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Wind farms have large fluctuations in grid connection, imbalance between supply and demand, etc. In order to solve the above problems, this paper studies the capacity optimization configuration of wind farm energy storage system based on full life cycle economic analysis. Firstly, the optimization model of energy storage capacity is established in this paper for computing wind farms require minimal storage capacity for load shifting, reducing peak and valley fluctuation; considering the loss cost of wind discarded, the fixed costs of energy storage device and equipment maintenance, the cost of the battery is analyzed in the whole life cycle by the point of view of economics; multi-objective optimization of the minimized ratio of peak-valley fluctuations around peak load shifting volatility and maximized economic benefits, by the global particle swarm algorithm for getting the optimal solution, the system capacity optimization control scheme is proposed. By analyzing the actual data, it is proved that the rational capacity allocation of the energy storage system can effectively reduce the ratio of peak-valley fluctuations around peak load shifting volatility, improve the economic benefit of the wind power plant, and improve the economic benefits of wind power plant storage.

Participating in the bidding of the electricity market is a new profit way for electric energy storage system. In the existing electricity market, the calculation model of bidding strategy for electricity energy storage technology is relatively single, and the dynamic energy characteristics of battery energy storage are neglected. Therefore, taking the battery energy storage system as the research object, a bidding strategy based on energy distribution for battery energy storage power market is proposed in the paper. Firstly, the dynamic energy model of battery is established. Secondly, the energy of batteries is allocated according to the time-of-use (TOU) electricity price policy. With the maximum economic benefit of electricity energy storage system and bidding income of the energy storage market in the day as the optimization targets, the energy bidding strategy of battery energy storage system was formulated. Finally, the power, state of charging (SOC) and revenue of lithium battery energy storage under different bidding strategies are calculated and analyzed according to the TOU price of a certain area. The case study results show that the energy market's revenue can be increased by 57.8% using the energy bidding strategy proposed in the paper. The research results can provide a reference for bidding strategy of electric energy storage market, and it also provide a theoretical basis for economic evaluation of energy storage technology.

This paper proposes a new active damping control strategy with LC filter for stator ironless axial-flux permanent magnet (AFPM) generator system. AFPM machines with enough power density and efficiency are the preferred choice for the propulsion of electric aircrafts. The LC filter connects the generator and the converter to increase the generator inductance, which improves the control effect. An active damping control strategy is introduced to weaken the resonant spike caused by the LC filter, which improves the stability of system according to the Bode diagram. The root locus indicates that active damping control can also enhance the dynamic performance of the stator ironless AFPM generator system. Finally, a model is designed in accordance with the active damping strategy and the simulation results prove the superiority of the proposed control strategy.

The cycloid drive usually has a cycloidal wheel meshing with pins to achieve transmission. The profile of the cycloidal wheel is generally a curve derived from an epitrochoid. This paper presents two design types of cycloid drive, the cycloidal wheel profile of one is internal offset of epicycloid and the other is external offset of hypocycloid. Parametric 3-D models of the two design types are constructed in AutoCAD. Dynamics simulation is carried out using machinery system dynamics simulation technology, and the correctness of the dynamics model is verified by the motion curve. The dynamic meshing force between the cycloidal wheel and the pins of the two design types is solved, and transmission characteristics are compared. The results show that the hypocycloidal drive design produces more stable output speed, and can improve meshing force.

In recent years, CBIR (content-based image retrieval) becomes a new hotspot. In the technology, image querying is achieved based on the characteristics of the color, shape, texture, spatial position of the object or the combination of these features. As the images are the most intuitive contents in the multimedia, content-based image retrieval is a very important problem in the multimedia information processing. Spiking cortical model (SCM) used in this paper is a neural network algorithm that generates a series of binary pulse images when excited by the grayscale or color images. And it has a superior performance in the feature extraction and the texture retrieval of images due to the properties of anti-noise and the geometry invariant of rotation, scale and translation. In order to improve the speed of texture retrieval, SCM is modeled based on FPGA in this paper.

Precision reducer is one of the key parts of an industrial robot, which generally incorporates two-stage cycloidal planetary drive. The theoretical engagement of cycloidal wheel and pin is a gapless engagement, but in practice, there is a gap between the cycloidal wheel and the pins due to manufacturing errors. Most of the previous research on the gap only involves the impact on the transmission accuracy, the force of engagement between the cycloidal wheel and pins is the main form of the transmission torque of the reducer. Thus, this paper aims to use the finite element method to explore the specific impact on meshing force caused by the gap due to manufacturing error of the pins, and obtain the general rule, provides guidance for the precision requirement in the manufacture of the pins.

To predict the key performance index (compressive strength) of Iron ore pellets, a prediction model based on Kernel principal component analysis (KPCA) and RBF neural network was proposed. This paper determined the input variable through the analysis of the chain grate machine — rotary kiln — ring cold pellet production process, deal with the sample data and simplified the model structure with Kernel principal component analysis algorithm (KPCA) and then established the pellet compression strength prediction model with RBF neural network. Using a global optimization performance of the simulated annealing algorithm to optimize the parameters of the network model, obtain the high precision prediction model of the system. The simulation results show that the proposed model can accurately predict the compressive strength of pellets, can overcome the disadvantage of large lag in the original compressive strength test method. The prediction model laid a foundation for the automatic control of the pellets compressive strength.

Capacities of li-ion batteries are difficult to estimate quickly and accurately when second use batteries are in large scale with dispersed parameters. This may result in costing too much time and money to reuse batteries. By analyzing the capacity and resistance characteristics, there is no functional relationship between them. In order to solve this nonlinear problem, a SVM model with 3 parameters (i.e. penalty coefficient, kernel function parameter and loss function parameter) is established. The inputs are five calculated values of resistance and the output is the capacity value of batteries. Data of 70 battery modules are adopted to train the model while data of other 30 battery modules are used to test the model. In this process, two parameters optimization methods using genetic algorithm have been proposed. By comparison, the coefficient of determination (COD) value of method 2 is higher than method 1 both in training model and testing model. The average error of method 2 between measured values and estimated values is 0.67% whereas that of method 1 is 1.35%. In method 2, 90% of the estimation errors are under 2.5%. The results provide valuable insights for large-scale retired li-ion batteries into second use.

LOD model based on quad-trees is improved on the basis of former researches. This model is applied to manage and organize terrain data. Node judgment rule is established to determine the detail degree of the present visual area. A new bidirectional way was given to recover the cracks according to distance. By programming, this method is proved to be able to generate multi-resolution terrain model continuously, improve the efficiency of terrain navigation, and realize smooth transition of the models with different resolution.