Youjun Yue

A study of distribution network fault location including Distributed Generator based on improved genetic algorithm

In the distribution network fault section positioning, the standard genetic algorithm can be used effectively. But the network including Distributed Generator power supply has its own characteristics, and the genetic algorithm appear some defects. Therefore, aiming at accurate positioning of faults in the network including DGs, the genetic algorithm coding and fitness function are improved in the paper. Through simulation, the effectiveness of the proposed algorithm is verified.

Research of night vision image denoising method based on the improved FastICA

When apple harvesting robot operates at night, there are lots of noise in the apples night vision image captured by image processing system. And the noise is mainly Gaussian noise, and mixed with some Salt and Pepper noise. In order to improve the harvesting efficiency and precision, the Independent Component Analysis (ICA) is introduced into the denoising method for night vision image. Mixture ICA model with noise based on the Maximum Likelihood Estimation regards likelihood of the observed signals as the objective function, which leads to the estimation to the mixed matrix poor. Aiming at the problem, this paper improves the objective function by bias removal technique, which can reduce the bias caused by the noise. And ICA is optimized by Fixed-point algorithm, so that the denoising method can operate efficiently. Through ICA transform, the robot image processing system gets the images independent components, and then these components with noise-free are estimated by using certain denoising method. Finally, this algorithm realizes the night vision image denoising. In order to verify the effectiveness of the improved FastICA, both Median-Average Filtering denoising and FastICA algorithm based on Maximum Likelihood estimation are simulated by MATLAB to compare the denoising effect. From the Relative Peak Signal-to-Noise Ratio (RPSNR), the improved FastICA denoising method in this paper relative to the other two denoising methods, respectively increases by 19.32%, 4.65%. In conclusion, the improved FastICA algorithm has unique advantage for night vision image denoising, which provides a solid foundation for the night operation of apple harvesting robot.

MPPT control method of PV system based on active disturbance rejection control

This paper presents a maximum power point tracking method based on the active disturbance rejection controller. The tracking-differentiator is used to obtain the differential signal of current and voltage, and the system is controlled by the active disturbance rejection controller to ensure the system works at the maximum power point. The proposed strategy is validated in Matlab/Simulink, and the feasibility and superiority of the algorithm are verified by comparing with the traditional incremental conductance method based on PI controller.

Study on DV-HOP node location algorithm for Wireless Sensor Networks

The traditional DV-Hop localization algorithm uses the number of hops multiplied by the average jump distance to estimate the distance and uses the maximum likelihood estimation method to locate. In view of this positioning method leads to greater error, in this paper, an improved DV-Hop algorithm is proposed based on hop number correction and improved particle swarm optimization. On the one hand, the minimum number of hops of the unknown node to the anchor node is corrected by introducing limit the number of hops mechanism, thus obtaining the optimal hops value; On the other hand, the improved particle swarm optimization algorithm is used to calculate the node coordinates instead of the maximum likelihood estimation method. Verified by matlab simulation software, compare with traditional DV-HOP and PSO-DVhop, the results of the improved algorithm are superior and feasible.

Research on data processing for condition monitoring of wind turbine based on Hadoop platform

The status monitoring data of wind turbines have large, multi-source, heterogeneous, complex and rapid growth of large data characteristics. The existing data processing methods are difficult to guarantee efficiency when handling massive amounts of data, and may miss the best time to troubleshoot. How to deal with the monitoring data more efficiently is of great significance to the accurate judgment of the fault. This paper proposes the use of cloud platform to deal with massive data to improve efficiency. Firstly, the state monitoring model of wind turbine is put forward. Then, the fuzzy C means clustering algorithm is introduced, and the algorithm process is realized by MapReduce model. Finally, the experiment is carried out with Hadoop platform, using distributed database HBase to store data, and using distributed programming framework MapReduce to calculate data. It is found that with the increase of the data volume and the number of nodes, the cloud platform is able to store and calculate data at a faster speed.

Study on prediction method of hot metal temperature in blast furnace

In order to improve the accuracy of the blast furnace hot metal temperature prediction, the Least Squares Support Vector Machine optimized by an improved Particle Swarm Optimization is proposed to solve this problem. By using the improved optimization algorithm to optimize the support vector machine parameters, the prediction model can obtain well prediction effect. Whats more, to avoid particle swarm easily falling into local minim problem during the process of the optimization, the chaotic method is introduced to help the particle swarm to jump out of the local optimal solution in the process of particle swarm optimization. Besides, the varying learning factors and the quadratic decreasing of the inertia weight are introduced to enhance the local search ability of the algorithm and pick up the convergence speed. The prediction model is established by the least squares support vector machine based on the chaos particle swarm optimization, then this method is compared with other prediction methods. The simulation result indicates that the prediction model has higher prediction accuracy. Therefore, a more accurate prediction method of the blast furnace hot metal temperature is proposed.

Research on LEACH algorithm based on double cluster head cluster clustering and data fusion

For the problems of uneven cluster distribution and excessive route energy consumption, which are caused by the clustering of low energy adaptive clustering algorithm (LEACH) in wireless sensor networks, an algorithm based on the clustering of double cluster heads and the data fusion mechanism of information entropy are proposed: In the cluster head selection algorithm, Select two levels of cluster heads in a well-divided cluster, two cluster heads perform different duties, this can be better to share the energy consumption, extend the network life cycle. Cluster head nodes use information entropy for classification and fusion, making the fusion results more accurate and data transmission more efficient. The algorithm is a good way to balance the energy consumption of nodes of a wireless sensor network composed of static location nodes, and extend the survival time of the network.

A study on modular multilevel converter topology to inhibit DC voltage drop

Flexible high voltage direct current transmission system based on modular multilevel converter (MMC-HVDC) have great potential for long-distance power transmission in wind farms. Modular multilevel converter (MMC) start-up is the key to solve the problem for MMC-HVDC. This paper designs a new topology based on VSC-MMC and a step-by-step method is proposed to suppress the DC voltage drop of the momentary valve in the traditional topology. Based on the PSCAD / EMTDC simulation platform, a 11-level simulation model is proposed to verify the feasibility of this topology.

Short-term wind speed combined prediction for wind farms

Accurate prediction of short-term wind speed for wind farms will help to reduce the impact of wind power on the grid. In order to improve the prediction accuracy, a combined forecasting method is proposed. Firstly, the Ensemble Empirical Mode Decomposition (EEMD) of the original wind speed sequence is carried out to reduce the interaction between different feature scale sequences. Meanwhile, the Sample Entropy (SE) of each sub-sequence is calculated, and the sequences with similar complexity are merged to improve the prediction efficiency. Then the kernel width and regularization parameters of the Least Squares Support Vector Machine (LSSVM) are optimized by Particle Swarm Optimization (PSO) algorithm. Then the prediction model are used to predict the wind speed of the components, and the results of each component are superimposed, the final wind speed prediction result is obtained and compared with the results of other methods. The simulation results show that the proposed method can improve the prediction accuracy and have practical engineering application value.

Study on prediction model of blast furnace hot metal temperature

In order to improve the accuracy of blast furnace hot metal temperature prediction in iron and steel enterprises, the Grey Relational Analysis and Support Vector Machine optimized by Genetic Algorithm Optimization method is proposed to solve this. Firstly, the main factors which influence the blast furnace hot metal temperature are determined by the Grey Relational Analysis, and then the prediction model is established by the support vector machine based on Genetic Algorithm Optimization. This method is compared with other prediction methods. The simulation result indicates GRA-GA-SVM have higher prediction accuracy. A new and more effective method is put forward for blast furnace hot metal temperature of prediction.