Real-time Phasor Measurement Units Research Articles

Real-time coordinated control of voltage regulation devices in a high PV penetrated weak distribution network

In the weak distribution network, on-load tap-changer (OLTC) needs to operate frequently to regulate the voltage fluctuations. Substantial solar photovoltaic (SPV) penetration affects OLTC life, which leads to increased maintenance cost. On the other hand, battery energy storage systems (BESS) are the most appropriate solution. However, frequent charging/discharging affects the BESS's life and make it uneconomic. Authors have proposed a control to efficiently utilize the existing voltage regulation devices without new communication channels. The proposed algorithm utilizing a real-time phasor-measurement unit (PMU) data to coordinate BESS, OLTC, and SPV inverters for the optimal control. In addition, for further improvement, the bidirectional state of charge (SOC) regulation method is also proposed. It further optimizes the existing solution to bring back SOC faster to the reference level to make it available for coming contingencies. Quantitatively, BESS processed energy and OLTC tapping operation have been reduced by 81.9% and 52.9%, respectively.

Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. This paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

Review of the Techniques of the Data Analytics and Islanding Detection of Distribution Systems Using Phasor Measurement Unit Data

The application of the Phasor Measurement Unit (PMU) in the power system is expanding day by day since it provides a higher reliability through fast symmetrically monitoring and protection and assists in controlling power systems. For power systems, islanding is a significant event due to its hazardous consequences. To detect islanding events, several schemes have been previously proposed but inappropriate threshold setting, higher computational time, and false tripping are the main limitations. In addition, differentiating between real island events and transient faults is another limitation. However, appropriate threshold setting plays a considerable part in detecting the island event, which is also important to differentiate between real and non-island events. Phasor Measurement Unit can assist in islanding detection, but it can generate 30 samples/s, so there is always the possibility of making particular period data disappear. The principal contribution of this review article is its detailed discussion of real-time symmetrical PMU data and it further presents different PMU data analytic techniques and the proposed schemes for the islanding detection system. An appropriate methodology tried to understand how to incorporate missing PMU data techniques along with the islanding detection system to ensure the higher reliability of the network.

Application of IIA Method and Virtual Bus Theory for Backup Protection of a Zone Using PMU Data in a WAMPAC System

Many wide area monitoring, protection, and control (WAMPAC) systems are being deployed by grid operators to deal with critical operational conditions that may occur in power systems. Thanks to the real-time measurements provided by a set of distributed phasor measurement units (PMUs), different protection algorithms can be run in a central location. In this context, this article presents and validates a novel method that can be used as a backup protection for a selected area in a power system. It merges the integrated impedance angle (IIA) protection method with the theory of virtual buses in wide area electrical power systems. The backup protection works this way: once a fault is detected (pickup time), another delay (added to the pickup time) is defined in order to wait for the primary protection to act. If this does not happen, the algorithm generates its backup trip. The proposed method has been called the zone integrated impedance angle (Zone IIA). A real-time PMU laboratory has been used to test the proposed algorithm using a real-time digital simulator (RTDS). The algorithm has been programmed in a real-time automation controller (RTAC). It has been tested in two different simulated setups: first, a 400 kV transmission system, with and without the use of renewable energy sources (RES); second, a 150 kV submarine line between the Greece mainland and an island, which is currently the longest submarine alternating current connection in the world. The results obtained during the tests have yielded tripping times for area protection in the order of 48 ms, if no time delay is used between the fault detection and the trip order. According to the test results, the proposed method is stable, reliable, obedient, and secure, also with RES installed in the power system. Additionally, the method is selective, i.e., during the tests no trip was executed for external faults, no trip was executed in no-fault condition, and all the applied internal faults were detected and tripped correctly. Finally, the protection method is easy to implement. The method is also applicable to protection against short circuits in distribution systems. According to the trip times observed during the tests, it is clear that these algorithms are well suited to implement backup protections in transmission grids, even in scenarios with high penetration of renewable energies. Considering that backup trip times in transmission grids are usually set between 400 and 1000 ms, and that the actuation times obtained by the proposed algorithm are under 100 ms, the method is suitable for its use as a backup protection.

RBFNN assisted transient instability-based risk assessment of cyber-physical power system

Wide area monitoring and control of modern power system sprawling over large geographical area, has emerged as a viable technology employing phasor measurement units (PMUs) as its backbone. The PMUs are optimally placed to make the whole power system observable. Due to tripping of some generating units, or transmission lines the power balance gets disrupted. As a consequence, the power system becomes prone to risk of transient instability. Also, loss of observability becomes an additional risk due to tripping of transmission lines, which is instrumental for contributing to indirect observability. The purpose of this paper is to develop a predictive approach for identifying vulnerable generating units for proactive control actions to ensure transient stability. The methodology involves use of extended Kalman filter (EKF) for processing real-time PMU data for predicting the dynamic capability of generating unit so as to assess the power swings during transients arising due to contingencies. The radial basis feed-forward neural network (RBFNN) assisted algorithm categorizes generating units into marginal and risky categories based in their trend of swings with respect to dynamic capability curve of generating unit. The case-studies pertaining to IEEE-39 bus system under different contingencies, validate the efficacy of the proposed approach for real world applications. Since, the PMUs communicate synchronized data to PDCs the cyber system is an integral part of the wide area monitoring and control system. Therefore, a cyber physical approach takes into account the loss of observability as a risk from the cyber perspective. Both the risk associated with the physical as well as the cyber layers have been investigated.

Measurement Setup Based on Zynq SoC FPGA for Real Time Monitoring of Grid System Parameters

The present-day grid is dynamic with bidirectional power flow because of the integration of Distributed Generation Units with conventional power generation units. Because of the grid dynamics and complexity, real-time monitoring of the grid system parameters is a major challenge. Conventional phasor measurement units (PMU) based on Fast Fourier Transform (FFT) do not give an accurate estimate of grid parameters under dynamic varying conditions. This paper proposes a System-on-Chip compact framework for real-time monitoring of various grid parameters. The designed unit is adaptive and hence suited for dynamically varying conditions. Also, as the monitoring algorithm is implemented on SoC hardware, it gives real-time performance. The response time is slightly greater than three-fourth of a cycle, while measurement delay is less than three-fourth of a cycle. The Total Vector Error of the designed system is less than 1% during measurements and hence the unit conforms with M class requirements of the IEEE standard on synchrophasor measurements. The monitoring unit occupies less than 45% of the programmable fabric of the SoC FPGA and is power efficient. It can adapt to any grid voltage and current levels and can be used in real-time SoC PMU.

Real-time low-frequency oscillations monitoring.

A major concern for interconnected power grid systems is low-frequency oscillation, which limits the scalability and transmission capacity of power systems. Undamped or poorly damped oscillations will lead to undesirable conditions or even a catastrophic system blackout. Real-time synchrophasor data can be used to reliably detect and control these low-frequency oscillations in order to mitigate their catastrophic impact. In this paper, two low complexity tracking algorithms are proposed to identify and monitor low-frequency oscillations; namely, a fast subspace tracking algorithm and a gradient descent based fast recursive algorithm. Initially, both methods perform a one-time matrix pencil method on the power spectrum matrix of real-time Phasor Measurement Unit (PMU) data to detect low-frequency oscillations. This is then followed by two different low-complexity algorithms to fast track the low-frequency oscillations. While the first method uses a recursive fast data projection method-based algorithm, the latter runs a gradient-descent based fast recursive algorithm on every PMU to track and monitor low-frequency oscillations. Both methods have been compared to other state-of-the-art techniques, such as matrix pencil method, frequency domain decomposition, and TLS-ESPRIT. We have shown that the proposed approaches are capable of achieving performance with high accuracy, especially in terms of computational complexity for a large system with many PMUs.

Real-Time Analysis of the Impedance–Temperature Relationship in Electric Distribution Lines Using PMUs

In this paper, an efficient method to compute the phase impedance of a power distribution line is presented. This paper’s main interest is to collect and analyze the impedance–temperature profiles in distribution lines by employing real-time phasor measurement units (PMUs) voltage and current measurements. The monitoring system has been developed for microgrids integrating ocean energies within the scope of the Mexican Ocean Energy Innovation Centre (CEMIE-Océano), contributing to marine energy tools’ innovation and development. The development tool can easily be applied to other distribution network components or to monitor microgrids with renewable-energy generation.

Measurement-Based Transmission Capability Estimation of Power Grid Using Load Response-Based Equivalent Model

In this paper, the load response-based equivalent model is proposed to develop a more sturdy method for the transmission capability estimation of the power grid. Based on real-time phasor measurement unit (PMU) measurements, the power grid is modelled as the multi-port equivalent model based on the coupled single-port models. The associated critical equivalent branch in the multi-port model is identified and modified by the shaping factor to construct the proposed model. According to the modification, the dissimilarity of the load response index between the critical equivalent branch and the approximate profile of the actual load voltage can indeed be reduced to obtain the perfect matching between the critical load voltage and the actual load voltage. Accordingly, the accurate and sturdy transmission capability estimation can be achieved in the proposed model. Moreover, the proposed model is further extended to design a complete solution algorithm for the limit-induced transmission capability estimation considering generator reactive power limits. Simulations on IEEE 14-bus and IEEE 118-bus test systems are presented to validate the accuracy and the sturdiness of the proposed method.

Fault diagnosis, service restoration, and data loss mitigation through multi-agent system in a smart power distribution grid

ABSTRACT Smart power distribution grid is equipped with different sensors and smart meters for getting measurements at different nodes. Additionally, for monitoring and control purpose Intelligent Electronic Devices (IEDs) are also installed. On the occurrence of fault in the grid, power utility implement a Fault Detection, Isolation and Restoration (FDIR) scheme to restore the power in healthy section of the network, this process is known as Service Restoration (SR). In the existing scenario, a centralized control scheme is used for monitoring and control of distribution grid. This control scheme is slow in terms of deployment of FDIR plan. So, the authors propose a multi-agent-based de-centralized control scheme for FDIR. For this multi-agent system, different agents are defined to perform tasks of fault detection, fault isolation, and restoration. Also, the aspect of communication among agents is presented and to tackle data loss problem, a Compressive Sensing (CS) based technique is applied. A novel Support Vector Machine (SVM) approach is proposed for fault classification. For SR, a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) based method is used and is validated through its application for different fault scenarios in an IEEE 69 bus test system. Also, for testing of data loss mitigation agent, a real-time Phasor Measurement Unit (PMU) data is taken. The whole framework is designed in a python-based Smart Python Agent Development Environment (SPADE) and the related computation is done using MATLAB.

Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid

Abstract The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.

A data-driven approach for online dynamic security assessment with spatial-temporal dynamic visualization using random bits forest

With the continuous expansion of the power system scale and extensive application of phasor measurement units (PMUs), the secure operation of power systems has been increasingly concerned. To construct an efficient dynamic security assessment (DSA) model and make it convenient for practical applications, an integrated framework for online DSA with spatial-temporal dynamic visualization is proposed in this paper. The proposed framework consists of DSA model based on random bits forest (RBF) and spatial-temporal dynamic visualization. By using a feature selection process based on the bagging nearest-neighbor prediction independence test (BNNPT) and Pearson correlation coefficient (PCC), the key features are selected for model training. Once the real-time PMU data from the wide area measurement system (WAMS) are received, the trained DSA model can rapidly provide the corresponding transient stability margin (TSM). Particularly, the spatial-temporal dynamic visualization is presented to describe the quickly captured dynamic security information. The encouraging performance of the framework is demonstrated by tests on a 23-bus test system and a practical 1648-bus system. Especially, some impact factors that influence the practical operation of power systems are considered in the robustness examination, including variations of the topology, power distribution among generators/loads, peak/minimum load and load characteristics.

Hybrid voltage stability and security assessment using synchrophasors with consideration of generator Q‐limits

Increased power demands, push for economics and limited investment in grid infrastructure have led utilities to operate power systems closer to their stability limits. Voltage instability may trigger cascade tripping, wide-area voltage collapse, and power blackouts. Real-time voltage stability monitoring possible with deployment of phasor measurement units (PMUs) is essential to take proactive control actions and minimise the impact on the system. This study presents a novel online algorithm for (a) hybrid perturbation analysis based voltage stability monitoring (HPVSM), (b) including Q-limit in voltage stability index, and (c) real-time security analysis using voltage stability index. HPVSM-based voltage stability index is computed using the data obtained from linear state estimator and PMU measurements. Typically measurement-based schemes ignore the impact of generator Q-limits and security analysis is not feasible. The proposed HPVSM-based index considers the impact of generator Q-limits violations by anticipating the critical generators using real-time PMU measurements. Contingencies are ranked using the proposed voltage stability index for security analysis. Results simulated for the 9-bus WECC, IEEE 14, 57, and 118 bus systems highlight the superiority of the proposed method in real-time voltage stability and security analysis.

Experimental Validation of High-Performance HIL-Based Real-Time PMU Model for WAMS

This article presents a novel real-time hardware-in-loop-based model for synchronized phasor measurement unit (PMU) which incorporates an adaptive observer to estimate the phasor quantities of the actual hardware-based PMU model. The proposed model is feasible for a wide-area monitoring system (WAMS) in modern power grids. The performance of the proposed model is compared to the conventional algorithms of the performance (P)-class and measurement (M)-class PMU models in terms of total vector error and rate of change of frequency under different scenarios (frequency ramp, unbalance with interharmonics, and out-of-bound offnominal frequency). The main advantages of the proposed model compared to existing models are as follows: (1) combining the advantages of both P and M classes in terms of achieving required standard steady-state performance (IEEE C37.118-2005) while capturing the dynamic system performance, (2) improving the redundancy due to PMU loss, (3) allowing faster implementations and advanced visualization tools, and (4) enhancing WAMS accuracy performance. Despite the advantageous introduced utilizing the proposed model, there is still a tradeoff gap between the investment cost of the installed hardware-based PMUs and system redundancy. Moreover, a laboratory-scale six-bus setup is built and tested to demonstrate the practical validity of the proposed model.

Multi-Task Logistic Low-Ranked Dirty Model for Fault Detection in Power Distribution System

This paper proposes a Multi-task Logistic Low-Ranked Dirty Model (MT-LLRDM) for fault detection in power distribution networks by using the distribution Phasor Measurement Unit (PMU) data. The MT-LLRDM improves the fault detection accuracy by utilizing the similarities in the fault data streams among multiple locations across a power distribution network. The captured similarities supplement the information to the task of fault detection at a location of interest, creating a multi-task learning framework and thereby improving the learning accuracy. The algorithm is validated with real-time PMU streams from a hardware-in-the-loop testbed that emulates real field communication and monitoring conditions in distribution networks. The results showed that the MT-LLRDM outperforms other state-of-the-art classification methods using actual synchrophasor data achieved from a power hardware-in-the-loop testbed.

Design of the Pacific DC Intertie Wide Area Damping Controller

This paper describes the design and implementation of a proof-of-concept Pacific dc Intertie (PDCI) wide area damping controller and includes system test results on the North American Western Interconnection (WI). To damp inter-area oscillations, the controller modulates the power transfer of the PDCI, a ±500 kV dc transmission line in the WI. The control system utilizes real-time phasor measurement unit (PMU) feedback to construct a commanded power signal which is added to the scheduled power flow for the PDCI. After years of design, simulations, and development, this controller has been implemented in hardware and successfully tested in both open and closed-loop operation. The most important design specifications were safe, reliable performance, no degradation of any system modes in any circumstances, and improve damping to the controllable modes in the WI. The main finding is that the controller adds significant damping to the modes of the WI and does not adversely affect the system response in any of the test cases. The primary contribution of this paper, to the state of the art research, is the design methods and test results of the first North American real-time control system that uses wide area PMU feedback.

Stepwise robust distribution system state estimation considering PMU measurement

In order to deal with the problem of robustness and computing speed of distribution system state estimation with the development of phasor technology, a stepwise distribution system state estimation with robust performance and considering real-time phasor measurement unit (PMU) measurements is proposed. The proposed method utilizes the absolute value of residuals to construct the weight function and uses the unit weight variance to standardize the residuals so that the residual precision can be unified on the same scale to improve robustness. Besides, the state estimation is divided into two steps. The first step makes use of supervisory control and data acquisition (SCADA) measurements and branch current phasors measured by PMU to perform the robust estimation, and due to the high sparsity of the Jacobian matrix, the computational efficiency can be ensured. The second step combines the estimation results of the first step with the voltage phasors measured by PMU to perform linear state estimation, which can update the result of the first step and realize the combination of SCADA and PMU measurements in the state estimation. Finally, the proposed method is verified by the Institute of Electrical and Electronics Engineers (IEEE) standard systems.

A Novel Event Detection Method Using PMU Data With High Precision

To take full advantage of the considerably high reporting rate of phasor measurement units (PMU) data, this paper develops a novel PMU-based event detection methodology. Considering the huge amount of streaming PMU data, a data compression algorithm, swinging door trending (SDT), is first used to compress the PMU data and generate multiple compression intervals. Then, dynamic programming is utilized to solve the optimization problem, which is recursively constituted by a score function. Based on predefined PMU event rules, dynamic programming merges adjacent compression intervals with the same slope direction. Finally, all the PMU event features are characterized. A conventional wavelet-based event detection method is compared with the developed dynamic programming based SDT (DPSDT) method. Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision. Also, the PMU event features, including the magnitude and duration of strokes, are characterized.

Validation of a PMU‐based fault location identification method for smart distribution network with photovoltaics using real‐time data

A fault location (FL) identification method for smart distribution network is presented and validated using a digital real-time simulator (DRTS). The method can accurately identify the FL in a distribution network in the presence of distributed generation (DG). This method is based on state estimation (SE) algorithm which uses real-time data from simulated phasor measurement units (PMUs), placed in the distribution network. SE needs the fault currents of the generators and voltage measurements of an optimal number of nodes to perform the FL algorithm. The method was validated using the IEEE 37 node test feeder with DGs. PMUs are placed on the real-time model of the system. The real-time model was implemented on a DRTS which streams phasor data over the Internet using C37.118 protocol. OpenPDC is used to collect real-time PMU data coming from the DRTS. Microsoft SQL is used as a database management server to store data coming from OpenPDC. In the last step of the FL process, data stored in OpenPDC is fed into a FL identification algorithm to locate the fault. Both balanced and unbalanced fault types are applied to different nodes and an accurate estimation of the FL (over 90% of the cases) is achieved.

Co-Optimal Placement of PMUs and Their Communication Infrastructure for Minimization of Propagation Delay in the WAMS

The phasor measurement unit (PMU) has evolved as an indispensable tool for real-time monitoring and control of the power system, thereby leading to its large-scale deployment in a wide-area measurement system (WAMS). Communication system plays a significant role for transferring measured real-time PMU data to the phasor data concentrator (PDC) for monitoring and control purposes. The propagation delay depends on the spatial position of the PMUs with respect to the PDC and the communication network. This paper deals with the problem of co-optimal placement of PMUs and their communication infrastructure for minimization of propagation delay in a WAMS. The novelty of the work is to explore the feasibility of microwave communication technology as an alternative to the existing fiber-based communication infrastructure in the WAMS. The proposed approach takes the link reliability and the geographical topological variations into consideration for the placement of the microwave links so as to minimize the propagation delay. The merit of the microwave communication technology in the framework of the WAMS is evaluated in terms of its cost and reliability. Simulation results and the propagation delay evaluations for the Eastern power grid of India validate the efficacy of the proposed approach in practical field applications.