Active Power Correction Strategies Based on Deep Reinforcement Learning Part II:A Distributed Solution for Adaptability

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part I:A Simulation-driven Solution for Robustness

Employing the novel Deep Reinforcement Learning approach,this paper addresses the active power corrective control in modern power systems.Seeking to minimize the joint effect engendered by operation cost and blackout penalty,this correction strategy focuses on evaluating the robustness and adaptability aspects of the control agent.In Part I of this paper,where robustness is the primary focus,the agent is developed to handle unexpected incidents and guide the stable operation of power grids A Simulation-driven Graph Attention Reinforcement Learning method is proposed to perform robust active power corrective control.The aim of the graph attention networks is to determine the representation of power system states considering the topological features.Monte Carlo tree search is adopted to select the best suitable action set out of the large action space,including generator redispatch and topology control actions.Finally,driven by simulation,a guided training mechanism along with a long-short-term action deployment strategy are designed to help the agent better evaluate the action set while training and to operate more stably when deployed.The efficacy of the proposed method has been demonstrated in the“2020 I earning to Run a Power Network.Neurips Track 1”global competition and the associated cases.Part II of this paper deals with the adaptability case,where the agent is equipped to better adapt to a grid that has an increasing share of renewable energies through the years.

Performance Evaluation of a Permanent Magnet Electric-Drive- Reconfigured Onboard Charger with Active Power Factor Correction

This paper presents a comprehensive charging operation for an electric-drive-reconfigured onboard charger(EDROC)with active power factor correction(APFC).The charging topology exclusively utilizes the three-phase permanent magnet synchronous motor(PMSM)propulsion system as a three-channel boost-type converter in which only a contactor and a small diode bridge are added.First,the operation scenario of the EDROC is introduced.Second,the relationship between electromagnetic torque and rotor position is investigated.Third,the current ripple cancellation of the EDROC is discussed in detail.Moreover,to implement the single-phase APFC along with charging voltage/current regulation of propulsion battery,control strategies including current balancing and synchronous/interleaving PWM strategies are incorporated.Finally,200W proof-of-concept prototype-based tests are conducted under different operation scenarios.