CN113595151A - Wind power generation coordination configuration system based on big data planning - Google Patents

Wind power generation coordination configuration system based on big data planning Download PDF

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CN113595151A
CN113595151A CN202110931070.9A CN202110931070A CN113595151A CN 113595151 A CN113595151 A CN 113595151A CN 202110931070 A CN202110931070 A CN 202110931070A CN 113595151 A CN113595151 A CN 113595151A
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power generation
wind power
analysis
area
energy storage
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王国欣
李满
童珊珊
于育民
宋苏罗
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Nanyang Institute of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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  • Wind Motors (AREA)
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Abstract

The invention discloses a wind power generation coordination configuration system based on big data planning, which relates to the technical field of wind power generation coordination configuration and solves the technical problem that the practicability of wind power generation is reduced due to the single operation mode of a wind power generation device in the prior art; by adjusting the operation mode, the practicability brought by wind power generation is improved, the defect of low energy density of a wind power plant can be overcome to the greatest extent, and the development and operation cost can be greatly reduced; the wind power generation device can complement the wind direction change to the maximum extent in the running process of wind power generation, can stably and continuously provide electric energy, and improves the running efficiency and the practicability of the power generation device.

Description

Wind power generation coordination configuration system based on big data planning
Technical Field
The invention relates to the technical field of wind power generation coordination configuration, in particular to a wind power generation coordination configuration system based on big data planning.
Background
Wind energy is a clean and pollution-free renewable energy, along with the aggravation of the global energy crisis and the gradual pollution of the global environment, the research, the development and the utilization of clean new energy and renewable energy are more emphasized by many countries, since the middle of the 70 th 20 th century, the development and the utilization of wind energy resources are increasingly emphasized by main developed countries and developing countries in the world, at present, wind power generation becomes a main form of wind energy utilization, and meanwhile, the wind power generation technology is mature day by day, meanwhile, the rapidly developed energy storage technology can be regarded as a power supply capable of flexibly responding to power under different time scales in an electric power system, and the large-scale application of the energy storage technology provides a new solution for the problem brought by large-scale wind power grid connection;
however, in the prior art, the wind power generation device has a single operation mode, cannot perform multi-mode operation according to real-time requirements, so that the practicability of wind power generation is reduced, the power generation cost is increased, the influence caused by wind direction change cannot be reduced to the greatest extent, and the operation efficiency of wind power generation is reduced; in addition, intelligent allocation can not be carried out, and the wind power generation device and the energy storage device are reasonably matched, so that the matched device can not be subjected to efficiency detection, and the labor cost is directly increased.
Disclosure of Invention
The invention aims to provide a wind power generation coordination configuration system based on big data planning, which analyzes the state of a wind power generation device in a wind power generation area, detects hardware, improves the working efficiency of wind power generation, and prevents the generation capacity from being influenced by low efficiency caused by abnormal operation of the wind power generation device; by adjusting the operation mode, the practicability brought by wind power generation is improved, the defect of low energy density of a wind power plant can be overcome to the greatest extent, and the development and operation cost can be greatly reduced; the wind power generation device can complement the wind direction change to the maximum extent in the running process of wind power generation, can stably and continuously provide electric energy, and improves the running efficiency and the practicability of the power generation device.
The purpose of the invention can be realized by the following technical scheme:
a wind power generation coordination configuration system based on big data planning comprises a processing front end and an operation terminal, wherein the processing front end comprises a region analysis unit and an address selection unit; the operation terminal comprises a server, a state analysis unit, a mode selection unit and a storage analysis unit;
the processing front end is used for analyzing and allocating a wind power generation region, analyzing the region through a region analysis unit, judging whether the region is suitable for being used as the wind power generation region according to the environment of the region, analyzing each sub-region in a preselected region, and dividing the sub-region into an analysis qualified region and an analysis unqualified region; selecting the qualified analysis area through an address selection unit, and screening out an optimal area as a wind power generation address;
the operation terminal is used for carrying out coordination configuration on wind power generation, carrying out state analysis on the wind power generation devices in the wind power generation area through the state analysis unit, and maintaining the wind power generation devices with abnormal states; and the mode selection unit is used for selecting qualified running modes, and the storage analysis unit is used for carrying out state analysis and adjustment on the energy storage device in the normal state device.
Further, the specific analysis and determination process of the area analysis unit is as follows:
acquiring a region boundary, acquiring a preselected region according to the region boundary, dividing the preselected region into i sub-regions, wherein i is a natural number greater than 1, and acquiring the wind power density, the average daily wind speed and the wind direction variation frequency of each sub-region within analysis time by taking historical six months as analysis time; obtaining a regional analysis coefficient of a subregion through analysis; comparing the region analysis coefficient of the subregion to a region analysis coefficient threshold:
if the area analysis coefficient of the sub-area is larger than or equal to the area analysis coefficient threshold, marking the corresponding sub-area as an analysis qualified area, and sending the analysis qualified area to an address selection unit;
if the area analysis coefficient of the sub-area is smaller than the area analysis coefficient threshold value, the corresponding sub-area is marked as an analysis unqualified area, and the analysis unqualified area is sent to the address selection unit.
Further, the address selecting unit specifically selects the process as follows:
collecting the qualified analysis area, analyzing and selecting the qualified analysis area, collecting the number of high-rise buildings and the number of residential population in the qualified analysis area, and comparing the number of high-rise buildings and the number of residential population in the qualified analysis area with the number threshold of high-rise buildings and the number threshold of residential population respectively:
if the corresponding numerical values in the number of the high-rise buildings and the number of the residential population in the analyzed qualified area are not less than the corresponding threshold values, judging that the corresponding analyzed qualified area is suitable for being used as a wind power generation address, marking the wind power generation address as a wind power generation area, and sending the wind power generation area to an operation terminal;
and if any numerical value of the number of the high-rise buildings and the number of the residential population in the analysis qualified area is less than the corresponding threshold value, judging that the corresponding analysis qualified area is not suitable for being used as a wind power generation address, and marking the area as a non-wind power generation area.
Further, the specific analysis and detection process of the state analysis unit is as follows:
randomly selecting a wind power generation device in a wind power generation area, monitoring the rotating speed of a generator and the rotating speed of a wind wheel of the wind power generation device, and monitoring and acquiring the reaction duration of overspeed protection when the rotating speed of the generator and the rotating speed of the wind wheel reach corresponding rotating speed thresholds; monitoring the temperature of a gear box of the wind power generation device, and monitoring and collecting the reaction time of overload protection after the temperature of the gear box reaches a temperature threshold; acquiring the speed of temperature reduction of the gearbox after the gearbox reaches a temperature threshold; acquiring a state coefficient of the wind power generation device through a state analysis model;
comparing the state coefficient to a state coefficient threshold: if the state coefficient is larger than or equal to the state coefficient threshold value, judging that the corresponding wind power generation device is marked as a state abnormal device, generating an abnormal device signal, sending the abnormal device signal and the state abnormal device to a server together, and after receiving the abnormal device signal, the server carries out maintenance detection on the wind power generation device in the wind power generation area; and if the state coefficient is less than the state coefficient threshold value, judging that the corresponding wind power generation device is marked as a normal state device, and sending the normal state device to the server.
Further, the specific selection process of the mode selection unit is as follows:
when the electric quantity transmission needs to be carried out on the area which is not covered by the power grid, the normal state device independently operates, the generator in the normal state device generates electricity and sends the generated electric quantity to the energy storage device for storage, and the energy storage device outputs the electric quantity for a user to use; when the wind power generation cost needs to be reduced, the normal state devices perform grid-connected operation, the normal state devices are in covering connection with a power grid and output electric quantity by the power grid, and a plurality of normal state devices and the power grid construct a temporary wind power electric field; when the wind direction changes frequently, the normal state devices run cooperatively, and the normal state devices in the corresponding areas are in communication connection with the wind power generation devices in the peripheral areas to form a cluster power generation group.
Further, the specific analysis process of the storage analysis unit is as follows:
marking the maximum storage value and the minimum storage value of the energy storage device as CLmax and CLmin respectively, taking ninety percent to eighty percent of the maximum storage value as an overcharge interval, and taking ten percent to five percent of the maximum storage value as an overdischarge interval;
acquiring the time length and frequency of charging the energy storage device and the electric quantity in an overcharge interval, and acquiring the charge coefficient CDX of the energy storage device through analysis; the method comprises the following steps of collecting the discharge time and frequency of the energy storage device, analyzing and obtaining the discharge coefficient FDX of the energy storage device, and comparing the charge coefficient and the discharge coefficient of the energy storage device with corresponding thresholds respectively: if the charging coefficient and the discharging coefficient of the energy storage device are both smaller than the corresponding threshold values, judging that the state of the energy storage device is normal, generating a qualified signal of the state of the energy storage device, and sending the qualified signal of the state of the energy storage device to the server; if any value of the charging coefficient and the discharging coefficient of the energy storage device is larger than or equal to the corresponding threshold value, judging that the state of the energy storage device is abnormal, generating an unqualified state signal of the energy storage device, and sending the unqualified state signal of the energy storage device to a server;
after receiving the state qualified signal of the energy storage device, the server matches the specification of the corresponding energy storage device with the model of the wind power generation device, and if the wind power generation device with the same model is newly added in the region, the server matches the energy storage device with the corresponding specification; and after receiving the unqualified state signal of the energy storage device, the server matches the specification of the corresponding energy storage device with the model of the wind power generation device, and if the wind power generation devices with the same model exist in the region and the specification of the matched energy storage device is the same, the energy storage device is replaced.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, whether the wind power generation area is suitable for being used as the wind power generation area is judged according to the environment of the area, and the wind power generation area is analyzed, so that the reduction of the wind power generation efficiency caused by environmental factors is prevented, a large amount of economic loss is caused, and manpower and material resources are wasted; selecting the qualified analysis area, screening out the optimal area as a wind power generation address, and giving full play to the efficiency of natural wind power, thereby improving energy generation, reducing power generation risk and preventing cost waste caused by low efficiency;
2. according to the invention, the state of the wind power generation device in the wind power generation area is analyzed, and hardware is detected, so that the working efficiency of wind power generation is improved, and the influence on the generating capacity caused by low efficiency due to abnormal operation of the wind power generation device is prevented;
3. according to the invention, the practicability brought by wind power generation is improved by adjusting the operation mode, and meanwhile, the defect of low energy density of a wind power plant can be made up to the greatest extent, so that the development and operation cost can be greatly reduced; the wind power generation device can complement the wind direction change to the maximum extent in the running process of wind power generation, can stably and continuously provide electric energy, and improves the running efficiency and the practicability of the power generation device.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a wind power generation coordination configuration system based on big data planning includes a processing front end and an operation terminal, wherein the processing front end includes a region analysis unit and an address selection unit; the operation terminal comprises a server, a state analysis unit, a mode selection unit and a storage analysis unit, wherein the server is in bidirectional communication connection with the state analysis unit, the mode selection unit and the storage analysis unit;
the regional analysis unit is used for carrying out the analysis to the region, judges whether be fit for as wind power generation region according to the environment in region, carries out regional analysis to wind power generation, prevents that environmental factor from resulting in wind power generation efficiency to reduce, causes a large amount of economic loss, and has wasted manpower and materials, and concrete analysis judges the process as follows:
acquiring a region boundary and acquiring a preselected region according to the region boundary, dividing the preselected region into i sub-regions, wherein i is a natural number greater than 1, acquiring the wind power density, the average daily wind speed and the wind direction variation frequency of each sub-region within analysis time by taking six historical months as analysis time, and respectively marking the wind power density, the average daily wind speed and the wind direction variation frequency of each sub-region as MDi, FSi and PLi; by the formula
Figure BDA0003210719350000061
Obtaining a regional analysis coefficient Xi of the subregion, wherein a1, a2 and a3 are all proportional coefficients, a1 is greater than a2 is greater than a3 is greater than 0, and beta is an error correction factor and takes a value of 2.14; the region analysis coefficient is a numerical value used for evaluating the probability of the sub-regions by carrying out normalization processing on the characteristic parameters of the sub-regions; the larger the wind power density and the average daily wind speed obtained by a formula are, or the smaller the frequency of wind direction change is, the larger the area analysis coefficient is, the larger the probability of sub-area selection is represented; the wind power density and the average daily wind speed are in direct proportion to the area analysis coefficient, and the frequency of wind direction variation is in inverse proportion to the area analysis coefficient;
comparing the region analysis coefficient Xi of the subregion with a region analysis coefficient threshold: if the area analysis coefficient Xi of the subarea is larger than or equal to the area analysis coefficient threshold, marking the corresponding subarea as an analysis qualified area, and sending the analysis qualified area to an address selection unit; if the area analysis coefficient Xi of the sub-area is smaller than the area analysis coefficient threshold, marking the corresponding sub-area as an analysis unqualified area, and sending the analysis unqualified area to an address selection unit;
after the address selecting unit receives the analysis qualified area and the analysis unqualified area, the analysis qualified area is selected, the optimal area is selected to serve as a wind power generation address, the efficiency of natural wind power is exerted to the maximum, the power generation risk is reduced while the energy generation is improved, the cost waste caused by low efficiency is prevented, and the specific selecting process is as follows:
collecting the qualified analysis area, analyzing and selecting the qualified analysis area, collecting the number of high-rise buildings and the number of residential population in the qualified analysis area, and comparing the number of high-rise buildings and the number of residential population in the qualified analysis area with the number threshold of high-rise buildings and the number threshold of residential population respectively: if the corresponding numerical values in the number of the high-rise buildings and the number of the residential population in the analyzed qualified area are not less than the corresponding threshold values, judging that the corresponding analyzed qualified area is suitable for being used as a wind power generation address, marking the wind power generation address as a wind power generation area, and sending the wind power generation area to an operation terminal; if any numerical value of the number of the high-rise buildings and the number of the residential population in the analyzed qualified area is smaller than the corresponding threshold value, judging that the corresponding analyzed qualified area is not suitable for being used as a wind power generation address, and marking the area as a non-wind power generation area; the influence of airflow formed by a high-rise building on a wind power generation device is reduced, and meanwhile, a wind power generation area is reasonably selected, so that the influence of wind power generation on the normal life of residents is prevented;
after the operation terminal receives the wind power generation area, a wind power generation device is arranged in the wind power generation area, a state analysis signal is generated, and the state analysis signal is sent to a state analysis unit;
after the state analysis unit receives the state analysis signal, the state analysis unit carries out state analysis on the wind power generation device in the wind power generation area, detects hardware, improves the work efficiency of wind power generation, prevents that the wind power generation device from running abnormally, leads to inefficiency to influence the generated energy, and the concrete analysis and detection process is as follows:
step one, randomly selecting a wind power generation device in a wind power generation area, monitoring the rotating speed of a generator and the rotating speed of a wind wheel of the wind power generation device, monitoring and acquiring the reaction duration of overspeed protection when the rotating speed of the generator and the rotating speed of the wind wheel reach corresponding rotating speed thresholds, and respectively marking the reaction duration as FDZ and FLS;
monitoring the temperature of a gear box of the wind power generation device, monitoring and acquiring the reaction time of overload protection after the temperature of the gear box reaches a temperature threshold value, marking the reaction time as CLS, acquiring the speed of temperature reduction after the temperature of the gear box reaches the temperature threshold value, and marking the corresponding speed of temperature reduction as WJS;
step two, acquiring the state coefficient of the wind power generation device through a state analysis model, namely the state analysis model is
Figure BDA0003210719350000081
Wherein b1 and b2 are both proportional coefficients, b1 is greater than b2 is greater than 0, e is a natural constant, alpha is an error correction factor, the value is 1.06, ZT is a state coefficient of the wind power generation device, and the state coefficient is obtained by normalizing characteristic parameters of the wind power generation device to evaluate the wind power generation deviceValue of electrical device status; the larger the state coefficient is, the worse the corresponding wind power generation device is in the operation state;
step three, comparing the state coefficient ZT with a state coefficient threshold value: if the state coefficient ZT is not less than the state coefficient threshold value, judging that the corresponding wind power generation device is marked as a state abnormal device, generating a signal of the abnormal device, sending the signal of the abnormal device and the state abnormal device to a server, and after receiving the signal, the server carries out maintenance detection on the wind power generation device in the wind power generation area; if the state coefficient ZT is less than the state coefficient threshold value, the corresponding wind power generation device is judged to be marked as a normal state device, and the normal state device is sent to a server;
the mode selection unit is used for selecting the operation modes of the normal state device, the operation modes comprise independent operation, grid-connected operation and matched operation, and the specific selection process comprises the following steps:
when the electric quantity transmission needs to be carried out on the area which is not covered by the power grid, the normal state device independently operates, the generator in the normal state device generates electricity and sends the generated electric quantity to the energy storage device for storage, and the energy storage device outputs the electric quantity for a user to use; the generated electric quantity can be transmitted to the area uncovered by the power grid, so that the practicability brought by wind power generation is improved, and meanwhile, the working efficiency of the wind power generation is also improved;
when the wind power generation cost needs to be reduced, the normal state devices perform grid-connected operation, the normal state devices are in covering connection with a power grid and output electric quantity by the power grid, and a temporary wind power electric field is constructed by the multiple normal state devices and the power grid, so that the wind energy is fully and efficiently utilized, the defect of low energy density of a wind power plant is overcome to the greatest extent, and the development and operation cost can be greatly reduced;
when the wind direction changes frequently, the normal state devices run in a matched mode, the normal state devices in the corresponding areas are in communication connection with the wind power generation devices in the peripheral areas to form a cluster type power generation group, the normal state devices in the corresponding areas can complement each other to the greatest extent in the face of the change of the wind direction in the running process of wind power generation, the stable and continuous supply of electric energy is guaranteed, and the running efficiency and the practicability of the power generation devices are improved;
the storage analysis unit is used for carrying out state analysis and adjustment on the energy storage device in the normal state device, improves the utilization rate of generated electric quantity, prevents that the generated electric quantity from consuming too much to cause that wind power generation efficiency is too low, and the concrete analysis process is as follows:
marking the maximum storage value and the minimum storage value of the energy storage device as CLmax and CLmin respectively, taking ninety percent to eighty percent of the maximum storage value as an overcharge interval, and taking ten percent to five percent of the maximum storage value as an overdischarge interval;
acquiring the time length and frequency of the energy storage device for charging and the electric quantity in the overcharge interval, and respectively marking the time length and the frequency of the electric quantity in the overcharge interval as GCS and GCP; by the formula
Figure BDA0003210719350000091
Acquiring a charge coefficient CDX of the energy storage device, wherein v1 and v2 are proportionality coefficients, and v1 is greater than v2 is greater than 0;
acquiring the duration and frequency of the energy storage device for discharging and the electric quantity in the over-discharge interval, and respectively marking the duration and frequency of the electric quantity in the over-discharge interval as GFS and GFP; by the formula
Figure BDA0003210719350000092
Obtaining a discharge coefficient FDX of the energy storage device, wherein v3 and v4 are proportionality coefficients, and v3 is greater than v4 is greater than 0;
comparing the charge coefficient and the discharge coefficient of the energy storage device with corresponding thresholds respectively: if the charging coefficient and the discharging coefficient of the energy storage device are both smaller than the corresponding threshold values, judging that the state of the energy storage device is normal, generating a qualified signal of the state of the energy storage device, and sending the qualified signal of the state of the energy storage device to the server; if any value of the charging coefficient and the discharging coefficient of the energy storage device is larger than or equal to the corresponding threshold value, judging that the state of the energy storage device is abnormal, generating an unqualified state signal of the energy storage device, and sending the unqualified state signal of the energy storage device to a server;
after receiving the state qualified signal of the energy storage device, the server matches the specification of the corresponding energy storage device with the model of the wind power generation device, and if the wind power generation device with the same model is newly added in the region, the server matches the energy storage device with the corresponding specification; after receiving the unqualified state signal of the energy storage device, the server matches the specification of the corresponding energy storage device with the model of the wind power generation device, and if the wind power generation devices with the same model exist in the region and the specification of the matched energy storage device is the same, the energy storage device is replaced; the energy storage device is reasonably matched with the wind power generation equipment, the failure rate is reduced, and meanwhile the working efficiency of electric quantity transmission is improved.
The working principle of the invention is as follows: a wind power generation coordination configuration system based on big data planning is characterized in that during operation, a wind power generation region is analyzed and allocated through a processing front end, the region is analyzed through a region analysis unit, whether the region is suitable for being used as a wind power generation region is judged according to the environment of the region, each sub-region in a preselected region is analyzed, the sub-regions are divided into analysis qualified regions and analysis unqualified regions, and therefore the wind power generation efficiency is prevented from being reduced due to environmental factors, a large amount of economic loss is prevented, and manpower and material resources are wasted; the address selection unit is used for selecting the qualified analysis area, the optimal area is selected as a wind power generation address, the efficiency of natural wind power is exerted to the maximum, the power generation risk is reduced while the energy generation is improved, and the cost waste caused by low efficiency is prevented; the operation terminal is used for carrying out coordination configuration on wind power generation, carrying out state analysis on the wind power generation devices in the wind power generation area through the state analysis unit, and maintaining the wind power generation devices with abnormal states; and the mode selection unit is used for selecting qualified running modes, and the storage analysis unit is used for carrying out state analysis and adjustment on the energy storage device in the normal state device.
The above formulas are all calculated by taking the numerical value of the dimension, the formula is a formula which obtains the latest real situation by acquiring a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (6)

1. A wind power generation coordination configuration system based on big data planning is characterized by comprising a processing front end and an operation terminal, wherein the processing front end comprises a region analysis unit and an address selection unit; the operation terminal comprises a server, a state analysis unit, a mode selection unit and a storage analysis unit;
the processing front end is used for analyzing and allocating a wind power generation region, analyzing the region through a region analysis unit, judging whether the region is suitable for being used as the wind power generation region according to the environment of the region, analyzing each sub-region in a preselected region, and dividing the sub-region into an analysis qualified region and an analysis unqualified region; selecting the qualified analysis area through an address selection unit, and screening out an optimal area as a wind power generation address;
the operation terminal is used for carrying out coordination configuration on wind power generation, carrying out state analysis on the wind power generation devices in the wind power generation area through the state analysis unit, and maintaining the wind power generation devices with abnormal states; and the mode selection unit is used for selecting qualified running modes, and the storage analysis unit is used for carrying out state analysis and adjustment on the energy storage device in the normal state device.
2. The big data planning-based wind power generation coordination configuration system according to claim 1, wherein the area analysis unit specifically analyzes and determines the process as follows:
acquiring a region boundary, acquiring a preselected region according to the region boundary, dividing the preselected region into i sub-regions, wherein i is a natural number greater than 1, and acquiring the wind power density, the average daily wind speed and the wind direction variation frequency of each sub-region within analysis time by taking historical six months as analysis time; obtaining a regional analysis coefficient of a subregion through analysis; comparing the region analysis coefficient of the subregion to a region analysis coefficient threshold:
if the area analysis coefficient of the sub-area is larger than or equal to the area analysis coefficient threshold, marking the corresponding sub-area as an analysis qualified area, and sending the analysis qualified area to an address selection unit;
if the area analysis coefficient of the sub-area is smaller than the area analysis coefficient threshold value, the corresponding sub-area is marked as an analysis unqualified area, and the analysis unqualified area is sent to the address selection unit.
3. The wind power generation coordination configuration system based on big data planning according to claim 1, characterized in that the address selection unit specifically selects the process as follows:
collecting the qualified analysis area, analyzing and selecting the qualified analysis area, collecting the number of high-rise buildings and the number of residential population in the qualified analysis area, and comparing the number of high-rise buildings and the number of residential population in the qualified analysis area with the number threshold of high-rise buildings and the number threshold of residential population respectively:
if the corresponding numerical values in the number of the high-rise buildings and the number of the residential population in the analyzed qualified area are not less than the corresponding threshold values, judging that the corresponding analyzed qualified area is suitable for being used as a wind power generation address, marking the wind power generation address as a wind power generation area, and sending the wind power generation area to an operation terminal;
and if any numerical value of the number of the high-rise buildings and the number of the residential population in the analysis qualified area is less than the corresponding threshold value, judging that the corresponding analysis qualified area is not suitable for being used as a wind power generation address, and marking the area as a non-wind power generation area.
4. The big data planning-based wind power generation coordination configuration system according to claim 1, wherein the specific analysis and detection process of the state analysis unit is as follows:
randomly selecting a wind power generation device in a wind power generation area, monitoring the rotating speed of a generator and the rotating speed of a wind wheel of the wind power generation device, and monitoring and acquiring the reaction duration of overspeed protection when the rotating speed of the generator and the rotating speed of the wind wheel reach corresponding rotating speed thresholds; monitoring the temperature of a gear box of the wind power generation device, and monitoring and collecting the reaction time of overload protection after the temperature of the gear box reaches a temperature threshold; acquiring the speed of temperature reduction of the gearbox after the gearbox reaches a temperature threshold; acquiring a state coefficient of the wind power generation device through a state analysis model;
comparing the state coefficient to a state coefficient threshold: if the state coefficient is larger than or equal to the state coefficient threshold value, judging that the corresponding wind power generation device is marked as a state abnormal device, generating an abnormal device signal, sending the abnormal device signal and the state abnormal device to a server together, and after receiving the abnormal device signal, the server carries out maintenance detection on the wind power generation device in the wind power generation area; and if the state coefficient is less than the state coefficient threshold value, judging that the corresponding wind power generation device is marked as a normal state device, and sending the normal state device to the server.
5. The big data planning-based wind power generation coordination configuration system according to claim 1, wherein the specific selection process of the mode selection unit is as follows:
when the electric quantity transmission needs to be carried out on the area which is not covered by the power grid, the normal state device independently operates, the generator in the normal state device generates electricity and sends the generated electric quantity to the energy storage device for storage, and the energy storage device outputs the electric quantity for a user to use; when the wind power generation cost needs to be reduced, the normal state devices perform grid-connected operation, the normal state devices are in covering connection with a power grid and output electric quantity by the power grid, and a plurality of normal state devices and the power grid construct a temporary wind power electric field; when the wind direction changes frequently, the normal state devices run cooperatively, and the normal state devices in the corresponding areas are in communication connection with the wind power generation devices in the peripheral areas to form a cluster power generation group.
6. The big data planning-based wind power generation coordination configuration system according to claim 1, wherein the specific analysis process of the storage analysis unit is as follows:
marking the maximum storage value and the minimum storage value of the energy storage device as CLmax and CLmin respectively, taking ninety percent to eighty percent of the maximum storage value as an overcharge interval, and taking ten percent to five percent of the maximum storage value as an overdischarge interval;
acquiring the time length and frequency of charging the energy storage device and the electric quantity in an overcharge interval, and acquiring the charge coefficient CDX of the energy storage device through analysis; the method comprises the following steps of collecting the discharge time and frequency of the energy storage device, analyzing and obtaining the discharge coefficient FDX of the energy storage device, and comparing the charge coefficient and the discharge coefficient of the energy storage device with corresponding thresholds respectively: if the charging coefficient and the discharging coefficient of the energy storage device are both smaller than the corresponding threshold values, judging that the state of the energy storage device is normal, generating a qualified signal of the state of the energy storage device, and sending the qualified signal of the state of the energy storage device to the server; if any value of the charging coefficient and the discharging coefficient of the energy storage device is larger than or equal to the corresponding threshold value, judging that the state of the energy storage device is abnormal, generating an unqualified state signal of the energy storage device, and sending the unqualified state signal of the energy storage device to a server;
after receiving the state qualified signal of the energy storage device, the server matches the specification of the corresponding energy storage device with the model of the wind power generation device, and if the wind power generation device with the same model is newly added in the region, the server matches the energy storage device with the corresponding specification; and after receiving the unqualified state signal of the energy storage device, the server matches the specification of the corresponding energy storage device with the model of the wind power generation device, and if the wind power generation devices with the same model exist in the region and the specification of the matched energy storage device is the same, the energy storage device is replaced.
CN202110931070.9A 2021-08-13 2021-08-13 Wind power generation coordination configuration system based on big data planning Withdrawn CN113595151A (en)

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