stvsd1314

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A-Barrier-Lyapunov-Actor-Critic-Reinforcement-Learning-Approach-for-Safe-and-Stable-Control

A-novel-DRO-model-for-self-scheduling-problem

This study is using distributionally robust optimization (DRO) algorithm with conditional value-at-risk (CVaR) to solve self-scheduling problem to obtain a suitable and adjustable self-scheduling strategy

Bayesian-Network

贝叶斯网络

CNN_basic

找工作之前的复习

Data-driven-deep-reinforcement-learning-controller-for-DC-DC-buck-converter-feeding-CPLs

Source code for deep reinforcement learning in MATLAB

EmbeddedSystem

:books: 计算机体系架构、嵌入式系统基础与主流编程语言相关内容总结

joint-optimization

This folder contains the codes and data used for papers: Shi, Yuanyuan, Bolun Xu, Di Wang, and Baosen Zhang. "Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains." IEEE Transactions on Power System.

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.

spatiotemporal_prediction

This code is for the paper "A spatio-temporal deep learning approach for airspace complexity prediction" that is submitted to the TRB

stvsd1314/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.

stvsd1314/Data-driven-deep-reinforcement-learning-controller-for-DC-DC-buck-converter-feeding-CPLs

Source code for deep reinforcement learning in MATLAB

stvsd1314/A-Barrier-Lyapunov-Actor-Critic-Reinforcement-Learning-Approach-for-Safe-and-Stable-Control

stvsd1314/ADSR

A Distributed Security and Robust Power Management Framework for More Electric Aircraft

stvsd1314/attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

stvsd1314/Battery_SOC_Estimation

Battery state of charge estimation using kalman filter in Matlab

stvsd1314/BayesianRL_AVC

This is the repository for Bayesian Reinforcement Learning for Automatic Voltage Control in Power Transmission Systems

stvsd1314/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。

stvsd1314/Fleet_Energy_Analysis

Energy performance analysis of two cruise ships focusing on efficiency, propulsion, power generation, and fuel efficiency. Includes data preprocessing, exploratory analysis, and detailed visualizations.

stvsd1314/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

stvsd1314/ki

stvsd1314/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

stvsd1314/MARL_local_electricity

Multi-agent reinforcement learning for privacy-preserving, scalable residential energy flexibility coordination

stvsd1314/microgrids

Code for the lab's published articles on the topic of "Economic Dispatch of a Single Micro-Gas Turbine Under CHP Operation"

stvsd1314/ol-ems

Online learning algorithm for microgrid energy management based on MPC

stvsd1314/Paper_Result

This is the experiment code and result of my research paper, including both my own method and the method used for comparision.

stvsd1314/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

stvsd1314/Real-Time-Outage-Management-Active-DNR-GRL

stvsd1314/ReinforcementLearning

reinforcement learning

stvsd1314/RiskAwareLearning_VoltageOpt_DistGrid

stvsd1314/RL-AL-for-Power-Converter-Design

Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design

stvsd1314/RL-based-event-triggered-MPC

stvsd1314/Safe-Policy-Optimization

This is a benchmark repository for safe reinforcement learning algorithms

stvsd1314/safe-reinforcement-learing-for-microgrid-control

stvsd1314/SHIP

stvsd1314/ship_in_transit_simulator

stvsd1314/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"

stvsd1314/time_varying_ADP

Several ADP algorithms code for the data-driven optimal control of linear time-varying systems

stvsd1314/tinynn

A lightweight deep learning library

stvsd1314/TRM_tutorial

Transformer在CV和NLP领域的变体模型的从零解读：Transformer；VIT；Swin Transformer