A continuous fractional order sliding mode controller based on a developed output feedback feature selection neural network (OFFSNN) for an active power filter (APF) is studied in this article to effectively compensate grid harmonic current and improve power quality. A fractional order sliding mode manifold is introduced first. Then, a continuous fractional order sliding mode controller is adopted to resolve the shortcoming of chattering phenomenon in the conventional one by designing a continuous control law. Furthermore, considering the unknown part of APF system, a new neural structure called OFFSNN is established to estimate the unknown dynamic characteristic with high precision and low computational burden. Compared with general neural network, the proposed OFFSNN is adept at adjusting neural structure and parameters by selecting beneficial nodes as well as deleting useless ones. To demonstrate the effectiveness and superiority of the presented scheme, experiments in various cases on the APF platform are given.

In view of the fact that most devices are nonlinear systems, this paper proposes a recursive probabilistic compensation fuzzy neural network (RPCFNN) control scheme based on global fast terminal sliding mode control (GFTSMC) for a class of nonlinear systems with uncertainties. Firstly, GFTSMC is developed to impose a finite-time convergence feature for the considered systems. Secondly, a novel RPCFNN framework is designed to further enhance the ability to deal with uncertainty. Due to the added probabilistic estimation and dynamic fuzzy operator, the developed RPCFNN controller possesses superior nonlinearity handling capability. Meanwhile, the stability of RPCFNN is proved by Lyapunov theory. Finally, active power filter (APF) is taken as the representative of the nonlinear system to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results show that the novel RPCFNN controller has good steady-state and dynamic performance, and has excellent robustness to deal with uncertain disturbances.

In this paper, a control system based on evolutionary emotional neural network is proposed for active power filters (APF) to improve power quality. First, the dynamic model of APF containing external disturbances and component parameter perturbations is introduced. The global fast terminal sliding mode(GFTSM) control method is proposed for APF, and its finite-time convergence and global robustness are demonstrated. In addition, an emotional neural network based on Hermite orthogonal polynomials as the activation function is constructed and combined with an evolutionary mechanism to form a self-evolving emotional neural network (SEENN). Then, a model-free control system based on SEENN is designed to address the model dependence of the GFTSM controller design. The parameter update law is designed under the Lyapunov framework to ensure stability. Finally, the results of prototype experiments show the excellent performance of the proposed control algorithm.

With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion batteries. In order to avoid the local optimal value problem of traditional BP neural networks and the fixed reasoning mechanism of traditional fuzzy neural networks, the PTCFNN combines the advantages of a probabilistic fuzzy neural network and a compensation fuzzy neural network so as to improve the learning convergence speed and optimize the fuzzy reasoning mechanism. Finally, in order to verify the estimation performance of the PTCFNN, a 18650-20R Li-ion battery was used to carry out the estimation test. The results show that the mean absolute error and mean square error are very small under the conditions of a low-current test and dynamic-current test, and the overall estimation error is less than 1%, which further indicates that this method has good estimation ability.

To tackle harmonic issue in the power grid, an intelligent control scheme consists of fast integral terminal sliding-mode control and hippocampus-based fuzzy neural network (HBFNN) is proposed and applied to active power filter (APF) to eliminate harmonics in this article. At first, the mathematical model of APF under the influence of external interference and parameter perturbation is derived in accordance with its topological structure. Afterwards, a fast integral terminal sliding-mode controller is developed for APF. The system stability is subsequently proved according to Lyapunov stability criterion, and the finite-time convergence of tracking error is certified. In addition, based on the biological structure and characteristics of the hippocampus, an innovative HBFNN is constructed to approximate the controller. Furthermore, the adaptive laws derived by the Lyapunov's theorem can realize the automatic adjustment of parameters and ensure closed-loop stability. Ultimately, relevant simulation and experimental results corroborate the availability and superiority of the designed intelligent control strategy in harmonic elimination.

During feeder outages in the distribution network, localized power restoration using distribution resources (e.g., PVs) can ensure supply to critical loads and mitigate adverse impacts, especially when main grid support is unavailable. This study presents a power restoration strategy aiming at maximizing the restoration duration of critical loads to ensure their prioritized recovery, thereby significantly improving power system reliability. The methodology begins with load enumeration via breadth-first search (BFS) and utilizes a long short-term memory (LSTM) neural network to predict microgrid generation output. Then, an adaptive multipoint crossover genetic solving algorithm (AMCGA) is proposed, which can dynamically adjust crossover and mutation rates, enabling rapid convergence and requiring fewer parameters, thus optimizing island partitioning to prioritize critical load demands. Experimental results show that AMCGA improves convergence speed by 42.5% over the traditional genetic algorithm, resulting in longer restoration durations. Compared with other strategies that do not prioritize critical load recovery, the proposed strategy has shown superior performance in enhancing critical load restoration, optimizing island partitioning, and reducing recovery fluctuations, thereby confirming the strategy’s effectiveness in maximizing restoration and improving stability.