A distributed new energy grid-connected data processing method and system

Abstract

The present invention discloses a distributed new energy grid-connected data processing method and system, relates to the field of new energy, solves the problem of poor functionality of the distributed new energy grid-connected data processing method, comprises step S1: obtaining first node power data, second node power data and power generation efficiency monitoring period, and obtaining node power data; step S2: obtaining wind energy monitoring coefficients corresponding to multiple first type new energy nodes according to the node power data, and obtaining environmental wind data; step S3: obtaining solar energy monitoring coefficients corresponding to multiple second type new energy nodes according to the node power data, and obtaining environmental solar energy data; step S4: performing power early warning according to the node power data, environmental wind data and environmental solar energy data. The present invention can provide a basis for power resource allocation and improve the overall power generation efficiency of the distributed power grid.

Description

Technical Field

[0001] The present invention belongs to the field of new energy, relates to data processing technology, and specifically is a distributed new energy grid-connected data processing method and system. Background Art

[0002] The existing distributed renewable energy grid-connected data processing methods have the following defects:

[0003] 1. The existing distributed renewable energy grid-connected data processing method cannot judge the power generation efficiency of each distributed power plant through the grid-connected data, and it is difficult to accurately evaluate the power generation efficiency of each power plant, resulting in some power plants continuing to generate electricity when operating inefficiently, which easily leads to a waste of resources;

[0004] 2. The existing distributed renewable energy grid-connected data processing method only relies on single power data to judge power generation anomalies, which cannot fully reflect the actual operating conditions of the power plant and the judgment results lack accuracy.

[0005] To this end, we propose a distributed renewable energy grid-connected data processing method and system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a distributed new energy grid-connected data processing method and system. The present invention is based on dividing new energy power generation nodes into first-type new energy nodes and second-type new energy nodes, obtaining the average power generation power corresponding to each first-type new energy node, obtaining the first node power data, obtaining each corresponding average power generation power, obtaining the second node power data, obtaining the power generation efficiency monitoring period, defining the power generation efficiency monitoring period, the first node power data and the second node power data as node power data, obtaining the wind energy monitoring coefficient corresponding to each first-type new energy node according to the node power data, defining the wind energy monitoring coefficients corresponding to multiple first-type new energy nodes as environmental wind data, obtaining the solar energy monitoring coefficient corresponding to each second-type new energy node according to the node power data, defining the solar energy monitoring coefficients corresponding to multiple second-type new energy nodes as environmental solar energy data, and performing power warnings for the first-type new energy nodes and the second-type new energy nodes respectively according to the node power data, the environmental wind data and the environmental solar energy data.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: a distributed new energy grid-connected data processing method, comprising the following specific steps:

[0008] Step S1: Divide the new energy power generation nodes into first-type new energy nodes and second-type new energy nodes, obtain the average power generation corresponding to each first-type new energy node, obtain the first node power data, obtain each corresponding average power generation, obtain the second node power data, obtain the power generation efficiency monitoring period, and define the power generation efficiency monitoring period, the first node power data and the second node power data as node power data;

[0009] Step S2: acquiring a wind energy monitoring coefficient corresponding to each first-type new energy node according to the node power data, and defining the wind energy monitoring coefficients corresponding to the plurality of first-type new energy nodes as environmental wind data;

[0010] Step S3: acquiring a solar energy monitoring coefficient corresponding to each second-type new energy node according to the node power data, and defining the solar energy monitoring coefficients corresponding to a plurality of second-type new energy nodes as environmental solar energy data;

[0011] Step S4: Step S4: Power warnings are respectively provided to the first type of new energy nodes and the second type of new energy nodes according to the node power data, the environmental wind data and the environmental solar energy data.

[0012] Furthermore, the step S1 further includes the following specific steps:

[0013] Step S11: Obtain distributed renewable energy grid-connected data, and mark a power generation efficiency monitoring period in the distributed renewable energy grid-connected data;

[0014] Step S12: dividing the new energy generation nodes existing in the distributed new energy network into first-type new energy nodes and second-type new energy nodes according to the power generation mode;

[0015] Step S13: acquiring the average power generation power of each first-type new energy node in the power generation efficiency monitoring period according to the distributed new energy grid-connected data, obtaining multiple average power generation powers, and naming them as first node power data;

[0016] Step S14: acquiring the average power generation power of each second type new energy node in the power generation efficiency monitoring period according to the distributed new energy grid-connected data, obtaining multiple average power generation powers, and naming them as second node power data;

[0017] Step S15: defining the power generation efficiency monitoring period, the first node power data and the second node power data as node power data.

[0018] Furthermore, the step S2 further includes the following specific steps:

[0019] Step S21: Obtain node power data, and obtain a power generation efficiency monitoring period according to the node power data;

[0020] Step S22: randomly selecting a first type of new energy node in the distributed new energy network as a first type of sample node;

[0021] Step S23: within the power generation efficiency monitoring period, marking n wind data monitoring points, and naming them as the first wind data monitoring point to the nth wind data monitoring point respectively;

[0022] Step S24: obtaining the wind energy monitoring coefficient corresponding to the first type of sample node;

[0023] Step S25: acquiring the wind energy monitoring coefficient for each of the first type new energy nodes respectively to obtain a plurality of wind energy monitoring coefficients, and defining the wind energy monitoring coefficients corresponding to the plurality of first type new energy nodes as environmental wind data.

[0024] Furthermore, the step S24 further includes the following specific steps:

[0025] Step S241: respectively acquiring wind strength values ​​corresponding to the first type sample nodes at the first wind data monitoring point to the nth wind data monitoring point, to obtain the first wind strength value to the nth wind strength value;

[0026] Step S242: Calculate the average of the first wind strength value to the nth wind strength value to obtain a monitored average wind strength value;

[0027] Step S243: obtaining a monitored wind stability coefficient by calculating the first wind strength value to the nth wind strength value and the monitored average wind strength value;

[0028] The wind stability coefficient is calculated, and the specific formula is configured as follows: ;

[0029] Wherein, Jfw is the monitored wind stability coefficient, Fq1 to Fqn are the first wind strength value to the nth wind strength value respectively, Fqp is the monitored average wind strength value, and n is the number value corresponding to the wind data monitoring point;

[0030] Step S244: Calculate the monitored average wind intensity value and the monitored wind stability coefficient to obtain the monitored wind turbulence intensity;

[0031] To calculate the intensity of wind turbulence monitoring, the specific formula is as follows: ;

[0032] Among them, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value;

[0033] Step S245: Calculate the wind energy monitoring coefficient corresponding to the first type of sample node by monitoring the wind turbulence intensity, the average wind intensity value and the wind stability coefficient;

[0034] To calculate the wind energy monitoring coefficient, the specific formula is configured as follows: ;

[0035] Among them, Nfl is the wind energy monitoring coefficient, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value.

[0036] Furthermore, the step S3 further includes the following specific steps:

[0037] Step S31: Acquire node power data, and acquire a power generation efficiency monitoring period according to the node power data;

[0038] Step S32: randomly selecting a second type of new energy node in the distributed new energy network as a second type of sample node;

[0039] Step S33: within the power generation efficiency monitoring period, marking j solar energy data monitoring points, and naming them as the first solar energy data monitoring point to the jth solar energy data monitoring point respectively;

[0040] Step S34: obtaining the solar energy monitoring coefficient corresponding to the second type sample node;

[0041] Step S35: acquiring the solar energy monitoring coefficient for each second type new energy node respectively to obtain a plurality of solar energy monitoring coefficients, and defining the solar energy monitoring coefficients corresponding to the plurality of second type new energy nodes as environmental solar energy data.

[0042] Furthermore, the step S34 further includes the following specific steps:

[0043] Step S341: respectively acquiring light intensity values ​​corresponding to the second type sample nodes at the first solar energy data monitoring point to the jth solar energy data monitoring point, to obtain the first light intensity value to the jth light intensity value;

[0044] Step S342: Calculate the average of the first light intensity value to the jth light intensity value to obtain a monitored average light intensity value;

[0045] Step S343: Calculate the first light intensity value to the jth light intensity value and the monitored average light intensity value to obtain a monitored solar energy stability coefficient;

[0046] To calculate the stability coefficient of monitored solar energy, the specific formula is configured as follows: ;

[0047] Wherein, Jtw is the monitored solar energy stability coefficient, Tq1 to Tqj are the first light intensity value to the jth light intensity value respectively, Tqp is the monitored average light intensity value, and j is the quantity value corresponding to the solar energy data monitoring point;

[0048] Step S344: Calculate the monitored average light intensity value and the monitored solar energy stability coefficient to obtain the solar energy monitoring coefficient corresponding to the second type of sample node;

[0049] To calculate the solar energy monitoring coefficient, the specific formula is as follows: Wherein, Ntl is the solar energy monitoring coefficient, Jtw is the monitored solar energy stability coefficient, and Tqp is the monitored average light intensity value.

[0050] Furthermore, the step S4 further includes the following specific steps:

[0051] Step S41: Acquire node power data, and acquire first node power data and second node power data respectively according to the node power data;

[0052] Step S42: Performing a power warning for the first type of new energy node according to the first node power data;

[0053] Step S43: Perform power warning for the second type of new energy node according to the second node power data.

[0054] Furthermore, the step S42 further includes the following specific steps:

[0055] Step S421: acquiring the average power generation corresponding to each first-type new energy node according to the first node power data;

[0056] Step S422: Acquire environmental wind data, and acquire a wind energy monitoring coefficient corresponding to each first-type new energy node according to the environmental wind data;

[0057] Step S423: Calculate the ratio of the average power generation to the wind energy monitoring coefficient to obtain the wind energy generation efficiency output ratio;

[0058] Step S424: obtaining a wind power generation efficiency output ratio threshold value, and comparing the wind power generation efficiency output ratio with the wind power generation efficiency output ratio threshold value;

[0059] Step S425: when the wind power generation efficiency output ratio is less than the wind power generation efficiency output ratio threshold, issuing a power abnormality warning to the corresponding first type new energy node;

[0060] Step S426: When the wind power generation efficiency output ratio is greater than or equal to the wind power generation efficiency output ratio threshold, continue to issue a power warning to the corresponding first type new energy node.

[0061] Furthermore, the step S43 further includes the following specific steps:

[0062] Step S431: acquiring the average power generation corresponding to each second type new energy node according to the second node power data;

[0063] Step S432: Acquire environmental solar energy data, and acquire a solar energy monitoring coefficient corresponding to each second type new energy node according to the environmental solar energy data;

[0064] Step S433: Calculate the ratio of the average power generation to the solar energy monitoring coefficient to obtain the solar energy power generation efficiency output ratio;

[0065] Step S434: obtaining a solar power generation efficiency output ratio threshold value, and numerically comparing the solar power generation efficiency output ratio with the solar power generation efficiency output ratio threshold value;

[0066] Step S435: when the solar power generation efficiency output ratio is less than the solar power generation efficiency output ratio threshold, issuing a power abnormality warning to the corresponding second type new energy node;

[0067] Step S436: When the solar power generation efficiency output ratio is greater than or equal to the solar power generation efficiency output ratio threshold, power warning is continued to be issued to the corresponding second type new energy node.

[0068] A distributed new energy grid-connected data processing system, comprising:

[0069] Data acquisition module: used for dividing the new energy power generation nodes into first-type new energy nodes and second-type new energy nodes, obtaining the average power generation corresponding to each first-type new energy node, obtaining the first node power data, obtaining each corresponding average power generation, obtaining the second node power data, obtaining the power generation efficiency monitoring period, and defining the power generation efficiency monitoring period, the first node power data and the second node power data as node power data;

[0070] Wind power generation module: used to obtain the wind energy monitoring coefficient corresponding to each first-type new energy node according to the node power data, and define the wind energy monitoring coefficients corresponding to multiple first-type new energy nodes as environmental wind data;

[0071] Solar power generation module: used for obtaining the solar energy monitoring coefficient corresponding to each second type new energy node according to the node power data, and defining the solar energy monitoring coefficients corresponding to multiple second type new energy nodes as environmental solar energy data;

[0072] Power warning module: used to provide power warning for the first type of new energy nodes and the second type of new energy nodes respectively according to the node power data, environmental wind data and environmental solar energy data.

[0073] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0074] 1. The present invention determines the power generation efficiency of each distributed power plant through grid-connected data, and can monitor the power generation efficiency of each distributed power plant in real time, provide a basis for power resource allocation, and improve the overall power generation efficiency of the distributed power grid;

[0075] 2. The present invention judges power anomalies of distributed power plants in combination with environmental factors, and can more accurately judge abnormal power generation and reduce false alarms and missed alarms of power anomaly warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0077] Figure 1 It is a diagram of the implementation steps of the present invention;

[0078] Figure 2 is the overall system block diagram of the present invention;

[0079] Figure 3 This is a data interaction diagram in the present invention. DETAILED DESCRIPTION

[0080] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] Example 1

[0082] See also Figure 1 ,The present invention provides a technical solution: a distributed new energy grid-connected data processing method, including the following specific steps;

[0083] Step S1: Obtain node power data;

[0084] Step S11: Obtain distributed renewable energy grid-connected data, and mark a power generation efficiency monitoring period in the distributed renewable energy grid-connected data;

[0085] Step S12: dividing the new energy generation nodes existing in the distributed new energy network into first-type new energy nodes and second-type new energy nodes according to the power generation mode;

[0086] Step S13: acquiring the average power generation power of each first-type new energy node in the power generation efficiency monitoring period according to the distributed new energy grid-connected data, obtaining multiple average power generation powers, and naming them as first node power data;

[0087] Step S14: acquiring the average power generation power of each second type new energy node in the power generation efficiency monitoring period according to the distributed new energy grid-connected data, obtaining multiple average power generation powers, and naming them as second node power data;

[0088] Step S15: defining the power generation efficiency monitoring period, the first node power data and the second node power data as node power data;

[0089] Step S2: acquiring wind data of the first type of new energy node according to the node power data to obtain environmental wind data;

[0090] Step S21: Obtain node power data, and obtain a power generation efficiency monitoring period according to the node power data;

[0091] Step S22: randomly selecting a first type of new energy node in the distributed new energy network as a first type of sample node;

[0092] Step S23: within the power generation efficiency monitoring period, marking n wind data monitoring points, and naming them as the first wind data monitoring point to the nth wind data monitoring point respectively;

[0093] Step S24: obtaining the wind energy monitoring coefficient corresponding to the first type of sample node;

[0094] The step S24 further includes the following specific steps:

[0095] Step S241: respectively acquiring wind strength values ​​corresponding to the first type sample nodes at the first wind data monitoring point to the nth wind data monitoring point, to obtain the first wind strength value to the nth wind strength value;

[0096] Step S242: Calculate the average of the first wind strength value to the nth wind strength value to obtain a monitored average wind strength value;

[0097] Step S243: obtaining a monitored wind stability coefficient by calculating the first wind strength value to the nth wind strength value and the monitored average wind strength value;

[0098] The wind stability coefficient is calculated, and the specific formula is configured as follows: ;

[0099] Wherein, Jfw is the monitored wind stability coefficient, Fq1 to Fqn are the first wind strength value to the nth wind strength value respectively, Fqp is the monitored average wind strength value, and n is the number value corresponding to the wind data monitoring point;

[0100] Step S244: Calculate the monitored average wind intensity value and the monitored wind stability coefficient to obtain the monitored wind turbulence intensity;

[0101] To calculate the intensity of wind turbulence monitoring, the specific formula is as follows: ;

[0102] Among them, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value;

[0103] Step S245: Calculate the wind energy monitoring coefficient corresponding to the first type of sample node by monitoring the wind turbulence intensity, the average wind intensity value and the wind stability coefficient;

[0104] To calculate the wind energy monitoring coefficient, the specific formula is configured as follows: ;

[0105] Among them, Nfl is the wind energy monitoring coefficient, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value;

[0106] Step S25: acquiring a wind energy monitoring coefficient for each of the first type new energy nodes respectively to obtain a plurality of wind energy monitoring coefficients, and defining the wind energy monitoring coefficients corresponding to the plurality of first type new energy nodes as environmental wind data;

[0107] Step S3: acquiring solar energy data for the second type of new energy node according to the node power data to obtain environmental solar energy data;

[0108] Step S31: Acquire node power data, and acquire a power generation efficiency monitoring period according to the node power data;

[0109] Step S32: randomly selecting a second type of new energy node in the distributed new energy network as a second type of sample node;

[0110] Step S33: within the power generation efficiency monitoring period, marking j solar energy data monitoring points, and naming them as the first solar energy data monitoring point to the jth solar energy data monitoring point respectively;

[0111] Step S34: obtaining the solar energy monitoring coefficient corresponding to the second type sample node;

[0112] The step S34 further includes the following specific steps:

[0113] Step S341: respectively acquiring light intensity values ​​corresponding to the second type sample nodes at the first solar energy data monitoring point to the jth solar energy data monitoring point, to obtain the first light intensity value to the jth light intensity value;

[0114] Step S342: Calculate the average of the first light intensity value to the jth light intensity value to obtain a monitored average light intensity value;

[0115] Step S343: Calculate the first light intensity value to the jth light intensity value and the monitored average light intensity value to obtain a monitored solar energy stability coefficient;

[0116] To calculate the stability coefficient of monitored solar energy, the specific formula is configured as follows: ;

[0117] Wherein, Jtw is the monitored solar energy stability coefficient, Tq1 to Tqj are the first light intensity value to the jth light intensity value respectively, Tqp is the monitored average light intensity value, and j is the quantity value corresponding to the solar energy data monitoring point;

[0118] Step S344: Calculate the monitored average light intensity value and the monitored solar energy stability coefficient to obtain the solar energy monitoring coefficient corresponding to the second type of sample node;

[0119] To calculate the solar energy monitoring coefficient, the specific formula is as follows: Wherein, Ntl is the solar energy monitoring coefficient, Jtw is the monitored solar energy stability coefficient, and Tqp is the monitored average light intensity value;

[0120] Step S35: acquiring a solar energy monitoring coefficient for each of the second type new energy nodes respectively to obtain a plurality of solar energy monitoring coefficients, and defining the solar energy monitoring coefficients corresponding to the plurality of second type new energy nodes as environmental solar energy data;

[0121] Step S4: Performing power warning for the first type of new energy nodes and the second type of new energy nodes respectively according to the node power data, the environmental wind power data and the environmental solar power data;

[0122] Step S41: Acquire node power data, and acquire first node power data and second node power data respectively according to the node power data;

[0123] Step S42: Performing a power warning for the first type of new energy node according to the first node power data;

[0124] The step S42 further includes the following specific steps:

[0125] Step S421: acquiring the average power generation corresponding to each first-type new energy node according to the first node power data;

[0126] Step S422: Acquire environmental wind data, and acquire a wind energy monitoring coefficient corresponding to each first-type new energy node according to the environmental wind data;

[0127] Step S423: Calculate the ratio of the average power generation to the wind energy monitoring coefficient to obtain the wind energy generation efficiency output ratio;

[0128] Step S424: obtaining a wind power generation efficiency output ratio threshold value, and comparing the wind power generation efficiency output ratio with the wind power generation efficiency output ratio threshold value;

[0129] Step S425: when the wind power generation efficiency output ratio is less than the wind power generation efficiency output ratio threshold, issuing a power abnormality warning to the corresponding first type new energy node;

[0130] Step S426: When the wind power generation efficiency output ratio is greater than or equal to the wind power generation efficiency output ratio threshold, continue to issue a power warning to the corresponding first type of new energy node;

[0131] Step S43: Performing a power warning for the second type of new energy node according to the second node power data;

[0132] The step S43 further includes the following specific steps:

[0133] Step S431: acquiring the average power generation corresponding to each second type new energy node according to the second node power data;

[0134] Step S432: Acquire environmental solar energy data, and acquire a solar energy monitoring coefficient corresponding to each second type new energy node according to the environmental solar energy data;

[0135] Step S433: Calculate the ratio of the average power generation to the solar energy monitoring coefficient to obtain the solar energy power generation efficiency output ratio;

[0136] Step S434: obtaining a solar power generation efficiency output ratio threshold value, and numerically comparing the solar power generation efficiency output ratio with the solar power generation efficiency output ratio threshold value;

[0137] Step S435: when the solar power generation efficiency output ratio is less than the solar power generation efficiency output ratio threshold, issuing a power abnormality warning to the corresponding second type new energy node;

[0138] Step S436: When the solar power generation efficiency output ratio is greater than or equal to the solar power generation efficiency output ratio threshold, continue to perform power warning for the corresponding second type new energy node.

[0139] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0140] Example 2

[0141] See also Figure 2 and Figure 3 Based on another concept of the same invention, a distributed new energy grid-connected data processing system is now proposed, including a data acquisition module, a wind power generation module, a solar power generation module, a power warning module and a server, wherein the data acquisition module, the wind power generation module, the solar power generation module and the power warning module are respectively connected to the server, and the server controls the data acquisition module, the wind power generation module, the solar power generation module and the power warning module respectively;

[0142] The data acquisition module acquires node power data;

[0143] Obtain distributed renewable energy grid-connected data, and mark a power generation efficiency monitoring period in the distributed renewable energy grid-connected data;

[0144] The new energy generation nodes existing in the distributed new energy network are divided into first-type new energy nodes and second-type new energy nodes according to the power generation mode;

[0145] According to the distributed new energy grid-connected data, the average power generation power of each first type new energy node in the power generation efficiency monitoring period is obtained to obtain multiple average power generation powers, which are named as first node power data;

[0146] According to the distributed new energy grid-connected data, the average power generation power of each second type new energy node in the power generation efficiency monitoring period is obtained to obtain multiple average power generation powers, which are named as second node power data;

[0147] It should be noted here that:

[0148] In this application, the power generation method corresponding to the first type of new energy node is wind power generation, and the power generation method corresponding to the second type of new energy node is solar power generation;

[0149] The distributed renewable energy grid-connected data involved here include the real-time power data and historical power data of each distributed power plant in the distributed renewable energy network;

[0150] The new energy nodes involved here are specifically distributed power plants in distributed new energy;

[0151] The power generation efficiency monitoring period, the first node power data and the second node power data are defined as node power data;

[0152] The data acquisition module acquires the node power data and transmits it to the wind power generation module, the solar power generation module and the power warning module;

[0153] The wind power generation module acquires wind power data of the first type of new energy node according to the node power data to obtain environmental wind power data;

[0154] Obtain node power data, and obtain a power generation efficiency monitoring period based on the node power data;

[0155] Randomly selecting a first type of new energy node in the distributed new energy network as a first type of sample node;

[0156] During the power generation efficiency monitoring period, n wind data monitoring points are marked and named as the first wind data monitoring point to the nth wind data monitoring point respectively;

[0157] Respectively acquiring wind strength values ​​corresponding to the first wind data monitoring point to the nth wind data monitoring point to obtain the first wind strength value to the nth wind strength value;

[0158] It should be noted here that:

[0159] n is the number value corresponding to the wind data monitoring point, and n is an integer greater than 0;

[0160] In this application, the wind intensity value is specifically the average wind speed in the area where the new energy node is located;

[0161] Calculate the average of the first wind strength value to the nth wind strength value to obtain a monitored average wind strength value;

[0162] The first wind strength value to the nth wind strength value and the monitored average wind strength value are calculated to obtain a monitored wind stability coefficient;

[0163] The wind stability coefficient is calculated, and the specific formula is configured as follows: ;

[0164] Wherein, Jfw is the monitored wind stability coefficient, Fq1 to Fqn are the first wind strength value to the nth wind strength value respectively, Fqp is the monitored average wind strength value, and n is the number value corresponding to the wind data monitoring point;

[0165] The monitored average wind intensity value and the monitored wind stability coefficient are calculated to obtain the monitored wind turbulence intensity;

[0166] To calculate the intensity of wind turbulence monitoring, the specific formula is as follows: ;

[0167] Among them, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value;

[0168] The wind energy monitoring coefficient corresponding to the first type of sample node is obtained by calculating the monitored wind turbulence intensity, the monitored average wind intensity value and the monitored wind stability coefficient;

[0169] To calculate the wind energy monitoring coefficient, the specific formula is configured as follows: ;

[0170] Among them, Nfl is the wind energy monitoring coefficient, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value;

[0171] Acquire wind energy monitoring coefficients for each first-type new energy node respectively to obtain multiple wind energy monitoring coefficients, and define the wind energy monitoring coefficients corresponding to the multiple first-type new energy nodes as environmental wind data;

[0172] The wind power generation module acquires environmental wind data and transmits it to the power warning module;

[0173] The solar power generation module acquires solar energy data of the second type of new energy node according to the node power data to obtain environmental solar energy data;

[0174] Obtain node power data, and obtain a power generation efficiency monitoring period based on the node power data;

[0175] Randomly selecting a second type of new energy node in the distributed new energy network as a second type of sample node;

[0176] In the power generation efficiency monitoring period, j solar energy data monitoring points are marked and named as the first solar energy data monitoring point to the jth solar energy data monitoring point respectively;

[0177] Acquire the light intensity values ​​corresponding to the first solar energy data monitoring point to the j-th solar energy data monitoring point respectively, and obtain the first light intensity value to the j-th light intensity value;

[0178] It should be noted here that:

[0179] j is the quantity value corresponding to the solar energy data monitoring point, and j is an integer greater than 0;

[0180] Calculate the average of the first light intensity value to the jth light intensity value to obtain a monitored average light intensity value;

[0181] The first light intensity value to the jth light intensity value and the monitored average light intensity value are calculated to obtain a monitored solar energy stability coefficient;

[0182] To calculate the stability coefficient of monitored solar energy, the specific formula is configured as follows: ;

[0183] Wherein, Jtw is the monitored solar energy stability coefficient, Tq1 to Tqj are the first light intensity value to the jth light intensity value respectively, Tqp is the monitored average light intensity value, and j is the quantity value corresponding to the solar energy data monitoring point;

[0184] The solar energy monitoring coefficient corresponding to the second type of sample node is obtained by calculating the monitored average light intensity value and the monitored solar energy stability coefficient;

[0185] To calculate the solar energy monitoring coefficient, the specific formula is as follows: Wherein, Ntl is the solar energy monitoring coefficient, Jtw is the monitored solar energy stability coefficient, and Tqp is the monitored average light intensity value;

[0186] Acquire a solar energy monitoring coefficient for each second type new energy node respectively to obtain a plurality of solar energy monitoring coefficients, and define the solar energy monitoring coefficients corresponding to the plurality of second type new energy nodes as environmental solar energy data;

[0187] The solar power generation module acquires environmental solar energy data and transmits it to the power warning module;

[0188] The power warning module performs power warning for the first type of new energy nodes and the second type of new energy nodes respectively according to the node power data, the environmental wind data and the environmental solar energy data;

[0189] Acquire node power data, and acquire first node power data and second node power data respectively according to the node power data;

[0190] Performing power warning on the first type of new energy node according to the first node power data;

[0191] The details are as follows:

[0192] According to the first node power data, respectively obtain the average power generation corresponding to each first type new energy node;

[0193] Acquire environmental wind data, and acquire a wind energy monitoring coefficient corresponding to each first-type new energy node according to the environmental wind data;

[0194] Calculate the ratio of average power generation to wind energy monitoring coefficient to obtain wind energy generation efficiency output ratio;

[0195] Obtaining a wind power generation efficiency output ratio threshold value, and comparing the wind power generation efficiency output ratio with the wind power generation efficiency output ratio threshold value;

[0196] When the wind power generation efficiency output ratio is less than the wind power generation efficiency output ratio threshold, a power abnormality warning is issued to the corresponding first type of new energy node;

[0197] When the wind power generation efficiency output ratio is greater than or equal to the wind power generation efficiency output ratio threshold, the corresponding first type of new energy node continues to issue a power warning;

[0198] Performing power warning on the second type of new energy node according to the second node power data;

[0199] The details are as follows:

[0200] According to the second node power data, respectively obtain the average power generation corresponding to each second type new energy node;

[0201] Acquire environmental solar energy data, and acquire a solar energy monitoring coefficient corresponding to each second type new energy node according to the environmental solar energy data;

[0202] Calculate the ratio of average power generation to solar monitoring coefficient to obtain the solar power generation efficiency output ratio;

[0203] Obtaining a solar power generation efficiency output ratio threshold value, and numerically comparing the solar power generation efficiency output ratio with the solar power generation efficiency output ratio threshold value;

[0204] When the solar power generation efficiency output ratio is less than the solar power generation efficiency output ratio threshold, a power abnormality warning is issued to the corresponding second type of new energy node;

[0205] When the solar power generation efficiency output ratio is greater than or equal to the solar power generation efficiency output ratio threshold, the power warning continues to be issued to the corresponding second type of new energy node.

[0206] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification.

Claims

1. A distributed new energy grid-connected data processing method, characterized in that: include: Step S1: Divide multiple new energy power generation nodes into first-type new energy nodes and second-type new energy nodes, obtain the average power generation corresponding to each first-type new energy node, obtain first node power data, obtain each corresponding average power generation, obtain second node power data, obtain power generation efficiency monitoring period, and define the power generation efficiency monitoring period, first node power data and second node power data as node power data; Step S2: acquiring a wind energy monitoring coefficient corresponding to each first-type new energy node according to the node power data, and defining the wind energy monitoring coefficients corresponding to the plurality of first-type new energy nodes as environmental wind data; Step S3: acquiring a solar energy monitoring coefficient corresponding to each second-type new energy node according to the node power data, and defining the solar energy monitoring coefficients corresponding to a plurality of second-type new energy nodes as environmental solar energy data; Step S4: Performing power warning for the first type of new energy nodes and the second type of new energy nodes respectively according to the node power data, the environmental wind power data and the environmental solar power data; The step S2 further includes the following specific steps: Step S21: Obtain node power data, and obtain a power generation efficiency monitoring period according to the node power data; Step S22: randomly selecting a first type of new energy node in the distributed new energy network as a first type of sample node; Step S23: within the power generation efficiency monitoring period, marking n wind data monitoring points, and naming them as the first wind data monitoring point to the nth wind data monitoring point respectively; Step S24: obtaining the wind energy monitoring coefficient corresponding to the first type of sample node; Step S25: acquiring a wind energy monitoring coefficient for each of the first type new energy nodes respectively to obtain a plurality of wind energy monitoring coefficients, and defining the wind energy monitoring coefficients corresponding to the plurality of first type new energy nodes as environmental wind data; The step S24 further includes the following specific steps: Step S241: respectively acquiring wind strength values ​​corresponding to the first type sample nodes at the first wind data monitoring point to the nth wind data monitoring point, to obtain the first wind strength value to the nth wind strength value; Step S242: Calculate the average of the first wind strength value to the nth wind strength value to obtain a monitored average wind strength value; Step S243: obtaining a monitored wind stability coefficient by calculating the first wind strength value to the nth wind strength value and the monitored average wind strength value; The wind stability coefficient is calculated, and the specific formula is configured as follows: ； Wherein, Jfw is the monitored wind stability coefficient, Fq1 to Fqn are the first wind strength value to the nth wind strength value respectively, Fqp is the monitored average wind strength value, and n is the number value corresponding to the wind data monitoring point; Step S244: Calculate the monitored average wind intensity value and the monitored wind stability coefficient to obtain the monitored wind turbulence intensity; To calculate the intensity of wind turbulence monitoring, the specific formula is as follows: ； Among them, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value; Step S245: Calculate the wind energy monitoring coefficient corresponding to the first type of sample node by monitoring the wind turbulence intensity, the average wind intensity value and the wind stability coefficient; To calculate the wind energy monitoring coefficient, the specific formula is configured as follows: ； Among them, Nfl is the wind energy monitoring coefficient, Jtl is the monitored wind turbulence intensity, Jfw is the monitored wind stability coefficient, and Fqp is the monitored average wind intensity value.

2. A distributed new energy grid-connected data processing method according to claim 1, characterized in that: The step S1 further includes the following specific steps: Step S11: Obtain distributed renewable energy grid-connected data, and mark a power generation efficiency monitoring period in the distributed renewable energy grid-connected data; Step S12: dividing the new energy generation nodes existing in the distributed new energy network into first-type new energy nodes and second-type new energy nodes according to the power generation mode; Step S13: acquiring the average power generation power of each first-type new energy node in the power generation efficiency monitoring period according to the distributed new energy grid-connected data, obtaining multiple average power generation powers, and naming them as first node power data; Step S14: acquiring the average power generation power of each second type new energy node in the power generation efficiency monitoring period according to the distributed new energy grid-connected data, obtaining multiple average power generation powers, and naming them as second node power data; Step S15: defining the power generation efficiency monitoring period, the first node power data and the second node power data as node power data.

3. A distributed new energy grid-connected data processing method according to claim 1, characterized in that: The step S3 further includes the following specific steps: Step S31: Acquire node power data, and acquire a power generation efficiency monitoring period according to the node power data; Step S32: randomly selecting a second type of new energy node in the distributed new energy network as a second type of sample node; Step S33: within the power generation efficiency monitoring period, marking j solar energy data monitoring points, and naming them as the first solar energy data monitoring point to the jth solar energy data monitoring point respectively; Step S34: obtaining the solar energy monitoring coefficient corresponding to the second type sample node; Step S35: acquiring the solar energy monitoring coefficient for each second type new energy node respectively to obtain a plurality of solar energy monitoring coefficients, and defining the solar energy monitoring coefficients corresponding to the plurality of second type new energy nodes as environmental solar energy data.

4. A distributed new energy grid-connected data processing method according to claim 3, characterized in that: The step S34 further includes the following specific steps: Step S341: respectively acquiring light intensity values ​​corresponding to the second type sample nodes at the first solar energy data monitoring point to the jth solar energy data monitoring point, to obtain the first light intensity value to the jth light intensity value; Step S342: Calculate the average of the first light intensity value to the jth light intensity value to obtain a monitored average light intensity value; Step S343: Calculate the first light intensity value to the jth light intensity value and the monitored average light intensity value to obtain a monitored solar energy stability coefficient; To calculate the stability coefficient of monitored solar energy, the specific formula is configured as follows: ； Wherein, Jtw is the monitored solar energy stability coefficient, Tq1 to Tqj are the first light intensity value to the jth light intensity value respectively, Tqp is the monitored average light intensity value, and j is the quantity value corresponding to the solar energy data monitoring point; Step S344: Calculate the monitored average light intensity value and the monitored solar energy stability coefficient to obtain the solar energy monitoring coefficient corresponding to the second type of sample node; To calculate the solar energy monitoring coefficient, the specific formula is as follows: ； Wherein, Ntl is the solar energy monitoring coefficient, Jtw is the monitored solar energy stability coefficient, and Tqp is the monitored average light intensity value.

5. A distributed new energy grid-connected data processing method according to claim 1, characterized in that: The step S4 further comprises the following specific steps: Step S41: Acquire node power data, and acquire first node power data and second node power data respectively according to the node power data; Step S42: Performing a power warning for the first type of new energy node according to the first node power data; Step S43: Perform power warning for the second type of new energy node according to the second node power data.

6. A distributed new energy grid-connected data processing method according to claim 5, characterized in that: The step S42 further includes the following specific steps: Step S421: acquiring the average power generation corresponding to each first-type new energy node according to the first node power data; Step S422: Acquire environmental wind data, and acquire a wind energy monitoring coefficient corresponding to each first-type new energy node according to the environmental wind data; Step S423: Calculate the ratio of the average power generation to the wind energy monitoring coefficient to obtain the wind energy generation efficiency output ratio; Step S424: obtaining a wind power generation efficiency output ratio threshold value, and comparing the wind power generation efficiency output ratio with the wind power generation efficiency output ratio threshold value; Step S425: when the wind power generation efficiency output ratio is less than the wind power generation efficiency output ratio threshold, issuing a power abnormality warning to the corresponding first type new energy node; Step S426: When the wind power generation efficiency output ratio is greater than or equal to the wind power generation efficiency output ratio threshold, continue to issue a power warning to the corresponding first type new energy node.

7. A distributed new energy grid-connected data processing method according to claim 5, characterized in that: The step S43 further includes the following specific steps: Step S431: acquiring the average power generation corresponding to each second type new energy node according to the second node power data; Step S432: Acquire environmental solar energy data, and acquire a solar energy monitoring coefficient corresponding to each second type new energy node according to the environmental solar energy data; Step S433: Calculate the ratio of the average power generation to the solar energy monitoring coefficient to obtain the solar energy power generation efficiency output ratio; Step S434: obtaining a solar power generation efficiency output ratio threshold value, and numerically comparing the solar power generation efficiency output ratio with the solar power generation efficiency output ratio threshold value; Step S435: when the solar power generation efficiency output ratio is less than the solar power generation efficiency output ratio threshold, issuing a power abnormality warning to the corresponding second type new energy node; Step S436: When the solar power generation efficiency output ratio is greater than or equal to the solar power generation efficiency output ratio threshold, power warning is continued to be issued to the corresponding second type new energy node.

8. A distributed new energy grid-connected data processing system, applicable to a distributed new energy grid-connected data processing method as claimed in any one of claims 1 to 7, characterized in that: The data processing system comprises: Data acquisition module: used for dividing the new energy power generation nodes into first-type new energy nodes and second-type new energy nodes, obtaining the average power generation corresponding to each first-type new energy node, obtaining the first node power data, obtaining each corresponding average power generation, obtaining the second node power data, obtaining the power generation efficiency monitoring period, and defining the power generation efficiency monitoring period, the first node power data and the second node power data as node power data; Wind power generation module: used to obtain the wind energy monitoring coefficient corresponding to each first-type new energy node according to the node power data, and define the wind energy monitoring coefficients corresponding to multiple first-type new energy nodes as environmental wind data; Solar power generation module: used for obtaining the solar energy monitoring coefficient corresponding to each second type new energy node according to the node power data, and defining the solar energy monitoring coefficients corresponding to multiple second type new energy nodes as environmental solar energy data; Power warning module: used to provide power warning for the first type of new energy nodes and the second type of new energy nodes respectively according to the node power data, environmental wind data and environmental solar energy data.

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