CN116192038A - Photovoltaic system fault diagnosis method and device and computer equipment - Google Patents

Photovoltaic system fault diagnosis method and device and computer equipment Download PDF

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Publication number
CN116192038A
CN116192038A CN202211649484.3A CN202211649484A CN116192038A CN 116192038 A CN116192038 A CN 116192038A CN 202211649484 A CN202211649484 A CN 202211649484A CN 116192038 A CN116192038 A CN 116192038A
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data
target
photovoltaic system
photovoltaic
inverter
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Inventor
宋玮琼
郭帅
王诜
赵成
宋威
仝霞
董宇
董浩
郑伟龙
赵磊
李季巍
吕凤鸣
韩柳
芦昳娜
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
    • H02S40/30—Electrical components
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
    • H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
    • H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy

Abstract

The invention discloses a photovoltaic system fault diagnosis method, a photovoltaic system fault diagnosis device and computer equipment. Wherein the method comprises the following steps: in edge processing equipment of a photovoltaic system, acquiring target power data of target equipment in the photovoltaic system, wherein the target power data is acquired based on a sensor; determining actual measurement data of the target device based on the target power data; predicting the state of the target equipment based on a target state model corresponding to the target equipment to obtain prediction result data of the target equipment; and detecting whether the target equipment fails or not based on the actual measurement data and the predicted result data, and obtaining a detection result. The invention solves the technical problem of low monitoring efficiency in the related art of monitoring the photovoltaic system.

Description

Photovoltaic system fault diagnosis method and device and computer equipment
Technical Field
The invention relates to the technical field of electric power, in particular to a photovoltaic system fault diagnosis method, a photovoltaic system fault diagnosis device and computer equipment.
Background
In the related art, based on the rising of the photovoltaic industry, state monitoring and operation and maintenance optimization of a photovoltaic system are also attracting more and more attention, so as to reduce the total cost of the photovoltaic power station in the photovoltaic system in the whole life cycle. For large photovoltaic power stations, the accurate state monitoring system can be installed to improve the investment income ratio. However, for consumer and commercial photovoltaic systems, the state monitoring system is not widely popularized in consideration of the cost of software and hardware.
However, in the related art, in order to realize the monitoring of the photovoltaic system, a method is generally adopted in which a plurality of types of acquisition devices are arranged at the place of a photovoltaic power station of the photovoltaic system, and then the acquisition devices transmit acquired data to a remote background, and the monitoring state of the photovoltaic system is determined based on the processing of the data by the background. However, when the photovoltaic system is monitored by adopting the processing mode, the time for obtaining the monitoring result is generally longer, the response is slower, and the monitoring efficiency is lower.
Accordingly, in the related art, there is a problem in that monitoring efficiency is low for monitoring a photovoltaic system.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a photovoltaic system fault diagnosis method, a photovoltaic system fault diagnosis device and computer equipment, which at least solve the technical problem that monitoring efficiency is low in monitoring a photovoltaic system in the related technology.
According to an aspect of the embodiment of the present invention, there is provided a photovoltaic system fault diagnosis method, including: in edge processing equipment of a photovoltaic system, acquiring target power data of target equipment in the photovoltaic system, wherein the target power data is acquired based on a sensor; determining actual measurement data of the target device based on the target power data; predicting the state of the target equipment based on a target state model corresponding to the target equipment to obtain prediction result data of the target equipment; and detecting whether the target equipment fails or not based on the actual measurement data and the prediction result data to obtain a detection result.
Optionally, in the case that the target device is a photovoltaic array in the photovoltaic system, the acquiring the target power data of the target device in the photovoltaic system includes: receiving direct-current voltage data acquired by a direct-current voltage sensor at a direct-current side of the photovoltaic system in the photovoltaic system, and receiving direct-current data acquired by a direct-current sensor at the direct-current side of the photovoltaic system in the photovoltaic system, wherein the target power data comprise the direct-current voltage data and the direct-current data; the determining actual measurement data of the target device based on the target power data includes: and determining the measured power of the photovoltaic array based on the direct current voltage data and the direct current data, wherein the actual measured data comprises the measured power.
Optionally, the predicting the state of the target device based on the target state model corresponding to the target device, to obtain predicted result data of the target device, includes: and predicting the state of the photovoltaic array based on the photovoltaic array state model to obtain the predicted power of the photovoltaic array, wherein the predicted result data comprises the predicted power.
Optionally, before the predicting the state of the photovoltaic array based on the state model of the photovoltaic array, obtaining the predicted power of the photovoltaic array, the method further includes: acquiring initial illumination intensity and initial temperature values of the photovoltaic array, moment illumination intensity and moment temperature values of the photovoltaic array at preset moment and a first model coefficient; determining the photovoltaic array state model based on the initial illumination intensity, the initial temperature value, the temporal illumination intensity, the temporal temperature value, and the first model coefficient.
Optionally, the determining the photovoltaic array state model based on the initial illumination intensity, the initial temperature value, the temporal illumination intensity, temporal temperature value, and the first model coefficient includes: the photovoltaic array state model is determined by:
Figure BDA0004011302430000031
wherein m is 2 Is square meters, W is watt,
Figure BDA0004011302430000032
for watt per square meter, P mp (t) the predicted power at the predetermined time, G 0 For the initial illumination intensity, G (T) is the illumination intensity at the predetermined time, and DeltaT (T) is the time temperature value T (T) at the predetermined time and the initial temperature value T 0 Difference between m 0 ～m 2 N is as follows 0 ～n 3 Is the first model coefficient.
Optionally, in the case that the target device is an inverter in the photovoltaic system, the acquiring the target power data of the target device in the photovoltaic system includes: receiving alternating voltage data acquired by an alternating voltage sensor at an alternating side of a photovoltaic system in the photovoltaic system, and receiving alternating current data acquired by an alternating current sensor at the alternating side of the photovoltaic system in the photovoltaic system, wherein the target power data comprise the alternating voltage data and the alternating current data; the determining actual measurement data of the target device based on the target power data includes: based on the alternating voltage data and the alternating current data, a measured energy of the inverter is determined, wherein the actual measured data includes the measured energy.
Optionally, the predicting the state of the target device based on the target state model corresponding to the target device, to obtain predicted result data of the target device, includes: and predicting the state of the inverter based on the inverter state model to obtain the predicted energy of the inverter, wherein the predicted result data comprises the predicted energy.
Optionally, before the predicting the state of the inverter based on the inverter state model, the predicting the predicted energy of the inverter further includes: acquiring initial energy output by the inverter, moment energy of the inverter at a preset moment and a second model coefficient; the inverter state model is determined based on the initial energy, the time of day energy, and the second model coefficient.
Optionally, the determining the inverter state model based on the initial energy, the moment energy, and the second model coefficient includes: the inverter state model is determined by:
E pac ＝h 0 +h 1 E dc
Figure BDA0004011302430000041
wherein the E dc Average output energy for one hour photovoltaic array, E pac Predicting energy for the inverter at the predetermined time, the P dc (t) is the output power of the photovoltaic array in the photovoltaic system at the preset moment, and the h 0 ～h 1 And the second model coefficient.
Optionally, the detecting whether the target device fails based on the actual measurement data and the prediction result data, to obtain a detection result includes: acquiring a difference value between the actual measurement data and the prediction result data; and determining that the target equipment fails under the condition that the difference value is larger than a preset threshold value.
According to another aspect of the present invention, there is provided a photovoltaic system fault diagnosis apparatus including: the photovoltaic system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring target power data of target equipment in the photovoltaic system in edge processing equipment of the photovoltaic system, and the target power data is acquired based on a sensor; a determining module for determining actual measurement data of the target device based on the target power data; the second acquisition module is used for predicting the state of the target equipment based on a target state model corresponding to the target equipment to obtain prediction result data of the target equipment; and the detection module is used for detecting whether the target equipment fails or not based on the actual measurement data and the prediction result data to obtain a detection result.
According to still another aspect of the present invention, there is provided a computer apparatus including: a memory and a processor, the memory storing a computer program; the processor is configured to execute a computer program stored in the memory, where the computer program when executed causes the processor to execute the photovoltaic system fault diagnosis method described in any one of the above.
In the embodiment of the invention, based on the fact that the actual measurement data and the predicted result data are acquired in the edge processing equipment of the photovoltaic system, and the actual measurement data and the predicted result data are compared to obtain the detection result of whether the target equipment is in fault or not, compared with the prior art that only the data of the photovoltaic system are acquired in the edge processing equipment of the photovoltaic system and the related processing is transmitted to the remote control center for processing, the time for data transmission and the processing of the remote control center is effectively saved, the data processing efficiency of the photovoltaic system is effectively improved, and the technical problem that the monitoring efficiency of the photovoltaic system is low in the related art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a photovoltaic system fault diagnosis method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a photovoltaic system status intelligent diagnosis device according to an embodiment of the present invention;
fig. 3 is a block diagram of a photovoltaic system fault diagnosis apparatus according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, a method embodiment of a photovoltaic system fault diagnosis method is provided, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
Fig. 1 is a flowchart of a photovoltaic system fault diagnosis method according to an embodiment of the present invention, as shown in fig. 1, the method comprising the steps of:
step S102, acquiring target power data of target equipment in a photovoltaic system in edge processing equipment of the photovoltaic system, wherein the target power data is acquired based on a sensor;
as an optional embodiment, the execution subject of the above-mentioned photovoltaic system fault diagnosis method may be an edge processing device of the above-mentioned photovoltaic system, where the device is located locally to the photovoltaic system, and belongs to an edge computing device in a network formed by the photovoltaic system and a remote control center. For example, when the photovoltaic system corresponds to a photovoltaic array located at a specific location, the remote control center may remotely communicate with a plurality of photovoltaic systems corresponding to photovoltaic arrays located at different locations, which may have their own corresponding edge processing devices.
As an alternative embodiment, the target power data acquired is different when monitoring different target devices in the photovoltaic system. For example, in the case where the target device is a photovoltaic array in a photovoltaic system, acquiring the target power data of the target device in the photovoltaic system includes: and receiving direct-current voltage data acquired by a direct-current voltage sensor at the direct-current side of the photovoltaic system in the photovoltaic system, and receiving direct-current data acquired by a direct-current sensor at the direct-current side of the photovoltaic system in the photovoltaic system, wherein the target power data comprise direct-current voltage data and direct-current data.
As an alternative embodiment, in a case where the target device is an inverter in a photovoltaic system, acquiring target power data of the target device in the photovoltaic system includes: and receiving alternating voltage data acquired by an alternating voltage sensor at the alternating current side of the photovoltaic system in the photovoltaic system, and receiving alternating current data acquired by an alternating current sensor at the alternating current side of the photovoltaic system in the photovoltaic system, wherein the target power data comprise alternating voltage data and alternating current data.
Step S104, determining actual measurement data of the target equipment based on the target power data;
as an alternative embodiment, correspondingly, in case the target device is a photovoltaic array in a photovoltaic system, determining actual measurement data of the target device based on the target power data comprises: based on the direct voltage data and the direct current data, a measured power of the photovoltaic array is determined, wherein the actual measured data includes the measured power.
As an alternative embodiment, correspondingly, determining actual measurement data of the target device based on the target power data comprises: determining measured energy of the inverter based on the ac voltage data and the ac current data, wherein the actual measured data includes the measured energy
Step S106, predicting the state of the target equipment based on a target state model corresponding to the target equipment to obtain predicted result data of the target equipment;
as an alternative embodiment, the corresponding target state model may also be different when the target devices are different. For example, when the target device is a photovoltaic array, the target state model corresponding to the photovoltaic array is a photovoltaic array state model, and when the target device is an inverter, the target state model corresponding to the inverter is an inverter state model. The photovoltaic array and the inverter are each illustrated below with the target device.
For example, when the target device is a photovoltaic array, predicting the state of the target device based on a target state model corresponding to the target device, to obtain prediction result data of the target device may include: the target state model corresponding to the photovoltaic array is a photovoltaic array state model, the state of the photovoltaic array is predicted based on the photovoltaic array state model, and the predicted power of the photovoltaic array is obtained, wherein the predicted result data comprise the predicted power.
As an alternative embodiment, before predicting the state of the photovoltaic array based on the state model of the photovoltaic array to obtain the predicted power of the photovoltaic array, the manner of obtaining the state model of the photovoltaic array may be various, for example, the following manner may be adopted: acquiring initial illumination intensity and initial temperature value of a photovoltaic array, moment illumination intensity and moment temperature value of the photovoltaic array at preset moment and a first model coefficient; a photovoltaic array state model is determined based on the initial illumination intensity, the initial temperature value, the temporal illumination intensity, the temporal temperature value, and the first model coefficient.
As an alternative embodiment, determining a photovoltaic array state model based on an initial illumination intensity, an initial temperature value, a temporal illumination intensity, a temporal temperature value, and a first model coefficient, comprises: the photovoltaic array state model is determined by:
Figure BDA0004011302430000081
wherein m2 is square meter, W is watt,
Figure BDA0004011302430000082
is watt per square meter, is the basic unit of illumination intensity, P mp (t) the predicted power at a predetermined time, G 0 For the initial illumination intensity, G (T) is the illumination intensity at the time of the predetermined time, and DeltaT (T) is the time temperature value T (T) and the initial temperature value T at the predetermined time 0 Difference between m 0 ～m 2 N is as follows 0 ～n 3 Is the first model coefficient.
For another example, when the target device is an inverter, predicting the state of the target device based on a target state model corresponding to the target device, to obtain prediction result data of the target device, including: the target state model corresponding to the inverter is an inverter state model, the state of the inverter is predicted based on the inverter state model, and predicted energy of the inverter is obtained, wherein the predicted result data comprise the predicted energy.
As an alternative embodiment, before predicting the state of the inverter based on the inverter state model, to obtain the predicted energy of the inverter, the method further includes: acquiring initial energy output by an inverter, moment energy of the inverter at a preset moment and a second model coefficient; an inverter state model is determined based on the initial energy, the time of day energy, and the second model coefficients.
As an alternative embodiment, determining the inverter state model based on the initial energy, the time of day energy, and the second model coefficients, comprises: an inverter state model is determined by:
E pac ＝h 0 +h 1 E dc
Figure BDA0004011302430000091
wherein, the liquid crystal display device comprises a liquid crystal display device,e above dc For one hour of average output energy of the photovoltaic array, E pac Predicting energy for inverter at predetermined time, P dc (t) is the output power of the photovoltaic array in the photovoltaic system at a preset moment, h 0 ～h 1 Is the second model coefficient. Step S108, detecting whether the target equipment fails or not based on the actual measurement data and the prediction result data, and obtaining a detection result.
As an alternative embodiment, when detecting whether the target device is faulty based on the actual measurement data and the predicted result data, various manners may be adopted to obtain the detection result, for example, the difference between the actual measurement data and the predicted result data may be obtained first; then, based on the comparison of the difference value and the threshold value, a detection result is obtained, and for example, it may be determined that the target device fails in a case where the difference value is greater than a predetermined threshold value.
Through the steps, based on the fact that the actual measurement data and the predicted result data are obtained in the edge processing equipment of the photovoltaic system, and the actual measurement data and the predicted result data are compared to obtain the detection result of whether the target equipment is in fault or not, compared with the fact that only the data of the photovoltaic system are collected in the edge processing equipment of the photovoltaic system in the related technology, the related processing is transmitted to the remote control center for processing, the time of data transmission and the processing of the remote control center is effectively saved, the data processing efficiency of the photovoltaic system is effectively improved, and the problem that monitoring efficiency of the photovoltaic system is low in the related technology is solved.
Based on the above examples and preferred embodiments, an alternative implementation is provided.
In the related art, photovoltaic monitoring systems, i.e., for monitoring of photovoltaic systems, are commonly used for diagnosing and maintaining performance of photovoltaic power generation systems. There are currently a number of commercial photovoltaic monitoring system products that can track the status of the photovoltaic system from the component layer and the inverter layer, evaluate system performance, and detect system faults. However, these commercial products often do not locate abnormal photovoltaic panels or help repair damaged photovoltaic panels. For example, some low cost photovoltaic monitoring systems employ multi-sensor architectures such as voltage sensors, current sensors, light sensors, and temperature sensors to monitor the status of the photovoltaic panel. And the obtained sampling data is sent to a central server through a wireless sensor network to perform performance evaluation. In addition, some photovoltaic monitoring systems employ low cost power line carrier communication schemes for data transmission. These commercial monitoring system products typically have two limitations. Firstly, data of the photovoltaic panel and the inverter are simply collected and transmitted, specific data analysis and fault diagnosis work are carried out in a remote background, and response speed is low. Second, the edge prediction capability of system performance is not enabled.
Based on the above-mentioned problems, in this alternative embodiment, a photovoltaic system status intelligent diagnosis apparatus is provided, and fig. 2 is a schematic diagram of a photovoltaic system status intelligent diagnosis apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus is located locally to a photovoltaic system, that is, on an edge device of the photovoltaic system, which is opposite to a remote control center. The relevant functional processing at the apparatus includes the following.
(1) Real-time voltage v of grid-connected point of alternating current side of photovoltaic system is obtained through power data measurement function of device ac (t) and current i ac (t) data. An inverter in a photovoltaic system acquires output voltage v of a direct-current side light array in real time dc (t) and current i dc (t) data and transmitting the data to the device through the communication interface of the inverter. The power data measurement function may be to read voltage data and current data of the grid-connected point of the inverter through the voltage transformer PT and the current transformer CT. The above-mentioned interface may be a wired communication interface such as RS-485, CAN, ethernet, or the like, or may be a wireless communication interface such as 4G, zigbee, or the like.
(2) The device utilizes the edge computing capability of the device to calculate and process alternating-current side voltage and current, direct-current side voltage and current data, and respectively obtain the output power P of the photovoltaic array dc (t) and the output power P of the inverter ac (t). Wherein, the self edge computing capability can be an embedded position with high computing power built in the deviceAnd the processor is used for carrying out calculation processing on the related sampling data and carrying out decision judgment on a calculation result.
(3) The photovoltaic array generates real-time data T by the temperature sensor and the illumination sensor which are assembled by the photovoltaic array A And G A To the device via a communication interface.
(4) The device predicts the maximum output power P of the photovoltaic array through a built-in photovoltaic array state model and an inverter state model respectively mp (t) and the output power P of the inverter g (t). For the predicted result, calculating the average output energy E of the photovoltaic array within a predetermined time (for example, within 1 hour) by using a sliding time window method dc And the average output energy E of the inverter pac . The photovoltaic array state model can select a specific mechanism model according to physical characteristics of photovoltaic arrays made of different materials. The inverter state model can select a specific unitary linear model according to a linear relation between the output energy and the input energy of the inverter in a certain load range. The sliding time window method can average all data in a certain time window, and can filter noise in the data to a certain extent.
The mechanism model selected by the photovoltaic array state model is made of crystalline silicon material, and can be
Figure BDA0004011302430000121
Wherein m is 2 Is square meters, W is watt,
Figure BDA0004011302430000122
is watt per square meter, is the basic unit of illumination intensity, G 0 G (t) is illumination intensity data acquired by an illumination sensor in real time for initial illumination intensity; delta T (T) is temperature data T (T) acquired by a temperature sensor in real time and initial temperature T 0 Is a difference in (c). m is m 0 ～m 2 N is as follows 0 ～n 3 Is a model coefficient. In the present embodiment, the light intensity of more than 200W/m is mainly considered 2 Is the case in (a).
The model parameters of the photovoltaic array state model can also be obtained in various manners, for example, the parameters can be determined by a least squares regression method based on historical data of a certain period. For example, for convenience of description, the related variables are represented by the following symbols, the illumination intensity G, the ambient temperature T, and the maximum output power P. Suppose that k sets of observations (G 1 ,T 1 ,P 1 )，(G 2 ,T 2 ,P 2 )，…，(G k ,T k ,P k ) Objective function
Figure BDA0004011302430000126
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004011302430000123
Figure BDA0004011302430000124
Figure BDA0004011302430000125
regression model q=m T A T Am-2m T A T P+P T P. Minimum points can be obtained by partial derivative of the regression model function as follows:
m＝(A T A) -1 A T P。
when the inverter state model selects the unitary linear model, the unitary linear model may be represented as follows:
E pac ＝h 0 +h 1 E dc
Figure BDA0004011302430000131
wherein h is 0 ～h 1 Model coefficients, E, which are the inverter state model dc For one hour of average output energy of the photovoltaic array, E pac Predicting energy for inverter at predetermined time, P dc And (t) is the output power of the photovoltaic array in the photovoltaic system at a preset moment.
The model coefficients of the inverter state model described above may be obtained in the following manner: based on historical data of a certain period, parameter identification is carried out through a least square regression method. Suppose that k sets of observations are obtained (E dc1 ,E pac1 )，(E dc2 ,E pac2 )，…，(E dck ,E pack ) Objective function
Figure BDA0004011302430000132
Wherein E is pacie For estimation, E paci Is a measurement value. Regression model +.>
Figure BDA0004011302430000133
Minimum points can be obtained by partial derivative of the regression model function as follows:
Figure BDA0004011302430000134
/>
Figure BDA0004011302430000135
(5) The output power P of the photovoltaic array obtained by actual measurement dc (t) photovoltaic array output power P obtained by model prediction mp (t) comparing, and calculating the relative error delta P (t) of the two in real time. When deltaP (t) exceeds a certain threshold value |delta p And when the voltage is I, judging that the performance of the photovoltaic array is seriously reduced or fails. Wherein the threshold value |delta p The I can be adjusted according to the actual design of the photovoltaic array and the selection of parts, and is selected according to historical experience values and an expert database. The inverter output energy E obtained by actual measurement ac Inverter output energy E obtained by model prediction pac The comparison is performed, and the relative error delta E of the two is calculated in real time. When delta E exceeds a certain threshold delta e When the inverter efficiency is judged to be severely reduced or a failure occurs. Wherein the threshold value delta e The inverter can be adjusted according to the actual design of the inverter and the selection of parts, and the inverter is selected according to historical experience values and an expert database.
(6) The actual measurement data and the model prediction result can be transmitted to a background control center through a communication interface of the equipment for real-time monitoring, analysis and storage. The background control center can be located in the power distribution automatic control system. Meanwhile, when the performance of the photovoltaic system is reduced and the photovoltaic system fails, the equipment can report the problem to a background control center. After the background control center is researched and judged, a decision instruction is issued to the equipment. Finally, the device performs relevant processing on the problematic photovoltaic system through the communication interface.
In an alternative embodiment of the invention, relevant parameters in the photovoltaic system are monitored and analyzed in real time by the data acquisition function of the inverter or a special intelligent acquisition terminal at the local of the photovoltaic system. The relevant state of the photovoltaic system can be estimated based on an advanced model and algorithm and compared with data acquired in real time. The function is beneficial to timely finding out the performance degradation or internal faults of the photovoltaic system, and is beneficial to improving the accuracy and the purpose of operation and maintenance. Meanwhile, the safety and reliability of the power distribution network can be improved.
In an embodiment of the present invention, there is further provided a photovoltaic system fault diagnosis apparatus, and fig. 3 is a block diagram of a photovoltaic system fault diagnosis apparatus according to an embodiment of the present invention, as shown in fig. 3, where the apparatus includes: the first acquisition module 32, the determination module 34, the second acquisition module 36 and the detection module 38 are described below.
The first obtaining module 32 is configured to obtain, in an edge processing device of the photovoltaic system, target power data of a target device in the photovoltaic system, where the target power data is acquired based on a sensor; a determining module 34, connected to the first obtaining module 32, for determining actual measurement data of the target device based on the target power data; a second obtaining module 36, connected to the determining module 34, configured to predict a state of the target device based on a target state model corresponding to the target device, so as to obtain predicted result data of the target device; the detection module 38 is connected to the second acquisition module 36, and is configured to detect whether the target device fails based on the actual measurement data and the predicted result data, so as to obtain a detection result.
In an embodiment of the present invention, there is also provided a computer apparatus including: a memory and a processor, the memory storing a computer program; and the processor is used for executing the computer program stored in the memory, and the computer program enables the processor to execute the photovoltaic system fault diagnosis method.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of units may be a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (12)

1. A photovoltaic system fault diagnosis method, characterized by comprising:
in edge processing equipment of a photovoltaic system, acquiring target power data of target equipment in the photovoltaic system, wherein the target power data is acquired based on a sensor;
determining actual measurement data of the target device based on the target power data;
predicting the state of the target equipment based on a target state model corresponding to the target equipment to obtain prediction result data of the target equipment;
and detecting whether the target equipment fails or not based on the actual measurement data and the prediction result data to obtain a detection result.
2. The method of claim 1, wherein, in the case where the target device is a photovoltaic array in the photovoltaic system,
the obtaining the target power data of the target device in the photovoltaic system includes: receiving direct-current voltage data acquired by a direct-current voltage sensor at a direct-current side of the photovoltaic system in the photovoltaic system, and receiving direct-current data acquired by a direct-current sensor at the direct-current side of the photovoltaic system in the photovoltaic system, wherein the target power data comprise the direct-current voltage data and the direct-current data;
the determining actual measurement data of the target device based on the target power data includes: and determining the measured power of the photovoltaic array based on the direct current voltage data and the direct current data, wherein the actual measured data comprises the measured power.
3. The method according to claim 2, wherein predicting the state of the target device based on the target state model corresponding to the target device, to obtain the predicted result data of the target device, includes:
and predicting the state of the photovoltaic array based on the photovoltaic array state model to obtain the predicted power of the photovoltaic array, wherein the predicted result data comprises the predicted power.
4. The method of claim 3, further comprising, prior to said predicting a state of said photovoltaic array based on said photovoltaic array state model, obtaining a predicted power of said photovoltaic array:
acquiring initial illumination intensity and initial temperature values of the photovoltaic array, moment illumination intensity and moment temperature values of the photovoltaic array at preset moment and a first model coefficient;
determining the photovoltaic array state model based on the initial illumination intensity, the initial temperature value, the temporal illumination intensity, temporal temperature value, and the first model coefficient.
5. The method of claim 4, wherein the determining the photovoltaic array state model based on the initial illumination intensity, the initial temperature value, the temporal illumination intensity, temporal temperature value, and the first model coefficient comprises:
the photovoltaic array state model is determined by:
Figure FDA0004011302420000021
wherein m is 2 Is square meters, W is watt,
Figure FDA0004011302420000022
for watt per square meter, P mp (t) the predicted power at the predetermined time, G 0 For the initial illumination intensity, G (T) is the illumination intensity at the predetermined time, and DeltaT (T) is the time temperature value T (T) at the predetermined time and the initial temperature value T 0 Difference between m 0 ～m 2 N is as follows 0 ～n 3 Is the first model coefficient.
6. The method of claim 1, wherein, in the case where the target device is an inverter in the photovoltaic system,
the obtaining the target power data of the target device in the photovoltaic system includes: receiving alternating voltage data acquired by an alternating voltage sensor at an alternating side of a photovoltaic system in the photovoltaic system, and receiving alternating current data acquired by an alternating current sensor at the alternating side of the photovoltaic system in the photovoltaic system, wherein the target power data comprise the alternating voltage data and the alternating current data;
the determining actual measurement data of the target device based on the target power data includes: based on the alternating voltage data and the alternating current data, a measured energy of the inverter is determined, wherein the actual measured data includes the measured energy.
7. The method of claim 6, wherein predicting the state of the target device based on a target state model corresponding to the target device, to obtain predicted result data of the target device, comprises:
and predicting the state of the inverter based on the inverter state model to obtain the predicted energy of the inverter, wherein the predicted result data comprises the predicted energy.
8. The method of claim 7, further comprising, prior to said predicting a state of the inverter based on the inverter state model, obtaining a predicted energy of the inverter:
acquiring initial energy output by the inverter, moment energy of the inverter at a preset moment and a second model coefficient;
the inverter state model is determined based on the initial energy, the time of day energy, and the second model coefficients.
9. The method of claim 8, wherein the determining the inverter state model based on the initial energy, the time of day energy, and the second model coefficients comprises:
the inverter state model is determined by:
E pac ＝h 0 +h 1 E dc
Figure FDA0004011302420000031
wherein the E dc Average output energy for one hour photovoltaic array, E pac Predicting energy for the inverter at the predetermined time, the P dc (t) is the output power of the photovoltaic array in the photovoltaic system at the preset moment, and the h 0 ～h 1 And the second model coefficient.
10. The method according to any one of claims 1 to 9, wherein the detecting whether the target device is malfunctioning based on the actual measurement data and the predicted result data, to obtain a detection result, comprises:
acquiring a difference value between the actual measurement data and the prediction result data;
and determining that the target equipment fails under the condition that the difference value is larger than a preset threshold value.
11. A photovoltaic system fault diagnosis apparatus, characterized by comprising:
the photovoltaic system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring target power data of target equipment in the photovoltaic system in edge processing equipment of the photovoltaic system, and the target power data is acquired based on a sensor;
a determining module for determining actual measurement data of the target device based on the target power data;
the second acquisition module is used for predicting the state of the target equipment based on a target state model corresponding to the target equipment to obtain prediction result data of the target equipment;
and the detection module is used for detecting whether the target equipment fails or not based on the actual measurement data and the prediction result data to obtain a detection result.
12. A computer device, comprising: a memory and a processor, wherein the memory is configured to store,
the memory stores a computer program;
the processor configured to execute a computer program stored in the memory, the computer program when executed causing the processor to perform the photovoltaic system fault diagnosis method of any one of claims 1 to 10.
CN202211649484.3A 2022-12-21 2022-12-21 Photovoltaic system fault diagnosis method and device and computer equipment Pending CN116192038A (en)

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