CN117391499A - Photovoltaic power station reliability evaluation method and device - Google Patents
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Abstract
The application provides a photovoltaic power station reliability evaluation method and device, and relates to the technical field of new energy and the technical field of data processing. The method comprises the following steps: candidate attribute data and target evaluation requirements of the photovoltaic power station are obtained, and a target evaluation index and a target evaluation strategy are determined according to the target evaluation requirements; carrying out multidimensional analysis and classification on the candidate attribute data according to target evaluation requirements to obtain target attribute data corresponding to a plurality of influence factors; acquiring the association degree of a plurality of influence factors and target evaluation indexes based on a target evaluation strategy, and further determining index weights corresponding to the plurality of influence factors; and acquiring reliability evaluation values of the photovoltaic power station under the current evaluation requirement according to target attribute data and index weights corresponding to the influence factors. The method and the device can comprehensively evaluate the reliability of the photovoltaic power station, so that the state and the reliability of the distributed photovoltaic power station are effectively analyzed, and the reliability analysis efficiency of the photovoltaic power station is improved.
Description
Technical Field
The application relates to the technical field of new energy and the technical field of data processing, in particular to a reliability evaluation method and device for a photovoltaic power station.
Background
In the related technology, the intermittent performance and the randomness of the photovoltaic power generation are extremely strong due to the influence of various factors such as the external environment and the quality of equipment, and meanwhile, the difficulty of evaluating the reliability of the photovoltaic power station is also enhanced. In order to ensure that the photovoltaic power station can safely, economically and reliably operate, how to deeply excavate the problems existing in the photovoltaic power station on the basis of acquiring mass data and comprehensively evaluate the reliability of the photovoltaic power station, the state and the reliability of the distributed photovoltaic power station are effectively analyzed, and the reliability analysis efficiency of the photovoltaic power station is improved, so that the method has become one of important research directions.
Disclosure of Invention
The present application aims to solve, at least to some extent, one of the technical problems in the related art. For this purpose, an object of the present application is to propose a photovoltaic power plant reliability evaluation method.
A second object of the present application is to provide a photovoltaic power station reliability evaluation device.
A third object of the present application is to propose an electronic device.
A fourth object of the present application is to propose a non-transitory computer readable storage medium.
A fifth object of the present application is to propose a computer programme product.
To achieve the above object, an embodiment of a first aspect of the present application provides a method for evaluating reliability of a photovoltaic power station, including:
candidate attribute data and target evaluation requirements of a photovoltaic power station are obtained, and a target evaluation index and a target evaluation strategy are determined according to the target evaluation requirements, wherein the candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station;
performing multi-dimensional analysis and classification on the candidate attribute data according to the target evaluation requirement to obtain target attribute data corresponding to a plurality of influence factors;
acquiring the association degree of the plurality of influence factors and the target evaluation index based on the target evaluation strategy, and determining index weights corresponding to the plurality of influence factors according to the association degree;
and inputting target attribute data corresponding to the influence factors and index weights corresponding to the influence factors into a target neural network for prediction to obtain a reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
The method and the device can deeply excavate the problems existing in the photovoltaic power station on the basis of acquiring mass data, comprehensively evaluate the reliability of the photovoltaic power station, effectively analyze the state and the reliability of the distributed photovoltaic power station, and improve the reliability analysis efficiency of the photovoltaic power station.
To achieve the above object, an embodiment of a second aspect of the present application provides a photovoltaic power station reliability evaluation device, including:
the first determining module is used for acquiring candidate attribute data and target evaluation requirements of the photovoltaic power station, and determining target evaluation indexes and target evaluation strategies according to the target evaluation requirements, wherein the candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station;
the first acquisition module is used for carrying out multi-dimensional analysis and classification on the candidate attribute data according to the target evaluation requirement to acquire target attribute data corresponding to a plurality of influence factors;
the second determining module is used for acquiring the association degrees of the plurality of influence factors and the target evaluation index based on the target evaluation strategy, and determining index weights corresponding to the plurality of influence factors according to the association degrees;
the second acquisition module is used for inputting the target attribute data corresponding to the influence factors and the index weights corresponding to the influence factors into a target neural network for prediction so as to acquire the reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the photovoltaic power plant reliability evaluation method provided in the embodiments of the first aspect of the present application.
To achieve the above object, an embodiment of a fourth aspect of the present application proposes a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the photovoltaic power plant reliability evaluation method provided in the embodiment of the first aspect of the present application.
To achieve the above object, an embodiment of a fifth aspect of the present application proposes a computer program product comprising a computer program which, when executed by a processor, implements the photovoltaic power plant reliability evaluation method provided in the embodiment of the first aspect of the present application.
Drawings
FIG. 1 is a flow chart of a photovoltaic power plant reliability evaluation method of one embodiment of the present application;
FIG. 2 is a schematic diagram of a photovoltaic power plant reliability evaluation method according to one embodiment of the present application;
FIG. 3 is a schematic diagram of a photovoltaic power plant reliability evaluation method according to one embodiment of the present application;
FIG. 4 is a flow chart of a photovoltaic power plant reliability evaluation method of one embodiment of the present application;
FIG. 5 is a flow chart of a photovoltaic power plant reliability evaluation method of an embodiment of the present application;
FIG. 6 is a block diagram of a photovoltaic power plant reliability evaluation apparatus according to one embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The terms in the examples of the present application are explained below:
principal component analysis (Principal Component Analysis, PCA) is a statistical method. A set of variables which may have correlation is converted into a set of variables which are not linearly correlated through positive-negative conversion, and the converted set of variables is called a main component.
Association analysis, also known as association mining, is the finding of frequent patterns, associations, correlations, or causal structures that exist between collections of items or objects in transactional data, relational data, or other information carriers.
The following describes a photovoltaic power station reliability evaluation method and a device thereof according to an embodiment of the present application with reference to the accompanying drawings.
Fig. 1 is a flowchart of a photovoltaic power plant reliability evaluation method according to an embodiment of the present application, as shown in fig. 1, the method includes the steps of:
s101, candidate attribute data and target evaluation requirements of the photovoltaic power station are obtained, and a target evaluation index and a target evaluation strategy are determined according to the target evaluation requirements.
The candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station.
In some embodiments, the power generation parameters, the device parameters, and the environmental parameters of the photovoltaic power plant stored in the database may be obtained as candidate attribute data.
In some implementations, device, grid and environment information, etc. Guan Canliang can be collected as candidate attribute data.
In this embodiment, the evaluation requirement may be a requirement for evaluating performance of the photovoltaic power generation system and an operation condition of the monitoring system, and the evaluation requirement is a system-level evaluation, such as an evaluation of performance reliability of the photovoltaic power generation system, in some embodiments, an evaluation requirement is an equipment-level evaluation, such as an evaluation of health of a component, a prediction of lifetime of the component, and an equipment failure diagnosis.
In the embodiment of the application, the mapping relation among the candidate evaluation requirement, the candidate evaluation index and the candidate evaluation strategy is obtained. And matching the target evaluation requirement with the candidate evaluation requirement to obtain the matching degree. And determining the candidate evaluation index and the candidate evaluation strategy corresponding to the candidate evaluation requirement with the highest matching degree as a target evaluation index and a target evaluation strategy.
Alternatively, the candidate evaluation index may be a device failure mode, a failure location, and a failure severity; alternatively, the candidate evaluation index may be the power generation utilization time, the power generation time deviation, the pearson correlation coefficient under different time ranges.
In the embodiment of the application, the candidate evaluation policy may include an analysis algorithm for mining the association relationship between the attribute data and the evaluation index, such as a big data core mining analysis method including statistical analysis, pattern recognition, principal component analysis, association analysis, and the like.
S102, carrying out multi-dimensional analysis and classification on the candidate attribute data according to target evaluation requirements, and obtaining target attribute data corresponding to a plurality of influence factors.
As shown in fig. 2, in some embodiments, the candidate attribute data is analyzed and classified from multiple dimensions such as a time dimension, a state dimension, a hierarchy dimension, a degree dimension and the like, so that reliability analysis of the photovoltaic power station is realized, related data is mined, and main influencing factors of reliability are mined from heterogeneous multi-source data of the photovoltaic power station.
In some implementations, the time dimension may include a time range of minute, hour, day, month, year, etc. levels. In some embodiments, the state dimensions may include a health state, a critical state, and a risk state, where the health state may include, for example, a component lifetime, a component temperature performance, and the like, and the risk state includes a plurality of risk levels divided in advance, and further, a threshold may be set to distinguish the critical state, where if a voltage value of the inverter is within a preset high voltage range, it is determined that the inverter is in the critical state, and the risk state is a high risk level.
In some implementations, the hierarchy dimension can include a device component level, a region level, and a system level, where the component level can include component performance metrics, failure rates, etc., the region level can include component region performance metrics, failure rates, etc., and the system level can include system stability, loss rates, etc.
In some embodiments, after obtaining the target attribute data corresponding to the plurality of influencing factors, the method further includes: aiming at target attribute data corresponding to any influence factor, carrying out data cleaning on the target attribute data, wherein the data cleaning comprises one or more of the following steps: missing value processing, outlier processing, data normalization processing and standardization processing.
In this embodiment of the present application, the missing value processing method may include deleting the missing value, or may perform mean interpolation, median interpolation, mode interpolation, or regression interpolation processing on the missing value.
In the embodiment of the present application, the method for processing an outlier may include deleting the outlier, or may replace the outlier with another reasonable value. The replacement method may be selected according to the specific situation, and for example, the average value, the median value, or some fixed value may be used to replace the outlier.
S103, acquiring the association degree of the plurality of influence factors and the target evaluation index based on the target evaluation strategy, and determining index weights corresponding to the plurality of influence factors according to the association degree.
In some embodiments, according to the target evaluation policy, for the specified influencing factors and the target evaluation indexes, a method such as multivariate statistical analysis, multidimensional association analysis and the like is applied to determine the association degree of the influencing factors and the target evaluation indexes, and the association degree is determined as the index weight corresponding to the influencing factors.
And S104, inputting target attribute data corresponding to the plurality of influence factors and index weights corresponding to the plurality of influence factors into a target neural network for prediction to obtain a reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
As shown in fig. 3, in the embodiment of the present application, the structure of the neural network includes an input neuron, an implicit neuron, and an output neuron, in the embodiment of the present application, target attribute data corresponding to a plurality of influence factors and index weights corresponding to a plurality of influence factors are input into the target neural network to predict, an evaluation scale T is selected according to requirements, and an index value O (t+t) at a time (t+t) is selected as output based on the influence factors, so as to predict a reliability evaluation value after T time.
The method and the device can deeply excavate the problems existing in the photovoltaic power station on the basis of acquiring mass data, comprehensively evaluate the reliability of the photovoltaic power station, effectively analyze the state and the reliability of the distributed photovoltaic power station, and improve the reliability analysis efficiency of the photovoltaic power station.
In some embodiments, the training process of the target neural network includes: and acquiring a training data set, wherein the training data set comprises a plurality of influence factors, training attribute data corresponding to any influence factor and training index weights. And inputting the training data set into an initial neural network, and performing prediction processing by the initial neural network to obtain a prediction reliability evaluation value. Obtaining a predicted reliability evaluation value and a loss function of a preset reference reliability evaluation value, and training the initial neural network according to the loss function until the training is finished to generate a target neural network.
Optionally, after the loss function converges to a preset loss threshold or reaches a preset training number, training may be determined to be completed, so as to obtain the target neural network.
In some implementations, dynamic maintenance of the index system can be realized through regular or irregular data analysis, and validity and rationality of the parameter set are ensured.
In the embodiment of the application, the influence factors with higher relevance to the evaluation value to be predicted are introduced into the training sample of the prediction model, so that the prediction precision of the model can be improved.
Fig. 4 is a flowchart of a photovoltaic power plant reliability evaluation method according to an embodiment of the present application, as shown in fig. 4, the method includes the steps of:
s401, candidate attribute data and target evaluation requirements of the photovoltaic power station are obtained, and a target evaluation index and a target evaluation strategy are determined according to the target evaluation requirements, wherein the candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station.
S402, carrying out multi-dimensional analysis and classification on the candidate attribute data according to target evaluation requirements, and obtaining target attribute data corresponding to a plurality of influence factors.
The description of step S401 to step S402 may be referred to the relevant content in the above embodiment, and will not be repeated here.
S403, identifying target attribute data corresponding to a plurality of influence factors based on a preset device parameter type, and determining key parameters of device operation.
In response to evaluating a demand for equipment fault diagnosis, the target evaluation index includes at least an equipment fault mode, a fault location, and a fault severity.
Identifying target attribute data corresponding to a plurality of influence factors based on a preset device parameter type, deleting target attribute data of a non-device parameter type, and acquiring key parameters of device operation.
S404, quantifying the association degree of the target evaluation index and different key parameters by a data mining algorithm based on association rules.
And quantifying the association degree of the equipment fault mode, the fault part and the key parameters of the equipment operation, corresponding to the fault severity and a plurality of influence factors, by utilizing a data mining algorithm of the association rule, optionally, the input data adopted during the identification come from the actual operation data of the photovoltaic power station, and the actual maximum power operation point data of the photovoltaic module and the key control parameters of the inverter can be obtained through the parameter identification of the photovoltaic power station, so that the reliability evaluation of the photovoltaic power station has higher precision and is closer to the actual operation state of the photovoltaic power station.
S405, determining index weights corresponding to a plurality of influence factors according to the association degree.
S406, inputting target attribute data corresponding to the influence factors and index weights corresponding to the influence factors into a target neural network for prediction to obtain a reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
The description of step S405 to step S406 may be referred to the relevant content in the above embodiment, and will not be repeated here.
The method and the device can deeply excavate the problems existing in the photovoltaic power station on the basis of acquiring mass data, comprehensively evaluate the reliability of the photovoltaic power station, effectively analyze the state and the reliability of the distributed photovoltaic power station, and improve the reliability analysis efficiency of the photovoltaic power station.
Fig. 5 is a flowchart of a photovoltaic power plant reliability evaluation method according to an embodiment of the present application, as shown in fig. 5, the method includes the steps of:
s501, candidate attribute data and target evaluation requirements of the photovoltaic power station are obtained, and a target evaluation index and a target evaluation strategy are determined according to the target evaluation requirements, wherein the candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station.
S501, carrying out multi-dimensional analysis and classification on the candidate attribute data according to target evaluation requirements, and obtaining target attribute data corresponding to a plurality of influence factors.
The description of step S501 to step S502 may be referred to the relevant content in the above embodiment, and will not be repeated here.
And responding to the evaluation requirement for evaluating the power generation state, wherein the plurality of influence factors at least comprise environmental factors (irradiation and temperature) and electrical factors (current, voltage, power generation and power generation data), and after target attribute data corresponding to the plurality of influence factors are acquired, the digital fir alignment is carried out according to a time axis, so that the subsequent indexing and analysis are convenient.
Further, the daily power generation amount sample data is obtained by taking a station as a unit according to a K-means clustering method through carrying out operations such as feature distribution analysis, missing value filling, abnormal value processing and the like on the power generation amount data and the power generation power data.
S502, performing cluster analysis on target attribute data of a plurality of influence factors, and acquiring characteristic parameters in different time ranges.
And carrying out cluster analysis on target attribute data of a plurality of influence factors, constructing a daily power generation typical curve, processing the generated energy into daily utilization hours, and calculating the daily utilization hours and the monthly utilization hours.
Further, a daily power generation power typical curve is constructed: the solar energy generation capacity sample data is obtained by performing operations such as feature distribution analysis, missing value filling, abnormal value processing and the like on the power data according to a K-means clustering method by taking a station as a unit, and a power generation power typical curve is obtained by taking 15 minutes as a sampling frequency.
And S503, performing multi-feature comprehensive judgment on the feature parameters and the target evaluation indexes in different time ranges based on an entropy method, and obtaining the association degree.
In response to the evaluation demand being the power generation state evaluation, the target evaluation index includes power generation utilization time, power generation time deviation, pearson correlation coefficient under different time ranges.
For example, in the embodiment of the present application, the target evaluation index may include a day usage hour number, a month usage hour number, a day usage hour number deviation (day usage hour number minus typical month usage hour number of the area), and a month usage hour number deviation (month usage hour number minus typical month usage hour number of the area), a fitting correlation coefficient of a day power generation curve and a typical curve of the area, a correlation coefficient of a month power generation curve and a typical curve of the area (pearson correlation coefficient), and 5 target evaluation indexes as characteristic parameters.
And (3) performing multi-feature comprehensive judgment on the characteristic parameters and the target evaluation indexes in different time ranges by adopting an entropy method, and determining index weights corresponding to a plurality of influence factors according to the association degree conveniently by the difference degree among the characteristic values.
S504, determining index weights corresponding to the influence factors according to the association degrees.
S505, inputting target attribute data corresponding to the influence factors and index weights corresponding to the influence factors into a target neural network for prediction to obtain a reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
The description of steps S504 to S505 may be referred to the relevant content in the above embodiments, and will not be repeated here.
The method and the device can deeply excavate the problems existing in the photovoltaic power station on the basis of acquiring mass data, comprehensively evaluate the reliability of the photovoltaic power station, effectively analyze the state and the reliability of the distributed photovoltaic power station, and improve the reliability analysis efficiency of the photovoltaic power station.
Fig. 6 is a block diagram of a photovoltaic power plant reliability evaluation apparatus according to an embodiment of the present disclosure, and as shown in fig. 6, a photovoltaic power plant reliability evaluation apparatus 600 includes:
the first determining module 610 is configured to obtain candidate attribute data and a target evaluation requirement of the photovoltaic power station, and determine a target evaluation index and a target evaluation policy according to the target evaluation requirement, where the candidate attribute data includes a power generation parameter, an equipment parameter, and an environmental parameter of the photovoltaic power station;
the first obtaining module 620 is configured to perform multidimensional analysis and classification on the candidate attribute data according to the target evaluation requirement, and obtain target attribute data corresponding to a plurality of influence factors;
the second determining module 630 is configured to obtain association degrees between the plurality of influence factors and the target evaluation index based on the target evaluation policy, and determine index weights corresponding to the plurality of influence factors according to the association degrees;
the second obtaining module 640 is configured to input target attribute data corresponding to the plurality of influencing factors and index weights corresponding to the plurality of influencing factors into the target neural network for prediction, so as to obtain a reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
In some embodiments, the evaluation requirement is a device fault diagnosis, and the second determining module 630 is further configured to:
identifying target attribute data corresponding to a plurality of influence factors based on a preset equipment parameter type, and determining key parameters of equipment operation;
and quantifying the association degree of the target evaluation index and different key parameters by a data mining algorithm based on association rules.
In some embodiments, the evaluation requirement is a power generation status evaluation, and the second determining module 630 is further configured to:
performing cluster analysis on target attribute data of a plurality of influence factors to obtain characteristic parameters in different time ranges;
and carrying out multi-feature comprehensive judgment on the characteristic parameters and the target evaluation indexes in different time ranges based on an entropy method to obtain the association degree.
In some embodiments, the first determining module 610 is further configured to:
responding to the evaluation requirement for equipment fault diagnosis, wherein the target evaluation index at least comprises an equipment fault mode, a fault part and a fault severity;
in response to the evaluation demand being the power generation state evaluation, the target evaluation index includes power generation utilization time, power generation time deviation, pearson correlation coefficient under different time ranges.
In some embodiments, the training process of the target neural network includes:
acquiring a training data set, wherein the training data set comprises a plurality of influence factors, training attribute data corresponding to any influence factor and training index weights;
inputting the training data set into an initial neural network, and performing prediction processing by the initial neural network to obtain a prediction reliability evaluation value;
obtaining a predicted reliability evaluation value and a loss function of a preset reference reliability evaluation value, and training the initial neural network according to the loss function until the training is finished to generate a target neural network.
In some embodiments, the first determining module 610 is further configured to:
obtaining a mapping relation among candidate evaluation requirements, candidate evaluation indexes and candidate evaluation strategies;
matching the target evaluation requirement with the candidate evaluation requirement to obtain a matching degree;
and determining the candidate evaluation index and the candidate evaluation strategy corresponding to the candidate evaluation requirement with the highest matching degree as a target evaluation index and a target evaluation strategy.
In some embodiments, the first acquisition module 620 is further configured to:
aiming at target attribute data corresponding to any influence factor, carrying out data cleaning on the target attribute data, wherein the data cleaning comprises one or more of the following steps: missing value processing, outlier processing, data normalization processing and standardization processing.
The method and the device can deeply excavate the problems existing in the photovoltaic power station on the basis of acquiring mass data, comprehensively evaluate the reliability of the photovoltaic power station, effectively analyze the state and the reliability of the distributed photovoltaic power station, and improve the reliability analysis efficiency of the photovoltaic power station.
Based on the same application conception, the embodiment of the application also provides electronic equipment.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic device 700 includes a memory 701, a processor 702, and a computer program product stored in the memory 701 and executable on the processor 702, and when the processor executes the computer program, the aforementioned photovoltaic power plant reliability evaluation method is implemented.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Based on the same application concept, the embodiment of the present application further provides a computer readable storage medium having computer instructions stored thereon, where the computer instructions are configured to cause a computer to execute the photovoltaic power plant reliability evaluation method in the above embodiment.
Based on the same application concept, the embodiments of the present application further provide a computer program product, including a computer program, which when executed by a processor, is configured to perform the photovoltaic power plant reliability evaluation method in the above embodiments.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. A method for evaluating reliability of a photovoltaic power station, comprising:
candidate attribute data and target evaluation requirements of a photovoltaic power station are obtained, and a target evaluation index and a target evaluation strategy are determined according to the target evaluation requirements, wherein the candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station;
performing multi-dimensional analysis and classification on the candidate attribute data according to the target evaluation requirement to obtain target attribute data corresponding to a plurality of influence factors;
acquiring the association degree of the plurality of influence factors and the target evaluation index based on the target evaluation strategy, and determining index weights corresponding to the plurality of influence factors according to the association degree;
and inputting target attribute data corresponding to the influence factors and index weights corresponding to the influence factors into a target neural network for prediction to obtain a reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
2. The method of claim 1, wherein the evaluation requirement is a device fault diagnosis, the obtaining the association degree of the plurality of influencing factors and the target evaluation index based on the target evaluation policy comprises:
identifying target attribute data corresponding to the influence factors based on a preset equipment parameter type, and determining key parameters of equipment operation;
and quantifying the association degree of the target evaluation index and different key parameters by a data mining algorithm based on association rules.
3. The method of claim 1, wherein the evaluation requirement is a power generation status evaluation, the obtaining the association degree of the plurality of influencing factors and the target evaluation index based on the target evaluation policy comprises:
performing cluster analysis on the target attribute data of the plurality of influence factors to obtain characteristic parameters in different time ranges;
and carrying out multi-feature comprehensive judgment on the characteristic parameters and the target evaluation indexes in different time ranges based on an entropy method to obtain the association degree.
4. A method according to claim 2 or 3, further comprising:
responding to the evaluation requirement for equipment fault diagnosis, wherein the target evaluation index at least comprises an equipment fault mode, a fault part and a fault severity;
and responding to the evaluation requirement as power generation state evaluation, wherein the target evaluation index comprises power generation utilization time, power generation time deviation and Pelson correlation coefficient in different time ranges.
5. The method of claim 1, wherein the training process of the target neural network comprises:
acquiring a training data set, wherein the training data set comprises a plurality of influence factors, training attribute data corresponding to any influence factor and training index weights;
inputting the training data set into an initial neural network, and performing prediction processing by the initial neural network to obtain a prediction reliability evaluation value;
obtaining the predicted reliability evaluation value and a loss function of a preset reference reliability evaluation value, and training the initial neural network according to the loss function until training is finished to generate a target neural network.
6. The method of claim 1, wherein the determining a target evaluation index and a target evaluation policy based on the target evaluation requirement comprises:
obtaining a mapping relation among candidate evaluation requirements, candidate evaluation indexes and candidate evaluation strategies;
matching the target evaluation requirement with the candidate evaluation requirement to obtain a matching degree;
and determining the candidate evaluation index and the candidate evaluation strategy corresponding to the candidate evaluation requirement with the highest matching degree as a target evaluation index and a target evaluation strategy.
7. The method according to claim 1, wherein after the target attribute data corresponding to the plurality of influencing factors is obtained, further comprising:
aiming at target attribute data corresponding to any influence factor, carrying out data cleaning on the target attribute data, wherein the data cleaning comprises one or more of the following steps: missing value processing, outlier processing, data normalization processing and standardization processing.
8. The device for evaluating the reliability of the photovoltaic power station is characterized by comprising the following components:
the first determining module is used for acquiring candidate attribute data and target evaluation requirements of the photovoltaic power station, and determining target evaluation indexes and target evaluation strategies according to the target evaluation requirements, wherein the candidate attribute data comprise power generation parameters, equipment parameters and environment parameters of the photovoltaic power station;
the first acquisition module is used for carrying out multi-dimensional analysis and classification on the candidate attribute data according to the target evaluation requirement to acquire target attribute data corresponding to a plurality of influence factors;
the second determining module is used for acquiring the association degrees of the plurality of influence factors and the target evaluation index based on the target evaluation strategy, and determining index weights corresponding to the plurality of influence factors according to the association degrees;
the second acquisition module is used for inputting the target attribute data corresponding to the influence factors and the index weights corresponding to the influence factors into a target neural network for prediction so as to acquire the reliability evaluation value of the photovoltaic power station under the current evaluation requirement.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the steps of the method according to any one of claims 1-6.
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| CN202311286578.3A CN117391499A (en) | 2023-10-07 | 2023-10-07 | Photovoltaic power station reliability evaluation method and device |
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| CN202311286578.3A CN117391499A (en) | 2023-10-07 | 2023-10-07 | Photovoltaic power station reliability evaluation method and device |
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