Data Mining Based Anomaly Detection In PMU Measurements And Event Detection

Transcription

1 Data Mining Based Anomaly Detection In PMU Measurements And Event Detection P. Banerjee, S. Pandey, M. Zhou, A. Srivastava, Y. Wu Smart Grid Demonstration and Research Investigation Lab (SGDRIL) Energy System Innovation center, Washington State University Contact: JSIS May 2017

2 Outline Synchrophasor Data Quality (SDQ) Ensemble based Anomaly Detection Architecture of the developed technique The proposed Maximum Likelihood Estimator (MLE) Inference and Outlier Detection Synchrophasor Anomaly Detection Tool Base Detectors- Chebyshev and Linear Regression. Quality Measures and Results Event Detection Event Detection Flowchart DBSCAN Algorithm Results Summary

3 Synchrophasor Data Quality issues The synchrophasor data are prone to anomalies. Anomalies can be outlier or missing data. Loss of data, GPS sync problem, inaccurate measurements etc. Impact of SDQ on Applications Performance deterioration of monitoring application. Effect output of control application. Solution Need for real time quality check required by data driven application. A Data Mining based Ensemble Technique, with less or no parameter tuning and unsupervised learning method.

4 X Data Window from PMU/PDC Regression Chebyshev DBSCAN D1 D2 D3 Learning Algorithm X Outlier Scores Base Detectors f i, f j, f k Normalization of Base Detector Scores F Normalized MLE-Ensemble Inference Algorithm Y MLE (α, β) Data Anomaly Detected 4

5 The goal of linear regression is to find the equation of the straight line Where is the y-intercept, is the slope. which would provide a "best" fit for the data points. Here the "best" will be understood as in the leastsquares approach: a line that minimizes the sum of squared residuals of the linear regression model.

6 Chebyshev method Chebyshev s was designed to determine a lower bound of the percentage of data that exists within k number of standard deviations from the mean. When the data distribution is unknown, Chebyshev s inequality can be used, as shown by where X represents the data, μ is the data mean, σ is the standard deviation of the data, and k represents the number of standard deviations from the mean.

7 Normalized Scores F Normalized Data Set X Compute Sensitivity Ψ and Specificity Ƞ Ψ, Ƞ Learn Weights α and β F Normalized Using EM algorithm fit Y MLE MLE-Ensemble α, β Final learned weights α, β

8 After the MLE-Ensemble step, weights of each base detector is learned which is Y MLE. New data set using these weights and the Normalized scores of the base detectors the inference algorithm makes decision on bad data. α,β i.e. the learned model Normalized Score F Normalized Using Y MLE and new Data Set label Outliers Inference and Outlier Detector Outliers Detected

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10 Given a PMU detector D and PMU data X, denote the actual anomaly data set as, and the anomaly reported by D as, the performance of D is evaluated using three metrics as follows. Precision Precision measures the fraction of true anomaly data in the reported ones from D, defined as Recall Recall measures the ability of D in finding all outliers, defined as False Positive False positive (FP) evaluates the possibility of false anomaly data detection; the smaller, the better.

11 Tests on the RTDS simulated PMU data (1.5 hours) Recall Precision False positive Linear Regression DBSCAN Chebyshev MLE ensemble Tests on the RTDS simulated PMU data (1.5 hours, 5% bad data points, 5%-10% range) Recall Precision False positive Linear Regression DBSCAN Chebyshev MLE ensemble Tests on the RTDS simulated PMU data (1.5 hours, 10% bad data points, 10%-20% range) 11

12 X Data Window from SyncAD Get New Data Window V I Fz Computation of Active and Reactive Flows P Q DBSACN Algorithm Cluster Change Detected? No Yes No Events Detected? Decision Tree Collection Of Cluster Change instances in V, I, P, Q and Fz Undetected Events Yes 12

13 DBSCAN algorithm uses two thresholds radius ε and minpts. During events the data points get out of reach of a cluster i.e. its greater than radius ε of the boundary points. New Clusters is formed after the event ends. Change in cluster using DBSCAN and determine the type of events. Current Time Event Detection Using DBSCAN

14 S No. Actual Events Detected Events 1 Cap Bank Operation Detected Successfully 2 Transformer tap Change Detected Successfully 3 Generator Drop Detected Successfully 4 Faults Detected Successfully 5 Heavy Load Changes Detected Successfully 6 Small Load Changes Not Detected

15 Current Plot An Active Power Event was detected at time instance seconds.

16 Anomaly Detection A single method is not sufficient to solve the bad data problem. An integrated methods called as base detectors is required. The MLE-Ensemble produces a result equivalent to or better than all the base detectors. Event Detection Event Detection Algorithm detects the events simulated in RTDS in terms of Active Power Event, Reactive Power Event and Faults. Event Detection Algorithm on Industry data was also applied. Advantage of the ensemble based method Plug in more base detectors as needed, which will continuously improve the method. Little or no effort in parameter tuning Unsupervised, but works well with more training data Established and well-supported data mining and machine learning. Suitable for real time streaming PMU, and can help in realizing close-loop Control Systems. Integrating Anomaly Detection into applications can result into qualityaware applications. 16