As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power grids, demand response loads are also considered, including residential price response loads and thermostatically controlled loads (TCLs). Then, a novel wind-storage cooperative decision-making mechanism is proposed, which combines the direct control of TCLs with the indirect control of residential price response loads. In addition, a kind of deep reinforcement learning algorithm called D3QN is utilized to solve the wind-storage cooperative decision-making problem. Finally, the numerical results verify the effectiveness of D3QN for optimizing the decision-making strategy of a wind-storage cooperation system.

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.

In this article, an emotional intelligent terminal sliding mode controller (EITSMC) is exploited. First, the dynamic model of a second-order nonlinear system considering unknown external disturbances is presented. The fast terminal sliding mode control (FTSMC) scheme is proposed in order to impose superior convergence feature. In addition, from the perspective of further improving the system performance and solving the problem of a prior knowledge dependence in FTSMC design, an emotional intelligent control framework with a self-construction mechanism is proposed to develop a model-free EITSMC. Moreover, an adaptive auxiliary item is developed to further strengthen the system robustness. Finally, the superior control abilities of the proposed strategy are evaluated by simulation and prototype experiments.