Search

This paper presents a fast model-agnostic method for recovering noisy Phasor Measurement Unit (PMU) datastreams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMUchannels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop as well as simultaneous data erasure across multiple PMU channels.

In this paper, we present SoDa, an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) output power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant’s 30-min power output, based on the configuration of the panel. The working hypothesis to generate high-resolution (e.g. 1 second) solar data is that the conditional distribution of the time series of solar power output given the cloud density is the same for different locations. We therefore propose a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, and train our stochastic model parameters for the cloudy states on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU). In the paper we introduce the stochastic model, and the methodology used for the training of its parameters. The numerical results show that our tool creates synthetic solar time series at high resolutions that are statistically representative of the measured solar power and illustrate how to make use of the tool to create synthetic data for arbitrary sites in the footprint covered by the NSRDB.

To mitigate the resonance between the full converter-based wind generation (FCWG) and the grids, their modal interaction is usually coordinated off-line and requires the full system model. In this paper, an on-line coordination strategy is proposed to achieve appropriate modal coordination in a model-free manner. Firstly, the dynamic interaction mechanism is examined to quantify the modal interaction impact, which provides theoretical foundation of how FCWG controller parameter tuning contributes to modal coordination. With phasor measurement unit (PMU) equipped in the power grid, the essential oscillation information (e.g. the critical mode that relates to modal resonance) is acquired without detailed modeling of the power grid. According to the frequency range of critical mode, the proper controller in FCWG is selected for parameter tuning. On this basis, we employ an on-line coordination strategy by adjusting relevant controller parameters to accommodate corresponding changes of system operational conditions and thus mitigate the potential risk of modal resonance. Case studies are carried out to demonstrate how FCWG adjusts the controller parameters timely to improve the oscillatory stability under various operating conditions, and thus in turn substantiate the robustness and adaptability of the proposed strategy.

This article offers synchrophasor transient and steady compliance with IEC/IEEE 60,255-118-1:2018 standard. This power system standard is mainly designed for the synchronized phasor measurement and specifies the synchronized voltage and current phasors, frequency and derivative requirements, time tag, and synchronization of all measurements. The Synchrophasor testbed includes three-phase high-precision power and current signal voltage, system time unit, GPS receiver, server PC, electricity standards, PMUCAL and test-based PMU software. Three-Phase Measurement Units (PMUs) from different machines have been tested individually to confirm compliance with the state-of-the-art international standard, which is essentially a dual logo standard. Such measured information is being used to control the power grid by smart communication utility. The test results of synchrophasor have been analyzed and discussed.

Time synchronized phasors obtained using Phasor Measurement Units (PMU) spread across wide areas have revolutionized power system monitoring and control. These synchronized measurements must be accurate and fast in order to comply with the latest IEEE standards for synchrophasor measurements. The speed at which a PMU provides an output depends on the group delay associated with that PMU and the permissible group delay in-turn decides the utility of a PMU for either control or measurement application. Based on the group delay compensation techniques, in the literature, two individual types of PMUs, such as causal and non-causal PMUs have been introduced. This paper presents an approach where both causal and non-causal PMUs are combined in an integrated PMU architecture. This method not only illustrates the group delay performance of two PMUs in a single module, but also can be used for multiple functions. In this environment several PMU algorithms have been compared with respect to their group delays and their effect on the response time. Application of the integrated PMU architecture to a four-machine 10-bus power system has been demonstrated using a six-input PMU with three-phase voltage and current signals as inputs. Different causal compensation schemes are introduced due to the availability of voltage and current-based frequency and ROCOF signals. Impact of these compensation schemes on PMU accuracy is evaluated through the Total Vector Error (TVE) index. The influence of these compensation schemes on measurements like power and impedance is also investigated. Finally, outputs from the integrated PMU architecture are fed into a Power System Stabilizer (PSS) to control the small-signal stability performance of a power system during dynamic conditions.

State estimation aims to estimate the optimal state variables that can represent the current state of a system based on the measurement set in the power system. The PMU (phasor measurement unit) is a key technology that can directly improve the performance of the monitoring, control and state estimation in the power system. The PMU enables the direct measurement of state variables such as voltage magnitude and phase angle. It also makes it possible to synchronize the measurement set using the GPS time stamp. To improve the performance of state estimation, it is not economical to place PMUs on all buses in the power system, so the PMU optimal placement must be determined. PMU optimal placement can be expressed as a limited optimization problem that secures the observability of the system with the placement of minimal PMUs by identifying the locations at which the PMUs should be installed. In this paper, we present the development of a PMU optimal placement algorithm that can determine the observability by searching the spanning tree of the graph, by which a topological connection relationship is created by measurement set.

GPS receiver is the source of synchronization in phasor measurement unit (PMU) networks. While it guarantees time precision, it is vulnerable to cyberattacks, which may modify the time offset and results in mismatches in time. This modification (GPS spoofing attack) changes the phase angle of signals measured by PMU. This article deals with three important challenges in GPS spoofing attack detection; first, multiattack detection while there are probable attacks on different PMUs, second, attack detection considering the dynamic model of power systems, and third, measurement correction. For providing solution to the abovementioned problems, an anti-GPS spoofing attack mechanism is suggested, in which, a dynamic filter with the help of PMU and supervisory control and data acquisition (SCADA) systems measurements estimate the phase shifts caused by spoofing attacks. The results of data fusion of PMUs and SCADA in the detection method are shown. Numerical results demonstrate that this approach can detect dynamic spoofing attacks in a reasonable time and as soon as multiple attacks are detected, corrected measurements are provided.

This paper is focused on the hardware and software development of a compact phasor measurement unit (PMU) device. This project seeks to provide a flexible, compact and development environment of hardware and software platform for advanced monitoring of a living lab Campus network. The development PMU ( $$D_{ev}-PMU$$ ) allows the exploration of the phenomena involved in the living lab Campus networks and can be very useful for the development of control and protection advanced applications in Low-Voltage (LV) power systems. The $$D_{ev}-PMU$$ is built on a general hardware platform and is able to process single as well as three-phase voltages. The performance of the proposed device is evaluated using the PMUCal Fluke set up as the benchmark and under real low-voltage distribution networks. Results confirm the accuracy of the proposed device for providing voltage phasors complying with the Standard limits for class P and M.

While various promising phasor measurement unit (PMU) data-driven applications have been developed for modern smart grids, how to improve the overall PMU data quality to ensure the reliability of these applications in practice still remains an open issue. Considering the challenging task of missing PMU data correction (MPDC) in practical complicated and noisy measurement contexts, this article develops a novel spatiotemporal correlation learning scheme (SCLS) for online MPDC in smart grids. In particular, the SCLS is strategically realized with two successive modules. First, from four complementary spatiotemporal perspectives, statistical missing data imputation is carried out to derive initial correction results in noisy contexts. Second, a well-designed deep learning architecture with the integration of convolutional neural network (CNN) and residual learning techniques is introduced to refine the correction results. With the help of these two modules, the SCLS is capable of performing precise MPDC for regional PMU measurements as well as filtering out potential noises. Extensive numerical test results on the IEEE 39-bus test system and the real-world Guangdong power grid in South China demonstrate the efficacy of the SCLS in practical complicated contexts.

The voltage stability margin is an important load margin measure used in power system operating centers to prevent a voltage collapse. However, oscillatory problems that arise with increasing load can also compromise the performance and stability of the power system. Thus, it is essential to determine a load margin that meets the requirements for voltage stability and small-signal stability in dynamic security assessment. This article proposes to use a artificial neural network, a supervised machine learning technique, to predict the load margin range of the power system considering the requirements of voltage stability and small-signal stability and using data of electrical quantities of certain buses that have a phasor measurement unit. A direct method based on a power systems model that determines the load margin meeting the voltage and small-signal stability requirements will be applied to generate the database for the training and testing stages of artificial neural network. The sequential forward selection algorithm was used in this research to select the buses to have a phasor measurement unit. Case studies are presented and discussed to verify the proposed load margin monitoring system based on artificial neural networks.

THE PURPOSE. To consider the problem of detecting changes in a power grid topology that occurs as a result of the power line outage / turning on. Develop the algorithm for detecting changes in the status of transmission lines in real time by using voltage and current phasors captured by phasor measurement units (PMUs) are placed on buses. Carry out experimental research on IEEE 14-bus test system. METHODS. This paper proposes a method from the field of artificial intelligence such as machine learning in particular "Deep Learning" to solve the problem. Deep Learning arises as a computational learning technique in which high level abstractions are hierarchically modelled from raw data. One of the means to effectively extract the inherent hidden features in data are Convolutional Neural Networks (CNNs). RESULTS. The article describes the topic relevance, offers to apply the method for detecting status of lines using a CNN classifier. The combination of different CNN architectures and the number of time slices from the moment of line status change are used to detect the power grid topology. The effectiveness of the joint use of PMUs and CNN in solving this problem has been proven. CONCLUSION. A solution for the line status change detection in the transient states using a CNN classifier is proposed. A high accuracy of the line status detection was obtained despite the influence of noise on measurement data. A change in the network topology is detected at the very beginning of the transient state almost instantly. It will allow the operator several times during the first seconds to identify the line state in order to make sure that the decisions made are correct.

This article presents a novel data recovery framework for missing synchrophasor measurements. The imputation accuracy for the existing data recovery methods is significantly reduced when there are consecutive data losses across multiple data streams. Besides, the recovered data do not necessarily meet the physical constraints of the power grid. To tackle these issues, a regularized low rank tensor completion (LRTC) method is proposed to incorporate the domain knowledge (e.g., Kirchhoff’s voltage and current laws, and three phase circuit relationships) as the regularization terms in order to efficiently exploit the data inter-dependencies for a better recovery. We leverage the tensor decomposition and completion as powerful tools to extract the latent structures of phasor measurement unit (PMU) data for the recovery process. Specifically, we first construct the tensor model of the PMU data and then formulate the LRTC problem as a rank minimization by leveraging the low rank property of the PMU measurements and adding the regularization terms into the LRTC problem in order to improve the imputation accuracy. An efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the regularized LRTC problem. The experiments using the real PMU dataset show that the proposed approach exhibits better imputation accuracy, compared with the conventional matrix completion methods.

Recent advances in distribution-level phasor measurement unit (D-PMU) technology have enabled the use of voltage phase angle measurements for direct load sharing control in distribution-level microgrid interconnections with high penetration of renewable distributed energy resources (DERs). In particular, D-PMU enabled voltage angle droop control has the potential to enhance stability and transient performance in such microgrid interconnections. However, these angle droop control designs are vulnerable to D-PMU angle measurement losses that frequently occur due to the unavailability of a GPS signal for synchronization. In the event of such measurement losses, angle droop controlled microgrid interconnections may suffer from poor performance and potentially lose stability. In this paper, we propose a novel distributed mixed voltage angle and frequency droop control (D-MAFD) framework to improve the reliability of angle droop controlled microgrid interconnections. In this framework, when the D-PMU phase angle measurement is lost at a microgrid, conventional frequency droop control is temporarily used for primary control in place of angle droop control to guarantee stability. We model the microgrid interconnection with this primary control architecture as a nonlinear switched system and design distributed secondary controllers to guarantee transient stability of the network. Further, we incorporate performance specifications such as robustness to generation-load mismatch and network topology changes in the distributed control design. We demonstrate the performance of this control framework by simulation on a test 123-feeder distribution network., Comment: 11 pages, IEEE Open Access Journal of Power and Energy (accepted)

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi ( $\pi$ ) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved $\pi $ model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.

Due to global warming problems and increasing environmental pollution, there is a strong tendency to install and apply renewable energy power plants (REPPs) around the world. On the other hand, with the increasing development of information and communication technology (ICT) infrastructures, power systems are using these infrastructures to act as smart grids. In fact, future modern power systems should be considered as smart grids with many small and large scale REPPs. One of the main problems and challenges of the REPPs is uncertainty and fluctuation of electrical power generation. Accordingly, a suitable solution can be combination of different types of REPPs. So, the penetration rate of large-scale wind-PV farms (LWPF) is expected to increase sharply in the coming years. Given that the LWPFs are added to the grid or will replace fossil fuel power plants, they should be able to play the important roles of synchronous generators such as power low-frequency oscillation (LFO) damping. In this paper, an LFO damping system is suggested for a LWPF, based on a phasor measurement unit (PMU)-based fractional-order proportional–integral–derivative (FOPID) controller with wide range of stability area and proper robustness to many power system uncertainties. Finally, the performance of the proposed method is evaluated under different operating conditions in a benchmark smart system.

Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.

Information representative of actual power system dynamics is usually buried in masses of phasor measurement unit (PMU) data. To take full advantage of these data in early anticipation of stability loss, we propose to implement the high dimensional stability index (HDSI). This method allows the extraction of more than 500-labeled attributes describing generator response signals, such as speed and rate of change of transient energy function (RoCoTE). A combined 31 functions are computed from spectrum analysis based on the Periodogram and Welch methods, Lyapunov exponents, and wavelet transform approaches. The test databases are built by simulating faults on each line in the IEEE 39- and 68-bus networks. Applying comparative time-series analysis to such signal responses to disturbances then highlights the texture matrix of the stability attributes. A 10-fold support vector machine (SVM) is used to implement a HDSI-based stability prediction model, with its performance then compared to the artificial neural network (ANN), decision trees (DT), random forest (RF), and adaptive boosting (AdaBoost) models available in the statistical package R. While most methods performed similarly, with ~100% accuracy on test cases using the same set of HDSI-based attributes, the RF classifier with its associated Gini feature importance allows for explicit feature ranking and interpretation, which results in prioritization of frequency-domain over time-domain features.

Event classification is one of the central components of automated disturbance analysis based on phasor measurement unit (PMU) measurements. Obtaining high-quality event labels remains a challenge for supervised learning-based classification of local and system-wide events in power grids due to its labor-intensive requirement. We present a sensitivity study considering rapidly refined, partially, and fully inspected event labels that leads to evidence that hierarchical convolutional neural networks (HCNNs) outperform traditional classification models regardless of the quality of the available event labels. It is demonstrated that performance similar to the one obtained using entirely domain-driven labeling can be achieved as long as the involved expert does not mislabel more than ~5% of the event data captured by PMU measurements.

As the Distributed Generation (DG) is evolving, the distribution network has undergone tremendous change. The DG sources like wind and solar are intermittent in nature. Hence to estimate the Active distribution network’s (ADN) parameters (Voltage and Current) accurately with the DG variation, the traditional offline method like long-term smart distribution network planning (with off-line variant) method is not feasible in terms of network’s reliability and operation. In this work, an online voltage estimation technique using PMU (Phasor Measurement Unit) technology is proposed to enhance a distribution network’s reliable operation and real-time monitoring. The proposed estimation method does not require PMU units at all nodes in the network. The estimation of unknown states information can be achieved from available PMU unit’s data. The proposed methodology is economical and feasible for voltage estimation, and system observability as the number of PMU units required is less. The standard IEEE Distribution network systems are considered for checking the feasibility of the recommended technique. For the simulation of the case studies, MATLAB programming is used. A Forward and Backward sweep (FBS) load-flow algorithm is used to authenticate the proposed methodology.

This paper introduces a new model to improve the wind power plant performance by modeling its reactive power demand. It develops a probabilistic model based on prediction interval to help better modeling of the reactive power demands of wind unit which needs to be compensated by the static VAr compensator (SVC). This is made possible by the use of a non-parametric neural network (NN) based model using the lower and upper bound estimation (LUBE) method. To avoid the instability arising due to the nonlinear and complex nature of NN, the idea of combined prediction intervals is used here. Due to the highly nonlinear and non-stationary characteristics of the reactive power pattern consumed in the wind power plant, a new optimization algorithm based on $\theta $ -symbiotic organisms search ( $\theta $ -SOS) is proposed to train the LUBE model parameters in the polar coordinates. In addition, a two-phase modification method is developed to enhance the local search ability of SOS and avoid premature convergence issue. The performance of the proposed model on the experimental Phasor Measurement Unit (PMU) data of a wind unit shows that the model can help to improve the performance of the wind SVC, effectively.

Phasor measurement unit (PMU) testing is desired to ensure the continued power system monitoring and the maneuvering precision of synchrophasor-based control. IEEE Std C37.118 and IEEE synchrophasor measurement test suite specification (TSS) clarify the different requirements for both steady-state and dynamic test scenarios with varied error ranges. Existing PMU testing requires taking PMUs out of service and test in the lab typically manually. This article presents an architecture for remote PMU testing utilizing the PMU performance analyzer (PPA) test suite, which can considerably reduce the cost while achieving the substation automation. The proposed field-programmable gate array (FPGA) programming method is demonstrated to meet the TSS requirements for signal source generators (SSG). The accuracy of the frequency and harmonic distortion factors by SSG using the proposed method are compared with the built-in signal generation module of the national instruments (NI) cRIO system. The steady-state and dynamic test results of remote PMU testing based on the proposed architecture are presented in this article. The satisfactory performance test results demonstrate the feasibility of the architecture.

In order to improve the reliability and continuity of the power distribution system, power Internet of Things analysis monitoring system was constructed in this article, and the system was used for comprehensive applications such as data acquisition and fault analysis. Fault location methods considering distribution phasor measurement unit (D-PMU) information and network symmetry characteristics are proposed. With the measurement information provided by the D-PMU, the fault sections and the fault types are determined by the phase difference between the positive and negative sequence currents. Different location algorithms are obtained based on the load symmetry of the distribution network. The fault location method based on the line distribution parameters is used for the three-phase symmetric system, and the fault location method based on line impedance is proposed for the three-phase asymmetric system. Finally, the effect of different nodes with a D-PMU on the fault location accuracy is analyzed, and the configuration principle of the D-PMU was proposed. The PSCAD/MATLAB simulation software is used to analyze the example and verify the effectiveness of the proposed method. The simulation results demonstrate that high-precision fault location could be realized and that the practicality and economy are improved.

PurposeThis paper aims to report how one can assess the transient stability of a power system by using stacked auto-encoders.Design/methodology/approachThe proposed algorithm works in a power system equipped with the wide area measurement system. To be more exact, it needs pre- and post-disturbance values of frequency sent from phasor measurement units.FindingsThe authors have investigated the performance of the proposed method. Going through details, the authors have simulated many contingencies, and then have predicted the transient stability in each of which by using the proposed algorithm.Originality/valueThe results demonstrate that the algorithm is fast, and it has acceptable performance under different circumstances including the change of system topology and failures of telecommunication channels.

The advancements in the field of wind turbine, solar photovoltaic (PV), fuel cell coupled with improved power electronics have increased reliability of renewable energy sources. The environmental benefits of these sources have forced the power industry to switch over for more distributed generations to meet the increasing load demand. Islanding in distribution system occurs when a portion of distribution system gets isolated from the rest of the grid and continues to supply the local load. Practically, all distributed generations (DG) are required to be disconnected immediately after the formation of island. It is done primarily to take care of safety of the operating personnel and to prevent power quality issues. Effective detection of islanding is an important area of concern. Prior to the integration of DG to the main electrical grid, each DG must be equipped with a suitable anti-islanding detection technique. In this paper, phasor measurement unit (PMU)-based islanding detection technique is presented. The requirement of channel limits of PMUs is also incorporated, and significant results of industry and utility have been obtained. The study has been carried out using MATLAB/Simulink (version 2018a) creating several islanding and non-islanding cases in a PV integrated distribution grid.

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.

The smart grid control applications necessitate real-time communication systems with time efficiency for real-time monitoring, measurement, and control. Time-efficient communication systems should have the ability to function in severe propagation conditions in smart grid applications. The data/packet communications need to be maintained by synchronized timing and reliability through equally considering the signal deterioration occurrences, which are propagation delay, phase errors and channel conditions. Phase synchronization plays a vital part in the digital smart grid to get precise and real-time control measurement information. IEEE C37.118 and IEC 61850 had implemented for the synchronization communication to measure as well as control the smart grid applications. Both IEEE C37.118 and IEC 61850 experienced a huge propagation and packet delays due to synchronization precision issues. Because of these delays and errors, measurement and monitoring of the smart grid application in real-time is not accurate. Therefore, it has been investigated that the time synchronization in real-time is a critical challenge in smart grid applications, and for this issue, other errors raised consequently. The existing communication systems are designed with the phasor measurement unit (PMU) along with communication protocol IEEE C37.118 and uses the GPS timestamps as the reference clock stamps. The absence of GPS increases the clock offsets, which surely can hamper the synchronization process and the full control measurement system that can be imprecise. Therefore, to reduce this clock offsets, a new algorithm is needed which may consider any alternative reference timestamps rather than GPS. The revolutionary Artificial Intelligence (AI) enables the industrial revolution to provide a significant performance to engineering solutions. Therefore, this article proposed the AI-based Synchronization scheme to mitigate smart grid timing issues. The backpropagation neural network is applied as the AI method that employs the timing estimations and error corrections for the precise performances. The novel AIFS scheme is considered the radio communication functionalities in order to connect the external timing server. The performance of the proposed AIFS scheme is evaluated using a MATLAB-based simulation approach. Simulation results show that the proposed scheme performs better than the existing system.

Within the context of time dissemination techniques for power systems applications, this paper discusses the use of the white rabbit (WR) protocol for synchrophasor networks. Specifically, this paper presents a phasor measurement unit (PMU) integrating the WR technology and its experimental validation with a focus on the synchrophasor phase estimation in steady state conditions, by using a PMU calibrator generating the reference signals. We further compare the accuracy of the developed PMU with other state-of-the-art time synchronization technologies for PMUs, i.e., global positioning system (GPS) and precision time protocol (PTP), demonstrating applicability of WR for PMU sensing networks.

This paper introduces a dynamic network model together with a phasor measurement unit (PMU) measurement model suitable for power system state estimation under spoofing attacks on the global positioning system (GPS) receivers of PMUs. The spoofing attacks may introduce time-varying phase offsets in the affected PMU measurements. An algorithm is developed to jointly estimate the state of the network, which amounts to the nodal voltages in rectangular coordinates, as well as the time-varying attacks. The algorithm features closed-form updates. The effectiveness of the algorithm is verified on the standard IEEE transmission networks. It is numerically shown that the estimation performance is improved when the dynamic network model is accounted for compared with a previously reported static approach.

The increase of renewable penetration in power grids calls for loads to participate in frequency regulation to avoid under-frequency load shedding after large resource contingencies. The motivation of this paper is to fulfill a fast residential load control for frequency stability enhancement. For this purpose, a novel mobile distribution-level phasor measurement unit (MDPMU) is developed for disturbance event detection, power mismatch estimation and fast load control. First, the systematic design of the MDPMU introduced. The proposed MDPMU has the major advantages of low cost and high frequency measurement accuracy. Second, the distribution-level measurement is utilized for the rate of change of frequency (ROCOF) calculation. Upon detection of a power system frequency event, power mismatch is estimated at the early stage of the event. Details of a robust approach to calculate ROCOF and determine the event starting point are presented. Practical issues including the measurement reporting rate and estimation time are also considered. Last, with the proposed control system, residential load responses can be controlled coordinately based on load availability and compensation prices offered by customers, providing adaptive frequency regulation service after large resource contingencies. The accuracy of frequency measurement and power mismatch estimation are evaluated via experimental analysis. The frequency stability improvement by load control is validated via PSS/E simulation.

Phase angle measurements from phasor measurement unit (PMU) data are sensitive to several issues, such as timing error, global positioning system (GPS) signal spoofing attack, transducer sensitivities and latency, and phasor computation algorithms. In some applications, highly accurate phase angle values are essential. In this paper, two methods are developed for correcting the phase angle difference (PAD) between the PMUs at both ends of a line. The method is based on the assumption that the line parameters are unchanged during the measurement period. The proposed scheme does not require knowing the parameters of the transmission line connecting the two PMUs, and uses multi-point measurements to estimate possible PAD bias. An additional benefit of the methods is that the parameters of the transmission line can also be calculated.

Phasor measurement unit (PMU) is among the most important measurement devices in modern power systems. It measures the voltage and current phasors which have both magnitude and phase angle using a common timing reference. These measurements are utilized in real time applications of electrical power systems. The main drawback of PMUs is its high cost so if PMUs are used to collect or send online real data from a system to a data centre or a decision maker, the used number of PMUs should be minimum with full observability of system measurements. This paper proposes a flower pollination algorithm (FPA) to find the optimal number and locations of PMUs in power systems. The optimization objectives of the work are the minimization of PMUs number, the achievement of complete observability of power system states, and the maximization of measurement redundancy. Existence of zero-injection buses in the system decreases the number of PMUs that is required to fulfil the complete observability of system states. Furthermore, additional constraints for remaining system full observable following failure of single PMU and single line outage are also included to increase the system reliability. The performance and efficiency of the proposed FPA is tested on IEEE standard networks such as 14-bus, 30-bus, 57-bus and 118-bus and new England 39-bus network. The obtained simulation results approve the ability of the proposed optimization method to find the optimal allocation of PMUs in different cases with fulfilling complete observability and reliability of electric power systems. The superiority of FPA is also verified by comparing its results of other optimization algorithms.

Phasor measurement unit (PMU), which could provide high-frequency phasors including the amplitude and phase angle synchronized with Global Positioning System (GPS)/BeiDou Navigation Satellite System (BDS) signal, plays a vital role in dynamic monitoring of power systems. However, if the GPS/BDS signal could not be received, the time of PMU, which depends on the crystal oscillator (CO), would cause measurement deviation due to frequency shifts of CO, and then the PMU data would engage deviations and deteriorate the relevant applications. In recognizing the above problems, this article proposes a method to detect the frequency deviation of the crystal oscillator (FDCO) based on the correlation coefficient, according to its relation with the characteristic of the voltage phase angle difference (VPAD) between the two ends of the line. In detail, firstly, the quality of the VPAD of PMU between the two ends of the line is analyzed and the related problem is stated. Secondly, the characteristics of PMU data, when FDCO exists, are derived and analyzed, especially the VPAD between the line, and the suitability of the data for detecting the FDCO is discussed. Thirdly, the ideas and specific methods of detecting the FDCO in case of malfunction of GPS/BDS signal are proposed. Furthermore, the validity of the proposed method with simulation data and modified measured data is presented. Finally, the conjectures on the reasons for the approximately constant VPAD deviation in the previous section are discussed.

With the increase in wind power penetration, wind power ramping events have increasingly influenced tie line power control in the power grid. Large power changes during ramping events make it difficult to accurately track the scheduling plans of tie lines and can even lead to overrun. Determining how to evaluate the control performance of automatic generation control (AGC) for wind power ramping has become an urgent problem. In this context, this paper studies the control performance of AGC for wind power ramping based on deep reinforcement learning. First, a tie line power control model of a power system with an AGC module is established. Then, measured data, which include thermal power, wind power, hydropower output and tie line power data, and a deep reinforcement learning method are combined for AGC parameter estimation based on the deep Q-network (DQN) algorithm. Next, the AGC parameter in different scenarios are fit by using measured phasor measurement unit (PMU) data, and on the basis of the fitted model, AGC performance evaluation is performed for wind power ramping events. Finally, the simulation results verify the feasibility and effectiveness of analysing the relationship between wind power ramping and AGC performance based on the AGC parameter fitting model.

The emerging distribution-level phasor measurement unit (D-PMU) is expected to play an important role in enhancing distribution system observability and situational-awareness. It is a demanding yet challenging task to develop advanced D-PMU data management and analytics tools to improve D-PMU data usability and further promote D-PMU projects deployment. This paper focuses on D-PMU data processing and storage. It presents a brief review of existing D-PMU data storage systems and points out their limitations on high-performance, flexibility, and scalability. To overcome the limitations, a unified PMU-data storage system (UPS) is proposed. Specifically, a unified I/O interface between storage servers and computing jobs is developed to effectively reduce the overhead of managing various computing jobs and data analytics over multiple storage infrastructures; and PMUCache with PMUCache partitioning and PMUCache replacement algorithms are designed to support in-situ data processing and shared distributed data storage and further serve the computing jobs/queries with different quality of service (QoS) requirements. Through a series of experiments, it is demonstrated that UPS achieves high performance on fast and big data processing and storage, and efficiently increases the flexibility and scalability of PMU data management systems.

Protection systems are one of the most critical components in the transmission system and are becoming more digital with ongoing automation. These digital systems are prone to vulnerabilities/attacks, and exploitation of these vulnerabilities may cause major impacts on the electric grid performance. Multiple alarms reported in the control center could be a result of the faults (expected operations) or failures in the protection system (anomalies/ unexpected operation). Situational awareness gained through sensors such as a phasor measurement unit (PMU) and data acquired through the cyber system provide an opportunity to develop continuous cyber-physical monitoring of the system. Note that relay data are not reported in the control center continuously. This paper presents a cyber-physical data analytics based technique to monitor transmission protection system and detect malicious activity. Initially, continuous monitoring of PMU data is utilized for data anomaly detection, which includes bad or missing data using long short-term memory (LSTM). Then, PMU data of interest are utilized for failure diagnosis, using a semisupervised deep autoencoder model. In this research, cyber anomalies are modeled by manipulating the setting/logic design of protective devices, and a ridge regression based classifier with a feature engineering pipeline is used to detect cyber anomalies. The results from the deep autoencoder model and ridge regression based classifier are then utilized for detailed investigation to find the root causes of the observed events assisted by the cyber log data from the protection devices. The algorithm is validated using a real-time simulation of the IEEE test system with industrial hardware relays and PMUs in the loop. Data analytics algorithm running on server utilizes these real-time data continuously for anomaly detection and classification for the developed use cases.

In response to recent concerns regarding deteriorating frequency quality in the Nordic power system, the Nordic transmission system operators (TSOs) have developed draft requirements for the provision of frequency containment reserves (FCR). These requirements outline extensive tests, which the power plant owners must perform to qualify to deliver FCR. In this paper we demonstrate that one can check these requirements by standard system identification techniques while the plant is in normal operation and by using measurements readily available from the plants control system or from a phasor measurement unit (PMU). The validity of the proposed method is demonstrated using Monte Carlo Simulation (MCS), where we investigate the effects of the main assumption and potential nonlinearities affecting the approach. Data from real power system operation is used to demonstrate the performance of the method. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.

The application of the network technology in the power grid makes the Load Frequency Control (LFC) system more vulnerable to various kinds of network attacks. The Denial of Service (DOS) attack can block the data collected by the Phasor measurement unit from being transmitted to the LFC center, thereby affecting the decision of the control center and generation of control signals, and can not adjust the frequency of the power grid timely. Aiming at the DOS attack on LFC, a defense method based on data prediction is proposed. Through the combination of the deep learning algorithm and the Extreme Learning Machine (ELM) algorithm, the Deep auto-encoder Extreme Learning Machine (DAELM) algorithm combines the advantages of the fast speed of the extreme learning machine and the advantages of high accuracy of the deep learning. We can predict and supplement the lost data based on the DAELM algorithm, and ensure the normal operation of the LFC system, thus can prevent DOS attacks. The experiments verified the effectiveness of the proposed method.

The market penetration of electric vehicles (EVs) has increased drastically. However, the high integration of EV fast-charging stations (EVFCS) into the power systems makes them more vulnerable to severe grid disturbances. In case of a disturbance driving the power system to instability, a fast prediction of stability status is vital for allowing sufficient time to take intelligent emergency control actions. Although various types of machine learning (ML) and deep learning (DL) algorithms have been developed for early detection of instability, the lack of reliable ML/DL models, trained with a realistic dataset, limits their practical application. This paper presents a reliable, accurate DL-based model for early detection of instability in power systems, and compares the results with/without coupling of the EVFCSs. For training our ML/DL models, a large set of realistic phasor measurement unit (PMU) data is generated through a new approach involving a hybrid-type simulation, as an alternative to conventional approaches in data generation. In our experiments, time-synchronized measurements of voltage signals obtained from PMUs are taken as raw input data. Through our proposed method, raw PMU data are encoded into images for developing a reliable and robust convolutional neural network (CNN) model, predicting the stability status of power systems.

Community microgrids are set to change the landscape of future energy markets. The technology is being deployed in many cities around the globe. However, a wide-scale deployment faces three major issues: initial synchronization of microgrids with the utility grids, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. To validate the concept, a unique intelligent DC microgrid architecture that employs the proposed Phasor Measurement Unit (PMU) Assisted Inverter (PAI) is also presented, alongside the cloud-based Artificial Intelligence (AI), which harnesses energy from community shared resources, such as batteries and the community’s rooftop solar resources. The results show that the proposed system produces quality output and is 98.5% efficient.

In this paper micro phasor measurement unit (μPMU) placement for maximum observability in smart distribution network is proposed. PMUs were only considered for transmission system monitoring and due to the advancements in the distribution system μPMUs are adopted for online monitoring. The distribution system is very complex and prone to lot of changes due to rapid penetration of distributed generators (DG), distributed energy resources (DER) and electric vehicles (EV). The maximum or complete observability of a distribution network is a big challenge. IEEE-13 and IEEE-33 test bus systems are considered for simulation and the placement of μPMU in distribution network. The results demonstrate that 5 and 11 μPMUs are sufficient to monitor IEEE 13 and IEEE 33 test bus system completely.

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.

Understanding of Phasor Measurement Unit (PMU) technology is enhanced when the student or researcher has detailed access to the signal and data flows within the instrument. In this paper, the authors discuss how the OpenPMU system can be used as a toolkit to provide such access to PMU data flows, enabling signals to be extracted, interpreted, analyzed and visualized at each stage of PMU signal processing. This toolkit is enhanced by the introduction of the authors’ affordable PC based polyphase signal generator, which allows researchers to inject physical signals into PMUs. This widens opportunities for use of the toolkit amongst teaching and research applications were expensive professional signal generators would not be available. The signal generator concept, implementation and method of operation are discussed and it is evaluated for its performance.

The Phasor Measurement Unit (PMU) has revolutionized power system management by providing high-resolution, time-stamped, spatial, and temporal measurements of the state of power systems. However, the standards defining the measured quantities have ambiguities that cause uncertainty, irreproducibility, and loss of interoperability during periods of change where the measurements should have the greatest value. This paper examines PMU frequency measurement to highlight the problems. It develops some suggestions for the scope and the next evolutionary improvements to the documentary standards. The aim is to improve the meaningfulness and trustworthiness of dynamic power system measurements.

Power grids must be more and more capable of adapting to a range of sometimes rapidly fluctuating conditions, originating by both fast variations of absorbed and generated power, especially from renewable sources. The implementation of substation automation is rapidly increasing, fostered by the 'digitalization' of the substations themselves. In this context, where voltage and current Instrument Transformers (ITs) have digital output or their output is digitized by the Stand Alone Merging Units (SAMUs), the natural evolution of a Phasor Measurement Unit is to become all-digital, that is not to have analog input but to process directly the Sampled Values (SVs) to extract the desired quantities. However, since these instruments can be used to implement critical tasks for the power system (stability control, remote diagnostics and so on) it is very important to assure the accuracy of the measurements. Therefore, this paper presents the results of a preliminary study where the performance of a PMU implemented using the SVs from a commercial SAMU are experimentally tested in various test conditions suggested or inspired by the international standards.

This paper investigates the use of Machine Learning (ML) algorithms for Anomaly Detection (AD) in Phasor Measurement Unit (PMU) data. Specifically, this is an evolution of previous heuristic techniques which investigated constraint and temporal anomaly detection in PMU data. This work found that K-Means Clustering is suitable for AD in static PMU data, a set of PMU data at one time stamp, and Auto Regressive Integrated Moving Average (ARIMA) to detect anomalies in time series PMU data. A smart grid test bed with 10 PMUs and a physical power grid emulator was used to generate PMU data sets for this work. The K-Means and ARIMA ML models were designed, trained, and validated offline using a baseline dataset to build a representation of how the power grid performs under normal operating conditions. Once the models were validated, they were then subjected to a test data set to evaluate performance and suitability for this application. For static AD, the K-Means model evaluates distortion from new, or real-time, PMU data. For the temporal AD, the ARIMA model predicts the next PMU measurement and evaluates the delta between the prediction and actual data. Distortion values, or delta values, exceeding predefined thresholds are indicative of anomalies. In practical implementation this Anomaly Detection System (ADS) addresses the need to analyze large volumes of PMU data while also alerting system operators of significant deviations from normal behavior. This paper describes an implementation of ML in this ADS and presents initial results which highlight the feasibility of this approach.

The complexity and dynamics of the modern power system are continuously changing due to the penetration of a large number of renewable energy sources and changing load patterns. These growing complexities have caused numerous outages around the world, primarily due to the lack of situational awareness about the grid operating states. Rectification of this problem requires advanced sensing technology to accurately capture the dynamics of the system for better monitoring and control. Measurement of synchrophasors is a potential solution to improve situational awareness in the grid. The synchrophasors technology is now widely accepted throughout the world and has the potential to replace the existing SCADA system in monitoring and control of the power system. Their installation enables efficient resolution to substantially improve transmission system planning, maintenance, operation, and energy trading. This paper reviews the state of the art potential applications that the PMU based WAMC offers to the power system. It also includes technical perspectives, challenges, and future possibilities.

The penetration of distributed generation in the power distribution system has increased significantly in recent years, bringing serious protection challenges. Hence, it is mandated to develop effective and reliable fault detection scheme for present-day microgrid environment. This study presents a phasor measurement unit-based (PMU) wide-area phase angle criterion for building a protection system for the microgrid. The absolute value of the rate of change of voltage phase angle difference between the point of common coupling and the bus closest to the fault point is utilised for fault detection in grid connected as well as in the islanded modes of operation. C37.118.1 complied PMUs are employed at the appropriate locations for accurate and faithful measurement of voltage phase angle and the rate of angle difference (RAD) for detecting faulted conditions. PMUs are connected to phasor data concentrator, which receives the time synchronised phasor data and acts as a centralised processing unit. The proposed protection index has been extensively validated on MATLAB/Simulink environment as well as the real-time digital simulator with external PMUs. The test results show that the proposed RAD is a potential candidate for providing effective fault detection measure for microgrid including large variations in operating conditions.

The aim of this paper is to investigate the use of the ever increasing penetration of renewable energy into the power grid to solve challenging problems such as inter area power oscillations without the use of expensive power electronic devices and power system stabilizers. The increase in size of interconnected power systems, energy demand, and installation of remote renewable energies with relatively weak tie-lines has witnessed different stability problems such as low-frequency inter-area oscillations. Inter-area oscillation reduces system stability and transmission capacity. Without effective damping control mechanism, these oscillations could prolong and threaten the security of the system. This paper proposes a supplementary controller from a photovoltaic (PV) solar plant for damping inter-area oscillations. Due to its strong correlation to active power flow and monitoring system stress, area phase-angle difference is employed for the remote signal input of the controller. The signal can be obtained from phasor measurement unit (PMU) through wide-area measurements systems (WAMS). To deal with a wide range of variable delay in the signal input, an adaptive compensator is designed to reduce the impact of the communication latency using neuro-fuzzy inference system. A two-area four-machine test system is used and simulated with a Simulink-based package developed for the work of this study. The time-domain simulations, modal and frequency response analysis demonstrate the capability of the proposed controller to effectively damp inter-area oscillations, under a small- and large-scale disturbances and against a wide range of time delays.