VSG-PMSG's starred repositories
DRLforPowerSystemRecovery
Master Thesis Project for DRL in power system restoration using renewables
Control-and-Dynamics-in-Power-Systems-and-Microgrids
codes from book "Control and Dynamics in Power Systems and Microgrids" CRC Press 2017
power_system_simulation
Power system small signal analysis, multi synchronous machine model linearisation
Compressed-Air-Energy-Storage-for-wind-energy-storage
Compressed Air Energy Storage (CAES) as a popular technology for wind energy storage, is mathematically integrated with a novel hydraulic wind power system. The integration of compressed air energy storage has improved the quality of power delivery while maintaining a stable frequency generation in the 600 kW hydraulic wind power system under variable wind speeds.
Solar-Wind-Hybrid-Power-plant-simulation-with-simulink-matlab
This project is done by our team for power system lab. There may be many shortcomings but we tried our best to make it better.
Power-System-Fault-Analysis
MATLAB Unbalanced Power System Fault Analysis
Power-system-optimization
This is the project containing the code developed for mulity objective optimization of microgrid energy management systems with user cooperation
Simscape-Triplex-Pump
Predictive maintenance algorithm developed using digital twin of hydraulic pump modeled in Simscape
Digital-twin-approach-for-damage-tolerant-mission-planning-under-uncertainty
The digital twin paradigm that integrates the information obtained from sensor data, physics models, as well as operational and inspection/maintenance/repair history of a system (or a component) of interest, can potentially be used to optimize operational parameters of the system in order to achieve a desired performance or reliability goal. In this article, we develop a methodology for intelligent mission planning using the digital twin approach, with the objective of performing the required work while meeting the damage tolerance requirement. The proposed approach has three components: damage diagnosis, damage prognosis, and mission optimization. All three components are affected by uncertainty regarding system properties, operational parameters, loading and environment, as well as uncertainties in sensor data and prediction models. Therefore the proposed methodology includes the quantification of the uncertainty in diagnosis, prognosis, and optimization, considering both aleatory and epistemic uncertainty sources. We discuss an illustrative fatigue crack growth experiment to demonstrate the methodology for a simple mechanical component, and build a digital twin for the component. Using a laboratory experiment that utilizes the digital twin, we show how the trio of probabilistic diagnosis, prognosis, and mission planning can be used in conjunction with the digital twin of the component of interest to optimize the crack growth over single or multiple missions of fatigue loading, thus optimizing the interval between successive inspection, maintenance, and repair actions.
Electric_Drives_Power_Electronics
This drive contains all the Matlab and Simulink codes and files for the Electric Drives and Power Electronics Design, Simulation and Analysis.
Python-Core-50-Courses
Python语言基础50课
Python-100-Days
Python - 100天从新手到大师