46579results about "Photovoltaic energy generation" patented technology

The invention relates to the technical field of power supply or power distribution circuit systems, in particular to a photovoltaic module fault monitoring system and method, and the system comprises an acquisition module, a power supply dynamic compensation module, a control optimization module, a power supply module and a digital twin module. The acquisition module acquires multi-source data through the infrared thermal imaging sensor, the electroluminescent detection unit and the current and voltage characteristic curve acquisition module, the power supply dynamic compensation module dynamically adjusts wavelet basis function parameters based on an improved whale optimization algorithm, and the power supply module fuses hot spot distribution, current and voltage abnormity and aging trend data. And driving the dynamic adjustment of the protection threshold. And the digital twin module synchronizes real and virtual system data through transfer learning, rehearses a fault path to generate a maintenance instruction and feeds back the maintenance instruction to an optimization prediction model, so that a monitoring, protection and self-optimization closed-loop system is formed, and the fault diagnosis precision and the system reliability in a complex environment are improved.

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

The invention relates to the technical field of data processing, in particular to a building light storage and charging integrated energy management system, which is used for solving the problems of data processing delay and resource coordination mismatch caused by insufficient multi-source heterogeneous data fusion in photovoltaic power generation, energy storage and grid connection links. The system comprises a data acquisition module, a protocol conversion module, a data fusion module, a scheduling optimization module, an instruction generation module and a feedback module. The protocol conversion module carries out Modbus, CAN bus and IEC 61850 protocol analysis on the photovoltaic output time sequence data, the energy storage charging and discharging state data and the power grid dispatching instruction to generate standardized data streams. And the safety verification module intercepts the overrun instruction and triggers an amplitude limiting strategy. Through protocol unification, dynamic fusion and closed-loop control, the system realizes improvement of photovoltaic consumption rate, reduction of energy storage circulation loss and optimization of power grid frequency modulation deviation, and significantly improves the efficiency and economy of light storage and charging cooperative operation.

The invention discloses an optical storage and charging integrated micro-grid energy management system, which relates to the related technical field of micro-grids and comprises an electric energy collection layer, a digital twinborn layer, a collaborative optimization layer, an application execution layer, a photovoltaic power generation unit, an energy storage unit, a charging load unit and a distributed measurement and control terminal. The invention further discloses an energy management method of the photovoltaic, storage and charging integrated micro-grid. The energy management method comprises the steps of data acquisition, digital twin modeling, multi-target strategy generation and optimization, strategy evaluation and screening, strategy issuing and execution and closed-loop feedback and dynamic correction. According to the invention, through short-term prediction of a digital twinborn layer and real-time generation of a charging and discharging strategy by a collaborative optimization layer, fluctuating renewable energy sources are preferentially consumed, the light abandoning rate is reduced, energy storage charging is automatically triggered in an illumination peak period, and power overflow is avoided; based on the simulation result of the digital twinborn model, the power distribution of energy storage and load is dynamically adjusted, so that the photovoltaic utilization rate is improved, and the dependence on a traditional power grid is reduced.

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.

An organic molecule is disclosed comprising:a first chemical moiety with a structure of formula I,andtwo second chemical moieties, each at each occurrence independently from another consisting of a structure of formula II,wherein the first chemical moiety is linked to each of the two second chemical moieties via a single bond.

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention relates to the technical field of intelligent energy storage control, and discloses an intelligent energy storage control method, device and equipment based on bidirectional energy management. The method comprises the following steps: carrying out real-time acquisition and power grid state analysis on power grid parameters, energy storage module parameters, load requirements and photovoltaic power generation data to obtain power grid state evaluation indexes and a system operation mode division result; executing a multi-objective optimization algorithm to obtain an energy scheduling plan including a charging and discharging power instruction; converting the energy scheduling plan into charging and discharging current control parameters, and inputting the charging and discharging current control parameters into a control structure composed of a prediction control layer and a real-time response control layer in the three-phase hybrid inverter to obtain power output data; the power output data of the multiple three-phase hybrid inverters are uploaded to the intelligent management cloud platform, multi-machine parallel coordination control is executed, a distributed energy network management scheme is obtained, the dynamic characteristics of the system under different electric energy flow directions are predicted, and the stability and adaptability of the distributed energy network are enhanced.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention provides a photovoltaic intelligent sensing fault diagnosis method and system for an energy internet of things, and relates to the field of photovoltaic technology, and the method comprises the steps: deploying a multi-dimensional sensor network based on a star topology structure; a deep learning model based on a graph attention network and a bidirectional gating loop unit is utilized to extract space-time correlation features, and matching learning is carried out on the space-time correlation features and historical fault samples to generate fault feature mapping; constructing a fault diagnosis classifier by adopting a comparative learning method and optimizing the fault diagnosis classifier through a knowledge distillation technology, and classifying the fault feature mapping to obtain a fault type probability distribution matrix and a fault early warning level; constructing a multi-dimensional fault evaluation model based on Bayesian reasoning and time sequence correlation analysis, and evaluating fault credibility; and finally, constructing a fault evolution prediction model based on deep reinforcement learning, predicting the fault of the photovoltaic module in combination with the fault credibility and historical data, and generating a health assessment report.

The invention relates to the field of computer vision, and discloses a photovoltaic panel defect category detection algorithm based on feature pyramid and cascade group attention, comprising the following steps: S1, data acquisition, labeling and preprocessing; s2, carrying out model training by using an improved YOLOv11 algorithm; and S3, predicting the input infrared picture, accurately positioning the defect and marking the category. According to the photovoltaic panel defect category detection algorithm based on the feature pyramid and the cascading group attention, the feature pyramid and the cascading group attention can be detected according to the internal circuit structure of the photovoltaic panel and an infrared imaging picture; accurately positioning, marking and classifying the defects on the photovoltaic panel; a newest YOLOv11 target detection algorithm is adopted and improved, and part of C3k2 modules are replaced by SCC3k2 modules, so that information capture on space and channels is more accurate, and adverse effects caused by redundant information extraction are reduced; and meanwhile, different input segmentation is provided for each attention head in the attention module, and the diversity of the characteristics input to the attention heads is enhanced by cross-head cascade output characteristics.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

The invention relates to the technical field of operation prediction, in particular to an optical storage flexible direct current operation prediction method based on deep learning, and the method achieves the precise evaluation of photovoltaic power fluctuation through the analysis of historical operation data and the combination of a power state conversion matrix, guarantees that the energy storage charging and discharging rate can be matched with the load demand distribution, and improves the prediction precision. Power scheduling is optimized, topology analysis and power flow path identification are carried out, topology weight calculation is carried out by using a graph convolutional network, power flow can be dynamically adjusted, the adaptability of an operation mode is improved, the influence of power fluctuation on system stability is reduced, power matching is carried out by adopting an adversarial generative network, photovoltaic output is optimized, and the system stability is improved. The energy storage release path is more reasonable, the power loss is reduced, the overall power distribution efficiency is improved, and the load compensation strategy is optimized through short-period fluctuation detection and long-period trend fitting. And variable correction is performed based on error calculation and anomaly detection, so that the reliability of regulation and control response is improved.

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 embodiment of the invention relates to the photovoltaic field, and provides a main-grid-free back contact battery and a photovoltaic module, and the main-grid-free back contact battery comprises a battery substrate which is provided with a first welding region and a second welding region which are alternately arranged along a first direction, two edge regions which are opposite along a second direction, and a central region located between the two edge regions; the first fine grid and the second fine grid are positioned on the battery substrate and are alternately arranged along a second direction; the bonding pads are located in the edge area, and each bonding pad is at least in contact connection with two adjacent homopolar fine grids; the welding spots are located in the central area, each welding spot is in contact connection with each same-polarity fine grid, and M same-polarity fine grids are arranged between the bonding pads and the welding spots which are adjacent in the second direction; the connecting line is in contact connection with the bonding pad and is also in contact connection with at least one welding spot close to the bonding pad along the second direction; the welding area is a first welding area and the same-polarity fine grid is a first fine grid, and / or the welding area is a second welding area and the same-polarity fine grid is a second fine grid, so that the photoelectric conversion efficiency of the main-grid-free back contact battery is at least improved.

The invention provides a photovoltaic module fault detection system, method and device based on infrared image hot spot detection, and a storage medium, and belongs to the field of photovoltaic module fault detection. The system comprises a temperature difference boundary extraction module, an image sequence registration module, a hot spot track identification module, an abnormal dynamic screening module and a fault hot spot confirmation module. Pixel difference screening is carried out by setting a temperature difference threshold value, a boundary communication structure is established, frame-level displacement calculation between time sequence images is introduced to realize coordinate alignment, hot spot center points in continuous frames are extracted to form a motion path, abnormal point screening and time node labeling are carried out in combination with path displacement characteristics, and the time sequence image is obtained. According to the method, the boundary area growth rate and temperature change double factors are fused to screen fault areas, dynamic tracking and accurate detection of abnormal hot spots are achieved, the logic relevance between abnormal behavior recognition and fault judgment is enhanced, and the integrity of photovoltaic module fault information extraction and the accuracy of hot spot recognition are guaranteed.

The invention relates to the technical field of power system communication, in particular to a protocol conversion and communication adaptation system for an optical storage and charging grid-connected device. Comprising a protocol conversion unit used for dynamically analyzing heterogeneous protocols of a photovoltaic inverter, an energy storage converter, a charging pile and power grid side equipment; a communication adaptation unit; an energy collaboration unit; a power grid interface unit; and a man-machine interaction unit. According to the method, the protocol mapping rule base is dynamically updated through the plug-in type architecture module and the machine learning algorithm, self-adaptive analysis of multiple heterogeneous protocols and variant protocols is achieved, the protocol analysis accuracy and generalization ability are improved, and the problem that a traditional static rule base is insufficient in adaptability to non-standard protocols is solved; according to the invention, the distributed edge computing architecture is adopted to construct the bidirectional communication link, so that the real-time performance and security of the communication link are improved, and the defects of high data delay and incomplete security mechanism in the traditional communication architecture are improved.

A mounting assembly disposable on a nail strip seam rib of a building surface includes a mounting body having an exterior, which in turn comprises a slot configured to separately receive a plurality of different nail strip seam rib profiles. The mounting assembly also includes an insert that is at least partially disposable in the slot, and a threaded fastener that extends through the mounting body, into the slot, and engages the insert.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention relates to the technical field of target detection, in particular to a photovoltaic panel detection method and system based on machine vision, and the method comprises the following steps: obtaining the displacement data of a photovoltaic panel cell, calculating the displacement gradient of a connection point, dividing a rigid region and a non-rigid region, generating a deformation classification result, and constructing a global deformation field. Generating a time sequence deformation trend matrix; calculating a displacement mean value of a bracket fixing point; adjusting a deformation compensation parameter; generating an adaptive compensation parameter, adjusting a welding point position, calculating an abnormal region Euclidean distance, and generating a defect matching identification result. According to the method, rigid and non-rigid areas are accurately divided by calculating the displacement gradient of a connection point, a global deformation field is constructed in combination with the displacement change of a feature point, trend continuity is ensured, a compensation parameter is adjusted by utilizing a fixed point displacement mean value, a deformation increment factor is calculated, the weight and stability are corrected, the position of a welding point is adjusted, and inverse deformation correction is optimized. The Euclidean distance is combined to match defects, so that the detection precision and stability are improved.

Disclosed in the present invention are a voltage control method and system for clusters of a power distribution network having a high proportion of photovoltaics. The method comprises: acquiring parameters of a power distribution network, and constructing a comprehensive electrical distance by means of adjustable active power and reactive power reserves of each node in the parameters of the power distribution network; forming an improved weight matrix by means of distributed photovoltaic reactive power and active power supporting capacities, a reactive power sensitivity matrix and an active power sensitivity matrix, and obtaining an optimal reactive power cluster and active power cluster division result by a community algorithm by means of a node coupling degree index in each cluster and the comprehensive electrical distance; and selecting, by means of a clustering algorithm on the basis of the division result, a key node in each cluster to act, to improve the cluster voltage regulation and control capability. Starting from the relationship between active power sensitivity and reactive power sensitivity, and considering the impact of different changes on other nodes, efficient utilization of the reactive power regulation capability of a grid-connected PV inverter can be achieved, the complexity of control strategy computation is greatly reduced, and the situation where there are too many adjustable distributed photovoltaics in clusters is avoided.

The present invention discloses a self-assembled monolayer hole transport material based on an alkyl polyphosphate anchoring group, a synthesis method thereof, and an application in an inverted perovskite solar cell. The present invention uses carbazole or 3,6-diphenylcarbazole with good hole transport properties as a parent core, and achieves complete coverage and stable self-assembly of the material on the substrate surface by improving the anchoring group from traditional alkyl monophosphate to alkyl diphosphate and triphosphate. The synthesis method of the present invention is simple and the synthesis cost is low. The prepared material can achieve the dual functions of hole transport and perovskite interface passivation, while enhancing the self-assembly strength and stability. When the material is used as a hole transport layer in an inverted perovskite solar cell, a photoelectric conversion efficiency of >20% can be obtained without doping, and the material has broad application prospects.

The invention discloses a flywheel energy storage system control method based on data center photovoltaic power supply, and belongs to the technical field of power system and new energy grid-connected control, and the method comprises the steps: obtaining the output prediction data of photovoltaic power generation, the real-time load power, the voltage fluctuation parameter of a commercial power grid, and the real-time rotating speed state of flywheel energy storage; generating a power gap predicted value by taking the real-time rotating speed state of flywheel energy storage as a dynamic constraint variable; generating a flywheel commercial power cooperative control strategy according to the commercial power voltage fluctuation parameter; the temperature change rate of the flywheel motor is detected in real time, and a control instruction is generated; and synchronously sending the control instruction to the photovoltaic inverter and the flywheel motor, adjusting the parameters of the photovoltaic inverter and the flywheel rotating speed response data, and dynamically updating the control parameter set. According to the invention, a dual adjustment mechanism of flywheel rotating speed dynamic constraint and voltage fluctuation ratio weight distribution is adopted, and a temperature gradient harmonic collaborative suppression strategy is combined, so that millisecond-level power compensation response can be realized, the power grid dependence degree is reduced, and the service life of an energy storage system is prolonged.

The invention discloses a photovoltaic power station equipment abnormal state detection method and system, and relates to the technical field of photovoltaic power station detection, and the method comprises the steps: obtaining photovoltaic module operation data, environment temperature data and historical operation data, and generating a monitoring data set; and generating a correction factor based on the real-time illumination intensity data and the historical data, performing environment classification by combining the environment temperature and the real-time illumination intensity, and matching a corresponding basic threshold. And calculating the temperature deviation degree between the real-time temperature and the historical temperature mean value, dynamically adjusting a basic threshold value in combination with a correction factor, and screening potential abnormal data points. And taking a component corresponding to the potential abnormal data point as a target component, adjusting a data acquisition neighborhood range according to real-time illumination intensity change, and calculating a temperature slope and a power change rate of the target component. And determining adjacent components of the target component, calculating a hot spot effect degree mean value and a standard deviation, and when the mean value exceeds an abnormal data screening threshold value and the standard deviation is lower than a preset threshold value, judging that systematic abnormality occurs.

The invention discloses a thiapyran molecule modified solar cell and a preparation method thereof. The cell sequentially comprises an ITO (Indium Tin Oxide) conductive glass layer, a 4-aminotetrahydro-2H-thiapyran-1, 1-dioxide hydrochloride modified [4-(3, 6-dimethoxy-9H-carbazole-9-yl) butyl] phosphonic acid hole transport layer, a Cs0.05 (FA < 0.77 > MA < 0.23 >) < 0.95 > Pb < (I < 0.77 > Br < 0.23 >) 3 wide-band gap perovskite thin film, a phenethyl ammonium iodide passivation layer, an electron transport layer of [6, 6]-phenyl-C61-butyric acid isomethyl ester, a 2, 9-dimethyl-4, 6, 7-tetrahydro-2H-thiapyran-1, 1-dioxide hydrochloride modified [4-(3, 6-dimethoxy-9H-carbazole-9-yl) butyl] the hole barrier layer is made of 1, 7-biphenyl-1, 10-phenanthroline; the electrode layer is made of metal Ag; according to the scheme, the hole transport layer is modified through thiapyran molecules, agglomeration of the hole transport layer is relieved, and a more compact and more uniform self-assembled single layer is generated by inducing favorable crystal orientation. A good bottom interface is obtained based on thiapyran molecule modification, and ordered growth of high-quality perovskite crystal grains is promoted. In addition, the energy level of the hole transport layer is effectively adjusted through thiapyran molecule treatment, and the extraction and transmission of carriers by the hole transport layer are enhanced.