A wind-solar complementary power supply method and system

Abstract

The present invention relates to the field of power supply, and in particular to a wind-solar complementary power supply method and system, the method comprising: obtaining environmental parameters of a wind-solar system at different time points; calculating the wind power synergy at each time point; calculating the photovoltaic synergy at each time point, the photovoltaic synergy is positively correlated with the light intensity and negatively correlated with the light power generation; calculating the correlation strength at each time point, the correlation strength represents the change of the wind power synergy and photovoltaic synergy at the corresponding time point; classifying different time points according to wind speed to obtain multiple groups; calculating the data anomaly of the wind-solar system at each time point, when the data anomaly is greater than a preset threshold, storing and detecting the operating parameters of the wind-solar system to enable the wind-solar system to operate stably. The present invention can determine whether the wind-solar system is in an abnormal state according to the wind power data anomaly, which is convenient for classifying and managing the operating parameters.

Description

Technical Field

[0001] The present invention relates to the field of power supply, and in particular to a wind-solar complementary power supply method and system. Background Art

[0002] Wind-solar complementary power generation is the use of the complementarity of wind and solar resources to generate electricity. Wind-solar complementary power generation has a high cost-effectiveness and is a new energy generation system with good application prospects. The wind-solar complementary power generation system uses solar photovoltaic arrays and wind turbines to store the generated electricity in battery packs. When users need electricity, the inverter converts the DC power stored in the battery pack into AC power and sends it to the user's load through the transmission line. The wind turbine and solar photovoltaic array are two types of power generation equipment that generate electricity together. During wind-solar complementary power generation, it is necessary to monitor whether the status of the power generation system is abnormal. When the system is abnormal, it is necessary to adjust the corresponding parameters in time or stop the system to ensure the safety of the system.

[0003] A Chinese patent document with announcement number CN112803577B discloses a power supply device, a power quality monitoring system and a system power supply control method, the method comprising: obtaining a measured power parameter value of a circuit to be tested; determining power quality status information according to the measured power parameter value; controlling a power conversion module and a power supply status of a backup power supply according to the status information; wherein the power quality status information comprises the power quality being in an abnormal state and a normal state; determining the power quality status information according to the measured power parameter value.

[0004] In the process of wind power complementary power supply monitoring, there are many types of operating parameters, so the data needs to be compressed to facilitate the storage and transmission of the operating parameters. Data compression often uses a run-length encoding algorithm. Based on the real-time changing characteristics of wind-solar complementary system data, the compression effect of the traditional run-length encoding algorithm is poor, and lossy compression will cause data damage, resulting in the inability to obtain the operating parameters of the wind-solar complementary power supply system when the state is abnormal, which is not conducive to the maintenance of the wind-solar system. Summary of the invention

[0005] In order to identify operating parameters of a wind-solar system in an abnormal state, the present invention provides a wind-solar complementary power supply method and system.

[0006] In a first aspect, the present invention provides a wind-solar complementary power supply method, which adopts the following technical solution:

[0007] Obtain the environmental parameters of the wind and solar system at different time points, including wind speed, light intensity, wind power generation and light power generation; calculate the wind power synergy at each time point, which is positively correlated with wind speed and negatively correlated with wind power generation; calculate the photovoltaic synergy at each time point, which is positively correlated with light intensity and negatively correlated with light power generation;

[0008] Calculate the correlation strength at each time point, where the correlation strength represents the change in wind power synergy and photovoltaic synergy at the corresponding time point; classify different time points according to wind speed to obtain multiple groups;

[0009] Calculate the data anomaly of the wind and solar system at each time point. When the data anomaly is greater than a preset threshold, store and detect the operating parameters of the wind and solar system to ensure stable operation of the wind and solar system.

[0010] The expression of data abnormality is:

[0011] ;

[0012] In the formula, represents the abnormality of the wind and solar system at time point i, represents the association strength at time point i, Indicates the first l The strength of the association at each time point, represents the wind power coordination amount at time point i, Indicates the first l The wind power coordination amount at a time point, represents the photoelectric synergy at time point i, Indicates the first l The photoelectric synergy of time point i is represented by m, where m represents the total number of time points in the group where time point i belongs.

[0013] The effect is that, according to the abnormality of wind power data, it is possible to judge whether the wind and solar system is in an abnormal state, which is convenient for the classification management of operating parameters, and provides a theoretical basis for the staff to understand the operating status of the wind and solar system, so as to facilitate the staff to detect the cause of the abnormality in time and adjust the operating parameters of the wind power generation system and photovoltaic system in time to improve the power generation efficiency.

[0014] Preferably, the expression of wind power synergy is:

[0015] ;

[0016] In the formula, represents the wind power coordination amount at time point i, represents the wind speed data at time point i, represents the amount of electricity generated by wind power at time point i, Represents the normalization function.

[0017] The effect is that the wind power synergy can reflect the synergy between the wind power generation system and the wind speed at the corresponding time point, and can preliminarily reflect the operating status of the wind power generation system.

[0018] Preferably, the expression of photoelectric synergy is:

[0019] ;

[0020] In the formula, represents the photoelectric synergy at time point i, represents the light intensity at time point i, represents the amount of electricity generated by light at time point i, Represents the normalization function.

[0021] The effect is that the photoelectric synergy can reflect the synergy between the photovoltaic power generation system and the light intensity at the corresponding time point, and can preliminarily reflect the operating status of the photovoltaic power generation system.

[0022] Preferably, the expression of the correlation strength at a time point is:

[0023] ;

[0024] in, represents the association strength at time point i, represents the wind power coordination amount at time point i, represents the wind power synergy at the jth time point adjacent to the time point i, represents the photoelectric synergy at time point i, represents the photoelectric synergy at the jth time point adjacent to the time point i, represents the normalization function, represents an exponential function with base e, and n represents the total number of adjacent historical time points.

[0025] The effect is that the correlation strength reflects the correlation between the wind power generation system and the photovoltaic power generation system, which facilitates further understanding of the synergy between the wind power generation system and the photovoltaic power generation system.

[0026] Preferably, the expression of the correlation strength at a time point is:

[0027] ;

[0028] in, represents the association strength at time point i, represents the wind power coordination amount at time point i, represents the wind power synergy at the jth time point adjacent to the time point i, represents the photoelectric synergy at time point i, represents the photoelectric synergy at the jth time point adjacent to the time point i, represents the normalization function, represents the hyperparameter, represents a logarithmic function, and n represents the total number of adjacent historical time points.

[0029] Preferably, the method for classifying different time points according to wind speed is: obtain the wind speed at each time point in the current time period, take any time point as the center point, calculate the absolute value of the difference between the wind speed at other time points and the center point, and in response to the absolute value of the difference being less than a preset threshold, group the corresponding time point and the center point; repeatedly select any data point from the remaining data points as the center point, and calculate the wind speed difference between the center point and other times, until all time points are grouped.

[0030] Preferably, the method for classifying different time points according to wind speed is: obtaining the wind speed at each time point in the current time period, and clustering the obtained multiple wind speeds using the DBSCAN algorithm to obtain multiple groups.

[0031] The effect is that by clustering the wind speed data, it is easier to compare the correlation strength, photovoltaic synergy, and wind power synergy corresponding to the wind speed data in the same group, thereby improving the accuracy of the abnormality of the wind and solar system.

[0032] In a second aspect, the present invention provides a wind-solar hybrid power supply system, which adopts the following technical solution:

[0033] A wind-solar hybrid power supply system, a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a wind-solar hybrid power supply method according to the above is implemented.

[0034] The effect is that the above-mentioned wind-solar complementary power supply method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0035] The present invention has the following technical effects:

[0036] 1. According to the abnormality of wind power data, it is possible to determine whether the wind and solar system is in an abnormal state, which is convenient for classifying and managing the operating parameters according to the abnormal state, providing a theoretical basis for the staff to understand the operating status of the wind and solar system, and facilitating the staff to promptly detect the cause of the abnormality and adjust the operating parameters of the wind power generation system and photovoltaic system in time to improve the power generation efficiency.

[0037] 2. According to the abnormality of wind power data, it is possible to determine whether the wind-solar system is in an abnormal state. The operating parameters of the wind-solar system when it is in an abnormal state are stored separately, which ensures the integrity of the operating parameters and provides data support for monitoring the wind-solar system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0039] Figure 1 It is a method flow chart of a wind-solar complementary power supply method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0042] The embodiment of the present invention discloses a wind-solar complementary power supply method, referring to Figure 1 , including the following steps, as follows:

[0043] S1: Obtain environmental parameters of the wind-solar system at different time points, including wind speed, light intensity, wind power generation and light power generation.

[0044] For the wind and solar system, during the operation of the power supply system, the environmental parameters of the wind and solar system are obtained every hour. The unit of wind speed data is m / s, the unit of light intensity is W / m², and the unit of wind power generation and light power generation is kw·h.

[0045] S2: Calculate the wind power synergy at each time point. The wind power synergy is positively correlated with wind speed and negatively correlated with wind power generation. Calculate the photovoltaic synergy at each time point. The photovoltaic synergy is positively correlated with light intensity and negatively correlated with light power generation.

[0046] The expressions of wind power synergy and photovoltaic synergy are:

[0047] ;

[0048] ;

[0049] In the formula, represents the wind power coordination amount at time point i, represents the wind speed data at time point i, represents the amount of electricity generated by wind power at time point i; represents the photoelectric synergy at time point i, represents the light intensity at time point i, represents the amount of electricity generated by light at time point i, Represents the normalization function.

[0050] In a wind power generation system, the amount of electricity generated by wind changes with the wind speed. Specifically, the greater the wind speed, the more electricity is generated per unit time, and the smaller the wind speed, the more electricity is generated per unit time. The wind power synergy reflects the synergy between the wind power generation system and the wind speed at the corresponding time point. Under normal circumstances, the wind power synergy should be maintained within a stable range. Similarly, in a photovoltaic power generation system, the amount of electricity generated by illumination changes with the light intensity. Specifically, the greater the light intensity, the more electricity is generated per unit time, and the smaller the light intensity, the less electricity is generated per unit time. The photovoltaic synergy reflects the synergy between the photovoltaic power generation system and the light intensity at the corresponding time point. Under normal circumstances, the photovoltaic synergy should be maintained within a stable range.

[0051] S3: Calculate the correlation strength at each time point. The correlation strength represents the change in wind power synergy and photovoltaic synergy at the corresponding time point.

[0052] In one embodiment, the expression of the correlation strength at a time point is:

[0053] ;

[0054] in, represents the association strength at time point i, represents the wind power coordination amount at time point i, represents the wind power synergy at the jth time point adjacent to the time point i, represents the photoelectric synergy at time point i, represents the photoelectric synergy at the jth time point adjacent to the time point i, represents the normalization function, represents an exponential function with e as the base, and n represents the total number of adjacent historical time points. For example, for the day, the environmental parameters of the wind and solar system are collected from 8 am, the collection interval is 1 hour, and a total of 5 data are collected at 12 o'clock. Therefore, when calculating the correlation strength at 12 o'clock, n is 5.

[0055] The correlation strength reflects the correlation between the wind power generation system and the photovoltaic power generation system. Under normal circumstances, the power generation capacity of the wind power generation system and the photovoltaic power generation system at adjacent time points changes gradually. The value of should be kept within a reasonable range. The value of becomes larger, indicating that the wind power generation system and / or the photovoltaic power generation system has undergone a large change. At this time, the synergy of the complementary power supply of the wind power generation system and the photovoltaic power generation system is poor, which further indicates that the correlation strength between the wind power generation system and the photovoltaic power generation system is poor at time point i. In summary, The larger the value of , the greater the association strength at time point i. Conversely, the smaller the association strength at time point i.

[0056] In one embodiment, the expression of the correlation strength at a time point is:

[0057] ;

[0058] in, represents the association strength at time point i, represents the wind power coordination amount at time point i, represents the wind power synergy at the jth time point adjacent to the time point i, represents the photoelectric synergy at time point i, represents the photoelectric synergy at the jth time point adjacent to the time point i, represents the normalization function, Represents a hyperparameter, γ is greater than 1, γ prevents the numerator from being 0, represents a logarithmic function, and n represents the total number of adjacent historical time points.

[0059] S4: Classify different time points according to wind speed to obtain multiple groups.

[0060] In one embodiment, the wind speed at each time point in the current time period is obtained, and the multiple wind speeds obtained are clustered using the DBSCAN algorithm to obtain multiple groups. For example, the wind speed at different time points within 7 days is obtained to obtain multiple wind speed data, and the multiple wind speeds obtained are clustered using the DBSCAN algorithm to obtain multiple groups.

[0061] In one embodiment, the wind speed at each time point in the current time period is obtained, and the wind speed at any time point is taken as the center point, and the absolute value of the difference between the wind speed at other time points and the center point is calculated. In response to the absolute value of the difference being less than a preset threshold, the wind speed at the corresponding time point and the center point are grouped together; any wind speed among the wind speeds at the remaining time points is repeatedly selected as the center point, and the wind speed difference between the center point and other times is calculated until all time points are grouped. The threshold is set manually according to actual conditions.

[0062] For example, the threshold is 0.05, and the wind speed at each time point ( , , …, ),by As the center point, calculate and The absolute value of the difference is calculated and The absolute value of the difference, …, calculate and The absolute value of the difference, where , If the absolute value of the corresponding difference is less than 0.05, , , into a group; then, As the center point, calculate and The absolute value of the difference, and The absolute value of the difference, …, and The absolute value of the difference is calculated and grouped according to the absolute value of the difference until all 20 wind speed data are grouped.

[0063] S5: Calculate the data anomaly of the wind and solar system at each time point.

[0064] The expression of data abnormality is:

[0065] ;

[0066] In the formula, represents the abnormality of the wind and solar system at time point i, represents the association strength at time point i, Indicates the first l The strength of the association at each time point, represents the wind power coordination amount at time point i, Indicates the first l The wind power coordination amount at a time point, represents the photoelectric synergy at time point i, Indicates the first l The photoelectric synergy of time point i is represented by m, where m represents the total number of time points in the group where time point i belongs. Indicates the overall association status of the wind and solar system. represents the overall association state of the wind and solar system at time point i, The larger the value is, the worse the coordination between the wind power generation system and the photovoltaic power generation system at that time point is, and the greater the possibility of abnormality in the wind-solar system is.

[0067] S6: When the data abnormality is greater than a preset threshold, the operating parameters of the wind-solar system are stored and detected to ensure stable operation of the wind-solar system.

[0068] When the data abnormality is greater than the preset threshold, it indicates that the wind-solar system is in an abnormal state at the current time point. The operating parameters of the wind-solar system are stored separately, and the operating parameters are adjusted to make the wind-solar system operate stably. The threshold is set manually according to the actual situation.

[0069] For example, the threshold is 0.8. If the abnormality of the data at 12 o'clock in a day is greater than 0.8, it indicates that the wind and solar system is in an abnormal state and the operating parameters need to be adjusted. At the same time, the wind and solar system operating parameters at 12 o'clock are stored separately, and the operating parameters at 8 o'clock, 9 o'clock, 10 o'clock, and 11 o'clock are calculated and stored as their averages, which facilitates the analysis of the operating parameters of the wind and solar system in abnormal and normal states.

[0070] For example, at 12 o'clock, it is detected that the load power is large, resulting in a small supply voltage. At this time, the supply voltage is adjusted by connecting to a backup power generation device.

[0071] An embodiment of the present invention further discloses a wind-solar complementary power supply system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a wind-solar complementary power supply method according to the present invention is implemented.

[0072] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0073] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0074] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0075] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A wind-solar complementary power supply method, characterized in that: Includes steps: Obtain environmental parameters of the wind and solar system at different time points, including wind speed, light intensity, wind power generation and light power generation; Calculate the wind power synergy at each time point. The wind power synergy is positively correlated with wind speed and negatively correlated with wind power generation; Calculate the photovoltaic synergy at each time point. The photovoltaic synergy is positively correlated with the light intensity and negatively correlated with the light power generation; Calculate the correlation strength at each time point, where the correlation strength represents the change in wind power synergy and photovoltaic synergy at the corresponding time point; classify different time points according to wind speed to obtain multiple groups; Calculate the data anomaly of the wind and solar system at each time point. When the data anomaly is greater than a preset threshold, store and detect the operating parameters of the wind and solar system to ensure stable operation of the wind and solar system. The expression of data abnormality is: ； In the formula, represents the abnormality of the wind and solar system at time point i, represents the association strength at time point i, Indicates the first l The strength of the association at each time point, represents the wind power coordination amount at time point i, Indicates the first l The wind power coordination amount at a time point, represents the photoelectric synergy at time point i, Indicates the first l The photoelectric synergy of time point i is represented by m, where m represents the total number of time points in the group where time point i belongs.

2. A wind-solar complementary power supply method according to claim 1, characterized in that: The expression of wind power synergy is: ； In the formula, represents the wind power coordination amount at time point i, represents the wind speed data at time point i, represents the amount of electricity generated by wind power at time point i, Represents the normalization function.

3. The wind-solar complementary power supply method according to claim 1, characterized in that: The expression of photoelectric synergy is: ； In the formula, represents the photoelectric synergy at time point i, represents the light intensity at time point i, represents the amount of electricity generated by light at time point i, Represents the normalization function.

4. The wind-solar hybrid power supply method according to claim 1, characterized in that: The expression of the correlation strength at a time point is: ； in, represents the association strength at time point i, represents the wind power coordination amount at time point i, represents the wind power synergy at the jth time point adjacent to the time point i, represents the photoelectric synergy at time point i, represents the photoelectric synergy at the jth time point adjacent to the time point i, represents the normalization function, represents an exponential function with base e, and n represents the total number of adjacent historical time points.

5. The wind-solar complementary power supply method according to claim 1, characterized in that: The expression of the correlation strength at a time point is: ； in, represents the association strength at time point i, represents the wind power coordination amount at time point i, represents the wind power synergy at the jth time point adjacent to the time point i, represents the photoelectric synergy at time point i, represents the photoelectric synergy at the jth time point adjacent to the time point i, represents the normalization function, represents the hyperparameter, represents a logarithmic function, and n represents the total number of adjacent historical time points.

6. The wind-solar complementary power supply method according to claim 1, characterized in that: The method for classifying different time points according to wind speed is as follows: obtain the wind speed at each time point in the current time period, take any time point as the center point, calculate the absolute value of the difference between the wind speed at other time points and the center point, and in response to the absolute value of the difference being less than a preset threshold, group the corresponding time point and the center point; repeatedly select any data point from the remaining data points as the center point, and calculate the wind speed difference between the center point and other times, until all time points are grouped.

7. The wind-solar complementary power supply method according to claim 1, characterized in that: The method for classifying different time points according to wind speed is: obtaining the wind speed at each time point in the current time period, and clustering the obtained multiple wind speeds using the DBSCAN algorithm to obtain multiple groups.

8. A wind-solar hybrid power supply system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a wind-solar complementary power supply method according to any one of claims 1 to 7 is implemented.

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