steven Song's repositories
PPGN-Physics-Preserved-Graph-Networks
The increasing number of variable renewable energy (solar and wind power) causes power grids to have more abnormal conditions or faults. Faults may further trigger power blackouts or wildfires without timely monitoring and control strategy. Machine learning is a promising technology to accelerate the automation and intelligence of power grid monitoring systems. Unfortunately, the black-box machine learning methods are weak to the realistic challenges in power grids: low observation, insufficient labels, and stochastic environments. To overcome the vulnerability of black-box machine learning, we preserve the physics of power grids through graph networks to efficiently and accurately locate the faults even with limited observability and low label rates. We first calculate the graph embedding of power grid infrastructure by establishing a reduced graph network with the observed nodes, then efficiently locate the fault on the node level using the low-dimensional graph embedding. To augment the location accuracy at low label rates, we build another graph network representing the physical similarity of labeled and unlabeled data samples. Importantly, we provide the physical interpretations of the benefits of the graph design through a random walk equivalence. We conduct comprehensive numerical experiments in the IEEE 123-node. Our proposed method shows superior performance than three baseline classifiers for different fault types, label rates, and robustness to out-of-distribution (OOD) data. Additionally, we extend the proposed method to the IEEE 37-node benchmark system and validate the effectiveness of the proposed training strategy.
Data-driven-deep-reinforcement-learning-controller-for-DC-DC-buck-converter-feeding-CPLs
Source code for deep reinforcement learning in MATLAB
ADSR
A Distributed Security and Robust Power Management Framework for More Electric Aircraft
attention-is-all-you-need-pytorch
A PyTorch implementation of the Transformer model in "Attention is All You Need".
Battery_SOC_Estimation
Battery state of charge estimation using kalman filter in Matlab
BayesianRL_AVC
This is the repository for Bayesian Reinforcement Learning for Automatic Voltage Control in Power Transmission Systems
d2l-zh
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被55个国家的300所大学用于教学。
Hands-On-Intelligent-Agents-with-OpenAI-Gym
Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch
machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
MARL_local_electricity
Multi-agent reinforcement learning for privacy-preserving, scalable residential energy flexibility coordination
microgrids
Code for the lab's published articles on the topic of "Economic Dispatch of a Single Micro-Gas Turbine Under CHP Operation"
ol-ems
Online learning algorithm for microgrid energy management based on MPC
Paper_Result
This is the experiment code and result of my research paper, including both my own method and the method used for comparision.
Physics-Informed-Neural-Networks-for-AC-Optimal-Power-Flow
This repository contains the code for Physics-Informed Neural Network for AC Optimal Power Flow applications and the worst case guarantees
pumpkin-book
《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book
ReinforcementLearning
reinforcement learning
RL-AL-for-Power-Converter-Design
Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design
Safe-Policy-Optimization
This is a benchmark repository for safe reinforcement learning algorithms
single-period-two-critic-DRL
code for paper "Single-Period Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Control in Active Distribution Networks"
tinynn
A lightweight deep learning library
TRM_tutorial
Transformer在CV和NLP领域的变体模型的从零解读：Transformer；VIT；Swin Transformer