to quantify the impact of measurement errors, the residual errors are introduced into the fault location equations. Levenberg-Marquardt (L-M) algorithm has been used to solve the equations. Then the original fault location data at each time after the occurrence of one cycle is obtained. A filtering method is proposed to filter the possible interference such as noise and abnormally greater numbers in fault location sequence. The abnormal data that deviating from the mean can be detected by the probability distribution and replaced by smooth filtering data. The simulation results on IEEE 39-bus systems show that proposed algorithm can effectively improve the location accuracy, is not affected by the fault location, transition resistance, fault type, and has strong anti-interference ability and good robustness.

Cross-variance analysis to online estimate power flow Jacobian matrix using limited PMU data

This paper proposes a data-driven approach to estimate the power flow Jacobian matrix online with only small-scale data set collected by phasor measurement unit (PMU). For data in limited amount, one of the greatest challenges to estimate the Jacobian matrix is how to quantify and treat the interference of multiple uncertainties, which are analytically intractable by traditional approaches. To tackle these uncertainties, in this work, through analyzing the cross-variance matrix between voltage measurements and load data, the joint density probability of uncertainties is modeled in matrix-level. Furthermore, a random matrix theory (RMT) based approach is proposed to shrink the singular values of the cross-variance matrix to alleviate the unfavorable influence of uncertainties. This approach is based on the property that the singular values of a random matrix are asymptotically converged to a deterministic distribution. Numerous cases prove that the proposed method is capable of obtaining more precise estimation results even with limited PMU data. Besides, this estimation would supply extra information assistant for power system monitoring, such as voltage stability assessment (VSA) and topology change detection (TCD).

A New Backup Protection Scheme for Transmission Lines with Limited Phasor Measurement Units

— This paper proposes a new scheme for backup protection of transmission lines with a limited number of phasor measurement units (PMUs). In the presented methodology, the PMUs are located (or installed) in accordance with a scheme allowing all faults to be observable. The network is divided into a few zones with 1, 2 or 3 transmission lines. Following this, the nearest bus to the fault location, named as the faulted bus, is selected through the voltage data measured by PMUs. A faulted area including the suspected zones, the zones connected to the faulted bus, is then identified. The faulted zone is subsequently recognized by using the phase deviation between the positive- (negative-) sequence voltage of the faulted bus and the positive- (negative-) sequence of the input current to the suspected zones. The faulted line is finally detected based on the type of the faulted zone and corresponding algorithm. The effectiveness of the presented scheme is assessed under several fault situations in MATLAB/Simulink platform. Simulation results demonstrate that the proposed method detects the faulted line accurately for all fault conditions. Moreover, the proposed scheme protects the shunt-compensated transmission lines as properly as the uncompensated ones without using the pre-fault data.

Targeted False Data Injection Attacks Against AC State Estimation Without Network Parameters

State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system. Relying heavily on meter measurements, state estimation has proven to be vulnerable to cyber attacks. In this paper, a novel targeted false data injection attack (FDIA) model against AC state estimation is proposed. Leveraging on the intrinsic load dynamics in ambient conditions and important properties of the Ornstein-Uhlenbeck process, we, from the viewpoint of intruders, design an algorithm to extract power network parameters purely from PMU data, which are further used to construct the FDIA vector. Requiring no network parameters and relying only on limited phasor measurement unit (PMU) data, the proposed FDIA model can target specific states and launch large deviation attacks. Sufficient conditions for the proposed FDIA model are also developed. Various attack vectors and attacking regions are studied in the IEEE 39-bus system, showing that the proposed FDIA method can successfully bypass the bad data detection and launch targeted large deviation attacks with very high probabilities.

Optimal PMU arrangement considering limited channel capacity and transformer tap settings

The transition from the conventional power systems to smart grids has led to the modernisation of the measuring infrastructure used for their monitoring and control. Among the devices used to accomplish these tasks, phasor measurement units (PMUs) play a key role since they can provide extremely accurate synchronised voltage and current phasor measurements. The optimal PMU placement (OPP) problem, which focuses on minimising the number of PMUs for full system observability, has long been a popular research topic. The vast majority of the available OPP techniques have treated each transformer tap setting (voltage turns ratio and phase-shifting angle) as a fixed network parameter. Such an assumption can lead to misdirecting residuals in adjacent valid measurements when the modelled tap setting is incorrect. The aim of this study is to propose a substation-oriented OPP method based on a binary semidefinite programming algorithm, considering the observability of the transformer tap settings and the limited PMU channel capacity. The method is illustrated using an 8-bus test system. Numerical results using different size IEEE systems are presented and discussed. The proposed approach is further applied to the Polish 3120-bus system to show its efficacy in solving the OPP problem for large-scale power systems.

Enhanced Optimal PMU Placements With Limited Observability Propagations

When the power grid encounters unexpected contingencies, conventional optimal phasor measurement units (PMUs) placement formulations with unlimited observability propagations may increase the risk of losing the observability. In order to tackle out this difficulty, an enhanced optimal PMU placement (OPP) formulation with considering bounded observability propagation constraints is proposed in this paper by introducing the concept of observability propagation depth (OPD). The OPD can characterize the depth of observability propagations applied to observe a bus from its nearest PMU measurements. To depict OPDs under a mathematical programming (MP) framework, new observability propagation rules, which are also valid for a power grid remaining incomplete observability with limited PMUs, are investigated. Extensive simulations are conducted on several IEEE test-bed systems as well as the Polish-2383 system to demonstrate the feasibility and the effectiveness of the proposed formulation.

Dominant inter-area oscillation mode identification using local measurement and modal energy for large-scale power systems with high grid-tied VSCs penetration

Appropriate phasor measurement unit (PMU) signals must be selected to monitor electromechanical oscillations for large-scale renewable energy connected power system. Considering the response mechanism of the active and reactive power of grid-tied voltage source converters (VSCs) in the electromechanical time scale, a local measurement signal selection strategy for monitoring inter-area oscillations (0.1–0.7 Hz) based on the inherent electromechanical characteristics of the synchronous generators (SGs) is proposed. Moreover, optimized dynamic mode decomposition (OpDMD) algorithm is used to extract dominant modal parameters (e.g., oscillation frequency, damping ratio, and mode shape). First, from the perspective of frequency response characteristics, the dynamic interaction mechanism between grid-tied VSCs and the AC grid is revealed in the electromechanical time scale. Second, considering the intrinsic relationship between the inherent inertia constant of the SG and observability of response after a disturbance, a local measurement signal selection strategy for monitoring the inter-area electromechanical oscillation of power systems with high grid-tied VSCs penetration is proposed. On the basis of the selected local measurement signals, the OpDMD algorithm identifies dominant inter-area oscillation parameters. Results of the IEEE 16-generator 5-area system show that the proposed method can accurately extract electromechanical modal parameters from the limited PMU measurements, verifying the effectiveness of the local measurement signal selection strategy for inter-area oscillation monitoring of power systems with high grid-tied VSCs penetration.

Unbiased Minimum Variance Filter-Based Generator State Estimation Using PMU Measurements for Unknown Generator Input

Estimating the values of internal states representing the synchronous generators is very much essential in various monitoring and control strategies based on the state-space model. Kalman filter-based techniques are used to estimate the states locally using the input and output measurements of the generator so that the expectation of error is minimum. Wide area power system (WAPS) control requires remote monitoring of these states. Phasor measurement units (PMU) are widely used for remote monitoring and control in WAPS, but the remote measurements are limited to output measurements like voltage, current, and frequency. In the case of Kalman filter-based techniques in addition to the output measurements, input measurements like field and torque input are also required in order to estimate the states. This paper proposes an input invariant filter technique for remote estimation of generator states using the limited PMU measurements, even in the absence of mechanical input torque and field voltage measurements. The performance of the filter under various grid conditions and change in input signals are analyzed in this paper. The effect of measurement error, PMU refresh rate, and execution time are also analyzed.

Efficient Location Identification of Multiple Line Outages With Limited PMUs in Smart Grids

The efficient location identification of multiple line outages is critical to not only cascading failure elimination but also repair cost reduction. Most of existing methods, however, fail to handle location identification well. This failure typically occurs because the methods cannot overcome two challenges: the very limited number of phasor measurement units (PMUs) and the high computational complexity. This paper presents an efficient algorithm inspired by the ambiguity group theory to identify the locations of line outages with limited PMUs. Using 14-, 57-, 118-, 300-, and 2383-bus systems, our experimental study demonstrates that the proposed technique successfully identifies the most likely multiple line outages while attaining a 500 × speedup when compared to the method of exhaustive search.