Visible to the public Neural Network Model for False Data Detection in Power System State Estimation

TitleNeural Network Model for False Data Detection in Power System State Estimation
Publication TypeConference Paper
Year of Publication2019
AuthorsTabakhpour, Adel, Abdelaziz, Morad M. A.
Conference Name2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)
Date PublishedMay 2019
ISBN Number978-1-7281-0319-8
Keywords13-bus IEEE test system, Artificial neural networks, composability, correlated data sets, cyber physical systems, Data models, data preprocessing technique, False Data Detection, False Data Injection, feature extraction, historical data, Human Behavior, Linear programming, Measurement false data, Multi-layer perceptron, neural network model, Noise measurement, outlier detection scheme, perceptron model, perceptron models, perceptrons, power engineering computing, Power measurement, power system measurement, Power system measurements, power system state estimation, Power systems state estimation, principal component analysis, pubcrawl, resilience, Resiliency, trained network, Training, training sets, Voltage measurement

False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.

Citation Keytabakhpour_neural_2019