Photovoltaic power station operation state monitoring management system

Abstract

The invention provides a photovoltaic power station operation state monitoring management system, and belongs to the technical field of state management. The system comprises a meteorological module which is used for installing a sensor in an environment near a photovoltaic power station and collecting original meteorological parameter data in real time; the operation module is used for detecting original operation parameter data of the photovoltaic power station under the preset parameter index group in real time; the data processing module is used for performing data cleaning and unit preprocessing on the original operation parameter data and the original meteorological parameter data to obtain standard operation parameter data and standard meteorological parameter data; the early warning module is used for establishing a fault early warning mechanism to analyze the standard operation parameter data and the standard meteorological parameter data and judging the abnormity of the photovoltaic power station; and the exception handling module is used for carrying out interactive analysis and optimizing the compatibility of the power station layout and the surrounding meteorological environment. Operation and maintenance are optimized through real-time monitoring and early warning, efficiency is improved, faults are reduced, and the service life of equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of status management, and in particular to a photovoltaic power station operation status monitoring and management system. Background Art

[0002] At present, photovoltaic power stations, as an important form of renewable energy generation, have been widely used around the world in recent years. With the continuous advancement of photovoltaic power generation technology, the scale of photovoltaic power stations has become larger and larger, and the speed of construction has also accelerated year by year. However, the operating status of photovoltaic power stations is affected by many factors, such as light intensity, ambient temperature, humidity, equipment aging, and system load. In particular, the performance of photovoltaic power generation systems varies greatly under different climatic and environmental conditions.

[0003] Therefore, the present invention proposes a photovoltaic power station operation status monitoring and management system. Summary of the invention

[0004] The present invention provides a photovoltaic power station operation status monitoring and management system, which is used to collect environmental data and power station operation data in real time through meteorological sensors, perform fault warning and abnormality analysis after data processing, optimize the compatibility of power station layout and meteorological environment, and improve the operation efficiency and stability of photovoltaic power stations.

[0005] In one aspect, the present invention provides a photovoltaic power station operation status monitoring and management system, comprising:

[0006] Meteorological module: Install sensors in the vicinity of the photovoltaic power station to collect raw meteorological parameter data in real time;

[0007] Operation module: real-time detection of the original operation parameter data of the photovoltaic power station under the preset parameter index group;

[0008] Data processing module: performs data cleaning and unit preprocessing on the original operating parameter data and the original meteorological parameter data to obtain standard operating parameter data and standard meteorological parameter data;

[0009] Early warning module: Establish a fault early warning mechanism to analyze standard operating parameter data and standard meteorological parameter data to determine abnormalities in the photovoltaic power station;

[0010] Exception handling module: interactively analyzes the exception, standard operating parameter data of the photovoltaic power station and standard meteorological parameter data to optimize the compatibility of the power station layout with the surrounding meteorological environment.

[0011] On the other hand, the meteorological module comprises:

[0012] Environmental area unit: Determine the meteorological monitoring area based on the geographical location of the photovoltaic power station;

[0013] Target recognition unit: Use a high-resolution image sensor to scan the initial ground area to obtain a three-dimensional environmental model of the meteorological monitoring area; identify and process the three-dimensional environmental model according to a convolutional neural network algorithm to identify multiple installation locations of the meteorological monitoring area.

[0014] On the other hand, the meteorological module further includes:

[0015] Matching degree unit: Analyze the installation matching degree of meteorological parameter type sensor at any installation location, and construct a relationship diagram between installation matching degree, meteorological parameter type sensor and installation location;

[0016] Sensor unit: Select any meteorological parameter type sensor and install it at the installation location with the highest matching degree in the relationship diagram as the preferred installation location, and configure a unique first number for each installation location, and configure a unique second number for each sensor. Install and deploy all sensors according to the unique correspondence between the sensor and the installation location.

[0017] On the other hand, the operation module includes:

[0018] Index definition unit: defines the initial operating parameter index group according to the design requirements and actual operating environment of the photovoltaic power station;

[0019] Key indicator unit: According to the preset operating state mathematical parameter library of the photovoltaic power station, the parameter importance of any operating parameter indicator in the initial operating parameter indicator group is determined as follows:

[0020] Among them, D i Indicates the parameter importance of the i-th operating parameter indicator, t i represents the number of occurrences of the ith operating parameter index in the preset operating state mathematical parameter library, sum(t) represents the total number of preset operating state mathematical parameter libraries, t jmax Indicates the historical maximum number of times the j-th type of operating parameter indicator appears in the preset operating state mathematical parameter library, t ji represents the number of occurrences of the i-th operating parameter indicator in the j-th type of operating parameter indicator, t jmin It indicates the historical minimum number of times the j-th type of operating parameter indicator appears in the preset operating state mathematical parameter library, sigma(t ij ) represents the entropy function of the number of occurrences of the i-th operating parameter indicator in the j-th type of operating parameter indicator relative to the total number of occurrences, and ln() represents the logarithmic function;

[0021] All operating parameter indicators in the initial operating parameter indicator group are sorted according to parameter importance, and indicators are screened according to the minimum parameter importance threshold to form a preset parameter indicator group.

[0022] On the other hand, the operation module further includes:

[0023] Index monitoring unit: according to the preset parameter index group, a data acquisition module is configured in the corresponding operating equipment of the photovoltaic power station;

[0024] Data transmission unit: Based on the data transmission protocol, the data transmission interface is connected to all data acquisition modules and the original operating parameter data is obtained in real time.

[0025] On the other hand, the data processing module includes:

[0026] Matrix replacement unit: constructs the original operation matrix and original meteorological matrix corresponding to the original operation parameter data and the original meteorological parameter data, where each row of the original matrix represents a different parameter type, and the data in each row that cannot be aligned in time is replaced by a labeled vector;

[0027] Missing value unit: Check the blank values ​​of the original operation matrix and the original meteorological matrix, count the missing proportion of each column, determine the distribution pattern of the missing values, and fill in the missing values ​​based on the distribution pattern to obtain the first operation matrix and the first meteorological matrix;

[0028] Unit processing unit: determining the standard unit of parameter type data of any row of the first operation matrix and the first meteorological matrix according to the parameter type-standard unit mapping table, and performing unit conversion based on the standard unit to obtain the second operation matrix and the second meteorological matrix;

[0029] Consistency unit: perform time series alignment processing on the row vectors with labeled vectors in the second operation matrix and the second meteorological matrix, and standardize them to obtain a standard operation parameter matrix and a standard meteorological parameter matrix, wherein each row of the standardized matrix is ​​standard operation parameter data or standard meteorological parameter data of one parameter type.

[0030] On the other hand, the early warning module includes:

[0031] Threshold definition unit: obtains the standard operating parameter matrix and the standard meteorological parameter matrix, obtains the parameter type standard data of any row in any matrix, and obtains the threshold range of the parameter type:

[0032] Among them, ex high represents the upper threshold, ex low represents the lower threshold, avg(x) represents the mean value of the parameter type, and x jrepresents the jth parameter value in the row vector to which the parameter type belongs, x0 represents the reference standard value of the row vector to which the parameter type belongs, h represents a total of h parameter values ​​in the row vector to which the parameter type belongs, D represents the preset weight coefficient of the row vector to which the parameter type belongs, k represents the adjustment constant, and σ represents the standard deviation of the row vector to which the parameter type belongs;

[0033] Construct a fault warning range comparison table based on all parameter types of the standard operating parameter matrix and the standard meteorological parameter matrix;

[0034] Early warning unit: if any standard data of any parameter type is not within the threshold range of the fault early warning range comparison table, the standard data is determined to be an abnormal value;

[0035] When abnormal values ​​appear continuously or in a periodic pattern, it is determined that the parameter type has a parameter state abnormality.

[0036] On the other hand, the exception handling module includes:

[0037] Abnormal processing unit: obtains meteorological parameters of the photovoltaic power station during the time period when the abnormal parameter status exists;

[0038] Data association unit: based on a machine learning framework, using standard operating parameter data and standard meteorological parameter data, modeling the relationship between the operating parameters of the photovoltaic power station and the meteorological parameters to obtain a first model;

[0039] Analysis unit: Based on the first model, analyze the relationship between the output power of the photovoltaic power station and all parameter types of meteorological parameters, and identify the influence coefficient of different meteorological parameters on the photovoltaic power generation state;

[0040] Optimization unit: According to the abnormal state of any parameter type, combined with the influence degree coefficient, analyze the unreasonable factors of the power station layout and the surrounding meteorological environment, and adjust the optimization strategy based on the unreasonable factors to obtain the optimal layout with the least abnormal state.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a photovoltaic power station operation status monitoring and management system, which is used to collect environmental data and power station operation data in real time through meteorological sensors, perform fault warning and abnormality analysis after data processing, optimize the compatibility of power station layout and meteorological environment, and improve the operation efficiency and stability of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a structural diagram of a photovoltaic power station operation status monitoring and management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Embodiment 1:

[0047] like Figure 1 As shown, an embodiment of the present invention provides a photovoltaic power station operation status monitoring and management system, including:

[0048] Meteorological module: Install sensors in the vicinity of the photovoltaic power station to collect raw meteorological parameter data in real time;

[0049] Operation module: real-time detection of the original operation parameter data of the photovoltaic power station under the preset parameter index group;

[0050] Data processing module: performs data cleaning and unit preprocessing on the original operating parameter data and the original meteorological parameter data to obtain standard operating parameter data and standard meteorological parameter data;

[0051] Early warning module: Establish a fault early warning mechanism to analyze standard operating parameter data and standard meteorological parameter data to determine abnormalities in the photovoltaic power station;

[0052] Exception handling module: interactively analyzes the exception, standard operating parameter data of the photovoltaic power station and standard meteorological parameter data to optimize the compatibility of the power station layout with the surrounding meteorological environment.

[0053] In this embodiment, the photovoltaic power station is also called a solar power station, which is a facility that converts sunlight into electrical energy through solar photovoltaic technology.

[0054] In this embodiment, the sensor is a device used to detect, sense and output physical quantities (such as temperature, humidity, pressure, light, wind speed, etc.) or chemical quantities (such as gas concentration, pH value, etc.).

[0055] In this embodiment, the original meteorological parameter data refers to the basic meteorological information collected by meteorological sensors near the photovoltaic power station without any processing.

[0056] In this embodiment, the preset parameter index group refers to a group of key performance parameters set during the operation of the photovoltaic power station based on the power station design, technical standards and operation requirements, including: output power, temperature, power generation efficiency, voltage, current, etc.

[0057] In this embodiment, the original operating parameter data refers to unprocessed real-time data directly collected from the system during the actual operation of the photovoltaic power station.

[0058] In this embodiment, data cleaning improves data quality by removing or correcting noise, errors, missing values, and inconsistencies in the original data, thereby ensuring the accuracy of subsequent analysis and modeling.

[0059] In this embodiment, unit preprocessing refers to uniformly converting and standardizing the units in the original data during data processing.

[0060] In this embodiment, the standard operating parameter data refers to the photovoltaic power station operating parameter data after data cleaning, unit preprocessing and standardization.

[0061] In this embodiment, the standard meteorological parameter data is meteorological data in a unified format and standard obtained after cleaning, unit conversion, and standardization of meteorological data.

[0062] In this embodiment, the fault warning mechanism is a mechanism that helps identify and predict potential system faults or performance degradation based on real-time data monitoring and analysis.

[0063] In this embodiment, the abnormality refers to the state of the photovoltaic power station outside the normal operating range, including: equipment failure abnormality, power generation abnormality, and meteorological condition abnormality.

[0064] In this embodiment, interactive analysis refers to identifying the interrelationships and impacts between different data sources by jointly analyzing them. For example, by combining meteorological conditions with the operating performance of a power plant, it is possible to reveal how specific meteorological factors (such as temperature, humidity, and irradiance) affect the power generation efficiency and equipment status of the power plant.

[0065] In this embodiment, optimizing the power station layout refers to adjusting the position, direction and configuration of each component in the photovoltaic power station (such as photovoltaic modules, inverters, sensors, etc.) to maximize the power generation efficiency of the power station, extend the service life of the equipment, and reduce the risk of failure caused by environmental factors.

[0066] In this embodiment, compatibility refers to the adaptability of photovoltaic power station equipment (such as photovoltaic modules, inverters, energy storage systems, etc.) to the surrounding meteorological environment.

[0067] The working principle and beneficial effects of the above technical solution are: by collecting meteorological and operating data in real time, conducting data processing and early warning analysis, abnormalities of photovoltaic power stations can be discovered in a timely manner. Interactive analysis is used to optimize the compatibility of power station layout with the meteorological environment, and to improve the operating efficiency, stability and fault handling capabilities of the power station.

[0068] Embodiment 2:

[0069] Based on the above embodiment 1, the meteorological module includes:

[0070] Environmental area unit: Determine the meteorological monitoring area based on the geographical location of the photovoltaic power station;

[0071] Target recognition unit: Use a high-resolution image sensor to scan the initial ground area to obtain a three-dimensional environmental model of the meteorological monitoring area; identify and process the three-dimensional environmental model according to a convolutional neural network algorithm to identify multiple installation locations of the meteorological monitoring area.

[0072] In this embodiment, the geographical location refers to the specific spatial coordinates of the site selected for the photovoltaic power station.

[0073] In this embodiment, the meteorological monitoring area refers to a specific area used to collect and analyze the meteorological conditions of the region during the construction and operation of the photovoltaic power station.

[0074] In this embodiment, the high-resolution image sensor is a sensor that can capture clear and detailed images and has a high pixel density.

[0075] In this embodiment, the initial ground area refers to the ground space to be scanned that is selected before performing meteorological monitoring and target recognition.

[0076] In this embodiment, the three-dimensional environmental model is constructed by collecting geographic data, object features and environmental information in the real world to build a virtual model based on three-dimensional space, showing the spatial layout, topography, object location, size, structure and other information of an area.

[0077] In this embodiment, the convolutional neural network algorithm can automatically identify areas suitable for installing photovoltaic panels by recognizing the three-dimensional environmental model.

[0078] In this embodiment, the installation location refers to a location within the meteorological monitoring area that meets certain conditions and is suitable for installing a photovoltaic panel.

[0079] The working principle and beneficial effects of the above technical solution are: a three-dimensional environmental model is obtained by scanning with a high-resolution image sensor, and a convolutional neural network algorithm is used for identification to accurately locate the installation location within the meteorological monitoring area. This improves the meteorological monitoring accuracy and layout optimization capabilities of photovoltaic power stations and enhances the intelligence level of the system.

[0080] Embodiment 3:

[0081] Based on the above embodiment 2, the meteorological module further includes:

[0082] Matching degree unit: Analyze the installation matching degree of meteorological parameter type sensor at any installation location, and construct a relationship diagram between installation matching degree, meteorological parameter type sensor and installation location;

[0083] Sensor unit: Select any meteorological parameter type sensor and install it at the installation location with the highest matching degree in the relationship diagram as the preferred installation location, and configure a unique first number for each installation location, and configure a unique second number for each sensor. Install and deploy all sensors according to the unique correspondence between the sensor and the installation location.

[0084] In this embodiment, the meteorological parameter type sensor refers to equipment used to measure and monitor weather and climate related equipment, and the types include: temperature, humidity, wind speed, light intensity, etc.

[0085] In this embodiment, the installation matching degree refers to the degree of adaptation between the environmental conditions provided by a specific installation location and the working conditions required by the sensor when a certain meteorological parameter type sensor is installed at the specific installation location.

[0086] In this embodiment, the relationship diagram of installation matching degree-meteorological parameter type sensor-installation location is a graphical tool for showing the compatibility between different installation locations and various types of meteorological parameter sensors.

[0087] In this embodiment, the preferred installation position refers to selecting an installation position with a higher matching degree under a certain sensor type as the optimal installation point based on analyzing the installation matching degree of different installation positions for each meteorological parameter type sensor.

[0088] In this embodiment, the first number refers to a unique number assigned to each installation location.

[0089] In this embodiment, the second number refers to a unique number assigned to each sensor.

[0090] The working principle and beneficial effects of the above technical solution are: by analyzing the matching degree between the installation location and the meteorological parameter sensor, a relationship diagram is constructed to optimize the sensor deployment. Each installation location and sensor is assigned a unique number to ensure accurate installation and signal reception, thereby improving monitoring accuracy and system efficiency.

[0091] Embodiment 4:

[0092] Based on the above embodiment 1, the operation module includes:

[0093] Index definition unit: defines the initial operating parameter index group according to the design requirements and actual operating environment of the photovoltaic power station;

[0094] Key indicator unit: According to the preset operating state mathematical parameter library of the photovoltaic power station, the parameter importance of any operating parameter indicator in the initial operating parameter indicator group is determined as follows:

[0095] Among them, D i Indicates the parameter importance of the i-th operating parameter indicator, t i represents the number of occurrences of the ith operating parameter index in the preset operating state mathematical parameter library, sum(t) represents the total number of preset operating state mathematical parameter libraries, t jmax Indicates the historical maximum number of times the j-th type of operating parameter indicator appears in the preset operating state mathematical parameter library, t ji represents the number of occurrences of the i-th operating parameter indicator in the j-th type of operating parameter indicator, t jmin It indicates the historical minimum number of times the j-th type of operating parameter indicator appears in the preset operating state mathematical parameter library, sigma(t ij ) represents the entropy function of the number of occurrences of the i-th operating parameter indicator in the j-th type of operating parameter indicator relative to the total number of occurrences, and ln() represents the logarithmic function;

[0096] All operating parameter indicators in the initial operating parameter indicator group are sorted according to parameter importance, and indicators are screened according to the minimum parameter importance threshold to form a preset parameter indicator group.

[0097] In this embodiment, the design requirements refer to the requirements and specifications for the design of the product project during the production process, such as equipment selection, equipment configuration, product process, etc.

[0098] In this embodiment, the actual operating environment refers to the actual physical, climatic, technical and operating conditions of the power station.

[0099] In this embodiment, the initial operating parameter index group is a group of preliminary monitoring and control indicators defined based on the design requirements and actual operating environment of the photovoltaic power station, such as power generation power, temperature, voltage, current, etc.

[0100] In this embodiment, the preset operation state mathematical parameter library is a database for defining and representing mathematical operation parameters required in the process of calculating the state of the photovoltaic power station.

[0101] In this embodiment, the parameter importance is a value that measures the relative importance of an operating parameter in the operating state of the photovoltaic power station.

[0102] In this embodiment, the entropy function is used to measure the size of the uncertainty parameter in the system.

[0103] In this embodiment, the minimum parameter importance threshold is a standard value used to determine which operating parameters should be selected into the final preset parameter indicator group.

[0104] The working principle and beneficial effect of the above technical solution are: by defining the initial operating parameter index group, and evaluating the importance of each parameter according to the preset operating state mathematical parameter library, the key indicators are screened out. Through sorting and threshold screening, the operation monitoring indicators of the photovoltaic power station are optimized, and the operation efficiency and accuracy are improved.

[0105] Embodiment 5:

[0106] Based on the above embodiment 4, the operation module further includes:

[0107] Index monitoring unit: according to the preset parameter index group, a data acquisition module is configured in the corresponding operating equipment of the photovoltaic power station;

[0108] Data transmission unit: Based on the data transmission protocol, the data transmission interface is connected to all data acquisition modules and the original operating parameter data is obtained in real time.

[0109] In this embodiment, the operating equipment refers to the hardware facilities that are directly involved in the power generation, monitoring, management and maintenance of the photovoltaic power generation system, including: inverters, combiner boxes, distribution equipment, etc.

[0110] In this embodiment, the data acquisition module is a software unit in the photovoltaic power station for collecting, acquiring and transmitting data from various operating devices.

[0111] In this embodiment, the data transmission protocol refers to the rules, formats and standards followed when transmitting data between different devices, such as Modbus protocol, CAN protocol, OPC protocol, etc.

[0112] In this embodiment, the data transmission interface refers to a software component used to connect different devices, data acquisition modules and monitoring systems for data exchange and transmission.

[0113] The working principle and beneficial effects of the above technical solution are: by configuring a data acquisition module in the photovoltaic power station equipment and transmitting the original operating parameter data in real time through the data transmission protocol, real-time monitoring and data acquisition of the system are ensured, thereby improving the operation and management efficiency and data accuracy of the photovoltaic power station.

[0114] Embodiment 6:

[0115] Based on the above embodiment 1, the data processing module includes:

[0116] Matrix replacement unit: constructs the original operation matrix and original meteorological matrix corresponding to the original operation parameter data and the original meteorological parameter data, where each row of the original matrix represents a different parameter type, and the data in each row that cannot be aligned in time is replaced by a labeled vector;

[0117] Missing value unit: Check the blank values ​​of the original operation matrix and the original meteorological matrix, count the missing proportion of each column, determine the distribution pattern of the missing values, and fill in the missing values ​​based on the distribution pattern to obtain the first operation matrix and the first meteorological matrix;

[0118] Unit processing unit: determining the standard unit of parameter type data of any row of the first operation matrix and the first meteorological matrix according to the parameter type-standard unit mapping table, and performing unit conversion based on the standard unit to obtain the second operation matrix and the second meteorological matrix;

[0119] Consistency unit: perform time series alignment processing on the row vectors with labeled vectors in the second operation matrix and the second meteorological matrix, and standardize them to obtain a standard operation parameter matrix and a standard meteorological parameter matrix, wherein each row of the standardized matrix is ​​standard operation parameter data or standard meteorological parameter data of one parameter type.

[0120] In this embodiment, the original operation matrix is ​​a preliminary data matrix containing different operation parameters, each row represents an operation parameter type, and each column represents a parameter value corresponding to a different time point.

[0121] In this embodiment, the original meteorological matrix is ​​a preliminary data matrix containing different meteorological parameters, each row represents a meteorological parameter type, and each column represents a parameter value corresponding to a different time point.

[0122] In this embodiment, the label vector is used to represent data points that cannot be aligned in time in the original data matrix.

[0123] In this embodiment, the blank value refers to missing data or unrecorded data in the matrix.

[0124] In this embodiment, the missing ratio refers to the ratio of missing values ​​in a row to the total amount of data in the row in the data set.

[0125] In this embodiment, the distribution pattern refers to the regularity of the missing values ​​appearing in the matrix. For example, some meteorological parameters may be missing in certain seasonal time periods, or the operating data of some equipment may be missing in a certain period of time.

[0126] In this embodiment, the first operating matrix is ​​an original operating parameter matrix obtained after missing values ​​are filled.

[0127] In this embodiment, the first meteorological matrix is ​​an original meteorological parameter matrix obtained after missing values ​​are filled.

[0128] In this embodiment, the parameter type-standard unit mapping table is a table indicating the mapping relationship between parameter types and standard units.

[0129] In this embodiment, the standard unit refers to the unit used for uniformly converting various parameter values ​​according to certain standards and specifications.

[0130] In this embodiment, unit conversion refers to the process of converting the value of a parameter from one measurement unit to another measurement unit.

[0131] In this embodiment, the second operation matrix refers to the first operation matrix after performing the unit processing unit.

[0132] In this embodiment, the second meteorological matrix refers to the first meteorological matrix after the unit processing unit is performed.

[0133] In this embodiment, the time series alignment process is an operation of aligning time nodes. First, a standard time scale needs to be determined, and then linear scaling is used to arrange all data at the same time point.

[0134] In this embodiment, the standard operating parameter matrix is ​​a standard matrix obtained after the second operating matrix preprocessing step.

[0135] In this embodiment, the standard meteorological parameter matrix is ​​a standard matrix obtained after the second meteorological matrix preprocessing step.

[0136] The working principle and beneficial effects of the above technical solution are: by constructing and processing the original operation and meteorological matrix, filling in missing values, unit conversion and time alignment, a standardized operation and meteorological parameter matrix is ​​finally obtained, ensuring data consistency, integrity and comparability, and providing reliable data support for accurate analysis and optimization of photovoltaic power plants.

[0137] Embodiment 7:

[0138] Based on the above embodiment 6, the early warning module includes:

[0139] Threshold definition unit: obtains the standard operating parameter matrix and the standard meteorological parameter matrix, obtains the parameter type standard data of any row in any matrix, and obtains the threshold range of the parameter type:

[0140] Among them, ex high represents the upper threshold, exlow represents the lower threshold, avg(x) represents the mean value of the parameter type, and x j represents the jth parameter value in the row vector to which the parameter type belongs, x0 represents the reference standard value of the row vector to which the parameter type belongs, h represents a total of h parameter values ​​in the row vector to which the parameter type belongs, D represents the preset weight coefficient of the row vector to which the parameter type belongs, k represents the adjustment constant, and σ represents the standard deviation of the row vector to which the parameter type belongs;

[0141] Construct a fault warning range comparison table based on all parameter types of the standard operating parameter matrix and the standard meteorological parameter matrix;

[0142] Early warning unit: if any standard data of any parameter type is not within the threshold range of the fault early warning range comparison table, the standard data is determined to be an abnormal value;

[0143] When abnormal values ​​appear continuously or in a periodic pattern, it is determined that the parameter type has a parameter state abnormality.

[0144] In this embodiment, the threshold range refers to a reasonable value interval of a certain parameter type under normal operation.

[0145] In this embodiment, the upper threshold is used to define the maximum reasonable value of a certain parameter type.

[0146] In this embodiment, the lower limit threshold is used to define the minimum reasonable value of a certain parameter type.

[0147] In this embodiment, the preset weight coefficient is used to assign a higher or lower priority to certain specific parameter types when calculating the threshold.

[0148] In this embodiment, the fault warning range comparison table is a table constructed based on standard parameter data and threshold calculation results, including the normal range of each parameter, and the abnormalities of different parameters are determined based on these ranges.

[0149] In this embodiment, an abnormal value refers to an actual value of a parameter that is not within a preset threshold range.

[0150] In this embodiment, the periodic regularity means that within a certain time interval, the occurrence of abnormal values ​​follows a certain regular repetitive pattern.

[0151] In this embodiment, the abnormal parameter state means that the parameter value of a certain parameter deviates from the normal range, and this deviation shows a certain regularity or persistence.

[0152] The working principle and beneficial effect of the above technical solution are: by defining the threshold range of parameter types and establishing a fault warning range comparison table, real-time monitoring of standard data can be achieved. When the data deviates from the threshold range and becomes abnormal, the parameter status can be effectively determined to be abnormal, providing a basis for equipment fault warning and maintenance.

[0153] Embodiment 8:

[0154] Based on the above embodiment 7, the exception handling module includes:

[0155] Abnormal processing unit: obtains meteorological parameters of the photovoltaic power station during the time period when the abnormal parameter status exists;

[0156] Data association unit: based on a machine learning framework, using standard operating parameter data and standard meteorological parameter data, modeling the relationship between the operating parameters of the photovoltaic power station and the meteorological parameters to obtain a first model;

[0157] Analysis unit: Based on the first model, analyze the relationship between the output power of the photovoltaic power station and all parameter types of meteorological parameters, and identify the influence coefficient of different meteorological parameters on the photovoltaic power generation state;

[0158] Optimization unit: According to the abnormal state of any parameter type, combined with the influence degree coefficient, analyze the unreasonable factors of the power station layout and the surrounding meteorological environment, and adjust the optimization strategy based on the unreasonable factors to obtain the optimal layout with the least abnormal state.

[0159] In this embodiment, the machine learning framework is a set of software tools for building, training, and deploying machine learning models, including: TensorFlow, XGBoost, LightGBM, etc.

[0160] In this embodiment, the modeling is to analyze the relationship between the operation of the photovoltaic power station and the meteorological parameters and construct a model.

[0161] In this embodiment, the first model refers to a mathematical model constructed in the data association unit and based on the relationship between the operating parameters of the photovoltaic power station and the meteorological parameter data.

[0162] In this embodiment, the influence degree coefficient refers to the strength of the relationship between the output power of the photovoltaic power station and various meteorological parameters.

[0163] In this embodiment, unreasonable factors include: shading problems, equipment layout problems, meteorological influences, soil factors, etc.

[0164] In this embodiment, the optimal layout refers to a layout plan that maximizes power generation efficiency, reduces interference between photovoltaic modules, reduces failure rates, and improves overall system stability by comprehensively considering the internal components, equipment configuration, and surrounding environment of the power station.

[0165] The working principle and beneficial effects of the above technical solution are: through machine learning modeling, the relationship between the operating parameters and meteorological parameters of the photovoltaic power station is analyzed, the influencing factors are identified, and the power station layout and meteorological environment are optimized according to abnormal conditions, so as to reduce the occurrence of faults and improve the efficiency and reliability of photovoltaic power generation.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic power station operation status monitoring and management system, characterized in that: include: Meteorological module: Install sensors near the photovoltaic power station to collect raw meteorological parameter data in real time; Operation module: real-time detection of the original operation parameter data of the photovoltaic power station under the preset parameter index group; Data processing module: performs data cleaning and unit preprocessing on the original operating parameter data and the original meteorological parameter data to obtain standard operating parameter data and standard meteorological parameter data; Early warning module: Establish a fault early warning mechanism to analyze standard operating parameter data and standard meteorological parameter data to determine abnormalities in the photovoltaic power station; Exception handling module: interactively analyzes the exception, standard operating parameter data of the photovoltaic power station and standard meteorological parameter data to optimize the compatibility of the power station layout with the surrounding meteorological environment.

2. A photovoltaic power station operation status monitoring and management system according to claim 1, characterized in that: The meteorological module comprises: Environmental area unit: Determine the meteorological monitoring area based on the geographical location of the photovoltaic power station; Target recognition unit: Use a high-resolution image sensor to scan the initial ground area to obtain a three-dimensional environmental model of the meteorological monitoring area; identify and process the three-dimensional environmental model according to a convolutional neural network algorithm to identify multiple installation locations of the meteorological monitoring area.

3. A photovoltaic power station operation status monitoring and management system according to claim 2, characterized in that: The meteorological module further comprises: Matching degree unit: Analyze the installation matching degree of meteorological parameter type sensor at any installation location, and construct a relationship diagram between installation matching degree, meteorological parameter type sensor and installation location; Sensor unit: Select any meteorological parameter type sensor and install it at the installation location with the highest matching degree in the relationship diagram as the preferred installation location, and configure a unique first number for each installation location, and configure a unique second number for each sensor. Install and deploy all sensors according to the unique correspondence between the sensor and the installation location.

4. A photovoltaic power station operation status monitoring and management system according to claim 1, characterized in that: The operation module comprises: Index definition unit: defines the initial operating parameter index group according to the design requirements and actual operating environment of the photovoltaic power station; Key indicator unit: According to the preset operating state mathematical parameter library of the photovoltaic power station, the parameter importance of any operating parameter indicator in the initial operating parameter indicator group is determined as follows: Among them, D i Indicates the parameter importance of the i-th operating parameter indicator, t i represents the number of occurrences of the ith operating parameter index in the preset operating state mathematical parameter library, sum(t) represents the total number of preset operating state mathematical parameter libraries, t jmax Indicates the historical maximum number of times the j-th type of operating parameter indicator appears in the preset operating state mathematical parameter library, t ji represents the number of occurrences of the i-th operating parameter indicator in the j-th type of operating parameter indicator, t jmin It indicates the historical minimum number of times the j-th type of operating parameter indicator appears in the preset operating state mathematical parameter library, sigma(t ij ) represents the entropy function of the number of occurrences of the i-th operating parameter indicator in the j-th type of operating parameter indicator relative to the total number of occurrences, and ln() represents the logarithmic function; All operating parameter indicators in the initial operating parameter indicator group are sorted according to parameter importance, and indicators are screened according to the minimum parameter importance threshold to form a preset parameter indicator group.

5. A photovoltaic power station operation status monitoring and management system according to claim 4, characterized in that: The operation module further includes: Index monitoring unit: according to the preset parameter index group, a data acquisition module is configured in the corresponding operating equipment of the photovoltaic power station; Data transmission unit: Based on the data transmission protocol, the data transmission interface is connected to all data acquisition modules and the original operating parameter data is obtained in real time.

6. A photovoltaic power station operation status monitoring and management system according to claim 1, characterized in that: The data processing module comprises: Matrix replacement unit: constructs the original operation matrix and original meteorological matrix corresponding to the original operation parameter data and the original meteorological parameter data, where each row of the original matrix represents a different parameter type, and the data in each row that cannot be aligned in time is replaced by a labeled vector; Missing value unit: Check the blank values ​​of the original operation matrix and the original meteorological matrix, count the missing proportion of each column, determine the distribution pattern of the missing values, and fill in the missing values ​​based on the distribution pattern to obtain the first operation matrix and the first meteorological matrix; Unit processing unit: determining the standard unit of parameter type data of any row of the first operation matrix and the first meteorological matrix according to the parameter type-standard unit mapping table, and performing unit conversion based on the standard unit to obtain the second operation matrix and the second meteorological matrix; Consistency unit: perform time series alignment processing on the row vectors with labeled vectors in the second operation matrix and the second meteorological matrix, and standardize them to obtain a standard operation parameter matrix and a standard meteorological parameter matrix, wherein each row of the standardized matrix is ​​standard operation parameter data or standard meteorological parameter data of one parameter type.

7. A photovoltaic power station operation status monitoring and management system according to claim 6, characterized in that: The early warning module comprises: Threshold definition unit: obtains the standard operating parameter matrix and the standard meteorological parameter matrix, obtains the parameter type standard data of any row in any matrix, and obtains the threshold range of the parameter type: Among them, ex high represents the upper threshold, ex low represents the lower threshold, avg(x) represents the mean value of the parameter type, and x j represents the jth parameter value in the row vector to which the parameter type belongs, x0 represents the reference standard value of the row vector to which the parameter type belongs, h represents a total of h parameter values ​​in the row vector to which the parameter type belongs, D represents the preset weight coefficient of the row vector to which the parameter type belongs, k represents the adjustment constant, and σ represents the standard deviation of the row vector to which the parameter type belongs; Construct a fault warning range comparison table based on all parameter types of the standard operating parameter matrix and the standard meteorological parameter matrix; Early warning unit: if any standard data of any parameter type is not within the threshold range of the fault early warning range comparison table, the standard data is determined to be an abnormal value; When abnormal values ​​appear continuously or in a periodic pattern, it is determined that the parameter type has a parameter state abnormality.

8. A photovoltaic power station operation status monitoring and management system according to claim 7, characterized in that: The exception handling module comprises: Abnormal processing unit: obtains meteorological parameters of the photovoltaic power station during the time period when the abnormal parameter status exists; Data association unit: based on a machine learning framework, using standard operating parameter data and standard meteorological parameter data, modeling the relationship between the operating parameters of the photovoltaic power station and the meteorological parameters to obtain a first model; Analysis unit: Based on the first model, analyze the relationship between the output power of the photovoltaic power station and all parameter types of meteorological parameters, and identify the influence coefficient of different meteorological parameters on the photovoltaic power generation state; Optimization unit: According to the abnormal state of any parameter type, combined with the influence degree coefficient, analyze the unreasonable factors of the power station layout and the surrounding meteorological environment, and adjust the optimization strategy based on the unreasonable factors to obtain the optimal layout with the least abnormal state.

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