This study constructs a novel virtual synchronous generator system based on a transfer function, and optimizes the parameters of the model by using the improved whale algorithm to improve the frequency control ability of virtual synchronous generator. Virtual synchronous generator technology helps to solve the problem that the integration of large-scale renewable energy generation into the power system leads to the deterioration of system frequency stability. It can maintain the symmetry of grid-connected scale and system stability. The virtual synchronous generator technology makes the inverter to have the inertia and damping characteristics of a synchronous generator. The inverter has the inertia characteristics and damps to reduce the frequency instability of high penetration renewable energy power system. The improved whale algorithm is efficient to find the best combination of control parameters and the effectiveness of the algorithm is verified by microgrid and power system. The results show that the proposed frequency coordination control scheme suppresses the frequency deviation of power system and keep the system frequency in a reasonable range.

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

To address the problems of grid connection and power dispatching caused by non-stationary wind power output, an improved Jellyfish Search algorithm optimization support vector regression (IJS-SVR) model was proposed in this study to achieve high-precision wind power prediction. The random selection of internal parameters of SVR model will affect its performance. In this study, the Jellyfish Search (JS) algorithm was selected and improved to propose an Improved Jellyfish Search (IJS) algorithm. Compared with the comparative algorithms, the optimized values of IJS algorithm are closer to 0. It exhibits good convergence ability, search stability, and optimization-seeking ability, as well as being more suitable for solving optimization problems. Therefore, IJS was used to optimize SVR, and the prediction model of IJS-SVR was established. Different weather and seasons affect wind power and model prediction accuracy. The wind power in spring and winter was selected for model prediction verification in this study. Compared with other methods, the IJS-SVR model proposed in this study could achieve better prediction results than other models in both seasons, and its prediction performance was better, which could improve the prediction accuracy of wind power. This study provides a more economical and effective method of wind power to solve its uncertainties and can be used as a reference for grid power generation planning and power system economic dispatch.

The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model–improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model–fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm–grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm–grey neural network model.

In this study, the adsorption material is fabricated from the aluminum residues using inductively coupled plasma (ICP) technique and applied as a raw material to manufacture porous solid desiccant wheel. An ICP system is developed to refine aluminum hydroxide extracted from aluminum residues into activated alumina by utilizing its high energy densities. A six-turn induction coil is suitable for our match box to produce alternating electromagnetic field, to sustain space discharge, and to have well reaction zone. The ICP generator power is set at 800 W, and the batch of aluminum residues is purified to generate activated alumina with a BET surface area of 122 m²/g. The porous desiccant wheel is manufactured using activated alumina with a 3-D random porous structure and applied to adsorb moisture from the air. The equilibrium time for the solid desiccant wheel and humidity are determined by observed kinetics in the adsorption experiment. The adsorption equilibrium time of the desiccant wheel (60 pores per inch) with 15-cm diameter and 5-cm thickness is 12 minutes. The maximum adsorption capacity is 51.4 g (at 12 min) and per gram of the desiccant wheel can adsorb 0.184-g water.