Impedance models of power systems are useful when state-space models of apparatus such as inverter-based resources (IBRs) have not been made available and instead only black-box impedance models are available.
This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL).
Based on dual synchronous idea, a dual synchronous generator (DSG) control is applied in VSC to form inertial current source.
Value factorisation proves to be a useful technique for multi-agent reinforcement learning (MARL) in global reward game, but the underlying mechanism is not yet fully understood.
Power electronic converters for integrating renewable energy resources into power systems can be divided into grid-forming and grid-following inverters.
The SG-dominated grid is traditionally analyzed in a mechanical-centric view which ignores fast electrical dynamics and focuses on the torque-speed dynamics.
The large-scale integration of converter-interfaced resources in electrical power systems raises new threats to stability which call for a new theoretic framework for modelling and analysis.
This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the system.
We test HDNO on MultiWoz 2. 0 and MultiWoz 2. 1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing improvements on the performance evaluated by automatic evaluation metrics and human evaluation.
To deal with this problem, we i) introduce a cooperative-game theoretical framework called extended convex game (ECG) that is a superset of global reward game, and ii) propose a local reward approach called Shapley Q-value.