CN117131321A - Electric energy data acquisition method - Google Patents
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Abstract
The invention discloses an electric energy data acquisition method, which relates to the technical field of electric energy acquisition digital processing, and comprises the following steps of acquiring a plurality of data of too strong radiation intensity fs in a renewable energy power generation system and analyzing to obtain a solar radiation intensity coefficient Fsx; deploying a meteorological sensor to obtain meteorological data; calculating to obtain a weather influence coefficient ys; collecting and obtaining a plurality of working powers GP, and calculating and obtaining a production efficiency coefficient xlx; establishing an electric energy data model, and carrying out associated calculation on a solar radiation intensity coefficient Fsx, a weather influence coefficient ys and a production efficiency coefficient xlx to obtain a stability coefficient wdx; collecting a set historical threshold Q; comparing the stability coefficient wdx with a historical threshold Q, calculating a data fluctuation value or a data growth value for a period of time; and evaluating the electric energy anomaly Yc according to the data fluctuation value or the data increase value, diagnosing based on the anomaly Yc, and obtaining a diagnosis scheme for repairing.
Description
Technical Field
The invention relates to the technical field of electric energy acquisition and digital processing, in particular to an electric energy data acquisition method.
Background
With increasing importance and demand for renewable energy sources, solar power generation has received a great deal of attention as a clean, renewable energy source. However, the performance and stability of solar power generation systems are critical to achieving high energy conversion. In the prior art, in the process of collecting electric energy data, the collection and analysis of environmental impact values and electric energy stability are lacking, and in order to ensure the reliability and efficiency of a solar power generation system, accurate collection, monitoring and analysis of the electric energy data are necessary steps.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an electric energy data acquisition method for supporting the monitoring, analysis and repair of a renewable energy power generation system.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a method for collecting electric energy data comprises the following steps,
in a renewable energy power generation system, an illumination sensor is deployed at a plurality of positions to monitor illumination radiation intensity, so as to obtain a plurality of data of too strong radiation intensity fs, and a solar radiation intensity coefficient Fsx is obtained through analysis; deploying a meteorological sensor to obtain meteorological data; calculating influence of meteorological data on a solar radiation intensity coefficient Fsx to obtain a weather influence coefficient ys;
collecting production electric energy data of a generator in a renewable energy power generation system by adopting an electric energy meter, obtaining a plurality of working powers GP, and calculating to obtain a production efficiency coefficient xlx;
establishing an electric energy data model, correlating a solar radiation intensity coefficient Fsx, a weather influence coefficient ys and a production efficiency coefficient xlx, and sending the correlated solar radiation intensity coefficient Fsx, the weather influence coefficient ys and the production efficiency coefficient xlx into the electric energy data model for analysis and calculation to obtain a stability coefficient wdx; the stability factor wdx is obtained by the following formula:
wherein p is represented as a stabilization factor, 0 is less than or equal to sigma < 1,0 is less than or equal to theta < 1, sigma+theta=1, sigma and theta are weights, and C is a constant correction coefficient, and a specific value of the constant correction coefficient can be adjusted and set by a user or generated by fitting an analysis function; r is a correlation coefficient between solar radiation intensity coefficient Fsx and production efficiency coefficient xlx;
the method comprises the steps of calling production electric energy data of a historical time axis in a daily, weekly or monthly mode, obtaining historical production data, calculating an average value and obtaining a historical threshold Q; comparing the stability coefficient wdx with a historical threshold Q, calculating a data fluctuation value or a data growth value for a period of time; and evaluating the electric energy anomaly degree Yc according to the data fluctuation value or the data increase value, analyzing according to the anomaly degree Yc, indicating that the system is abnormal in operation and exists, diagnosing based on the anomaly degree Yc, and obtaining a diagnosis scheme for repairing.
Preferably, the solar radiation intensity coefficient Fsx is obtained by the following formula:
Fsx＝(1/N)*∑(fx*Time)
where N represents the number of solar radiation intensity data, Σ represents a summation symbol, fx represents the value of each solar radiation intensity, and Time represents the corresponding radiation Time parameter; collecting data of a plurality of solar radiation intensities fx, and calculating the product of each solar radiation intensity fx value and a corresponding radiation time parameter; summing the product results to obtain a sum value; dividing the sum by the number of solar radiation intensity data N to obtain a solar radiation intensity coefficient Fsx.
Preferably, a meteorological sensor is deployed to obtain meteorological data;
calculating influence of meteorological data on a solar radiation intensity coefficient Fsx to obtain a weather influence coefficient ys; the weather effect coefficient ys is calculated by the following formula,
Fsx＝a1*Fsx+a2*wd+a3*zs+a4*sd+a5*Other Factor+...
wherein, in this formula, a1, a2, a3 and a4 are regression coefficients indicating the degree of influence of each meteorological factor on the solar radiation intensity coefficient; wd represents temperature, sd represents irradiation time, sd represents air humidity, and other factors that may have an effect.
Preferably, data of several operating powers GP are collected, which are continuously acquired or measured results within a specific time interval; the operating power reflects the electrical energy production capacity of the generator over a specific period of time;
averaging the collected working power GP data to obtain average working power avg_GP;
calculating theoretical maximum Power generation capacity Max_energy according to the actual Power generation time GenTime and the Rated Power Rated_Power of the generator by referring to the Power generation time and the Power generation intensity;
the production efficiency coefficient xlx is obtained by the following formula:
xlx＝Avg_GP/Max_Energy
the meaning in the formula is: the production efficiency coefficient xlx, i.e. the ratio of the average operating power avg_gp to the theoretical maximum power generation max_energy, is used to measure the actual power generation efficiency of the generator.
Preferably, collecting data of a current period, and collecting the data of the current period by using a current sensor, wherein the current period is set to be one complete period of a current waveform;
calculating the average value of the current period: average calculation is carried out on the collected current Period data to obtain an average current Period avg_period;
calculating the standard deviation of the current period: calculating standard deviation of the collected current period data, wherein the standard deviation represents fluctuation degree of the current period;
calculating a stability factor P: the stability factor P is obtained by dividing the average current Period (avg_period) by the standard deviation of the current Period:
P＝Avg_Period/Standard Deviation
the significance of the stability factor P calculation is: the stability factor P represents the smoothness of the current period; a higher value of the stability factor indicates a more stable current period and a lower value indicates a greater fluctuation in the current period.
Preferably, daily, weekly or monthly production power data is extracted from the historical timeline; the production electric energy data comprise generating capacity data, generating efficiency data, current and voltage data;
calculating an average of the production power data for each time period, including daily, weekly, and monthly; calculated by summing the historical production data and then dividing by the number of days, weeks or months in the time period; the average value obtained is the history threshold Q.
Preferably, the stability factor wdx is compared to a historical threshold Q to calculate a fluctuation value or a data growth value of the data;
calculating a difference or percentage difference between the stability coefficient wdx and the historical threshold Q to measure the degree of fluctuation of the data; a larger variance indicates that the data has higher volatility over time;
according to the data fluctuation value or the data increment value, evaluating the abnormality Yc of the electric energy;
the standard threshold value Qz is set to judge the degree of the abnormality degree, the fluctuation degree or the growth condition of the data is compared with the standard threshold value Qz to obtain the abnormality degree Yc, a higher abnormality degree Yc value indicates that the abnormality degree of the electric energy data is higher, and a lower abnormality degree Yc value indicates that the electric energy data is relatively normal.
Preferably, the calculated power anomaly Yc is compared with a standard threshold Qz, in the following manner,
if the electric energy anomaly Yc is less than or equal to Qz: the electric energy anomaly degree is shown to be within an acceptable range, namely the electric energy data are relatively normal, and no further repair measures are needed;
if power anomaly Yc > Qz: the abnormal degree of the electric energy exceeds a standard threshold value Qz, namely the abnormal degree of the electric energy data is repaired according to a repairing scheme obtained through diagnosis.
Preferably, the repair scheme comprises the steps of performing corresponding diagnosis and repair measures according to the magnitude and trend of the electric energy anomaly Yc;
for small abnormal degree increase or fluctuation, routine maintenance and overhaul are carried out to ensure that the system operates normally;
for larger abnormal degree increase or fluctuation, the fault cause needs to be deeply diagnosed, and the specific cause causing the abnormality is found out and repaired by checking the power equipment, detecting the sensor and performing a system calibration method;
based on the diagnostic results, corresponding corrective actions are taken, including repairing the faulty device, replacing the damaged component, and recalibrating the sensor to resume normal operation of the electrical energy system.
(III) beneficial effects
The invention provides an electric energy data acquisition method. The beneficial effects are as follows:
(1) The electric energy data acquisition method can monitor key indexes such as solar radiation intensity, meteorological conditions, power generation efficiency and the like in real time, identify potential problems and abnormal conditions, and take corresponding measures to optimize the performance and stability of the system. The technology is beneficial to improving the operation efficiency of the renewable energy power generation system, reducing the fault risk and promoting the sustainable utilization of renewable energy.
(2) According to the electric energy data acquisition method, the solar radiation intensity, the weather influence and the production efficiency are comprehensively considered, and the operation condition of an electric energy system is analyzed and diagnosed through the steps of stability coefficient wdx, data fluctuation value, abnormality degree evaluation and the like, so that the purposes of fault detection and repair are achieved.
(3) According to the electric energy data acquisition method, the purpose of calculating the stability factor P is to evaluate the stability of a current period. By comparing the magnitudes of the stability factors, it can be determined whether the current period has higher stability, and further analysis and determination of the electrical energy data can be performed. This helps to detect fluctuations in the current period and provides a reference for the operation and management of the renewable energy power generation system.
(4) According to the electric energy data acquisition method, the degree of abnormality Yc of electric energy is evaluated, and whether further repair measures are needed or not is determined according to a comparison result of the degree of abnormality Yc and a standard threshold Qz. A method for comprehensively considering historical data, fluctuation values and standard threshold Qz is provided, and is used for evaluating the abnormality degree of electric energy data and carrying out corresponding repair and optimization according to the result.
Drawings
FIG. 1 is a schematic diagram of a data analysis step according to the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
With increasing importance and demand for renewable energy sources, solar power generation has received a great deal of attention as a clean, renewable energy source. However, the performance and stability of solar power generation systems are critical to achieving high energy conversion. In the prior art, in the process of collecting electric energy data, the collection and analysis of environmental impact values and electric energy stability are lacking, and in order to ensure the reliability and efficiency of a solar power generation system, accurate collection, monitoring and analysis of the electric energy data are necessary steps.
Example 1
The invention provides a method for collecting electric energy data, referring to fig. 1, comprising the following steps,
in a renewable energy power generation system, illumination sensors are deployed at a plurality of positions to monitor illumination radiation intensity, and the illumination radiation intensity can be monitored at different positions. Thus, more comprehensive and accurate solar radiation intensity data can be obtained, the monitoring and optimizing capacity of a solar power generation system is improved, a plurality of pieces of data of too strong radiation intensity fs are obtained, and the solar radiation intensity coefficient Fsx is obtained through analysis; deploying a meteorological sensor to obtain meteorological data, and further analyzing the influence of meteorological factors on the solar radiation intensity coefficient; calculating influence of meteorological data on a solar radiation intensity coefficient Fsx to obtain a weather influence coefficient ys;
collecting production electric energy data of a generator in a renewable energy power generation system by adopting an electric energy meter, obtaining a plurality of working powers GP, and calculating to obtain a production efficiency coefficient xlx;
establishing an electric energy data model, correlating a solar radiation intensity coefficient Fsx, a weather influence coefficient ys and a production efficiency coefficient xlx, and sending the correlated solar radiation intensity coefficient Fsx, the weather influence coefficient ys and the production efficiency coefficient xlx into the electric energy data model for analysis and calculation, so that the influence of solar radiation, weather and power generation data can be comprehensively considered, a more comprehensive and accurate stability evaluation index is provided, and a stability coefficient wdx is obtained; the stability factor wdx is obtained by the following formula:
wherein p is represented as a stabilization factor, 0 is less than or equal to sigma < 1,0 is less than or equal to theta < 1, sigma+theta=1, sigma and theta are weights, and C is a constant correction coefficient, and a specific value of the constant correction coefficient can be adjusted and set by a user or generated by fitting an analysis function; r is a correlation coefficient between solar radiation intensity coefficient Fsx and production efficiency coefficient xlx;
the method comprises the steps of calling production electric energy data of a historical time axis in a daily, weekly or monthly mode, obtaining historical production data, calculating an average value and obtaining a historical threshold Q; comparing the stability coefficient wdx with a historical threshold Q, calculating a data fluctuation value or a data growth value for a period of time; and evaluating the electric energy anomaly degree Yc according to the data fluctuation value or the data increase value, analyzing according to the anomaly degree Yc, indicating that the system is abnormal in operation and exists, diagnosing based on the anomaly degree Yc, and obtaining a diagnosis scheme for repairing. The system analysis and diagnosis are carried out based on the electric energy anomaly Yc, so that the anomaly and operation anomaly of the system can be found in time, a corresponding diagnosis scheme is provided for repairing, and the stability and performance of the system are improved.
In this embodiment, the electric energy data collection method comprehensively considers the solar radiation intensity, the weather effect and the production efficiency, and analyzes and diagnoses the operation condition of the electric energy system through the steps of stability coefficient wdx, data fluctuation value, abnormality degree evaluation and the like, thereby achieving the purposes of fault detection and repair.
Example 2, which is an illustration made in example 1, specifically, the solar radiation intensity coefficient Fsx is obtained by the following formula:
Fsx＝(1/N)*∑(fx*Time)
where N represents the number of solar radiation intensity data, Σ represents a summation symbol, fx represents the value of each solar radiation intensity, and Time represents the corresponding radiation Time parameter; collecting data of a plurality of solar radiation intensities fx, and calculating the product of each solar radiation intensity fx value and a corresponding radiation time parameter; summing the product results to obtain a sum value; dividing the sum by the number of solar radiation intensity data N to obtain a solar radiation intensity coefficient Fsx.
In this embodiment, the calculation method may comprehensively consider the numerical value of the solar radiation intensity and the radiation time parameter to obtain the comprehensive index of the solar radiation intensity. By calculating the solar radiation intensity coefficient Fsx, the utilization efficiency of the solar resources can be more accurately estimated and used to support energy analysis and optimization of the renewable energy power generation system.
Example 3, which is an explanation made in example 1, specifically, a meteorological sensor is deployed to obtain meteorological data;
calculating influence of meteorological data on a solar radiation intensity coefficient Fsx to obtain a weather influence coefficient ys; the weather effect coefficient ys is calculated by the following formula,
Fsx＝a1*Fsx+a2*wd+a3*zs+a4*sd+a5*Other Factor+...
wherein, in this formula, a1, a2, a3 and a4 are regression coefficients indicating the degree of influence of each meteorological factor on the solar radiation intensity coefficient; wd represents temperature, sd represents irradiation time, sd represents air humidity, and other factors that may have an effect.
In this embodiment, the influence degree of the weather factor on the solar radiation intensity coefficient is comprehensively considered by calculating the weather influence coefficient ys. A higher weather factor indicates a greater impact of the weather factor on the intensity of solar radiation, while a lower weather factor indicates a lesser impact of the weather factor on the intensity of solar radiation. By comprehensively considering meteorological factors such as temperature, irradiation time, air humidity and the like, the influence degree of meteorological data on the solar radiation intensity coefficient can be estimated more accurately. This helps optimize energy yield prediction and management of the renewable energy power generation system.
Embodiment 4, which is an explanation made in embodiment 1, specifically, collecting data of several operating powers GP, which are continuously collected or measured results in a specific time interval; the operating power reflects the electrical energy production capacity of the generator over a specific period of time;
averaging the collected working power GP data to obtain average working power avg_GP;
calculating theoretical maximum Power generation capacity Max_energy according to the actual Power generation time GenTime and the Rated Power Rated_Power of the generator by referring to the Power generation time and the Power generation intensity;
the production efficiency coefficient xlx is obtained by the following formula:
xlx＝Avg_GP/Max_Energy
the meaning in the formula is: the production efficiency coefficient xlx, i.e. the ratio of the average operating power avg_gp to the theoretical maximum power generation max_energy, is used to measure the actual power generation efficiency of the generator.
In this embodiment, the actual power generation efficiency of the generator is measured by calculating the production efficiency coefficient xlx. A higher production efficiency coefficient indicates that the actual power generation efficiency of the generator is higher in a specific period of time, and a lower coefficient indicates that the actual power generation efficiency of the generator is lower. This helps to assess the operating conditions and performance of the generator and provides a reference for the operation and management of the renewable energy power generation system.
Embodiment 5, which is an explanation made in embodiment 1, specifically, collecting data of a current period by using a current sensor, the current period being set to one complete period of a current waveform;
calculating the average value of the current period: average calculation is carried out on the collected current Period data to obtain an average current Period avg_period;
calculating the standard deviation of the current period: calculating standard deviation of the collected current period data, wherein the standard deviation represents fluctuation degree of the current period;
calculating a stability factor P: the stability factor P is obtained by dividing the average current Period (avg_period) by the standard deviation of the current Period:
P＝Avg_Period/Standard Deviation
the significance of the stability factor P calculation is: the stability factor P represents the smoothness of the current period; a higher value of the stability factor indicates a more stable current period and a lower value indicates a greater fluctuation in the current period.
In this embodiment, the purpose of calculating the stability factor P is to evaluate the stability of the current period. By comparing the magnitudes of the stability factors, it can be determined whether the current period has higher stability, and further analysis and determination of the electrical energy data can be performed. This helps to detect fluctuations in the current period and provides a reference for the operation and management of the renewable energy power generation system.
Example 6, which is an explanation made in example 1, specifically, the daily, weekly, or monthly production power data is extracted from the historical time axis; the production electric energy data comprise generating capacity data, generating efficiency data, current and voltage data;
calculating an average of the production power data for each time period, including daily, weekly, and monthly; calculated by summing the historical production data and then dividing by the number of days, weeks or months in the time period; the average value obtained is the history threshold Q.
Comparing the stability coefficient wdx with a historical threshold Q to calculate a fluctuation value or a data growth value of the data;
calculating a difference or percentage difference between the stability coefficient wdx and the historical threshold Q to measure the degree of fluctuation of the data; a larger variance indicates that the data has higher volatility over time;
according to the data fluctuation value or the data increment value, evaluating the abnormality Yc of the electric energy;
the standard threshold value Qz is set to judge the degree of the abnormality degree, the fluctuation degree or the growth condition of the data is compared with the standard threshold value Qz to obtain the abnormality degree Yc, a higher abnormality degree Yc value indicates that the abnormality degree of the electric energy data is higher, and a lower abnormality degree Yc value indicates that the electric energy data is relatively normal.
Comparing the calculated electric energy anomaly degree Yc with a standard threshold value Qz, the specific method is as follows,
if the electric energy anomaly Yc is less than or equal to Qz: the electric energy anomaly degree is shown to be within an acceptable range, namely the electric energy data are relatively normal, and no further repair measures are needed;
if power anomaly Yc > Qz: the abnormal degree of the electric energy exceeds a standard threshold value Qz, namely the abnormal degree of the electric energy data is repaired according to a repairing scheme obtained through diagnosis.
In this embodiment, through the above steps, the degree of abnormality Yc of the electric energy can be estimated, and whether further repair measures are required or not can be determined according to the result of comparing the degree of abnormality Yc with the standard threshold Qz. A method for comprehensively considering historical data, fluctuation values and standard threshold Qz is provided, and is used for evaluating the abnormality degree of electric energy data and carrying out corresponding repair and optimization according to the result.
Example 7, which is an explanation of example 6, specifically, the repair scheme includes performing corresponding diagnosis and repair measures according to the magnitude and trend of the power anomaly Yc;
for small abnormal degree increase or fluctuation, routine maintenance and overhaul are carried out to ensure that the system operates normally;
for larger abnormal degree increase or fluctuation, the fault cause needs to be deeply diagnosed, and the specific cause causing the abnormality is found out and repaired by checking the power equipment, detecting the sensor and performing a system calibration method;
based on the diagnostic results, corresponding corrective actions are taken, including repairing the faulty device, replacing the damaged component, and recalibrating the sensor to resume normal operation of the electrical energy system.
In this embodiment, the purpose of the repair measures is to solve faults and anomalies in the electrical energy system, and to resume the normal operation of the system. Depending on the degree and trend of the anomaly, appropriate measures are taken to repair the device or system and ensure that it meets the expected performance and operational requirements.
Specific examples: some solar power plant is used, and the illumination sensor monitors: 10 illumination sensors are deployed at various locations of the solar power plant for monitoring illumination radiation intensity. The 10 solar radiation intensity data obtained by the illumination sensor are: [800,850,900,950,1000,1050,1100,1150,1200,1250] W/m 2;
meteorological data are obtained by a meteorological sensor: and deploying a meteorological sensor to obtain meteorological data such as temperature, irradiation time, air humidity and the like. The meteorological data obtained is assumed to be: the temperature is 30 ℃, the irradiation time is 8 hours, and the air humidity is 50%;
calculating a solar radiation intensity coefficient Fsx: from the illumination sensor data, a solar radiation intensity coefficient Fsx is calculated. Obtaining fsx= (1/10), (800+850+900+950+1000+1050+1100+1150+1200+1250) =1050W/m 2 according to the formula;
calculating a weather influence coefficient ys: and calculating weather influence coefficient ys according to the meteorological data and the solar radiation intensity coefficient Fsx. Let a1=0.5, a2=0.3, a3=0.2, and the other factors affect the coefficient 0. The calculation formula is: ys=0.5×30+0.3×8+0.2×50=23.5;
collecting production electric energy data of a generator: the method comprises the steps of collecting production electric energy data of a generator by using an electric energy meter, and assuming that the collected working efficiency data GP per hour are: [500,550,600,650,700,750,800,850,900,950,1000] kW;
calculating a production efficiency coefficient xlx: from the collected operating power data, a production efficiency coefficient xlx is calculated. Assuming that the Rated Power of the generator, rated_power, is 800kW, the average operating Power avg_gp is (500+550+600+650+700+750+800+850+900+950+1000)/11= 772.73kW. The theoretical maximum Power generation max_energy is rated_power for 8 hours=800 kw×8=6400 kW. The production efficiency coefficient xlx is 772.73/6400= 0.1207;
calculate stability factor wdx: the stability factor wdx is calculated from the solar radiation intensity factor Fsx and the production efficiency factor xlx. Assuming that the stability factor p is 1.2 and r is 0.8, the stability factor wdx is 1.2 (1050/6400) 0.8=0.156;
historical data analysis: daily production power data, such as power generation for the past 30 days, is extracted from the historical time axis: [45000,48000,46000,47000,49000,50000,52000,51000,47000,48000,49000,50000, 51000,52000,50000,48000,47000,49000,50000,52000,53000,52000,51000,53000,54000,55000,56000] kwh.
Calculating a history threshold value Q: the 30-day power generation amount data are added and divided by 30 to obtain a history threshold value Q: (45000+48000+46000+ & gt 55000+56000)/30=49800 kWh.
Calculating a data fluctuation value or a data growth value: the stability factor wdx is compared with the historical threshold Q to calculate a fluctuation value or a data growth value of the data. Assume that the data fluctuation value is the absolute value of the difference between the historical threshold Q and the stability factor wdx: |49800-0.156|= 49699.844kWh.
Evaluating the abnormality degree Yc of the electric energy: and evaluating the abnormality degree Yc of the electric energy according to the data fluctuation value or the data increment value. Assuming that the standard threshold Qz is set to 1000kWh, the abnormality Yc of the electric power is a difference between the data fluctuation value or the data increase value and the standard threshold Qz: 49699.844-1000= 48699.844kWh.
Electric power anomaly Yc evaluation: and comparing the calculated electric energy anomaly degree Yc with a standard threshold Qz to judge the anomaly degree of the electric energy data. If the electric energy anomaly degree Yc is less than or equal to the standard threshold value Qz, the electric energy anomaly degree is within an acceptable range, namely the electric energy data are relatively normal; if the electric energy anomaly degree Yc is greater than the standard threshold value Qz, the electric energy anomaly degree exceeds the standard threshold value, namely the electric energy data is abnormal, and corresponding repair measures are needed.
This particular example demonstrates how the monitoring, analysis and repair process of a solar power plant can be performed using an electrical energy data collection method to improve the efficiency and reliability of the system. This is beneficial to improving the operating efficiency of the renewable energy power generation system and reducing the risk of potential failure and loss.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The electric energy data acquisition method is characterized by comprising the following steps of: comprises the steps of,
in a renewable energy power generation system, an illumination sensor is deployed at a plurality of positions to monitor illumination radiation intensity, so as to obtain a plurality of data of too strong radiation intensity fs, and a solar radiation intensity coefficient Fsx is obtained through analysis; deploying a meteorological sensor to obtain meteorological data; calculating influence of meteorological data on a solar radiation intensity coefficient Fsx to obtain a weather influence coefficient ys;
collecting production electric energy data of a generator in a renewable energy power generation system by adopting an electric energy meter, obtaining a plurality of working powers GP, and calculating to obtain a production efficiency coefficient xlx;
establishing an electric energy data model, correlating a solar radiation intensity coefficient Fsx, a weather influence coefficient ys and a production efficiency coefficient xlx, and sending the correlated solar radiation intensity coefficient Fsx, the weather influence coefficient ys and the production efficiency coefficient xlx into the electric energy data model for analysis and calculation to obtain a stability coefficient wdx; the stability factor wdx is obtained by the following formula:
wherein p is represented as a stabilization factor, 0 is less than or equal to sigma < 1,0 is less than or equal to theta < 1, sigma+theta=1, sigma and theta are weights, and C is a constant correction coefficient, and a specific value of the constant correction coefficient can be adjusted and set by a user or generated by fitting an analysis function; r is a correlation coefficient between solar radiation intensity coefficient Fsx and production efficiency coefficient xlx;
the method comprises the steps of calling production electric energy data of a historical time axis in a daily, weekly or monthly mode, obtaining historical production data, calculating an average value and obtaining a historical threshold Q; comparing the stability coefficient wdx with a historical threshold Q, calculating a data fluctuation value or a data growth value for a period of time; and evaluating the electric energy anomaly degree Yc according to the data fluctuation value or the data increase value, analyzing according to the anomaly degree Yc, indicating that the system is abnormal in operation and exists, diagnosing based on the anomaly degree Yc, and obtaining a diagnosis scheme for repairing.
2. The method for collecting electrical energy data according to claim 1, wherein: the solar radiation intensity coefficient Fsx is obtained by the following formula:
Fsx＝(1/N)*∑(fx*Time)
where N represents the number of solar radiation intensity data, Σ represents a summation symbol, fx represents the value of each solar radiation intensity, and Time represents the corresponding radiation Time parameter; collecting data of a plurality of solar radiation intensities fx, and calculating the product of each solar radiation intensity fx value and a corresponding radiation time parameter; summing the product results to obtain a sum value; dividing the sum by the number of solar radiation intensity data N to obtain a solar radiation intensity coefficient Fsx.
3. The method for collecting electrical energy data according to claim 1, wherein: deploying a meteorological sensor to obtain meteorological data;
calculating influence of meteorological data on a solar radiation intensity coefficient Fsx to obtain a weather influence coefficient ys; the weather effect coefficient ys is calculated by the following formula,
Fsx＝a1*Fsx+a2*wd+a3*zs+a4*sd+a5*Other Factor+...
wherein, in this formula, a1, a2, a3 and a4 are regression coefficients indicating the degree of influence of each meteorological factor on the solar radiation intensity coefficient; wd represents temperature, sd represents irradiation time, sd represents air humidity, and other factors that may have an effect.
4. The method for collecting electrical energy data according to claim 1, wherein: collecting data of a plurality of working powers GP, wherein the data are continuously acquired or measured results in a specific time interval; the operating power reflects the electrical energy production capacity of the generator over a specific period of time;
averaging the collected working power GP data to obtain average working power avg_GP;
calculating theoretical maximum Power generation capacity Max_energy according to the actual Power generation time GenTime and the Rated Power Rated_Power of the generator by referring to the Power generation time and the Power generation intensity;
the production efficiency coefficient xlx is obtained by the following formula:
xlx＝Avg_GP/Max_Energy
the meaning in the formula is: the production efficiency coefficient xlx, i.e. the ratio of the average operating power avg_gp to the theoretical maximum power generation max_energy, is used to measure the actual power generation efficiency of the generator.
5. The method for collecting electrical energy data according to claim 1, wherein: collecting data of a current period, wherein the current period is set as a complete period of a current waveform by adopting a current sensor to collect the data of the current period;
calculating the average value of the current period: average calculation is carried out on the collected current Period data to obtain an average current Period avg_period;
calculating the standard deviation of the current period: calculating standard deviation of the collected current period data, wherein the standard deviation represents fluctuation degree of the current period;
calculating a stability factor P: the stability factor P is obtained by dividing the average current Period (avg_period) by the standard deviation of the current Period:
P＝Avg_Period/Standard Deviation
the significance of the stability factor P calculation is: the stability factor P represents the smoothness of the current period; a higher value of the stability factor indicates a more stable current period and a lower value indicates a greater fluctuation in the current period.
6. The method for collecting electrical energy data according to claim 1, wherein: extracting daily, weekly or monthly production power data from the historical timeline; the production electric energy data comprise generating capacity data, generating efficiency data, current and voltage data;
calculating an average of the production power data for each time period, including daily, weekly, and monthly; calculated by summing the historical production data and then dividing by the number of days, weeks or months in the time period; the average value obtained is the history threshold Q.
7. The method for collecting electrical energy data according to claim 6, wherein: comparing the stability coefficient wdx with a historical threshold Q to calculate a fluctuation value or a data growth value of the data;
calculating a difference or percentage difference between the stability coefficient wdx and the historical threshold Q to measure the degree of fluctuation of the data; a larger variance indicates that the data has higher volatility over time;
according to the data fluctuation value or the data increment value, evaluating the abnormality Yc of the electric energy;
the standard threshold value Qz is set to judge the degree of the abnormality degree, the fluctuation degree or the growth condition of the data is compared with the standard threshold value Qz to obtain the abnormality degree Yc, a higher abnormality degree Yc value indicates that the abnormality degree of the electric energy data is higher, and a lower abnormality degree Yc value indicates that the electric energy data is relatively normal.
8. The method for collecting electrical energy data according to claim 7, wherein: comparing the calculated electric energy anomaly degree Yc with a standard threshold value Qz, the specific method is as follows,
if the electric energy anomaly Yc is less than or equal to Qz: the electric energy anomaly degree is shown to be within an acceptable range, namely the electric energy data are relatively normal, and no further repair measures are needed;
if power anomaly Yc > Qz: the abnormal degree of the electric energy exceeds a standard threshold value Qz, namely the abnormal degree of the electric energy data is repaired according to a repairing scheme obtained through diagnosis.
9. The method for collecting electrical energy data according to claim 8, wherein: the repairing scheme comprises the steps of performing corresponding diagnosis and repairing measures according to the magnitude and trend of the electric energy anomaly Yc;
for small abnormal degree increase or fluctuation, routine maintenance and overhaul are carried out to ensure that the system operates normally;
for larger abnormal degree increase or fluctuation, the fault cause needs to be deeply diagnosed, and the specific cause causing the abnormality is found out and repaired by checking the power equipment, detecting the sensor and performing a system calibration method;
based on the diagnostic results, corresponding corrective actions are taken, including repairing the faulty device, replacing the damaged component, and recalibrating the sensor to resume normal operation of the electrical energy system.
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