Prof. Trapti Jain

Research

Research Interests

  • Data analytics in smart grid
  • Cyber-attack detection and mitigation in smart grid
  • Synchrophasor applications to Power System
  • Artificial Intelligence applications to power systems
  • Integration of Electric Vehicles into the power grid
  • Stability and Control of Microgrid
  • Power system security analysis

Research Highlights

Synchrophasor measurement based data analytics researchPhasor Area Criterion research visualization

Synchrophasor measurement based data analytics

Developed a real-time event detection algorithm cum software to identify and localize the disturbances utilizing synchrophasor measurements.

Phasor Area Criterion for wide area stability assessment research

Phasor Area Criterion for wide area stability assessment

Phasor area criterion, inspired from equal area criterion, is developed to assess the qualitative and quantitative aspects of the transient stability of power systems using synchrophasor measurements.

Grid-forming control strategy research result 2Grid-forming control strategy research result 4Grid-forming control strategy research result 3Grid-forming control strategy research result 1

Grid forming control strategies to improve stability of islanded AC microgrids

Developed various grid-forming control strategies to improve stability and dynamic performance of islanded AC microgrids and validated them through controller hardware-in-loop simulation.

Cybersecurity of power-system state estimation research result 1Cybersecurity of power-system state estimation research result 2

Cybersecurity of Power-System State Estimation

Developed a cyber-physical framework to investigate the impact of cyber attacks on power-system measurements and state estimation. The work integrates RTDS-based power-system simulation, PMU/RTU measurements, and communication networks to evaluate the effect of manipulated measurements on grid applications.

PMU Anomaly Detection

Developed a deep-learning-based framework for detecting anomalous patterns in synchrophasor measurements using reconstruction-based learning. The approach identifies abnormal measurement windows without relying exclusively on labelled attack data and was evaluated using PMU datasets from standard power-system test systems.

PMU measurement prediction and error analysis result 1PMU measurement prediction and error analysis result 2PMU measurement prediction and error analysis result 3PMU measurement prediction and error analysis result 4

PMU Measurement Prediction and Error Analysis

Developed machine-learning-based prediction methods for power-system measurements under measurement loss and communication disruptions. The prediction performance was evaluated through error distributions and statistical performance analysis for different PMU measurement variables.

Research Facilities

Research facility at IIT IndorePower systems simulation laboratory at IIT IndoreResearch laboratory facility at IIT IndorePower systems research equipment at IIT Indore

Some of the major facilities available include dSPACE, RTDS System, and PMU, along with a simulation laboratory equipped with software such as PSCAD, MATLAB, DIGSILENT, ETAP, and GAMS.