Set-Estimation based Networked Model Predictive Control for Energy Management of Faulty Microgrids
원문 초록
This paper addresses the issue of power flow control for partially faulty microgrids. In microgrid control systems, faults may occur in both electrical and communication layers. This may have severe effects on the operation of microgrids. In addition, disturbances always coexist with faults in microgrids, which may further deteriorate system performance. To address the faults and disturbances simultaneously, a model predictive control (MPC) method based on set-membership estimation (SME) that transmits information via a communication network is proposed. When electrical devices are nonfunctional or communication failures occur, the corresponding system states will become unavailable. To this end, the SME method is employed to estimate the states with the existence of unknown-but-bounded process and measurement disturbances. The networked MPC method is designed to schedule the power dispatch by using the forecasts of photovoltaic (PV) generation and load demand. With these two methods, the fault-tolerant control can be achieved. Further, a deviation compensation method is proposed to compensate for the forecast errors. The effectiveness of the proposed control strategy is demonstrated through wireless communication tests using Raspberry Pis.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Model Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures
HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management
Algorithmic Control Improves Residential Building Energy and EV Management when PV Capacity is High but Battery Capacity is Low
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
Coordinated Control of Autonomous Vehicles for Traffic Density Reduction at a Signalized Junction: An MPC Approach
Behavioral Generative Agents for Energy Operations
토론
이 논문과 연결된 포럼 글이 아직 없습니다.