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Abstract Wind power is an exceptionally clean source of energy; its rational utilization can fundamentally alleviate the energy, environment, and development problems, especially under the goals of ‘carbon peak’ and ‘carbon neutrality’. A combined short‐term wind power prediction based on long short‐term memory (LSTM) artificial neural network has been studied aiming at the non‐linearity and volatility of wind energy. Due to the large amount of historical data required to predict the wind power precisely, the ambient temperature and wind speed, direction, and power are selected as model input. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise has been introduced as data preprocessing to decompose wind power data and reduce the noise. And the Particle Swarm Optimization is conducted to optimize the LSTM network parameters. The combined prediction model with high accuracy for different sampling intervals has been verified by the wind farm data of Chongli Demonstration Project in Hebei Province. The results illustrate that the algorithm can effectively overcome the abnormal data influence and wind power volatility, thereby providing a theoretical reference for precise short‐term wind power prediction.

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified.

Wind power forecasting plays a key role in balancing the power supply and load demand of the system. To achieve reasonable processing and decomposition of input data for power prediction, a combined forecasting model is proposed, in which Sparrow Search Algorithm (SSA) is adopted to optimize Variational Mode Decomposition (VMD) parameters to solve the problem that VMD is difficult to achieve optimal decomposition by manually setting parameters. Firstly, the SSA is used to optimize the VMD parameters, and then the optimized VMD is used to decompose the data. At the same time, the entropy weight-grey relational analysis method is used to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components is selected as the input of the LSTM prediction model to obtain more accurate prediction results. The example results show that the SSA-VMD-LSTM method can effectively improve the prediction accuracy and reduce the wind power prediction error compared with other methods, which verifies the effectiveness of the prediction model.

In order to further improve the accuracy of wind power forecasting, a combined forecasting method based on sparrow search algorithm (SSA) optimizing variational mode decomposition (VMD) parameters was proposed. Firstly, the SSA was used to optimize the VMD parameters, then the optimized VMD was used to decompose the data. Secondly, the entropy weight method and grey relational analysis were combined to analyze the correlation of environmental variables, and the combination of the most relevant influencing factors and the decomposed modal components were selected as the input of the LSTM prediction model to obtain more accurate prediction results. Finally, a wind power probability prediction model based on NKDE was established to effectively quantify the uncertainty of wind power prediction results. The results show that the MAE, RMSE and MAPE of the proposed combination model decrease by 3951%, 33.22% and 40.39%, respectively, compared with the VMD-LSTM model. The SSA-VMD-LSTM-NKDE combination model can not only effectively improve the accuracy of deterministic prediction, but also effectively quantify the uncertainty of wind power prediction results, which provides scientific decision-making basis for wind power prediction.

Non grid connected wind power hydrogen production technology is of great significance for the large-scale comprehensive utilization of hydrogen energy and accelerating the development of clean energy. In this paper, an electrolyzer power allocation and alternate control method for non grid connected wind power hydrogen production is proposed, and the optimized control strategy are combined to predict the maximum wind power of certain time interval. While retaining the required data characteristics, the instantaneous fluctuation of some wind power data is eliminated, which provides a reliable basis for power distribution in the alternation control strategy of electrolyzer array. The case simulation verifies the effectiveness of the electrolyzer array control principle and the prediction of the maximum wind power. While ensuring the absorption effect and hydrogen production rate, the service life and operation safety of the electrolyzer array are effectively improved by balancing the working state of each electrolyzer.

The imbalance of capacitor voltage on the DC side of the inverter will cause the third-order current harmonics and the device will be damaged greatly with the increase of voltage stress. The mechanism of imbalance capacitors voltage and the third-order current harmonics generated by the double frequency fluctuation are analyzed. This paper presents a feedforward capacitor differential pressure control strategy based on PR controller, which is aimed at the single-phase three-level NPC inverter and considers the cause of double modulation wave. The PR controller is used in the current loop to suppress the third-order harmonic by static-free tracking of the AC signal, and the voltage balance is realized by the feedforward DC-link capacitor voltage difference to offset the DC component of the output side. The simulation model and 1kW prototype driven by the control strategy have been established to demonstrate that the capacitor voltage of the inverter is balanced and the third-order harmonic is suppressed efficiently.

A combined ultra-short-term wind power prediction strategy with high robustness based on least squares support vector machine (LSSVM) has been proposed, in order to solve the wind abandonment caused by wind power randomness and realize efficient hydrogen production under wide power fluctuation. Firstly, the original wind power data is decomposed into sub-modes with different bandwidth by variational modal decomposition (VMD), which reduces the influence of random noise and mode mixing significantly. Then dragonfly algorithm (DA) is introduced to optimize LSSVM kernel function and the combined ultra-short-term wind power prediction strategy which meets the time resolution and accuracy requirements of electrolytic cell control has been established finally. This model is validated by a wind power hydrogen production demonstration project output in the middle east of China. The superior prediction accuracy for high volatility wind power data is verified and the algorithm provides theoretical basis to improve the control of wind power hydrogen production system.

A novel multi-objective robust optimization model of an integrated energy system with hydrogen storage (HIES) considering source–load uncertainty is proposed to promote the low-carbon economy operation of the integrated energy system of a park. Firstly, the lowest total system cost and carbon emissions are selected as the multi-objective optimization functions. The Pareto front solution set of the objective function is applied by compromise planning, and the optimal solution among them is obtained by the maximum–minimum fuzzy method. Furthermore, the robust optimization (RO) approach is introduced to cope with the source–load uncertainty effectively. Finally, it is demonstrated that the illustrated HIES can significantly reduce the total system cost, carbon emissions, and abandoned wind and solar power. Meanwhile, the effectiveness of the proposed model and solution method is verified by analyzing the influence of multi-objective solutions and a robust coefficient on the Chongli Demonstration Project in Hebei Province.

In order to reduce the electromagnetic vibration noise caused by the radial electromagnetic force of permanent magnet synchronous motor,an optimization method based on magnetic bridge offset was proposed based on the finite element analysis method of electromagnetic field,structure field and acoustic field.Taking an interior radial permanent magnet synchronous motor for vehicle as the research object,the position of the magnetic isolation bridge of the motor was shifted to the magnetic pole direction along the circumferential track of the outer diameter of the rotor,and the adjacent distance between the magnetic isolation bridge and the length of the isolation magnetic bridge were optimized.The offset distance of the magnetic bridge was set as 4 mm considering the vibration noise level and the performance of the motor.The radial electromagnetic force,rotor force,motor modal and electromagnetic vibration noise were simulated and analyzed before and after the motor optimization.The results show that the average electromagnetic vibration noise of the motor can be reduced by [BF]16.77%[BFQ] by this optimization method,the noise of the motor can be effectively reduced,but it has little impact on the performance of the motor.The optimization method based on the magnetic bridge offset can improve the motor noise level,which provides some reference for the optimal design of permanent magnet synchronous motor and the suppression of electromagnetic vibration noise for vehicle.[JP][HQ]

Abstract Background The newly designed cervical disc prosthesis, Pretic-I, had been finished its limited clinical use for over 5 years. At a short-term follow-up of 2 years, we obtained satisfactory clinical results. The long-term clinical efficacy and safety of Pretic-I will now be analyzed. Methods Peri-operative parameters included intra-operative blood loss, operation time, off-bed time. Clinical parameters included visual analogue scale (VAS) for arm and neck, neck disability index (NDI), and Japanese Orthopaedic Association (JOA) score. Radiological parameters included C2–7 Cobb angle, Shell angle, and the range of motion (ROM) of C2–7, functional segment unit (FSU), and adjacent FSU. The CDA-related complications included adjacent segment degeneration (ASDeg), adjacent segment disease (ASDis), heterotopic ossification (HO), prosthesis subsidence, prosthesis displacement, and dysphagia. Results A total 64 patients from two independent centers received a single-level CDA with Discover (n = 32) and Pretic-I (n = 32), and all of patients finished a 5-year follow-up. There’re no significant differences between two groups in peri-operative parameters. The clinical parameters improved greatly in Pretic-I group (p

A high step-up integrated coupled inductor-capacitor DC-DC converter for renewable energy applications has been proposed in this paper. Because of relative low output voltage of renewable energy systems, a high step-up converter is necessary. In this paper, a new configuration has been given by integrating coupled inductor-capacitor to get the desire voltage gain. The voltage gain dramatically boosted up by using a coupled inductor to charge a capacitor and the voltage across the switch are clamped inherently. The proposed converter possesses of only six components, which makes it uncomplicated and more efficient. The derivation of the steady state operation's principles has been analyzed in detail, this paper. Prototype hardware is implemented in the laboratory and the experimental results are given to verify the theoretical analysis.

When an input terminal is connected to a solar panel with a low voltage level and large power range fluctuation, a Boost type CLLC converter controlled by fixed frequency pulse width modulation (PWM) reduces the utilization of PV electric energy, and the synchronous drive of the secondary side and primary side ensures that the converter operates with low efficiency when the duty cycle is shifted by 0.5. To maximize the utilization of photovoltaic (PV) energy in a PV power generation system and improve the conversion efficiency, this paper studies a Boost CLLC converter with high gain and soft switching controlled by PWM-pulse frequency modulation (PFM) hybrid modulation for PV power generation. By controlling the duty cycle and switching frequency of the primary MOSFETs, the converter can stabilize the output voltage while tracking the PV maximum power point. Under such a hybrid control, the resonant gain of the converter is greater than 1, the voltage gain is large, one of the MOSFETs at the primary side is in a zero-voltage-switching-open (ZVS-ON) state, two diodes are used to realize zero-current-switching (ZCS) soft switching on the secondary side, and the efficiency of the converter is high. In addition, this paper studies the phenomenon of backflow power in the resonant circuit and the influence of a change in the switching frequency on the magnitude of the backflow power. Finally, a 500W experimental prototype was constructed to verify the accuracy of the theoretical analysis and the feasibility of the control method.

A duty-cycle predictive control is proposed for dc grid integration of front-end isolated quasi-Z-source modular cascaded converter (qZS-MCC) photovoltaic (PV) power system. The post-stage qZS half-bridge dc-dc converter deals with PV maximum power point tracking, dc grid integration, and dc-link voltage balance; whereas, the front-end isolation converters operate at a constant duty cycle of 50%. Thus, it saves control hardware resources while overcoming challenges from PV-panel voltage variations and dc-bus voltage limit. The proposed control uses the derived circuit model to predict the global active-state duty cycle for grid-connected current control and predict the shoot-through duty cycles for dc-link voltage balance, achieving a fast and accurate tracking target. The proposed control method has advantages of: i) eliminating weighting factors that exist in conventional model predictive control (MPC), ii) no sophisticated loop parameters design that exists in proportional-integral (PI) control, iii) operating at constant switching frequency that is different from the conventional MPC with variable switching frequency. Simulation and experimental tests are carried out to verify the effectiveness of the proposed control method and compare with the PI-based control system.

In order to solve the problem that the current geophysical method is difficult to locate in the mud of bored piles, the problem of effective exploration of karst caves at the bottom of piles, the author puts forward the application of Internet of Things technology and sonar technology in the construction of bored pile foundation in a high altitude environment. Combined with the propagation characteristics of sonar stress waves, a sonar detection method for karst caves at the bottom of bored piles is proposed, and a sonar detector for karst caves at the bottom of piles (JL-SONAR) and signal analysis software (PBCA) are developed. The JL-SONAR detector realizes the emission and acquisition of onsite sonar signals, and the PBCA software completes the analysis and arrangement of the detection data. This technology makes full use of the mud conditions of bored piles, the development of karst caves within 10 m of the pile bottom can be tracked and detected in the process of hole formation, which has the advantages of low cost, high speed, and high precision. Experimental results show that Project A has been drilled in multiple directions at the bottom of the pile, the rock cores on the west side of the pile bottom are mostly fragmented, the rock quality index RQD is 15–30, and the quality of the foundation rock at the pile bottom is extremely poor, which verify the results of sonar detection. No slurry leakage occurred in Project B after drilling, and the cores were mostly short columns. The rock quality index RQD was 75 to 90, and the quality of the foundation rock at the bottom of the pile was good, which verifies the results of sonar detection. Conclusion. The research results provide a new solution for karst cave exploration in karst areas, especially in liquid environments.

A bipolar DC microgrid shows good performance since it improves the efficiency and system’s reliability. To transmit power to the load, several DC-DC converters are required by the conventional bipolar DC microgrid, which results into large weight, volume, and high cost. To integrate bipolar DC microgrid with the photovoltaic (PV) module, a novel bipolar output converter is proposed in this paper. This converter possesses very simple circuit configuration and can achieve a high step-up voltage gain by adopting active-switched-inductor (ASL) network; meanwhile, bipolar output voltage is between positive line (P), negative line (N), and common ground (O), which can offer three voltage classes (Vdc, +0.5 Vdc and -0.5 Vdc). To verify the precision of the analysis of the proposed converter, a prototype has been designed and experiments are performed in the lab.

As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speed-power, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.

In this article, a topology based on the single-phase full-bridge is proposed to decouple control of phase current in current source grid-connected inverters. The DC side of this current source inverter topology can operate with a common DC bus or independently, and the AC side can be independently integrated into the three-phase grid or operate in parallel. This topology needs only a simple control logic and phase information to achieve 3-phase current decoupling control, which can accommodate a wide range of voltage fluctuations. Then, dual-carrier driving and hybrid damping correction strategies are designed to achieve flexible combinatorial operation and eliminate the common-mode voltage. Meanwhile, the method is simple and easy to implement in practice. The effectiveness of the proposed algorithm is validated with experiments.

The image preprocessing is an important part in the field of digital image processing, and it’s also the premise for the image detection of cold steel strip surface defects. The factors including the complicated on-site environment and the distortion of the optical system will cause image degradation, which will directly affects the feature extraction and classification of the images. Aiming at these problems, a method combining the adaptive median filter and homomorphic filter is proposed to preprocess the image. The adaptive median filter is effective for image denoising, and the Gaussian homomorphic filter can steadily remove the nonuniform illumination of images. Finally, the original and preprocessed images and their features are analyzed and compared. The results show that this method can improve the image quality effectively.

In some areas, the problem of wind and solar power curtailment is prominent. Hydrogen energy has the advantage of high storage density and a long storage time. Multi-energy hybrid systems including renewable energies, batteries and hydrogen are designed to solve this problem. In order to reduce the power loss of the converter, an AC-DC hybrid bus is proposed. A multi-energy experiment platform is established including a wind turbine, photovoltaic panels, a battery, an electrolyzer, a hydrogen storage tank, a fuel cell and a load. The working characteristics of each subsystem are tested and analyzed. The multi-energy operation strategy is based on state monitoring and designed to enhance hydrogen utilization, energy efficiency and reliability of the system. The hydrogen production is guaranteed preferentially and the load is reliably supplied. The system states are monitored, such as the state of charge (SOC) and the hydrogen storage level. The rated and ramp powers of the battery and fuel cell and the pressure limit of the hydrogen storage tank are set as safety constraints. Eight different operation scenarios comprehensively evaluate the system’s performance, and via physical experiments the proposed operation strategy of the multi-energy system is verified as effective and stable.

In some rural areas of northern China, brucellosis is an endemic zoonotic infection caused by a bacteria of the genus Brucella. As a result of brucellosis, osteoarticular involvement is the most common complication. Here, we report the case of a 50-year-old male who presented with severe swelling and pain in the right knee in players. Brucella arthritis was diagnosed based on his contact history, clinical manifestations, and results of serological tests, synovial fluid cultures, and radiological imaging. As part of the treatment plan, surgery including an arthrotomy, debridement, and irrigation of the joint cavity was carried out. In the weeks following surgery, the patient reported significant improvement in his right knee joint's function and a significant reduction in the intensity of his joint pain in players. [ABSTRACT FROM AUTHOR]