CN113762713A - Photovoltaic power generation real-time power measurement data quality evaluation method and device - Google Patents
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Abstract
The invention relates to the technical field of measurement data quality evaluation, and particularly provides a photovoltaic power generation real-time power measurement data quality evaluation method and device, wherein the method comprises the following steps: calculating a reliability value corresponding to the daily power measurement data to be measured by using the set power expected data; determining an evaluation value of the measured daily power measurement data according to the reliability value corresponding to the measured daily power measurement data; and evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data. According to the scheme, a normal distribution function of the power measurement data in the corresponding time period in the vicinity of the photovoltaic power generation data in the typical day is constructed, and the data reliability of the corresponding real-time data is further judged, so that bad recorded data are eliminated, and the accurate judgment and prediction of the power quality in the next step are guaranteed.
Description
Technical Field
The invention relates to the field of measurement data quality evaluation, in particular to a photovoltaic power generation real-time power measurement data quality evaluation method and device.
Background
The electric energy is an indispensable secondary energy source in the present society, can be converted from primary energy sources such as coal, petroleum, natural gas, wind energy, water energy and the like through processing, and is a secondary energy source with convenient use, easy utilization and high quality. The power quality is used for measuring the quality of the power. The power supply data quality assessment is an important key process in the power quality, and plays a great role in analyzing whether the power quality is successful or not. In the research work on the power quality, attention is paid to the discussion of a data analysis algorithm, and the research on the quality processing of power supply data before data analysis is neglected. Some more sophisticated algorithms have certain requirements on the data sets processed by the algorithms, such as good data integrity, low data redundancy, and correlation between attributes. However, data in an actual measurement system generally has the problems of incompleteness, redundancy, ambiguity and the like, and the requirements of a data analysis algorithm can be rarely and directly met, which seriously affects the execution efficiency of the data analysis algorithm, and the improvement of the power supply data quality becomes a key problem in the implementation process of the data analysis system because the noise interference in the data analysis algorithm can also cause invalid induction. Especially in a new energy station, the photovoltaic power station is greatly influenced by climate and environment, and the evaluation of data quality is especially important.
The abnormal data of the distributed power supply cluster can reduce the quality of the monitoring data and the uploading scheduling data of the distributed power supply to a great extent, so that the work of estimation, regulation and analysis and the like of data parameters is influenced, and more serious people can cause the inaccuracy of electric quantity prediction control or regulation and control calculation, and the loss of manpower and material resources is caused. The effective data is the premise of carrying out the prediction and regulation and control management of the generated energy of the distributed power supply, and provides basic support for the scheduling decision and statistical analysis of the distributed power supply. The invalid data not only influences the judgment of the operation state of the distributed power supply by the scheduling personnel, but also influences the convergence of the analysis application of the power grid. The general condition of distributed power supply is generally judged by selecting a sample board machine at present, but the number of the selected sample board machines is small, the actual condition of the distributed power supply is difficult to reflect, the accuracy of power prediction and analysis calculation results is low, and the sample board machine is not fixed and can be regularly adjusted according to needs.
The document [ Application of data mining failure detection and prediction in Boiler of Power plant using local network. International Conference on Power Engineering and Electrical Drives,2009,473-478] proposes to use a neural network for data failure monitoring of a Boiler combustion system of a Power plant, and to use a two-stage neural network with four hidden layers. Document [ correction of bad data of power system based on neural network, grid technology, 2007, 31 (S2): 173-175 proposes to continue to use the neural network to correct the bad data. However, both methods highly depend on the training process of the network, and when a proper threshold is selected, the method is subjective, and residual inundation and residual pollution are easy to occur, so that missed detection and false detection are caused. Literature [ outlier data mining and its application in power load prediction, power system automation, 2004, 28 (11): 41-44, cluster data mining is applied to power load prediction, basic parameters in a clustering process are selected by combining the advantages of a hierarchical clustering method and an information entropy principle, and bad data are corrected by extracting a characteristic curve of a load curve by using an artificial neural network. Although the method can avoid residual errors and residual error pollution, the method has certain subjectivity due to the selection of proper membership. It is easy to make an offset to the overall evaluation of the data. Document [ identification and correction of load prediction failure data in EMS, power system automation, 2006, 30 (15): 85-88, the reasons of bad data in EMS (energy management system) of the power system dispatching center are researched, and the abnormal data mainly comprises abnormal data caused by two conditions of automatic system failure and large load sudden and accidental fluctuation, and the abnormal data are respectively processed by a total value dynamic multi-source processing method and a method for scanning the load data of the power grid terminal one by one, but the method needs to rely on the support of multi-party resources in the energy management system and is not simple and convenient to implement. And (3) establishing a quality evaluation index in a document [ power grid planning basic data quality evaluation model application research, 2019], and evaluating by adopting an analytic hierarchy process. Meanwhile, a power load prediction hierarchical structure is established by combining an analytic hierarchy process, and the analysis of the power data of the whole society and the power prediction research of different industries are carried out by using practical application, so that effective data support is provided for the planning and prediction of the power grid. However, the analytic hierarchy process has a certain subjectivity, which causes the weight difference of the evaluation index to influence the final data scoring. Patent [ an evaluation method for power data fingerprints, CN112580078A, 2021-03-30] proposes that subjective and objective weights are respectively determined by an entropy weight method and an analytic hierarchy process, and then an optimal weighting method is used to obtain a most weighted solution, so that subjectivity is reduced, and data accuracy is increased.
Disclosure of Invention
In order to overcome the defects, the invention provides a photovoltaic power generation real-time power measurement data quality assessment method and device capable of objectively reflecting the power measurement data quality.
In a first aspect, a photovoltaic power generation real-time power measurement data quality assessment method is provided, and the photovoltaic power generation real-time power measurement data quality assessment method includes:
calculating a reliability value corresponding to the daily power measurement data to be measured by using the set power expected data;
determining an evaluation value of the measured daily power measurement data according to the reliability value corresponding to the measured daily power measurement data;
and evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data.
Preferably, before calculating the reliability value corresponding to the measured daily power measurement data by using the predetermined expected power data, the method includes:
acquiring a plurality of typical daily power measurement data and a plurality of historical daily power measurement data;
respectively calculating a first evaluation index and a second evaluation index between single typical daily power measurement data and all historical daily power measurement data;
and selecting, as the given expected power data, typical daily power measurement data in which a difference between the second evaluation index and the first evaluation index is the largest among the plurality of typical daily power measurement data.
Further, the calculation formula of the first evaluation index between the single typical daily power measurement data and the total historical daily power measurement data is as follows:
in the above formula, z1Is a first evaluation index between the typical daily power measurement data and the total historical daily power measurement data, gammadPower generation rate of typical day, gammajThe power generation rate of the jth historical day is n, and the total number of the historical days is n;
wherein the power generation rate γ of a typical daydIs calculated as follows:
in the above formula, qidIs the power measurement at the ith time of the typical day.
Further, the calculation formula of the second evaluation index between the single typical daily power measurement data and the total historical daily power measurement data is as follows:
in the above formula, rdjIs a correlation coefficient between typical daily power measurement data and jth historical daily power measurement data.
Preferably, the calculation formula of the reliability value corresponding to the daily electric power measurement data to be measured is as follows:
in the above formula, p (x) is a reliability value corresponding to the measured daily power measurement data, Φ (x) is an accumulated probability density function related to a normalized value of the measured daily power measurement data, x is a normalized value of the measured daily power measurement data, and μ is a normalized value of the expected predetermined power data;
wherein the cumulative probability density function phi (x) of the normalized value of the measured daily power measurement data*) Is calculated as follows:
in the above formula, f (x)*) The normal distribution function of the normalized value of the measured daily power measurement data is calculated as follows:
in the above formula, σ is a standard deviation of the normalized value of the daily power measurement data to be measured.
Preferably, the calculation formula of the evaluation value of the power measurement data on the day to be measured is as follows:
in the above formula, g is the evaluation value of the measured daily power measurement data, p (x)i) And the reliability value is the reliability value corresponding to the power measurement data at the ith moment of the day to be measured.
Preferably, the quality evaluation of the measured daily power data by using the evaluation value of the measured daily power data includes:
if the evaluation value of the electric power measurement data of the day to be measured is larger than the preset value, the quality of the electric power measurement data of the day to be measured is qualified, otherwise, the quality of the electric power measurement data of the day to be measured is unqualified.
Further, the preset value is 90.
In a second aspect, a photovoltaic power generation real-time power measurement data quality assessment device is provided, which includes:
the calculation module is used for calculating the reliability value corresponding to the daily power measurement data to be measured by utilizing the set power expected data;
the determining module is used for determining the evaluation value of the measured daily power data according to the reliability value corresponding to the measured daily power data;
and the evaluation module is used for evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data.
In a third aspect, a storage device is provided, wherein a plurality of program codes are stored, wherein the program codes are suitable to be loaded and executed by a processor to execute the photovoltaic power generation real-time power measurement data quality evaluation method.
One or more technical schemes of the invention at least have one or more of the following beneficial effects:
the invention provides a photovoltaic power generation real-time power measurement data quality evaluation method, which comprises the following steps: calculating a reliability value corresponding to the daily power measurement data to be measured by using the set power expected data; determining an evaluation value of the measured daily power measurement data according to the reliability value corresponding to the measured daily power measurement data; and evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data. According to the technical scheme, the power measurement data obtained through historical experience are analyzed, wherein the power measurement data of the photovoltaic power station and the power measurement data obtained through the historical experience are in normal distribution, and the power measurement data of the photovoltaic power station fluctuate nearby the power measurement data obtained through the historical experience and do not change greatly at will, so that the power measurement data quality evaluation performed through the power measurement data obtained through the historical experience has certain objectivity, and the reliability level of each data can be accurately judged;
furthermore, the quality of the power measurement data is judged and evaluated by normal distribution, residual pollution is not easy to occur, and meanwhile, the data can be judged by grading without the support of multi-party resources, so that the average evaluation quality of the more detailed overall data is obtained.
Drawings
FIG. 1 is a schematic flow chart of the main steps of a photovoltaic power generation real-time power measurement data quality evaluation method according to an embodiment of the invention;
FIG. 2 is a graph of generated power measured in real time in an embodiment of the present invention;
FIG. 3 is a graph of the generated power of a typical solar photovoltaic plant in an embodiment of the present invention;
FIG. 4 is a graph of a normal distribution function for a normalized value of generated power measured in real time in an embodiment of the present invention;
FIG. 5 is a graph of reliability values corresponding to photovoltaic power plant generated power data in an embodiment of the present invention;
fig. 6 is a main structural block diagram of a photovoltaic power generation real-time power measurement data quality evaluation device according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Data quality relates to several aspects such as numerical size, data storage, data usage, and data transmission, and requires that data must accurately reflect the actual measured size, that records be free of duplication or deletion, be valid for the required time, satisfy existing constraints, and that interrelated data be logically consistent.
Factors influencing the data quality mainly include numerical errors caused by faults of the data acquisition device at the site end, data loss or data repetition caused by faults of a data transmission channel, data jumping caused by faults of a main station system, data errors caused by human errors and the like.
Therefore, in order to ensure the reliability of the real-time data of the power measurement, the data reliability of the corresponding real-time data is obtained by constructing a function by utilizing the characteristic that the photovoltaic power generation data of the historical typical day and the power measurement data of the corresponding time period are in normal distribution. Therefore, bad recorded data are eliminated, and accurate judgment and prediction of the power quality in the next step are guaranteed.
Referring to fig. 1, fig. 1 is a schematic flow chart of main steps of a photovoltaic power generation real-time power measurement data quality evaluation method according to an embodiment of the invention. As shown in fig. 1, the method for evaluating the quality of photovoltaic power generation real-time power measurement data in the embodiment of the present invention mainly includes the following steps:
step S101: calculating a reliability value corresponding to the daily power measurement data to be measured by using the set power expected data;
step S102: determining an evaluation value of the measured daily power measurement data according to the reliability value corresponding to the measured daily power measurement data;
step S103: and evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data.
Specifically, before calculating the reliability value corresponding to the measured daily power measurement data by using the predetermined power expectation data, the method first acquires the predetermined power expectation data, and specifically includes:
acquiring a plurality of typical daily power measurement data and a plurality of historical daily power measurement data, wherein the typical daily power measurement data can be used for artificially selecting historical power measurement data with excellent data quality;
further, the calculation formula of the typical daily power measurement data may be μ ═ il ×, η, where μ is the typical daily power measurement data, il is the photovoltaic irradiance of the typical day, and η is the comprehensive conversion efficiency of the photovoltaic power station;
respectively calculating a first evaluation index and a second evaluation index between single typical daily power measurement data and all historical daily power measurement data;
and selecting, as the given expected power data, typical daily power measurement data in which a difference between the second evaluation index and the first evaluation index is the largest among the plurality of typical daily power measurement data.
In one embodiment, the first evaluation indicator between the single typical daily electricity measurement data and the total historical daily electricity measurement data is calculated as follows:
in the above formula, z1Is a first evaluation index between the typical daily power measurement data and the total historical daily power measurement data, gammadPower generation rate of typical day, gammajThe power generation rate of the jth historical day is n, and the total number of the historical days is n;
wherein the power generation rate γ of a typical daydIs calculated as follows:
in the above formula, qidIs the power measurement at the ith time of the typical day.
In one embodiment, the calculation of the second evaluation indicator between the single typical daily electricity measurement data and the total historical daily electricity measurement data is as follows:
in the above formula, rdjThe correlation coefficient between the typical daily power measurement data and the jth historical daily power measurement data is, in this embodiment, the correlation coefficient may be a pearson correlation coefficient;
in this embodiment, the calculation formula of the reliability value corresponding to the daily power measurement data to be measured is as follows:
in the above formula, p (x)*) The reliability value, phi (x), corresponding to the measured daily power measurement data*) Cumulative probability density function, x, being normalized values of the daily power measurement data to be measured*For the daily power measurement data normalization value, mu*Normalizing the values for the given power expectation data;
wherein the cumulative probability density function phi (x) of the normalized value of the measured daily power measurement data*) Is calculated as follows:
in the above formula, f (x)*) The normal distribution function of the normalized value of the measured daily power measurement data is calculated as follows:
in the above formula, σ is a standard deviation of the normalized value of the daily power measurement data to be measured.
In this embodiment, the calculation formula of the evaluation value of the power measurement data of the day to be measured is as follows:
in the above formula, g is the evaluation value of the measured daily power measurement data, p (x)i) And the reliability value is the reliability value corresponding to the power measurement data at the ith moment of the day to be measured.
In this embodiment, the quality evaluation of the measured daily power data by using the evaluation value of the measured daily power data includes:
if the evaluation value of the electric power measurement data of the day to be measured is larger than the preset value, the quality of the electric power measurement data of the day to be measured is qualified, otherwise, the quality of the electric power measurement data of the day to be measured is unqualified.
In one embodiment, the preset value is 90.
Further, the invention also provides an optimal implementation scheme, which comprises the following specific steps:
1) photovoltaic irradiance for a typical day of the local area is obtained as shown in table 1 and the generated power is measured in real time as shown in fig. 2, which data records a point for 15 minutes.
TABLE 1 photovoltaic irradiance (W/m2) for a typical day of the local area
| 0：00 | 0 |
| 5：00 | 0 |
| 6：00 | 7.751 |
| 11：00 | 310.072 |
| 12：00 | 313.231 |
| 13：00 | 303.07 |
| 19：00 | 0 |
| 23：00 | 0 |
2) The generated power of a typical solar photovoltaic power plant is calculated from the average photovoltaic conversion efficiency of the photovoltaic power plant, as shown in fig. 3.
3) And calculating a first index and a second index between the generated power of the typical daily photovoltaic power plant and the generated power of the historical solar photovoltaic power plant.
4) The smaller the first index is, the larger the second index is, and the better the selecting effect of the power generation power of the typical sunlight photovoltaic power plant is. And comparing the first index and the second index of the selected data. The data with the best quality is selected as the expected value.
5) And (3) carrying out normalization processing on all the data to obtain the per unit value of the data in the range of (0, 1) in each time period.
6) Constructing a normal distribution function of the generated power normalized value measured in real time, as shown in fig. 4;
7) calculating a reliability value corresponding to the power data generated by the photovoltaic power station at each moment, as shown in fig. 5;
8) and performing overall average scoring on all the retained data, and evaluating the quality according to the score.
It can be seen from fig. 5 that the recorded data are recorded with obvious errors at 1:00 and 4:15, and have slight errors at 12:45 and 15:15, and the evaluation data score is better in other cases. The overall average data score was 95.68. The overall data is excellent.
Based on the same inventive concept, the invention also provides a photovoltaic power generation real-time power measurement data quality evaluation device, as shown in fig. 6, the photovoltaic power generation real-time power measurement data quality evaluation device comprises:
the calculation module is used for calculating the reliability value corresponding to the daily power measurement data to be measured by utilizing the set power expected data;
the determining module is used for determining the evaluation value of the measured daily power data according to the reliability value corresponding to the measured daily power data;
and the evaluation module is used for evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data.
Preferably, before calculating the reliability value corresponding to the measured daily power measurement data by using the predetermined expected power data, the method includes:
acquiring a plurality of typical daily power measurement data and a plurality of historical daily power measurement data;
respectively calculating a first evaluation index and a second evaluation index between single typical daily power measurement data and all historical daily power measurement data;
and selecting, as the given expected power data, typical daily power measurement data in which a difference between the second evaluation index and the first evaluation index is the largest among the plurality of typical daily power measurement data.
Further, the calculation formula of the first evaluation index between the single typical daily power measurement data and the total historical daily power measurement data is as follows:
in the above formula, z1Is a first evaluation index between the typical daily power measurement data and the total historical daily power measurement data, gammadPower generation rate of typical day, gammajFor the jth historical dayThe power generation rate is n, and n is the total number of historical days;
wherein the power generation rate γ of a typical daydIs calculated as follows:
in the above formula, qidIs the power measurement at the ith time of the typical day.
Further, the calculation formula of the second evaluation index between the single typical daily power measurement data and the total historical daily power measurement data is as follows:
in the above formula, rdjIs a correlation coefficient between typical daily power measurement data and jth historical daily power measurement data.
Preferably, the calculation formula of the reliability value corresponding to the daily electric power measurement data to be measured is as follows:
in the above formula, p (x)*) The reliability value, phi (x), corresponding to the measured daily power measurement data*) Cumulative probability density function, x, being normalized values of the daily power measurement data to be measured*For the daily power measurement data normalization value, mu*Normalizing the values for the given power expectation data;
wherein the cumulative probability density function phi (x) of the normalized value of the measured daily power measurement data*) Is calculated as follows:
in the above formula, f (x)*) For classifying the measured data of the electric power on the day to be measuredA normalized normal distribution function calculated as:
in the above formula, σ is a standard deviation of the normalized value of the daily power measurement data to be measured.
Preferably, the calculation formula of the evaluation value of the power measurement data on the day to be measured is as follows:
in the above formula, g is the evaluation value of the measured daily power measurement data, p (x)i) And the reliability value is the reliability value corresponding to the power measurement data at the ith moment of the day to be measured.
Preferably, the quality evaluation of the measured daily power data by using the evaluation value of the measured daily power data includes:
if the evaluation value of the electric power measurement data of the day to be measured is larger than the preset value, the quality of the electric power measurement data of the day to be measured is qualified, otherwise, the quality of the electric power measurement data of the day to be measured is unqualified.
Further, the preset value is 90.
Further, the present invention also provides a storage device, wherein a plurality of program codes are stored, wherein the program codes are suitable to be loaded and run by a processor to execute the photovoltaic power generation real-time power measurement data quality evaluation method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A photovoltaic power generation real-time power measurement data quality assessment method is characterized by comprising the following steps:
calculating a reliability value corresponding to the daily power measurement data to be measured by using the set power expected data;
determining an evaluation value of the measured daily power measurement data according to the reliability value corresponding to the measured daily power measurement data;
and evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data.
2. The method as claimed in claim 1, wherein before calculating the reliability value corresponding to the measured daily power measurement data by using the predetermined expected power data, the method comprises:
acquiring a plurality of typical daily power measurement data and a plurality of historical daily power measurement data;
respectively calculating a first evaluation index and a second evaluation index between single typical daily power measurement data and all historical daily power measurement data;
and selecting, as the given expected power data, typical daily power measurement data in which a difference between the second evaluation index and the first evaluation index is the largest among the plurality of typical daily power measurement data.
3. The method of claim 2, wherein the first evaluation metric between the single typical daily electricity measurement and the total historical daily electricity measurement is calculated as follows:
in the above formula, z1Is a first evaluation index between the typical daily power measurement data and the total historical daily power measurement data, gammadPower generation rate of typical day, gammajIs the power generation rate of the jth historical day, and n isTotal number of historical days;
wherein the power generation rate γ of a typical daydIs calculated as follows:
in the above formula, qidIs the power measurement at the ith time of the typical day.
4. The method of claim 3, wherein the second evaluation metric between the single typical daily electricity measurement and the total historical daily electricity measurement is calculated as follows:
in the above formula, rdjIs a correlation coefficient between typical daily power measurement data and jth historical daily power measurement data.
5. The method of claim 1, wherein the reliability value corresponding to the daily power measurement data is calculated as follows:
in the above formula, p (x)*) The reliability value, phi (x), corresponding to the measured daily power measurement data*) Cumulative probability density function, x, being normalized values of the daily power measurement data to be measured*For the daily power measurement data normalization value, mu*Normalizing the values for the given power expectation data;
wherein the cumulative probability density function phi (x) of the normalized value of the measured daily power measurement data*) Is calculated as follows:
in the above formula, f (x)*) The normal distribution function of the normalized value of the measured daily power measurement data is calculated as follows:
in the above formula, σ is a standard deviation of the normalized value of the daily power measurement data to be measured.
6. The method according to claim 1, wherein the evaluation value of the daily electric power measurement data to be measured is calculated by:
in the above formula, g is the evaluation value of the measured daily power measurement data, p (x)i) And the reliability value is the reliability value corresponding to the power measurement data at the ith moment of the day to be measured.
7. The method of claim 1, wherein the quality evaluation of the daily electric power measurement data to be measured using the evaluation value of the daily electric power measurement data to be measured comprises:
if the evaluation value of the electric power measurement data of the day to be measured is larger than the preset value, the quality of the electric power measurement data of the day to be measured is qualified, otherwise, the quality of the electric power measurement data of the day to be measured is unqualified.
8. The method of claim 7, wherein the predetermined value is 90.
9. A photovoltaic power generation real-time electricity measurement data quality evaluation device, characterized in that the device includes:
the calculation module is used for calculating the reliability value corresponding to the daily power measurement data to be measured by utilizing the set power expected data;
the determining module is used for determining the evaluation value of the measured daily power data according to the reliability value corresponding to the measured daily power data;
and the evaluation module is used for evaluating the quality of the measured daily power data by using the evaluation value of the measured daily power data.
10. A storage device having a plurality of program codes stored therein, wherein the program codes are adapted to be loaded and run by a processor to perform the photovoltaic power generation real-time electricity measurement data quality assessment method of any one of claims 1 to 8.
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