1901 results about "Photovoltaic power station" patented technology

The invention discloses a photovoltaic power station unmanned inspection method and system based on multi-mode fusion detection, and relates to the technical field of intelligent operation and maintenance of photovoltaic power stations. The method mainly comprises the following steps: (1) collecting multi-modal data of a photovoltaic module and carrying out preprocessing operation; (2) uniformly mapping the heterogeneous data into discrete marks by using each pre-trained modal data marker; (3) training a multi-modal fusion detection network based on a Transform encoder-decoder architecture by using the training sample, and adjusting network parameters to obtain a trained model; and (4) outputting a defect type, a position bounding box and a severity score by utilizing the trained model according to the obtained multi-modal data or single-modal data of the photovoltaic module. The problems that traditional single-mode detection is high in omission ratio and multi-mode fusion is low in efficiency are solved, the inspection collection module, the data processing module, the multi-mode fusion detection module and the fault decision module are provided in a targeted mode, and full-process automation from data collection to intelligent decision is achieved. The operation and maintenance cost of the photovoltaic power station is obviously reduced; and the fault response efficiency is improved.

The invention provides a method and system for monitoring the running state of a photovoltaic power station in real time, and relates to the technical field of photovoltaic power station monitoring, and the method comprises the steps: integrating a multi-mode sensor array in a photovoltaic module junction box, and collecting the sensor data of the photovoltaic power station in real time; performing localization preprocessing on the sensor data through an edge computing node, and transmitting different priority data to a cloud based on a dynamic hybrid communication protocol; sensor data are fused at the cloud, a four-dimensional digital twinborn model is constructed, and time-space continuous meteorological prediction, component aging and dust retention evolution data are fused; recognizing the fault mode of the photovoltaic power station, and dynamically optimizing the string topological structure of the photovoltaic power station in combination with the real-time operation state of the photovoltaic power station, the equipment health data and the fault mode recognition result; by integrating the distributed sensor, the edge calculation module and the intelligent alarm mechanism, the problems of delay, insufficient intelligent analysis and response lag in the prior art can be effectively solved.

The invention discloses a self-generating and self-using distributed photovoltaic power station power anti-countercurrent control method and system, and relates to the technical field of photovoltaic anti-countercurrent control, and the method comprises the steps: obtaining the real-time load power of a grid-connected point of a power grid and the output power of a photovoltaic inverter, and calculating the net load power deviation; when the net load power deviation is smaller than a preset countercurrent risk threshold value, it is judged that a countercurrent risk exists, and an anti-countercurrent adjusting instruction is generated; in response to the anti-countercurrent regulation instruction, dynamically correcting the maximum output power limit value of the photovoltaic inverter, and synchronously activating the charge and discharge compensation mode of the energy storage system; an anti-countercurrent control coefficient is calculated, and the power reduction proportion of the photovoltaic inverter and the compensation power of the energy storage system are synchronously adjusted based on the anti-countercurrent control coefficient, so that the power flow of the grid-connected point of the power grid is kept to be zero or forward flow; according to the method, dynamic and accurate cooperation of photovoltaic reduction and energy storage compensation power in time and magnitude is realized, and the efficiency of a self-generating and self-using distributed photovoltaic power station is improved.

The invention relates to the technical field of photovoltaic power station operation and maintenance, and discloses a photovoltaic power station intelligent operation and maintenance and fault early warning system. A multi-band image fusion algorithm and a temperature gradient enhancement algorithm are applied to identify composite defects; establishing a defect interaction influence model to analyze the synergistic effect of different defects; constructing a defect evolution prediction algorithm to predict a defect development trend; and developing a defect-performance mapping model and a processing priority algorithm to optimize a maintenance decision. According to the method, the defect detection sensitivity and accuracy are improved, accurate identification and evolution prediction of composite defects are realized, a prospective maintenance decision is effectively supported, the operation and maintenance efficiency of a photovoltaic power station is remarkably improved, the service life of the power station is prolonged, and the safety risk is reduced.

The invention provides an intelligent cleaning decision-making method and system for a photovoltaic power station based on machine learning, and relates to the technical field of photovoltaic power stations, and the method comprises the steps: collecting images through a visible light camera and an infrared thermal imaging camera carried by an unmanned plane, segmenting a pollution region based on color, texture and temperature features through a deep learning model after image registration and preprocessing, and obtaining a cleaning decision-making result; and calculating a pollution area proportion and establishing an evaluation index in combination with the temperature difference value so as to analyze the power generation efficiency loss and generate a pollution distribution map and an evaluation report. The pollution area can be accurately identified, the influence of the pollution degree on the power generation efficiency is quantitatively evaluated, and a decision basis is provided for power station cleaning maintenance.

The invention discloses a health degree evaluation system and method based on a photovoltaic string, and belongs to the technical field of photovoltaic operation and maintenance intellectualization, and the method comprises the steps: firstly collecting SCADA operation data of a plurality of strings of a photovoltaic power station, including voltage, current, assembly temperature, environment irradiance and power factors; secondly, constructing a whole-station feature reference model, and generating a state vector fusing environment and load characteristics through a nonlinear projection algorithm; aiming at the target group string, extracting a health reference track, calculating a disturbance propagation factor, and identifying a health abnormal state; when the deviation degree exceeds a threshold value, dynamically screening heterogeneous reference group strings, constructing a health score distribution model, and further calculating a confidence score and a prediction residual error; if the score is low and the residual error is unstable, determining that the string is in a sub-health or hidden fault state, and generating an intervention instruction; the method has high accuracy, strong adaptability and good interpretability, and can be widely applied to intelligent diagnosis and refined operation and maintenance management of the photovoltaic power station.

Provided in the present invention is a power prediction method for distributed photovoltaic power stations, the method comprising: acquiring a satellite cloud image of distributed photovoltaic power stations at a historical moment and a satellite cloud image of the distributed photovoltaic power stations at a current moment to predict satellite cloud image features at a future moment; selecting several photovoltaic power stations to install solar irradiance collection apparatuses, and on the basis of the satellite cloud images, collected solar irradiance, and location information of the photovoltaic power stations, calculating the solar irradiance of all the photovoltaic power stations; on the basis of the satellite cloud image features at the future moment and the collected solar irradiance of the photovoltaic power stations, obtaining the solar irradiance of the distributed photovoltaic power stations at the future moment; and on the basis of the solar irradiance of the distributed photovoltaic power stations at the future moment and weather forecast information at the future moment, predicting the power of the distributed photovoltaic power stations at the future moment. In the solution of the present invention, solar irradiance data is expanded, and a mapping relationship between satellite cloud images and solar irradiance and a mapping relationship between solar irradiance and power are constructed by means of prediction, so that the power of distributed photovoltaic power stations is predicted, thereby improving the prediction precision.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention belongs to the technical field of new energy power generation monitoring, and discloses a photovoltaic power station intelligent monitoring method and device, and the method comprises the steps: carrying out the feature extraction of a standardized multi-modal data set, constructing an attention weighting matrix, carrying out the weighted fusion of multi-modal features, and forming a multi-modal fusion feature vector; a hybrid prediction network of a residual connection structure and a bidirectional long-short-term memory network is adopted to predict the future operation state of the photovoltaic power station to obtain a predicted value, and real-time operation data are synchronously collected to obtain an actual value; by comparing the residual error of a predicted value and an actual value, identifying a potential abnormal mark and abnormal duration by adopting an adaptive dynamic threshold mechanism; and according to the amplitude, the change rate and the abnormal duration of the residual error, evaluating the severity of the fault, and generating early warning information. According to the method, the multi-modal data is collected and standardized, so that the data quality and consistency are effectively improved, and the reliability of subsequent analysis is ensured.

The invention provides a photovoltaic power station intelligent inspection task planning and scheduling method and system, and relates to the technical field of photovoltaic power stations, and the method comprises the steps: obtaining real-time monitoring data of edge calculation nodes to evaluate an equipment state, constructing an inspection task priority matrix, employing distributed edge calculation nodes to make a collaborative decision, and generating a global task distribution result, and dynamically optimizing an inspection task scheme based on robot state information, and continuously updating the priority matrix according to task execution state data. According to the invention, intelligent distribution and dynamic optimization of the inspection tasks of the photovoltaic power station are realized, and the inspection efficiency and the equipment operation and maintenance quality are improved.

The invention provides a photovoltaic power station energy efficiency evaluation method. A data acquisition layer module, a dynamic energy efficiency model module, an energy efficiency evaluation module and a fault positioning module are included. Wherein the dynamic energy efficiency model module establishes a theoretical power generation model based on an AI prediction technology, and the model dynamically updates model parameters according to real-time environment parameters and equipment states; the energy efficiency evaluation module calculates the actual energy efficiency according to the theoretical generating capacity predicted by the dynamic energy efficiency model and the actually collected inverter output power data, and performs energy efficiency loss grading; the fault positioning module discriminates an abnormal group string through a group string current dispersion rate and determines a hot spot position by means of an infrared image, relates to the technical field of photovoltaic power generation, and achieves precise and real-time power station energy efficiency management through fusion of dynamic environment correction, equipment health degree diagnosis and energy efficiency loss classification, thereby improving the efficiency of power station energy efficiency management. The problems that the evaluation deviation is large and the accuracy is reduced along with time in the existing evaluation technology are effectively solved.

The invention relates to the technical field of solar panel maintenance, in particular to a photovoltaic power station solar cell panel intelligent flushing system, which comprises an image analysis module, an intelligent control module, an image processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the image analysis module is used for monitoring smudginess data of the surface of a solar cell panel and transmitting the smudginess data to the intelligent control module in real time; the multispectral camera collects surface images of a current cell panel and a next cell panel, the surface images comprise cell panel surface images collected under different spectral bands or angles, and smudginess distribution and smudginess degree of the next cell panel are obtained; a cleaning strategy is generated according to the cleaning requirement through a preset intelligent algorithm, a control instruction is output and sent to the intelligent control module through the communication unit, and the intelligent control module adjusts the working state of the flushing device. According to the system, the precise recognition capability of the system on the dirty area is improved, it is ensured that water flow can effectively cover the part needing to be cleaned in the cleaning process, and waste of the non-dirty area is avoided.

Provided in the present invention are a power-distribution-network self-healing method and system taking photovoltaic output into consideration. The method comprises: acquiring historical operation data of a photovoltaic power station and irradiance observation data from a meteorological station; on the basis of the historical operation data of the photovoltaic power station and the irradiance observation data from the meteorological station, predicting the generated power of the photovoltaic power station by using a convolutional long-short-term memory recurrent neural network model that takes sparrow search into consideration; on the basis of the generated power of the photovoltaic power station, a segment-switch state of a power distribution network and a network topology of the power distribution network, constructing an objective function and a constraint condition for a power-distribution-network self-healing model, and obtaining the power-distribution-network self-healing model; solving the power-distribution-network self-healing model by using a propagation search algorithm, so as to obtain an optimal recovery strategy; and executing the optimal recovery strategy by means of segmented switches and node loads. The present invention can realize self-healing of a power distribution network while ensuring the minimum power generation cost of a distributed power source, the minimum network loss and the minimum node voltage deviation.

The invention provides a photovoltaic power station energy storage management system and method, and relates to the technical field of power system energy storage. The photovoltaic power station energy storage management system comprises the following modules: a data acquisition module, a prediction analysis module, an optimization scheduling module, a health management module, a real-time control module, a power grid interaction module and an energy efficiency evaluation module. And the data acquisition module is used for cleaning photovoltaic array output voltage / current data by adopting an improved Kalman filtering algorithm based on an Internet of Things sensing network, integrating environmental parameters such as irradiance and temperature of a meteorological station through a multi-source data fusion technology, and generating a standardized operation data set. According to the method, photovoltaic array output data is cleaned and integrated through an improved Kalman filtering algorithm and a multi-source data fusion technology, a high-precision standardized data set is constructed, a dual prediction architecture of an attention mechanism long-short term memory network and a time convolution network is combined, and the space-time correlation between photovoltaic output and load demand prediction is remarkably improved.

The invention relates to the technical field of photovoltaic inspection, in particular to an unmanned aerial vehicle photovoltaic inspection positioning method and system based on extended Kalman filtering, and the method comprises the steps: obtaining the GPS positioning data, attitude data and photovoltaic panel image data of an unmanned aerial vehicle in real time based on a sensor carried by the unmanned aerial vehicle; according to the image data of the photovoltaic panel, fault features of the fault photovoltaic panel are identified, and pixel coordinates of the fault photovoltaic panel are extracted; converting the pixel coordinates of the fault photovoltaic panel into initial position information under a geographic coordinate system; constructing an extended Kalman filtering model, inputting the positioning data, attitude data and initial position information of the unmanned aerial vehicle into the extended Kalman filtering model, and establishing a state vector and an observation vector; and iteratively calculating an optimal estimation value through an extended Kalman filtering algorithm according to the state vector, and outputting geographic coordinates of the fault photovoltaic panel. According to the invention, the method achieves the quick and precise positioning of the fault photovoltaic panel, improves the operation and maintenance efficiency of a photovoltaic power station, and reduces the operation and maintenance cost.

A digital-twin-based whole-station stress early-warning method and system for a flexible photovoltaic support. The method comprises: collecting real-time stress data of a single photovoltaic array, properties of a photovoltaic support, and meteorological prediction data of a whole photovoltaic power station, performing data cleaning and preprocessing, and extracting features; establishing a digital twin model in view of the real-time stress data of the single photovoltaic array, the meteorological prediction data and the properties of the support, which real-time stress data, meteorological prediction data and properties have been subjected to data cleaning and preprocessing; training the digital twin model by using historical data, and inputting the real-time stress data of the single photovoltaic array into the trained digital twin model, such that the change trend of stress on the flexible support of the whole photovoltaic power station is predicted; and on the basis of a prediction result, analyzing whether the flexible support of the whole photovoltaic power station has potential safety hazards. A digital twin model is established by means of a neural network method, and has relatively high prediction accuracy and stability; and potential safety hazards can be found in a timely manner, thereby improving the safety and stability of a photovoltaic power station.

The invention relates to the technical field of photovoltaic power prediction, in particular to a photovoltaic power station cluster power prediction method and system based on multi-task learning, and the method comprises the following steps: obtaining the three-dimensional position and shielding characteristics of a power station, constructing a sun incidence vector, recognizing the array shielding relation and space illumination distribution, and extracting the wind speed disturbance characteristics. And deducing a propagation path and a response time sequence, and executing multi-task prediction dynamic adjustment output. According to the method, a three-dimensional space shielding relation is established by combining topographic features and a solar radiation path, space shielding characteristics are measured and calculated through an earth surface inclination angle, an orientation angle and a shielding angle, the influence of the topography on illumination distribution is effectively captured, the interference of landform differences on photovoltaic output is reflected, and meteorological and space cooperation features are fused; the prediction precision is improved, non-linear errors caused by terrain and space distribution are considered, the adaptive capacity of power prediction to dynamic disturbance is enhanced, and steady-state operation guarantee of a photovoltaic power station cluster under the complex terrain condition is achieved.

The invention relates to the field of photovoltaic power station operation and maintenance, and discloses an operation and maintenance system for a photovoltaic power station, and the system comprises a data collection module which is used for collecting the equipment state data, environment parameters and power grid information of the photovoltaic power station, and forming multi-source information; the preprocessing module is used for performing space-time alignment, abnormal value filtering and feature extraction on the multi-source information, generating equipment-level health features and constructing a standardized data set based on the equipment-level health features; and the coupling modeling module is used for establishing a multi-equipment collaborative degradation model and outputting equipment collaborative health indexes and residual life prediction based on the standardized data set. Multi-source information is formed by collecting equipment state data, environmental parameters and power grid information of a photovoltaic power station, after space-time alignment, abnormal value filtering and feature extraction, a standardized data set containing equipment-level health features is constructed, and then a coupling modeling module is used for modeling the equipment-level health features on the basis of the standardized data set. And outputting an equipment collaborative health index capable of reflecting the overall degradation state and residual life prediction.

The invention relates to the technical field of photovoltaic power generation management systems, in particular to a photovoltaic power generation management, control and regulation system, which realizes multi-target collaborative optimization by fusing AGC active dynamic distribution, AVC reactive collaborative compensation, virtual inertia frequency response and reverse power rapid protection technologies. The system adopts a priority regulation and control strategy, a hybrid reactive power distribution algorithm and a multi-stage interlocking protection mechanism, the stability and the regulation precision of the power grid are remarkably improved, and the grid-connected requirement of a large-scale photovoltaic power station is met.

The invention relates to the technical field of voiceprint recognition, in particular to a photovoltaic equipment fault detection method and system based on voiceprint recognition, and the method comprises the following steps: collecting the sound of the surrounding environment of a current photovoltaic power station, and building scene associated equipment spectrum peak features; according to the method, classification of environment sound is introduced in the sound acquisition stage, and scene association is performed on the operation sound of the photovoltaic equipment and the surrounding background sound in combination with Mel-frequency cepstrum coefficient extraction and acoustic scene recognition, so that a more targeted reference framework is provided for subsequent analysis. Modeling of multi-source sound features is achieved by synchronously collecting operation sound of an inverter and a cooling fan in equipment and extracting spectrum peak frequency points and amplitudes. In the signal processing process, multiple sub-bands are divided on the basis of scene correlation spectrum peak frequency points, so that each sub-band is aligned with the acoustic characteristics of equipment, the structural precision of short-time energy envelope extraction is improved, and it is ensured that the cross-correlation analysis result is more reliable.

The invention discloses a real-time calculation method and system for distributed photovoltaic power generation meteorological elements, and solves the technical problem of regional distributed photovoltaic power generation meteorological information loss. The method comprises the following steps: selecting adjacent sites based on power station operation data, and repairing target site data by using adjacent site data; constructing an irradiance calculation model by using the reconstructed power data, and establishing a model among numerical weather forecast, distributed photovoltaic station power, irradiance and environment temperature; and based on the established model, calculating the solar irradiance and temperature of the position of the distributed photovoltaic station by taking the real-time power and the current value of the numerical weather forecast as input. In order to complement the power of the distributed photovoltaic station, a meteorological information calculation model is established, a high-cost-performance and high-precision solution is provided for complementing the meteorological information of distributed photovoltaic power generation, and the accuracy of output power prediction of the distributed photovoltaic station is improved.

The invention discloses a wind wave-seabed-pile foundation-photovoltaic array multi-physics field coupling calculation system for an ocean photovoltaic power station. The system comprises a wave model construction module, a photovoltaic array wind load numerical simulation module and a multi-physics field coupling numerical model integration module. The wave model construction module is used for extracting wave elements and constructing a wave model; the photovoltaic array wind load numerical simulation module is used for performing photovoltaic array wind load numerical simulation based on the extracted wave elements and the constructed wave model; and the multi-physics field coupling numerical model integration module is used for carrying out multi-physics field coupling numerical model integration, solving and verification based on a numerical simulation structure. According to the method, the wind wave load, the seabed seepage field, the pile foundation stress field and the photovoltaic array wind field are cooperatively calculated by building the multi-field dynamic coupling model, and therefore the accuracy and reliability of offshore photovoltaic power station pile foundation bearing performance evaluation are improved.

The invention discloses a distributed photovoltaic equipment detection method and system based on big data analysis, through multi-source data acquisition including equipment operation data, state data, electric energy quality data and environment data, comprehensive working information of a photovoltaic power station can be obtained, rich data support is provided for subsequent analysis, and the detection efficiency is improved. Data cleaning and standardization technologies are adopted, the quality and consistency of data are ensured, the accuracy of subsequent model training and fault diagnosis is improved, fault types are given through a multi-model fusion diagnosis method in combination with feature indexes and a detection method, and a fault knowledge base is constructed, so that the accuracy of fault diagnosis is improved, and the fault diagnosis efficiency is improved. And valuable experience and data support are provided for future fault prevention and maintenance.

The invention discloses an operation state evaluation method and system for a photovoltaic power station, and relates to the technical field of photovoltaic power generation, and the method comprises the following steps: carrying out the real-time monitoring of a photovoltaic module of the photovoltaic power station through a sensor and a camera device, and obtaining the electrical parameters and surface image data of the photovoltaic module; the intelligent monitoring system based on the combination of the electrical parameters and the image data can detect and position the hot spot problem of the photovoltaic module in real time, rapidly identify the hot spot area and analyze the influence of the hot spot area on the performance of the module, thereby triggering an alarm and taking countermeasures, effectively avoiding the risks of overheating, aging or burning of equipment, and improving the reliability of the photovoltaic module. Equipment damage and shutdown cost is reduced, and the service life of the assembly is prolonged. In combination with multispectral analysis and infrared thermal imaging technologies, the system provides an accurate analysis report for operation and maintenance personnel, automatically generates maintenance suggestions, optimizes operation and maintenance management, reduces labor cost, improves operation and maintenance efficiency and power generation efficiency, and ensures stable operation of a photovoltaic power station.

A method and system for evaluating cleaning demand of a photovoltaic panel based on environmental meteorological influence is provided. The method includes: determining a photovoltaic-panel generated power attenuation rate based on a photovoltaic-panel pollution environmental meteorological comprehensive index; determining a maximum surplus of a photovoltaic power station based on the attenuation rate, where a cleaning cycle corresponding to the maximum surplus is an optimal cleaning cycle; determining a cumulative amount of the comprehensive index in the optimal cleaning cycle as an optimal cumulative amount of the comprehensive index; calculating a standardized clean index of the photovoltaic panel based on the optimal cumulative amount of the comprehensive index; determining an influence index of meteorological elements on the photovoltaic panel in a predetermined time; and evaluating the cleaning cycle and determining an optimal cleaning time based on the influence index and the standardized clean index of the photovoltaic panel.

The invention relates to a robust distributed photovoltaic power station generation power prediction method and system, and the method comprises the steps: obtaining historical data of a distributed photovoltaic power station, and carrying out the preprocessing; selecting weather characteristic data with strong correlation with photovoltaic output power as input data; obtaining a robust distributed photovoltaic prediction model; training a weight parameter of the distributed photovoltaic prediction model, and selecting an optimal hyper-parameter; and obtaining a real-time prediction result. The method has the beneficial effects that the outliers are considered in the designed system model, so that the interference of noise on a prediction result in photovoltaic output power prediction can be effectively reduced; and the distributed model takes the system as a whole, and a plurality of stations are cooperatively trained, so that the prediction precision can be effectively improved.