• Micro-grid/Smart-grid Optimization and Control
      The power networks around the world are undergoing a transformation like never before. The transformation, from traditional passive distribution networks to active distribution networks with distributed energy resource (DER) penetration, is encouraged as this augments the conventional generation sources. These DERs may be interconnected to form a micro-grid. Stable, economical and environment friendly operation of micro-grid/smart-grid is becoming very critical for overall power quality and reliability. Our efforts are geared towards ensuring fault tolerant and economical micro-grid/smart-grid operation.
  • Distributed State Estimation
      Distributing calculations of a central Kalman filter requires subsystem level expressions for the propagation and update steps of the Kalman filter. It is difficult to obtain subsystem level expressions due to the inverse term present in the update step. Our focus is on achieveing truely distributed estimation, by distributing the coputation burden across subsystems.
  • Data Driven Process Monitoring and Fault Diagnosis
      For large scale systems, obtaining an accurate model is a herculean task and it is relatively easy to rely on data for process monitoring and fault diagnosis. At the same time, large scale processes suffer from data overload because of increasing amounts of data that are being logged. Such historical data contain wealth of information, which when carefully mined can give important insights required for the task of process monitoring and fault diagnosis. Identifying important patterns from such historical data is our prime interest. Similarly, we are actively working on batch process monitoring and diagnosis.
  • AI/ML based controller design for Multivariable systems
      Multivariable systems require carefully designed controller to ensure stability and performance during closed-loop operation. With large amount of operation data, it may be possible to design controller that give much better performance compared to existing controllers. Using machine learning algorithms, it is possible to adapt and tune controller parameters to operating condition.