CN111313830A - Inverter diagnosis method and system - Google Patents
Inverter diagnosis method and system Download PDFInfo
- Publication number
- CN111313830A CN111313830A CN202010088162.0A CN202010088162A CN111313830A CN 111313830 A CN111313830 A CN 111313830A CN 202010088162 A CN202010088162 A CN 202010088162A CN 111313830 A CN111313830 A CN 111313830A
- Authority
- CN
- China
- Prior art keywords
- power
- inverter
- value
- determining
- fluctuation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000002159 abnormal effect Effects 0.000 claims abstract description 62
- 230000005856 abnormality Effects 0.000 claims abstract description 6
- 238000002405 diagnostic procedure Methods 0.000 claims abstract 8
- 238000005070 sampling Methods 0.000 claims description 38
- 238000010248 power generation Methods 0.000 abstract description 12
- 230000007423 decrease Effects 0.000 abstract description 3
- 238000005286 illumination Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- JIXYFZKFYITQCJ-UHFFFAOYSA-N 3-(4-methoxyphenyl)-4-phenyl-1,2,4-triazole Chemical compound C1=CC(OC)=CC=C1C1=NN=CN1C1=CC=CC=C1 JIXYFZKFYITQCJ-UHFFFAOYSA-N 0.000 description 1
- 238000000418 atomic force spectrum Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
-
- 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
Landscapes
- Inverter Devices (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Photovoltaic Devices (AREA)
Abstract
The embodiment of the invention discloses an inverter diagnosis method and system. The diagnostic method comprises the following steps: acquiring operation historical data of a photovoltaic power station; determining the number of power extreme values of each inverter in each typical day photovoltaic power station and the occurrence time of each power extreme value based on the operation historical data; determining the abnormal fluctuation duration of the corresponding inverter based on the number of the power extreme values and the occurrence time of each power extreme value; acquiring each path of MPPT voltage of the corresponding inverter; and determining whether the power fluctuation abnormality exists in the corresponding inverter or not based on the MPPT voltage and the abnormal fluctuation time length. The method and the device for determining the power fluctuation of the inverter are used for determining whether the inverter has abnormal power fluctuation or not based on the operation historical data of the photovoltaic power station. The problem of among the prior art inverter because self trouble can't in time be found and lead to the decline of inverter generating efficiency is solved, can find the inverter that has the latent fault in advance, reduce the inverter trouble and miss the report rate, reduce the power generation loss of photovoltaic power plant.
Description
Technical Field
The embodiment of the invention relates to a photovoltaic power generation technology, in particular to an inverter diagnosis method and system.
Background
The inverter has low power generation efficiency caused by the fact that hardware of the inverter operates in a half-fault state, the inverter cannot be detected completely (the detection range of hardware faults of the inverter of the old generation is less), and operation and maintenance personnel of a power station cannot relatively and comprehensively process various abnormal working conditions of the inverter, so that the inverter cannot exert the optimal working efficiency, and further the power generation efficiency of the photovoltaic power station is reduced.
Disclosure of Invention
The embodiment of the invention provides an inverter diagnosis method and system, which are used for diagnosing an inverter with abnormal power, finding out a potential fault inverter in advance and improving the power generation amount of a photovoltaic power station.
In a first aspect, an embodiment of the present invention provides an inverter diagnosis method, which is applied to a photovoltaic power station, and the method includes:
acquiring operation historical data of a photovoltaic power station;
determining the number of power extreme values of each inverter in the photovoltaic power station on each typical day and the occurrence time of each power extreme value based on the operation historical data;
determining the abnormal fluctuation time length of the corresponding inverter based on the number of the power extreme values and the occurrence time of each power extreme value;
acquiring each path of MPPT voltage of the corresponding inverter;
and determining whether the corresponding inverter has power fluctuation abnormity or not based on the MPPT voltage and the abnormal fluctuation time length.
Optionally, the determining, based on the operation history data, the number of power extreme values of each inverter in the photovoltaic power plant on each typical day and the occurrence time of each power extreme value includes:
determining the all-day active power of each inverter in the photovoltaic power station on each typical day based on the operation historical data;
and determining the number of power extreme values of each inverter based on the active power of each typical day, and recording the occurrence time of each power extreme value.
Optionally, the power extreme value includes a power peak value or a power valley value, and the power extreme value is determined according to the following method:
comparing the active power at any moment with the active power at two adjacent moments;
if the active power at any moment is greater than the active power at two adjacent moments, determining the active power at the moment as a power wave peak value;
and if the active power at any moment is smaller than the active power at two adjacent moments, determining that the active power at the moment is a power wave valley value.
Optionally, the determining, based on the number of the power extremum values and the occurrence time of each power extremum value, an abnormal fluctuation duration of the corresponding inverter includes:
judging whether the number of the power extreme values meets a preset condition, wherein the preset condition comprises the following steps: the number of the power extreme values is larger than the threshold value of the number of fluctuation points, and the time interval of any two adjacent power extreme values in the power extreme values is smaller than the threshold value of the fluctuation duration;
and if the number of the power extreme values meets a preset condition, determining the difference value between the appearance moment of the last power extreme value and the appearance moment of the first power extreme value as the abnormal fluctuation time length of the corresponding inverter.
Optionally, the determining whether the power fluctuation abnormality exists in the corresponding inverter based on the MPPT voltage includes:
calculating a second order difference absolute value of each path of MPPT voltage in the inverter corresponding to the abnormal fluctuation duration;
and if the second-order difference absolute value of any MPPT voltage is greater than the voltage second-order difference threshold value, determining that the corresponding inverter has power fluctuation abnormity.
Optionally, the second order difference absolute value of the MPPT voltage is determined according to the following formula:
wherein, CiThe second order difference absolute value of the ith MPPT voltage is obtained; v. oftThe voltage value of the t sampling point is obtained; v. ofmaxIs the maximum voltage value of the current day; v. oft+2The voltage value of the t +2 sampling point is obtained; v. oft+1Is the voltage value of the t +1 th sampling point.
Optionally, after determining that there is a power fluctuation abnormality in the corresponding inverter, the method further includes:
and carrying out fault diagnosis on the inverter with the abnormal power fluctuation.
Optionally, the performing fault diagnosis on the inverter with power fluctuation abnormality includes:
if the second-order difference absolute value of partial MPPT voltage in the inverter is larger than the voltage second-order difference threshold value, determining that a boost plate of the partial MPPT is abnormal;
and if the second-order difference absolute values of all MPPT voltages in the inverter are larger than the voltage second-order difference threshold value, determining that the DSP control board of the inverter is abnormal.
Optionally, the typical day is determined according to the following method:
selecting a string with the largest daily generated energy based on the operation historical data;
calculating a second-order difference absolute value of the current of the string with the maximum daily generated energy according to the following formula:
wherein itCurrent data of the t sampling point; i.e. it+2Current data of a t +2 sampling point; i.e. it+1Current data of a t +1 th sampling point; i.e. imaxIs the maximum value of the current on the day; diThe current second-order difference absolute value at the ith moment is obtained;
and determining the generation day when the absolute values of the second-order current differences are all smaller than a second-order current difference threshold as the typical day.
In a second aspect, an embodiment of the present invention further provides an inverter diagnosis system, where the diagnosis system includes:
the data acquisition module is used for acquiring the operation historical data of the photovoltaic power station;
the power extreme value determining module is used for determining the number of power extreme values of each inverter in the photovoltaic power station on each typical day and the occurrence time of each power extreme value on the basis of the operation historical data;
the abnormal fluctuation duration determining module is used for determining the corresponding abnormal fluctuation duration of the inverter based on the number of the power extreme values and the occurrence time of each power extreme value;
the MPPT voltage acquisition module is used for acquiring the MPPT voltages of all paths of the corresponding inverters;
and the fluctuation abnormity determining module is used for determining whether the power fluctuation abnormity exists in the corresponding inverter or not based on the MPPT voltage.
According to the embodiment of the invention, the number of the power extreme values of the inverters on each typical day is determined by acquiring the operation historical data of the photovoltaic power station, the occurrence time of each power extreme value is recorded, the abnormal fluctuation time length of each inverter is further obtained, and whether the fluctuation range of the MPPT voltage of each inverter in the abnormal fluctuation time length range meets the requirement or not is determined, so that whether the inverter has abnormal power fluctuation or not is determined. The problem of among the prior art inverter because self trouble can't in time be found and lead to the decline of inverter generating efficiency is solved, realized can diagnosing the operating condition of inverter according to photovoltaic power plant's historical operating data, found the inverter that has the latent fault in advance, reduce the inverter trouble and miss the report rate, rethread replacement has the inverter of latent fault, let each inverter exert best work efficiency, reduced photovoltaic power plant's power generation loss.
Drawings
Fig. 1 is a flowchart of an inverter diagnosis method according to an embodiment of the present invention;
fig. 2 is a flowchart of an optimized inverter diagnosis method according to a second embodiment of the present invention;
fig. 3 is a block diagram of an inverter diagnostic system according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of an inverter diagnosis method according to an embodiment of the present invention, and this embodiment is applicable to a case where an inverter of a photovoltaic power station is subjected to fault diagnosis based on operating data of the photovoltaic power station in operation. The method may be performed by an inverter diagnostic system, for example, by a computing device configured with a processor, the method specifically including:
and S110, obtaining operation historical data of the photovoltaic power station.
The operation historical data of the photovoltaic power station comprises operation data of each inverter in the photovoltaic power station. For example, the active power parameter of each inverter, the generated current parameter of each inverter group string, the maximum power point voltage data of each inverter, and the like.
And S120, determining the number of power extreme values of each inverter in the photovoltaic power station on each typical day and the occurrence time of each power extreme value based on the operation historical data.
Wherein, the typical day refers to the sampling day with smooth force curve all day. The curve smoothing shows that the current day is not affected by cloud disturbance. The selection of a typical day usually needs to consider the weather condition of the location of the photovoltaic power station and search for a date or a period with stable illumination amplitude. If the illumination amplitude is unstable, suddenly rises and suddenly falls, and when the illumination amplitude changes rapidly, the actual tracking response precision of the inverter is different, the power fluctuation of the inverter can be caused, and the judgment result can be influenced. By selecting a typical day and analyzing the operation data of the typical sunlight photovoltaic power station, the interference of meteorological factors can be avoided.
The power extreme value refers to the fact that the attack power at a certain sampling moment has the maximum value or the minimum value compared with the active power at the front and the back adjacent sampling moments, and the maximum value or the minimum value is the power wave peak value or the power wave valley value. In this embodiment, the power peak value or the power valley value may be selected as the power extreme value, and the occurrence time of each power peak value or the occurrence time of each power valley value may be recorded.
In one embodiment, the power limit is specifically determined by:
comparing the active power at any moment with the active power at two adjacent moments;
if the active power at any moment is greater than the active power at two adjacent moments, determining the active power at the moment as a power wave peak value;
and if the active power at any moment is smaller than the active power at two adjacent moments, determining that the active power at the moment is a power wave valley value.
For example, if the inverter samples data every fifteen minutes, 96 sampling moments exist in 24 hours a day, and if the active power value at the 80 th sampling moment is greater than the active power values at the 79 th and 81 th sampling moments, the active power value at the 80 th sampling moment is recorded as one power wave peak value. Similarly, if the active power value at the 81 th sampling time is smaller than the active power values at the 80 th and 82 th sampling times, the active power at the 81 th sampling time is defined as one power valley value.
The embodiment may count only the peak value of the power wave as the power extreme value, or count only the trough value of the power wave as the power extreme value. And when the quantity of the power extreme values is counted, the occurrence time of the power extreme values is recorded at the same time, and the occurrence time of each power extreme value is used for carrying out follow-up statistics on the total abnormal fluctuation time length of each inverter.
After the typical day is determined, based on a certain inverter, determining the distribution of each power extreme value (including the number of power extreme values and the occurrence time of the power extreme values) of the inverter on a certain typical day, and then counting the distribution of the power extreme values of the inverter on other typical days according to the same method; and then, counting the power extreme value distribution of other inverters on each typical day according to the same method.
In general, when the inverter normally operates, the active power output by the inverter at each sampling time is distributed in a smooth parabolic shape. In the embodiment, the active power distribution condition of each inverter on each typical day is obtained by analyzing the active power of each inverter on each typical day, whether the active power of the inverter changes smoothly or not is determined, and if the active power changes unsmoothly and a power extreme value exists, the number of each power extreme value and the occurrence time of each power extreme value are counted when the power extreme value exists.
S130, determining the abnormal fluctuation duration of the corresponding inverter based on the number of the power extreme values and the occurrence time of each power extreme value.
And the number of the power extreme values is used for judging the power abnormity of the inverter. The abnormal fluctuation time length refers to the time span of the power fluctuation of the inverter. For example, if the first power limit of an inverter occurs at a time of 8:30 and the last power limit of the inverter occurs at a time of 15:30, the abnormal fluctuation time period of the inverter is 7 hours.
In one embodiment, before diagnosing a photovoltaic power station, a corresponding parameter threshold is configured for the photovoltaic power station, and then the obtained parameter is compared with the parameter threshold to determine the abnormal fluctuation duration of an inverter in the photovoltaic power station, where the process specifically includes:
judging whether the number of the power extreme values meets a preset condition, wherein the preset condition comprises the following steps: the number of the power extreme values is larger than the threshold value of the number of fluctuation points, and the time interval of any two adjacent power extreme values in the power extreme values is smaller than the threshold value of the fluctuation duration;
and if the number of the power extreme values meets a preset condition, determining the difference value between the appearance moment of the last power extreme value and the appearance moment of the first power extreme value as the abnormal fluctuation time length of the corresponding inverter.
The fluctuation point number threshold value and the fluctuation time length threshold value are adjusted in advance according to the operation age of the photovoltaic power station. Generally, for a photovoltaic power station with a longer service life, instability of an inverter is increased, so that a larger fluctuation point number threshold value and a fluctuation time length threshold value need to be set; on the contrary, if the photovoltaic power station with a shorter service life is started, the fluctuation point number threshold value and the fluctuation duration threshold value are set to relatively smaller values so as to perform more precise detection on the photovoltaic power station.
Before determining the abnormal fluctuation time length, the embodiment first determines whether the time interval between each adjacent power extreme value exceeds the preset interval range, screens each recorded power extreme value, and then calculates the abnormal fluctuation time length for each screened power extreme value. For example, if the number of power extremes of a certain inverter on a certain typical day is 10, the time interval between each adjacent power extreme is sequentially determined from the first power extreme. And if the time interval between the second power extreme value and the third power extreme value is greater than the fluctuation time length threshold value, and the time intervals between other adjacent power extreme values are smaller than the fluctuation time length threshold value, the third power extreme value to the tenth power extreme value are effective power extreme values, and when the abnormal fluctuation time length is calculated, the time difference between the appearance moment of the tenth power extreme value and the appearance moment of the third power extreme value is taken as the abnormal fluctuation time length of the inverter on the typical day.
And S140, acquiring the MPPT voltage of each path of the corresponding inverter.
Wherein, the mppt (maximum Power Point tracking) voltage is the maximum Power Point tracking voltage. Each inverter has multiple MPPT controllers, and accordingly, each MPPT voltage in each inverter needs to be obtained. For example, one inverter includes 3 MPPTs, and all 3 MPPT voltages in the inverter need to be obtained.
S150, determining whether the corresponding inverter has power fluctuation abnormity or not based on the MPPT voltage and the abnormal fluctuation time length.
The variation relation of the MPPT voltage of the same path at different sampling moments can be analyzed through the acquired MPPT voltage, so that the fluctuation range of each MPPT voltage is analyzed within the determined fluctuation duration, and the MPPT voltage fluctuation is compared with a preset condition to determine whether the MPPT voltage fluctuation meets the requirements or not. And finally, determining whether the inverter has abnormal power fluctuation or not by analyzing each path of MPPT voltage in the inverter.
The working principle of the inverter diagnosis method is as follows: the power fluctuation condition of the inverters is judged by analyzing historical operation data of the inverters on a typical day, and whether power fluctuation abnormity exists in each inverter is judged based on the MPPT voltage of the inverter.
According to the technical scheme, the number of the power extreme values of the inverters on each typical day is determined by obtaining the operation historical data of the photovoltaic power station, the occurrence time of each power extreme value is recorded, the abnormal fluctuation time length of each inverter is further obtained, and whether the fluctuation range of the MPPT voltage of each inverter in the abnormal fluctuation time length range meets the requirement or not is determined, so that whether the inverter has abnormal power fluctuation or not is determined, and the diagnosis of the operation state of the inverter is realized. The problem of among the prior art inverter's self trouble can't in time be found and lead to the decline of inverter generating efficiency is solved, realized can diagnosing the operating condition of inverter according to photovoltaic power plant's historical operating data, found the inverter that has the latent fault in advance, reduce the inverter trouble and miss the report rate, rethread replacement has the inverter of latent fault, lets each inverter exert best work efficiency, has reduced photovoltaic power plant's power generation loss.
Example two
Fig. 2 is a flowchart of an optimized inverter diagnosis method according to a second embodiment of the present invention, where the present embodiment is optimized based on the foregoing embodiments, specifically, the method includes:
s210, obtaining operation historical data of the photovoltaic power station.
S220, determining the number of power extreme values of each inverter in the photovoltaic power station on each typical day and the occurrence time of each power extreme value based on the operation historical data.
In one embodiment, a typical day is determined by analyzing the variation relationship of the power generation amount of the photovoltaic string with the maximum daily power generation amount at different sampling moments, and the method specifically comprises the following steps:
selecting a string with the largest daily generated energy based on the operation historical data;
calculating a second-order difference absolute value of the current of the string with the maximum daily generated energy according to the following formula:
wherein itCurrent data of the t sampling point; i.e. it+2Current data of a t +2 sampling point; i.e. it+1Current data of a t +1 th sampling point; i.e. imaxIs the maximum value of the current on the day; diThe current second-order difference absolute value at the ith moment is obtained;
and determining the generation day when the absolute values of the second-order current differences are all smaller than a second-order current difference threshold as the typical day.
Alternatively, the maximum current second-order difference absolute value may be selected from the current second-order difference absolute values according to formula (3), and when the maximum current second-order difference absolute value is greater than the set current difference threshold, the power generation day is determined to be the typical day.
Wherein D is a set current difference threshold.
According to the analysis, each power extreme value is determined based on the active power of the inverter, so that the determination of the number and the occurrence time of the power extreme values of each inverter in each typical day photovoltaic power station can be specifically optimized as follows:
determining the all-day active power of each inverter in the photovoltaic power station on each typical day based on the operation historical data;
and determining the number of power extreme values of each inverter based on the active power of each typical day, and recording the occurrence time of each power extreme value.
The active power of the inverter in the whole day is the active power of the inverter at each sampling moment in the whole day. By analyzing the distribution of the active power of all the inverters on each typical day, the number of power extreme values of each inverter can be determined, and the occurrence time of each power extreme value is recorded.
And S230, determining the abnormal fluctuation duration of the corresponding inverter based on the number of the power extreme values and the occurrence time of each power extreme value.
And S240, acquiring the MPPT voltage of each path of the corresponding inverter.
And S250, determining whether the corresponding inverter has power fluctuation abnormity or not based on the MPPT voltage and the abnormal fluctuation time length.
In one embodiment, the fluctuation range of each MPPT voltage can be analyzed by solving the second order difference absolute value of each MPPT voltage. Accordingly, determining whether a power fluctuation anomaly exists for the corresponding inverter may be specifically optimized as:
calculating a second order difference absolute value of each path of MPPT voltage in the inverter corresponding to the abnormal fluctuation duration;
and if the second-order difference absolute value of any MPPT voltage is greater than the voltage second-order difference threshold value, determining that the corresponding inverter has power fluctuation abnormity.
The second order difference absolute value of the MPPT voltage is determined according to the following formula:
wherein, CiThe second order difference absolute value of the ith MPPT voltage is obtained; v. oftThe voltage value of the t sampling point is obtained; v. ofmaxIs the maximum voltage value of the current day; v. oft+2The voltage value of the t +2 sampling point is obtained; v. oft+1Is the voltage value of the t +1 th sampling point.
When the second order difference absolute value of a certain path of MPPT voltage is larger than the voltage second order difference threshold value, the MPPT voltage of the path is shown to have larger fluctuation, the MPPT controller of the path cannot track and maintain the optimal value, and therefore the corresponding inverter has power fluctuation abnormity.
And S260, carrying out fault diagnosis on the inverter with the abnormal power fluctuation.
When the operation state of a boost plate or a DSP (Digital Signal Processing) control plate of the inverter is poor (i.e., in a pseudo-normal state), the MPPT voltage may not track the optimal value, and the power may fluctuate abnormally. Therefore, based on the fluctuation range of each MPPT voltage, the specific fault of the inverter can be further positioned.
In one embodiment, diagnosing the fault of the inverter based on the second order difference absolute value of the MPPT voltage specifically includes:
if the second-order difference absolute value of partial MPPT voltage in the inverter is larger than the voltage second-order difference threshold value, determining that a boost plate of the partial MPPT is abnormal;
and if the second-order difference absolute values of all MPPT voltages in the inverter are larger than the voltage second-order difference threshold value, determining that the DSP control board of the inverter is abnormal.
For example, a certain inverter comprises three MPPT voltages, and when the second-order difference absolute value of one MPPT voltage is greater than the voltage second-order difference threshold, it is indicated that the booster plate of the MPPT controller has a problem; when the second-order difference absolute value of all three MPPT voltages in the inverter is larger than the voltage second-order difference threshold value, the fact that the DSP control panel of the inverter is abnormal is indicated. The voltage second-order differential threshold is adjusted according to the operation age of the photovoltaic power station, the longer the operation age of the photovoltaic power station is, the correspondingly increased voltage second-order differential threshold is increased, and on the contrary, if the starting age of the photovoltaic power station is shorter, the correspondingly decreased voltage second-order differential threshold is obtained.
Through analyzing the second order difference absolute value of the MPPT voltage, the specific potential fault of the inverter can be positioned, specific overhauling basis is provided for overhauling and maintenance, the accuracy of inverter diagnosis is improved, and the maintenance efficiency is improved.
According to the technical scheme of the embodiment, second-order difference absolute value analysis is carried out according to the power generation current of the photovoltaic string with the largest daily power generation amount, and a typical day is selected; the active power of each inverter on a typical day is analyzed to obtain the abnormal fluctuation time length of each inverter, the MPPT voltage in the inverters is determined to be in the corresponding abnormal fluctuation time length, whether the fluctuation range of the inverters exceeds the set range is judged, and whether the inverters have power fluctuation abnormity is determined; when the power abnormal fluctuation of the corresponding inverter is determined, the potential fault position of the inverter is directly positioned based on the second-order difference absolute value of the MPPT voltage in the inverter, and the problem that the potential fault of the inverter cannot be diagnosed in the prior art is solved; meanwhile, the power fluctuation range of the inverter is obtained through calculation based on the operation data of the photovoltaic power station, and the fault diagnosis precision and the diagnosis efficiency of the inverter are improved.
EXAMPLE III
Fig. 3 is a block diagram of an inverter diagnostic system according to an embodiment of the present invention, where the inverter diagnostic system includes: a data acquisition module 310, a power limit determination module 320, an abnormal fluctuation duration determination module 330, an MPPT voltage acquisition module 340, and a fluctuation abnormality determination module 350, wherein,
the data acquisition module 310 is used for acquiring operation history data of the photovoltaic power station;
the power extreme value determining module 320 is configured to determine, based on the operation history data, the number of power extreme values of each inverter in each typical-day photovoltaic power station and occurrence time of each power extreme value;
an abnormal fluctuation duration determination module 330, configured to determine an abnormal fluctuation duration of a corresponding inverter based on the number of the power extrema and the occurrence time of each power extrema;
an MPPT voltage obtaining module 340, configured to obtain each MPPT voltage of the corresponding inverter;
and a fluctuation anomaly determination module 350, configured to determine whether a power fluctuation anomaly exists in the corresponding inverter based on the MPPT voltage.
Optionally, the power extreme value determining module 320 is specifically configured to:
determining the all-day active power of each inverter in each typical day photovoltaic power station based on the operation historical data;
and determining the number of power extreme values of each inverter based on the active power of each typical day, and recording the occurrence time of each power extreme value.
Optionally, on the basis of the above technical solution, the power extreme value includes a power peak value or a power valley value, and the power extreme value is determined according to the following method:
comparing the active power at any moment with the active power at two adjacent moments;
if the active power at any moment is greater than the active power at two adjacent moments, determining the active power at the moment as a power wave peak value;
and if the active power at any moment is smaller than the active power at two adjacent moments, determining that the active power at the moment is a power wave valley value.
Optionally, the abnormal fluctuation duration determining module 330 is specifically configured to:
judging whether the number of the power extreme values meets preset conditions or not, wherein the preset conditions comprise: the number of the power extreme values is larger than the threshold value of the number of the fluctuation points, and the time interval of any two adjacent power extreme values in the power extreme values is smaller than the threshold value of the fluctuation duration;
and if the number of the power extreme values meets the preset condition, determining the difference value between the appearance moment of the last power extreme value and the appearance moment of the first power extreme value as the abnormal fluctuation time length of the corresponding inverter.
Optionally, the fluctuation anomaly determination module 350 is specifically configured to:
calculating a second-order difference absolute value of each path of MPPT voltage in the corresponding inverter within the abnormal fluctuation time length;
and if the second-order difference absolute value of any MPPT voltage is greater than the voltage second-order difference threshold value, determining that the corresponding inverter has power fluctuation abnormity.
Optionally, on the basis of the above technical solution, the second order difference absolute value of the MPPT voltage is determined according to the following formula:
wherein, CiThe second order difference absolute value of the ith MPPT voltage is obtained; v. oftThe voltage value of the t sampling point is obtained; v. ofmaxIs the maximum voltage value of the current day; v. oft+2The voltage value of the t +2 sampling point is obtained; v. oft+1Is the voltage value of the t +1 th sampling point.
Optionally, on the basis of the above technical solution, the inverter diagnosis system further includes a diagnosis module, and the diagnosis module is configured to perform fault diagnosis on the inverter with the abnormal power fluctuation.
Optionally, the diagnostic module is specifically configured to:
if the second-order difference absolute value of partial MPPT voltage in the inverter is larger than a voltage second-order difference threshold value, determining that a boost plate of partial MPPT is abnormal;
and if the second-order difference absolute values of all MPPT voltages in the inverter are larger than the voltage second-order difference threshold value, determining that the DSP control board of the inverter is abnormal.
Optionally, the diagnosis module is further configured to generate warning information for the inverter with the fault and the corresponding fault information;
on the basis of the above technical solution, optionally, the inverter diagnosis system further includes a display module, configured to display the warning information sent by the diagnosis module on a page;
optionally, the inverter diagnosis system further includes a communication module, configured to send the alarm signal to a power station manager.
Optionally, on the basis of the above technical scheme, the typical day is determined according to the following method:
selecting a string with the largest daily generated energy based on the operation historical data;
and calculating a second-order difference absolute value of the current of the string with the maximum daily generated energy according to the following formula:
wherein itCurrent data of the t sampling point; i.e. it+2Current data of a t +2 sampling point; i.e. it+1Current data of a t +1 th sampling point; i.e. imaxIs the maximum value of the current on the day; diThe current second-order difference absolute value at the ith moment is obtained;
and determining the generation day when the absolute values of the second-order current differences are all smaller than the second-order current difference threshold value as a typical day.
The product can execute the method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An inverter diagnosis method is applied to a photovoltaic power station and is characterized by comprising the following steps:
acquiring operation historical data of a photovoltaic power station;
determining the number of power extreme values of each inverter in the photovoltaic power station on each typical day and the occurrence time of each power extreme value based on the operation historical data;
determining the abnormal fluctuation time length of the corresponding inverter based on the number of the power extreme values and the occurrence time of each power extreme value;
acquiring each path of MPPT voltage of the corresponding inverter;
and determining whether the corresponding inverter has power fluctuation abnormity or not based on the MPPT voltage and the abnormal fluctuation time length.
2. The inverter diagnostic method of claim 1, wherein determining the number of power extremes for each inverter in the photovoltaic power plant on each typical day and the time of occurrence of each power extreme based on the operational history data comprises:
determining the all-day active power of each inverter in the photovoltaic power station on each typical day based on the operation historical data;
and determining the number of power extreme values of each inverter based on the active power of each typical day, and recording the occurrence time of each power extreme value.
3. The inverter diagnostic method of claim 2, wherein the power extremum comprises a power peak or a power valley, the power extremum being determined as follows:
comparing the active power at any moment with the active power at two adjacent moments;
if the active power at any moment is greater than the active power at two adjacent moments, determining the active power at the moment as a power wave peak value;
and if the active power at any moment is smaller than the active power at two adjacent moments, determining that the active power at the moment is a power wave valley value.
4. The inverter diagnostic method according to claim 1, wherein the determining the abnormal fluctuation time period of the corresponding inverter based on the number of the power extreme values and the occurrence time of each of the power extreme values comprises:
judging whether the number of the power extreme values meets a preset condition, wherein the preset condition comprises the following steps: the number of the power extreme values is larger than the threshold value of the number of fluctuation points, and the time interval of any two adjacent power extreme values in the power extreme values is smaller than the threshold value of the fluctuation duration;
and if the number of the power extreme values meets a preset condition, determining the difference value between the appearance moment of the last power extreme value and the appearance moment of the first power extreme value as the abnormal fluctuation time length of the corresponding inverter.
5. The inverter diagnostic method of claim 1, wherein the determining whether a power fluctuation anomaly exists for the corresponding inverter based on the MPPT voltage comprises:
calculating a second order difference absolute value of each path of MPPT voltage in the inverter corresponding to the abnormal fluctuation duration;
and if the second-order difference absolute value of any MPPT voltage is greater than the voltage second-order difference threshold value, determining that the corresponding inverter has power fluctuation abnormity.
6. The inverter diagnostic method of claim 5, wherein the second order difference absolute value of the MPPT voltage is determined according to the following equation:
wherein, CiThe second order difference absolute value of the ith MPPT voltage is obtained; v. oftThe voltage value of the t sampling point is obtained; v. ofmaxIs the maximum voltage value of the current day; v. oft+2The voltage value of the t +2 sampling point is obtained; v. oft+1Is the voltage value of the t +1 th sampling point.
7. The inverter diagnostic method of claim 5, wherein after the determining that the corresponding inverter has a power fluctuation anomaly, the method further comprises:
and carrying out fault diagnosis on the inverter with the abnormal power fluctuation.
8. The inverter diagnosis method according to claim 7, wherein the performing fault diagnosis on the inverter in which the power fluctuation abnormality exists includes:
if the second-order difference absolute value of partial MPPT voltage in the inverter is larger than the voltage second-order difference threshold value, determining that a boost plate of the partial MPPT is abnormal;
and if the second-order difference absolute values of all MPPT voltages in the inverter are larger than the voltage second-order difference threshold value, determining that the DSP control board of the inverter is abnormal.
9. The inverter diagnostic method according to any one of claims 1 to 8, wherein the representative day is determined according to the following method:
selecting a string with the largest daily generated energy based on the operation historical data;
calculating a second-order difference absolute value of the current of the string with the maximum daily generated energy according to the following formula:
wherein itCurrent data of the t sampling point; i.e. it+2Current data of a t +2 sampling point; i.e. it+1Current data of a t +1 th sampling point; i.e. imaxIs the maximum value of the current on the day; diThe current second-order difference absolute value at the ith moment is obtained;
and determining the generation day when the absolute values of the second-order current differences are all smaller than a second-order current difference threshold as the typical day.
10. An inverter diagnostic system, comprising:
the data acquisition module is used for acquiring the operation historical data of the photovoltaic power station;
the power extreme value determining module is used for determining the number of power extreme values of each inverter in the photovoltaic power station on each typical day and the occurrence time of each power extreme value on the basis of the operation historical data;
the abnormal fluctuation duration determining module is used for determining the corresponding abnormal fluctuation duration of the inverter based on the number of the power extreme values and the occurrence time of each power extreme value;
the MPPT voltage acquisition module is used for acquiring the MPPT voltages of all paths of the corresponding inverters;
and the fluctuation abnormity determining module is used for determining whether the power fluctuation abnormity exists in the corresponding inverter or not based on the MPPT voltage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010088162.0A CN111313830B (en) | 2020-02-12 | 2020-02-12 | Inverter diagnosis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010088162.0A CN111313830B (en) | 2020-02-12 | 2020-02-12 | Inverter diagnosis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111313830A true CN111313830A (en) | 2020-06-19 |
| CN111313830B CN111313830B (en) | 2022-05-24 |
Family
ID=71150972
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010088162.0A Active CN111313830B (en) | 2020-02-12 | 2020-02-12 | Inverter diagnosis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111313830B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112213584A (en) * | 2020-09-29 | 2021-01-12 | 合肥阳光智维科技有限公司 | Inverter power-limiting operation diagnosis method and device and monitoring equipment |
| CN112288285A (en) * | 2020-10-30 | 2021-01-29 | 西安热工研究院有限公司 | Photovoltaic power station string attenuation rate evaluation method based on generalized extreme value distribution |
| CN113629862A (en) * | 2021-07-07 | 2021-11-09 | 上海远景科创智能科技有限公司 | Method, device and equipment for prompting peak clipping state and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017028917A (en) * | 2015-07-25 | 2017-02-02 | ミヨシ電子株式会社 | Power conditioner monitoring system and solar power generation plant |
| CN106992540A (en) * | 2017-04-20 | 2017-07-28 | 中南大学 | A kind of photovoltaic system and its open fault diagnostic method without power of communications optimizer |
| CN107589318A (en) * | 2016-07-06 | 2018-01-16 | 新疆金风科技股份有限公司 | The method for detecting abnormality and device of inverter in photovoltaic plant |
| CN109901003A (en) * | 2017-12-11 | 2019-06-18 | 中国电力科学研究院有限公司 | A kind of inverter power fault detection method and system |
-
2020
- 2020-02-12 CN CN202010088162.0A patent/CN111313830B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017028917A (en) * | 2015-07-25 | 2017-02-02 | ミヨシ電子株式会社 | Power conditioner monitoring system and solar power generation plant |
| CN107589318A (en) * | 2016-07-06 | 2018-01-16 | 新疆金风科技股份有限公司 | The method for detecting abnormality and device of inverter in photovoltaic plant |
| CN106992540A (en) * | 2017-04-20 | 2017-07-28 | 中南大学 | A kind of photovoltaic system and its open fault diagnostic method without power of communications optimizer |
| CN109901003A (en) * | 2017-12-11 | 2019-06-18 | 中国电力科学研究院有限公司 | A kind of inverter power fault detection method and system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112213584A (en) * | 2020-09-29 | 2021-01-12 | 合肥阳光智维科技有限公司 | Inverter power-limiting operation diagnosis method and device and monitoring equipment |
| CN112213584B (en) * | 2020-09-29 | 2024-04-09 | 阳光智维科技股份有限公司 | Inverter power-limited operation diagnosis method, device and monitoring equipment |
| CN112288285A (en) * | 2020-10-30 | 2021-01-29 | 西安热工研究院有限公司 | Photovoltaic power station string attenuation rate evaluation method based on generalized extreme value distribution |
| CN112288285B (en) * | 2020-10-30 | 2023-04-07 | 西安热工研究院有限公司 | Photovoltaic power station string attenuation rate evaluation method based on generalized extreme value distribution |
| CN113629862A (en) * | 2021-07-07 | 2021-11-09 | 上海远景科创智能科技有限公司 | Method, device and equipment for prompting peak clipping state and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111313830B (en) | 2022-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111313830B (en) | Inverter diagnosis method and system | |
| CN112465237B (en) | Fault prediction method, device, equipment and storage medium based on big data analysis | |
| CN117074965A (en) | Lithium ion battery remaining life prediction method and system | |
| CN117150216B (en) | Regression analysis method and system for power data | |
| CN116418293B (en) | Photovoltaic power station intelligent diagnosis system based on big data of Internet of things | |
| CN116660672A (en) | Power grid equipment fault diagnosis method and system based on big data | |
| CN118432000B (en) | High-voltage isolation protection method and system | |
| CN117767271A (en) | Electric power emergency automatic management system | |
| CN117131321A (en) | Electric energy data acquisition method | |
| CN115754743A (en) | Battery voltage inconsistency identification method, device, equipment and medium | |
| CN117331790A (en) | Machine room fault detection method and device for data center | |
| CN116755964A (en) | Fault prediction and health management system for reinforcement server | |
| CN112782495A (en) | String abnormity identification method for photovoltaic power station | |
| EP4224707A1 (en) | A method and computing device for monitoring performance of a photovoltaic panel | |
| CN113176962B (en) | Computer room IT equipment fault accurate detection method and system for data center | |
| CN110869607A (en) | Method, device and system for wind power converter management | |
| Sarquis Filho et al. | Practical recommendations for the design of automatic fault detection algorithms based on experiments with field monitoring data | |
| US20230238918A1 (en) | Method for detecting pv anomaly and determining long-term degradation | |
| CN117272844B (en) | Method and system for predicting service life of distribution board | |
| CN118367682B (en) | Intelligent state identification regulation method and system for electrical equipment | |
| CN117527611B (en) | Gaussian distribution-based fault dynamic prediction method, system, electronic equipment and storage medium | |
| CN118245917B (en) | Fault detection method and system for servo inverter | |
| CN118232425B (en) | Intelligent management method and system for photoelectric integrated unit | |
| US20240210479A1 (en) | Battery performance monitoring and optimization using unsupervised clustering | |
| EP4312035A1 (en) | Determination and monitoring of series resistance of a photovoltaic device or system from voltage factor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: High tech Zone of Hefei city of Anhui Province in 230088 Lake Road No. 2 Applicant after: Sunshine New Energy Development Co.,Ltd. Address before: High tech Zone of Hefei city of Anhui Province in 230088 Lake Road No. 2 Applicant before: Sunshine New Energy Development Co.,Ltd. Address after: High tech Zone of Hefei city of Anhui Province in 230088 Lake Road No. 2 Applicant after: Sunshine New Energy Development Co.,Ltd. Address before: 230088 6th floor, R & D center building, no.1699 Xiyou Road, high tech Zone, Hefei City, Anhui Province Applicant before: HEFEI SUNGROW RENEWABLE ENERGY SCI. & TECH. Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |