2993 results about "Photovoltaic system" patented technology

The invention relates to the technical field of energy management, and discloses a multi-source photovoltaic energy storage collaborative management method and system. The method comprises the steps of obtaining a multi-source photovoltaic system data generation task set; constructing a multi-dimensional data fusion model, and defining a collaborative optimization coordinate system; determining an initial cooperative constraint condition; co-scheduling simulation is executed based on the model to generate a preliminary strategy; and dynamically correcting and generating a multi-source collaborative optimization strategy by using a self-adaptive optimization algorithm. The method also relates to refining processing of each axis in the model, adoption of an improved algorithm optimization strategy, a calculation framework and a fault-tolerant mechanism in simulation, anomaly detection and re-optimization, construction of a scene library for predicting conflicts and the like. The system comprises a data acquisition and classification module, a model construction module, a constraint setting module, a simulation scheduling module and a strategy optimization module. The energy utilization efficiency of the multi-source photovoltaic energy storage system can be effectively improved, stable operation of the system is guaranteed, and equipment loss and cost are reduced.

An optical storage system designed to compensate for the water hammer effect in hydropower turbines and its cooperative frequency modulation method are provided. The method involves detecting the water hammer effect and utilizing a pre-established system model integrating a hydropower unit, photovoltaic system, and hybrid energy storage system. Through model predictive control combined with a whale optimization algorithm, the governor parameters of the hydropower unit are optimized. The system then obtains the photovoltaic active power output and the battery's state of charge (SOC) within the hybrid energy storage system. Based on these parameters, a collaborative control strategy for the photovoltaic and hybrid energy storage systems is determined and executed. This strategy enables effective control of the photovoltaic system and / or hybrid energy storage to compensate for reverse power adjustments in the hydropower unit, addressing the water hammer effect and ensuring stable power grid operation.

The invention aims to provide an intelligent diagnosis method and system for a temporarily-built photovoltaic fault based on a knowledge graph, and belongs to the technical field of temporarily-built photovoltaic fault diagnos.The method comprises the steps that firstly, current, voltage, temperature and irradiance data of a temporarily-built photovoltaic system are collected, and multi-dimensional feature parameters are generated through abnormal value elimination, noise filtering and feature fusion; secondly, constructing a temporarily built photovoltaic knowledge graph containing equipment entity nodes, fault mode nodes and a topological connection relation, and calculating a topological connection weight based on historical fault data and an equipment rated power difference value; then, the multi-dimensional characteristic parameters are matched with fault mode nodes of the knowledge graph, fault propagation path reasoning is carried out through bidirectional breadth-first search and path weight screening, and fault types and positioning information are output; and finally, dynamically updating the association weight of the knowledge graph according to operation and maintenance feedback. The rapid and accurate diagnosis of the fault is realized, the downtime is obviously reduced, and the operation efficiency and the economic benefit of the temporarily built photovoltaic system are improved.

The invention discloses a self-adaptive photovoltaic tracking control method and system, and the method comprises the steps: collecting and fusing multi-source data, detecting the operation environment of a photovoltaic system in real time, and carrying out the real-time processing of the collected environment data; constructing a self-adaptive photovoltaic tracking control model, training the self-adaptive photovoltaic tracking control model, and dynamically adjusting a tracking strategy of the photovoltaic panel according to historical data and real-time environment change; a dynamic error compensation mechanism is introduced, errors of a tracking system are corrected in real time, a multi-objective optimization strategy is designed, and tracking modes are automatically switched according to different environmental conditions. The system comprises a multi-source data acquisition module, a photovoltaic tracking control module and a tracking mode switching module. According to the invention, the precision and efficiency of operation monitoring of the photovoltaic system are improved; high-quality data input is provided for a photovoltaic tracking control model, and the decision-making ability of the model is improved; the intelligent and automatic photovoltaic panel tracking strategy is realized, and the power generation efficiency of a photovoltaic system is improved.

A fault alarm device for solar power generation belongs to the field of solar fault monitoring. The system includes an edge monitoring module, a multimodal data fusion module, a fault classification module, a trend prediction module, a dynamic adjustment module and an alarm response module. The present invention improves the accuracy of fault identification by integrating multidimensional features such as photovoltaic panel current, voltage, power, ambient temperature and light intensity by using a deep learning fusion model. By introducing an attention mechanism and dynamically adjusting the alarm threshold, it effectively distinguishes environmental fluctuations from actual faults, reduces the false alarm rate and missed alarm rate, and can adapt to the complex and changeable photovoltaic system operating environment without manual intervention.

The invention relates to the technical field of photovoltaic systems, and particularly discloses a fault identification method and system for a photovoltaic system, and the method comprises the steps: collecting data in real time through environment, electrical parameters and an equipment state monitoring sensor, carrying out the cleaning and standardization, extracting time domain, frequency domain and time frequency features, and screening a feature subset through a correlation analysis and feature importance sorting algorithm; detecting and classifying faults by using a hybrid model composed of an isolated forest algorithm and a random forest classifier; positioning a fault subsystem and analyzing a root cause by means of a hierarchical diagnosis strategy, a graph neural network and a Bayesian reasoning algorithm; and periodically updating the model based on the new data. The method can quickly and accurately identify and position faults, adapts to a complex environment, reduces the operation and maintenance cost, and improves the operation reliability and operation and maintenance efficiency of a photovoltaic system.

A busbar-free electrode structure, IBC solar cell, and a photovoltaic system, used in the technical field of solar cells. Since first end welding points close to a third edge and disposed at a first break position are not connected to a first fine grid, but are electrically connected to a part of a second fine grid closest to the third edge, second fine grids close to a first edge can be connected together. On this basis, the first end welding points can be disposed, by means of the described electric connection manner, at a positions distant from the first edge of the silicon substrate, so that when a welding strip is subsequently welded to a first end welding point, because there is a relatively large distance between a disposed first end welding point and the first edge, the risk of hidden cracks during welding can be reduced, the cell yield can be improved, and overall convergence of the second fine grid can be achieved.

The APSO-SA-based photovoltaic system maximum power point tracking control method comprises the following steps: constructing a photovoltaic system model, and carrying out parameter and particle swarm initialization on a preset APSO-SA algorithm in MPPT control; voltage and current of the photovoltaic system model are collected in real time, the initialized APSO-SA algorithm is input for optimization iteration, and the optimal duty ratio is obtained; and adjusting the working state of the boost converter based on the optimal duty ratio, so that the photovoltaic system operates at the optimal duty ratio, and tracking of the maximum power point is realized. According to the method, the MPPT system can rapidly relocate the maximum power point when illumination suddenly changes, meanwhile, high-precision tracking is maintained in a steady state, and the energy capture efficiency of a photovoltaic system is remarkably improved.

The invention provides a renewable energy equipment fault intelligent diagnosis method based on deep learning. The method comprises the following steps: S1, collecting data of a photovoltaic system and the like, carrying out data preprocessing, and constructing a training sample set with a fault label; s2, constructing a fractional order physical information neural network in a weighted shift Grunwald-Letnikov format, and outputting an equipment fault probability prediction value; s3, establishing a segmented Poisson regression model, taking a time variable and an environment virtual variable as independent variables, performing dynamic trend modeling on the equipment fault probability predicted value, and outputting the fault probability after statistical correction; s4, fusing the equipment fault probability prediction value and the corrected fault probability to generate a final fault probability; and S5, optimizing prediction strategy parameters based on a reinforcement learning method of a relative reward regression mechanism. According to the invention, the identification capability of implicit fault features is improved, and the fault diagnosis accuracy is significantly improved.

The present application relates to a solar cell, a method for manufacturing the same, a photovoltaic module and a photovoltaic system. The solar cell includes: a substrate (110), including a first surface (S1) and a second surface (S2) being opposite to each other, wherein the first surface (S1) has a first region (A) and a second region (B) adjacent to each other in a first direction; a passivating contact layer (120), located in the first region (A) of the first surface (S1); a polysilicon layer (130) located on at least a part of a surface of the passivating contact layer (120) away from the substrate (110); the passivating contact layer (120) including a first tunneling layer (121) and a first doped layer (122), the first tunneling layer (121) and the first doped layer (122) being sequentially stacked on the first region (A) of the first surface (S1) of the substrate (110) in a direction away from the second surface (S2); and a first passivation layer (140), located on a surface of the polysilicon layer (130) away from the passivating contact layer (120) and on the second region (B) of the first surface (S1).

The invention relates to the technical field of back contact cells, and particularly discloses a back contact cell and a preparation method thereof, and a photovoltaic system, the back contact cell comprises a silicon substrate, and the backlight surface of the silicon substrate is provided with a plurality of first areas and second areas which are alternately arranged; the first semiconductor composite layer is arranged in the first region and comprises a tunneling passivation layer and a first doping layer which are sequentially arranged in the direction away from the silicon substrate; the second semiconductor composite layer is at least partially arranged in the second area and comprises a passivation contact layer and a second doping layer which are sequentially arranged in the direction away from the silicon substrate, and the polarity of the second doping layer is opposite to that of the first doping layer; the composite TCO layer is arranged in at least partial areas of the sides, back to the silicon substrate, of the first doping layer and the second doping layer, the composite TCO layer comprises a first TCO layer and a second TCO layer which are sequentially arranged in the direction away from the silicon substrate, a plurality of holes are distributed in the first TCO layer, and the holes are provided with openings at least in the side, away from the second TCO layer, of the first TCO layer. According to the back contact battery structure, the back reflection effect on light can be effectively improved, the light utilization rate is improved, and then the battery conversion efficiency is improved.

The invention provides a photovoltaic system-based generating capacity loss intelligent evaluation method, which comprises the following steps of: 1, acquiring optical parameters in real time, and synchronously acquiring predicted irradiation data of a meteorological station; step 2, performing feature extraction on the current and voltage curve, identifying abnormal inflection points in the curve, dividing mismatch types, determining theoretical output power of each group of strings, and generating a future theoretical generating capacity baseline; 3, obtaining a group cascade stage power loss rate, and determining a current loss reason in combination with a mismatch type; 4, identifying a current system-level fault area; and step 5, based on a historical fault case library, extracting a group cascade power loss rate, a mismatch type and spatial distribution characteristics of a system-level fault area, matching a loss mode of a current system with a historical fault type, generating an evaluation report including a loss quantized value, a fault position and a repair priority, and carrying out fault recovery. And an optimization operation and maintenance instruction including a personnel scheduling scheme and a spare part demand list is generated. And the operation efficiency and the maintenance effect of the photovoltaic system are improved.

The invention discloses a micro-grid power control method, system and device based on fuzzy modulation and a storage medium, and relates to the technical field of intelligent grid integration of a photovoltaic system and a micro-grid, and the method comprises the steps: firstly obtaining the real-time power data of a photovoltaic system, carrying out the control processing through fuzzy logic, and calculating a reference instruction; according to the reference instruction, hysteresis time is dynamically adjusted through modulation hysteresis control, and an inverter switching signal is generated; converting direct current into alternating current based on the switching signal and a power switching device, injecting the alternating current into the micro-grid, calculating active and reactive power reference values, ensuring that the system operates according to a unit power factor, and giving active power to the reference value; according to the method, the power control precision and adaptability of the micro-grid are improved, excessive switching of the inverter is avoided, loss is reduced, the service life is prolonged, power matching and stable transmission are achieved, reactive loss is reduced, bus voltage is stabilized, harmonic waves can be reduced, the electric energy quality is improved, and efficient and stable operation of the micro-grid is guaranteed.

The invention discloses a wind and light storage coordination control method and system for a virtual synchronous machine, and the method comprises the steps: building a wind power plant-energy storage power station double-layer response mechanism at a first stage, and employing a multi-time scale coordination control strategy for a second-stage energy storage system; and in combination with the virtual synchronous control characteristics of the optical storage power station, establishing a multi-scene optimization model of a wind and light storage multi-source collaborative double-layer coupling mechanism, and realizing dynamic optimization of output of each unit among inertia support, frequency modulation capacity and energy storage health state. According to the method, wind power plant rotor kinetic energy release combined overspeed load shedding control, a fuzzy control means based on an energy storage charge state and a VSG control means based on a virtual inertia and damping coefficient self-adaption photovoltaic system are considered, then wind and light energy storage frequency modulation coordination is optimized, and the system inertia safety is effectively improved on various working conditions and different time scales.

The invention discloses a photovoltaic module performance degradation rate prediction method based on environmental influence factors. The method comprises the steps of building a small photovoltaic system, and obtaining an initial photovoltaic module performance degradation condition; the photovoltaic module is monitored, and data of performance change of the photovoltaic module under different environmental factors are collected respectively; obtaining a preliminary photovoltaic module performance attenuation prediction model through the coupling effect of various environmental factors; based on the preliminary photovoltaic module performance degradation prediction model, coupling various environmental factor influence data to obtain a complete photovoltaic module performance degradation prediction model; and based on the complete photovoltaic module performance attenuation prediction model, speculating and calculating performance attenuation conditions of the photovoltaic module operating in different environments. The method not only has academic research value, but also can calculate and design the service life of the photovoltaic module in each region according to meteorological data.

An inertia support control method and apparatus for a grid-forming photovoltaic system. When a grid-forming photovoltaic system needs to be started, a VSC inversion unit can be locked, and a DC / DC boost circuit is switched to a charging mode; real-time monitoring is then performed on a direct-current bus voltage, and when the duration during which the direct-current bus voltage remains at a preset target voltage is greater than a preset target duration, the inversion unit is started, and the boost circuit is switched to a maximum power tracking mode; then, a dispatching requirement of the photovoltaic system can be acquired; and the maximum power tracking mode of the boost circuit is maintained when the dispatching requirement is that power generation takes priority, and the boost circuit is switched to a reserved active mode when the dispatching requirement is that inertia support takes priority. A VSC inversion unit and a DC / DC boost circuit are used to control a photovoltaic system, so that flexible inertia support can be realized on the basis of improving the system stability under weak power grid conditions, thereby improving the stability and reliability of a power system.

The invention relates to the technical field of power quality monitoring, and discloses a method for identifying power quality disturbance characteristics of a distributed photovoltaic interference power supply, which comprises the following steps: S1, data acquisition and preprocessing: acquiring a power quality signal of a photovoltaic system and carrying out denoising, normalization and time alignment; and S2, carrying out disturbance signal adaptive decomposition based on improved variational mode decomposition, and automatically selecting a mode number by utilizing information geometric optimization. An improved variational mode decomposition method is adopted, information geometric optimization and Bayesian optimization are combined, self-adaptive mode decomposition of disturbance signals is achieved, the optimal mode number and penalty factors can be automatically determined through improved variational mode decomposition, decomposition errors caused by manual parameter setting in traditional variational mode decomposition are avoided, and the method is suitable for large-scale popularization and application. Compared with a fixed parameter variational mode decomposition method in the prior art, the method has the advantages that the analysis capability of complex disturbance signals is improved, and the disturbance mode in the photovoltaic system is accurately decomposed.

The invention relates to the technical field of photovoltaic intelligent operation and maintenance, and discloses a distributed photovoltaic intelligent operation and maintenance system. The method comprises the following steps: acquiring operation parameters such as voltage output, current intensity, temperature and ambient light of a plurality of photovoltaic units in real time; classifying the performance state of each photovoltaic unit based on the parameters, and identifying an efficient operation area, a performance attenuation area and a potential fault area; secondly, extracting rules from the maintenance strategy database, and constructing a mapping relation between the performance partition and the maintenance strategy; then determining a key partition according to the partition priority weight and the system impact factor, and optimizing the matching degree of the maintenance strategy combination; then, the real-time operation state of the system is checked according to the optimized combination, the maintenance demand change is recognized, and the mapping relation is updated; and finally, determining a maintenance target and an execution sequence according to the updated mapping relationship, and generating a structured operation and maintenance management library to guide subsequent maintenance activities. The method can improve the operation and maintenance efficiency and accuracy, and guarantees the stable and efficient operation of the photovoltaic system.

The embodiment of the present application provides a photovoltaic module and a photovoltaic system. The photovoltaic module includes a battery module; the battery module includes a plurality of battery strings, the battery string includes a battery string, the interconnection bar in the battery string connects the plurality of battery cells in series by welding and extends from the end of the battery string; the first collector electrode and the second collector electrode of the battery cell are both located on the back of the substrate; an intermediate bus bar is provided between the first battery string group and the second battery string group in the battery string group, the intermediate bus bar is electrically connected to the interconnection bar extending from the end of the battery string, and the intermediate bus bar has a bending portion; the adjacent interconnection bars on the same battery cell are respectively connected to the collector electrodes of different polarities and the spacing between the adjacent interconnection bars is L, the minimum distance between an interconnection bar adjacent to the bending portion and the bending portion is D1, and the photovoltaic module satisfies: 3mm<D1<13mm; or, L<D1<2L. The embodiment of the present application can reduce the risk of cold welding and short circuit of the photovoltaic module.

The invention discloses a photovoltaic arc detection method and system based on a neural network. The method comprises the following steps: generating a data set; preprocessing the data set to obtain a training data set; a hybrid architecture detection model combining a CNN model and a Transform model is constructed, the CNN model processes the voltage time sequence data to extract feature information, the Transform model fuses the feature information at each moment to extract fused feature information, and a predicted value is calculated to adjust parameters of the hybrid architecture detection model to obtain an arc detection model; sampling voltage time sequence data of the photovoltaic system are collected and imported into the arc detection model, and the arc detection model detects whether an arc occurs or not and outputs detection information. According to the method, the accuracy of arc fault detection in the photovoltaic system is greatly improved by fusing the convolutional neural network CNN and the Transform technology, and the safety and stability of operation of the photovoltaic system are improved. The key features of the arc fault can be accurately captured from the complex electric signal, so that the detection is more efficient and stable.

The invention relates to the technical field of intelligent building energy management, in particular to a power management optimization method based on an intelligent building green energy system, which is used for solving the problem of energy supply and demand imbalance caused by high time-varying characteristics of photovoltaic output and load demand in an intelligent building. The method comprises the following steps: firstly, establishing models of an intelligent building energy system, including a photovoltaic system, an energy storage system, a controllable load and a power grid interaction model; performing time-frequency domain analysis on photovoltaic output and load demand by using short-time Fourier transform, and decomposing a time sequence into a trend term, a periodic term and a random fluctuation term; a multi-time scale prediction model is constructed based on the decomposition result, and different prediction methods are adopted for different frequency domain features; and a hierarchical model prediction control framework including a day-ahead planning layer, an hour-level economic dispatching layer and a minute-level real-time control layer is further established, and multi-time-scale coordination control is realized.

The invention relates to the technical field of photovoltaic equipment, and discloses a cable-supported marine photovoltaic floating system, which comprises floating units, a cable net structure, a frame structure and a mooring structure, the cable net structure comprises longitudinal cables and transverse cables, the longitudinal cables and the transverse cables intersect to form a grid shape, and the longitudinal cables and the transverse cables intersect and are fixed in the floating units; the outer ends of the longitudinal cables and the outer ends of the transverse cables are connected with the frame structure, the frame structure is connected with the mooring structure, and the mooring structure is used for keeping pretension of the transverse cables and the longitudinal cables. According to the scheme, the longitudinal cables and the transverse cables are stressed in a face mode, a traditional point stress mode is replaced, external force can be dispersed more evenly, the local damage risk is reduced, and high fatigue resistance is achieved; the longitudinal cables and the transverse cables are fixed through the floating units, relative displacement of the longitudinal cables and the transverse cables is avoided, the integrity of the cable net structure is guaranteed, the frame structure and the mooring structure fluctuate up and down along with stormy waves and adapt to dynamic changes of the cable net structure, and the stability and durability of the whole floating type offshore photovoltaic system in the complex marine environment are guaranteed.

The present invention provides a distributed photovoltaic power prediction method and system, which relates to the photovoltaic power prediction technology field. The specific scheme is as follows: obtaining and preprocessing known distributed photovoltaic system data to obtain photovoltaic data; building a multi-head time series model, building an initial model and training according to photovoltaic data to obtain a prediction model and training error data, and fitting the multi-head time series model with the prediction model to obtain a hybrid prediction model; building a correction model and using the training error data to obtain an error correction model; obtaining data to be predicted, assigning weights to the data to be predicted based on the hybrid prediction model, extracting features to obtain time series features, and predicting to obtain preliminary prediction results and their prediction error sequences; fitting the prediction error sequence based on the error correction model and correcting the preliminary prediction results to obtain photovoltaic power prediction values. The present invention improves the shortcomings of the prior art in the accuracy of photovoltaic power prediction, and improves the prediction accuracy and adaptability of the model.

The invention relates to the technical field of building energy conservation, in particular to an edge computing cooperative communication method for multiple heat pumps and a photovoltaic system, which comprises an edge server, a cloud server, multiple heat pumps, a variable frequency pump and an indoor and outdoor heat supply device, and is characterized in that the edge server realizes time synchronization of equipment through an OPC UA over TCSN protocol, a multi-level energy topology network is established, and the multi-level energy topology network is connected with the cloud server; in combination with physical energy flow and information flow, based on a building heat balance equation, an edge server controls a heat pump to work at an energy-saving operation point according to indoor and outdoor temperatures and user requirements, and the flow of a variable frequency pump is adjusted. In addition, multi-mode state sensing is achieved through the distributed sensor network, a layered self-adaptive control strategy is executed, photo-thermal collaborative optimization and heat pump group collaborative control are achieved, the system collaborative control precision is remarkably improved through the method, the time synchronization error is reduced by three orders of magnitude, and the system response speed reaches the millisecond level and is 10 times faster than that of a traditional system.

The invention provides a photovoltaic module support amplitude stabilizing device, which relates to the technical field of photovoltaic equipment and comprises a support amplitude stabilizing execution module, a data acquisition module, a windproof control type determination module and a windproof control module. By constructing a three-level windproof control strategy system, accurate response to different wind levels is achieved, a first-level windproof control strategy is adapted to the vibration frequency and amplitude of a support in real time by dynamically adjusting the damping coefficient of a damping shock absorber, and a second-level windproof control strategy is adapted to the length of a windproof hydraulic cylinder based on wind speed and wind direction data. According to the three-level windproof control strategy, when strong wind exceeds a mechanical counteracting range, the dip angle of the photovoltaic panel is adjusted to a safe and efficient conversion angle, the wind resistance and the light energy utilization rate of the structure are balanced, and the wind energy utilization rate of the photovoltaic panel is increased. And the reliability and the economical efficiency of the photovoltaic system under the severe weather condition are improved.

The invention discloses a self-cleaning photovoltaic super-hydrophobic coating based on a nano-porous structure and a preparation method of the self-cleaning photovoltaic super-hydrophobic coating, and belongs to the technical field of photovoltaic panel coatings. The coating comprises the following components in percentage by mass: 15-25% of nano porous silicon dioxide particles, 5-10% of a low surface energy modifier, 3-8% of photocatalytic nano titanium dioxide, 30-40% of organic silicon resin and the balance of deionized water. The coating has super-hydrophobicity, photocatalytic decomposition of organic matters and wear resistance, can solve the problems of photovoltaic panel dust accumulation, bird droppings, organic matter pollution and the like, and improves the power generation efficiency of a photovoltaic system.

The invention relates to the technical field of encrypted transmission, in particular to an encrypted transmission method and system for distributed photovoltaic operation and maintenance data. The method comprises the following steps: installing edge computing equipment at each node of a distributed photovoltaic system, and collecting photovoltaic operation and maintenance data in real time through a photovoltaic sensor; on the edge computing device, photovoltaic operation and maintenance data of the multiple photovoltaic sensors are aggregated and preprocessed; establishing encryption connection through an encryption transmission protocol TLS and a network protocol HTTPS; based on the encrypted connection, the edge computing device encrypts the aggregated photovoltaic operation and maintenance data by using a symmetric encryption AES algorithm and an asymmetric encryption RSA algorithm; and through the SD-WAN technology, the encrypted photovoltaic operation and maintenance data are transmitted through different network paths. According to the design of the invention, an advanced encryption technology (such as TLS, AES and RSA) is adopted to ensure that the photovoltaic operation and maintenance data is not stolen or tampered in the transmission process from the edge computing device to the cloud platform.

The invention relates to the technical field of agricultural management, in particular to a tea garden temperature and humidity regulation and control method based on photovoltaic light supplement. The method comprises the following steps: acquiring temperature and humidity data of a tea garden; obtaining tea tree distribution data, and obtaining a tea tree temperature and humidity environment model according to the tea tree distribution data and the tea garden temperature and humidity data; obtaining photovoltaic parameter data, and performing photovoltaic light supplement simulation according to the photovoltaic parameter data and the tea tree temperature and humidity environment model to obtain tea tree temperature and humidity photovoltaic light supplement data; performing temperature and humidity light supplement coupling relation calculation according to the tea tree temperature and humidity photovoltaic light supplement data to obtain temperature and humidity light supplement coupling relation data; and obtaining tea tree variety data, and performing light supplement effect optimization matching according to the temperature and humidity light supplement coupling relation data and the tea tree variety data to obtain light supplement effect optimization data. The light supplementing intensity and duration can be dynamically adjusted according to environment changes and tea tree growth requirements, and the utilization efficiency and the environment regulation and control effect of a photovoltaic system are improved.

The invention relates to the technical field of building structure reinforcement, and discloses a calculation method for increasing photovoltaic reinforcement on the roof of an existing building, which comprises the following steps of: constructing a load distribution model containing a vertical load layer (self weight of a photovoltaic module and accumulated snow load) and a horizontal load layer (wind load and earthquake load), and cooperatively transmitting the load through a node support system and an anchoring connecting piece; generating a roof reinforcement analysis model containing a supporting node model and a connecting component model; generating a reinforcement assembly sequence according to the photovoltaic system installation position distribution information and the reinforcement analysis model; generating a load distribution scheme based on the component sequence; and the distribution scheme and the reinforcement analysis model are combined, and the supporting node arrangement density and the connecting component material specification are determined through multi-objective optimization. According to the method, multi-load and structure aging are comprehensively considered, multi-target optimization is achieved, the safety and economical efficiency of the reinforcement scheme are improved, and the method is suitable for photovoltaic reinforcement calculation of the existing building roof.

The invention relates to the field of new energy, and discloses a photovoltaic system efficiency evaluation method and system based on unmanned aerial vehicle multi-source image fusion, and the method comprises the steps: collecting a visible light orthoimage, thermal imaging data and environment parameters of a photovoltaic system, and obtaining a registration result through employing an improved feature point matching algorithm; based on a registration result, combining spatial features of a visible light image and temperature features of thermal imaging, realizing accurate segmentation of the photovoltaic panel through a deep learning model, and identifying the type, the arrangement mode and the installation angle of the photovoltaic panel at the same time; establishing a mathematical model of the relation between the photovoltaic panel temperature distribution and the power generation efficiency, distinguishing the normal working temperature difference and the fault hot spot, and analyzing and determining the efficiency attenuation degree and the fault type of the photovoltaic system through a heat distribution mode. The method is accurate in image registration, good in data fusion effect, accurate in temperature anomaly detection and comprehensive in efficiency evaluation, and provides more efficient and accurate technical support for management and maintenance of the distributed photovoltaic system.