CN112803592A - Intelligent fault early warning method and system suitable for distributed power station - Google Patents
Intelligent fault early warning method and system suitable for distributed power station Download PDFInfo
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Abstract
The invention discloses an intelligent fault early warning method and system suitable for a distributed power station, which comprises the steps of collecting power generation data of a historical photovoltaic power station during fault; establishing an expert database by utilizing the historical data, and performing classified storage according to different equipment data of the power station; establishing a fault tree analysis model according to the collected fault data to perform equipment fault probability analysis; collecting real-time data of the photovoltaic power station, and inputting the real-time data into the expert database for fault analysis and prediction according to an analysis result of the fault tree analysis model; and carrying out intelligent fault early warning according to the prediction condition. The workload of monitoring the stations is reduced, the unified monitoring and multilayer monitoring of various photovoltaic power stations of different types are realized, and the unattended operation mode with few persons is realized; a new fault early warning method is established, the accuracy and speed of photovoltaic power station fault early warning are improved, and the stability, reliability and operation management efficiency of power station operation are improved.
Description
Technical Field
The invention relates to the technical field of distributed power station fault early warning, in particular to an intelligent fault early warning method and system suitable for a distributed power station.
Background
In recent years, the new energy industry is rapidly developed, the number of photovoltaic power stations put into production of development companies is increased day by day, the governed power stations are wide in distribution region and scattered in site, and a power station monitoring system is simple in function and has no efficient data integration, statistics and analysis capability. The photovoltaic power stations are wide in distribution region and large in equipment quantity, so that early warning is difficult to carry out on operation faults of the power stations, the reason of the faults is difficult to find out quickly and accurately when the faults occur, the daily office of a power station operation and maintenance team has no standard operation distance and rule system, the number of routing inspection projects is large, the field personnel skills are insufficient, closed-loop management is avoided, when low-efficiency and faulty operation devices exist, active reminding cannot be carried out through a fault early warning and self-diagnosis method, the equipment runs in a sub-health state for a long time, a large amount of electric quantity loss can be caused, the operation cost of the equipment is improved, an intelligent fault early warning method of the distributed power stations needs to be established, and the management.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned problems with the conventional photovoltaic power plant.
Therefore, the technical problem solved by the invention is as follows: the power station equipment is numerous, the data acquisition amount is large, and rapid fault prediction is difficult to perform; when an operating device with low efficiency and faults exists, active reminding cannot be achieved through a fault early warning and self-diagnosis method, and the equipment operates in a sub-health state for a long time; after the early warning occurs, no clear idea for solving the fault problem exists; and the maintenance of the daily equipment adopts a manual mode, so that the efficiency is low, the error is easy, and the storage and the search are inconvenient.
In order to solve the technical problems, the invention provides the following technical scheme: collecting power generation data of historical photovoltaic power station faults; establishing an expert database by utilizing the historical data, and performing classified storage according to different equipment data of the power station; establishing a fault tree analysis model according to the collected fault data to perform equipment fault probability analysis; collecting real-time data of the photovoltaic power station, and inputting the real-time data into the expert database for fault analysis and prediction according to an analysis result of the fault tree analysis model; and carrying out intelligent fault early warning according to the prediction condition.
As an optimal scheme of the intelligent fault early warning method applicable to the distributed power station, the method comprises the following steps: the establishing of the expert database comprises the steps that the collected historical fault data comprise power station data and power station KPI data, the data are pre-judged according to fixed telephones or customized expert experiences, pre-judgment results are classified and stored according to different equipment data to form the expert database, and the expert database is updated in a month period to ensure the real-time performance of the information of the expert database.
As an optimal scheme of the intelligent fault early warning method applicable to the distributed power station, the method comprises the following steps: the classified storage according to different equipment data of the power station comprises the current and voltage of the string, the inverter power generation data, the operation state of the combiner box, the voltage power of the box transformer substation, the output power of the photovoltaic module and the operation state of the transformer.
As an optimal scheme of the intelligent fault early warning method applicable to the distributed power station, the method comprises the following steps: the establishing of the fault tree analysis model for equipment fault probability analysis comprises the steps of analyzing the fault probability of different equipment by the fault tree analysis model, calculating the fault probability of each equipment, inputting real-time data of the equipment into the expert database from high to low in sequence for fault analysis according to the calculated probability condition, and further rapidly predicting the fault condition.
As an optimal scheme of the intelligent fault early warning method applicable to the distributed power station, the method comprises the following steps: the fault tree analysis model comprises that a power station is set to be composed of n devices, the normal running state of the devices is represented by '0', the abnormal running state is represented by '1', and the power station fault is caused by the power station device fault, so the running state of the power station device determines the running condition of the power station and is represented by Q (x)1,x2,…xn) To represent the operating state of the power station equipment, the fault tree analysis model is as follows:
if the failure rate of the device x in the time t is set to be gamma, the calculation formula of the failure rate is as follows:
wherein: and oc is the number of failures of the equipment x in the T time period, the failure rate is used for predicting the failure probability of the equipment x in the T running time period, and the calculation formula is as follows:
P(x)＝1-e-γt
w(x)＝γ(1-P(x))
wherein: p (x) is the probability of the power equipment failing in time t, and w (x) is the frequency of the power equipment failing in time t.
As an optimal scheme of the intelligent fault early warning method applicable to the distributed power station, the method comprises the following steps: the fault analysis and prediction comprises the steps of carrying out fault waveform analysis on the real-time data, selecting the real-time data within a certain time to carry out image drawing, analyzing the change of an image curve by using a fitting formula, further deducing the data of the equipment at the next moment, and inputting the predicted data into an expert database to carry out fault prediction, wherein the fitting formula is expressed as follows:
y＝ax+b
wherein: y is the device data, x is time, a is the slope of the fitted image curve, and b is the fitting coefficient.
As an optimal scheme of the intelligent fault early warning method applicable to the distributed power station, the method comprises the following steps: the intelligent fault early warning system comprises an intelligent fault early warning device, wherein the intelligent fault early warning device can realize on-line comprehensive processing, display and thrust of prediction information, support collection and processing of various kinds of early warning information, carry out classified management and synthesis and compression on a large number of early warning information, form different early warning display schemes for different requirements, provide comprehensive and comprehensive early warning prompts by utilizing an image visual mode, and display the early warning information in a centralized manner, wherein the content comprises early warning content description, early warning grade, early warning time, whether to reset, whether to confirm and the like, and can carry out classified retrieval, display and printing of the early warning information according to different conditions.
As an optimal scheme of the intelligent fault early warning system applicable to the distributed power station, the invention comprises the following steps: the power station data acquisition module is used for acquiring historical fault data of the distributed power station and operation data of implementation equipment; the fault early warning analysis module is connected with the power station data acquisition module, a fault analysis model is established by using historical data acquired by the power station data acquisition module, and fault prediction is carried out according to the acquired real-time data; and the intelligent early warning module is connected with the fault early warning analysis module and is used for carrying out intelligent early warning on the prediction result of the fault early warning analysis module.
As an optimal scheme of the intelligent fault early warning system applicable to the distributed power station, the invention comprises the following steps: the fault early warning analysis module comprises an expert database connected with the power station data acquisition module, and is used for carrying out expert experience analysis on the historical fault data and classifying and storing a prediction result; the fault tree analysis model is connected with the power station data acquisition module, and is established by utilizing the acquired historical data to calculate the fault probability of each device; the fault prediction unit is connected with the power station data acquisition module, the expert database and the fault tree analysis model, sequentially carries out fault analysis prediction on real-time data of the power station equipment according to a calculation result of the fault tree analysis model, and inputs the result into the expert database to obtain a prediction result.
As an optimal scheme of the intelligent fault early warning system applicable to the distributed power station, the invention comprises the following steps: the intelligent early warning module comprises an early warning classification unit, a fault prediction unit and a fault detection unit, wherein the early warning classification unit is connected to the fault prediction unit, acquires a plurality of early warning data sources and reasonably classifies a large amount of early warning information according to respective characteristics; the early warning information synthesis and compression unit is connected with the fault prediction unit, combines a plurality of early warning information caused by the same reason in the system, only gives core early warning or causes of faults, and does not display all the early warning information; the early warning intelligent reasoning unit is connected with the expert database and the early warning classification unit, and is used for carrying out time sequence analysis on the classified early warning information, giving a fault report and providing a fault type and a fault process. The early warning intelligent display unit is connected with the early warning classification unit, the early warning information synthesis and compression unit and the early warning intelligent reasoning unit and displays the type and reason of the early warning information and the early warning report.
The invention has the beneficial effects that: the workload of monitoring the stations is reduced, the unified monitoring and multilayer monitoring of various photovoltaic power stations of different types are realized, and the unattended operation mode with few persons is realized; the new fault early warning method is established, the accuracy and the speed of photovoltaic power station fault early warning are improved, the stability, the reliability and the operation management efficiency of power station operation are improved, the production operation management level is improved, and the production operation and equipment maintenance cost is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of an intelligent fault early warning method for a distributed power station according to a first embodiment of the present invention;
FIG. 2 is a graph of a fitted image of the intelligent fault early warning method for a distributed power station according to the first embodiment of the present invention;
fig. 3 is a schematic flow chart of an intelligent fault early warning system for a distributed power station according to a second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 2, a first embodiment of the present invention provides an intelligent fault early warning method suitable for a distributed power station, including:
s1: and collecting power generation data of historical photovoltaic power station faults. In which it is to be noted that,
information is collected by utilizing equipment such as a photovoltaic array (a photovoltaic module temperature detector), a sun tracking device, an inverter (the convergence condition is monitored through the inverter), an environment monitor, a camera and the like.
S2: and establishing an expert database by utilizing the historical data, and performing classified storage according to different equipment data of the power station. In which it is to be noted that,
the collected historical fault data comprises power station data and power station KPI data, the data is pre-judged according to fixed-line or self-defined expert experience, the pre-judged result is classified and stored according to different equipment data to form an expert database, and the expert database is updated in a month period to ensure the real-time performance of the information of the expert database;
further, the classification and storage according to different equipment data of the power station comprises the current and voltage of the string, the power generation data of the inverter, the operation state of the combiner box, the voltage and power of the box transformer, the output power of the photovoltaic module and the operation state of the transformer, the current and voltage of the string comprise photovoltaic string output direct current voltage, output direct current power and the like, the inverter power generation data comprise direct current voltage, current, power, alternating current voltage, current, temperature in the inverter, clock, frequency, power factor and the like, the running state of the junction box comprises junction box output voltage, junction box output power, current monitoring tolerance alarm, transmission cable/short circuit fault alarm, air switch state and the like, the output power of the photovoltaic module comprises junction box collected data, the output power of each photovoltaic simulation is displayed, and the abnormal photovoltaic module is located.
S3: and establishing a fault tree analysis model according to the collected fault data to perform equipment fault probability analysis. In which it is to be noted that,
the fault tree analysis model analyzes the fault probability of different equipment, calculates the fault probability of each equipment, and inputs the real-time data of the equipment into an expert database from high to low in sequence for fault analysis according to the calculated probability condition so as to quickly predict the fault condition;
further, the set power station is composed of n devices, the device operation state is normally represented by "0", and the operation state is abnormal"1" indicates that the power plant fault is caused by a power plant equipment fault, and thus the operating state of the power plant equipment determines the operating condition of the power plant, and is represented by Q (x)1,x2,…xn) To represent the operating state of the power station equipment, the fault tree analysis model is as follows:
if the failure rate of the device x in the time t is set to be gamma, the calculation formula of the failure rate is as follows:
wherein: and oc is the number of failures of the equipment x in the T time period, the failure probability of the equipment x in the T time period is predicted by using the failure rate, and the calculation formula is as follows:
P(x)＝1-e-γt
w(x)＝γ(1-P(x))
wherein: p (x) is the probability of the power equipment failing in time t, and w (x) is the frequency of the power equipment failing in time t.
S4: and collecting real-time data of the photovoltaic power station, and inputting the real-time data into an expert database for fault analysis and prediction according to an analysis result of the fault tree analysis model. In which it is to be noted that,
performing fault analysis and prediction comprises performing fault waveform analysis on real-time data, selecting the real-time data within a certain time to perform image drawing, analyzing the change of an image curve by using a fitting formula, further deducing the data of the equipment at the next moment, and inputting predicted data into an expert database to perform fault prediction, wherein the fitting formula is expressed as follows:
y＝ax+b
wherein: y is the device data, x is time, a is the slope of the fitted image curve, and b is the fitting coefficient.
For example, given 6 sets of data: (0.08,0), (0.24,0.2), (0.37,0.4), (0.68,0.6), (0.87,0.8) and (1,1) image curve plotting is performed according to the 6 groups of data, the plotting result refers to fig. 2, the solid line in the plot is a curve plotted by the 6 groups of data, the dotted line is a fitting formula obtained by the curve, and it can be seen that the fitting formula of the image curve is: y is 0.97 x-0.03.
S5: and carrying out intelligent fault early warning according to the predicted condition. In which it is to be noted that,
the intelligent fault early warning comprises the steps that the intelligent fault early warning can realize on-line comprehensive processing, display and thrust of prediction information, support collection and processing of various kinds of early warning information, carry out classified management and synthesis and compression on a large number of early warning information, form different early warning display schemes for different requirements, provide comprehensive and comprehensive early warning prompts in an image and visual mode, intensively display the early warning information, and carry out classified retrieval, display and printing on the early warning information according to different conditions, wherein the contents comprise early warning content description, early warning grade, early warning time, whether to reset, whether to confirm and the like.
In order to better verify and explain the technical effects adopted in the method of the invention, a fault early warning method based on an interval motion curve is selected for testing in the embodiment, and the test results are compared by means of scientific demonstration to verify the real effect of the method;
selecting running state data of the Harvest Oriental Red station to carry out fault prediction, wherein the frequency of a basic power grid in a photovoltaic power station is 60.0HZ, carrying out prediction analysis on the fault condition of the Harvest Oriental Red station in the future 5 days by using the method and the traditional fault early warning method based on the interval motion curve, wherein when the method is used for carrying out fault prediction on the power station, collecting fault data of the previous month of the power station, establishing an expert database, carrying out fault probability analysis on each device by using a fault tree model, and carrying out fault prediction on real-time running data of the power station in 5 days; the traditional fault early warning method based on the interval motion curve is used, equipment is detected through a networking technology, information is collected, a cloud database is deployed to store the collected information, the health state of the equipment is evaluated and abnormal conditions are early warned through data interaction between an application layer and the cloud database, MATLB software is used for testing the fault early warning time and accuracy of the two methods, and the test results are shown in the following table 1:
table 1: and (5) predicting the fault of the photovoltaic power station.
As can be seen from table 1, from the aspect of fault early warning time, the time using the conventional interval motion method is stabilized at about 20s, but the time required by the method is about 15 s although the fluctuation is large, and the early warning is performed only for 8s on day 4, so that the method can save the early warning time; on the other hand, from the accuracy point of view, the accuracy rate of the traditional method is more than 70%, but the method is kept above 80%, and obviously, the method has good effect in the aspect of accuracy rate.
Example 2
Referring to fig. 3, a second embodiment of the present invention is different from the first embodiment in that an intelligent fault pre-warning system for a distributed power station is provided, which includes: the system comprises a power station data acquisition module 100, a fault early warning analysis module 200 and an intelligent early warning module 300, wherein the power station data acquisition module 100 is used for acquiring historical fault data of a distributed power station and operation data of implementation equipment; the fault early warning analysis module 200 is connected with the power station data acquisition module 100, establishes a fault analysis model by using historical data acquired by the power station data acquisition module 100, and carries out fault prediction according to acquired real-time data; the intelligent early warning module 300 is connected with the fault early warning analysis module 200 and is used for carrying out intelligent early warning on the prediction result of the fault early warning analysis module 200;
further, the fault early warning analysis module 200 includes an expert database 201 connected with the power station data acquisition module 100, and performs expert experience analysis on historical fault data, and performs classified storage on prediction results; the fault tree analysis model 202 is connected with the power station data acquisition module 100, and is established by utilizing the acquired historical data to calculate the fault probability of each device; the fault prediction unit 203 is connected to the power station data acquisition module 100, the expert database 201 and the fault tree analysis model 202, sequentially performs fault analysis prediction on real-time data of the power station equipment according to a calculation result of the fault tree analysis model 202, and inputs the result into the expert database 201 to obtain a prediction result;
the intelligent early warning module 300 comprises an early warning classification unit 301 connected to the fault prediction unit 203, acquiring a plurality of early warning data sources, and reasonably classifying a large amount of early warning information according to respective characteristics; the early warning information synthesis and compression unit 302 is connected with the fault prediction unit 203, combines a plurality of early warning information caused by the same reason in the system, only gives core early warning or causes of faults, and does not display all the early warning information; the early warning intelligent reasoning unit 303 is connected to the expert database 201 and the early warning classification unit 301, and performs time sequence analysis on the classified early warning information, and provides a fault report and a fault type and a fault process. The early warning intelligent display unit 304 is connected to the early warning classification unit 301, the early warning information synthesis and compression unit 302 and the early warning intelligent inference unit 303, and displays the type and reason of the early warning information and the early warning report;
furthermore, the connection between the module and the unit uses optical fiber for information transmission, the information capacity is large, the loss is low, the transmission distance is long, the anti-electromagnetic interference capability is strong, the safety performance and the confidentiality are good, errors of data in the transmission process can be effectively avoided, and the information transmission time is shortened.
It should be understood that the system provided in the present embodiment, which relates to the power station data acquisition module 100, the fault early warning analysis module 200, and the intelligent early warning module 300, may be, for example, a computer readable program, and is implemented by improving program data interfaces of the modules.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, the operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described herein includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein. A computer program can be applied to input data to perform the functions described herein to transform the input data to generate output data that is stored to non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. An intelligent fault early warning method suitable for a distributed power station is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting power generation data of historical photovoltaic power station faults;
establishing an expert database by utilizing the historical data, and performing classified storage according to different equipment data of the power station;
establishing a fault tree analysis model according to the collected fault data to perform equipment fault probability analysis;
collecting real-time data of the photovoltaic power station, and inputting the real-time data into the expert database for fault analysis and prediction according to an analysis result of the fault tree analysis model;
and carrying out intelligent fault early warning according to the prediction condition.
2. The intelligent fault early warning method for distributed power plants as claimed in claim 1, wherein: said establishing an expert database comprises establishing a database of experts,
the collected historical fault data comprises power station data and power station KPI data, the data is pre-judged according to fixed-line or self-defined expert experience, pre-judgment results are classified and stored according to different equipment data to form an expert database, and the expert database is updated in a month period to ensure the real-time performance of the information of the expert database.
3. The intelligent fault pre-warning method for distributed power stations as claimed in claim 1 or 2, wherein: the classified storage according to the different equipment data of the power station comprises,
the current and voltage of the string, the inverter power generation data, the operation state of the combiner box, the voltage and power of the box transformer, the output power of the photovoltaic module and the operation state of the transformer.
4. The intelligent fault early warning method for distributed power plants as claimed in claim 1, wherein: the establishing of the fault tree analysis model for equipment fault probability analysis comprises the following steps,
the fault tree analysis model analyzes the probability of faults of different equipment, calculates the probability of faults of each equipment, and inputs real-time data of the equipment into the expert database in sequence from high to low according to the calculated probability condition to perform fault analysis, so as to predict the fault condition quickly.
5. The intelligent fault pre-warning method for distributed power plants as claimed in claim 4, wherein: the fault tree analysis model includes a fault tree analysis model that includes,
the method comprises the steps of setting a power station to be composed of n devices, wherein the normal operation state of the devices is represented by 0, the abnormal operation state is represented by 1, and the power station fault is caused by the power station device fault, so that the operation state of the power station device determines the operation state of the power station and is Q (x)1，x2，...xn) To represent the operating state of the power station equipment, the fault tree analysis model is as follows:
if the failure rate of the device x in the time t is set to be gamma, the calculation formula of the failure rate is as follows:
wherein: and oc is the number of failures of the equipment x in the T time period, the failure rate is used for predicting the failure probability of the equipment x in the T running time period, and the calculation formula is as follows:
P(x)＝1-e-γτ
w(x)＝γ(1-P(x))
wherein: p (x) is the probability of the power equipment failing in time t, and w (x) is the frequency of the power equipment failing in time t.
6. The intelligent fault early warning method for distributed power plants as claimed in claim 5, wherein: the performing of the fault analysis prediction includes,
analyzing the fault waveform of the real-time data, selecting the real-time data within a certain time to draw an image, analyzing the change of an image curve by using a fitting formula, further deducing the data of the equipment at the next moment, and inputting the predicted data into an expert database to perform fault prediction, wherein the fitting formula is expressed as follows:
y＝ax+b
wherein: y is the device data, x is time, a is the slope of the fitted image curve, and b is the fitting coefficient.
7. The intelligent fault pre-warning method for distributed power plants as claimed in claim 6, wherein: the performing of the intelligent fault pre-warning includes,
the intelligent fault early warning can realize on-line comprehensive processing, display and thrust of prediction information, support collection and processing of various kinds of early warning information, carry out classification management and synthesis and compression on a large amount of early warning information, form different early warning display schemes to different demands, provide comprehensive and comprehensive early warning prompts by utilizing an image visual mode, intensively display the early warning information, and the content comprises early warning content description, early warning grade, early warning time, whether to regress, whether to confirm and the like, and can carry out classification retrieval, display and printing of the early warning information according to different conditions.
8. The utility model provides an intelligence fault early warning system suitable for distributed power station which characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the power station data acquisition module (100) is used for acquiring historical fault data of the distributed power station and operation data of implementation equipment;
the fault early warning analysis module (200) is connected with the power station data acquisition module (100), a fault analysis model is established by using historical data acquired by the power station data acquisition module (100), and fault prediction is carried out according to the acquired real-time data;
and the intelligent early warning module (300) is connected with the fault early warning analysis module (200) and is used for intelligently early warning the prediction result of the fault early warning analysis module (200).
9. The intelligent fault early warning system for a distributed power plant of claim 8, wherein: the fault pre-warning analysis module (200) comprises,
an expert database (201) is connected with the power station data acquisition module (100), and is used for carrying out expert experience analysis on the historical fault data and classifying and storing prediction results;
the fault tree analysis model (202) is connected with the power station data acquisition module (100), and is established by utilizing the acquired historical data to calculate the fault probability of each device;
the fault prediction unit (203) is connected with the power station data acquisition module (100), the expert database (201) and the fault tree analysis model (202), fault analysis prediction is sequentially carried out on real-time data of the power station equipment according to the calculation result of the fault tree analysis model (202), and the result is input into the expert database (201) to obtain a prediction result.
10. The intelligent fault early warning system for a distributed power plant of claim 8 or 9, wherein: the intelligent early warning module (300) comprises,
the early warning classification unit (301) is connected with the fault prediction unit (203), acquires a plurality of early warning data sources, and reasonably classifies a large amount of early warning information according to respective characteristics;
the early warning information synthesis and compression unit (302) is connected with the fault prediction unit (203), combines a plurality of early warning information caused by the same reason in the system, only gives core early warning or causes of faults, and does not display all the early warning information;
the early warning intelligent reasoning unit (303) is connected with the expert database (201) and the early warning classification unit (301), and is used for carrying out time sequence analysis on classified early warning information, giving a fault report and providing a fault type and a fault process.
The early warning intelligent display unit (304) is connected with the early warning classification unit (301), the early warning information synthesis and compression unit (302) and the early warning intelligent reasoning unit (303) and displays the type and reason of the early warning information and the early warning report.
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