CN112730968A - Neural network-based photovoltaic grid-connected power station fault type detection device - Google Patents

Neural network-based photovoltaic grid-connected power station fault type detection device Download PDF

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CN112730968A
CN112730968A CN202110046104.6A CN202110046104A CN112730968A CN 112730968 A CN112730968 A CN 112730968A CN 202110046104 A CN202110046104 A CN 202110046104A CN 112730968 A CN112730968 A CN 112730968A
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module
photovoltaic
current
output
value
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李博文
夏之秋
彭继慎
赵翀
殷孝雎
鞠振河
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Shenyang Furun Solar Energy Technology Development Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/25—Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
    • 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
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • 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
    • 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
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

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  • General Physics & Mathematics (AREA)
  • Photovoltaic Devices (AREA)
  • Photometry And Measurement Of Optical Pulse Characteristics (AREA)

Abstract

A photovoltaic grid-connected power station fault type detection device based on a neural network is characterized by comprising: the device comprises a photovoltaic module array, a measuring module, a control module, a communication module and an illumination measuring instrument; and detecting whether the photovoltaic array has fault types such as hot spots, hidden cracks, sheltering and the like by adopting a BP neural network calculation method according to the illumination value output by the current illumination measuring instrument and the current and voltage values of the photovoltaic array.

Description

Neural network-based photovoltaic grid-connected power station fault type detection device
Technical Field
The invention relates to a photovoltaic grid-connected power station fault type detection device based on environmental illumination measurement, and belongs to the technical field of solar photovoltaic power generation.
Background
With the urgent need of new energy application, the scale of solar photovoltaic power generation is continuously enlarged, and each link of operation, maintenance, management and the like of photovoltaic power generation urgently needs intelligent transformation and upgrading to improve efficiency and reduce cost. Therefore, the working state of each photovoltaic cell panel needs to be known accurately in real time. Although some monitoring methods exist at present, the operation state of the photovoltaic module cannot be judged correctly. Such as the technical method disclosed in patent application No. 2020203163891 "a component level photovoltaic power plant fault detection system". Because the working state of the photovoltaic cell module is greatly influenced by the illumination, the operating state of the photovoltaic module cannot be accurately judged only by detecting the voltage of the output end. Generally, the user can know the fault, the burning and even the development of a disaster accident of a certain battery panel only when the certain battery panel is in fault, the burning and even the development of the disaster accident, and the serious loss is caused at night. The existing photovoltaic power station can only monitor a string of photovoltaic modules, cannot consider the influence of environmental illumination, cannot determine the type of a fault, and cannot meet the operation and maintenance requirements of a large-scale photovoltaic power station.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a photovoltaic grid-connected power station fault type detection device based on a neural network, which can detect and judge the fault type of a photovoltaic component; it is characterized by comprising: the device comprises a photovoltaic module array, a measuring module, a control module, a communication module and an illumination measuring instrument; the photovoltaic array is formed by connecting a plurality of photovoltaic modules in series; the measuring module comprises a voltage sampling module and a current sampling module; the output end of the photovoltaic module array is connected with the input end of the measuring module; the output end of the measuring module is connected with the input end of the control module; the output end of the control module is connected with the input end of the communication module;
the communication module is used for transmitting the current system state and the fault detection result.
The illuminance measuring instrument is arranged near the field of the photovoltaic module, and the output end of the illuminance measuring instrument is connected with the input end of the control module and used for measuring the solar irradiation quantity of the current environmental field; the control module detects whether the photovoltaic array has faults such as hot spots, hidden cracks, sheltering and the like by adopting a BP neural network calculation method according to the illumination value output by the current illumination measuring instrument and the current and voltage values of the photovoltaic array, and the steps are as follows:
step 1 obtaining BP neural network learning sample
Selecting sunny weather, carrying out integral point division on time periods from 6 morning to 6 evening, respectively selecting three conditions of normal operation, shadow shielding, hot spot or hidden crack of a photovoltaic module, setting G as illumination, V as voltage, I as current and t as time, wherein t is 1,21,G2,...,GmVoltage value V1,V2,..., VmValue of current I1,I2,...,ImAnd collecting sample values for more than 50 days as BP neural network learning samples.
Step 2, learning by adopting BP neural network
1) Selecting input and output samples;
converting the values of time t, illumination G, voltage V and current I into binary; when the photovoltaic array generates electricity normally, the output d is equal to 0; when the photovoltaic array has hot spots or hidden crack faults, the output d is equal to 1; when the photovoltaic array has a shadow blocking fault, the output d is equal to-1. Then:
the input samples are:
Figure RE-GDA0002929150740000011
the output samples are:
Figure RE-GDA0002929150740000012
2) selecting a primary weight w;
3) repeating the following process, inputting all samples until convergence;
(1) input sample x, calculate the layer outputs:
yl＝f(vtl)＝f(wlyl-1)
(2) calculating an output layer error:
Figure RE-GDA0002929150740000021
(3) calculating the local gradient deltaj,δk
Figure RE-GDA0002929150740000022
Figure RE-GDA0002929150740000023
(4) Correcting the weight of the output layer:
wk←wk+ηΔwk
(5) correcting the weight of the hidden layer:
wj←wj+ηΔwj
(6) enter new sample x until all samples are entered and E<EmaxAnd then the process is finished.
Step 4, obtaining the current illumination value, voltage value and current value
Reading an illuminance value of the illuminance measuring instrument; acquiring a voltage value V through a photovoltaic array terminal voltage sampling module; and acquiring a current value I through a photovoltaic array end current sampling module, and sending the current value I to a control module.
Step 5, adopting BP neural network to detect
The current time t, the illumination value G, the voltage value V and the current value I are input into the BP neural network as an input vector x, and then an output value d of the network can be calculated. When the output value d is equal to 0, the photovoltaic array is considered to be normal, when d is equal to 1, the photovoltaic array is considered to have hot spots or hidden crack faults, and when d is equal to-1, the photovoltaic array is considered to have shadow shielding faults.
The photovoltaic module fault type detection method has the beneficial effects that the fault type detection of the photovoltaic module can be realized according to the measured value of an environment illumination measuring instrument and the current and voltage parameters of the photovoltaic module.
Drawings
FIG. 1 is a schematic structural diagram of a fault type detection device of a photovoltaic grid-connected power station based on a neural network.
FIG. 2 is a flow chart of a computing method employing a BP neural network.
FIG. 3 shows that whether the photovoltaic module has faults such as hidden cracks, hot spots and the like is judged according to the combination of the current value and the voltage value of the kth photovoltaic module.
Detailed description of the preferred embodiments
The device for detecting the fault type of the photovoltaic grid-connected power station based on the neural network as shown in the attached figure 1 is characterized by comprising the following components: the device comprises a photovoltaic array, a measuring module, a control module, a communication module and an illumination measuring instrument; the photovoltaic array is formed by connecting a plurality of photovoltaic modules in series; the measuring module comprises a voltage sampling module and a current sampling module; the output end of the photovoltaic array is connected with the input end of the measuring module; the output end of the measuring module is connected with the input end of the control module; the output end of the control module is connected with the input end of the communication module;
the communication module is used for transmitting the current system state and the fault detection result.
The illuminance measuring instrument is installed near the field of the photovoltaic module, and the output end of the illuminance measuring instrument is connected with the input end of the control module and used for measuring the solar irradiation amount of the current environment field.
As shown in fig. 2 and fig. 3, the control module detects whether the photovoltaic module has hot spots, hidden cracks, shelters and other faults by adopting a calculation method of a BP neural network according to the illuminance value output by the current illuminance measuring instrument and the current and voltage values of the photovoltaic module, and the steps are as follows:
step 1 obtaining BP neural network learning sample
Selecting sunny weather, carrying out integral point division on time periods from 6 morning to 6 evening, respectively selecting three conditions of normal operation, shadow shielding, hot spot or hidden crack of a photovoltaic module, setting G as illumination, V as photovoltaic module voltage, I as current and t as time, wherein t is 1,21,G2,...,GmVoltage value V of photovoltaic module1,V2,...,VmValue of current I1,I2,...,ImAnd collecting sample values for more than 50 days as BP neural network learning samples.
Step 2, learning by adopting BP neural network
1) Selecting input and output samples;
converting values of time t, illumination G, photovoltaic module voltage V and current I into binary systems; when the photovoltaic array generates electricity normally, the output d is equal to 0; when the photovoltaic array has hot spots or hidden crack faults, the output d is equal to 1; when the photovoltaic array has a shadow blocking fault, the output d is equal to-1. Then:
the input samples are:
Figure RE-GDA0002929150740000031
the output samples are:
Figure RE-GDA0002929150740000032
2) selecting a primary weight w;
3) repeating the following process, inputting all samples until convergence;
(1) input sample x, calculate the layer outputs:
yl＝f(vtl)＝f(wlyl-1)
(2) calculating an output layer error:
Figure RE-GDA0002929150740000033
(3) calculating the local gradient deltaj,δk
Figure RE-GDA0002929150740000034
Figure RE-GDA0002929150740000035
(4) Correcting the weight of the output layer:
wk←wk+ηΔwk
(5) correcting the weight of the hidden layer:
wj←wj+ηΔwj
(6) enter new sample x until all samples are entered and E<EmaxAnd then the process is finished.
Step 4, acquiring the current illumination value, the voltage value and the current value of the photovoltaic module
Reading an illuminance value of the illuminance measuring instrument; acquiring a voltage value V through a photovoltaic array terminal voltage sampling module; and acquiring a current value I through a photovoltaic array end current sampling module, and sending the current value I to a control module.
Step 5, adopting BP neural network to detect
The current time t, the illumination value G, the voltage value V and the current value I are input into the BP neural network as an input vector x, and then an output value d of the network can be calculated. When the output value d is equal to 0, the photovoltaic array is considered to be normal, when d is equal to 1, the photovoltaic array is considered to have hot spots or hidden crack faults, and when d is equal to-1, the photovoltaic array is considered to have shadow shielding faults.
Therefore, the states of all positions of the photovoltaic module are distinguished, the position illumination of normal power generation is normal, the position illumination shielded by the shadow can be reduced, and the position illumination with hot spots and hidden cracks can be increased.
The components, processes and letters that are not described in detail in this embodiment are well known in the art and are not described in detail herein.

Claims (2)

1. A photovoltaic grid-connected power station fault type detection device based on a neural network is characterized by comprising: the device comprises a photovoltaic array, a measuring module, a control module, a communication module and an illumination measuring instrument; the photovoltaic array is formed by connecting a plurality of photovoltaic modules in series;
the output end of the photovoltaic array is connected with the input end of the measuring module; the output end of the measuring module is connected with the input end of the control module; the output end of the control module is connected with the input end of the communication module;
the measuring module comprises voltage sampling modules and current sampling modules, the number of the voltage sampling modules is the same as that of the photovoltaic modules, the input end of each voltage sampling module is connected with the output end of the corresponding photovoltaic module, and the input end of each current sampling module is connected with the output end of any photovoltaic module and used for collecting the output voltage and the output current of the photovoltaic array in real time;
the communication module is used for transmitting the current system state and the fault detection result.
2. The device for detecting the fault type of the photovoltaic grid-connected power station based on the neural network as claimed in claim 1, wherein the illuminance measuring instrument is installed near a photovoltaic module field, and the output end of the illuminance measuring instrument is connected with the input end of the control module and is used for measuring the solar irradiation amount of the current environmental field; the control module detects whether the photovoltaic module has faults such as hot spots, hidden cracks, sheltering and the like by adopting a BP neural network calculation method according to the illumination value output by the illumination measuring instrument and the current and voltage values of the photovoltaic module at present, and the steps are as follows:
step 1 obtaining BP neural network learning sample
Selecting sunny weather, carrying out integral point division on time periods from 6 morning to 6 evening, respectively selecting three conditions of normal operation, shadow shielding, hot spot or hidden crack of a photovoltaic module, setting G as illumination, V as voltage of the photovoltaic module, I as current and t as time, wherein t is 1,21,G2,...,GmVoltage value V of photovoltaic module1,V2,...,VmValue of current I1,I2,...,ImCollecting sample values for more than 50 days as BP neural network learning samples;
step 2, learning by adopting BP neural network
1) Selecting input and output samples;
converting values of time t, illumination G, voltage V and current I of the photovoltaic module into binary systems; when the photovoltaic array generates electricity normally, the output d is equal to 0; when the photovoltaic array has hot spots or hidden crack faults, the output d is equal to 1; when the photovoltaic array has a shadow shielding fault, the output d is equal to-1
Then:
the input samples are:
Figure RE-FDA0002929150730000011
the output samples are:
Figure RE-FDA0002929150730000012
2) selecting a primary weight w;
3) repeating the following process, inputting all samples until convergence;
(1) input sample x, calculate the layer outputs:
yl＝f(vtl)＝f(wlyl-1)
(2) calculating an output layer error:
Figure DEST_PATH_GDA0002929150740000021
(3) calculating the local gradient deltaj，δk
Figure DEST_PATH_GDA0002929150740000022
Figure DEST_PATH_GDA0002929150740000023
(4) Correcting the weight of the output layer:
wk←wk+ηΔwk
(5) correcting the weight of the hidden layer:
wj←wj+ηΔwj
(6) enter new sample x until all samples are entered and E < EmaxThen the process is finished;
step 4, acquiring the current illumination value, the voltage value and the current value of the photovoltaic module
Reading an illuminance value of the illuminance measuring instrument; acquiring a voltage value V of the photovoltaic module through a voltage sampling module; acquiring a current value I through a current sampling module, and sending the current value I to a control module;
step 5, adopting BP neural network to detect
Inputting the current time t, the illumination value G, the voltage value V and the current value I of the photovoltaic module as input vectors x into the BP neural network, and calculating an output value d of the network;
when the output value d is equal to 0, the photovoltaic module is considered to be normal, when the output value d is equal to 1, the photovoltaic module is considered to have hot spot or hidden crack fault, and when the output value d is equal to-1, the photovoltaic module is considered to have shadow shielding fault.
CN202110046104.6A 2021-01-14 2021-01-14 Neural network-based photovoltaic grid-connected power station fault type detection device Pending CN112730968A (en)

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