Network multi-area electric power system load frequency control method based on self-adaptive event trigger mechanism
A technology of load frequency control and event triggering, applied to AC network circuits, electrical components, circuit devices, etc.
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Embodiment 1
[0057] see figure 1 and figure 2 , the networked multi-area power system load frequency control method based on an adaptive event trigger mechanism, is characterized in that the operation steps are:
[0058] (1) Establish a dynamic model of multi-regional power system load frequency control considering communication factors;
[0059] (2) Introduce an adaptive event trigger mechanism;
[0060] (3) Establish a time-delay dynamic model of ACE-dependent multi-regional power system load frequency control with the introduction of an adaptive event trigger mechanism;
[0061] (4) Given the H of the system ∞ performance conditions;
[0062] (5) Determine the trigger matrix Φ and the controller gain matrix K.
Embodiment 2
[0064] This embodiment is basically the same as Embodiment 1, and the special features are as follows:
[0065] 1. The step (1) establishes a multi-area power system load frequency control dynamic model considering communication factors:
[0066] x · ( t ) = A x ( t ) - B K C x ( t k ) + F ω ( t ) y ( t ) = ...
Embodiment 3
[0102] The load frequency control method of the networked multi-regional power system based on the adaptive event trigger mechanism is as follows:
[0103]1. Establishment of time-delay model for load frequency control of networked multi-regional power system with the introduction of adaptive event trigger mechanism
[0104] 1. Establish a dynamic model of multi-regional power system load frequency control considering communication factors
[0105] The power system is a complex nonlinear dynamic system. Since the load of the power system changes very little during normal operation, a linearized model can be used to represent the system dynamics near its operating point. The i-th subregion of the linearized multi-regional power system A dynamic model such as figure 1 shown. Depend on figure 1 The following relationship can be obtained:
[0106] Δf i = ...
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