Data Fusion and Data Mining for Power System Monitoring

Arturo Messina earned his PhD from Imperial College, London, UK, in 1991. Since 1997, he has been a professor in the Center for Research and Advanced Studies, Guadalajara, Mexico. He is on the editorial and advisory boards of Electric Power Systems Research, and Electric Power Components and Systems. From 2011 to 2018 he was Editor of the IEEE Trans. on Power Systems and Chair of the Power System Stability Control Subcommittee of the Power Systems Dynamic Committee of IEEE (2015-2018). A Fellow of the IEEE, he is the editor of Inter-Area Oscillations in Power Systems – A Nonlinear and Non-stationary Perspective (Springer, 2009) and the author of Robust Stability and Performance Analysis of Large-Scale Power Systems with Parametric Uncertainty: A Structured Singular Value Approach (Nova Science Publishers, 2009), and Wide-Area Monitoring of Interconnected Power Systems (IET, 2015).

• 1. Introduction. 2. Data mining and data fusion architectures. 3. Data parameterization, clustering and denoising. 4. Spatio-temporal data mining. 5. Multisensor data fusion. 6. Dimensionality reduction and feature extraction and classification. 7. Forecasting decision support systems. 8. Data fusion and data mining analysis and visualization. 9. Emerging topics in data mining and data fusion. 10. Experience with the application of data fusion and data mining for power system health monitoring.