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：Parameter identification of fractional-order chaotic power systems is a multidimensional optimization problem that plays a decisive role in the synchronization and control of fractional-order chaotic power systems. In this paper, a state transition algorithm based on the lens imaging learning strategy is proposed for parameter identification of fractional-order chaotic power systems. Taking a fractional-order six-dimensional chaotic power system mathematical model as an example, the mathematical model and chaotic state are analyzed. First, the Tent chaotic mapping is used to initialize the population, thus increasing population diversity. The randomness and ergodicity of the Tent chaotic sequence are used to enhance the global searching ability of the algorithm. Second, a maturity index is employed to determine population maturity. The lens imaging learning strategy is used to suppress the premature convergence of the state transition algorithm effectively and help the population jump out of local optima. Finally, the improved state transition algorithm is used to identify the parameters of the fractional-order six-dimensional chaotic power system model. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition and particle swarm optimization algorithms. The simulation results show that the parameters of the fractional-order chaotic power system are identified accurately even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm.

As a complex non‐linear system, the chaotic oscillation of power system seriously threatens the safe and stable operation of power grids. In this paper, a finite time adaptive synchronization control method is proposed to mitigate the problem of chaotic oscillation in the power system. The proposed method can realize chaos control and parameter identification by completely synchronizing the fractional‐order chaotic power system with the stable fractional‐order power system to identify parameters within a finite time. The fractional Lyapunov stability theory is used for numerical simulation. Theoretical and simulation results show that this method can effectively stabilize the system in a finite time. It is proved that compared with the adaptive synchronous control method, the control method is simpler in design, shorter in action time, and more meaningful in engineering practice.

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Fractional-order chaotic power system parameter estimation is a multidimensional function optimization problem, and it is a prerequisite for controlling such systems. Aiming at the problem that the parameter estimation of a fractional-order chaotic power system is easily affected by external interference and estimation is difficult, a state transition algorithm based on the reverse learning strategy of lens imaging is proposed, taking a fractional six-order chaotic power system as the study model. First, the tent chaotic mapping to initialize the population, thus increasing population diversity. Second, we optimize parameters \(\gamma\) and \(\delta\) of the basic state transition algorithm to improve its global and single-dimensional search abilities. We use the lens imaging learning strategies to avoid the local optima. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition, particle swarm optimization, genetic and gray wolf algorithms. The simulation results show that the parameters of the fractional sixth-order chaotic power system are identified accurately. even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm.