As a key reactive oxygen species (ROS), hypochlorous acid (HClO) plays an important role in many physiological and pathological processes. The mitochondria-targeting probes for the highly sensitive detection of HClO are desirable. In present work, we designed and synthesized an original mitochondria-localizing and turn-on fluorescent probe for detecting HClO. 4-Aminonaphthalimide was employed as the fluorescent section, the (2-aminoethyl)-thiourea unit was utilized as a typical sensing unit, and the quaternized pyridinium moiety was used as a mitochondria-targeted localization group. When HClO was absent, the probe showed weak fluorescence. In the existence of HClO, the probe revealed a blue fluorescence. Moreover, the turn-on fluorescent probe was able to function in a broad pH scope. There was an excellent linearity between the fluorescence emission intensity at 488 nm and the concentrations of HClO in the range of 5.0 × 10^–7 to 2.5 × 10^–6 mol·L^–1. Additionally, the probe had almost no cell toxicity and possessed an excellent mitochondria-localizing capability. Furthermore, the probe was able to image HClO in mitochondria of living PC-12 cells. The above remarkable properties illustrated that the probe was able to determine HClO in mitochondria of living cells.

Accurate forecasting is the key factor in promoting wind power consumption and improving the stable operation of power systems. A short-term wind power forecasting (WPF) and uncertainty analysis method based on whale optimization algorithm (WOA), least squares support vector machine (LSSVM), and nonparametric kernel density estimation (NPKDE) was proposed in this paper. The advantages of WOA (fast convergence speed and high convergence accuracy) were used to optimize the penalty factor and kernel function width of the LSSVM model, and the calculation speed and forecasting accuracy of the LSSVM model were improved. The training sample set is classified according to the wind speed interval, and the WOA-LSSVM forecasting model is trained by subclass after classification to further improve the accuracy of short-term WPF. The NPKDE method is used to accurately calculate the probability density distribution characteristics of the forecasting error of wind power, and the confidence interval of the WPF is accurately calculated based on the probability density distribution characteristics. The calculation results show that the forecasting accuracy of the WOA-LSSVM model is higher than those of the LSSVM, long short-term memory (LSTM), and particle swarm optimization and least squares support vector machine (PSO-LSSVM) models, and the forecasting accuracy of the WOA-LSSVM model can be further improved after classifying the training sample set. The coverage of the confidence intervals in different time scales is higher than the corresponding confidence level, indicating that the NPKDE method can accurately describe the probability density distribution characteristics of the WPF errors.

For large-scale constructed wind farms, reducing wake loss and improving the overall output power are the main objectives for their optimal operation. Therefore, a wind farm power maximization method based on a multi-strategy improved sparrow search algorithm (MS-ISSA) is proposed in this paper. Integrating the wake propagation mechanism of wind turbines and the characteristics of the classic Jensen wake model, the Jensen–Gaussian wake model and wake superposition model were constructed to accurately calculate the wind farm wake distribution. The constructed Jensen–Gaussian wake model and wake superposition model can accurately describe the non-uniform distribution characteristics of wake velocity. The Sin chaotic model, Cauchy distribution, and hyperparameter adaptive adjustment strategy were used to improve the sparrow search algorithm (SSA), and the optimization ability, convergence speed, and stability of the SSA were improved. Accordingly, considering the maximum output power of the wind farm as the optimization target and axial induction factor as the optimization variable, a coordinated optimization model for wind turbines based on MS-ISSA was proposed to realize the coordinated optimal operation of wind turbines with reduced wake loss. Considering the Danish Horns Rev wind farm as the research object, the results of optimization using particle swarm optimization algorithm, whale optimization algorithm, basic sparrow search algorithm, and MS-ISSA were calculated and analyzed. The calculation results revealed that under different incoming wind conditions, the MS-ISSA exhibited better optimization results than the other optimization algorithms.