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Found 12 papers, 0 papers with code
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation
This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT).
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties
Conventional control strategies usually produce large disturbances to buses during charging and discharging (C&D) processes of UCs, which significantly degrades the power quality and system performance, especially under fast C&D modes.
Submodular Load Clustering with Robust Principal Component Analysis
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS).
Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.
Probabilistic Load Forecasting via Point Forecast Feature Integration
In the first stage, all related features are utilized to train a point forecast model and also obtain the feature importance.
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response.
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach
However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters.
Evaluating Load Models and Their Impacts on Power Transfer Limits
Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid.
Optimization and Control Systems and Control Systems and Control
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process.
Coordinated Frequency Control through Safe Reinforcement Learning
With widespread deployment of renewables, the electric power grids are experiencing increasing dynamics and uncertainties, with its secure operation being threatened.
Deep Reinforcement Learning-Enabled Adaptive Forecasting-Aided State Estimation in Distribution Systems with Multi-Source Multi-Rate Data
Distribution system state estimation (DSSE) is paramount for effective state monitoring and control.
