In this paper, an innovative closed hydraulic wind turbine with an energy storage system is proposed. The hydraulic wind turbine consists of the wind rotor, the variable pump, the hydraulic bladder accumulator, the variable motor, and the synchronous generator. The wind energy captured by the wind rotor is converted into hydraulic energy by the variable pump, and then the hydraulic energy is transformed into electrical energy by the variable motor and generator. In order to overcome the fluctuation and intermittence shortcomings of wind power, the hydraulic bladder accumulator is used as an energy storage system in this system to store and release hydraulic energy. A double-loop speed control scheme is presented to allow the wind rotor to operate at optimal aerodynamic performance for different wind speeds and hold the motor speed at the synchronous speed to product constant frequency electrical power regardless of the changes of wind speed and load power. The parameter design and modeling of 600 kW hydraulic wind turbine are accomplished according to the Micon 600 kW wind turbine. Ultimately, time-domain simulations are completed to analyze the dynamic response of the hydraulic wind turbine under the step change conditions of wind speed, rotor speed input, and load power. The simulation results validate the efficiency of the hydraulic wind turbine and speed control scheme presented, moreover, they also show that the systems can achieve the automatic matching among turbine energy, accumulator energy, and generator output energy.

Under the time-varying temperature, the high-temperature radiation of forgings and the change of reflection characteristics of oxide skin on the surface of forgings lead to the difficulty of obtaining images to truly reflect the geometric characteristics of forgings. It is urgent to study the clear and reliable acquisition method of hot forging feature image under time-varying temperature to meet the requirements of visual measurement of hot geometric parameters of forgings. Based on this, this chapter first puts forward the quality evaluation method of forging feature image, which provides guarantee for the accurate evaluation of feature image quality. Furthermore, the factors that affect the image quality, such as the radiation characteristics of forgings and the photographic characteristics of cameras, are analyzed, and the imaging spectrum which can effectively suppress the radiation intensity of forgings is determined. Finally, aiming at the problem that the quality of image acquisition is difficult to guarantee due to the drastic change of radiation intensity of forgings under time-varying temperature, an image acquisition method based on minimum signal-to-noise ratio (SNR) based laser light intensity adaptation is proposed, which significantly improves the definition of feature light strips in forging images at high temperature, and finally realizes the clear acquisition of feature images of large-scale hot forging under time-varying temperature.

In the era of big data, the application of multi-source heterogeneous aggregation data is more and more extensive. If the quality of aggregation data is uneven, it will bring a lot of troubles to the subsequent data mining, and then lead to inaccurate decision-making. A comprehensive quality evaluation method for aggregation data is proposed in this paper, based on factor analysis and multivariate variance analysis which is from the perspective of multivariate statistical inference. The case study shows that the method proposed in this paper is feasible and adaptive for the long-term evaluation of the quality of multi-source heterogeneous aggregation data.

Impulsive noise, generated by inverters and other equipment used in the field, tends to easily enter test channels in scenarios where continuous frequency conversion signals are employed to test the frequency response of electrohydraulic proportional valves. Interference caused by such noise reduces the signal-to-response ratio of response signals, thereby influencing the accuracy of the frequency response of proportional valves. To address this concern, in this article, an integrated filtering method that combines ensemble empirical mode decomposition with median filtering is proposed. The proposed method first preprocesses the response signals of the systems and subsequently obtains frequency-response diagrams using fast Fourier transforms. Simulation results demonstrate that the proposed method reduces the root mean square error associated with the amplitude-frequency characteristic curves of the proportional directional valve considered from 2.1 to 0.3, whereas that associated with phase-frequency characteristic curves is reduced from 78.0 to 1.4 with signal-to-noise ratios in the high-frequency band being of the order of-20 dB. Experimental results also reveal that the proposed method reduces the root mean square error of the amplitude-frequency characteristic curves of the proportional directional valve by 52.5% and that of the phase-frequency characteristic curves by 71.2%.