CN114282683A - Early warning method and system for photovoltaic power station assembly - Google Patents
Early warning method and system for photovoltaic power station assembly Download PDFInfo
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Abstract
The application discloses a photovoltaic power station assembly early warning method and system, which comprises the following steps: acquiring weather information in weather forecast, and acquiring current values and voltage values of photovoltaic modules in different weather information; and marking as a fault value according to different gradients; performing correlation analysis according to the weather information and the standard value to obtain influence factors of the current value and the voltage value; abnormal data acquisition: acquiring first abnormal data and second abnormal data according to the time data and the standard value; and (3) comparison treatment: comparing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value; if the estimated value is not the fault value, the acquisition of the first abnormal data, the acquisition of the second abnormal data and the comparison processing are continuously and sequentially carried out; if the estimated value is in the fault value, outputting an early warning result. The early warning method and the early warning system can timely and effectively detect abnormal components and perform early warning, and the power output capacity and the stability of the photovoltaic power station are enhanced.
Description
Technical Field
The application relates to the field of solar photovoltaic power stations, in particular to an early warning method and system for a photovoltaic power station assembly.
Background
With the development of the photovoltaic industry, the most basic equipment of a photovoltaic electric field and the normal operation of a photovoltaic module are the basis for ensuring the continuous, stable and efficient power generation of a photovoltaic power station under the condition that the installed capacity of new photovoltaic energy is continuously increased. The photovoltaic module is the equipment that photovoltaic power plant quantity is the most, guarantees the normal operating of subassembly, needs in time to monitor the running state of subassembly, takes place unusual running state and in time carries out the early warning, not only can practice thrift the fortune dimension cost, can improve fortune dimension personnel's work efficiency moreover greatly to the subassembly that the discovery that enables fortune dimension personnel accuracy, quick has the problem. In order to maintain continuous, efficient and stable power generation of the photovoltaic module, a large amount of manpower and material resources must be input, a problem module is found in workload, and the maintenance early warning mode is difficult and low in timeliness. And install sensor, communications facilities and carry out the early warning to photovoltaic module below photovoltaic module, but the purchase of equipment, installation maintenance cost are higher, have increased the very not cost-effective of enterprise.
Therefore, the early warning method for the photovoltaic power station assembly solves the problems.
Disclosure of Invention
The early warning method and the early warning system for the photovoltaic power station component can predict whether the photovoltaic power station photovoltaic component is in a normal operation state in time, timely and effectively detect abnormal components and perform early warning, and power output capacity and stability of the photovoltaic power station are enhanced.
In order to solve the technical problem, the application provides an early warning method for a photovoltaic power station assembly, which comprises the following steps:
acquiring weather information in weather forecast;
acquiring current values and voltage values of the photovoltaic modules in different weather information as standard values;
acquiring the current and voltage change conditions of the photovoltaic module which has failed in different weather information before the first time, and marking the photovoltaic module as a failure value according to different gradients;
performing correlation analysis according to the weather information and the standard value to obtain influence factors of a current value and a voltage value; obtaining a historical weather time point similar to the current time weather as time data according to the influence factor;
abnormal data acquisition: acquiring first abnormal data and second abnormal data according to the time data and the standard value;
and (3) comparison treatment: comparing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value;
if the estimated value is not the fault value, continuing to perform the abnormal data acquisition step and the comparison processing step;
and if the estimated value is within the fault value, outputting an early warning result.
Wherein the weather information includes: wind speed, wind direction, cloud cover, temperature, humidity in weather forecast.
Wherein the frequency of executing the early warning method is 1-60 min.
Wherein the obtaining of the first anomaly data comprises:
s1, obtaining the difference value between the current maximum value and the current minimum value of the components in the time data to obtain a first difference value;
s2, acquiring the current mean value of all branches of each inverter at each moment in the first time;
s3, obtaining a branch circuit of which the current value at each moment is smaller than the current mean value in all branch circuits of each inverter in the first time to obtain a filtering branch circuit;
s4: obtaining a difference value between the current of the filtering branch and the current mean value to obtain a second difference value;
s5: and comparing the second difference with the first difference, outputting branches with the second difference smaller than the first difference, counting the occurrence times, and determining the first abnormal data.
Wherein the obtaining of the second anomaly data comprises:
step 1, subtracting the current at the previous moment from the current at the later moment in a plurality of time periods in all branches under each inverter in the first time to obtain a two-dimensional matrix of current difference values;
step 2: acquiring the weather information before the first time and the weather change factor of the weather information after the first time, and outputting the weather change factor as a current change factor;
and step 3: and comparing the current difference value of the two-dimensional matrix with the current change factor, outputting a branch circuit which is larger than the current change factor, counting the occurrence times, and determining the second abnormal data.
In addition, this application still provides a photovoltaic power plant subassembly's early warning system, includes:
the acquisition unit is used for acquiring weather information in weather forecast and acquiring current values and voltage values of the photovoltaic modules in different weather information as standard values; acquiring the current and voltage change conditions of the photovoltaic module which has failed in different weather information before the first time, and marking the photovoltaic module as a failure value according to different gradients;
the first processing unit is used for carrying out correlation analysis according to the weather information and the standard value to obtain influence factors of a current value and a voltage value; obtaining a historical weather time point similar to the current time weather as time data according to the influence factor;
the second processing unit is used for acquiring the abnormal data and acquiring first abnormal data and second abnormal data according to the time data and the standard value;
the third processing unit is used for comparing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value;
a judgment processing unit that judges whether the estimated value is within the failure value;
an output unit: if the estimated value is not the fault value, continuing to perform the abnormal data acquisition step and the comparison processing step;
and if the estimated value is within the fault value, outputting an early warning result.
Meanwhile, the application also provides a computer scale storage medium, wherein a computer program is stored in the storage medium, and the computer program realizes the steps of any one of the methods when being executed by a processor.
The present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.
According to the method, the early warning is carried out on the photovoltaic module which is possibly out of order in the photovoltaic power station by collecting weather data and real-time data of all branch current under the power station inverter and using a machine learning method, so that the manpower and material resources for operation and maintenance of the photovoltaic power station are reduced. Whether photovoltaic power plant photovoltaic module is in normal operating condition can be in time forecasted, unusual problem discovery to problem photovoltaic module in advance makes the conversion of power station fortune dimension completion from the solution problem to the prevention problem to reach the power station and last, stable, the efficient electricity generation, consequently reinforcing photovoltaic power plant output capacity and stability.
Drawings
In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without making any inventive changes.
Fig. 1 is a schematic flow chart of an early warning method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a first exception data processing according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a first abnormal data processing flow according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
Fig. 1 is a schematic flow chart of an early warning method according to an embodiment of the present invention; FIG. 2 is a flow chart illustrating a first exception data processing according to an embodiment of the present invention; (ii) a Fig. 3 is a schematic diagram of a first abnormal data processing flow according to an embodiment of the present invention. As shown in fig. 1-3, a method for pre-warning a photovoltaic power plant component includes:
and acquiring weather information in the weather forecast, and storing the weather data into a database. The real-time weather data and the historical weather information data in the photovoltaic module area can be obtained in real time through the weather data in the existing weather forecast database. Or different devices such as a temperature and humidity measuring instrument, a wind speed and direction measuring instrument, a temperature acquisition module and the like are used for acquiring, and the weather information data acquisition is not limited to the two modes. And the acquired data distributions are communicated and transmitted to the server in a bus time division multiplexing mode and are kept in the database. The partial data information obtained is shown in tables 1-1 and 1-2 below:
TABLE 1-1
Tables 1 to 2
Meanwhile, the current value and the voltage value of the photovoltaic module in the obtained different weather information are used as standard values; the current and voltage data values under the inverter are acquired in real time through the current and voltage acquisition module, and the voltage and current data under the same inverter are shown in the following tables 1-3
Tables 1 to 3
When the photovoltaic module is actually obtained, the current value and the voltage value of the photovoltaic module are monitored in real time through monitoring equipment and are matched with weather information data. Namely, the weather data in each time period is matched with the current and voltage data in each inverter at the time. Matching pass processor
As shown in fig. 1-3, fault module information exists in the acquired current and voltage information data, and PV8 current is abnormal data. And marking the current and voltage change conditions of the photovoltaic module which has faults in different weather information before the first time as the fault module with the fault value according to different gradients. The first time is a certain time and may be any time in data monitoring.
Performing correlation analysis according to the weather information and the standard value to obtain influence factors of the current value and the voltage value; and obtaining a historical weather time point similar to the current time weather as time data according to the influence factors. And determining the influence factor by adopting a Pearson correlation analysis method.
The analysis method similar to the weather at the current time uses the following calculation formula, coef ═ ((nWCoef-hWCoef) × 2+ (nTCoef1-hTCoef1) × 2+ (nDCoef-hDCoef) × 2+ (nTCoef2-hTCoef2) × 2, and finds the minimum value of coef as the date similar to the weather at the current time.
Abnormal data acquisition: acquiring first abnormal data and second abnormal data according to the time data and the standard value;
acquiring first abnormal data: performing horizontal data processing on components in the time data;
as shown in fig. 2: which comprises the following steps:
s1, obtaining the difference value between the current maximum value and the current minimum value of the components in the time data to obtain a first difference value;
s2, acquiring the current mean value of all branches of each inverter at each moment in the first time;
s3, obtaining a branch circuit of which the current value is smaller than the current mean value at each moment in all the branch circuits of each inverter in the first time to obtain a filtering branch circuit;
s4: obtaining a difference value between the current of the filtering branch and the current mean value to obtain a second difference value;
s5: and comparing the second difference with the first difference, outputting branches with the second difference smaller than the first difference, counting the occurrence times, and determining first abnormal data.
Through the horizontal data processing, the problematic components are easier to find in comparison with other components under the same weather conditions.
And acquiring second abnormal data: performing longitudinal processing according to the first abnormal data;
as shown in fig. 3, among others, it includes:
step 1, subtracting the current at the previous moment from the current at the later moment in a plurality of time periods in all branches under each inverter in the first time to obtain a two-dimensional matrix of current difference values;
step 2: acquiring weather information before the first time and weather change factors of the weather information after the first time, and outputting the weather change factors as current change factors;
and step 3: and comparing the current difference value of the two-dimensional matrix with the current change factor, outputting the branch circuit with the current change factor, counting the occurrence times, and determining second abnormal data.
The condition of instantaneous current and voltage change is determined through longitudinal processing, and whether the threshold value is exceeded or not and whether the component is abnormal or not are determined.
And (3) comparison treatment: comparing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value;
if the estimated value is not the fault value, the acquisition of the first abnormal data, the acquisition of the second abnormal data and the comparison processing are continuously and sequentially carried out;
if the estimated value is in the fault value, outputting an early warning result.
Wherein the weather information includes: wind speed, wind direction, cloud cover, temperature, humidity in weather forecast.
Wherein, the frequency of executing the early warning method is 1-60 min.
In addition, this application still provides a photovoltaic power plant subassembly's early warning system, includes:
the acquisition unit is used for acquiring weather information in weather forecast and acquiring current values and voltage values of the photovoltaic modules in different weather information as standard values; acquiring the current and voltage change conditions of the photovoltaic module which has failed in different weather information before the first time, and marking the photovoltaic module as a failure value according to different gradients;
the first processing unit is used for carrying out correlation analysis according to the weather information and the standard value to obtain influence factors of the current value and the voltage value; obtaining a historical weather time point similar to the current time weather as time data according to the influence factors;
the second processing unit is used for acquiring abnormal data, and acquiring first abnormal data and second abnormal data according to the time data and the standard value;
the third processing unit is used for comparing and processing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value;
a judgment processing unit for judging whether the estimated value is within the fault value;
an output unit: if the estimated value is not the fault value, continuing to perform the abnormal data acquisition step and the comparison processing step;
if the estimated value is in the fault value, outputting an early warning result.
In the present application, the embodiment of the warning system of the photovoltaic power station component is basically similar to the embodiment of the warning method of the photovoltaic power station component, and reference is made to the introduction of the embodiment of the warning method of the photovoltaic power station component of the photovoltaic power station in related places.
Meanwhile, the computer scale storage medium provided by the application stores a computer program, and the program realizes the steps of the early warning method of the photovoltaic power station assembly when being executed by the processor. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
The computer device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and the steps of the early warning method of the photovoltaic power station assembly are realized when the processor executes the computer program.
The computer device may also be a personal digital assistant, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The data operation and execution steps of the early warning method of the photovoltaic power station assembly are realized through a processor, a memory, an input device, an output device and the like.
According to the method, the early warning is carried out on the photovoltaic module which is possibly out of order in the photovoltaic power station by collecting weather data and real-time data of all branch current under the power station inverter and using a machine learning method, so that the manpower and material resources for operation and maintenance of the photovoltaic power station are reduced. Whether photovoltaic power plant photovoltaic module is in normal operating condition can be in time forecasted, unusual problem discovery to problem photovoltaic module in advance makes the conversion of power station fortune dimension completion from the solution problem to the prevention problem to reach the power station and last, stable, the efficient electricity generation, consequently reinforcing photovoltaic power plant output capacity and stability.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
All functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit. It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The above-described embodiments of the present application do not limit the scope of the present application.
Claims (8)
1. The early warning method for the photovoltaic power station component is characterized by comprising the following steps:
acquiring weather information in weather forecast;
acquiring current values and voltage values of the photovoltaic modules in different weather information as standard values;
acquiring the current and voltage change conditions of the photovoltaic module which has failed in different weather information before the first time, and marking the photovoltaic module as a failure value according to different gradients;
performing correlation analysis according to the weather information and the standard value to obtain influence factors of a current value and a voltage value; obtaining a historical weather time point similar to the current time weather as time data according to the influence factor;
abnormal data acquisition: acquiring first abnormal data and second abnormal data according to the time data and the standard value;
and (3) comparison treatment: comparing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value;
if the estimated value is not the fault value, continuing to perform the abnormal data acquisition step and the comparison processing step;
and if the estimated value is within the fault value, outputting an early warning result.
2. The warning method for a photovoltaic power plant component of claim 1, wherein the weather information comprises: wind speed, wind direction, cloud cover, temperature, humidity in weather forecast.
3. The warning method for a photovoltaic power plant component as claimed in claim 1, characterized in that the warning method is carried out at a frequency of 1-60 min.
4. The warning method for a photovoltaic power plant component of claim 1, wherein the obtaining of the first anomaly data comprises:
s1, obtaining the difference value between the current maximum value and the current minimum value of the components in the time data to obtain a first difference value;
s2, acquiring the current mean value of all branches of each inverter at each moment in the first time;
s3, obtaining a branch circuit of which the current value at each moment is smaller than the current mean value in all branch circuits of each inverter in the first time to obtain a filtering branch circuit;
s4: obtaining a difference value between the current of the filtering branch and the current mean value to obtain a second difference value;
s5: and comparing the second difference with the first difference, outputting branches with the second difference smaller than the first difference, counting the occurrence times, and determining the first abnormal data.
5. The warning method for a photovoltaic power plant component of claim 3, wherein the obtaining of the second anomaly data comprises:
step 1, subtracting the current at the previous moment from the current at the later moment in a plurality of time periods in all branches under each inverter in the first time to obtain a two-dimensional matrix of current difference values;
step 2: acquiring the weather information before the first time and the weather change factor of the weather information after the first time, and outputting the weather change factor as a current change factor;
and step 3: and comparing the current difference value of the two-dimensional matrix with the current change factor, outputting a branch circuit which is larger than the current change factor, counting the occurrence times, and determining the second abnormal data.
6. The utility model provides an early warning system of photovoltaic power plant subassembly which characterized in that includes:
the acquisition unit is used for acquiring weather information in weather forecast and acquiring current values and voltage values of the photovoltaic modules in different weather information as standard values; acquiring the current and voltage change conditions of the photovoltaic module which has failed in different weather information before the first time, and marking the photovoltaic module as a failure value according to different gradients;
the first processing unit is used for carrying out correlation analysis according to the weather information and the standard value to obtain influence factors of a current value and a voltage value; obtaining a historical weather time point similar to the current time weather as time data according to the influence factor;
the second processing unit is used for acquiring the abnormal data and acquiring first abnormal data and second abnormal data according to the time data and the standard value;
the third processing unit is used for comparing the first abnormal data, the second abnormal data and the fault value to obtain an estimated value;
a judgment processing unit that judges whether the estimated value is within the failure value;
an output unit: if the estimated value is not the fault value, continuing to perform the abnormal data acquisition step and the comparison processing step;
and if the estimated value is within the fault value, outputting an early warning result.
7. A computer scale storage medium, wherein a computer program is stored in the storage medium, which program, when executed by a processor, performs the steps of the method of any one of claims 1-5.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, the processor implementing the steps of the method of any one of claims 1-5 when executing the computer program.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115907307A (en) * | 2023-01-04 | 2023-04-04 | 南方电网数字电网研究院有限公司 | Power grid real-time data interaction-oriented online analysis method for carbon emission flow of power system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115907307A (en) * | 2023-01-04 | 2023-04-04 | 南方电网数字电网研究院有限公司 | Power grid real-time data interaction-oriented online analysis method for carbon emission flow of power system |
| CN115907307B (en) * | 2023-01-04 | 2023-06-27 | 南方电网数字电网研究院有限公司 | Power grid real-time data interaction-oriented online analysis method for carbon emission flow of power system |
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