An intelligent monitoring system for a photovoltaic power station

Abstract

The application provides a kind of photovoltaic power station intelligent monitoring system, it is related to intelligent monitoring technical field, comprising: data acquisition module: obtain power station operating data and power station state data, and carry out data processing and data classification, obtain first operating data set and first state data set;State determination module: first operating data set and first state data set are integrated with data, and the corresponding data weight is determined to determine the comprehensive equipment state: state judging module: the comprehensive equipment state is combined with environmental influence parameter to judge state, determine whether the target photovoltaic power station exists fault;Early warning judging module: for the target photovoltaic power station that exists fault, corresponding fault reason is judged, and fault early warning is carried out based on fault reason.Processing operating data, state data, to accurately determine the equipment state in combination with the corresponding weight, comprehensive state determination and early warning can make the state determination of photovoltaic power station more accurate and improve work efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system for photovoltaic power plants. Background Technology

[0002] Currently, due to the increasing depletion of traditional energy sources and the growing demand for higher quality electricity, the application of solar photovoltaic power generation technology is becoming more and more widespread.

[0003] However, at present, solar photovoltaic power plants are mainly used in areas far from the public power grid with no or little electricity and in some special places. Therefore, it is increasingly important to conduct intelligent monitoring of these energy systems in order to achieve efficient operation and to conduct accurate monitoring of photovoltaic power plants.

[0004] Existing intelligent monitoring systems for photovoltaic power plants generally compare real-time data with standard data to determine equipment status. However, this approach suffers from incomplete and inaccurate monitoring and analysis, resulting in untimely and inaccurate fault warnings.

[0005] Therefore, the present invention provides an intelligent monitoring system for photovoltaic power plants. Summary of the Invention

[0006] This invention provides an intelligent monitoring system for photovoltaic power plants, which solves the problems of incomplete monitoring and analysis and insufficient accuracy and timeliness of fault early warning in the prior art.

[0007] This invention provides an intelligent monitoring system for photovoltaic power plants, comprising:

[0008] Data acquisition module: used to acquire power plant operation data and power plant status data of photovoltaic power plant based on preset sensors, and to perform data processing and data classification to obtain a first set of operation data and a first set of status data;

[0009] Status determination module: This module integrates the first operational data set and the first status data set, obtains the corresponding data weights based on the integration results, and determines the equipment status of each device in the photovoltaic power station by combining the corresponding data, thereby determining the overall equipment status of the target photovoltaic power station.

[0010] Status determination module: used to determine whether there is a fault in the target photovoltaic power station by combining the status of comprehensive equipment with environmental impact parameters;

[0011] Early warning and judgment module: used to determine the cause of the fault in the target photovoltaic power station and to issue a fault warning based on the cause of the fault.

[0012] The data acquisition module provided by the present invention includes:

[0013] Data indicator determination unit: used to determine the monitoring indicators that need to be intelligently monitored based on the safety monitoring requirements of the target photovoltaic power station, and to classify the monitoring indicators into a first state indicator type and a first operation indicator type;

[0014] Status data acquisition unit: used to acquire initial power station status data corresponding to the first status index type of photovoltaic power station based on preset status sensors, and to make a first judgment on the equipment status of the target photovoltaic power station based on the initial power station status data combined with real-time environmental parameters;

[0015] Data classification unit: used to classify the initial power station state data based on the different first devices when the first judgment result is qualified, and obtain the first state data set;

[0016] Operational data acquisition unit: used to acquire initial power plant operation data corresponding to the first operation index type based on preset equipment sensors, perform data standardization processing to obtain power plant operation data, and classify the power plant operation data according to the different first equipment to obtain the first operation data set.

[0017] The status data acquisition unit provided by the present invention includes:

[0018] First Judgment Subunit: Used to make a first judgment on the equipment status of the target photovoltaic power station by combining real-time environmental parameters;

[0019] If the initial power plant status data does not fall within the preset standard status range corresponding to the real-time environmental parameters, then a fault warning will be issued based on the initial power plant status data.

[0020] If the initial power plant status data falls within the preset standard state range corresponding to the real-time environmental parameters, then the initial power plant status data is filtered and standardized to obtain the power plant status data.

[0021] The state determination module provided by the present invention includes:

[0022] Data integration unit: used to integrate the data belonging to the same first device in the first state data set and the first operation data set to obtain the first device data, and to obtain the first device data set based on the first device data of each first device in the target photovoltaic power station;

[0023] First weight determination unit: used to extract the indicator type of each first device data, and obtain the first initial device weight based on the correlation between the device data corresponding to each indicator type and the device operation;

[0024] First weight optimization unit: used to optimize the first device weight based on the data fluctuation of the device data corresponding to each first indicator type to obtain the first optimized device weight;

[0025] The second weight determination unit is used to obtain the historical device data corresponding to each first device data and to use the historical weight of the corresponding historical device data as the second device weight of the corresponding first device data.

[0026] The third weight determination unit is used to obtain the standard equipment data of the first device corresponding to each first device data, and to perform normalization processing based on the difference between the first device data and the corresponding standard equipment data, and to obtain the third device weight corresponding to the first device data based on the processing result.

[0027] Weight matrix construction unit: used to extract the first optimized device weight, second device weight and third device weight corresponding to each first device data, so as to obtain the first weight matrix of each first device;

[0028] Data weight determination unit: used to obtain the data weight of each first device data based on the first matrix coefficients of each first device data in the first weight matrix;

[0029] Equipment status determination unit: used to combine the first equipment data with the corresponding data weight to determine the equipment status of each first equipment;

[0030] Comprehensive Status Determination Unit: Used to integrate the equipment status of each first device in the target photovoltaic power station to obtain the comprehensive equipment status of the target photovoltaic power station;

[0031] Where T represents the overall equipment status of the target photovoltaic power station; n represents the quantity of the first piece of equipment in the target photovoltaic power station; α i Let A be the comprehensive operational impact index of the i-th first device of the target photovoltaic power station on the target photovoltaic power station; i The device status of the i-th first device in the target photovoltaic power plant; δ j Let m be the inter-device influence index of the remaining j-th first device in the target photovoltaic power plant on the i-th first device; where j is the number of devices (n-1); m j Let be the distance between the remaining j-th first device and the ith first device in the target photovoltaic power station; η is the distance conversion coefficient; and exp is the exponential function.

[0032] The weight matrix construction unit provided by the present invention includes:

[0033] Weight extraction subunit: used to extract the first optimized device weight, second device weight and third device weight corresponding to each first device data;

[0034] Weighting subunit: Used to sort the first device data based on the degree of influence of the first optimized weight, second device weight and third device weight corresponding to each first device data on the first device data, and obtain an ordered set of device weights;

[0035] Matrix construction sub-unit: used to construct the first weight matrix of the corresponding first device based on the ordered set of device weights and the corresponding first device data.

[0036] The state determination module provided by the present invention includes:

[0037] Parameter acquisition unit: used to acquire the first standard state of a photovoltaic power station of the same type as the target photovoltaic power station, and to acquire the real-time environmental parameters of the target photovoltaic power station;

[0038] State correction unit: used to determine the environmental impact index of the target photovoltaic power station based on the real-time environmental parameters of the target photovoltaic power station, and combine the environmental impact index with the first standard state to obtain the second standard state of the target photovoltaic power station;

[0039] State comparison unit: used to compare the second standard state with the overall equipment state of the target photovoltaic power station one by one to obtain the first comparison result;

[0040] Status judgment unit: used to analyze the first comparison result based on a preset status-deviation database, thereby judging the fault status of the comprehensive equipment of the target photovoltaic power station.

[0041] The state determination unit provided by the present invention includes:

[0042] Comparative analysis subunit: used to compare the first comparison result with the preset state-deviation database, thereby determining the deviation level corresponding to each sub-comparison result;

[0043] Fault Judgment Subunit: Used to determine the overall deviation level of the target photovoltaic power station based on each deviation level, thereby determining whether the target photovoltaic power station has a fault;

[0044] If the overall deviation level is higher than the preset maximum deviation level, the target photovoltaic power station is judged to have a fault.

[0045] Conversely, if the target photovoltaic power station does not have a fault, it is determined that there is no fault.

[0046] The early warning judgment module provided by the present invention includes:

[0047] First Fault Cause Unit: Used to obtain the comprehensive deviation level of the target photovoltaic power station with faults, and then extract the first fault cause set corresponding to the comprehensive deviation level from the deviation-cause database;

[0048] Fault data acquisition unit: used to acquire fault data types related to each first fault cause in the first fault cause set;

[0049] The second fault cause unit is used to obtain the corresponding equipment data based on the fault data type, thereby determining whether there is a deviation in the corresponding equipment data, and eliminating the first fault cause that does not have equipment data deviation, to obtain the second fault cause set.

[0050] Fault warning unit: used to provide fault warnings based on the second fault cause in the second fault cause set.

[0051] The present invention provides an intelligent monitoring system for photovoltaic power plants. By processing and classifying operational and status data, and combining corresponding weights, the system accurately judges the status of each device in the photovoltaic power plant, thereby obtaining a more accurate comprehensive status judgment of the photovoltaic power plant. Based on the status judgment results, the system issues early warnings, which can make the status judgment of the photovoltaic power plant more accurate and timely, and at the same time improve the working efficiency of the photovoltaic power plant. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0053] Figure 1 This is a structural diagram of a photovoltaic power plant intelligent monitoring system provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0055] Example 1:

[0056] This invention provides an intelligent monitoring system for photovoltaic power plants, such as... Figure 1 As shown, it includes:

[0057] Data acquisition module: used to acquire power plant operation data and power plant status data of photovoltaic power plant based on preset sensors, and to perform data processing and data classification to obtain a first set of operation data and a first set of status data;

[0058] Status determination module: This module integrates the first operational data set and the first status data set, obtains the corresponding data weights based on the integration results, and determines the equipment status of each device in the photovoltaic power station by combining the corresponding data, thereby determining the overall equipment status of the target photovoltaic power station.

[0059] Status determination module: used to determine whether there is a fault in the target photovoltaic power station by combining the status of comprehensive equipment with environmental impact parameters;

[0060] Early warning and judgment module: used to determine the cause of the fault in the target photovoltaic power station and to issue a fault warning based on the cause of the fault.

[0061] In this embodiment, the power plant operation data is the photovoltaic power plant operation data obtained by the preset equipment sensors. The power plant operation data includes: power data, equipment parameters under standard operating conditions (such as rated power, conversion efficiency, etc.), actual power generation, operating temperature, voltage, current, input power, output power, etc.

[0062] In this embodiment, the power plant status data is based on the status data of the photovoltaic power plant obtained from preset status sensors. The status data includes: equipment conversion efficiency, equipment operating temperature, equipment response speed, stability data, etc.

[0063] In this embodiment, data processing refers to filtering and standardizing power plant operation data and power plant status data, while data classification refers to classifying power plant status data and power plant operation data based on the different devices corresponding to them.

[0064] In this embodiment, the first operating data set refers to the classified data set obtained by classifying the power station operating data based on different first devices, and the first status data set refers to the classified data set obtained by classifying the power station status data based on different first devices. The first device is the device that needs to be monitored in the photovoltaic power station.

[0065] In this embodiment, data integration refers to organizing and combining the first operating data and the first status data of the same first device.

[0066] In this embodiment, data weight refers to the influence weight of each first operating data and first state data in the first device on the first device. The data weight is determined by comprehensively considering data correlation and volatility, historical weight of historical device data, and differences between device data and standard device data.

[0067] In this embodiment, the device status is the working status of the device determined by analyzing the comprehensive data determined by combining the data integration results of the first operating data set and the first status data set with the corresponding data weights.

[0068] In this embodiment, the overall equipment status is determined based on the overall equipment status of all equipment in the target photovoltaic power station.

[0069] In this embodiment, the environmental impact parameters include the external pressure, temperature, humidity, wind force, and height of each first device from the ground.

[0070] In this embodiment, the status judgment is to determine whether the target photovoltaic power station has a fault.

[0071] In this embodiment, the causes of failure include component failure, connection failure, grounding failure, cleaning problems, configuration failure, and environmental factors.

[0072] In this embodiment, fault warning refers to issuing a device warning for the corresponding faulty equipment based on the cause of the fault.

[0073] The beneficial effects of the above technical solution are: by processing and classifying the operation data and status data, and combining the corresponding weights, the equipment status of each device in the photovoltaic power station can be accurately judged, resulting in a more accurate overall status judgment of the photovoltaic power station. Based on the status judgment results, early warnings can be issued, making the status judgment of the photovoltaic power station more accurate and timely, and also improving the working efficiency of the photovoltaic power station.

[0074] Example 2:

[0075] Based on Example 1, the data acquisition module includes:

[0076] Data indicator determination unit: used to determine the monitoring indicators that need to be intelligently monitored based on the safety monitoring requirements of the target photovoltaic power station, and to classify the monitoring indicators into a first state indicator type and a first operation indicator type;

[0077] Status data acquisition unit: used to acquire initial power station status data corresponding to the first status index type of photovoltaic power station based on preset status sensors, and to make a first judgment on the equipment status of the target photovoltaic power station based on the initial power station status data combined with real-time environmental parameters;

[0078] Data classification unit: used to classify the initial power station state data based on the different first devices when the first judgment result is qualified, and obtain the first state data set;

[0079] Operational data acquisition unit: used to acquire initial power plant operation data corresponding to the first operation index type based on preset equipment sensors, perform data standardization processing to obtain power plant operation data, and classify the power plant operation data according to the different first equipment to obtain the first operation data set.

[0080] In this embodiment, safety monitoring requirements include equipment safety monitoring requirements, electrical safety monitoring requirements, environmental safety monitoring requirements, performance and power generation efficiency monitoring requirements, etc.

[0081] In this embodiment, the monitoring indicators refer to the monitoring indicators that the target photovoltaic power station needs to be monitored in real time, as determined based on safety monitoring requirements.

[0082] In this embodiment, the first classification refers to dividing the monitoring indicators into first status indicators and first operational indicators. The data type corresponding to the first status indicator is the first status indicator type, and the data type corresponding to the first operational indicator type is the first operational indicator type.

[0083] In this embodiment, the initial power plant status data is the status data of the target photovoltaic power plant and the first status index type obtained by the preset status sensor.

[0084] In this embodiment, the first judgment refers to combining the initial power station status data with real-time environmental parameters to make an initial judgment on the equipment status of the target photovoltaic power station. If the initial power station status data does not fall within the preset standard status range corresponding to the real-time environmental factors, a fault warning is issued based on the initial power station status data. If the initial power station status data falls within the preset standard status range corresponding to the real-time environmental factors, the initial power station status data is filtered and standardized to obtain the power station status data.

[0085] In this embodiment, the first state data set refers to the state data set obtained by classifying the initial power plant state data that have passed the first judgment result according to the different first devices. Each subset in the first state data set corresponds to a unique first device.

[0086] In this embodiment, the initial power plant operation data is the operation data corresponding to the first operation index type of the target photovoltaic power plant obtained by the preset equipment sensors.

[0087] In this embodiment, the first operating data set is a categorized data set obtained by classifying the power plant operating data based on the different first devices.

[0088] The beneficial effects of the above technical solution are: by classifying the monitoring indicators, the power station status data and power station operation data can be processed separately, making the data monitoring of photovoltaic power stations more accurate, and thus enabling more accurate fault early warning.

[0089] Example 3:

[0090] Based on Embodiment 2, the state data acquisition unit includes:

[0091] First Judgment Subunit: Used to make a first judgment on the equipment status of the target photovoltaic power station by combining real-time environmental parameters;

[0092] If the initial power plant status data does not fall within the preset standard status range corresponding to the real-time environmental parameters, then a fault warning will be issued based on the initial power plant status data.

[0093] If the initial power plant status data falls within the preset standard state range corresponding to the real-time environmental parameters, then the initial power plant status data is filtered and standardized to obtain the power plant status data.

[0094] In this embodiment, the real-time environmental parameters include the external pressure, temperature, humidity, wind force, and height of the device above the ground for each first device.

[0095] In this embodiment, the preset standard state range refers to the standard equipment state of the target photovoltaic power station under real-time environmental parameters.

[0096] In this embodiment, the power plant status data refers to the power plant status data obtained after data filtering and data standardization processing of the initial power plant status data when the initial power plant status data falls within the preset standard status range corresponding to the real-time environmental parameters.

[0097] The beneficial effects of the above technical solution are: by judging the initial power station status data, processing the data, and combining it with the power station operation data for monitoring, the data monitoring of the photovoltaic power station can be made more accurate, thereby enabling more precise fault early warning.

[0098] Example 4:

[0099] Based on Embodiment 3, the state determination module includes:

[0100] Data integration unit: used to integrate the data belonging to the same first device in the first state data set and the first operation data set to obtain the first device data, and to obtain the first device data set based on the first device data of each first device in the target photovoltaic power station;

[0101] First weight determination unit: used to extract the indicator type of each first device data, and obtain the first initial device weight based on the correlation between the device data corresponding to each indicator type and the device operation;

[0102] First weight optimization unit: used to optimize the first device weight based on the data fluctuation of the device data corresponding to each first indicator type to obtain the first optimized device weight;

[0103] The second weight determination unit is used to obtain the historical device data corresponding to each first device data and to use the historical weight of the corresponding historical device data as the second device weight of the corresponding first device data.

[0104] The third weight determination unit is used to obtain the standard equipment data of the first device corresponding to each first device data, and to perform normalization processing based on the difference between the first device data and the corresponding standard equipment data, and to obtain the third device weight corresponding to the first device data based on the processing result.

[0105] Weight matrix construction unit: used to extract the first optimized device weight, second device weight and third device weight corresponding to each first device data, so as to obtain the first weight matrix of each first device;

[0106] Data weight determination unit: used to obtain the data weight of each first device data based on the first matrix coefficients of each first device data in the first weight matrix;

[0107] Equipment status determination unit: used to combine the first equipment data with the corresponding data weight to determine the equipment status of each first equipment;

[0108] Comprehensive Status Determination Unit: Used to integrate the equipment status of each first device in the target photovoltaic power station to obtain the comprehensive equipment status of the target photovoltaic power station;

[0109] Where T represents the overall equipment status of the target photovoltaic power station; n represents the quantity of the first piece of equipment in the target photovoltaic power station; α i Let A be the comprehensive operational impact index of the i-th first device of the target photovoltaic power station on the target photovoltaic power station; i The device status of the i-th first device in the target photovoltaic power plant; δ j Let m be the inter-device influence index of the remaining j-th first device in the target photovoltaic power plant on the i-th first device; where j is the number of devices (n-1); m j Let be the distance between the remaining j-th first device and the ith first device in the target photovoltaic power station; η is the distance conversion coefficient; and exp is the exponential function.

[0110] In this embodiment, the first device data includes the first status data and the first operating data corresponding to the first device.

[0111] In this embodiment, the first device data set is a data set composed of all the first device data of the target photovoltaic power station.

[0112] In this embodiment, the first device data set refers to the data set obtained by integrating the first device data corresponding to each first device of the target photovoltaic power station.

[0113] In this embodiment, the first initial device weight is determined based on the correlation between the device data of each indicator type in the first device data and the device operation.

[0114] In this embodiment, the first optimized device weight is obtained by optimizing the first device weight based on the data fluctuation.

[0115] In this embodiment, historical device data refers to the device data corresponding to the first device during its historical operation.

[0116] In this embodiment, the historical weight refers to the weight of the impact of historical device data on the operation of the device in the corresponding historical working process. The historical weight is the second device weight of the first device.

[0117] In this embodiment, standard equipment data refers to the standard equipment data corresponding to the first equipment under standard operating conditions.

[0118] In this embodiment, the normalization process is performed on the first device data based on the difference between the first device data and the corresponding standard device data.

[0119] In this embodiment, the third device weight is the weight of the impact of each device data on the device operation determined based on the normalization processing result.

[0120] In this embodiment, the first weight matrix refers to the device weight matrix of the current first device constructed based on the sorting results of the first optimized device weight, the second device weight, and the third device weight. For example, the first weight matrix of device A is... Where a, b, and c are device weights, and 1, 2, and 3 are different data types.

[0121] In this embodiment, the first matrix coefficient refers to the degree of influence of each device weight in the first weight matrix on the first device data. The greater the degree of influence, the larger the first matrix coefficient. The value range of the first matrix coefficient is (0, 1).

[0122] In this embodiment, data weight refers to the data weight of the first device data of the first device determined by combining the first weight matrix with the first matrix coefficients of each device weight.

[0123] In this embodiment, the device status is the device status of the first device determined by combining the data weight with the device data of the corresponding first device. The device status is represented by the device index corresponding to the current device status.

[0124] In this embodiment, the overall equipment status refers to combining the status of each piece of equipment in the target photovoltaic power station to comprehensively judge the overall equipment status of the target photovoltaic power station. Among them, the more core and powerful the equipment corresponding to the first piece of equipment, the greater its influence on the overall equipment status.

[0125] In this embodiment, the remaining first equipment refers to the remaining equipment included in the target photovoltaic power station other than the current first equipment.

[0126] The beneficial effects of the above technical solution are: by combining the influence weights of different influencing factors on the first equipment and processing the data of the first equipment accordingly, the analysis of the equipment status can be more accurate, thereby obtaining a more accurate comprehensive equipment status and making timely and accurate status judgments.

[0127] Example 5:

[0128] Based on Example 3, the weight matrix construction unit includes:

[0129] Weight extraction subunit: used to extract the first optimized device weight, second device weight and third device weight corresponding to each first device data;

[0130] Weighting subunit: Used to sort the first device data based on the degree of influence of the first optimized weight, second device weight and third device weight corresponding to each first device data on the first device data, and obtain an ordered set of device weights;

[0131] Matrix construction sub-unit: used to construct the first weight matrix of the corresponding first device based on the ordered set of device weights and the corresponding first device data.

[0132] In this embodiment, the degree of influence refers to the degree of influence of the data correlation and volatility corresponding to the first optimized device weight, the historical device data corresponding to the second device weight, and the difference between the third device weight and the standard device data on the first device data. The sum of the degree of influence of the first optimized device weight, the second device weight, and the third device weight is 1.

[0133] In this embodiment, the device weight set refers to the set of device weights for each first device obtained based on the ordered device weights after sorting the first weights according to the degree of influence of the first optimization weight, the second device weight, and the third device weight on the first device data.

[0134] In this embodiment, the first weight matrix is ​​a comprehensive weight matrix constructed based on the set of equipment weights for each first device in the target photovoltaic power station. In the first weight matrix, the relative positions of the first optimized device weight, the second device weight, and the third device weight in each set of equipment weights are consistent.

[0135] The beneficial effects of the above technical solution are: by determining the degree of influence of each influence weight on the data of the first device and then sorting them, the weight matrix can be made clearer, thereby enabling more accurate corresponding processing and making the analysis of the device status more precise.

[0136] Example 6:

[0137] Based on Embodiment 5, the state determination module includes:

[0138] Parameter acquisition unit: used to acquire the first standard state of a photovoltaic power station of the same type as the target photovoltaic power station, and to acquire the real-time environmental parameters of the target photovoltaic power station;

[0139] State correction unit: used to determine the environmental impact index of the target photovoltaic power station based on the real-time environmental parameters of the target photovoltaic power station, and combine the environmental impact index with the first standard state to obtain the second standard state of the target photovoltaic power station;

[0140] State comparison unit: used to compare the second standard state with the overall equipment state of the target photovoltaic power station one by one to obtain the first comparison result;

[0141] Status judgment unit: used to analyze the first comparison result based on a preset status-deviation database, thereby judging the fault status of the comprehensive equipment of the target photovoltaic power station.

[0142] In this embodiment, the first standard state refers to the standard equipment state of a photovoltaic power station of the same type as the target photovoltaic power station.

[0143] In this embodiment, the environmental impact index is determined based on the impact of real-time environmental parameters on the first standard state. The worse the real-time environment, the higher the environmental impact index.

[0144] In this embodiment, the second standard state refers to the standard state obtained by optimizing the first standard state based on the environmental impact index.

[0145] In this embodiment, the first comparison result refers to the comparison result obtained by comparing the second standard state with each sub-state in the comprehensive equipment state of the target photovoltaic power station.

[0146] In this embodiment, the state-deviation database refers to a database that contains the deviation of each device state from the standard state and the corresponding device fault conditions for each deviation.

[0147] In this embodiment, fault judgment refers to analyzing the first comparison result based on the state-deviation database to determine whether a fault exists in the photovoltaic power station.

[0148] The beneficial effects of the above technical solution are: by comparing and analyzing the status of integrated equipment, the fault condition of the target photovoltaic power station can be determined, and fault warnings can be issued more accurately and in a timely manner.

[0149] Example 7:

[0150] Based on Embodiment 6, the state determination unit includes:

[0151] Comparative analysis subunit: used to compare the first comparison result with the preset state-deviation database, thereby determining the deviation level corresponding to each sub-comparison result;

[0152] Fault Judgment Subunit: Used to determine the overall deviation level of the target photovoltaic power station based on each deviation level, thereby determining whether the target photovoltaic power station has a fault;

[0153] If the overall deviation level is higher than the preset maximum deviation level, the target photovoltaic power station is judged to have a fault.

[0154] Conversely, if the target photovoltaic power station does not have a fault, it is determined that there is no fault.

[0155] In this embodiment, the deviation level refers to the deviation level corresponding to each sub-comparison result in the first comparison result in the preset state-deviation database. The smaller the deviation level, the smaller the value of the corresponding sub-comparison result.

[0156] In this embodiment, the comprehensive deviation level refers to the comprehensive deviation level of the photovoltaic power station obtained by combining each deviation level in the first comparison result.

[0157] In this embodiment, the preset maximum deviation level is determined in advance based on the monitoring accuracy of the target photovoltaic power station. The higher the monitoring accuracy, the smaller the preset maximum deviation level.

[0158] The beneficial effects of the above technical solution are: by analyzing the comparison results between the integrated equipment status and the second standard status, the fault status of the target photovoltaic power station can be determined, and fault warnings can be given more accurately and in a timely manner.

[0159] Example 8:

[0160] Based on Example 7, the early warning judgment module includes:

[0161] First Fault Cause Unit: Used to obtain the comprehensive deviation level of the target photovoltaic power station with faults, and then extract the first fault cause set corresponding to the comprehensive deviation level from the deviation-cause database;

[0162] Fault data acquisition unit: used to acquire fault data types related to each first fault cause in the first fault cause set;

[0163] The second fault cause unit is used to obtain the corresponding equipment data based on the fault data type, thereby determining whether there is a deviation in the corresponding equipment data, and eliminating the first fault cause that does not have equipment data deviation, to obtain the second fault cause set.

[0164] Fault warning unit: used to provide fault warnings based on the second fault cause in the second fault cause set.

[0165] In this embodiment, the first set of fault causes refers to the set of all fault causes corresponding to the comprehensive deviation level extracted from the deviation-cause database.

[0166] In this embodiment, the fault data type refers to the data type of the first device data extracted from the first device data that is related to the first fault cause.

[0167] In this embodiment, the second fault cause set refers to the set of fault causes that are formed by obtaining the corresponding device data based on the fault data type, determining whether there is a data deviation in the obtained device data, and if there is no data deviation, then the corresponding first fault cause does not exist, and the corresponding first fault cause is removed. The set of fault causes that remain after removing the first fault causes corresponding to all device data without data deviation is formed.

[0168] The beneficial effects of the above technical solution are: by judging the cause of the failure of the target photovoltaic power station, the corresponding equipment can be handled more timely and accurately, thereby improving the overall working efficiency of the photovoltaic power station.

[0169] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart monitoring system for photovoltaic power plants, characterized in that, include: Data acquisition module: used to acquire power plant operation data and power plant status data of photovoltaic power plant based on preset sensors, and to perform data processing and data classification to obtain a first set of operation data and a first set of status data; Status determination module: This module integrates the first operational data set and the first status data set, obtains the corresponding data weights based on the integration results, and determines the equipment status of each device in the photovoltaic power station by combining the corresponding data, thereby determining the overall equipment status of the target photovoltaic power station. Status determination module: used to determine whether there is a fault in the target photovoltaic power station by combining the status of comprehensive equipment with environmental impact parameters; Early warning and judgment module: used to determine the cause of the fault in the target photovoltaic power station and to issue a fault warning based on the cause of the fault; The status determination module includes: Data integration unit: used to integrate the data belonging to the same first device in the first state data set and the first operation data set to obtain the first device data, and to obtain the first device data set based on the first device data of each first device in the target photovoltaic power station; First weight determination unit: used to extract the indicator type of each first device data, and obtain the first initial device weight based on the correlation between the device data corresponding to each indicator type and the device operation; First weight optimization unit: used to optimize the first device weight based on the data fluctuation of the device data corresponding to each first indicator type to obtain the first optimized device weight; The second weight determination unit is used to obtain the historical device data corresponding to each first device data and to use the historical weight of the corresponding historical device data as the second device weight of the corresponding first device data. The third weight determination unit is used to obtain the standard equipment data of the first device corresponding to each first device data, and to perform normalization processing based on the difference between the first device data and the corresponding standard equipment data, and to obtain the third device weight corresponding to the first device data based on the processing result. Weight matrix construction unit: used to extract the first optimized device weight, second device weight and third device weight corresponding to each first device data, so as to obtain the first weight matrix of each first device; Data weight determination unit: used to obtain the data weight of each first device data based on the first matrix coefficients of each first device data in the first weight matrix; Equipment status determination unit: used to combine the first equipment data with the corresponding data weight to determine the equipment status of each first equipment; Comprehensive Status Determination Unit: Used to integrate the equipment status of each first device in the target photovoltaic power station to obtain the comprehensive equipment status of the target photovoltaic power station; Where T represents the overall equipment status of the target photovoltaic power station; n represents the quantity of the first equipment in the target photovoltaic power station; The comprehensive operational impact index of the i-th first device of the target photovoltaic power station on the target photovoltaic power station; The device status of the i-th first device in the target photovoltaic power station; Let be the inter-device influence index of the remaining j-th first device in the target photovoltaic power plant on the i-th first device; where j is the number of devices (n-1). Let be the distance between the remaining j-th first device and the ith first device in the target photovoltaic power station; This is the distance conversion factor; It is an exponential function.

2. The intelligent monitoring system for a photovoltaic power station according to claim 1, characterized in that, The data acquisition module includes: Data indicator determination unit: used to determine the monitoring indicators that need to be intelligently monitored based on the safety monitoring requirements of the target photovoltaic power station, and to classify the monitoring indicators into a first state indicator type and a first operation indicator type; Status data acquisition unit: used to acquire initial power station status data corresponding to the first status index type of photovoltaic power station based on preset status sensors, and to make a first judgment on the equipment status of the target photovoltaic power station based on the initial power station status data combined with real-time environmental parameters; Data classification unit: used to classify the initial power station state data based on the different first devices when the first judgment result is qualified, and obtain the first state data set; Operational data acquisition unit: used to acquire initial power plant operation data corresponding to the first operation index type based on preset equipment sensors, perform data standardization processing to obtain power plant operation data, and classify the power plant operation data according to the different first equipment to obtain the first operation data set.

3. The intelligent monitoring system for a photovoltaic power station according to claim 2, characterized in that, The status data acquisition unit includes: First Judgment Subunit: Used to make a first judgment on the equipment status of the target photovoltaic power station by combining real-time environmental parameters; If the initial power plant status data does not fall within the preset standard status range corresponding to the real-time environmental parameters, then a fault warning will be issued based on the initial power plant status data. If the initial power plant status data falls within the preset standard state range corresponding to the real-time environmental parameters, then the initial power plant status data is filtered and standardized to obtain the power plant status data.

4. The intelligent monitoring system for a photovoltaic power station according to claim 1, characterized in that, The weight matrix construction unit includes: Weight extraction subunit: used to extract the first optimized device weight, second device weight and third device weight corresponding to each first device data; Weighting subunit: Used to sort the first device data based on the degree of influence of the first optimized weight, second device weight and third device weight corresponding to each first device data on the first device data, and obtain an ordered set of device weights; Matrix construction sub-unit: used to construct the first weight matrix of the corresponding first device based on the ordered set of device weights and the corresponding first device data.

5. The intelligent monitoring system for a photovoltaic power station according to claim 1, characterized in that, The status determination module includes: Parameter acquisition unit: used to acquire the first standard state of a photovoltaic power station of the same type as the target photovoltaic power station, and to acquire the real-time environmental parameters of the target photovoltaic power station; State correction unit: used to determine the environmental impact index of the target photovoltaic power station based on the real-time environmental parameters of the target photovoltaic power station, and combine the environmental impact index with the first standard state to obtain the second standard state of the target photovoltaic power station; State comparison unit: used to compare the second standard state with the overall equipment state of the target photovoltaic power station one by one to obtain the first comparison result; Status judgment unit: used to analyze the first comparison result based on a preset status-deviation database, thereby judging the fault status of the comprehensive equipment of the target photovoltaic power station.

6. The intelligent monitoring system for a photovoltaic power station according to claim 5, characterized in that, The status determination unit includes: Comparative analysis subunit: used to compare the first comparison result with the preset state-deviation database, thereby determining the deviation level corresponding to each sub-comparison result; Fault Judgment Subunit: Used to determine the overall deviation level of the target photovoltaic power station based on each deviation level, thereby determining whether the target photovoltaic power station has a fault; If the overall deviation level is higher than the preset maximum deviation level, the target photovoltaic power station is judged to have a fault. Conversely, if the target photovoltaic power station does not have a fault, it is determined that there is no fault.

7. The intelligent monitoring system for a photovoltaic power station according to claim 6, characterized in that, The early warning judgment module includes: First Fault Cause Unit: Used to obtain the comprehensive deviation level of the target photovoltaic power station with faults, and then extract the first fault cause set corresponding to the comprehensive deviation level from the deviation-cause database; Fault data acquisition unit: used to acquire fault data types related to each first fault cause in the first fault cause set; The second fault cause unit is used to obtain the corresponding equipment data based on the fault data type, thereby determining whether there is a deviation in the corresponding equipment data, and eliminating the first fault cause that does not have equipment data deviation, to obtain the second fault cause set. Fault warning unit: used to provide fault warnings based on the second fault cause in the second fault cause set.

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