A distributed photovoltaic power supply safe grid connection method and system

Abstract

The present application relates to the field of photovoltaic power generation technology, and in particular to a distributed photovoltaic power supply safe grid connection method and system, which collects energy output information associated with a grid-connected demand source, obtains an associated initial energy output information table based on the energy output information; collects meta-information names of local grid-connected conditions, and extracts local photovoltaic power stations associated with each meta-information name; establishes a grid connection prediction value between local grid-connected conditions and the initial energy output information table based on the local photovoltaic power station; based on the grid connection prediction value, converts the initial energy output information meta-information in the initial energy output information table that matches the local photovoltaic power station into associated normalized energy output information meta-information; and performs credibility detection on the normalized energy output information. The present invention greatly reduces maintenance costs and manufacturing costs, and is suitable for different types of photovoltaic power stations to be grid-connected.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic power generation technology, and in particular to a method and system for safely connecting distributed photovoltaic power sources to the grid. Background Art

[0002] Distributed photovoltaic access to the grid is in different forms and has different technical requirements. It is not yet mature in terms of access design methods, grid connection safety system, and safety maintenance technology, which increases the risk of safe, stable, and reliable operation of the grid. Domestic research on large-scale access of distributed photovoltaics has just started, and the technical system for evaluating the safety of the distribution network for distributed photovoltaic power generation grid connection needs to be established. There is a lack of fault analysis and safety assessment tools; in the past, my country's distributed photovoltaics were mainly operated independently in small-capacity pilot projects, and lacked experience in the safe operation and management of distribution network connection; the relevant specifications for distributed photovoltaic power generation access to the grid are not perfect, and their guiding role in planning and safe operation is limited.

[0003] However, the existing comprehensive evaluation methods for power grid planning do not take into account factors such as the grid structure, equipment configuration, intelligence level, and operation and maintenance management level, which can easily lead to inaccurate evaluation results and ultimately affect the benefits of the power grid planning scheme. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims a method for safely connecting a distributed photovoltaic power source to the grid, the method comprising:

[0006] Collecting energy output information associated with the grid-connected demand source, and obtaining an associated initial energy output information table based on the energy output information;

[0007] Collecting the meta-information names of local grid-connected conditions, and extracting the local photovoltaic power stations associated with each of the meta-information names;

[0008] Based on the local photovoltaic power station, establishing a grid connection prediction value between the local grid connection condition and the initial energy output information table;

[0009] Based on the grid connection prediction value, converting the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into associated normalized energy output information meta-information;

[0010] Performing credibility detection on the normalized energy output information;

[0011] The establishing of the grid connection prediction value between the local grid connection condition and the initial energy output information table based on the local photovoltaic power station includes:

[0012] Collect each meta-information of the initial energy output information table, and determine the association relationship between the main meta-information and each slave meta-information; determine the association relationship between the main meta-information and the slave meta-information of the local grid-connected conditions, and respectively collect the local photovoltaic power station associated with the main meta-information and the slave meta-information of the local grid-connected conditions; based on the local photovoltaic power station of the main meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the main meta-information of the initial energy output information table and the main meta-information of the local grid-connected conditions, and determine it as the first grid-connected prediction value; based on the local photovoltaic power station of the slave meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the slave meta-information of the initial energy output information table and the slave meta-information of the local grid-connected conditions, and determine it as the second grid-connected prediction value; based on the first grid-connected prediction value and the second grid-connected prediction value, obtain the grid-connected prediction value between the local grid-connected conditions and the initial energy output information table.

[0013] Furthermore, the performing credibility detection on the normalized energy output information includes:

[0014] Based on the normalized energy output information and the basic information of the thermal power unit, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power unit belongs;

[0015] Collect local energy consumption trigger information;

[0016] Based on the local energy consumption trigger information, the credibility of the normalized energy output information of thermal power units of different user categories is detected respectively.

[0017] Furthermore, the basic information of the thermal power unit includes the distribution area of ​​the thermal power unit; the population characteristic energy consumption population analysis based on the normalized energy output information and the basic information of the thermal power unit to determine the user category to which the thermal power unit belongs includes:

[0018] Collect local population divisions;

[0019] Determining the thermal power plant cluster to which the thermal power plant belongs based on the distribution area and local population division of the thermal power plant;

[0020] Based on the normalized energy output information and the thermal power cluster to which the thermal power group belongs, fuzzy filtering is performed on each of the thermal power groups in the thermal power cluster, and information of the thermal power groups that fail the fuzzy filtering is deleted;

[0021] The user category to which the thermal power unit belongs is determined based on the normalized energy output information.

[0022] Furthermore, the energy consumption trigger information includes function trigger information and power disturbance trigger information; the local energy consumption trigger information is used to perform credibility detection on normalized energy output information of thermal power units of different user categories, including:

[0023] Comparing the function triggering information with the normalized energy output information of thermal power units of different user categories to obtain a comparison result;

[0024] When the comparison deviation between the normalized energy output information and the function trigger information is within a first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection;

[0025] Comparing the normalized energy output information that passes the fuzzy credibility detection with the power disturbance trigger information;

[0026] When a comparison deviation between the normalized energy output information and the power disturbance trigger information is within a second local deviation upper limit, it is determined that the normalized energy output information is credible.

[0027] According to a second aspect of the present invention, the present invention claims protection for a distributed photovoltaic power supply safe grid-connected system, the system comprising:

[0028] An energy output information collection unit, used to collect energy output information associated with the grid-connected demand source, and obtain an associated initial energy output information table based on the energy output information;

[0029] A local photovoltaic power station extraction unit, used to collect the meta-information names of local grid-connected conditions, and extract the local photovoltaic power stations associated with each of the meta-information names;

[0030] A grid connection prediction value establishing unit, configured to establish a grid connection prediction value between the local grid connection condition and the initial energy output information table based on the local photovoltaic power station;

[0031] A conversion unit, configured to convert the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into associated normalized energy output information meta-information based on the grid connection prediction value;

[0032] A credibility detection unit, used for performing credibility detection on the normalized energy output information;

[0033] The grid connection prediction value establishing unit is further used for:

[0034] Collect each meta-information of the initial energy output information table, and determine the association relationship between the main meta-information and each slave meta-information; determine the association relationship between the main meta-information and the slave meta-information of the local grid-connected conditions, and respectively collect the local photovoltaic power station associated with the main meta-information and the slave meta-information of the local grid-connected conditions; based on the local photovoltaic power station of the main meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the main meta-information of the initial energy output information table and the main meta-information of the local grid-connected conditions, and determine it as the first grid-connected prediction value; based on the local photovoltaic power station of the slave meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the slave meta-information of the initial energy output information table and the slave meta-information of the local grid-connected conditions, and determine it as the second grid-connected prediction value; based on the first grid-connected prediction value and the second grid-connected prediction value, obtain the grid-connected prediction value between the local grid-connected conditions and the initial energy output information table.

[0035] Furthermore, the credibility detection unit is also used for:

[0036] Based on the normalized energy output information and the basic information of the thermal power unit, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power unit belongs;

[0037] Collect local energy consumption trigger information;

[0038] Based on the local energy consumption trigger information, the credibility of the normalized energy output information of thermal power units of different user categories is detected respectively.

[0039] Furthermore, the credibility detection unit includes a user category determination unit, which is used to:

[0040] Collect local population divisions;

[0041] Determining the thermal power plant cluster to which the thermal power plant belongs based on the distribution area and local population division of the thermal power plant;

[0042] Based on the normalized energy output information and the thermal power cluster to which the thermal power group belongs, fuzzy filtering is performed on each of the thermal power groups in the thermal power cluster, and information of the thermal power groups that fail the fuzzy filtering is deleted;

[0043] The user category to which the thermal power unit belongs is determined based on the normalized energy output information.

[0044] Furthermore, the energy consumption trigger information includes function trigger information and power disturbance trigger information; the credibility detection unit is also used for:

[0045] Comparing the function triggering information with the normalized energy output information of thermal power units of different user categories to obtain a comparison result;

[0046] When the comparison deviation between the normalized energy output information and the function trigger information is within a first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection;

[0047] Comparing the normalized energy output information that passes the fuzzy credibility detection with the power disturbance trigger information;

[0048] When a comparison deviation between the normalized energy output information and the power disturbance trigger information is within a second local deviation upper limit, it is determined that the normalized energy output information is credible.

[0049] The present application relates to the field of photovoltaic power generation technology, and in particular to a distributed photovoltaic power supply safe grid connection method and system, which collects energy output information associated with a grid-connected demand source, obtains an associated initial energy output information table based on the energy output information; collects meta-information names of local grid-connected conditions, and extracts local photovoltaic power stations associated with each meta-information name; establishes a grid connection prediction value between local grid-connected conditions and the initial energy output information table based on the local photovoltaic power station; based on the grid connection prediction value, converts the initial energy output information meta-information in the initial energy output information table that matches the local photovoltaic power station into associated normalized energy output information meta-information; and performs credibility detection on the normalized energy output information. The present invention greatly reduces maintenance costs and manufacturing costs, and is suitable for different types of photovoltaic power stations to be grid-connected. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of an application environment of a distributed photovoltaic power supply safe grid-connected method claimed in an embodiment of the present application;

[0051] Figure 2 A flowchart of a distributed photovoltaic power supply safe grid connection method claimed in an embodiment of the present application;

[0052] Figure 3 A second working flow chart of a distributed photovoltaic power supply safe grid-connected method claimed in an embodiment of the present application;

[0053] Figure 4 This is a structural module diagram of a distributed photovoltaic power supply safe grid-connected system for which protection is requested in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0056] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] The distributed photovoltaic power supply safe grid connection method provided in this application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The server 104 collects the energy output information associated with the grid-connected demand source sent by the user at the terminal 102, obtains the associated initial energy output information table based on the energy output information, and collects the meta-information name of the local grid-connected condition, and extracts the local photovoltaic power station associated with each meta-information name. Based on the local photovoltaic power station, the server 104 establishes the grid-connected prediction value between the local grid-connected condition and the initial energy output information table, and then based on the grid-connected prediction value, converts the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into the associated normalized energy output information meta-information, and performs credibility detection on the normalized energy output information. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers and portable wearable devices, and the server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0058] In one embodiment, Figure 2 As shown, a distributed photovoltaic power supply safe grid connection method is provided, and the method is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0059] Step S202: collecting energy output information associated with the grid-connected demand source, and obtaining an associated initial energy output information table based on the energy output information.

[0060] Specifically, when a grid-connected demand source initiated by a thermal power unit at a terminal is received, energy consumption assessment information of the thermal power unit associated with the grid-connected demand source is collected, and information preprocessing is performed on energy output information associated with the energy consumption assessment information to obtain energy output information associated with the grid-connected demand source. Then, an associated initial energy output information table is obtained based on the energy output information, wherein the initial energy output information table includes a plurality of initial energy consumption meta-information.

[0061] Furthermore, by performing information preprocessing, i.e., information cleaning, on the energy output information associated with the energy consumption assessment information, the energy output information associated with the grid-connected demand source is obtained. Information cleaning includes information filtering and information deduplication. Information filtering is to filter out incomplete and non-sample information in the energy output information, and information deduplication is to remove duplicate information in the energy output information.

[0062] Step S204, collecting the meta-information names of the local grid-connected conditions, and extracting the local photovoltaic power stations associated with each meta-information name.

[0063] Step S206: Based on the local photovoltaic power station, a grid connection prediction value between the local grid connection condition and the initial energy output information table is established.

[0064] Specifically, by collecting each meta-information of the initial energy output information table, and determining the association between the master meta-information and each slave meta-information. At the same time, based on determining the association between the master meta-information and slave meta-information of the collected local grid-connected conditions, the master meta-information of the local grid-connected conditions and the local photovoltaic power station associated with the slave meta-information are collected respectively.

[0065] Furthermore, based on the local photovoltaic power station of the master information of the local grid-connected conditions, the master information of the initial energy output information table and the grid-connected prediction value of the master information of the local grid-connected conditions are established, and the grid-connected prediction value is determined as the first grid-connected prediction value. At the same time, based on the local photovoltaic power station of the slave information of the local grid-connected conditions, the grid-connected prediction value of the slave information of the initial energy output information table and the slave information of the local grid-connected conditions is established, and the grid-connected prediction value is determined as the second grid-connected prediction value. Furthermore, based on the first grid-connected prediction value and the second grid-connected prediction value, the grid-connected prediction value between the local grid-connected conditions and the initial energy output information table can be obtained.

[0066] Step S208, based on the grid connection prediction value, converting the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into associated normalized energy output information meta-information.

[0067] Specifically, based on the grid-connected prediction value, the local photovoltaic power stations in each normalized meta-information are matched with the initial energy output information meta-information respectively, the meta-information names matching the local key meta-information are determined from the initial energy output information meta-information, and the information associated with each meta-information name is extracted from the energy output information, and the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table is converted into associated normalized energy output information meta-information.

[0068] Step S210: performing credibility detection on the normalized energy output information.

[0069] Specifically, based on the normalized energy output information and the basic information of the thermal power units, the population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power units belong. By collecting local energy consumption trigger information and based on the local energy consumption trigger information, the normalized energy output information of thermal power units of different user categories is tested for credibility.

[0070] In one embodiment, the energy consumption trigger information includes function trigger information and power disturbance trigger information. Based on the local energy consumption trigger information, the normalized energy output information of thermal power units of different user categories is tested for credibility, specifically including:

[0071] The function trigger information is compared with the normalized energy output information of thermal power units of different user categories to obtain a comparison result. When the comparison deviation between the normalized energy output information and the function trigger information is within the first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection.

[0072] Then, by collecting the first local deviation upper limit, it is determined whether the comparison deviation of the comparison result between the normalized energy output information and the function trigger information is within the first local deviation upper limit. When the comparison deviation of the comparison result between the normalized energy output information and the function trigger information is within the first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility test.

[0073] Furthermore, the normalized energy output information that has passed the fuzzy credibility detection is compared with the power disturbance trigger information. When the comparison deviation between the normalized energy output information and the power disturbance trigger information is within the second local deviation upper limit, the normalized energy output information is determined to be credible.

[0074] In the above-mentioned distributed photovoltaic power supply safe grid connection method, by collecting the energy output information associated with the grid-connected demand source, the associated initial energy output information table is obtained based on the energy output information, and the meta-information name of the local grid-connected condition is collected to extract the local photovoltaic power station associated with each meta-information name. Then, based on the local photovoltaic power station, the grid connection prediction value between the local grid-connected condition and the initial energy output information table is established. Based on the grid connection prediction value, the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table is converted into the associated normalized energy output information meta-information, realizing the normalization of energy output information in different regions, avoiding the situation of information or information confusion in various regions. At the same time, by performing credibility detection on a large amount of integrated normalized energy output information, the behavioral capability evaluation of the thermal power unit can be realized based on the normalized energy output information that has passed the credibility detection, thereby improving the accuracy of the behavioral capability evaluation of the thermal power unit.

[0075] In one embodiment, Figure 3 As shown, the step of performing credibility detection on the normalized energy output information includes the following steps S302 to S306:

[0076] Step S302: Based on the normalized energy output information and the basic information of the thermal power units, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power units belong.

[0077] Specifically, by collecting local population divisions, and based on the distribution of thermal power units and local population divisions, the thermal power cluster to which the thermal power units belong is determined. Based on the normalized energy output information and the thermal power cluster to which the thermal power units belong, fuzzy filtering is performed on each thermal power unit in the thermal power cluster, and the information of the thermal power units that fail the fuzzy filtering is deleted, and then the user category to which the thermal power units belong is determined based on the normalized energy output information.

[0078] Step S304: collecting local energy consumption trigger information.

[0079] Step S306: Based on the local energy consumption trigger information, the credibility of the normalized energy output information of the thermal power units of different user categories is detected.

[0080] Specifically, the local energy consumption trigger information includes function trigger information and power disturbance trigger information. The function trigger information is compared with the normalized energy output information of thermal power units of different user categories to obtain a comparison result. When the comparison deviation between the normalized energy output information and the function trigger information is within the first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection.

[0081] Furthermore, the normalized energy output information that has passed the fuzzy credibility detection is compared with the power disturbance trigger information. When the comparison deviation between the normalized energy output information and the power disturbance trigger information is within the second local deviation upper limit, the normalized energy output information is determined to be credible.

[0082] In one embodiment, the step of evaluating the energy consumption level of thermal power plant crew members specifically includes:

[0083] Specifically, the associated evaluation method is used to evaluate the normalized energy output information of each thermal power unit under the associated user category, determine the energy consumption level of the thermal power unit, and obtain the behavioral capacity evaluation result of the thermal power unit based on the energy consumption level of the thermal power unit.

[0084] In one embodiment, the energy consumption level of a thermal power unit is determined using a correlation evaluation method, including:

[0085] Based on the first evaluation method, the fault information of the thermal power units under the first user category is collected, and the fault information is evaluated to determine the energy consumption level of the thermal power units; or

[0086] Collect the daily living activities scale based on the second assessment method, and determine the energy consumption level of the thermal power unit based on the daily living activities scale; or

[0087] Dementia assessment scales are collected based on the third assessment method, and the energy consumption level of the thermal power unit is determined based on the dementia assessment scales.

[0088] In the above steps, based on the normalized energy output information and the basic information of the thermal power units, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power units belong, and based on the local energy consumption trigger information, the normalized energy output information of the thermal power units of different user categories is subjected to credibility detection to avoid the problem that the collected credible information is single, and thus the user portrait obtained based on the single credible information is not universal.

[0089] In one embodiment, after the behavioral capability is evaluated based on the normalized energy output information, it also includes determining a re-evaluation time based on the evaluated energy consumption level, and sending the evaluated energy consumption level and the re-evaluation time to the user terminal where the thermal power unit is located.

[0090] Specifically, after the evaluation of the energy consumption level is completed, the associated energy consumption level evaluation results are obtained, and based on the failure, recovery period, and survival period information of the thermal power unit members, it is determined whether the thermal power unit members need re-evaluation and the re-evaluation time, and the energy consumption level evaluation results and re-evaluation time are sent to the thermal power unit members.

[0091] In the above steps, by sending the re-evaluation time determined based on the energy consumption level and the energy consumption level to the terminal where the thermal power unit is located, the thermal power unit can promptly learn the evaluation results of the energy consumption level, and can prepare the required materials or information based on the re-evaluation time so as to conduct a timely re-evaluation and obtain the energy consumption level assessment results.

[0092] It should be understood that although Figure 2-3 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-3 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0093] In one embodiment, Figure 4As shown, a distributed photovoltaic power supply safe grid-connected system is provided, including: an energy output information collection unit 402, a local photovoltaic power station extraction unit 404, a grid-connected prediction value establishment unit 406, a conversion unit 408 and a credibility detection unit 410, wherein:

[0094] The energy output information collection unit 402 is used to collect energy output information associated with the grid-connected demand source, and obtain an associated initial energy output information table based on the energy output information.

[0095] The local photovoltaic power station extraction unit 404 is used to collect the meta-information names of the local grid-connected conditions and extract the local photovoltaic power stations associated with each meta-information name.

[0096] The grid connection prediction value establishing unit 406 is used to establish the grid connection prediction value between the local grid connection condition and the initial energy output information table based on the local photovoltaic power station.

[0097] The conversion unit 408 is used to convert the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into associated normalized energy output information meta-information based on the grid connection prediction value.

[0098] The credibility detection unit 410 is used to perform credibility detection on the normalized energy output information.

[0099] In the above-mentioned distributed photovoltaic power supply safe grid-connected system, by collecting the energy output information associated with the grid-connected demand source, the associated initial energy output information table is obtained based on the energy output information, and the meta-information name of the local grid-connected condition is collected, and the local photovoltaic power station associated with each meta-information name is extracted. Then, based on the local photovoltaic power station, the grid-connected prediction value between the local grid-connected condition and the initial energy output information table is established. Based on the grid-connected prediction value, the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table is converted into the associated normalized energy output information meta-information, realizing the normalization of energy output information in different regions, avoiding the situation of information or information confusion in various regions. At the same time, by performing credibility detection on a large amount of integrated normalized energy output information, the behavioral capability evaluation of the thermal power unit can be realized based on the normalized energy output information that has passed the credibility detection, thereby improving the accuracy of the behavioral capability evaluation of the thermal power unit.

[0100] In one embodiment, the credibility detection unit is further used to:

[0101] Based on the normalized energy output information and the basic information of the thermal power units, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power units belong; local energy consumption trigger information is collected; based on the local energy consumption trigger information, the normalized energy output information of the thermal power units of different user categories is tested for credibility.

[0102] The above-mentioned credibility detection unit performs population characteristic energy consumption population analysis based on the normalized energy output information and the basic information of the thermal power unit, determines the user category to which the thermal power unit belongs, and based on the local energy consumption trigger information, performs credibility detection on the normalized energy output information of thermal power units of different user categories, so as to avoid the problem that the collected credible information is single, and thus the user portrait obtained based on the single credible information is not universal.

[0103] For the specific definition of the distributed photovoltaic power supply safe grid-connected system, please refer to the definition of the distributed photovoltaic power supply safe grid-connected method above, which will not be repeated here. Each unit in the above-mentioned distributed photovoltaic power supply safe grid-connected system can be implemented in whole or in part by software, hardware and their combination. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations associated with the above-mentioned units.

[0104] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0105] Collecting energy output information associated with the grid-connected demand source, and obtaining an associated initial energy output information table based on the energy output information;

[0106] Collect the meta-information names of local grid-connected conditions, and extract the local photovoltaic power stations associated with each meta-information name;

[0107] Based on the local photovoltaic power station, the grid connection prediction value between the local grid connection conditions and the initial energy output information table is established;

[0108] Based on the grid connection prediction value, the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table is converted into the associated normalized energy output information meta-information;

[0109] Perform credibility check on normalized energy output information.

[0110] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0111] Based on the normalized energy output information and the basic information of thermal power units, the population characteristic energy consumption population analysis is carried out to determine the user category to which the thermal power units belong;

[0112] Collect local energy consumption trigger information;

[0113] Based on the local energy consumption trigger information, the credibility of the normalized energy output information of thermal power units of different user categories is tested respectively.

[0114] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0115] Collect local population divisions;

[0116] Based on the distribution of thermal power units and the local population division, determine the thermal power cluster to which the thermal power units belong;

[0117] Based on the normalized energy output information and the thermal power plant cluster to which the thermal power plant belongs, fuzzy filtering is performed on each thermal power plant in the thermal power plant cluster, and the information of the thermal power plants that fail the fuzzy filtering is deleted;

[0118] The user category to which the thermal power unit belongs is determined based on the normalized energy output information.

[0119] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0120] The function triggering information is compared with the normalized energy output information of thermal power units of different user categories to obtain the comparison results;

[0121] When the comparison deviation between the normalized energy output information and the function trigger information is within the first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection;

[0122] The normalized energy output information that has passed the fuzzy credibility detection is compared with the power disturbance trigger information;

[0123] When the comparison deviation of the comparison result between the normalized energy output information and the power disturbance trigger information is within the second local deviation upper limit, it is determined that the normalized energy output information is credible.

[0124] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0125] Collecting each meta-information of the initial energy output information table, and determining the correlation between the main meta-information and each sub-meta-information;

[0126] Determine the association relationship between the master meta-information and the slave meta-information of the local grid-connected conditions, and respectively collect the local photovoltaic power stations associated with the master meta-information and the slave meta-information of the local grid-connected conditions;

[0127] Based on the local photovoltaic power station of the principal element information of the local grid-connected conditions, the principal element information of the initial energy output information table and the grid-connected prediction value of the principal element information of the local grid-connected conditions are established and determined as the first grid-connected prediction value;

[0128] Based on the local photovoltaic power station of the slave meta-information of the local grid-connected condition, the slave meta-information of the initial energy output information table and the grid-connected prediction value of the slave meta-information of the local grid-connected condition are established and determined as the second grid-connected prediction value;

[0129] Based on the first grid-connection prediction value and the second grid-connection prediction value, a grid-connection prediction value between the local grid-connection condition and the initial energy output information table is obtained.

[0130] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0131] Collecting energy output information associated with the grid-connected demand source, and obtaining an associated initial energy output information table based on the energy output information;

[0132] Collect the meta-information names of local grid-connected conditions, and extract the local photovoltaic power stations associated with each meta-information name;

[0133] Based on the local photovoltaic power station, the grid connection prediction value between the local grid connection conditions and the initial energy output information table is established;

[0134] Based on the grid connection prediction value, the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table is converted into the associated normalized energy output information meta-information;

[0135] Perform credibility check on normalized energy output information.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0137] Based on the normalized energy output information and the basic information of thermal power units, the population characteristic energy consumption population analysis is carried out to determine the user category to which the thermal power units belong;

[0138] Collect local energy consumption trigger information;

[0139] Based on the local energy consumption trigger information, the credibility of the normalized energy output information of thermal power units of different user categories is tested respectively.

[0140] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0141] Collect local population divisions;

[0142] Based on the distribution of thermal power units and the local population division, determine the thermal power cluster to which the thermal power units belong;

[0143] Based on the normalized energy output information and the thermal power plant cluster to which the thermal power plant belongs, fuzzy filtering is performed on each thermal power plant in the thermal power plant cluster, and the information of the thermal power plants that fail the fuzzy filtering is deleted;

[0144] The user category to which the thermal power unit belongs is determined based on the normalized energy output information.

[0145] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0146] The function triggering information is compared with the normalized energy output information of thermal power units of different user categories to obtain the comparison results;

[0147] When the comparison deviation between the normalized energy output information and the function trigger information is within the first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection;

[0148] The normalized energy output information that has passed the fuzzy credibility detection is compared with the power disturbance trigger information;

[0149] When the comparison deviation of the comparison result between the normalized energy output information and the power disturbance trigger information is within the second local deviation upper limit, it is determined that the normalized energy output information is credible.

[0150] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0151] Collecting each meta-information of the initial energy output information table, and determining the correlation between the main meta-information and each sub-meta-information;

[0152] Determine the association relationship between the master meta-information and the slave meta-information of the local grid-connected conditions, and respectively collect the local photovoltaic power stations associated with the master meta-information and the slave meta-information of the local grid-connected conditions;

[0153] Based on the local photovoltaic power station of the principal element information of the local grid-connected conditions, the principal element information of the initial energy output information table and the grid-connected prediction value of the principal element information of the local grid-connected conditions are established and determined as the first grid-connected prediction value;

[0154] Based on the local photovoltaic power station of the slave meta-information of the local grid-connected condition, the slave meta-information of the initial energy output information table and the grid-connected prediction value of the slave meta-information of the local grid-connected condition are established and determined as the second grid-connected prediction value;

[0155] Based on the first grid-connection prediction value and the second grid-connection prediction value, a grid-connection prediction value between the local grid-connection condition and the initial energy output information table is obtained.

[0156] In the several embodiments provided in the present application, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0157] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0158] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for safely connecting a distributed photovoltaic power source to the grid, the method comprising: Collecting energy output information associated with the grid-connected demand source, and obtaining an associated initial energy output information table based on the energy output information; Collecting the meta-information name of the local grid-connected condition, and extracting the local photovoltaic power station associated with the meta-information name; Based on the local photovoltaic power station, establishing a grid connection prediction value between the local grid connection condition and the initial energy output information table; Based on the grid connection prediction value, converting the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into associated normalized energy output information meta-information; Performing credibility detection on the normalized energy output information meta-information; The establishing of the grid connection prediction value between the local grid connection condition and the initial energy output information table based on the local photovoltaic power station includes: Collect each meta-information of the initial energy output information table, and determine the association relationship between the main meta-information and each slave meta-information; determine the association relationship between the main meta-information and the slave meta-information of the local grid-connected conditions, and respectively collect the local photovoltaic power station associated with the main meta-information and the slave meta-information of the local grid-connected conditions; based on the local photovoltaic power station of the main meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the main meta-information of the initial energy output information table and the main meta-information of the local grid-connected conditions, and determine it as the first grid-connected prediction value; based on the local photovoltaic power station of the slave meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the slave meta-information of the initial energy output information table and the slave meta-information of the local grid-connected conditions, and determine it as the second grid-connected prediction value; based on the first grid-connected prediction value and the second grid-connected prediction value, obtain the grid-connected prediction value between the local grid-connected conditions and the initial energy output information table.

2. The method according to claim 1, characterized in that The performing credibility detection on the normalized energy output information meta-information includes: Based on the normalized energy output information and the basic information of the thermal power unit, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power unit belongs; Collect local energy consumption trigger information; Based on the local energy consumption trigger information, the credibility of the normalized energy output information of thermal power units of different user categories is respectively detected.

3. The method according to claim 2, characterized in that The basic information of the thermal power unit includes the distribution area of ​​the thermal power unit; the population characteristic energy consumption population analysis based on the normalized energy output information and the basic information of the thermal power unit to determine the user category to which the thermal power unit belongs includes: Collect local population divisions; Based on the distribution area and local population division of the thermal power units, determining the thermal power cluster to which the thermal power units belong; Based on the normalized energy output information and the thermal power cluster to which the thermal power group belongs, fuzzy filtering is performed on each of the thermal power groups in the thermal power cluster, and information of the thermal power groups that fail the fuzzy filtering is deleted; The user category to which the thermal power unit belongs is determined based on the normalized energy output information.

4. The method according to claim 3, characterized in that The energy consumption trigger information includes function trigger information and power disturbance trigger information; the local energy consumption trigger information is used to perform credibility detection on normalized energy output information of thermal power units of different user categories, including: Comparing the function triggering information with the normalized energy output information of thermal power units of different user categories to obtain a comparison result; If the comparison result between the normalized energy output information and the function trigger information has a comparison deviation within a first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility test; Comparing the normalized energy output information that passes the fuzzy credibility detection with the power disturbance trigger information; If the comparison deviation between the normalized energy output information and the power disturbance trigger information is within the second local deviation upper limit, it is determined that the normalized energy output information is credible.

5. A distributed photovoltaic power supply safe grid-connected system, characterized in that: The system comprises: An energy output information collection unit, used to collect energy output information associated with the grid-connected demand source, and obtain an associated initial energy output information table based on the energy output information; A local photovoltaic power station extraction unit, used to collect the meta-information names of local grid-connected conditions, and extract the local photovoltaic power stations associated with each of the meta-information names; A grid connection prediction value establishing unit, configured to establish a grid connection prediction value between the local grid connection condition and the initial energy output information table based on the local photovoltaic power station; A conversion unit, configured to convert the initial energy output information meta-information matching the local photovoltaic power station in the initial energy output information table into associated normalized energy output information meta-information based on the grid connection prediction value; A credibility detection unit, used for performing credibility detection on the normalized energy output information; The grid connection prediction value establishing unit is further used for: Collect each meta-information of the initial energy output information table, and determine the association relationship between the main meta-information and each slave meta-information; determine the association relationship between the main meta-information and the slave meta-information of the local grid-connected conditions, and respectively collect the local photovoltaic power station associated with the main meta-information and the slave meta-information of the local grid-connected conditions; based on the local photovoltaic power station of the main meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the main meta-information of the initial energy output information table and the main meta-information of the local grid-connected conditions, and determine it as the first grid-connected prediction value; based on the local photovoltaic power station of the slave meta-information of the local grid-connected conditions, establish the grid-connected prediction value of the slave meta-information of the initial energy output information table and the slave meta-information of the local grid-connected conditions, and determine it as the second grid-connected prediction value; based on the first grid-connected prediction value and the second grid-connected prediction value, obtain the grid-connected prediction value between the local grid-connected conditions and the initial energy output information table.

6. The distributed photovoltaic power supply safe grid-connected system according to claim 5, characterized in that: The credibility detection unit is also used for: Based on the normalized energy output information and the basic information of the thermal power unit, a population characteristic energy consumption population analysis is performed to determine the user category to which the thermal power unit belongs; Collect local energy consumption trigger information; Based on the local energy consumption trigger information, the credibility of the normalized energy output information of thermal power units of different user categories is respectively detected.

7. The distributed photovoltaic power supply safe grid-connected system according to claim 6, characterized in that: The credibility detection unit includes a user category determination unit, which is used to: Collect local population divisions; Based on the distribution area and local population division of the thermal power units, determining the thermal power cluster to which the thermal power units belong; Based on the normalized energy output information and the thermal power cluster to which the thermal power group belongs, fuzzy filtering is performed on each of the thermal power groups in the thermal power cluster, and information of the thermal power groups that fail the fuzzy filtering is deleted; The user category to which the thermal power unit belongs is determined based on the normalized energy output information.

8. The distributed photovoltaic power supply safe grid-connected system according to claim 7, characterized in that: The energy consumption trigger information includes function trigger information and power disturbance trigger information; the credibility detection unit is also used for: Comparing the function triggering information with the normalized energy output information of thermal power units of different user categories to obtain a comparison result; When the comparison deviation between the normalized energy output information and the function trigger information is within a first local deviation upper limit, it is determined that the normalized energy output information passes the fuzzy credibility detection; Comparing the normalized energy output information that passes the fuzzy credibility detection with the power disturbance trigger information; When a comparison deviation between the normalized energy output information and the power disturbance trigger information is within a second local deviation upper limit, it is determined that the normalized energy output information is credible.

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