Optimal Placement of Phasor Measurement Unit in Smart Grids Considering Multiple Constraints

The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit(PMU)placement(OPP)problem.However,this is not always accurate in practice.This paper proposes a new OPP method for smart grids in which the effects of conventional measurements,limited channels of PMUs,zero-injection buses(ZIBs),single PMU loss contingency,state estimation error(SEE),and the maximum SEE variance(MSEEV)are considered.The SEE and MSEEV are both obtained using a robust t-distribution maximum likelihood estimator(MLE)because t-distribution is more flexible for modeling both Gaussian and non-Gaussian noises.The A-and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints.This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints.The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus,30-bus,and 118-bus systems as well as the 211-bus practical distribution system in China.

Phasor Measurement Unit Incorporated Adaptive Out-of-step Protection of Synchronous Generator

The existing out-of-step(OOS)protection schemes have proven to be deficient in the prevention of significant outages.OOS protection schemes must not operate in stable power swing,and rapidly isolate an asynchronous generator or group of generators from the rest of the power system in case of unstable power swing.The paper proposes a novel phasor measurement unit(PMU)incorporating a polygon-shaped graphical algorithm for OOS protection of the synchronous generator.The unique PMU-based logic works further to classify the type of swing once the graphical scheme detects it,which can identify the complex power swing produced in the modern power system.The proposed algorithm can take the correct relaying decision in the event of power swing due to renewable energy integration,load encroachment,and transient faults.In this paper,the original and modified Kundur two-area system with a power system stabilizer(PSS)is used to test the proposed algorithm.In the end,it provides assessment results of the proposed relay on the Indian power system during the blackout in July 2012.The results demonstrate that the proposed algorithm is fast,accurate,and adaptive in the modern power system and shows better performance than the existing OOS protection schemes.

Subsynchronous oscillation monitoring and alarm method based on phasor measurements

Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.

A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

Curvature Quantified Douglas-Peucker-based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.

Impacts of subsynchronous and supersynchronous frequency components on synchrophasor measurements

Phasor measurement units(PMU) are playing an increasingly important role in power system dynamic security monitoring and control. However, the wide-area deployments of the renewable energy sources and the high voltage direct current(HVDC) transmission bring a large number of inter-harmonics to the power grid, which may result in further power system security problems. The impacts of inter-harmonics on synchrophasor measurements are revealed. This paper derives the phasor expressions of the signal, which contains the fundamental component and the inter-harmonics. It is found that the inter-harmonics will lead to the subsynchronous oscillation of the phasor measurements. The frequency transmutation principle between the harmonic and the phasor oscillation is revealed. Then, the field PMU data recorded during a subsynchronous oscillation, which occurred in an area of China with a high concentration of wind farms and HVDC transmission lines, are studied. A geographical wiring diagram with the subsynchronous oscillation distribution depicts the severe consequences of the inter-harmonics. In addition, the correctness of the theoretical derivation and the possibility of the inter-harmonics monitoring are verified.

A SVM Based Condition Monitoring of Transmission Line Insulators Using PMU for Smart Grid Environment

A new methodology for the detection and identification of insulator arc faults for the smart grid environment based on phasor angle measurements is presented in this study and the real time phase angle data are collected using Phasor Measurement Units (PMU). Detection of insulator arcing faults is based on feature extraction and frequency component analysis. The proposed methodology pertains to the identification of various stages of insulator arcing faults in transmission lines network based on leakage current, frequency characteristics and synchronous phasor measurements of voltage. The methodology is evaluated for IEEE 14 standard bus system by modeling the PMU and insulator arc faults using MATLAB/Simulink. The classification of insulator arcs is done using Support Vector Machine (SVM) technique to avoid empirical risk. The proposed methodology using phasor angle measurements employing PMU is used for fault detection/classification of insulator arcing which further helps in efficient protection of the system and its stable operation. In addition, the methodology is suitable for wide area condition monitoring of smart grid rather than end to end transmission lines.

GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet

This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)measurements.We propose a detection method based on improved Capsule Neural Network(CapsNet)to handle this attack.In the improved CapsNet,the gated recurrent unit(GRU)is added to the front of the full connection layer of the CapsNet.The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid.The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously,which can accurately distinguish the attacked data from the normal data,to improve the detection accuracy.Finally,simulation experiments are carried out on IEEE 14-,IEEE 118-bus systems.The experimental results show that compared with other detection methods,our method is proved to be more efficient.

A Value-at-Risk Based Approach for PMU Placement in Distribution Systems

With the application of phasor measurement units(PMU)in the distribution system,it is expected that the performance of the distribution system state estimation can be improved obviously with the PMU measurements into consideration.How to appropriately place the PMUs in the distribution is therefore become an important issue due to the economical consideration.According to the concept of efficient frontier,a value-at-risk based approach is proposed to make optimal placement of PMU taking account of the uncertainty of measure errors,statistical characteristics of the pseudo measurements,and reliability of the measurement instrument.The reasonability and feasibility of the proposed model is illustrated with 12-node system and IEEE-33 node system.Simulation results indicated that uncertainties of measurement error and instrument fault result in more PMU to be installed,and measurement uncertainty is the main affect factor unless the fault rate of PMU is quite high.

Robust fault analysis in transmission lines using Synchrophasor measurements

As more electric utilities and transmission system operators move toward the smart grid concept,robust fault analysis has become increasingly complex.This paper proposes a methodology for the detection,classification,and localization of transmission line faults using Synchrophasor measurements.The technique involves the extraction of phasors from the instantaneous three-phase voltages and currents at each bus in the system which are then decomposed into their symmetrical components.These components are sent to the phasor data concentrator(PDC)for real-time fault analysis,which is completed within 2–3 cycles after fault inception.The advantages of this technique are its accuracy and speed,so that fault information may be appropriately communicated to facilitate system restoration.The proposed algorithm is independent of the transmission system topology and displays high accuracy in its results,even with varying parameters such as fault distance,fault inception angle and fault impedance.The proposed algorithm is validated using a three-bus system as well as the Western System Coordinating Council(WSCC)nine bus system.The proposed algorithm is shown to accurately detect the faulted line and classify the fault in all the test cases presented.

Real-time Synchrophasor Data Compression Technique with Phasor Interpolation and Extrapolation

To completely eliminate the time delays caused by phasor data compressions for real-time synchrophasor applications,a real-time synchrophasor data compression(RSDC)is proposed in this paper.The two-way rotation characteristic and elliptical trajectory of dynamic synchrophasors are introduced first to enhance the compressions along with a fast solving method for elliptical trajectory fitting equations.The RSDC for phasor data compression and reconstruction is then proposed by combining the interpolation and extrapolation compressions.The proposed RSDC is verified by both the actual phasor measurement data recorded in a two-phase short-circuit incident and a subsynchronous oscillation incident,and the synthetic dynamic synchrophasors.It is also compared with two previous real-time phasor data compression techniques,i.e.,phasor swing door trending(PSDT)and exception and swing door trending(SDT)data compression(ESDC).The verification results demonstrate that RSDC can achieve significantly higher compression ratios for offline applications with the interpolation and the zero-delay phasor data compression with the extrapolation for real-time applications simultaneously.

Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

High Impedance Fault Detection Based on Attention Mechanism and Object Identification

Detection of high impedance faults(HIFs)has been traditionally a main challenge in the protection of distribution systems,since they do not generate enough current to be reliably detected by conventional over-current relays.Data-based methods are alternative HIF detection methods which avoid threshold settings by training a classification or regression model.However,most of them lack interpretability and are not compatible with various distribution networks.This paper proposes an object detection-based HIF detection method,which has higher visualization and can be easily applied to different scenarios.First,based on the analysis of HIFs,a Butterworth band-pass filter is designed for HIF harmonic feature extraction.Subsequently,based on the synchronized data provided by distribution-level phasor measurement units,global HIF feature gray-scale images are formed through combining the topology information of the distribution network.To further enhance the feature information,a locally excitatory globally inhibitory oscillator region attention mechanism(LEGIO-RAM)is proposed to highlight the critical feature regions and inhibit useless and fake information.Finally,an object detection network based You Only Look Once(YOLO)v2 is established to achieve fast HIF detection and section location.The obtained results from the simulation of the proposed approach on three different distribution networks and one realistic distribution network verify that the proposed method is highly effective in terms of reliability and generalization.

High-accuracy and Low-complexity Phasor Estimation Method for PMU Calibration

Due to the increasing development of renewables in power systems,the requirements for phasor measurement units(PMUs)becomes higher.A PMU calibrator is an important tool to test and calibrate PMUs to ensure their measurement performance.This device can provide accurate reference values for error analysis of PMUs.In this paper,a phasor algorithm with low computational complexity and high accuracy is proposed for the PMU calibrator.This method reduces the processor requirements and development costs of the calibrator,thereby facilitating its popularization.At first,an enhanced discrete Fourier transform(DFT)method is put forward:1)the frequency response of the windowed DFT method is analyzed to reveal its large measurement errors under dynamic conditions;2)the parameter requirements of the DFT window that is regarded as a lowpass filter are analyzed,and thus a lowpass filter with better filtering performance is designed as the window coefficients to improve the estimation accuracy.Then,based on the enhanced DFT algorithm,a calibrator algorithm framework consisting of two-stage filters and a signal recognition module is established.This algorithm can consider the anti-interference ability and dynamic measurement accuracy at a low reporting rate.Simulation and experimental test results show that the proposed calibrator algorithm provides high-accuracy measurements of the static and dynamic signals with low computational complexity.

A review on synchrophasor communication system:communication technologies,standards and applications

The present-day power system is a complex network that caters to the demands of several applications with diverse energy requirements.Such a complex network is susceptible to faults caused due to several reasons such as the failure of the equipment,hostile weather conditions,etc.These faults if not detected in the real-time may lead to cascading failures resulting in a blackout.These blackouts have catastrophic consequences which result in a huge loss of resources.For example,a blackout in 2004 caused an economic loss of 10 billion U.S dollars as per the report of the Electricity Consumers Resource Council.Subsequent investigation of the blackout revealed that the catastrophe could have been prevented if there was an early warning system.Similar other blackouts across the globe forced the power system engineers to devise an effective solution for real-time monitoring and control of the power system.The consequence of these efforts is the wide area measurement system(WAMS).The WAMS consists of several sensors known as the phasor measurement units(PMUs)that collect the real information pertaining to the health of the power grid.This information in the form time synchronized voltage and current phasors is communicated to the central control center or the phasor data concentrator(PDC)where the data is analyzed for detection of power system anomalies.The communication of the synchrophasor data from each PMU to the PDC constitutes the synchrophasor communication system(SPCS).Thus,the SPCS can be considered as the edifice of the WAMS and its reliable operation is essential for the effective monitoring and control of the power system.This paper presents a comprehensive review of the various synchrophasor communication technologies,communication standards and applications.It also identifies the existing knowledge gaps and the scope for future research work.

Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements

Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.

A novel method for locating the source of sustained oscillation in power system using synchrophasors data

Large interconnected power systems are usually subjected to natural oscillation(NO)and forced oscillation(FO).NO occurs due to system transient response and is characterized by several oscillation modes,while FO occurs due to external perturbations driving generation sources.Compared to NO,FO is considered a more severe threat to the safe and reliable operation of power systems.Therefore,it is important to locate the source of FO so corrective actions can be taken to ensure stable power system operation.In this paper,a novel approach based on two-step signal processing is proposed to characterize FO in terms of its frequency components,duration,nature,and the location of the source.Data recorded by the Phasor Measurement Units(PMUs)in a Wide Area Monitoring System(WAMS)is utilized for analysis.As PMU data usually contains white noise and appears as multi-frequency oscillatory signal,the first step is to de-noise the raw PMU data by decomposing it into a series of intrinsic mode functions(IMF)using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)technique.The most appropriate IMF containing the vital information is selected using the correlation technique.The second step involves various signal processing and statistical analysis tools such as segmented Power Spectrum Density(PSD),excess kurtosis,cross PSD etc.to achieve the desired objectives.The analysis performed on the simulated two-area four-machine system,reduced WECC-179 bus 29 machine system,and the real-time power system PMU data set from ISO New England,demonstrates the accuracy of the proposed method.The proposed approach is independent of complex network topologies and their characteristics,and is also robust against measurement noise usually contained in PMU data.

Data-driven Transient Stability Assessment Using Sparse PMU Sampling and Online Self-check Function

Artificial intelligence technologies provide a newapproach for the real-time transient stability assessment (TSA)of large-scale power systems. In this paper, we propose a datadriven transient stability assessment model (DTSA) that combinesdifferent AI algorithms. A pre-AI based on the time-delay neuralnetwork is designed to locate the dominant buses for installingthe phase measurement units (PMUs) and reducing the datadimension. A post-AI is designed based on the bidirectionallong-short-term memory network to generate an accurate TSAwith sparse PUM sampling. An online self-check function of theonline TSA’s validity when the power system changes is furtheradded by comparing the results of the pre-AI and the post-AI.The IEEE 39-bus system and the 300-bus AC/DC hybrid systemestablished by referring to China’s existing power system areadopted to verify the proposed method. Results indicate that theproposed method can effectively reduce the computation costswith ensured TSA accuracy as well as provide feedback forits applicability. The DTSA provides new insights for properlyintegrating varied AI algorithms to solve practical problems inmodern power systems.

Field PMU Test and Calibration Method——PartⅡ:Test Signal Identification Methods and Field Test Applications

Synchrophasor measurement units(PMUs)provide synchronized measurement data for wide-area applications.To improve the effectiveness of synchrophasor-based applications,field PMUs must be tested to ensure their performance and data quality.In the companion paper(Part I),we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center.Part I presents the development and test of the PMU calibrator.This paper focuses on the analysis center and field test applications.First,the critical component of the analysis center is the signal identification module,for which the step and oscillation signal identification methods are proposed.Here,the performance evaluation criteria of PMU in these two cases are different from others.The methods include a step signal detection method based on singular value decomposition(SVD),which has the capability of weak step detection to account for energy leakage of the signal during the step process,and an oscillation signal identification method based on SVD and fast Fourier transform,which can accurately extract oscillation components that benefit from the adaptive threshold setting method.Second,the analysis center software is implemented based on identification results.By integrating the PMU calibrator in Part I with the analysis center in Part II,we can examine in depth the field PMU test applications in three test scenarios,including standard,playback,and field signal test.Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.