CN116187027A - Intelligent prediction and early warning method and system for photovoltaic power generation faults - Google Patents
Intelligent prediction and early warning method and system for photovoltaic power generation faults Download PDFInfo
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Abstract
The invention discloses an intelligent prediction and early warning method and system for photovoltaic power generation faults, and relates to the technical field of photovoltaic power generation, wherein the method comprises the following steps: acquiring a health monitoring category set of photovoltaic power generation, and determining a health monitoring index set; collecting a real-time index parameter set and calculating a real-time health index; if the preset health threshold is not reached, a preset fault prediction scheme is called; acquiring a historical photovoltaic power generation fault record, and analyzing to obtain a photovoltaic power generation fault tree; acquiring a first fault type, matching a first fault factor and collecting a first state characteristic parameter; establishing a photovoltaic power generation simulation model to obtain a first state analysis result; and carrying out fault prediction and early warning on the photovoltaic power generation. The problems that in the prior art, the operation inspection of the photovoltaic module by using the traditional manual inspection and other modes is low in working efficiency and untimely in fault discovery are solved. The abnormal recognition accuracy of the photovoltaic module is improved, intelligent early warning responsivity of the system is improved, and the normal and stable photovoltaic power generation effect is guaranteed.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to an intelligent prediction and early warning method and system for photovoltaic power generation faults.
Background
With further increase of energy demand and continuous development of photovoltaic industry, solar energy becomes an important energy source, and importance of a photovoltaic power generation system in energy supply is also gradually revealed. In the existing photovoltaic power generation process, the core component photovoltaic module is influenced by various factors in the actual environment operation, abnormal power generation state or fault event conditions can occur, and if supervision staff cannot timely take corresponding measures for the sudden accidents, the normal operation of the whole photovoltaic power generation system can be influenced by different degrees, and even uncontrollable events such as fire and the like can be caused. For example, the operation of the photovoltaic power generation assembly is affected by the environment such as illumination intensity, ambient temperature, ambient wind condition, relative humidity, etc., so that the power generation efficiency of the photovoltaic power generation system is changed, the active power, reactive power, apparent power and power factor of the photovoltaic power generation system are changed, the amplitude and phase of the harmonic voltage and current are changed, etc. Therefore, the research utilizes computer science and technology to dynamically monitor the running state of each component in the photovoltaic power generation system and intelligently predict and early warn faults, so that the fault risk is timely checked, and the overall efficiency of the photovoltaic power generation system is improved.
However, in the prior art, the photovoltaic module is affected by various factors in the actual environment operation in the photovoltaic power generation process, abnormal power generation state or fault event conditions can occur, the operation inspection of the photovoltaic module by using the traditional manual inspection and other modes has low working efficiency, the fault discovery is not timely, corresponding measures cannot be timely taken for sudden accidents, and finally the technical problem that the normal operation of the whole photovoltaic power generation system can be affected by different degrees is finally caused.
Disclosure of Invention
The invention aims to provide an intelligent prediction and early warning method and system for photovoltaic power generation faults, which are used for solving the technical problems that in the prior art, a photovoltaic module is influenced by various factors in actual environment work in the photovoltaic power generation process, abnormal power generation state or fault events occur, the operation and inspection of the photovoltaic module by using the traditional manual inspection and other modes are low in working efficiency, the faults are not found timely, corresponding measures cannot be taken in time for sudden accidents, and the normal operation of the whole photovoltaic power generation system is influenced by different degrees.
In view of the problems, the invention provides an intelligent prediction and early warning method and system for photovoltaic power generation faults.
In a first aspect, the invention provides an intelligent prediction and early warning method for a photovoltaic power generation fault, which is realized by an intelligent prediction and early warning system for the photovoltaic power generation fault, wherein the method comprises the following steps: acquiring a health monitoring category set of photovoltaic power generation, and determining a health monitoring index set based on the health monitoring category set; collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation; if the real-time health index does not reach the preset health threshold, a preset fault prediction scheme is called; acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme, and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree; obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor; carrying out principle and technology analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model, and combining with a first state characteristic parameter to obtain a first state analysis result; and carrying out fault prediction and early warning on the photovoltaic power generation according to the first state analysis result.
In a second aspect, the present invention further provides a photovoltaic power generation fault intelligent prediction and early warning system, configured to execute the photovoltaic power generation fault intelligent prediction and early warning method according to the first aspect, where the system includes: the system comprises an index determining module, a power generation module and a power generation module, wherein the index determining module is used for acquiring a health monitoring category set of photovoltaic power generation and determining the health monitoring index set based on the health monitoring category set; the health calculation module is used for collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation; the scheme calling module is used for calling a preset fault prediction scheme if the real-time health index does not reach a preset health threshold value; the first fault analysis module is used for acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree; the second fault analysis module is used for obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor; the fault prediction module is used for carrying out principle and technical analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model and combining with the first state characteristic parameters to obtain a first state analysis result; and the fault early warning module is used for carrying out fault prediction early warning on the photovoltaic power generation according to the first state analysis result.
One or more technical schemes provided by the invention have at least the following technical effects or advantages:
acquiring a health monitoring category set of photovoltaic power generation, and determining a health monitoring index set based on the health monitoring category set; collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation; if the real-time health index does not reach the preset health threshold, a preset fault prediction scheme is called; acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme, and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree; obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor; carrying out principle and technology analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model, and combining with a first state characteristic parameter to obtain a first state analysis result; and carrying out fault prediction and early warning on the photovoltaic power generation according to the first state analysis result. The technical aim of improving the intelligent degree of abnormal identification of the photovoltaic module in the photovoltaic power generation is achieved, the abnormal identification accuracy of the operation of the photovoltaic module is improved, the intelligent early warning responsivity of the system is further improved, and the normal and stable operation of the photovoltaic power generation system is finally ensured.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described below, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an intelligent prediction and early warning method for photovoltaic power generation faults;
FIG. 2 is a schematic flow chart of a first category health index calculated in the intelligent prediction and early warning method for photovoltaic power generation faults;
fig. 3 is a schematic flow chart of drawing a photovoltaic power generation fault tree in the intelligent photovoltaic power generation fault prediction and early warning method of the invention;
fig. 4 is a schematic flow chart of a photovoltaic power generation simulation model obtained in the intelligent prediction and early warning method of photovoltaic power generation faults;
fig. 5 is a schematic structural diagram of an intelligent prediction and early warning system for photovoltaic power generation faults.
Reference numerals: the system comprises an index determining module M100, a health calculating module M200, a scheme calling module M300, a first fault analyzing module M400, a second fault analyzing module M500, a fault predicting module M600 and a fault early warning module M700.
Detailed Description
The intelligent prediction and early warning method and system for the photovoltaic power generation faults solve the technical problems that in the prior art, the photovoltaic module is influenced by various factors in actual environment operation in the photovoltaic power generation process, abnormal power generation state or fault events occur, the operation and inspection of the photovoltaic module by means of traditional manual inspection and the like are low in working efficiency, faults are not found timely, corresponding measures cannot be taken in time for sudden accidents, and finally the normal operation of the whole photovoltaic power generation system is influenced by different degrees. The technical aim of improving the intelligent degree of abnormal identification of the photovoltaic module in the photovoltaic power generation is achieved, the abnormal identification accuracy of the operation of the photovoltaic module is improved, the intelligent early warning responsivity of the system is further improved, and the normal and stable operation of the photovoltaic power generation system is finally ensured.
The technical scheme of the invention obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.
In the following, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention, and that the present invention is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Example 1
Referring to fig. 1, the invention provides an intelligent prediction and early warning method for photovoltaic power generation faults, wherein the method is applied to an intelligent prediction and early warning system for photovoltaic power generation faults, and the method specifically comprises the following steps:
step S100: acquiring a health monitoring category set of photovoltaic power generation, and determining a health monitoring index set based on the health monitoring category set;
specifically, the intelligent prediction and early warning method for the photovoltaic power generation faults is applied to an intelligent prediction and early warning system for the photovoltaic power generation faults, and can realize intelligent monitoring of the real-time operation state of the photovoltaic module in the photovoltaic power generation system, so that abnormal conditions of the photovoltaic module are identified and predicted, and early warning is timely and pertinently performed, and normal and stable operation of the photovoltaic power generation system is guaranteed.
The health monitoring category set is analyzed by related technicians based on historical operation and maintenance and fault conditions, and meanwhile comprehensive determination is carried out by combining subjective feelings of the technicians, wherein the health monitoring category set comprises system categories which are easy to cause faults and anomalies in a photovoltaic power generation system. Exemplary factors include, for example, power generation efficiency, active, reactive, apparent power and power factor, voltage and frequency deviations across the grid, and the like in a photovoltaic power generation system. After the health monitoring category set is determined, related experts and technicians analyze each category in the health monitoring category set in turn, and factor indexes which can influence photovoltaic power generation in the surrounding environment are determined, so that the health monitoring index set is formed. Exemplary environmental parameters are temperature, humidity, irradiance, and the like. In addition, the operation parameters of the photovoltaic power generation system also reflect the operation health condition of the power generation system from the side, and the electrical parameters such as voltage, current, power, frequency and the like are exemplified. Thus, the relevant electrical parameter is also added to the health monitoring index set. Wherein, each index in the health monitoring index set is used for providing theoretical guidance and preference reference for monitoring of the intelligent predictive early warning system.
Step S200: collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation;
further, as shown in fig. 2, step S200 of the present invention includes:
step S210: extracting a first monitoring category in the health monitoring category set;
step S220: screening the health monitoring index set based on the first monitoring category to obtain a first monitoring index set;
further, step S220 of the present invention includes:
step S221: taking each health monitoring index in the health monitoring index set as an independent variable and taking the first monitoring category as a dependent variable;
step S222: drawing a scatter diagram according to the independent variables, the dependent variables and the mapping relation thereof, wherein the scatter diagram comprises a plurality of target scatter diagrams;
step S223: analyzing the plurality of target scatter diagrams to obtain a plurality of target maximum information coefficients, and screening to obtain a first target maximum information coefficient set;
step S224: and reversely matching the first monitoring index set based on the first target maximum information coefficient set.
Step S230: collecting parameters of each monitoring index in the first monitoring index set in real time to obtain a first index parameter set;
step S240: normalizing the first index parameter set to obtain a first parameter processing result, and correspondingly obtaining a first index weight coefficient set;
step S250: and calculating to obtain a first category health index based on the first parameter processing result and the first index weight coefficient set.
Specifically, after environmental factor indexes influencing the working of a photovoltaic module in the photovoltaic power generation process and electrical factor indexes reflecting the working state of a photovoltaic power generation system are analyzed and the health monitoring index set is obtained, the health monitoring index set in the photovoltaic power generation process is dynamically monitored and collected by using relevant intelligent equipment, so that the real-time index parameter set is obtained. Wherein, the real-time index parameter set and the health monitoring index set have a one-to-one correspondence.
And after the real-time index parameter set is acquired dynamically, carrying out objective analysis and evaluation on the actual state of each category in the health monitoring category set by the parameter information acquired in real time. Firstly, any one monitoring category in the health monitoring categories is extracted and is recorded as the first monitoring category, then, the health monitoring index set is subjected to correlation screening based on the first monitoring category, and only the electrical index and the environmental index which can influence the first monitoring category are reserved, so that the first monitoring index set is obtained. And acquiring parameters of each monitoring index in the first monitoring index set in real time to obtain a first index parameter set, and normalizing the first index parameter set to obtain a first parameter processing result. The normalization processing is used for eliminating dimension influence among different index factors, and subsequent calculation is facilitated. Furthermore, a first set of index weight coefficients corresponding to the first set of index parameters is obtained. Exemplary weight coefficients of the respective indicators in the first monitoring indicator set can be calculated by using a coefficient of variation method, for example, by combining the collected first indicator parameter set. And finally, obtaining a first category health index based on the first parameter processing result and the first index weight coefficient set, namely weighting calculation. That is, the first monitoring index set is an index factor that affects the first monitoring category, and the actual health condition of the first monitoring category can be obtained by performing calculation and analysis on each index parameter in the first monitoring index set.
When the correlation screening is carried out on the health monitoring index set based on the first monitoring category, in order to improve screening efficiency and ensure the science and rationality of the first monitoring index set obtained by screening, the correlation degree between each monitoring index and the corresponding monitoring category is sequentially calculated by using a maximum information coefficient method, and then the index with higher correlation degree is reserved, and other index factors with little influence on the first monitoring category are removed. Specifically, each health monitoring index in the health monitoring index set is first set as an independent variable, and the first monitoring category is set as an independent variable. And then drawing a scatter diagram according to the independent variables, the dependent variables and the mapping relation thereof, wherein the scatter diagram comprises a plurality of target scatter diagrams, namely, each health monitoring index and the first monitoring category form a scatter diagram. And then analyzing the plurality of target scatter diagrams and obtaining a plurality of target maximum information coefficients, and further screening to obtain a first target maximum information coefficient set according to the numerical value of the plurality of target maximum information coefficients. Exemplary, for example, the plurality of target maximum information coefficients are arranged in a descending order, and the first 50% of health indexes in the descending order list are reserved as the first health type objective monitoring indexes. And finally reversely matching the first monitoring index set based on the first target maximum information coefficient set. The method realizes the goal of quantifying the degree of correlation between each health monitoring index in the health monitoring index set and the first monitoring category through correlation analysis and calculation, and achieves the technical effects of improving the scientificity and rationality of index factor screening and improving the intelligent degree of the screening process.
Step S300: if the real-time health index does not reach the preset health threshold, a preset fault prediction scheme is called;
step S400: acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme, and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree;
further, as shown in fig. 3, step S400 of the present invention includes:
step S410: constructing a power generation fault set according to the historical photovoltaic power generation fault record, wherein the power generation fault set comprises a plurality of power generation faults;
step S420: sequentially taking the multiple power generation faults as overhead events;
step S430: analyzing an incident factor of the overhead event;
step S440: and drawing the photovoltaic power generation fault tree based on the overhead event and the accident factor.
Specifically, after health indexes of all monitoring categories in the health monitoring category set are sequentially analyzed and calculated, the overall health condition of the photovoltaic power generation is obtained through weighting calculation, and then the real-time health index is obtained. When the real-time health index does not reach the preset health threshold, the running state of the current photovoltaic power generation system is unhealthy, and faults are likely to occur, so that the intelligent prediction early warning system automatically calls a preset fault prediction scheme for intelligent analysis and prediction of possible faults and faults in the photovoltaic power generation system. The preset health threshold is a health index range of the health running state of the photovoltaic power generation system, which is determined by comprehensively analyzing the actual running years, running environment and running load conditions of the photovoltaic power generation system by related personnel and combining with related running experience. The preset fault prediction scheme is stored in the intelligent prediction early warning system in advance and is logic for carrying out fault monitoring analysis on photovoltaic power generation.
Further, a historical photovoltaic power generation fault record is obtained based on the preset fault prediction scheme, wherein the historical photovoltaic power generation fault record comprises records of related information of faults of the photovoltaic power generation in a historical operation process, so that a power generation fault set is built according to the historical photovoltaic power generation fault record, and a plurality of power generation faults are included. And sequentially taking the multiple power generation faults as overhead events, analyzing accident factors of the overhead events, and finally drawing the photovoltaic power generation fault tree based on the overhead events, the accident factors and the mutual correspondence. The photovoltaic power generation fault tree is obtained by analyzing the historical photovoltaic power generation fault records, and a personalized monitoring index scheme is provided for carrying out targeted fault prediction on various objects in a subsequent photovoltaic power generation system, so that the targets of the targeted and efficient fault prediction and early warning are improved.
Step S500: obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor;
specifically, the first fault type refers to any fault to be intelligently predicted and pre-warned by using the intelligent prediction and pre-warning system. Exemplary is to predict whether the generated power of the current photovoltaic power generation system is normal or not. After a certain fault type to be subjected to targeted analysis is determined, a first fault factor of the fault type is matched by combining the photovoltaic power generation fault tree, and then the relevant state indexes of the photovoltaic power generation system are targeted acquired based on the first fault factor, namely, a first state characteristic parameter is acquired. The technical effect of improving the individuation degree of fault prediction and early warning by targeted monitoring analysis of the photovoltaic power generation system is achieved.
Step S600: carrying out principle and technology analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model, and combining with a first state characteristic parameter to obtain a first state analysis result;
further, as shown in fig. 4, step S600 of the present invention includes:
step S610: acquiring a simulation training data set, and randomly dividing the simulation training data set to obtain a division result;
step S620: wherein the partitioning result includes a first data set, a second data set, and a third data set;
step S630: training to obtain a first model based on the first data set, training to obtain a second model based on the second data set, and training to obtain a third model based on the third data set;
step S640: and integrating and fusing the first model, the second model and the third model to obtain the photovoltaic power generation simulation model.
Further, step S640 of the present invention includes:
step S641: integrating and fusing the first model, the second model and the third model to obtain a plurality of integrated and fused models;
step S642: randomly extracting any one integrated fusion model in the plurality of integrated fusion models;
step S643: acquiring a primary learner and a meta learner of any one integrated fusion model;
step S644: performing verification analysis on the primary learner and the meta learner to obtain verification record information;
step S645: and determining the photovoltaic power generation simulation model based on the verification record information.
Further, step S645 of the present invention further includes:
step S6451: the verification record information comprises a verification state analysis result and verification duration;
step S6452: acquiring a preset state analysis result;
step S6453: comparing the verification state analysis result with the preset state analysis result to obtain verification deviation;
step S6454: and calculating to obtain a verification comprehensive index based on the verification duration and the verification deviation.
Specifically, the photovoltaic power generation simulation model is used for performing intelligent simulation analysis on a photovoltaic power generation process, and simultaneously realizing simulation monitoring in a virtual space by combining actual data, so that intelligent fault prediction and early warning are performed according to simulation monitoring results. Firstly, acquiring a simulation training data set based on big data, randomly dividing the simulation training data set to obtain a first data set, a second data set and a third data set, namely forming the division result, and then respectively utilizing the data sets obtained by division to conduct intelligent model training, namely obtaining a first model based on the first data set training, obtaining a second model based on the second data set training, and obtaining a third model based on the third data set training. The intelligent model obtained by training each data set is a model trained based on different computer supervision learning principles, so that each intelligent model has different model advantages. Exemplary are training neural network models to improve prediction accuracy, training gray wolf optimization models to improve prediction efficiency, etc. And finally, integrating and fusing the first model, the second model and the third model to obtain the photovoltaic power generation simulation model. An exemplary idea of using a Stacking algorithm is to use some two models as primary learners and use another model as a meta-learner to perform model integration, wherein the prediction results obtained after the multiple primary learners train the prediction are used as the training basis of the meta-learner, and new learning prediction is performed again. Therefore, after the first model, the second model and the third model are integrated and fused, a plurality of different integrated and fused models can be obtained.
And randomly extracting any one of the integrated fusion models to obtain a primary learner and a meta learner of the any one integrated fusion model, wherein the primary learner and the meta learner respectively correspond to intelligent models obtained by training according to different training principles. And then carrying out verification analysis on the primary learner and the meta learner, and recording related data information in the verification process to obtain verification record information, wherein the verification record information comprises verification state analysis results and verification duration. Further, a preset state analysis result is obtained, and the verification state analysis result and the preset state analysis result are compared and analyzed to obtain the verification deviation condition of any one integrated fusion model. And then, calculating to obtain a verification comprehensive index based on the verification duration and the verification deviation, and determining the photovoltaic power generation simulation model based on the verification comprehensive index. And taking the integrated fusion model with the highest verification comprehensive index as a final photovoltaic power generation simulation model, and combining the photovoltaic power generation simulation model with the first state characteristic parameters to obtain a first state analysis result. And the photovoltaic power generation simulation model is determined through integrated fusion analysis, so that the technical effect of ensuring the model fault prediction accuracy is achieved.
Step S700: and carrying out fault prediction and early warning on the photovoltaic power generation according to the first state analysis result.
Specifically, after the photovoltaic power generation simulation model is combined with a first state characteristic parameter to obtain a first state analysis result, the intelligent prediction early warning system performs fault prediction early warning on the photovoltaic power generation according to the first state analysis result. For example, a related technician performs analysis and presetting on an index factor parameter threshold value under the condition that whether the monitoring category fails or not, after the model intelligent analysis obtains a first state analysis result, the first state analysis result is compared with the preset parameter threshold value, and if each index parameter is not in a preset range, the parameter abnormality is indicated. The technical aim of improving the intelligent degree of abnormal recognition of the photovoltaic module in the photovoltaic power generation is achieved.
In summary, the intelligent prediction and early warning method for the photovoltaic power generation faults provided by the invention has the following technical effects:
acquiring a health monitoring category set of photovoltaic power generation, and determining a health monitoring index set based on the health monitoring category set; collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation; if the real-time health index does not reach the preset health threshold, a preset fault prediction scheme is called; acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme, and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree; obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor; carrying out principle and technology analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model, and combining with a first state characteristic parameter to obtain a first state analysis result; and carrying out fault prediction and early warning on the photovoltaic power generation according to the first state analysis result. The technical aim of improving the intelligent degree of abnormal identification of the photovoltaic module in the photovoltaic power generation is achieved, the abnormal identification accuracy of the operation of the photovoltaic module is improved, the intelligent early warning responsivity of the system is further improved, and the normal and stable operation of the photovoltaic power generation system is finally ensured.
Example two
Based on the same inventive concept as the intelligent prediction and early warning method for photovoltaic power generation faults in the foregoing embodiments, the invention also provides an intelligent prediction and early warning system for photovoltaic power generation faults, referring to fig. 5, the system comprises:
the index determining module M100 is used for acquiring a health monitoring category set of photovoltaic power generation and determining a health monitoring index set based on the health monitoring category set;
the health calculation module M200 is used for collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the real-time health index of the photovoltaic power generation by weight change;
the scheme calling module M300 is used for calling a preset fault prediction scheme if the real-time health index does not reach a preset health threshold value;
the first fault analysis module M400 is used for acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree;
the second fault analysis module M500 is used for obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor;
the fault prediction module M600 is used for carrying out principle and technology analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model and obtaining a first state analysis result by combining the first state characteristic parameters;
and the fault early warning module M700 is used for carrying out fault prediction early warning on the photovoltaic power generation according to the first state analysis result.
Further, the health calculation module M200 in the system is further configured to:
extracting a first monitoring category in the health monitoring category set;
screening the health monitoring index set based on the first monitoring category to obtain a first monitoring index set;
collecting parameters of each monitoring index in the first monitoring index set in real time to obtain a first index parameter set;
normalizing the first index parameter set to obtain a first parameter processing result, and correspondingly obtaining a first index weight coefficient set;
and calculating to obtain a first category health index based on the first parameter processing result and the first index weight coefficient set.
Further, the health calculation module M200 in the system is further configured to:
taking each health monitoring index in the health monitoring index set as an independent variable and taking the first monitoring category as a dependent variable;
drawing a scatter diagram according to the independent variables, the dependent variables and the mapping relation thereof, wherein the scatter diagram comprises a plurality of target scatter diagrams;
analyzing the plurality of target scatter diagrams to obtain a plurality of target maximum information coefficients, and screening to obtain a first target maximum information coefficient set;
and reversely matching the first monitoring index set based on the first target maximum information coefficient set.
Further, the first fault analysis module M400 in the system is further configured to:
constructing a power generation fault set according to the historical photovoltaic power generation fault record, wherein the power generation fault set comprises a plurality of power generation faults;
sequentially taking the multiple power generation faults as overhead events;
analyzing an incident factor of the overhead event;
and drawing the photovoltaic power generation fault tree based on the overhead event and the accident factor.
Further, the fault prediction module M600 in the system is further configured to:
acquiring a simulation training data set, and randomly dividing the simulation training data set to obtain a division result;
wherein the partitioning result includes a first data set, a second data set, and a third data set;
training to obtain a first model based on the first data set, training to obtain a second model based on the second data set, and training to obtain a third model based on the third data set;
and integrating and fusing the first model, the second model and the third model to obtain the photovoltaic power generation simulation model.
Further, the fault prediction module M600 in the system is further configured to:
integrating and fusing the first model, the second model and the third model to obtain a plurality of integrated and fused models;
randomly extracting any one integrated fusion model in the plurality of integrated fusion models;
acquiring a primary learner and a meta learner of any one integrated fusion model;
performing verification analysis on the primary learner and the meta learner to obtain verification record information;
and determining the photovoltaic power generation simulation model based on the verification record information.
Further, the fault prediction module M600 in the system is further configured to:
the verification record information comprises a verification state analysis result and verification duration;
acquiring a preset state analysis result;
comparing the verification state analysis result with the preset state analysis result to obtain verification deviation;
and calculating to obtain a verification comprehensive index based on the verification duration and the verification deviation.
In this description, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the foregoing intelligent prediction and early warning method and specific example for a photovoltaic power generation failure in the first embodiment of fig. 1 are also applicable to the intelligent prediction and early warning system for a photovoltaic power generation failure in the first embodiment, and by the foregoing detailed description of the intelligent prediction and early warning method for a photovoltaic power generation failure, those skilled in the art can clearly know the intelligent prediction and early warning system for a photovoltaic power generation failure in the first embodiment, so that the description is omitted herein for brevity. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and the equivalent techniques thereof, the present invention is also intended to include such modifications and variations.
Claims (8)
1. The intelligent prediction and early warning method for the photovoltaic power generation faults is characterized by comprising the following steps of:
acquiring a health monitoring category set of photovoltaic power generation, and determining a health monitoring index set based on the health monitoring category set;
collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation;
if the real-time health index does not reach the preset health threshold, a preset fault prediction scheme is called;
acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme, and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree;
obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor;
carrying out principle and technology analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model, and combining with a first state characteristic parameter to obtain a first state analysis result;
and carrying out fault prediction and early warning on the photovoltaic power generation according to the first state analysis result.
2. The intelligent predictive and early-warning method according to claim 1, wherein the collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain the real-time health index of the photovoltaic power generation comprises:
extracting a first monitoring category in the health monitoring category set;
screening the health monitoring index set based on the first monitoring category to obtain a first monitoring index set;
collecting parameters of each monitoring index in the first monitoring index set in real time to obtain a first index parameter set;
normalizing the first index parameter set to obtain a first parameter processing result, and correspondingly obtaining a first index weight coefficient set;
and calculating to obtain a first category health index based on the first parameter processing result and the first index weight coefficient set.
3. The intelligent predictive early warning method according to claim 2, wherein the screening the health monitoring index set based on the first monitoring category to obtain a first monitoring index set includes:
taking each health monitoring index in the health monitoring index set as an independent variable and taking the first monitoring category as a dependent variable;
drawing a scatter diagram according to the independent variables, the dependent variables and the mapping relation thereof, wherein the scatter diagram comprises a plurality of target scatter diagrams;
analyzing the plurality of target scatter diagrams to obtain a plurality of target maximum information coefficients, and screening to obtain a first target maximum information coefficient set;
and reversely matching the first monitoring index set based on the first target maximum information coefficient set.
4. The intelligent predictive early warning method according to claim 1, wherein the obtaining a historical photovoltaic power generation fault record based on the preset fault prediction scheme and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree comprises:
constructing a power generation fault set according to the historical photovoltaic power generation fault record, wherein the power generation fault set comprises a plurality of power generation faults;
sequentially taking the multiple power generation faults as overhead events;
analyzing an incident factor of the overhead event;
and drawing the photovoltaic power generation fault tree based on the overhead event and the accident factor.
5. The intelligent predictive and early-warning method according to claim 1, wherein the principle-technology analysis of the photovoltaic power generation and the establishment of the photovoltaic power generation simulation model comprise:
acquiring a simulation training data set, and randomly dividing the simulation training data set to obtain a division result;
wherein the partitioning result includes a first data set, a second data set, and a third data set;
training to obtain a first model based on the first data set, training to obtain a second model based on the second data set, and training to obtain a third model based on the third data set;
and integrating and fusing the first model, the second model and the third model to obtain the photovoltaic power generation simulation model.
6. The intelligent predictive early warning method according to claim 5, wherein the integrating and fusing the first model, the second model and the third model to obtain the photovoltaic power generation simulation model comprises:
integrating and fusing the first model, the second model and the third model to obtain a plurality of integrated and fused models;
randomly extracting any one integrated fusion model in the plurality of integrated fusion models;
acquiring a primary learner and a meta learner of any one integrated fusion model;
performing verification analysis on the primary learner and the meta learner to obtain verification record information;
and determining the photovoltaic power generation simulation model based on the verification record information.
7. The intelligent predictive early warning method according to claim 6, wherein the determining the photovoltaic power generation simulation model based on the verification record information comprises:
the verification record information comprises a verification state analysis result and verification duration;
acquiring a preset state analysis result;
comparing the verification state analysis result with the preset state analysis result to obtain verification deviation;
and calculating to obtain a verification comprehensive index based on the verification duration and the verification deviation.
8. An intelligent predictive early warning system for photovoltaic power generation faults, which is characterized by comprising:
the system comprises an index determining module, a power generation module and a power generation module, wherein the index determining module is used for acquiring a health monitoring category set of photovoltaic power generation and determining the health monitoring index set based on the health monitoring category set;
the health calculation module is used for collecting the health monitoring index set of the photovoltaic power generation to obtain a real-time index parameter set, and calculating the weight change to obtain a real-time health index of the photovoltaic power generation;
the scheme calling module is used for calling a preset fault prediction scheme if the real-time health index does not reach a preset health threshold value;
the first fault analysis module is used for acquiring a historical photovoltaic power generation fault record based on the preset fault prediction scheme and analyzing the historical photovoltaic power generation fault record to obtain a photovoltaic power generation fault tree;
the second fault analysis module is used for obtaining a first fault type, matching a first fault factor by combining the photovoltaic power generation fault tree, and acquiring a first state characteristic parameter based on the first fault factor;
the fault prediction module is used for carrying out principle and technical analysis on the photovoltaic power generation, establishing a photovoltaic power generation simulation model and combining with the first state characteristic parameters to obtain a first state analysis result;
and the fault early warning module is used for carrying out fault prediction early warning on the photovoltaic power generation according to the first state analysis result.
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