Xiao Li Xu

The Research on the Flexible Integrated Research Platform of the Solar Energy Photovoltaic System

On the basis of the general technology, high-tech, vast varieties and small-lot of the solar energy photovoltaic system, this paper comes up with the research conception of optical, mechanical and electronic integration and builds the optical, mechanical and electronic integration system. It also brings forward the design methodology and the integrated mechanism of the instant systematic integration based on the flexible interconnection, which leads to the building of the basic experimental and research environment of the automatic integrated design. The application of this new technology is based on the research of the highly-efficient Concentrator Photovoltaic System. It noticeably improves the technical level as well as the research efficiency.

Construction of Solar PV Power Generation Remote Monitoring System in the Architecture of Internet of Things

With the widening application of solar PV power generation, a way of solar utilization, the safe operation of PV power generation system is increasingly valued. To assure the operating safety and reliability of PV power plant, monitoring system has to be installed to identify and eliminate faults of the plant immediately. Solar remote monitoring system in the architecture of internet of things (IoT) is comprised of sensing layer, information transmission layer and application layer. The sensing layer consists of sensors for monitoring environment, PV module and inverter; the information transmission layer consists of ZigBee access unit, access network, WEB server and Internet; and the application layer consists of IoT application fault diagnosis module. The remote monitoring system provides scientific decision-making reference to the safe operation and daily maintenance and management of PV power generation system through data acquisition, fusion, analysis and evaluation.

The Turbine Machine Fault Prediction Based on Kernel Principal Component Analysis

Kernel principal component analysis (KPCA) is presented and is applied to predict the huge electro-mechanical system fault. Take the gas turbine set of Beijing Yanshan Petrochemical Refinery as the research object. KPCA uses the historical normal data of vibration intensity value to establish a prediction system. And then it is used to forecast the collected data for judging whether the turbine set is in normal. The simulation experiment result indicates the effectiveness of the method and the running state can be judged as normal or not from the result. And the experiment also shows KPCA can obtain a satisfactory prediction result.

Research on the Application of the Prediction of the Expressway Traffic Flow Based on the Neural Network with Genetic Algorithm

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

Universal-Joint Sun Tracking Method and Tracking Device

In order to enhance the sun tracking accuracy of photovoltaic power generation system and reduce the manufacturing cost of sun tracking devices, a new sun tracking method and corresponding tracking device was presented in this paper. The new method is to get two driving rotational angle parameters by conversing solar azimuth parameter and solar altitude parameter. According to the new parameters, the solar cell panel is driven to rotate as the universal-joint movement in order to track the sun. The new tracking device consists of solar direction monitoring module, parameter calculation module, driving device, direction error detection module, and feedback control module. It can acquire solar direction parameter to complete new angle parameters conversion and it can amend real-time interaction frequency and stride according to the direction error of solar cell panel. The new tracking device has features of high tracking accuracy, small tracking time intervals, simple structures and low manufacturing cost.

Modeling and Control Technology of the Z-Source Inverter PV Grid-Connected System in the New Energy Equipment

Z-source inverter is the key part of the new energy equipment. A model of Z-source inverter has been built by using the State Space Average Overall Modeling method. It can achieve the linkage control of the direct zero vector d and modulation factor m. According to the model, a control system adopted MPPT tracking control and voltage & current double loop grid-connected control is designed to realize the coordinated control. SHEPWM modulation strategy is used to ensure that the grid current waveform quality. Z-source photovoltaic grid-connected experimental system has been designed and carried out, and the experimental results verify the validity and correctness of the control system.

MWGANN Prediction Model for Electromechanical Equipment Running State

To ensure the rotary electromechanical equipment injection pump unit safe and stable operation, status assessment parameters should be predicted used by appropriate prediction model. This paper presents a genetic algorithm optimization neural network prediction model based on mean function new information-weighted theory (MWGANN prediction model). MWGANN prediction model can optimize the neural network structure parameters and improve the prediction accuracy and prediction timeliness by using the recency difference of time series data. Collecting large rotating injection pump unit vibration intensity time series in the industrial site, MWGANN prediction model and GANN prediction model are applied to predict trend. The results show that MWGANN model achieved good results in prediction accuracy and prediction timeliness.

Research on Precision Measuring System for Motor Speed

In order to improve accuracy of measuring motor speed in precision motor control systems, a method of precise measurement of speed with CPLD is proposed, which measures the truncated parts of the measured pulse on the basis of measuring raster count pulse within the equal period, and takes them as compensation, thus improving accuracy of measuring the raster pulse. In this paper, a speed measuring formula is given. Measuring system is provided with parallel and serial communication interfaces for output of measurement results.

Power Optimizing of Solar Photovoltaic Systems

Efficient energy yield is a major concern in solar photovoltaic (PV) systems. This paper describes a distributed control system to optimize the power output of the PV systems. The PV systems contain many PV modules. And every PV module has a monitoring and control network node. The communication data are successfully transmitted using a low-cost ZigBee wireless network. The field conditions are monitored by voltage, current, irradiance, and temperature sensors. The power operating point tracking is implemented at the PV module level. The reference voltage is calculated based on a neural network model, which is used to identify maximum power point. And the output voltage is regulated by a digital controller in the integrated converter according to the reference voltage. Experiments show that the power output can be greatly increased with this distributed control system under many shadow conditions.

Research on Operation Stability Deterioration Trend Prediction Faced to Wind Power Group

A operation stability prediction model faced to wind power equipment group based on Internet of Things(IOT) is research. It’s beneficial to improve the operation stability of key equipment group and promote the emerging IOT technology application in equipment maintenance. The prediction model mainly including: based on IOT constructing the system of remote online sample acquisition and operation stability prediction; proposing operation trend prediction algorithm based on energy decoupling faced to wind power rotating machinery, realizing of operation trend feature extraction of equipment group; constructing operation stability trend prediction model faced to equipment group. Operation trend feature is used to predict fault trend of wind power in order to ensure safe and stable operation of wind power group.