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Droop control has been widely used and researched as a characteristic method to achieve current distribution. The traditional droop control has inherent limitations. With the development of various control algorithms, the sec-ondary control of the DC microgrid droop control is no longer limited to the use of classical deviation adjustment or the general compensation method of the associated parameters for compensation. An improved droop control method based on the optimal compensation method. Compared with the traditional particle swarm optimization, the adaptive particle swarm optimization (ASAPSO) algorithm based on simulated annealing is adopted, which enhances the ability of the particle swarm to improve the convergence speed and convergence accuracy. By improving the algorithm, the optimization results are fed back to the droop parameters in real time, so as to improve the optimization effect. The effectiveness and viability of the proposed control method are verified by simulation experiments with MATLAB/Simulink.

This paper presents a method of forecasting and modeling of electric vehicle charging load in different regions of distribution network. Firstly, the fixed factors affecting the charging load of electric vehicles are analyzed. The electric vehicles are divided into pure electric and plug-in hybrid electric vehicles, and the charging equipment is divided into ordinary charging equipment and fast charging equipment. Then, the behavior characteristics and psychological factors of electric vehicle users are considered as the random factors influencing the charging load, the investigation statistics and hypotheses are carried out. Finally, the distribution network is divided into different regions, and the charging load model of each region is established based on Monte Carlo simulation. The established model shows that with the increase of penetration rate of electric vehicles in the future, large-scale charging into the power grid will cause impact load in different regions of the distribution network at different times, which provides some reference for the suppression of voltage fluctuation in distribution network.

At present, ip-iq algorithm based on instantaneous reactive power theory is the most widely used in the field of harmonic detection.However, the ip-iq algorithm based on instantaneous reactive power has coordinate transformation and inverse transformation, which leads to cumbersome calculation.the existence of low-pass filter leads to delay and poor dynamic performance of the algorithm.The harmonic detection based on BP neural network has the advantages of simple calculation, fast detection speed, strong adaptive ability and good dynamic performance.However, the initial weights and thresholds are random, so it is easy for the network to fall into local optimal solution during training, resulting in low detection accuracy. This paper presents a harmonic detection method based on ant colony algorithm optimized BP neural network. ACO-BP, BP and ip-iq algorithms are compared by Simulink simulation. The results show that ACO-BP harmonic detection algorithm is feasible and superior

The application of energy storage on wind power prediction error compensation is becoming an ideal scheme now. However, limited by energy storage technology, the implement speed of is quite slow. The error distribution characteristics of wind power prediction are studied based on several improved typical wind power prediction error distribution for the reason that the demand for energy storage capacity depends largely on the cumulative error distribution characteristics. The relationship between the capacity allocation requirements and the error distribution characteristics of the energy storage system is obtained on the whole. Then, a wind/storage power grid-connected plan forecasting optimization strategy based on the average absolute error is proposed to improve the forecast precision of wind power grid-connected generation and reduce the capacity demand of energy storage configuration. The energy storage capacity allocation with different confidence levels is obtained by the kernel density estimation method, which provides a reference for the actual wind farm. Finally, an example is given to test and verify the economic feasibility of the proposed method.

Photovoltaic power generation is changing day by day, but its system has drawbacks. To compensate for the shortcomings of PV power generation system, the micro-grid system of PV power generation with HES is constructed by using the complementary characteristics of battery and supercapacitor. On the basis of this, we introduce a fuzzy control to realize the fast response of hybrid energy system. A micro-grid model is built based on Matlab/Simulink to verify the feasibility and rationality of the proposed control strategy.

Because of the defect that Malikov criterion may produce wrong judgment in application, the invalidation reason of Malikov criterion was analyzed, and the necessary condition for Malikov criterion being valid using a t-test method is brought forward that the anterior half measure data are inconsistent with posterior half. Only in this way, the judgment according to Malikov criterion is correct.

The advantages and shortcomings of three methods to find linear systematic error were analyzed. Malikov criterion is the most common method, but it often produced wrong judgment in application. The invalidation reason of Malikov criterion was analyzed, and then the t-test method was developed to find linear systematic error. The t-test method can exactly judge whether there is linear systematic error in measure data list according to the consistency of the anterior and posterior half measure data.

The nonlinear identification of the input-output characteristic of the eddy current sensor is an important research task in the application of the eddy current sensor. At first, the working principle and the working characteristics of the eddy current sensor were introduced in this paper. Then the nonlinear identification of the eddy current sensor was analyzed in detail. To aim at the error problem of the eddy current sensor with the nonlinear factors, a feasible project of nonlinear identification of the eddy current sensor was researched applied the modeling method of artificial neural networks, and a new artificial neural network is presented to realize the nonlinear identification of the eddy current sensor without linearization processing. Its design, modeling and implement technology based on a new internal recurrent neural networks were advanced, and then the data experiments were processed with MATLAB software, thus the measure precision of the eddy current sensor was enhanced. In the end, the measuring error is no more than 0.1%, and the dynamic responding time is less than 0.5 second. This method can increase the measuring accuracy and be advantageous for the test analysis and the data statistics though the online data processing of the computer.

Firstly, the oculomotor characteristics and form of human eye are analyzed. Then, based on the human eye anatomical and physiological research, an oculomotor control system model for a single eye is developed according to the oculomotor neural paths. The simulation experiments are done on Matlab6.5 under the same condition as the physiological experiments. Comparisons with the results of physiological experiments show that the model can realize VOR (vestibulo- ocular reflex), OKR (optokinetic reflex), smooth pursuit and compound movement of human eye.

A novel control strategy of pan-tilt-zoom camera is described. Because the active camera is mounted on a moving autonomous helicopter in visual tracking system, and the tracked object is moving at same time, and there exists the vibration influence of the helicopter, image stabilization becomes poor, and all pixels are running. Therefore, a biomimetic control strategy of on-board pan-tilt-zoom camera is presented. In this paper, the biomimetic oculomotor control model is obtained based on physiological neural path of eye movement control. In order to validate the functions of the biomimetic control model, simulation experiments were done under the same condition as the physiological experiments in physiological researches. Then the biomimetic controller of onboard pan-tilt-zoom camera is developed. The results of flight tracking experiments show that the biomimetic controller can compensate the deflection caused by the flight platform, and enhance the visual tracking system performance.

This paper describes a novel control strategy of pan-tilt-zoom camera for tracking a ground moving object from an autonomous helicopter. Because the active camera is mounted on a moving helicopter, and the tracked object is moving at same time, and there exists the vibration influence of the helicopter, image stabilization becomes poor, and all pixels are running. Therefore, a biomimetic control strategy of on-board pan-tilt-zoom camera is presented. In this paper, the biomimetic oculomotor control model is obtained based on physiological neural path of eye movement control. In order to validate the functions of the biomimetic control model, simulation experiments were done under the same condition as the physiological experiments in physiological researches. Then the biomimetic controller of on-board pan-tilt-zoom camera is developed. The results of flight tracking experiments show that the biomimetic controller can compensate the deflection caused by the flight platform, and enhance the system performance.

Visual tracking is currently one of the most active research topics in computer vision. It has a broad range of applications and many visual-tracking Technologies are applied to the production auto-lines. In fact, the problem of visual tracking usually is regarded as the problem of combining control with computer vision. According to the result of image processing, production auto-lines can be monitored and operated automatically. The intelligent visual tracking system used on production auto-lines can take the place of inspector and decrease the amount of human labour needed. In this paper, we review the developments of visual tracking and present some methods of image processing and servo control related to visual tracking.

Generally the scene of the visual surveillance using one camera is confined within certain limits. If you want to extend the surveillance area, you need multiple cameras. It attempts to detect, recognize and track certain objects from image sequences. This paper presents a simple and effective approach segmenting and tracking a moving object from a moving platform in the air such as UAV or ballonet airship, etc. The moving platform can follow the tracked object during a long distance. It can be used to track the doubtful vehicle and strengthen the visual surveillance more. The proposed approach is efficient and robust. The object is searched only in a subwindow of the current frame. The position of sub-window is estimated by its position in the last two frames. The features of the object are updated in each frame. The approach produces good tracking results at a frame rate of 25 fps. It is available in tracking a moving object in various scenes.

In this paper, oculomotor characteristics and form were analyzed, the three-dimensional oculomotor control system model of biomimetic eye was developed based on the oculomotor neural paths, then the model was applied to the UAV on-board pan-tilt control system in the ground moving target low-altitude tracking system to coordinately control UAV and the on-board pan-tilt camera, thus the on-board camera can track the ground moving target continuously, smoothly, clearly and stably like human eyes.

Firstly, the characteristics of UAV visual tracking system were analysed, secondly the three-dimensional oculomotor control system model of biomimetic eye was developed based on the oculomotor neural paths. Then a new UAV visual tracking system was established. The on-board pan-tilt camera control system based on biomimetic eye can compensate the deflection caused by the UAV rotation and the movement of ground relative to the UAV. The pan-tilt camera can simulate human eye movements including smooth pursuit, VOR and OKR, and the control pattern of camera motor is velocity control, thus it can track the ground moving target continuously, smoothly, clearly and stably like human eye. Finally the experimental results were presented.