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AP3B2 is one of the subunits of vesicle coat protein AP3 and is specifically expressed in central nervous system neurons. AP3B2 antibody has been reported in patients with autoimmune cerebellar ataxia and various extracerebellar symptoms. However, there have been few reports on its clinical features and treatment response. We report a 47-year-old man with AP3B2 antibody who presented with insidious-onset paresthesia and gait disturbance. His serum and cerebrospinal fluid (CSF) showed reactivity with the cytoplasm of Purkinje cells and granular layer synapses comparable to the reported specific pattern of anti-AP3B2 IgG, and this was confirmed by a cell-based assay. His symptoms improved after the administration of intravenous immunoglobulin, and oral prednisone and mycophenolate mofetil. Extensive examination and long-term follow-up showed no evidence of malignancy. A literature review was included to emphasize the neurological syndrome associated with this rare autoantibody. Eleven cases with AP3B2 antibody, including our patient, were identified. The diversity of symptoms, including cerebellar and sensory ataxia, paresthesia, and weakness, was in line with the extensive binding of AP3B2 antibody to the spinal cord gray matter, dorsal root ganglia, cerebellar cortex, and nucleus. In the CSF, half of patients had elevated white blood cell counts, increased protein concentrations, or CSF-specific oligoclonal bands. All previous cases had subacute onsets and no improvement was noted after immunotherapy. Our case indicated that disorders associated with AP3B2 antibody can also start insidiously. Immunotherapy is warranted given the possibility of clinical improvement.

The output stability of the photovoltaic (PV) system is directly affected by temperature change of PV panels. In this paper, a novel temperature prediction method of PV panels with support vector machine (SVM) is proposed, which can solve the temperature prediction problem in a complex environment. In order to optimize parameters of SVM, a Pigeon-Inspired Optimization (PIO) method is given. Meanwhile, the delay factor (DF) is added to improve the PIO algorithm for avoiding the problem of local optimum. Moreover, a multisensor monitoring system of PV is established, and the collected data of temperature are used to train and verify the accuracy of the model. Finally, the proposed method is evaluated using synthetic and actual data sets. Simulation results show that the DFPIO-SVM can obtain better predictive performance.

For the nonlinear systems with delay output, the control performance of the system is affected by the previous output of the system, such as crawling robots of photovoltaic panels. In this paper, an improved controller design method based on echo state network with delay output (DO-ESN) is proposed for designing the controller of a class of nonlinear system. According to the internal characteristics of DO-ESN, the DO-ESN can match the system characteristics of nonlinear systems with delay output, such that the proposed controller can quickly meet the control performance of the nonlinear system. In order to ensure the stability of the controller, a sufficient condition is given for the echo state property of DO-ESN. Finally, a simulation example is used to illustrate the effectiveness of the proposed method.

The power generation efficiency and production capacity of photovoltaic power plants are seriously affected by dust on the surface of photovoltaic panels. To solve the above problem, a simulation for dust accumulation based on the discrete element method (DEM) is researched in this paper. Firstly, a contact model is established and the contact between dust particles and photovoltaic panels is simulated. Then, a Hertz-Mindlin with JKR Cohesion model suitable for dust distribution on the surface of photovoltaic panels is proposed to simulate different working conditions, for example, different inclination angles and different flow fields. Finally, the simulation results show that the proposed method can effectively predict the dust accumulation process of photovoltaic panels, and photovoltaic panels have less dust accumulation when inclination angle is 34 degrees. The proposed model can provide a theoretical reference for the operation and maintenance of photovoltaic panels.

In order to achieve a highly automated and low-cost solution to replace regular manual cleaning and improve the economic benefits of distributed photovoltaic (PV) system, an intelligent operation and maintenance cleaning robot and cleaning cycle optimization method was developed in this paper. The three-dimensional model of the moving vehicle, cleaning roller and control system of the cleaning robot are designed. And then the cleaning cycle optimization model of dust on PV modules was built based on the developed cleaning robot. Cleaning test and optimization results showed that using the robot to clean PV modules with dust accumulation of 4 g/m2, 6 g/m2 and 8 g/m2, and the cleaning efficiency reached 92.14%, 91.04%, and 90.09%, respectively. Compared with manual cleaning, the optimal cleaning cycle was reduced from 48.3 days to 26.5 days, and the cleaning cost was reduced from 223200 yuan to 30600 yuan for 2MW distributed PV system. The cleaning cost is greatly reduced, while the cleaning frequency is increased, and the power generation is improved.

In this paper, considering the idea of the master-slave structure, a new classification method based on master-slave residual network is proposed to classify the degree of dust accumulation on the photovoltaic (PV) panels. Through training the master-slave residual network, the structure parameters of residual network can be determined, such that the proposed classification method can obtain the better classification accuracy for different learning tasks. In order to determine the structure parameters of residual network, four learning algorithms are given to determine the input type, the number of convolution layers, weight learning method and the pooling method, respectively. In order to verify the accuracy of the classification method, an image dataset for different degree of dust accumulation is collected by artificial simulation experiment, and thus, the simulation results show the effectiveness of this proposed method.

Uneven dust accumulation can significantly influence the thermal balance between different regions of photovoltaic (PV) panels, leading to a sharp decrease in power generation efficiency and service life. In this paper, a new identification method for uneven dust accumulation on the surface of PV panels is developed to analyze the dust state (concentration and distribution) quantitatively. First, a novel deep residual neural network (DRNN) is proposed to obtain the regional dust concentration. The residual elements in the model are connected in the form of skipping layers to reduce the order of the weight matrices and improve the network flexibility and the feature learning accuracy. Second, an image preprocessing method is designed to classify the dust accumulation. It includes transformation and correction, removal of the silver grid, nonlinear interpolation, equivalent segmentation, and clustering. A new error evaluation method, error loop, is proposed to analyze the consistency between the measured and the experimental results. The results show that the DRNN has better prediction accuracy than other methods. The R2 and mean absolute error (MAE) of the DRNN are 78.7% and 3.67, respectively. In addition, three conditions are used to verify the performance of the identification method for determining uneven dust accumulation. The average evaluation coefficients of the error loop are 1.19, 0.77, and 1.10, respectively, meeting the design requirements. The proposed method can provide theoretical support for the intelligent operation and maintenance of PV systems.

Photovoltaic (PV) power generation has become one of the major energy in all the world. If the dust accumulation of PV power station is not cleaned in time, the production cost will be increased in power station. This paper seeks to develop a optimization model of cleaning to inform economic operation decision for PV power station. By setting the goal of the maximum output power and the minimum economic loss, a optimization method based on Adaptive Dynamic Programming (ADP) is proposed. A new cleaning strategy is proposed in this paper, and the simulation results show the effectiveness of this method. Taking a 12WM PV power station in western Jilin Province in China as an example, the total maintenance cost of cleaning every 17 days is the lowest.

A novel analysis method for energy efficiency loss is proposed in this paper, which is used to evaluate the effect of dust accumulation in PV system, quantitatively. Considering the nonlinear power generation characteristic of PV panels at low irradiance (below 200–300 W/m2), the coupling model of dust concentration and photoelectric conversion efficiency (DC-PCE) is established. To determine the selection range of unknown parameters (saturation irradiance, influence coefficient of dust accumulation) in the model, a PV power generation experiment with six representative dust pollutants (ash, laterite, stone dust, sandy, coal powder and cement) is designed. According to experimental fitting data, the saturation irradiance and corresponding standard deviation of influence coefficient for the six pollutants are given. Eventually, the effectiveness of the model is verified by using ash in natural conditions. Results show that the proposed model has excellent generalization ability and the accuracy is 83.12%. It offers a simple and reliable tool for evaluating the degree of dust accumulation on site.

With the reliability evaluation background of power electronics devices, the one-dimensional thermal network method was studied to obtain the operating junction temperature of press-pack IGBT (insulated gate bipolar transistor) devices. Based on the Cauer model, an improved method was proposed for the extraction of radiator parameters and heat conductivity area equivalent factor. The IGBT module branch was simplified into a one-dimensional geometric model through assumed conditions. The theoretical basis of radiator parameter extraction was explained as well as the fitting expression of heat conductivity area equivalent factor was obtained. Finally, the proposed method was compared with the finite element method. The results show that the proposed parameter extraction method can calculate fast and accurately.