load frequency control
Load frequency control is essential for maintaining power system stability, especially under uncertainties and input delays. This paper proposes a reinforcement learning-based dual-channel dynamic event-triggered fixed-time load frequency control approach for uncertain multi-area power systems with input delays. A non-singular fast terminal sliding mode technique is employed to guarantee that the tracking error converges within a fixed time.
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The efficient operation of interconnected power systems relies on Load Frequency Control (LFC) to maintain stable frequency and regulate tie-line power flow between distinct areas. In modern setups, LFC parameters are monitored and processed in real-time through cloud-based platforms, enabling centralized control and optimization. However, this introduces vulnerabilities, as demonstrated by the susceptibility to attacks such as scaling, ramp, and random manipulations of data transmitted between areas and the cloud server.
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In high renewable energy sources penetrated multiple microgrids (MMGs), conventional fast timescale load frequency control (LFC) and slow timescale economic dispatch become much less economically efficient. In this paper, the economy-oriented LFC problem of nonlinear MMGs is framed and a distributed optimal disturbance rejection control method is proposed to address this problem. In particular, this method has excellent anti-disturbance capability in dealing with power imbalances, critical parameter variations, and measurement noises.
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