Wide Area Measurement Research Articles

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

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

Model predictive control based coordinated control of multi-terminal HVDC for enhanced frequency oscillation damping

This paper presents a coordinated power control strategy based on Model Predictive Control (MPC) for Multi-Terminal HVDC (MTDC) to enhance the system frequency oscillation damping. Due to the available transmission capacity margin of HVDC links, MTDC buses have power regulation flexibility. With the proposed control strategy, the MTDC buses dispersed at different locations are efficiently coordinated to optimize the power flow of the grid to damp the frequency oscillations fast and timely. Besides, by the sensitivity analysis, the impact of the power injections at MTDC buses on the system frequency is quantified. By using the real-time sampling data of the Wide Area Measurement System (WAMS), the linearized power system model can be estimated on-line and used as the prediction model of the MPC. The simulation results show that the proposed controller can efficiently improve the system damping performance during frequency events.

Real‐time identification of electromechanical oscillations through dynamic mode decomposition

The increasing penetration of inverter-based resources is affecting the overall power system security, so that effective tools are needed to provide system awareness and identify the most suited countermeasures. To tackle this problem, real-time monitoring and assessment of transmission grids based on synchrophasor measurements have drawn the attention of many researchers over the last decade, thanks to the high temporal resolution of data provided by phasor measurement units. In this study, a new version of the dynamic mode decomposition (DMD) is proposed as a tool for the real-time identification of electromechanical oscillations. This approach is able to provide information on the dynamic characteristics (frequency and damping) and the spatial correlation (mode shape) of the modes identified. Besides, an improved criterion is used to track and discern the dominant modes. The effectiveness of the DMD method has been tested on the two-area Kundur system and validated using data from a real event on the European synchronous grid, giving promising results. Thanks to its reliability and robustness, the DMD is now implemented in the Terna (the Italian transmission system operator) Wide Area Measurement System in use in the control room.

Recognition method of wide area measurement abnormal data based on GRA-IForest

Aiming at the anomaly recognition method of large area measurement system (WAMS), a method based on Grey Mrrelatian Aru and Isolation Forest algorithm is proposed, considering the attributes of power data and the big data characteristics of large area measurement system. In this method, the relational matrix of power data attribute sequence is obtained through grey relational analysis, and the dependent attributes are selected according to the relational matrix for attribute sequence merging. Then, the data set is partitioned by cascaded isolated forest model and abnormal data are detected. Taking the data of a province’s wide-area measurement system as an example for experimental simulation, compared with LOF, Kmeans and IForest, under the same inspection time, the GRA-IForest model has achieved good inspection results and achieved a higher overall detection accuracy and lower standard deviation.

Frequency coordinated control strategy of HVDC sending system with wind power based on situation awareness

With the large-scale wind power, centralised access to high-voltage direct current (HVDC) asynchronous interconnected sending system, the random fluctuation of wind energy increases the uncertainty of frequency regulation. A frequency coordinated control (FCC) strategy based on situation awareness is proposed to make full use of the frequency regulation potential of wind turbines and HVDC transmission. Firstly, the influence of wind power permeability and HVDC power ratio on frequency regulation capability is analysed, and the mathematical model of FCC is established. Secondly, based on the information of FCC obtained by wide-area measurement system and data acquisition system, a non-linear mathematical prediction model is constructed, and the frequency response in finite time domain is predicted according to the frequency deviation and its change rate. According to the results of the prediction, the parameters of FCC are dynamically adjusted to realise the whole process variable objective optimal control on the time scale of primary frequency regulation. Finally, the simulation results show that the maximum frequency deviation of the proposed control method is reduced by about 46.6% and the frequency callback time is reduced by about 11.6% compared with the traditional control method with fixed parameters under the same disturbance.

Data-Driven Wide-Area Model-Free Adaptive Damping Control With Communication Delays for Wind Farm

The strong nonlinearity, time-varying structure, and complex operating conditions in interconnected power systems with the wind farm deteriorate the control performance of wide-area damping control (WADC) designed by conventional model-based methods. In this paper, an improved model-free adaptive control (MFAC), which employs the pseudo-partial derivative (PPD) to linearize the nonlinear power system dynamically, considering WADC requirements and system disturbances is proposed to damp out the inter-area oscillation for wind farm. In addition, to compensate for communication delays in a wide-area measurement system (WAMS), an adaptive delay compensator (ADC) is employed to reimbursed both constant and variable delays. The proposed controller depends only on online I/O data, which avoids the complicated system modeling process and can adapt to variations of system operating conditions and delay scenarios. Moreover, compared to existing MFAC damping approaches, the proposed approach provides additional advantages for wind farm on managing initial values and the stability region of PPD. The communication delays are compensated, which is not considered in other MFAC-based methods. Finally, case studies with various uncertainties and disturbances are conducted to validate the proposed control strategy.

Three-way unsupervised data mining for power system applications based on tensor decomposition

Sophisticated geospatial metering devices used in today's networks such as the advanced metering infrastructure (AMI), wide area measurement system (WAMS) and supervisory control and data acquisition (SCADA) open new opportunities to monitor the security of the system in real time. Consequently, these metering infrastructures have received significant attention in recent years from data mining communities because of the new challenges involved on managing this information. One of the main challenges is the analysis of multivariable data, which represents datasets containing variables of different nature, which are linked. In this document a data mining technique that allows the analysis of multivariate data is presented. Moreover, an innovative application of an unsupervised data mining algorithm for smart meters data, particularly to Electrical Load Profile using tensor decomposition is presented. Since the proposed tensor representation allows to assign a given dimension to a particular variable involved; data reduction, data compression, data visualization and data clustering is archived separately for every variable. To validate the effectiveness of the proposed methodology, a three-way tensor built with data from the Electrical Reliability Council of Texas (ERCOT) is presented. The results demonstrate that is possible to extract more information than using conventional approaches based on 2-way arrangements (matrices). Additionally, the proposed algorithm is solved using an iterative approach, which indirectly enable to estimate missing data.

Anomaly Detection and Mitigation for Wide-Area Damping Control Using Machine Learning

In an interconnected multi-area power system, wide-area measurement based damping controllers are used to damp out inter-area oscillations, which jeopardize grid stability and constrain the power flows below to their transmission capacity. The effect of wide-area damping control (WADC) significantly depends on both power and cyber systems. At the cyber system layer, an adversary can inflict the WADC process by compromising either measurement signals, control signals or both. Stealthy and coordinated cyber-attacks may bypass the conventional cybersecurity measures to disrupt the seamless operation of WADC. This paper proposes an anomaly detection (AD) algorithm using supervised Machine Learning and a model-based logic for mitigation. The proposed AD algorithm considers measurement signals (input of WADC) and control signals (output of WADC) as input to evaluate the type of activity such as normal, perturbation (small or large signal faults), attack and perturbation-and-attack. Upon anomaly detection, the mitigation module tunes the WADC signal and sets the control status mode as either wide-area mode or local mode. The proposed anomaly detection and mitigation (ADM) module works inline with the WADC at the control center for attack detection on both measurement and control signals and eliminates the need for ADMs at the geographically distributed actuators. We consider coordinated and primitive data-integrity attack vectors such as pulse, ramp, relay-trip and replay attacks. The performance of the proposed ADM algorithms was evaluated under these attack vector scenarios on a testbed environment for 2-area 4-machine power system. The ADM module shows effective performance with 96.5% accuracy to detect anomalies.

Method for Detecting Anomaly Data of WAMS System Based on GA-iForest

Wide area measurement system problems such as anomalies, data missing, and real-time response deeply affect the operation and maintenance of the power grid. Considering the accuracy requirements of the system for anomaly detection, the detection accuracy and stability of traditional isolation forest are poor, so this paper proposes a new data anomaly detection method named GA-iForest. This method uses genetic algorithms to select isolated trees with high accuracy and obvious differences to optimize the structure of isolated forests. The new data anomaly detection method GA-iForest improves the accuracy and stability of detection, and mean while maintaining the high efficiency. The experiment uses standard simulation data sets and data from one province’s wide-area measurement system for experimental simulation. The result shows that the GA-iForest method has significantly improved accuracy and stability compared with the traditional isolation forest, LOF, and K-means methods.

An Adaptive Underfrequency Load Shedding Scheme in the Presence of Solar Photovoltaic Plants

Load shedding is performed, as last option, for emergency measure when system frequency drifts below the allowed limit. Utilities use underfrequency load shedding schemes to bring back frequency within limit. In this article, an adaptive underfrequency load shedding scheme is proposed for the power system in the presence of solar photovoltaic plants using wide-area measurements. Stepwise load shedding is performed at selected buses with consideration of any change in photovoltaic power output and load/generation at each step. Share of load to be shed at a bus is based on the voltage stability index of the buses at that instant considering predefined load priority. The voltage stability index at a bus is obtained using the Thevenin equivalent of the system. For a solar photovoltaic plant integrated system, the Thevenin equivalent changes with solar power variation, where available methods become inaccurate. A Thevenin equivalent calculation approach is proposed in this article using an equivalent model of synchronous generators and photovoltaic plants. The proposed load shedding method is tested on the New England 39-bus system and the 233-bus North Eastern Indian grid with integrated photovoltaic plants. It is compared with available methods and found to be a better technique in bringing system frequency to normalcy with adaptive load shedding.

Study and Analysis of Advance Signal Processing Approaches in Wide Area Measurement System

Study and Analysis of Advance Signal Processing Approaches in Wide Area Measurement System

A security-based observability method for optimal PMU-sensor placement in WAMS

Optimal PMU placement (OPP) problem is one of the most important issues in wide area measurement systems (WAMS). Delivering the measured data to the control center is possible through communication infrastructure (CI) that is a part of the WAMS. However, to well estimate the state of the system, observability of the measurement system is necessary. In this paper, a security-based observability (SBO) method for the PMUs and related CI is proposed. A hybrid wireless sensor-based structure including energy harvesting sensor nodes (EHSNs) and plug-in powered sensor nodes (PPSNs) is utilized as the communication system. The contingency-constraint optimal PMU placement and related sensor-based CI is proposed and formulated. To increase the reliability of the CI, a reliability index for the wireless sensors is added as a constraint to the objective function which includes the total cost minimization subject to power system observability and communication constraints. The effect of zero-injection buses (ZIBs) and radial buses in the power system are also included in the model to get more practical results. A mixed-integer linear programming (MILP) is used for contingency constraint PMU placement and a binary-coded genetic algorithm (BCGA) is accordingly used for solving the examined communication optimization. The IEEE 13-bus and IEEE 37-bus test feeder systems are utilized to evaluate the effectiveness of the proposed method.

Global Harmonic Parameters for Estimation of Power Quality Indices: An Approach for PMUs

For wide-area measurement systems and smart grids, phasor measurement units (PMUs) have become key elements since they provide synchronized information related to the fundamental frequency components of voltages and currents. In recent years, some works have extended the concept of PMU to harmonic analysis due to the proliferation of nonlinear loads. In this work, as a first contribution, the reference model for P-class and M-class PMUs provided by the IEEE Standard C37.118.1 is expanded with the aim of obtaining the harmonic information and electric power quantities. Additionally, as a second contribution, the approach of global harmonic parameters (GHPs) for PMUs is proposed. Specifically, GHPs are introduced in this work as unified quantities regarding the overall harmonic content of voltages and currents signals. With the help of these parameters, the estimation of power quality indices (PQIs) according to the IEEE Standard 1459 can be carried out but with an important advantage, i.e., a reduced amount of data, which reduces the requirements of management, storage, and analysis. Finally, the mathematical formulations for PQIs using the proposal are also presented. It is important to mention that they are equivalent to classical formulations that use individual harmonic information; however, they exploit the advantage of PMUs that require a reduced amount of data. Several tests with synthetic and real signals are carried out to validate the proposal. Results demonstrate the effectiveness and usefulness of the proposed approach.

Low-Cost Real-Time Design and Implementation of Phasor Measurement Unit using Analog Discovery

The blackouts in the power grid are forced to premonitor the conditions of the grid. Phasor Measurement Units (PMU) have become highly inevitable in the Wide Area Measurement System (WAMS). Continuous measurement of current and voltage phasors has become more reliable owing to very high stability showcased by Non-recursive DFT algorithm. Design and Modelling of PMU are implemented both in software and hardware using NI LabVIEW Professional development system. The novelty in this paper is that the continuous monitoring and real-time implementation of portable Phasor Measurement Unit is achieved by using Analog Discovery 2 with modest design. The results are validated, and the proposed implementation is more cost effective than the conventional system.

Design and Application of a Distribution Network Phasor Data Concentrator

The wide area measurement system (WAMS) based on synchronous phasor measurement technology has been widely used in power transmission grids to achieve dynamic monitoring and control of the power grid. At present, to better realize real-time situational awareness and control of the distribution network, synchronous phasor measurement technology has been gradually applied to the distribution network, such as the application of micro multifunctional phasor measurement units (μMPMUs). The distribution network phasor data concentrator (DPDC), as a connection node between the μMPMUs and the main station, is also gaining more attraction. This paper first analyzes the communication network structure of DPDCs and μMPMUs and compares and analyzes the differences in the installation locations, functions, communication access methods and communication protocols of the phasor technology devices of the distribution network and the transmission network. It is pointed out that DPDCs not only need the functions of data collection, storage, and forwarding like transmission network PDCs, but also should be able to access more μMPMUs, and can aggregate the phasor data of the same time scale from μMPMUs by different communication methods. The communication protocol selected by DPDC should be expanded to support remote control, telemetry, fault diagnosis and other functions of distribution automation. The application requirements of DPDCs are clarified, and the key indicators of DPDCs are given as a method to evaluate the basic performance of DPDCs. Then, to address the problems of more μMPMU access, abnormal communication, and data collection with different delays that DPDC encountered, a DPDC that considers multiple communication methods is designed. Based on the Linux system and the libuv library, the DPDC is designed with event-driven mechanism and structured programming, runs multiple threads to implement multitasking, and invokes callbacks to perform asynchronous non-blocking operations. The DPDC test system and test methods are designed. The performance of the designed DPDC is evaluated through the test and the test results are analyzed. Lastly, its real-world application is disclosed, which further confirmed the value of our DPDC.

Speed of sound evaluation considering spatial resolution in a scanning acoustic microscopy system capable of observing wide spatial area

The dependence of the speed of sound (SoS) in sliced rat organs on the spatial resolution was investigated through ultrasound analysis at high frequencies (80 and 250 MHz). The radio-frequency echo signals from target were acquired by a scanning acoustic microscopy system developed by the authors that can observe a sample with a transducer. The SoS was evaluated by correcting for the time of flight difference obtained from the wide area measurement. The dependency of the SoS on the spatial resolution was evaluated by applying different filtering methods. Stable analysis was possible after time correction, and fine textures reflecting the microstructure could be observed. The application of different filters to the results obtained at 250 MHz resulted in average SoS values that were similar to those obtained at 80 MHz. These results suggest that the primary targets evaluated 80 and 250 MHz are connective tissue and single cells, respectively.

A Distributed Cooperative Control Strategy of Offshore Wind Turbine Groups with Input Time Delay

With large-scale development of offshore wind power and the increasing scale of power grid interconnection, more and more attention has been drawn to the stable operation of wind power units. When the wide area measurement system (WAMS) is applied to the power system, the time delay mainly occurs in the signal measurement and transmission of the power system. When 10MW wind turbines transmit information through complex communication network, time delay often exists, which leads to the degradation of performance and instability for system. This affects the normal operation of a wind farm. Therefore, in this paper, the distributed control problem of doubly fed wind turbines with input time delay is studied based on the Hamiltonian energy theory. Firstly, the Port-controlled Hamiltonian system with Dissipation (PCH-D) model is implemented with the Hamiltonian energy method. Then, the Casimir function is introduced into the PCH-D model of the single wind turbine system to stabilize the time delay. The wind turbine group is regarded as one network and the distributed control strategy is designed, so that the whole wind turbine cluster can remain stable given a time delay occurring in the range of 30–300 ms. Finally, simulation results show that the output power of the wind turbine cluster with input delay converges to the expected value rapidly and remains stable. Additionally, the system error caused by time delay is greatly reduced. This control method can effectively solve the problem of input time delay and improve the stability of the wind turbine cluster. Moreover, the method proposed in this paper can adopt the conventional time step of dynamic simulation, which is more efficient in calculation. This method has adaptability in transient stability analysis of large-scale power system, however, the third-order mathematical model used in this paper cannot be used to analyze the internal dynamics of the whole power converter.

Innovative primary frequency control in low‐inertia power systems based on wide‐area RoCoF sharing

Future plans for integration of large non-synchronous generation and the expansion of the power system in the Nordic countries are a concern to transmission system operators due to the common interconnections and electricity exchanges among these operative areas. The expected reduction in the inertia anticipates an alteration of the frequency response, provoking a high Rate of Change of Frequency (RoCoF) slopes that can jeopardise the security of the interconnected systems. Since power generation in the Nordic countries such as Sweden, Finland and Norway is hydro-dominated, here, the authors propose a novel solution to tackle this problem including wide area measurements to monitor and share the RoCoF in remote areas with lower inertia to enhance their primary frequency control. To demonstrate the effectiveness of the proposed solution, first a test benchmark control with optimised parameters is developed and later compared against the proposed method. Additionally, since the proposed solution is based on measurements from remote locations in order to guarantee the stability of the system the impact of delays in the communication channels is also included in the problem formulation.

Model-free angle stability assessment using wide area measurements

A power system experiences different disturbances frequently where stability assessment is important for reliable operation. Synchronized data, from various buses, provided by wide area measurement system are resourceful for real-time stability analysis. Following a disturbance, generator rotors swing differently. Such rotor swings can be fast or slow based on the disturbance and system conditions, and may die down gradually (stable) or go on increasing (unstable). Therefore, rotor angle stability assessment is important for proper system operation. In this work, slow and fast swings are classified based on the relative rotor angle trend of the most disturbed generator pair. A predictive approach using auto-regression technique is used for stability assessment of fast swing. For slow swing, an index is proposed using Prony analysis. The proposed method identifies if the system is stable or moving towards instability, well in advance of actual instability point, enabling the system operator to take actions for preventing system collapse. No power system model is used, making the method fast and free from modelling errors. The proposed method is tested for New England 39 bus system, IEEE 118 bus system and 233 bus North Eastern Indian grid and found to be accurate. Comparative results with available method indicate the strength of the proposed method.

Approach to Enhance the Robustness on PMU-Based Power System Dynamic Equivalent Modeling

Due to the maturity of wide area measurement technology, phasor measurement unit (PMU)-based dynamic equivalent modeling method (PDM) has been wildly used in large-scale power system modeling. However, traditional “event-based” PDM equivalent model may face serious robustness issues, such as although derived equivalent models show the good ability on reproduction of the system behaviors under recorded events where they derived from, they may not work well for other possible disturbances. In this article, to improve the robustness of equivalent model, a novel method on dynamic equivalent modeling of power system is proposed. Based on the result of coherency identification, generators inside the equivalent system can be aggregated into different coherent subgroups and represented by multimachine equivalent model. Key parameter selection, which can shrink the scale of identified parameters, is designed to avoid multiple solutions of the equivalent model. Moreover, a multiobjective parameter identification algorithm is developed to fully consider the PMU measurements in different system events. In addition, hybrid dynamic simulation is performed so that each equivalent submodel can be identified separately. Finally, the effectiveness of the proposed method is verified in an actual power grids in China.