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.

Optimal Placement of Phasor Measurement Units Using a Modified Canonical Genetic Algorithm for Observability Analysis

This paper proposes a method for optimal placement of synchronized PMUs （phasor measurement units） in electrical power systems using a MCGA （modified canonical genetic algorithm）, which the goal is to determine the minimum number of PMUs, as well as the optimal location of these units to ensure the complete topological observability of the system. In case of more than one solution, a strategy of analysis of the design matrix rank is applied to determine the solution with the lower number of critical measurements. In the proposed method of placement, modifications are made in the crossover and mutation genetic operators, as well as in the formation of the subpopulation, and are considered restrictive hypotheses in the search space to improve the performance in solving the optimization problem. Simulations are performed using the IEEE 14-bus, IEEE 30-bus and New England 39-bus test systems. The proposed method is applied on the IEEE 118-bus test system considering the presence of observable zones formed by conventional measurements.

Estimation Method of Center of Inertia Frequency Based on Phasor Measurement Data

In the world, recent increased disturbances, congestion management problems, and increases of complexity in operating power systems have brought the need for integrations and improvements of power systems. Advanced applications in WAMPAC （wide area monitoring, protection, and control） systems provide a cost effective solution to improve system planning, operation, maintenance, and energy trading. Synchronized measurement technology and the application are an important element of WAMPAC. In addition, PMUs （phasor measurement units） are the most accurate and advanced time-synchronized technology available for WAMPAC application. Therefore, the original measurement system of PMUs has been constructed in Japan. This paper describes the estimation method of a center of inertia frequency by applying actual measurement data. The application of this method enables us to extract power system oscillations from measurement data appropriately. Moreover, this proposed method will help to the clarification of power system dynamics and this application will make it possible to realize the monitoring of power system oscillations associated with the power system stability.

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.

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.

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.

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.

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.

Pragmatic multi-stage simulated annealing for optimal placement of synchrophasor measurement units in smart power grids

Conventional power grids across the globe are reforming to smart power grids with cutting edge technologies in real time monitoring and control methods. Advanced real time monitoring is facilitated by incorpor- ating synchrophasor measurement units such as phasor measurement units （PMUs） to the power grid monitoring system. Several physical and economic constraints limit the deployment of PMUs in smart power grids. This paper proposes a pragmatic multi-stage simulated annealing （PMSSA） methodology for finding the optimal locations in the smart power grid for installing PMUs in conjunction with existing conventional measurement units （CMUs） to achieve a complete observability of the grid. The proposed PMSSA is much faster than the conventional simulated annealing （SA） approach as it utilizes controlled uphill and downhill movements during various stages of optimiza- tion. Moreover, the method of integrating practical phasor measurement unit （PMU） placement conditions like PMU channel limits and redundant placement can be easily handled. The efficacy of the proposed methodology has been validated through simulation studies in IEEE standard bus systems and practical regional Indian power grids.

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.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

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.

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.

GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems

Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems.

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.

PMU Based Monitoring and Estimation of Inter-area Power Oscillation for Singapore-Malaysia Interconnection Power System

This paper proposes a PMU （phasor measurement unit） based monitoring and estimation scheme of power system small-signal stability in Singapore-Malaysia interconnection power system through a 50-Hz and 500 kV transmission line. Two PMUs are installed in the power system interconnection network of Singapore-Malaysia. One PMU is located in Singapore and the other one in Malaysia （Penang）. Both PMUs measure the single-phase voltage phasor. The data filtering technique based on FFT （Fast Fourier Transform） is employed to extract oscillation data for single mode. Finally, some analysis results of monitoring and estimation of Singapore-Malaysia interconnected power system based on application practice of the CampusWAMS are presented and analyzed.

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.

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.

An Adaptive Data-driven Method Based on Fuzzy Logic for Determining Power System Voltage Status

This paper proposes an adaptive method based on fuzzy logic that utilizes data from phasor measurement units(PMUs) to assess and classify generating-side voltage trajectories. The voltage variable and its associated derivatives are used as the input variables of a fuzzy-logic block. In addition, the voltage trajectory is compared with the pre-selected pilot-bus voltage to make a reliable decision about the voltage operational state. Different types of short-term voltage dynamics are considered in the proposed method. The fuzzy membership functions are determined using a systematic method that considers the current situation of the voltage trajectory. Finally, the voltage status is categorized into four classes to determine appropriate remedial actions. The proposed method is validated on a IEEE 73-bus power system in a MATLAB environment.

Local Hybrid Linear State Estimation for Electric Power Systems Using Stream Processing

The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation(SE).Meanwhile,we note that only a fraction of system states fluctuate at the millisecond level and require to be updated.As such,refreshing only those states with significant variation would enhance the computational efficiency of SE and make fast-continuous update of states possible.However,this is difficult to achieve with conventional SE methods,which generally refresh states of the entire system every 4–5 s.In this context,we propose a local hybrid linear SE framework using stream processing,in which synchronized measurements received from phasor measurement units(PMUs),and trigger/timingmode measurements received from remote terminal units(RTUs)are used to update the associated local states.Moreover,the measurement update process efficiency and timeliness are enhanced by proposing a trigger measurement-based fast dynamic partitioning algorithm for determining the areas of the system with states requiring recalculation.In particular,non-iterative hybrid linear formulations with both RTUs and PMUs are employed to solve the local SE problem.The timeliness,accuracy,and computational efficiency of the proposed method are demonstrated by extensive simulations based on IEEE 118-,300-,and 2383-bus systems.