Search

In recent years, under the development of the dual carbon goal, the energy crisis has become increasingly serious, and China has also experienced serious power rationing. However, the research on dynamic surface control technology in solar tracking systems in nonlinear control systems is mostly based on continuous-time systems, while adaptive dynamic surface control based on discrete-time nonlinear control systems can describe an actual control system more accurately in the production process. It can effectively suppress interference with extremely high stability and safety. To solve the problem of low efficiency in photovoltaic power generation, this research first built a photovoltaic power generation servo system model based on the parameter of uncertainty. Then, a discrete adaptive neural network dynamic surface (DANNDS) controller was designed to solve the problems in the design of the traditional backstepping method. Finally, based on the designed method of a dynamic surface controller, a discrete adaptive neural network quantization controller (DANNQC) for the photovoltaic power generation servo system was designed by introducing external disturbance. The control parameters and their studied ranges were as follows: The reference signals were o r 1 = sin (0.1 t) and o r 2 = cos (0.1 t) . The parameters of the virtual control law and the final control law were m 11 = 0.01 , m 22 = 0.01 , m 12 = 0.02 , m 13 = 0.02 , and m 23 = 0.02 . The time constant of the low-pass filter was ζ 12 = ζ 13 = ζ 22 = ζ 23 = 0.005 . The parameters of the parameter regulation law were ρ 12 = ρ 13 = ρ 22 = ρ 23 = 0.0005 and a 12 = a 13 = 20 , a 22 = a 23 = 22 . The research results show that the MTE, RMSTE, and 2NTE scores of the height angle servo motor of the DANNDS control method were 0.0026, 7.0279 × 10−4, and 0.3552, respectively. The scores for each index of the azimuth servo motor were 0.0028, 8.9237 × 10−4, and 0.4511, respectively. The height angle tracking error for the DANNQC control method was [−0.02,0.022]. The azimuth tracking error was [−0.03,0.03]. In summary, the photovoltaic power generation servo system based on the DANNQC has a better control performance. By controlling the height angle and azimuth angles, it can better track the position of the sun and adjust the position of the photovoltaic panel in real time. The sun's rays illuminate the photovoltaic panel at an appropriate angle to achieve maximum power generation efficiency, which is of great practical significance for the development of solar technology. [ABSTRACT FROM AUTHOR]

A concentrating photovoltaic/thermal (PV/T) integration device is normally confined to the mutual restriction of sun tracking accuracy and tracking range. In this study, a novel high-efficient solar tracking controller with dual-sensor joint control was designed to optimize the electrical and thermal performance of the PV/T system. The rough surface plane of glass was introduced to the PV/T integration device, where the sunlight was reflected to the concentrating cell components for multiple times concentrating, thereby increasing the power generation of solar cell per unit area. Part of concentrated light and heat was still collected, although the flowing water was cooling through the cells in the PV/T panel. A plurality of light-gathering units were fixed on the long axis of azimuth drive, while connected through the height angle linkage frame, for the tracking of sun azimuth via the azimuth push rod. A height angle push rod was used to push the parallelogram connecting rod for the height angle of the sun. The specific procedure was: 1) A power conditioning circuit was used to amplify small piece signals of the solar cell, and then to convert into a standard signal for a photoelectric sensor. Two sensors were used on the east and west frame to capture the azimuth angle of the sun whereas another two sensors were on the north and south for the altitude angle of the sun. Four photoelectric sensors were installed at the top of the light-concentrating frame on the side of a rectangular parallelepiped to form a rough tracking photoelectric sensor, where the condensed light spot was collected from the reflection of the plane mirror. Another four sensors were installed at the four corners of light-concentrating cell assembly for a fine tracking photoelectric sensor. 2) A single chip of Atmega 32 was selected as the core control circuit. The rough tracking photoelectric sensor was used to capture the position of the sun in a large range whereas the fine tracking photoelectric sensor was utilized to precisely locate the condensing spot on the entire photovoltaic panel. 3) A coordinated and stable performance of dual photoelectric sensors was achieved after the optimization of the tracking control strategy. Specifically, two parameters were optimized, including the tracking accuracy of the spotlight, and the driving frequency of the push rod. The angle sensors were installed on the azimuth and altitude drive shafts, where the azimuth and altitude tracking of the sun were collected per minute. A comparison was made on the theoretical and astronomical data. Furthermore, a dual photoelectric tracking sun controller for PV/T integration device was also tested in Xiamen City of South China (118.09°E, 24.56°N) on July 11, 2018. An optimal combination was obtained, where the sunlight was captured from 0°-180° with a tracking error of less than 0.5°. The movement of the angle push rod was slow-fast-slow, and the running time interval of the height angle push rod was always maintained at 4-5 min/time. The PV/T integration device generated 3.94 kWh on the same day, and the working temperature of concentrating cell module was between 53℃ -60℃. Light and heat of 18.25 MJ (5.07 kWh) were collected, 1.29 times of photovoltaic effect. [ABSTRACT FROM AUTHOR]

Soft-soil surface in a paddy field is usually left with unevenly distributed rice stalks, uncut stubbles, and machine ruts after harvesting by combines. A common treatment of rice straws is rotary burying using tractor-hitched rotary cultivators in southern China. However, misses and overlaps inevitably occur, because human tractor drivers mainly perform the current operation under the complex conditions of field surface. It is greatly urgent to develop an automatic navigation system for better operational efficiency and accuracy. In this study, a Dongfanghong LX954 tractor with a hitched rotary straw returning cultivator was taken as the research object. An automatic navigation system was developed to replace the original tractor-rotary cultivator combined one, with a novel electronic proportional control and hydraulic steering. The main hardware of the navigation system included an onboard PC, an electronic-control proportional hydraulic valve, a steering angle sensor, and a dual-antenna RTK-GNSS. The navigation software was performed on the MATLAB platform in the Windows 7 operating system. The RTK-GNSS was used to measure the real-time lateral deviations and orientation errors of the tractor. The onboard PC was used to plan the operation paths, process the GNSS measurement data, calculate the steering angles using the designed control algorithms, and finally send control commands to the steering controller. Specifically, the steering controller was used to receive the control commands through a CAN bus, thereby controlling the opening and closing of the electronic-control proportional hydraulic valve, and finally realizing automatic navigation. An estimation model of slip angle was derived using the steering angle control of the tractor, and a variable gain single-neuron PID controller was designed, in order to reduce the great slippages from the complex conditions of field surface during rotary burying of straws. The slip angles of the front-wheel and rear-wheel were added into the model to accurately calculate the control angle more suitable for the actual operation. The variable gain single-neuron PID controller output the values of steering angle by learning the previous control effects from the neuron and the adjusted gain, according to the differences between the current heading and the target heading. As such, the navigation control rapidly adapted to the subsequent soil surface in the field. MATLAB simulations were carried out to compare the control effects of a conventional, a single-neuron, and the variable gain single-neuron PID controller. The results showed that the variable gain single-neuron PID controller behaved the fastest convergence, the smallest overshoot, and the best performance of signal tracking. A road test was performed to further verify the feasibility of the designed navigation system. When the operation speed was about 2 m/s, the variable gain single-neuron PID effectively improved the performance of path tracking, in terms of speed and accuracy for both straight path and curve tracking. Furthermore, the maximum lateral deviation was 0.026 m during straight path tracking. In the curve tracking for a half circle with a diameter of 12 m, the maximum lateral deviation was 0.382 m and the mean was 0.170 m. A field experiment was performed on the rotary straw returning under the designed navigation system. In straight path tracking, the maximum lateral deviation was 0.071 m, and the mean was 0.031 m when the operation speed was 1.15 m/s. Compared with the traditional and single-neuron PID controllers, the control accuracy of the maximum error and mean absolute error were improved by 53.08% and 51.72% respectively, and compared with single neuron PID controller, the control accuracy of the maximum error and mean absolute error were improved by 39.00% and 28.21% respectively, the developed navigation controller can significantly reduce the interference from the various soft-soil surface in a paddy field, thereby enhancing the adaptability and robustness of the navigation system, particularly with the higher accuracy of path tracking. [ABSTRACT FROM AUTHOR]

Copyright of Turismo: Visão e Ação is the property of Turismo-Visao E Acao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)