Wind power is a renewable and clean energy, but for the transmission system of wind turbines operating at high altitude, its equipment inspection, maintenance and troubleshooting are more difficult, and if the fault is not found in time, it may cause more serious economic losses. Therefore, how to better apply the online fault monitoring and prevention technology to the operation and maintenance management of wind turbines is particularly important. This paper mainly combined the mechanical transmission system of a wind turbine to carry out online fault diagnosis technology and system research and development, mainly including: using vibration acceleration signal as monitoring signal to build a fault monitoring system platform; The relationship between mechanical fault and vibration of wind turbine, the principle, implementation, application analysis and development direction of online fault diagnosis technology are described. The configuration scheme and software and hardware requirements of online fault diagnosis technology of wind turbine are proposed for different models, and the fault diagnosis analysis process is discussed based on wind turbine operation data and engineering fault examples.

The grain boundary diffusion process (GBDP) has become one of the main methods to enhance the coercivity of Nd-Ce-Fe-B magnets. In this study, we examined how the magnetic properties of sintered Nd-Ce-Fe-B magnets are influenced by the combined impacts of diffusion depth, Tb-rich shell thickness, and surface grain coarsening after conducting grain boundary diffusion. There exists a trade-off between achieving a desired diffusion depth and avoiding excessive surface grain coarsening. To examine this trade-off, samples with varying diffusion depths were prepared through controlled diffusion time. Results revealed that compared to the original annealed magnets, the coercivity increments of the magnets diffused for 1 h and 3 h were 148 kA/m and 290 kA/m, respectively, while the coercivity of the magnet diffused for 9 h remained nearly the same as that diffused for 3 h. Microstructural analysis indicated that surface grain coarsening intensified with increasing diffusion time, leading to a reduction in the surface diffusion channels, thereby diminishing diffusion efficiency. In addition, strong mutual diffusion was observed between the magnet and the diffusion source. Furthermore, micromagnetic simulation studies revealed that severe surface grain coarsening limits the enhancement of coercivity even with increased depth of diffusion and thickness of the Tb-rich shell layer. This study offers valuable insights into the correlation between diffusion depth, Tb-rich shell thickness, surface grain coarsening, and the ultimate magnetic properties in sintered Nd-Ce-Fe-B magnets after GBDP, providing guidance for enhancing the efficiency of GBDP.

Grain boundary diffusion process (GBDP) presents an attractive approach for enhancing the coercivities of Rare Earth-Iron-Boron (Re-Fe-B) magnets. In this work, the evolution of microstructure and magnetic properties concerning the diffusion time was investigated in a sintered Re-Fe-B magnet (Ce/Re=49 %). The optimized diffusion time is 3 h, resulting in an increase in coercivity from the initial 840 kA/m to 1100 kA/m, and an increase in remanence from 1.165 T to 1.175 T. With further prolongation of the diffusion time the remanence decreases. The average surface grain size increases from 4.57 mu m to 5.21 mu m as the diffusion time increases from 1 h to 9 h. Micromagnetic simulations demonstrate that the distribution of surface grain size and the magnetism of the grain boundary phase both impact the coercivity and the direction of the reversal magnetic domain propagation during the demagnetization process.

To address the issue of measurement errors in ultrasonic water meters caused by various interfering factors during the flow calibration process, this study proposes a highly automated and intelligent ultrasonic water meter flow calibration system design. Firstly, by analyzing and studying the working principle of the water flow calibration device, and considering the actual flow calibration range, the calibration method that combines the master meter method and the dynamic weighing method is determined. To enhance the automation level of the flow calibration system, the flow calibration control algorithm is thoroughly analyzed and designed. Finally, a flow correction algorithm based on linear interpolation was designed and utilized for water meter flow calibration. Test data show that after the flow correction, the value error of the ultrasonic water meter in the "high area" is not more than 2%, in the "low area" it is not more than 3%, and the repeatability is less than 0.05%. Compared with manual calibration, the flow calibration system designed in this paper enables high-precision automated flow calibration of ultrasonic water meters, significantly enhancing detection efficiency and accuracy.

Aiming at the challenge of high-precision and high-reliability simulation of urban medium- and high-pressure natural gas pipeline networks, the model establishment is analyzed in detail, solution algorithm and optimization process in gas pipeline network simulation, and designs a modeling and solution method that is easy to analyze and implement to solve the complexity problem of existing natural gas pipeline network simulation algorithms. First, a steady-state simulation model was established, and the nonlinear equations were converted to linear equations using Newton’s iterative method. Then, Kirchhoff’s law and the nodal pipe segment correlation matrix are applied to establish the system of equations. Finally, by combining the iterative process of Newton’s method with the "numerical table" method, a simple calculation method based on the contribution value is established. To solve the sensitivity problem of Newton’s iterative method, the advantages of Multilayer Perceptron (MLP) in data prediction are utilized in this work. A new initial value prediction algorithm of Newton’s iterative method is proposed based on the multilayer perceptron in the real-time simulation system of gas pipeline network. In the simulation of a complex natural gas network with 134 nodes and a total length of 35.843 kilometers, the results show that the average number of iterations of the Newton’s iterative method after inputting the predicted values is reduced by 87.51% and the computation time of the simulation system is reduced by 47.32%. In addition, the absolute error between the pressure values calculated by our proposed algorithm and the pressure values calculated by the commercial software TGNET software is within 1.3% for a single point, and the average absolute error for multiple points is within 0.9%. The experimental results demonstrate the advantages of the proposed algorithm in terms of real-time, effectiveness and stability.

When tunable diode laser absorption spectroscopy is used to measure the concentration of gas, the second harmonic signal of demodulation is changed due to the influence of temperature change, and the error in concentration measurement is great. In order to solve the problem of large errors in atmospheric quality monitoring equipment due to the change in gas temperature, this paper, based on the tunable semiconductor laser absorption spectroscopy (TDLAS) theory, measured methane gas with 1000 ppm standard gas as the target and selected the central absorption wavelength of 1650 nm. The influence of temperature change on gas injection and the laser absorption spectrometer is studied. A temperature compensation algorithm based on an empirical formula is designed. Firstly, by analyzing the variable temperature test data of the detection module, it is proposed to divide the influence factors of temperature into two parts and study the influence of injection gas temperature and detector temperature, respectively. Secondly, the temperature compensation is carried out by polynomial fitting the concentration inversion results. Finally, according to the compensation effect, a scheme was proposed to compensate the measured gas by applying a constant temperature treatment to the detector at 313 K. After compensation, the average error of the system measurement is reduced from 8.4% to 1.08% when the gas temperature changes from 233 K to 343 K, which effectively reduces the deviation of the measured value caused by the abrupt temperature change. It further improves the accuracy and reliability of measuring gas concentration when gas inspection equipment is working outdoors and has strong practicability.

Measurement of flow is crucial for assuring product quality, increasing manufacturing effectiveness, and promoting the development of science and technology. With the advancement of calibration and automation, standard devices using the mass method, volumetric method, and master meter method have limitations, such as low calibration efficiency and automation, large size, and complex operation. Innovations in this area are desperately needed. To realize the automation of calibrating ultrasonic water meters, a piston-type flow standard device calibration system with a high degree of automation, high calibration efficiency, small size, and easy operation was designed. A piston-type flow standard device was designed, the standard device was modeled, the selection of the main hardware and the design of the automated control of the hardware parts were completed; an automation control system adapted to the flow standard device was developed; and, furthermore, a water meter flow point calibration algorithm integrating the start–stop method and the dual-time method, as well as a water meter flow correction algorithm, was devised to improve the efficiency of ultrasonic water meter calibration. An uncertainty assessment of the designed system was completed; the standard uncertainty and expanded uncertainty of the device were 0.013% and 0.026%. Meanwhile, flow calibration tests were conducted, validating the rationality of the automated calibration algorithm for ultrasonic water meters. The results show that ultrasonic water meters calibrated with flow correction have a flow error within ±3% in the “low flow range” and within ±2% in the “high flow range”, with a repeatability of less than 0.05%. This indicates that a piston-type flow standard device, coupled with an automation calibration control system, can efficiently, accurately, and conveniently perform water meter calibration, and the system has good practical value.

Aiming at the problems of transient over-current and over-voltage in the switching process of AC/DC hybrid microgrid in grid-connected mode and island mode, which leads to the sudden change of transmission power and seriously affects the power transmission quality. In this paper, an improved seamless switching control strategy of droop control with disturbance observer is designed. The main work is as follow: according to the diffident power transmission calculation methods of droop control in grid-connected mode and island mode, the adaptive switching is realized by detecting the current and frequency at Point of Common Coupling(PCC) of public power grid. Secondly, the switching from island mode to grid-connected mode will cause impact due to the difference of frequency, phase and amplitude between microgrid and public grid, and even cause system collapse. So the pre-synchronization control of frequency, phase and amplitude is designed. Based on the droop control, the disturbance observation(DOB) is added, which can quickly track the sudden change of system current, and suppress the sudden change by the difference between the tracking value and the actual value, so as to realize the smooth switching from island to grid. Finally, the effectiveness and feasibility of the control strategy are verify in Matlab/Simulink.

With the rapid development of renewable energy technologies, islanded DC microgrids have received extensive attention in the field of distributed power generation due to their plug-and-play, flexible operation modes and convenient power conversion, and are likely to be one of the mainstream structures of microgrids in the future. The islanded DC microgrid contains multiple distributed power generation units. The battery energy storage system (BESS) is the main controlled unit used to smooth power fluctuations. The main parameter of concern is the state of charge (SOC). In order to maintain the stability of the microgrid, this paper takes the islanded DC microgrid as the research object and designs a control strategy based on the SOC of the BESS. Additionally, in the control strategy, the BESS's energy balance control strategy and the microgrid's operation control strategy are emphatically designed. The designed BESS control strategy adjusts the droop coefficient in real time according to the SOC of the battery energy storage unit (BESU), and controls the charge and discharge power of the BESU to achieve the SOC balance among the BESUs. The microgrid operation control strategy takes the energy storage system (ESS) as the main controlled unit to suppress power fluctuations, and distributes the power of distributed power sources according to the SOC of the BESS to achieve power balance in the microgrid, and control the DC bus voltage fluctuation deviation within 4.5%.

At present, some achievements have been made in the research on the energy management of microgrid operation. However, the research is mainly on the operation of grid-connected microgrid, while the research on the energy management of islanded microgrid is still relatively few. Frequency is one of the characteristics that affects the reliability and power quality of the microgrid. The essence of controlling frequency stability is to maintain source-load balance and redistribution of active power. Therefore, this paper proposes a frequency control strategy based on dynamically cutting machine to reduce load by analyzing the use priority of different distributed power supply and the division of load importance degree, and combining the influence degree of different frequency variation range on microgrid. To coordinate and control distributed power supply, energy storage device, and load in different frequency change areas, this paper proposes different control strategies. The seed strategies of the control strategy are discussed one by one. Experimental results show that the frequency control strategy can significantly improve the frequency stability of the power supply system and reduce the operating cost of islanded microgrid.

In microgrids, paralleled converters can increase the system capacity and conversion efficiency but also generate zero-sequence circulating current, which will distort the AC-side current and increase power losses. Studies have shown that, for two paralleled three-phase voltage-source pulse width modulation (PWM) converters with common DC bus controlled by space vector PWM, the zero-sequence circulating current is mainly related to the difference of the zero-sequence duty ratio between the converters. Therefore, based on the traditional control ideal of zero-vector action time adjustment, this paper proposes a zero-sequence circulating current suppression strategy using proportional–integral quasi-resonant control and feedforward compensation control. Firstly, the dual-loop decoupled control was utilized in a single converter. Then, in order to reduce the amplitude and main harmonic components of the circulating current, a zero-vector duty ratio adjusting factor was initially generated by a proportional–integral quasi-resonant controller. Finally, to eliminate the difference of zero-sequence duty ratio between the converters, the adjusting factor was corrected by a feedforward compensation link. The simulation mode of Matlab/Simulink was constructed for the paralleled converters based on the proposed control strategy. The results verify that this strategy can effectively suppress the zero-sequence circulating current and improve power quality.

The AC/DC hybrid microgrid has a large-scale and complex control process. It is of great significance and value to design a reasonable power coordination control strategy to maintain the power balance of the system. Based on hierarchical control, this paper designs a reasonable power coordination control strategy for AC/DC hybrid microgrid. For lower control, this paper designs a variety of control modes for each converter in different application scenarios. For the higher control, this paper analyzes the working mode of the system and designs the power coordination control strategy under the grid-connected and isolated island mode. In grid-connected operation, the DC bus voltage can be stabilized by adjusting the operation mode of the DC energy storage and the on-off of the secondary load. In isolated island operation, the DC sub-microgrid is the main microgrid, and the DC energy storage is the main power regulating equipment. This is based on the principle of "energy is in short supply in the system, DC energy storage finally discharge, energy supply exceeds demand in the system, DC energy storage gives priority to charging" of DC energy storage. By adjusting the control strategy of the micro-source, the reference power, and the on-off of the secondary load, the overall power balance is maintained. The Matlab/Simulink simulation software was used to build the AC/DC hybrid microgrid simulation model, which verified the effectiveness and stability of the proposed power coordination control strategy under various operating conditions.

The development of industry 4.0 has put forward higher requirements for modern milling technology. Monitoring the degree of milling tool wear during machine tool processing can improve product quality and reduce production losses. In the machining process of machine tools, many kinds of tools are usually used, and the signal characteristics of various sensors of different tools are different. Therefore, before the tool wear assessment, this paper identified the tool type according to the spindle current data. After the tool type recognition, this paper evaluates the tool wear degree according to the tool force data, vibration data, acoustic emission signal, and other multi-sensor data. Firstly, the Elman neural network and Adaboost algorithm are combined to construct the Elman_Adaboost strong predictor. Then, the variance and mean of seven sensor data were selected as the characteristic quantities to input the strong predictor. Finally, three wear quantities were obtained to measure the wear degree of the tool. The method proposed in this paper is implemented by Matlab, and the validity of this method is verified using the competition data provided by PHM (Prognostics and Health Management) Society. The results show that the average evaluation accuracy of the same tool wear is more than 92%, and that of the similar tool wear is more than 85%.

In order to improve the accuracy of fault diagnosis on wind turbines, this paper presents a method of wind turbine fault diagnosis based on ReliefF algorithm and eXtreme Gradient Boosting (XGBoost) algorithm by using the data in supervisory control and data acquisition (SCADA) system. The algorithm consists of the following two parts: The first part is the ReliefF multi-classification feature selection algorithm. According to the SCADA history data and the wind turbines fault record, the ReliefF algorithm is used to select feature parameters that are highly correlated with common faults. The second part is the XGBoost fault recognition algorithm. First of all, we use the historical data records as the input, and use the ReliefF algorithm to select the SCADA system observation features with high correlation with the fault classification, then use these feature data to build the XGBoost multi classification fault identification model, and finally we input the monitoring data generated by the actual running wind turbine into the XGBoost model to get the operation status of the wind turbine. We compared the algorithm proposed in this paper with other algorithms, such as radial basis function-Support Vector Machine (rbf-SVM) and Adaptive Boosting (AdaBoost) classification algorithms, and the results showed that the classification accuracy using “ReliefF + XGBoost” algorithm was higher than other algorithms.

The current signal harmonic detection method(s) cannot reduce the errors in the analysis and extraction of mixed harmonics in the power grid. This paper designs a harmonic detection method based on discrete Fourier transform (DFT) and discrete wavelet transform (DWT) using Bartlett–Hann window function. It improves the detection accuracy of the existing methods in the low frequency steady-state part. In addition, it also separates the steady harmonics from the attenuation harmonics of the high frequency part. Simulation results show that the proposed harmonic detection method improves the detection accuracy of the steady-state part by 1.5175% compared to the existing method. The average value of low frequency steady-state amplitude detection of the proposed method is about 95.3375%. At the same time, the individual harmonic components of the signal are accurately detected and recovered in the high frequency part, and separation of the steady-state harmonics and the attenuated harmonics is achieved. This method is beneficial to improve the ability of harmonic analysis in the power grid.

The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in the aspect of the power supply reliability, the grid-connected inverter cost is also ignored. Considering the deficiency of the traditional energy supply system, this paper builds the CCHP system construction cost model. The particle swarm optimization (PSO) is adopted to find out the minimum value of the construction cost, and the optimal system construction scheme is constructed from three aspects which are system reliability, economic benefits and environmental benefits. In this paper, the typical daily data, as well as the meteorological data and the load data, in the last four years are taken as experimental dataset. The experimental results show that compared with the traditional SP system and power supply system, the CCHP system established in this paper not only achieves lower cumulative investment cost, but also has a good power supply reliability and environmental benefits.

The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

This paper presents an infrared infusion monitoring method based on data dimensionality reduction and a logistics classifier. In today's social environment, nurses with hospital infusion work are under excessive pressure. In order to improve the information level of the traditional medical process, hospitals have introduced a variety of infusion monitoring devices. The current infusion monitoring equipment mainly adopts the detection method of infrared liquid drop detection to realize non-contact measurements. However, a large number of experiments have found that the traditional infrared detection method has the problems of low voltage signal amplitude variation and low signal-to-noise ratio (SNR). Conventional threshold judgment or signal shaping cannot accurately judge whether droplets exist or not, and complex signal processing circuits can greatly increase the cost and power consumption of equipment. In order to solve these problems, this paper proposes a method for the accurate measurement of droplets without increasing the cost, that is, a method combining data drop and a logistics classifier. The dimensionalized data and time information are input into the logistics classifier to judge the drop landing. The test results show that this method can significantly improve the accuracy of droplet judgment without increasing the hardware cost.

The nonlinear load in AC/DC hybrid microgrid seriously affects the power transmission quality of bidirectional AC/DC interlinking converter (BIC). In this article, an improved Fryze-Buchholz-Depenbrock (FBD) harmonic current detection method combined with droop control is designed to suppress harmonic current control strategy. The decoupled double synchronous reference frame phase locked loop (DDSRF-PLL) with self decoupled network is used to eliminate the negative sequence fundamental frequency component, and the accurate phase detection can also be realized when affected by nonlinear load. The least mean square (LMS) algorithm adaptive filter can enhance the dynamic response ability when the load current suddenly changes. To solve the problem that the cut-off frequency of low pass filter (LPF) can not take into account the detection accuracy and response speed of FBD harmonic current detection method. Finally, Matlab/Simulink is used for simulation verification. The simulation results show that the improved control strategy not only realizes the bidirectional flow of power and maintains the DC bus voltage stability, but also reduces the total harmonic distortion (THD) at point of common coupling (PCC) by 0.45% compared with the traditional FBD control strategy. And the effectiveness and feasibility of the design method are proved.

Period detection technology for weak characteristic signals is very important in the fields of speech signal processing, mechanical engineering, etc. Average magnitude difference function (AMDF) is a widely used method to extract the period of periodic signal for its low computational complexity and high accuracy. However, this method has low detection accuracy when the background noise is strong. In order to improve this method, this paper proposes a new method of period detection of the signal with single period based on the morphological self-complementary Top-Hat (STH) transform and AMDF. Firstly, the signal is de-noised by the morphological self-complementary Top-Hat transform. Secondly, the average magnitude difference function of the noise reduction sequence is calculated, and the falling trend is suppressed. Finally, a calculating adaptive threshold is used to extract the peaks at the position equal to the period of periodic signal. The experimental results show that the accuracy of periodic extraction of AMDF after Top-Hat filtering is better than that of AMDF directly. In summary, the proposed method is reliable and stable for detecting the periodic signal with weak characteristics.