Wide Area Measurement System Research Articles

WAMS-based Hierarchical Active Power Differential Signal Algorithm for Backup Protection of a FACTS Compensated Transmission Network

This paper proposes a hierarchical active power differential signal-based generalized backup protection algorithm using Wide Area Measurement System (WAMS) data for Flexible AC Transmission System (FACTS)-compensated transmission networks. The proposed algorithm can be used for backup protection of transmission systems with any shunt and series-type FACTS devices. The increased number of FACT compensators affects the reliable operation of primary and backup protection of the transmission lines. Both shunt and series compensated lines cause malfunctioning of existing backup protection schemes. The proposed algorithm utilizes the sequence components of bus voltages and active power differential signals of lines to identify the faulty line. The algorithm is validated on a modified 9-bus system under MATLAB/SIMULINK platform. It is observed that the algorithm is suitable for identifying a faulty line in transmission systems containing both uncompensated and compensated lines with series or shunt-type FACTS controllers. This algorithm has the advantage that it uses a generalized backup protection logic and can be used for the accurate identification of a faulty line irrespective of the type of compensation devices.

Analysis of Oscillations during Out-of-Step Condition in Power Systems

Power systems interconnected by weak tie lines can be subject to low-frequency oscillations because of disturbances which excites the low-frequency modes of the system; furthermore, these oscillations can be stable or unstable. The latter, if not treated, can cause severe oscillations that divide the network into smaller groups which oscillate against each other, leading to out-of-step (OOS) condition in the network. The detection of OOS condition is a challenge for power system operators in real time as it is difficult with conventional measuring instruments, to identify the instant at which the bus voltage angle between two areas connected by tie line falls out of synchronism. Conventionally, the detection of OOS condition has been carried out with impedance-based relays along with power swing blocking. With the advent of synchrophasor-based measurement units, it is now possible to measure the bus voltage angle in real time leading to direct detection of OOS condition in power system and intentional islanding. In this article, a systematic analytical study and EMT time-domain simulation study have been performed to simulate OOS condition in the power systems, and its detection is based on the voltage angle difference with wide-area measurement systems (WAMSs). The article has been carried out on a single machine infinite bus (SMIB) to monitor generator OOS, Kundur’s two-area system to detect interarea OOS, and the IEEE39 bus system to identify OOS condition and with a new algorithm. Time-domain simulation studies carried out with OPAL-RT real-time simulator in HYPERSIM environment corroborates with analytical results.

Evaluation of Wavelet Transform Based Feature Extraction Techniques for Detection and Classification of Faults on Transmission Lines Using WAMS Data

The smart grid is an intelligent power system network that should be reliable and resilient for sustainable operation. Wide-Area Measurement Systems (WAMS) are deployed in the power grid to provide real-time situational awareness to the power grid operators. An excellent strategy for exploiting the WAMS data effectively is to extract relevant insights from the increasing volume of data collected. Feature extraction techniques are pivotal in developing data-driven models for power systems. This paper proposes an ensemble feature extraction method for developing intelligent data-driven models for transmission line fault detection and classification. A comparative efficacy analysis of the proposed ensemble feature extraction method is carried out with state-of-the-art feature extraction methods. The models developed and evaluated with the feature data derived with the proposed method give an accuracy of 100% for fault detection and 99.78% for fault classification. This method also has the advantage of significantly reducing training and testing time. Features are extracted from the WAMS data collected by simulating an IEEE39 bus test system in the PowerWorld simulator.

WAMS-Based Fuzzy Logic PID Secondary Voltage Control of the Egyptian Grid

This paper presents the application of fuzzy logic PID secondary voltage control to the Egyptian power system model. The study included tertiary voltage control, Wide Area Measurement System (WAMS) configuration, a selection of pilot buses, and fuzzy logic PID secondary voltage control to improve the system performance. The secondary voltage control was applied using a fuzzy PID coordinated controller, a reactive power integral controller, Automatic Voltage Regulators (AVRs), and regional generators. The tertiary voltage control was implemented based on the optimal power flow to maximize the reactive power reserve. A novel optimization technique is presented to select pilot buses based on different operating conditions and compared to other techniques. The optimal WAMS configuration included the best allocation of Phasor Measurement Units (PMUs), Phasor Data Concentrators (PDCs), and the required communication infrastructure considering geographical regions with minimum cost. The Egyptian power grid considering 500/220 kV level is simulated by using DIgSILENT software to perform static and dynamic analyses, while the WAMS optimization problems and fuzzy logic PID controller design are performed by employing MATLAB software.

Electrical Event Detection and Monitoring Data Storage from Wide Area Measurement System

Synchronized phasor measurement systems are being widely used around the world and have become essential elements in the evolution of the operation of large electrical power systems (EPS). These systems, called Phasor Measurement Units (PMUs), are capable of recording and communicating dynamic data from the EPSs in a synchronized way by GPS and with a high sampling rate, generate a huge set of data that, among many applications, has the capacity to detect events. In this way, this work presents a data management system architecture applied to a real PMU system located in the state of Paraná, Brazil that detects and storages events using principal component analysis and Pearson correlation. This method can detect and store electrical events that occurred during the operation of the national interconnected system of Brazil with good results.

Discrete-time analytic signals for power system phasor and frequency tracking

The tracking of phasor and frequency is becoming vital in power networks ranging from interconnections monitored via wide-area measurement systems to isolated microgrids operating autonomously. However, despite the progress in phasor measurement technology, the performance of the signal processing module is still an active research area due to the challenge of phasor and frequency estimation under dynamic conditions. This paper proposes a signal processing technique for frequency and phasor estimation based on discrete-time analytic (DTA) signals. First, a new method is proposed for computing the DTA signal directly in the time domain. The DTA signal is then approximated using a first-order Taylor series expansion that forms the basis of the tracking method, essentially ensuring that the DTA signal is generated at the exact value of the estimated frequency. The proposed method is accurate as it is based on time-domain least squares estimation; it also meets real-time computing requirements because it employs a single factorization of the gain matrix to achieve the solution. Numerical results are reported under both steady-state and dynamic conditions, showing the proposal’s viability.

An optimized dynamic state estimation method for voltage source converter under cyber-attacks

The measured data of the power system with voltage source converter (VSC) depending on data communication network, are monitored and analyzed by Dynamic state estimation (DSE), which can provide predictive data to control system. However, information communication technology may contain unknown risks and be vulnerable to cyber-attacks, resulting in inaccurate predictions of DSE. For example, the communication network of wide area measurement system (WAMS) will provide wrong data to DSE after being attacked. Thus, in this paper, an optimized dynamic state estimation method for voltage source converter considering cyber-attack is proposed. First, in order to consider more dynamic characteristics of VSC and provide a more complete model for DSE, the dynamic model of VSC which is affected by cyber-attacks is derived. Then, base on the model which is linearized around an equilibrium point, the Kalman filter considering anomalous events caused by false data injection attack (FDIA) is improved to obtain the optimal state estimated value. Finally, to verify the effectiveness of the proposed method and the accuracy of prediction values, a simulation has been carried out on a single-VSC infinite-bus power system. The simulation results show that the proposed method can predict dynamic state values effectively and the Root Mean Squared Error (RMSE) is around 4.6×10−5 in the power system with VSC considering cyber-attack.

WAMS-Based HVDC Damping Control for Cyber Attack Defense

Owing to the fast and large power regulating the capacity of the HVDC system, the wide-area measurement system (WAMS) based high voltage direct current (HVDC) system has been regarded as a prospective solution to deal with the low-frequency oscillation issue. However, due to the vulnerability of the WAMS communication, WAMS based HVDC system control can be a prime target of malicious penetrations that could lead to disastrous events. To remediate this adverse effect, an improved WAMS based HVDC damping control framework is proposed. First, a lightweight network named Attack Shuffle convolutional neural Networks (ASNet) is proposed to learn the characteristics of cyber attacks. Then, a model-free-based cyber attack defense framework is introduced to quickly identify the attack types based on the continuous wavelet transform and ASNet. Additionally, an improved control framework of the WAMS and HVDC-based wide-area power oscillation damping control (WH-PODC) is developed to provide different response control for mitigation of the impact of cyber attacks. Finally, the performance of the proposed WH-PODC control framework is evaluated with real PMU data in multiple scenarios in RTDS, where the results indicate that the response intensity can be kept under multiple types of cyber attacks while providing similar effectiveness in oscillation suppression to conventional controls.

Online estimation of controlled islanding time intervals using dynamic state trajectories through cascading failures from WAMS data

• Use the wide area measurement system to evaluate the system dynamic stability. • A new concept of estimating islanding time. • Proposing an islanding identification time (IIT) index based on the system dynamic behaviors. • Presenting an adaptive concept of IIT scheme with respect to fault severity. • Presenting an online and non-model-based index through real-time evaluations. • Online evaluation of Unstable Equilibrium Point (UEP) to estimate the stability margin of each created island. Islanding strategy is the last controlling action performed through different phases. In this case, fast and secure estimation of the islanding identification time (IIT) are two emergency parameters which play important roles on wide area stability. Due to nonlinear behaviors of the power system generators through severe faults, determining the best islanding scenario remains as an important challenge. By using wide area measuring system (WAMS) technology, an online and adaptive concept of IIT scheme is presented in this paper. To do this, at each time window, considering a set of proper IITs, the best islanding times are estimated. In this case, considering each islanding scenario, based on concept of Unstable Equilibrium Point (UEP) through online evaluations, the synchronism and stability margin of each created island is evaluated. By this way, the corresponding synchronous generators (GSs) are separated though a set of secure islanded subsystems which are operated within different stable operating points. In the case of estimating unstable islanding cases, by rescheduling GSs islanding scenarios through real-time evaluations, the most proper IITs are estimated and performed. Simulation results indicate the effectiveness of provided scheme for online estimation of proper islanding times through different wide area instability scenarios.

Development of methods for visual representation of low-frequency oscillation parameters in power systems

The development of wide area measurement system (WAMS) expands the analysis capabilities of the Unified power system based on computational processing of synchronized phasor measurements (SPM). The analysis of low-frequency oscillations (LFO) in the power system is one of the most relevant applications of WAMS data. The task of locating the source of forced LFO in real-time is highlighted. Due to the increasing complexity and heterogeneity of real power systems, a definitive solution to this problem does not currently exist. However, there are SPM data analysis methods that provide a high probability of identifying the source of oscillations. Informative representation of measurement signals, intermediate, and final LFO analysis results can serve as the basis for decision support tools for operational dispatch personnel. Direct visualization of raw data and computed parameters, such as a set of time function graphs, is inefficient due to the large volume. Therefore, compact high-level representations are in demand. This paper proposes some approaches to visualizing SPM data, computed LFO parameters, and the results of applying various oscillation source detection methods. The presented data visualization methods have been implemented by the authors in a LFO analysis software system.

Development of Methods for Calculation the Load Angle of a Synchronous Generator Based on PMU Data

The development of the Wide Area Measurement Systems (WAMS) in the Unified Electric Power System (UES) of Russia and using phasor measurement technology (or PMU technology) open up new prospects in the electric power system (EPS) operational dispatch and automatic control. PMU devices installation on synchronous generators (SG) allows to obtain information not only about currents and stator voltages phasors, but also information about the excitation current and excitation voltage. This set of high-precision measurements of the network state parameters, in the presence of generators mathematical models, provides the possibility of calculating such parameters, the direct measurements of which are very difficult. One of these parameters, which can be determined using PMU, is the load angle of the SG (the angle between the generalized voltage phasor and the generator EMF phasor in the dq axis system) in the steady states and transients. The value of the load angle can be used to analyze the static and dynamic stability, as well as to quantify the damping properties of the SG at low-frequency oscillations (LFO) of the power systems electrical parameters. This study analyzes the possibilities for calculating the load angle based on a typical set of generator PMU signals: stator current and voltage vectors, excitation current and voltage. Two methods for calculating the load angle in steady states and transients have been developed and implemented. The first method is based on solving a system of nonlinear algebraic equations, and the basis of the second method is the mathematical model of the SG. The presented methods were tested using the SimInTech software package. Further, using the developed methods, the load angles of two turbogenerators installed at the plants of the Russian UES were calculated according to the PMU data of the parameters of the electromechanical transient process.

A novel comprehensive optimal PMU placement considering practical issues in design and implementation of a wide-area measurement system

• Several WAMS-based applications are considered in the OPP problem. • Concept of the line-wise observability is presented. • Observability of multi-TSO power systems in the OPP problem is investigated • The proposed OPP model is originally a MILP one and its global solution can be easily found. Since phasor measurement units (PMUs) are increasingly installed in power systems, many research works have investigated the optimal PMU placement (OPP) problem with the sake of maximizing observability over the power grid, while minimizing the cost of purchasing PMUs. However, there are still some practical challenges that have not been considered in the previous research studies, and are needed to be addressed for a real wide-area measurement system (WAMS) implementation. In this paper, a comprehensive OPP model is proposed which employs a set of basic and novel constraints for a WAMS design. In this regard, the line-wise observability concept is proposed for the first time to consider the requirements of observability for a range of WAMS-based applications such as restoration management, model validation of power system components, and dynamic line rating (DLR) monitoring. In addition, observability constraints for multi-transmission system operator (multi-TSO) power systems that are operated by different legal entities are provided, which can considerably affect the OPP result. The proposed model is formulated based on a mixed-integer linear programming (MILP) formulation which enables the optimization problem to be solved easily and effectively by existing commercial optimization tools, without any need for linearization. The performance of the proposed OPP model is first verified through simulation results on an 86-bus real power system. Then, simulations are applied to IEEE 118-bus and 300-bus test systems, to certify the performance of the proposed model in comparison with other OPPs in the literature.

Phasor data concentrator placement in phasor measurement unit networks considering communication channel uncertainty and failure

AbstractOne challenge in Wide Area Measurement System is to design a reliable network which assures data availability. The availability of data in Phasor Measurement Unit (PMU) networks depends on two factors: 1‐Reliable data measurements which depend on performance of PMUs and transmission branches, 2‐Reliable data transmission which depends on the reliability of communication networks used to transfer data from PMUs to Phasor Data Concentrators (PDCs) and control centre. The reliability of data transmission can be improved by choosing proper locations for PDCs. In this paper, a novel approach for PDC placement is proposed based on two pre‐defined indices; observability reliability and Loss Of Data Expectation (LODE). In the proposed method, the probability of regional communication networks' failure and the uncertainty of failure data for all components are also included in the calculation of these two indices to achieve more realistic results. To handle uncertainty, fuzzy membership functions are obtained for repair time and failure rate of all components, and a fuzzy Monte Carlo method is presented to evaluate both indices for each possible PDCs' location. The minimum LODE is used to find the best locations for PDCs. The proposed method is investigated for three sample systems which prove the system efficacy.

Communication Resilience for Smart Grids Based on Dependence Graphs and Eigenspectral Analysis

Dependence analysis has been applied as a method to design resilient wide area measurement systems (WAMSs). Based on the graph theory, it is possible to distribute the importance, and therefore, the dependencies, among WAMS elements almost evenly, and thus, plan resilient-by-design infrastructures; since no element will be significantly more important than the others, the WAMS will not suffer from single points of failure. Our main goal in this article is the resilience of the communication layer of the WAMS. Although the cyber dependencies of WAMS infrastructures have been studied in the literature, there is a research gap regarding the analysis of the relevant physical dependencies. We propose a method for assessing both the cyber and physical dependencies in the WAMS through eigenspectral analysis. By concurrently analyzing the physical (electrical) and the cyber (informational) dependencies between the electrical buses and the communication routers and by reducing the deviation of their dependence values, it is possible to increase the structural resilience of the communication infrastructure. This will enable the network operators to properly distribute the level of the cyber and the physical dependencies among these components, by selectively applying communication redundancy and communication loops. Through simulations for two IEEE test cases, we demonstrate the effectiveness of the proposed method.

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. Case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

A Refined Taylor-Fourier Transform with Applications to Wideband Oscillation Monitoring

The recent increase in renewable energy adoption has enhanced the penetration rate of electronic equipment, leading to an increased risk of wideband oscillations. Existing wide-area measurement systems mainly focus on fundamental phasors, which cannot effectively monitor wideband oscillations. This study presents an accurate wideband oscillation monitoring method based on radial basis function (RBF) neural networks and Taylor–Fourier transform (TFT). First, discrete Fourier transform is used to obtain a preliminary estimation of the oscillation signal, and then, TFT is adopted to obtain a precise estimation even under dynamic conditions. To reduce the computational burden of TFT, an RBF neural network is used for noise intensity estimation, which adaptively determines the window length. Finally, the proposed method is verified by synthetic data and the field data collected from Guyuan and Hami, China. The experimental results show that the RBF neural network has an excellent denoising effect. When the signal-to-noise ratio is 45 dB, the maximum overall phasor error and the maximum frequency error are 1% and 0.01 Hz, respectively. Hence, it is expected to be useful for next-generation monitoring systems.

Power system abnormal pattern detection for new energy big data

Abstract The energy crisis is a problem that countries all over the world pay more and more attention to, and a series of ecological problems caused by it have become increasingly prominent. It is difficult for traditional fossil fuels to maintain a healthy and coordinated sustainable development of society and economy. The establishment of a sustainable energy system has become the development trend of various countries to solve energy problems. Electric energy is a secondary energy that all primary energy can be converted into, and an irreplaceable consumable for all industrial technologies and people’s lives. Electric power data has the characteristics of large rate span, numerous data sources, complicated interaction methods, and various types of data. The existence of abnormal data in the power system will greatly reduce the accuracy of the system state estimation and the state estimation convergence rate. This paper introduces the power grid industrial control system, combines the data flow of power big data, and analyzes the abnormal information detection process in detail. It takes the data stream acquired by the acquisition unit PMU of the wide area measurement system as the research object. The rapid development of the Hadoop big data platform provides important technical support for the research of power grid big data. Based on the Hadoop platform, the clustering algorithm is used to complete the anomaly detection of real-time data. The LOF algorithm has poor performance when dealing with a large amount of high-dimensional data, and has high time and space complexity. In order to make up for the shortcomings of the LOF algorithm, this paper uses the K-means clustering algorithm to propose an improved algorithm K-LOF of the density-based local abnormal factor detection algorithm LOF, and optimizes the neighborhood query process. It is verified by experiments that the K-LOF algorithm can effectively reduce the time complexity of the anomaly detection algorithm and improve the detection accuracy by 2–4.2%.

Dataplane Based Fast Failover in SDN-Enabled Wide Area Measurement System of Smart Grid

Dataplane Based Fast Failover in SDN-Enabled Wide Area Measurement System of Smart Grid

Fault Location and Faulty Line Selection in Transmission Networks: Application of Improved Gated Recurrent Unit

Wide-area measurement systems (WAMS) are efficient tools to estimate the fault location in the large-scale power systems. In this study, a fast and accurate deep learning based structure is proposed to detect the faulty lines and locate the fault based on the measured data of WAMS including phasor measurement units. To this end, a modulated gated recurrent unit deep learning based network named improved gated recurrent neural network has been proposed. The designed network, which has a recurrent neural network based structure, benefits from one update gate that can capture sudden changes in transient voltage and current signals and also enhance computational efficiency. Furthermore, to enhance robustness of the designed network under noisy conditions, a new informative-based loss function is formulated. The proposed loss function utilizes generalized form of mutual information to enhance robustness of designed network in the presence of Gaussian and non-Gaussian noises. The numerical results on IEEE 68-bus system verifies the effectiveness and superiority of the proposed method considering several operational conditions and comparison by two combined shallow-based networks and one deep learning based networks.

WAMS-based two-level robust detection methodology of power system events

With the advance of Wide-Area Measurement Systems (WAMS), power system operators have direct access to a large amount of data with valuable information about the power system dynamic performance. As a result, there is a clear need for new data-driven methodologies capable of extracting relevant information from this collected data. One of the key challenges is correctly detecting power system disturbances to avoid false alarms during real-time operation as well as off-line disturbance analysis. This paper proposes a two-level robust event detection methodology aiming to reduce false disturbance detection (false positives/alarms) and validate true events. The methodology is divided into two-levels: (i) signal processing analysis (ii) deep neural network (DNN) classification. In the first level, we apply a widely used spectral analysis based on the Discrete Wavelet Transform (DWT) to event detection. In the second level, the events detected by the DWT are processed by a DNN to check if they are real events or false alarms. Finally, the proposed methodology is evaluated using real synchrophasor event records from the Brazilian Interconnected Power System (BIPS).