Search Results for author: Baosen Zhang

Found 39 papers, 13 papers with code

Leveraging Predictions in Power System Frequency Control: an Adaptive Approach

no code implementations20 May 2023 Wenqi Cui, Guanya Shi, Yuanyuan Shi, Baosen Zhang

Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations.

Load Forecasting

Equilibria of Fully Decentralized Learning in Networked Systems

no code implementations15 May 2023 Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés

Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder their applicability to practical systems.

An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees

no code implementations3 Apr 2023 Ling Zhang, Daniel Tabas, Baosen Zhang

The challenge of finding good policies to approximate the second-stage decisions is that these solutions need to be feasible, which has been difficult to achieve with existing policies.

Solving Differential-Algebraic Equations in Power Systems Dynamics with Neural Networks and Spatial Decomposition

no code implementations17 Mar 2023 Jochen Stiasny, Spyros Chatzivasileiadis, Baosen Zhang

The involved Jacobian matrix is calculated with Automatic Differentiation and its size depends only on the network size but not on the component dynamics.

Efficient Reinforcement Learning Through Trajectory Generation

1 code implementation30 Nov 2022 Wenqi Cui, Linbin Huang, Weiwei Yang, Baosen Zhang

Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data.

LEMMA Offline RL +2

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

1 code implementation29 Nov 2022 Daniel Tabas, Ahmed S. Zamzam, Baosen Zhang

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms.


Optimal Inverter-Based Resources Placement in Low-Inertia Power Systems

no code implementations28 Oct 2022 Atinuke Ademola-Idowu, Baosen Zhang

An increase in the integration of Inverter Based Resources (IBRs) to the electric grid, will lead to a corresponding decrease in the amount of connected synchronous generators, resulting in a decline in the available rotational inertia system-wide.

Cyber-Physical Attack Leveraging Subsynchronous Resonance

no code implementations8 Jul 2022 Bosong Li, Baosen Zhang, Daniel S. Kirschen

This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system.

Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees

1 code implementation1 Jun 2022 Wenqi Cui, Yan Jiang, Baosen Zhang, Yuanyuan Shi

We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.

Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach

no code implementations1 May 2022 Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés

Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch.

reinforcement-learning Reinforcement Learning (RL)

Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach

no code implementations23 Mar 2022 Daniel Tabas, Baosen Zhang

Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization problem in real time.

Equilibrium-Independent Stability Analysis for Distribution Systems with Lossy Transmission Lines

no code implementations9 Mar 2022 Wenqi Cui, Baosen Zhang

Because of the intermittent nature of these resources, the stability of distribution systems under large disturbances and time-varying conditions is becoming a key issue in practical operations.

Data-Driven Modeling of Aggregate Flexibility under Uncertain and Non-Convex Load Models

no code implementations28 Jan 2022 Sina Taheri, Vassilis Kekatos, Harsha Veeramachaneni, Baosen Zhang

The feasible set of the aggregator is then approximated by an ellipsoid upon training a convex quadratic classifier using the labeled dataset.

LEMMA Scheduling

State-of-Charge Aware EV Charging

1 code implementation30 Nov 2021 Yize Chen, Baosen Zhang

Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids.


A Frequency Domain Approach to Predict Power System Transients

1 code implementation1 Nov 2021 Wenqi Cui, Weiwei Yang, Baosen Zhang

System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies.

Numerical Integration

Computationally Efficient Safe Reinforcement Learning for Power Systems

no code implementations20 Oct 2021 Daniel Tabas, Baosen Zhang

We propose a computationally efficient approach to safe reinforcement learning (RL) for frequency regulation in power systems with high levels of variable renewable energy resources.

reinforcement-learning Reinforcement Learning (RL) +1

Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach

no code implementations4 Oct 2021 Ling Zhang, Baosen Zhang

Using standard and modified IEEE 22-bus, 39-bus, and 118-bus networks, we show that our approach is able to obtain the globally optimal cost even when the training data is mostly comprised of suboptimal solutions.

Decentralized Safe Reinforcement Learning for Voltage Control

no code implementations3 Oct 2021 Wenqi Cui, Jiayi Li, Baosen Zhang

We explicitly engineer the structure of neural network controllers such that they satisfy the Lipschitz constraints by design.

reinforcement-learning Reinforcement Learning (RL) +1

An Iterative Approach to Improving Solution Quality for AC Optimal Power Flow Problems

no code implementations13 Sep 2021 Ling Zhang, Baosen Zhang

In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems.

Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability

1 code implementation5 Mar 2021 Wenqi Cui, Baosen Zhang

The learned neural Lyapunov function is then utilized as a regularization to train the neural network controller by penalizing actions that violate the Lyapunov conditions.

reinforcement-learning Reinforcement Learning (RL)

Multi-Agent Reinforcement Learning in Cournot Games

no code implementations14 Sep 2020 Yuanyuan Shi, Baosen Zhang

This is the first result (to the best of our knowledge) on the convergence property of learning algorithms with continuous action spaces that do not fall in the no-regret class.

Continuous Control Multi-agent Reinforcement Learning +2

Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov Approach

1 code implementation11 Sep 2020 Wenqi Cui, Yan Jiang, Baosen Zhang

As more inverter-connected renewable resources are integrated into the grid, frequency stability may degrade because of the reduction in mechanical inertia and damping.

reinforcement-learning Reinforcement Learning (RL)

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks

1 code implementation20 Mar 2020 Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang

This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.

reinforcement-learning Reinforcement Learning (RL) +1

Transfer Learning for HVAC System Fault Detection

1 code implementation4 Feb 2020 Chase P. Dowling, Baosen Zhang

Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gaining in popularity.

Fault Detection Transfer Learning

Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach

no code implementations14 Jun 2019 Yan Yang, Zhifang Yang, Juan Yu, Baosen Zhang

A deep neural network (DNN) is used to approximate the power flow calculation and is trained according to the physical power flow equations to improve its learning ability.

A tractable ellipsoidal approximation for voltage regulation problems

no code implementations9 Mar 2019 Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto Sangiovanni-Vincentelli, Baosen Zhang

We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation.

BIG-bench Machine Learning

Real-Time Prediction of the Duration of Distribution System Outages

no code implementations3 Apr 2018 Aaron Jaech, Baosen Zhang, Mari Ostendorf, Daniel S. Kirschen

This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors.

Bayesian Renewables Scenario Generation via Deep Generative Networks

1 code implementation2 Feb 2018 Yize Chen, Pan Li, Baosen Zhang

We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks.

An Unsupervised Deep Learning Approach for Scenario Forecasts

1 code implementation7 Nov 2017 Yize Chen, Xiyu Wang, Baosen Zhang

Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations.

Optimization and Control

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

2 code implementations30 Jul 2017 Yize Chen, Yishen Wang, Daniel Kirschen, Baosen Zhang

We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors.

Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints

no code implementations28 Apr 2017 Pan Li, Baihong Jin, Dai Wang, Baosen Zhang

We also show that this optimization problem is convex for a wide variety of probabilistic distributions.

Blocking Transferability of Adversarial Examples in Black-Box Learning Systems

no code implementations13 Mar 2017 Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, Radha Poovendran

Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.

Medical Diagnosis

Deceiving Google's Perspective API Built for Detecting Toxic Comments

no code implementations27 Feb 2017 Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples.

An Optimal Treatment Assignment Strategy to Evaluate Demand Response Effect

no code implementations2 Oct 2016 Pan Li, Baosen Zhang

The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program.

Experimental Design

Learning Temporal Dependence from Time-Series Data with Latent Variables

no code implementations27 Aug 2016 Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes.

Time Series Analysis

Online Active Linear Regression via Thresholding

no code implementations9 Feb 2016 Carlos Riquelme, Ramesh Johari, Baosen Zhang

We consider the problem of online active learning to collect data for regression modeling.

Active Learning regression

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

no code implementations5 Nov 2015 Pan Li, Baosen Zhang, Yang Weng, Ram Rajagopal

Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.

Two-sample testing

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