Wide Area Measurement Research Articles

Transient Stability Control Based on Kinetic Energy Changes Measured by Synchronized Angular Velocity

Real-time transient stability studies are based on voltage angle measures obtained with phasor measurement units (PMUs). A more precise calculation to address transient stability is obtained when using the rotor angles. However, these values are commonly estimated, which leads to possible errors. In this work, the kinetic energy changes in electric machines are used as a criterion for evaluating and correcting transient stability, and to determine the precise time of insertion of a special protection system (SPS). Data from the PMU of the wide-area measurement system (WAMS) are used to construct the SPS. Furthermore, it is assumed that a microcontroller can be located in each generation unit to obtain the synchronized angular velocity. Based on these measurements, the kinetic energy of the system and the respective control action are performed at the appropriate time. The results show that the proposed SPS effectively corrects the oscillations fast enough during the transient stability event. In addition, the proposed method has the advantage that it does not depend on commonly proposed methods, such as system models, the identification of coherent machine groups, or the structure of the network. Moreover, the synchronized angular velocity signal is used, which is not commonly measured in power systems. Validation of the method is carried out in the New England power system, and the findings show that the method is helpful for real-time operation on large power systems.

Twin-Delayed Deep Deterministic Policy Gradient for Low-Frequency Oscillation Damping Control

Due to the large scale of power systems, latency uncertainty in communications can cause severe problems in wide-area measurement systems. To resolve this issue, a significant amount of past work focuses on using emerging technology, including machine learning methods such as Q-learning, for addressing latency issues in modern controls. Although the method can deal with the stochastic characteristics of communication latency, the Q-values can be overestimated in Q-learning methods, leading to high bias. To address the overestimation bias issue, we redesign the learning structure of the deep deterministic policy gradient (DDPG). Then we develop a damping control twin-delayed deep deterministic policy gradient method to handle the damping control issue under unknown latency in the power network. The purpose is to address the damping control issue under unknown latency in the power network. This paper will create a novel reward algorithm, taking into account the machine speed deviation, the episode termination prevention, and the feedback from action space. In this way, the system optimally damps down frequency oscillations while maintaining the system’s stability and reliable operation within defined limits. The simulation results verify the proposed algorithm in various perspectives, including the latency sensitivity analysis under high renewable energy penetration and the comparison with conventional and machine learning control algorithms. The proposed method shows a fast learning curve and good control performance under varying communication latency.

SVM based intelligent predictor for identifying critical lines with potential for cascading failures using pre-outage operating data

The process of cascading failures is one of the most important threats to the security of the power system, which can lead to a number of blackouts. Therefore, predicting the potential of cascading failure occurrence (PCFO) using pre-outages information is very essential. In this study, PCFO, associated to each line outage which is an important factor for causing blackout, is predicted before line outage using pre-outage steady state operating data (PSSOD). In the proposed scheme, support vector machine (SVM) is used as an intelligent predicting tool with information of dominant operating variables (DOV) as its input vector. DOV is a subset of PSSOD which have rich information about line criticalness and associated PCFO of the line outage. DOV can be identified using the joint mutual information (JMI) mathematical method based on the theory of mutual information and entropy between operating variables. In a real-time environment, the DOV associated to each line outage are collected by wide area measurement system (WAMS) in the power system control center (PSCC) and given to the SVM as an input vector for PCFO prediction. Therefore, the proposed scheme can be used as an alarming system for evaluating the PCFO of lines. By predicting PCFO, there is a chance for changing operating point of the power system from insecure mode to secure one by preventive actions. The proposed scheme has been implemented on IEEE 39-bus test system and its performance is validated with the desired results.

Online DMDc Based Model Identification Approach for Transient Stability Enhancement Using Wide Area Measurements

This letter proposes an online control strategy to enhance the overall damping of low-frequency power system oscillations of large inter-connected electrical networks. To achieve this objective, the proposed approach employs wide area measured signals to estimate an accurate online model of power system dynamics using the dynamic mode decomposition with control (DMDc) method. The DMDc technique applies singular value decomposition (SVD) to the collected data matrices such that an accurate low-order approximation of system dynamics is obtained. The estimated model is thereafter utilized in designing the wide area damping controller (WADC) using discrete linear quadratic regulator (DLQR). Furthermore, kalman filtering is deployed to update the states of the designed controller and to remove the noise that is contained in the measured signals. The effectiveness of the proposed method is tested on the 10 machines, 39-bus New England test system.

Secured zone 3 protection during load encroachment using synchrophasor data

Three-phase fault and load encroachment are balanced phenomena. A load encroachment function is incorporated in distance relay to improve the security of protection scheme, confuses with three-phase fault during high reactive loading condition. Zone 3 malfunction during such a situation often causes cascade line tripping leading to wide-area disturbance. In this work, voltage phasors of selected buses from a wide area measurement system (WAMS) are used to prevent zone 3 maloperation of a distance relaying scheme during load encroachment. The change in magnitude and phase of bus voltages are applied to discriminate three-phase fault from load encroachment. Performance of the proposed method is tested with New England 39 bus and IEEE 118 bus system simulated in EMTDC/PSCAD. Results show high accuracy as well as the speed of operation, is very fast of the proposed method.

Risk-Constrained Minimization of Combined Event Detection and Decision Time for Online Transient Stability Assessment

This paper addresses the problem of `quickest possible' online transient stability assessment, by minimizing the decision time of combined event detection that might lead to a system split and unstable generator group prediction, from real-time wide area power system measurements. More importantly it does so by respecting predefined probabilistic error constraints for the prediction. The statistical theory of optimal detection is applied, firstly to choose the detection threshold and secondly to select a flexible assessment time, after using probabilistic neural networks to provide a temporal representation of the data. On simulated wide area measurements from the interconnected New England test system and New York power system this approach is between two and three times faster on average than strategies based on fixed assessment times, despite having comparable error rates.

Voltage instability identification with modified L-index using synchrophasor data

<p>In the prevailing open-access environment, one of the limitations for power exchanges has been voltage stability. The study of voltage stability necessitates a complete network representation. In this paper, the advantage of the dynamic behavior of generators is considered by incorporating dynamic models for generators. It has been shown dynamic models resulted in more accurate results compared to the conventional PV buses or ideal voltage source models that are used in most of the voltage stability studies. Moreover, the traditional L-index is augmented by incorporating real-time and synchronized phasor data collected from the optimally located phasor measurement units (PMU) in a wide-area measurement system (WAMS) to estimate more accurate voltage stability margins. Simulation studies carried out on IEEE 9-bus and IEEE 14-bus systems under various system conditions. It has been demonstrated that the inclusion of dynamic models and synchrophasor data from WAMS significantly improves the precision with which voltage stability analysis results are obtained.</p>

A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

Artificial Neural Network Approach for Fault Detection and Identification in Power Systems with Wide Area Measurement Systems

Artificial Neural Network Approach for Fault Detection and Identification in Power Systems with Wide Area Measurement Systems

Guest Editorial Special Issue on Resilience-Oriented Protection, Control, and Monitoring Systems for Power Grids

The papers in this special section focus on resilience-oriented protection control and monitoring systems for power grids. Existing protection and control systems have been developed based on the properties and requirements of the legacy power system. However, the severity, frequency, and diversity of large-scale disturbances that can disrupt normal operation of power grids have increased. For instance, the number of major climate disasters has risen substantially, and the grid has been exposed to cyber-attacks due to the expansion of a host of different communication-dependent features/elements. In the meantime, various emerging components of modern grids can potentially be a major asset to improve the grid resilience. For example, islanded microgrids can ensure the continuity of service during major disruptions in the transmission system. Wide-area measurement systems also can provide predictive data in the event of large-scale disturbances, thus expediting mitigative measures. These recent developments have led to renewed and intensified efforts to meet the demands for higher levels of grid resilience. Protection, monitoring, and control systems will play a major role in these efforts by either preventing largescale disruptions or enabling the grid to cope with their consequences.

A Robust Scheme for Coherency Detection in Power Systems

This paper presents an approach for online generator coherency identification based on windowed dynamic time warping (DTW). Generator rotor speed deviations measured by phasor measurement units (PMUs) are used as input data to compute a DTW dissimilarity matrix. Using the dissimilarity matrix together with Agglomerative Hierarchical Clustering (AHC) and Hubert-Levin index (C-index), generators are optimally grouped into coherent clusters. In addition to the clustering of generators, an index for characterizing the transmission delay of a Wide Area Measurement System (WAMS) is presented. A data delay factor that can indicate whether there is an inconsistent PMU data transmission delay is also proposed. The coherency identification technique and indices were tested using simulations carried out on the IEEE 39-bus system. The test results indicate that the proposed scheme accurately clusters generators into coherent groups. The suggested indices were also found to be valid. Keywords : coherence identification, dynamic time warping, speed deviation, phasor measurement unit, dissimilarity matrix

Model free Robust Real-Time severity analyser using PMU measurements

Inception of any disturbance may edge the power system towards transient unstable settings. To safeguard the system from transient instability, accurate and quick assessment of impact of disturbance on the system is essential. The majority of available state-of-art presents phasor measurement unit (PMU) assisted Transient Stability Assessment (TSA), which does not infer any information regarding the severity of disturbance on the system. This paper proposes model free Wide Area Transient Instability Severity Analyser (WATISA) for real-time severity assessment effectively. The WATISA presents first swing TSA in terms of severity assessments. Following an event, severity assessment is quantified by assessing the number of severely affected buses connected with generators and loads. The presented method first, utilizes synchronized bus frequency and voltage measurements to construct a novel Robust Severity Assessment Index (RSAI) to capture the real-time status of a power network. Then, to determine the severity of an event, Severity Indices (SI) based on RSAI and bus voltage measurements are calculated. Finally, Adaptive Boosting (AdaBoost) of decision estimators is developed to evaluate associated Severity Level (SL) for incurred disturbance. To validate robustness of the proposed WATISA, its performance is tested with system topological changes and wide-area measurement noise. The simulation results on IEEE 39 bus power system highlights method effectiveness in estimating severity level with topological changes and measurement noise.

Design of adaptive wide-area damping controller based on delay scheduling for improving small-signal oscillations

The main aim of this paper is to compensate the delays resulted from communication links transmitting wide-area measurement signals in a new large-scale power system. The proposed controller is based on adaptive delay compensation which is able to handle the uncertainty arising from the phase delay by means of scheduling the delay’s value. In this design, further to compensation of WAMS’s delays, the delay due to internal structure of closed-loop system is also modeled, and by using synthesis H∞/H2 technique, its robustness has been evaluated by defining an objective function. Then, by means of small-signal stability analysis, a wide-area damping controller has been employed for coordination of FACTS devices including STATCOM and HVDC with the controller of rotor-side-convertor (RSC) in DFIG. The simulations have been implemented on 8-area power system including wind farms and FACTS devices through different scenarios.

Data gaps and degraded space-time resolution for modal decomposition: A compensator approach

High-resolution empirical mapping methods of dynamic oscillation responses for large power systems have recently been considered to treat the data gaps and its space-time compensation. Although virtually-designed data of signals are not a real condition for monitoring systems, these can be implemented to study objectively the moving features and dominant paths of inter-area oscillation modes. This paper demonstrates the application of the space-time interpolation of wide-area measurements to compensate data gaps and degraded space-time resolution for modal decomposition. Contributions of empirical mode coherence for splitting and clustering data in density factors are also presented to the classification area. The method involves the space-time high-resolution interpolation of empirical mode components for splitting closely-related oscillation modes at intrinsic timescales, preserving their multivariate waveform features. Therefore, high-resolution empirical viewing maps of remotely-sensed dynamic responses, with degraded space-time resolutions, are derived to study the observance and dominant paths of wide-area oscillation modes. The proposed approach is numerically applied to the IEEE 16-generator 68-bus test system for efficiently defining empirical viewing maps of inter-area oscillation modes.

The New Detection Method of Ovarian Follicle Development Using Digitized Wide Area Measurement

METHODS: The research method used in this research was experimental laboratory with pre-and posttest only control group design. RESULTS: The result shows that the extradiol level which has range of 26.30–31.03 from 28 experimental animals measured, this showed more measurement diameter which has not had measurement addition compare with the wide percentage of measurement. The result shows strong correlation between digitalized measured wide follicles to the changing of estradiol level with value of 0.453. The result of comparation between estradiol level and measured diameter shows weak correlation. This shows that manual measurement of follicle diameter still weak to the changing of estradiol level. CONCLUSION: There is strong correlation between measured wide area follicle used ImageJ applications to the changing of estradiol level compare to the measurement of follicle diameter.

Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

Dynamic clustering-based model reduction scheme for damping control of large power systems using series compensators from wide area signals

This paper presents a new scheme of thyristor controlled series compensators (TCSC)-based damping controller for damping inter-area oscillations in bulk power systems based on a wide area measurement system (WAMS). In this regard, the proposed scheme provides an adaptive three-phase step through real-time working mode. In the first step, by proposing dynamic clustering-based technique, wide area signals evaluate the inter-area oscillations of the system which in a real-time procedure identify the corresponding oscillating areas. In the case of reducing the order of power systems through a set of aggregated coherent areas, the proposed scheme implements through the second step which utilizes as a damping controller for damping the inter-area oscillations. In this step, the wide area electromechanical signals use as input data through the proposed TCSC-based damping controller. The corresponding controlling signals consist of inter-area rotor angle (ΔδCOI) and inter-area speed deviation (ΔωCOI) evaluated through center of inertia (COI) frame. In the third step, at each time window (ΔT) through real-time working mode, the proposed scheme examines through a set of fault occurrences which based on online evaluations of the system dynamic responses, the controller parameters adjust adaptively. The proposed controller is an online and non-model-based scheme which properly reduces the system order through a set of coherent areas to provide proper damping performances of inter-area oscillations. The effectiveness of the proposed scheme evaluates through the Iran National Power Grid with the potential of two oscillating areas with proper damping performances for damping the unstable inter-area oscillations.

Mitigating inter-area oscillation of an interconnected power system considering time-varying delay and actuator saturation

A wide area damping controller (WADC) is designed in this paper to mitigate the frequency deviation of interconnected power system. The effect of time delay during wide area measurement system (WAMS) and saturation of the actuator is also considered in this work while designing the WADC. The effect of communication delay along with the saturation of a actuator can make the interconnected power system unstable. This paper focuses on finding a stabilization criterion of the power system connected with a WADC. The WADC is designed taking the effect of time-varying delay in the WAMS and saturation of the actuator into consideration. The saturation of the actuator is modelled with the help of dead zone type non-linearity. In this paper, a Lyapunov–Krasovskii functional is used to design a state feedback control law based on linear matrix inequality (LMI) taking time-varying delay and the actuator saturation into consideration. A thyristor controlled series capacitor (TCSC) is used as an actuator. A supplementary damping controller (SDC) is used as the WADC in this paper to provide sufficient damping. Simulation study on four machine 11 bus power system and New England 39 bus power system is performed to understand the performance of the designed controller. The results are also verified in real-time using OPAL-RT.

Convolutional neural network‐based power system frequency security assessment

Weak inertia characteristics of power systems with high penetrations of renewables have become a prominent problem for frequency security. To solve this problem, a convolutional neural network (CNN)-based deep learning approach is applied to realize rapid frequency security assessment (FSA). First, the time series frequency security feature is autonomously mined from the wide-area measurement data to serve as the input data. By doing so, the complex construction process of frequency security feature quantity is avoided. A deep learning structure is then used to establish a non-linear mapping relationship between time series features and frequency security indicators to realize end-to-end power system frequency security prediction. Next, the evaluation accuracy of the proposed approach is optimized by tuning the key parameters in the CNN-based evaluation model. Through data measurement error analysis and a wind penetration sensitivity study, the anti-interference performance of the proposed evaluation model is demonstrated. Finally, the effectiveness of the CNN-based FSA is verified by case studies of a modified 16-machine 68-node system and the China Southern Power Grid.

An ensemble model for wide-area measurement-based transient stability assessment in power systems

Transient stability assessment involves the determination of whether all generators in the power system would maintain synchronous operation when it is subjected to a large disturbance. Assessing the transient stability status of a power system in an accurate and timely manner is of high importance in order to plan and initiate relevant corrective control actions where required. The wide-area monitoring systems enable to acquire synchronized measurements of important system variables from geographically dispersed locations via the phasor measurement units (PMUs), which can be used for measurement-based transient stability assessment. In this work, a stacked ensemble model that uses post-contingency wide-area synchrophasor measurements of bus voltage magnitude, voltage angle, frequency and rate of change of frequency is proposed to classify the post-contingency transient stability status of a power system to be unstable or stable. The model includes four individual long short-term memory network classifiers as base-learners and a LightGBM classifier as a meta-learner. An important differentiation of this work is that the proposed method does not assume that each bus is equipped with a PMU. Furthermore, robustness of the proposed model is analyzed in the presence of measurement noise and against the scenario of missing measurements. The performance of the proposed ensemble model is investigated in two generic test systems, the Nordic and the 127-bus WSCC test systems, as well as the practical Turkish power system.