Real-time multiple event analysis is important for reliable situational awareness and secure operation of the power system. Multiple sequential events can induce complex superimposed pattern in the data and are challenging to analyze in real time. This paper proposes a method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data. For detection and temporal localization, a Teager–Kaiser energy operator (TKEO) based method is proposed. For event classification, a time series classification based method using energy similarity measure (ESM) is proposed. The proposed method is tested for simulated multiple event cases in the IEEE-118 bus system using DigSilent/PowerFactory and real PMU data for the Indian grid.

Real-Time Multiple Event Detection and Classification in Power System Using Signal Energy Transformations

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. The results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

With an increasing number of extreme events, grid components and complexity, more alarms are being observed in the power grid control centers. Operators in the control center need to monitor and analyze these alarms to take suitable control actions, if needed, to ensure the system's reliability, stability, security, and resiliency. Although existing alarm and event processing tools help in monitoring and decision making, synchrophasor data along with the topology and component location information can be used in detecting, classifying and locating the event, which is the focus of this work. Phasor Measurement Unit's (PMU's) data quality issue is also addressed before using data for event analysis. The developed algorithms include statistic, clustering, and Maximum Likelihood Criterion (MLE) based anomaly detection, Density-based spatial clustering of applications with noise (DBSCAN) for event detection and physics-based rule/ decision tree for event classification. Further, topology information, statistical techniques, and graph search algorithms are used for event localization. Developed algorithms have been validated with satisfactory results for IEEE 14 bus and 39 Bus as well as with real PMU data from the western US interconnection (WECC).

A Real Time Event Detection, Classification and Localization Using Synchrophasor Data

The wide-area measurement system provides a new data acquisition and supervisory control tool for a power system, and the data acquisition level is increased dramatically with its development in the smart grid environment Huge data associated with the power system operation are acquired, which are beneficial for enhancing situational awareness of a power system concerned Identifying the coherency among synchronous generators using real-time signals from phasor measurement units (PMUs) is one of the major tasks of situational awareness in power system operation Given this background, a data-driven coherency identification methodology is proposed based on the spectral clustering algorithm First, several trajectory dissimilarity indices for the rotor angle and rotor speed trajectories of generators as measured by PMUs are presented based on the trajectory similarity theory Second, a decision-making method based on the Gini coefficient and Kendall rank correlation coefficient is presented for integrating multiple indices describing trajectory dissimilarities Third, the spectral clustering algorithm is presented to identify the coherency of synchronous generators, and silhouette is presented for determining a reasonable number of coherent groups Finally, oscillation events happened/simulated in two actual power systems, ie, Guangdong power system in China and Western Interconnection power system in North America, are utilized to demonstrate the effectiveness of the proposed data-driven coherency identification methodology

Data-Driven Coherency Identification for Generators Based on Spectral Clustering

Renewable energy sources (RES) based distributed generation (DG) system results reduction of overall system inertia, which is likely to generate higher oscillations in the system during disturbance conditions. Therefore, DG penetration level has a significant impact on system stability and reliability. This study provides an in-depth analysis of battery energy storage system (BESS) impact in providing primary frequency control to support increased wind penetration level. The BESS is modeled as a storage system with DC/AC converter and other associated power electronics interfaces. The objective is to replace the existing synchronous generator in proportion with the increasing penetration level of wind units while maintaining power system stability and reliability. The BESS model is developed in DigSILENT/PowerFactory and the system performances are simulated and compared with and without the BESS considering different disturbances such as single-phase-to-ground fault, and temporary line outage and load demand increment with various DG penetration level. It is shown through simulation results that BESS exhibits the ability to reduce system oscillations following disturbances and supports the increment of DG penetration level in the existing power system. Therefore, BESS can be seen the most viable measure of stability enhancement with renewable oriented sustainable future electric grid.

The relevance of large-scale battery energy storage (BES) application in providing primary frequency control with increased wind energy penetration

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load), and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, thereby increasing overall situational awareness of the power system. Numerical case studies using both real data, collected from the U.K. power system, and simulated case studies, constructed using DigSilent PowerFactory, for islanding events, as well as both loss of load and generation dip events, are used to demonstrate the reliability of the proposed method.

/pdf/real-time-multiple-event-detection-and-classification-using-50uu73qdoo.pdf

Real-Time Multiple Event Detection and Classification Using Moving Window PCA

/pdf/real-time-multiple-event-detection-and-classification-using-230uwrm7ej.pdf

Real-time multiple event detection and classification using moving window PCA

PMU data has the potential of providing a wealth of information on power system operation, health, faults and anomalies. PMU tend to provide tens of measurements per second, therefore automated anomaly detection is required; especially for use in real or near-time applications by power system operators. This paper demonstrates a method of detecting local anomalies in PMU data utilizing multiple linear regression. A window of near-time data is employed to generate a regression function that predicts the live data that arrives. If the error between the observed and predicted values exceeds a threshold an exception is noted. The threshold is dynamically updated based on the error in the regression function, allowing the method to work equally well on data of varying regularity. This anomaly detection method is not tuned to particular events and should detect novel occurrences. The method is evaluated on two numerical case studies, the real data was collected from PMUs placed on the Irish power system.

/pdf/local-anomaly-detection-by-application-of-regression-7oygnskxkn.pdf

Local Anomaly Detection by Application of Regression Analysis on PMU Data

Rapid detection and diagnosis of events in power system wide area monitoring is of great interest to system operators, with event classification being a major aspect of diagnosing an event. Other event diagnostic aspects include the time the event occurred, location of the event, root cause of the event and magnitude of the event. Automatic event classification enhances the operators’ ability to identify the types of events occurring in a system quickly, which helps to assist fast decision making when restoring power to the system. This paper proposes an approach for classifying power system events, namely Generation Dip, Loss of Load and Line Trip Events, by employing Quadratic Discriminant Analysis (QDA) on Phasor Measurement Unit (PMU) data in combination with a forward selection technique. QDA is a commonly used supervised, statistical technique for data classification, and works by finding a combination of features that separates the data into different classes by modelling the difference between them. Historical power system event data is used to construct an event database, and as new events are detected the methodology automatically classifies the event based on the effect on power system variables. The reliability of the proposed method is demonstrated using simulated case studies, constructed using DigSilent Power Factory, and real data case studies, acquired from the UK and Irish Power System.

Automatic Power System Event Classification Using Quadratic Discriminant Analysis on PMU Data

Synthetic inertia devices must deliver power during maximum RoCoF, which is observed to occur during the first 500 ms post fault. The use of a droop characteristic with a deadband is demonstrated to be unfit for purpose. The ideal response (with present technologies) is demonstrated to be a maximum power ramp in minimum time post fault. This paper demonstrates a method of rapidly detecting and categorizing under frequency events by monitoring synchronous machines. PMU and point on wave data, sampled at 6.4 kHz, from a power station and a laboratory set up, under transient events, is presented. A rapid inertial power ramp signals the onset of the events. Sub-cycle voltage and current variations are demonstrated as a potential method for reliably detecting frequency transients and triggering a synthetic inertia response.

/pdf/triggering-bess-inertial-response-with-synchronous-machine-r7tuaxjlbm.pdf

Triggering BESS Inertial Response with Synchronous Machine Measurements

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