New energy power generation technology supervision and management method and system

Abstract

The invention provides a new energy power generation technology supervision and management method. The method comprises the following steps: obtaining new energy power generation data of new energy power generation technology supervision and management; carrying out fuzzification processing on the at least two kinds of new energy power generation data, and carrying out knowledge graph visualization construction to generate a fuzzified new energy power generation knowledge graph; establishing a new energy power generation field knowledge base, and constructing a fuzzy control rule set of new energy power generation data according to the new energy power generation field knowledge base; and obtaining a fuzzy reasoning conclusion according to the fuzzy control rule set in combination with the new energy power generation knowledge graph, carrying out defuzzification processing on the fuzzy reasoning conclusion, and carrying out feedback fuzzy regulation and control until an expected regulation and control target of new energy power generation supervision and management is achieved. Through fuzzy control and the knowledge graph, the intelligent management level of new energy power generation is improved, energy configuration is optimized, and the stability and reliability of a new energy power generation system are improved.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation technology, and in particular to a renewable energy power generation technology supervision and management method and system. Background Art

[0002] New energy generally refers to renewable energy that is developed and utilized based on new technologies, including solar energy, biomass energy, wind energy, geothermal energy, wave energy, ocean current energy and tidal energy, in addition, there is hydrogen energy, etc.; while coal, oil, natural gas, hydropower, nuclear fission energy and other energies that have been widely used are called conventional energy. New energy power generation uses existing technologies to achieve the process of power generation through the above-mentioned new energy sources.

[0003] A new energy data comprehensive analysis and management method with publication number CN109167389A adopts the "region-station-equipment" hierarchical model design to carry out panoramic modeling of new energy business data. Through basic model synchronization, operation analysis data collection, station management information maintenance and massive historical data storage, dynamic synchronization of new energy basic information and operation data collection of various provinces are realized; the new energy operation data is monitored for the entire network power generation operation curve, new energy power generation basic information management and new energy power generation basic analysis are carried out to realize unified comprehensive analysis and management of new energy data; based on the WebGIS development platform, with the panoramic map of new energy-related areas as the basis, the new energy power generation operation, basic information, unified scheduling, and consumption data are visualized from different spatial dimensions of "region-station-equipment"; data reports are dynamically generated, and report templates are managed to realize data integration and reporting.

[0004] At present, the large amount of data generated in the process of renewable energy power generation is often not fully mined and utilized, resulting in waste of data resources and low management efficiency. In addition, traditional control strategies often rely on experience judgment, lack of scientific data support and intelligent decision-making means, making it difficult to achieve precise control of the renewable energy power generation process and optimize energy allocation, resulting in energy waste and low power generation efficiency, thereby reducing the stability and reliability of the renewable energy power generation system. Summary of the invention

[0005] In view of this, the present invention proposes a new energy power generation technology supervision and management method and system, which can efficiently integrate and utilize new energy power generation data, improve the intelligent management level of new energy power generation through fuzzy control and knowledge graphs, optimize energy configuration, and improve the stability and reliability of the new energy power generation system.

[0006] The technical solution of the present invention is implemented as follows: In the first aspect, the present invention provides a new energy power generation technology supervision and management method, comprising the following steps:

[0007] S1, obtain new energy power generation data for new energy power generation technology supervision and management;

[0008] S2, fuzzifying at least two kinds of renewable energy power generation data, and visually constructing a knowledge graph for the fuzzified renewable energy power generation data to generate a fuzzified renewable energy power generation knowledge graph;

[0009] S3, establish a knowledge base in the field of renewable energy power generation, and construct a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation;

[0010] S4, based on the fuzzy control rule set and combined with the new energy power generation knowledge graph, obtain the fuzzy reasoning conclusion, defuzzify the fuzzy reasoning conclusion, and perform feedback fuzzy regulation until the expected regulation target of new energy power generation supervision and management is achieved.

[0011] On the basis of the above technical solutions, preferably, the new energy power generation data includes health status data of new energy power generation equipment, voltage data of new energy power generation, current data of new energy power generation, uncertain interference data of new energy power generation, sudden failure data of new energy power generation and network emergency processing data of new energy power generation.

[0012] On the basis of the above technical solution, preferably, the fuzzy processing of at least two kinds of renewable energy power generation data in step S2 includes the following sub-steps:

[0013] Acquire at least two kinds of renewable energy power generation data, and perform data filtering preprocessing on the renewable energy power generation data;

[0014] Extract the health status characteristics of the renewable energy power generation equipment, the voltage characteristics of renewable energy power generation, the current characteristics of renewable energy power generation, the interference characteristics of renewable energy power generation, the fault characteristics of renewable energy power generation and the network threat characteristics of renewable energy power generation from the pre-processed data;

[0015] According to the extracted features, the uncertain fuzzy sets of health status of new energy power generation equipment, the uncertain fuzzy sets of new energy power generation interference, the uncertain fuzzy sets of sudden failures of new energy power generation and the uncertain fuzzy sets of network emergency processing of new energy power generation are constructed respectively;

[0016] According to the qualitative level and membership relationship of the uncertain characteristic data of new energy power generation data, the membership function value of each fuzzy set is calculated, and the calculation expression is:

[0017] μ km (d k , f m )=μ k (d k )·μ m (f m )

[0018] In the formula, μ km (d k , f m ) represents the kth new energy power generation equipment d k The membership function value of the mth uncertain feature data, μ k (d k ) represents the kth new energy power generation equipment d k The probability membership function value, μ m (f m ) represents the membership function value of the mth uncertain feature data.

[0019] On the basis of the above technical solution, preferably, the step S2 of visually constructing the knowledge graph of the fuzzy processed new energy power generation data to generate a fuzzy new energy power generation knowledge graph set includes the following sub-steps:

[0020] Identify key entities from each fuzzy set of fuzzy processed renewable energy power generation data, and extract attribute information related to the corresponding entities;

[0021] Determine the logical associations between the identified key entities, integrate the identified entities and the determined logical associations to form a new energy power generation knowledge graph collection, and use visualization tools to display the knowledge graph objects in a graphical manner.

[0022] On the basis of the above technical solution, preferably, the step S3 of establishing a knowledge base in the field of renewable energy power generation and constructing a fuzzy control rule set for renewable energy power generation data according to the knowledge base in the field of renewable energy power generation includes the following sub-steps:

[0023] Establishing a knowledge base in the field of renewable energy power generation, wherein the knowledge base stores professional knowledge and operating methods in the field of renewable energy power generation, and extracting trigger conditions and conclusions or operating methods from the professional knowledge and operating methods in the field of renewable energy power generation;

[0024] Use IF-THEN structure to define fuzzy control rules, define the extracted trigger conditions as IF condition items, and define the conclusions or operation methods as THEN conclusion items or instruction items;

[0025] According to the defined fuzzy control rules, a fuzzy vector set is constructed, and the corresponding membership function value is defined for each fuzzy control rule in the fuzzy vector set, and all the fuzzy control rules are combined to form a fuzzy control rule set for new energy power generation data.

[0026] On the basis of the above technical solution, preferably, the step S3 constructs a fuzzy vector set according to the defined fuzzy control rules, defines a corresponding membership function value for each fuzzy control rule in the fuzzy vector set, and combines all fuzzy control rules to form a fuzzy control rule set for new energy power generation data, including the following sub-steps:

[0027] Define a trigger condition set C. Each element C in the trigger condition set i Represents a trigger condition for renewable energy power generation expertise; defines a conclusion set A, where each element A j Represents the conclusion or operation method corresponding to a trigger condition;

[0028] Construct a fuzzy vector set and convert each IF condition item C i and THEN conclusion item or instruction item A j As vectors in the fuzzy vector space, and defining corresponding membership function values, each vector in the fuzzy vector space is associated with its corresponding membership function value;

[0029] Each IF condition item and THEN conclusion item or instruction item and the corresponding membership function value are combined into a fuzzy control rule, and all fuzzy control rules are combined to form a fuzzy control rule set for renewable energy power generation data.

[0030] On the basis of the above technical scheme, preferably, the fuzzy control rule set described in step S4 is used to obtain a fuzzy reasoning conclusion in combination with the new energy power generation knowledge graph, the fuzzy reasoning conclusion is defuzzified, and feedback fuzzy regulation is performed until the expected regulation target of the new energy power generation supervision and management is achieved, including the following sub-steps:

[0031] Identify entities, attributes and relationships corresponding to fuzzy control rules in the renewable energy power generation knowledge graph, map the IF condition items of the fuzzy control rules with the entities and relationships of the renewable energy power generation knowledge graph, and associate the THEN conclusion items or instruction items of the fuzzy control rules with the knowledge results or equipment operation instructions in the knowledge graph;

[0032] Construct the inverse mapping of the fuzzy mapping relationship, that is, defuzzify the conclusion after fuzzy reasoning of the fuzzy new energy power generation knowledge graph;

[0033] According to the renewable energy power generation condition items combined with the renewable energy power generation knowledge graph, a fuzzy reasoning conclusion is generated, and the fuzzy reasoning conclusion is defuzzified. The renewable energy power generation equipment executes the defuzzified execution instructions to control the renewable energy power generation equipment and process, and obtains the actual output value;

[0034] Regulate the renewable energy power generation process, compare the regulation results with the expected output value of renewable energy power generation fuzzy control, and judge whether the expected effect is achieved;

[0035] If the actual output of the fuzzy control of the new energy power generation equipment reaches the expected output value, the control process of operating the new energy power generation equipment is set according to the current new energy power generation fuzzy control rule parameter;

[0036] If the actual output of the fuzzy control of the new energy power generation equipment does not reach the expected output value, the current new energy power generation fuzzy control rule parameters are adjusted, the new energy power generation fuzzy control rule parameters are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of the new energy power generation supervision and management is achieved.

[0037] On the basis of the above technical solution, preferably, the random change of the parameters of the fuzzy control rule of new energy power generation in step S4 is performed, wherein the parameters of the knowledge graph data corresponding to the fuzzy control rule of new energy power generation are randomly changed without repetition within their value range according to the random function, and the expression is:

[0038] p ij =random(r j )

[0039] Among them, p ij Represents the jth renewable energy generation fuzzy control rule r j The i-th parameter, random() represents a random function, p ij ∈P, P represents the set of unused parameters within the value range; the parameters include the number of equipment, the number of process sections, the number of expert experience rules or the time consumed by the renewable energy power generation process.

[0040] On the basis of the above technical solution, preferably, before randomly changing the parameters of the fuzzy control rule for renewable energy power generation in step S4, the method further includes:

[0041] Constructing a set of parameters for optimal solutions to be selected, wherein the set of parameters for optimal solutions to be selected contains a subset of parameters that achieve the expected control target of renewable energy power generation supervision and management after each control, and is associated with renewable energy power generation scenarios;

[0042] Before randomly changing the parameters of the fuzzy control rules for renewable energy power generation, a parameter subset of the expected control target that matches the current renewable energy power generation scenario is obtained, and the parameters of the fuzzy rules for renewable energy power generation are adjusted according to the parameter subset of the matched expected control target to determine whether the output of the renewable energy power generation equipment reaches the expected output value;

[0043] If all matched parameter subsets are not satisfied, the parameters of the fuzzy control rules of renewable energy power generation are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of renewable energy power generation supervision and management is achieved. The parameters of the optimized fuzzy control rules of renewable energy power generation are used as parameter subsets, and matched with the current renewable energy power generation scenario and stored in the parameter set to be selected as the optimal solution. If satisfied, the regulation process of the renewable energy power generation equipment is run according to the current parameters of the fuzzy control rules of renewable energy power generation.

[0044] In a second aspect, the present invention further provides a new energy power generation technology supervision and management system, which is implemented by the new energy power generation technology supervision and management method as described above, and the system includes:

[0045] Data collection module, used to obtain new energy power generation data for supervision and management of new energy power generation technology;

[0046] A knowledge graph construction module is used to perform fuzzification processing on at least two types of renewable energy power generation data, and to perform knowledge graph visualization construction on the fuzzified renewable energy power generation data to generate a fuzzified renewable energy power generation knowledge graph;

[0047] A rule building module is used to establish a knowledge base in the field of renewable energy power generation and to build a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation;

[0048] The reasoning control module is used to obtain fuzzy reasoning conclusions based on the fuzzy control rule set and in combination with the new energy power generation knowledge graph, defuzzify the fuzzy reasoning conclusions, and perform feedback fuzzy regulation until the expected regulation target of new energy power generation supervision and management is achieved.

[0049] The new energy power generation technology supervision and management method and system of the present invention have the following beneficial effects compared with the prior art:

[0050] (1) By acquiring renewable energy power generation data, fuzzifying these data and constructing a fuzzy renewable energy power generation knowledge graph, further establishing a knowledge base in the field of renewable energy power generation and constructing a fuzzy control rule set, and using the fuzzy control rule set and renewable energy power generation knowledge graph for fuzzy reasoning, defuzzification is performed after the reasoning conclusion is reached, and feedback fuzzy control is used to achieve the expected control objectives of renewable energy power generation supervision and management, thereby efficiently integrating and utilizing renewable energy power generation data, improving the intelligent management level of renewable energy power generation through fuzzy control and knowledge graph technology, optimizing energy configuration, and improving the stability and reliability of renewable energy power generation systems;

[0051] (2) Through the combination of fuzzy control rule set and renewable energy power generation knowledge graph, intelligent supervision, management and regulation of renewable energy power generation process is realized. Through continuous feedback and adjustment of control rule parameters, the fuzzy control rules of renewable energy power generation can be gradually optimized to make them more adaptable to the needs of the actual power generation process.

[0052] (3) The set of parameters for the optimal solution to be selected provides an effective parameter configuration reference, which enables the optimal power generation strategy to be found more quickly, thereby improving the management and control efficiency of power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flowchart of the new energy power generation technology supervision and management method of the present invention;

[0055] Figure 2 It is a structural schematic diagram of the new energy power generation technology supervision and management system of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with 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 only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a new energy power generation technology supervision and management method includes the following steps:

[0058] S1, obtain new energy power generation data for new energy power generation technology supervision and management.

[0059] Among them, the new energy power generation data includes health status data of new energy power generation equipment, voltage data of new energy power generation, current data of new energy power generation, uncertain interference data of new energy power generation, sudden failure data of new energy power generation and network emergency processing data of new energy power generation.

[0060] It should be noted that the health status data of new energy power generation equipment reflects the operating status of the equipment, including data such as the degree of wear, temperature, and vibration, which is conducive to timely detection of potential faults. The voltage data of new energy power generation includes data such as voltage fluctuations and voltage imbalance, reflecting the stability of voltage and power quality; the current data of new energy power generation reflects the power generation efficiency and equipment load conditions, including data such as current size and current waveform; the uncertain interference data of new energy power generation includes unstable power generation data caused by factors such as weather changes and equipment aging; the sudden failure data of new energy power generation records the sudden failure of the equipment, including data such as the failure type, occurrence time, and impact range; the network emergency processing data of new energy power generation includes emergency processing measures and recovery time in the event of power grid failure, reflecting the reliability and resilience of the power grid. The acquisition of these data is the basis for supervision and management, and can comprehensively reflect the operating status of the new energy power generation system.

[0061] S2, fuzzy processing is performed on at least two types of new energy power generation data, and a knowledge graph visualization is constructed for the fuzzy processed new energy power generation data to generate a fuzzy new energy power generation knowledge graph.

[0062] The fuzzy processing of at least two kinds of renewable energy power generation data in step S2 includes the following sub-steps:

[0063] Acquire at least two kinds of renewable energy power generation data, and perform data filtering preprocessing on the renewable energy power generation data;

[0064] Extract the health status characteristics of the renewable energy power generation equipment, the voltage characteristics of renewable energy power generation, the current characteristics of renewable energy power generation, the interference characteristics of renewable energy power generation, the fault characteristics of renewable energy power generation and the network threat characteristics of renewable energy power generation from the pre-processed data;

[0065] According to the extracted features, the uncertain fuzzy sets of health status of new energy power generation equipment, the uncertain fuzzy sets of new energy power generation interference, the uncertain fuzzy sets of sudden failures of new energy power generation and the uncertain fuzzy sets of network emergency processing of new energy power generation are constructed respectively;

[0066] According to the qualitative level and membership relationship of the uncertain characteristic data of new energy power generation data, the membership function value of each fuzzy set is calculated, and the calculation expression is:

[0067] μ km (d k , f m )=μ k (d k )·μ m (f m )

[0068] In the formula, μ km (dk , f m ) represents the kth new energy power generation equipment d k The membership function value of the mth uncertain feature data, μ k (d k ) represents the kth new energy power generation equipment d k The probability membership function value, μ m (f m ) represents the membership function value of the mth uncertain feature data.

[0069] It should be noted that at least two types of the renewable energy power generation data are fuzzy processed, and based on the characteristic qualitative level and empirical affiliation judgment of the uncertain data of renewable energy power generation, the uncertain data that cannot be accurately described quantitatively are represented by fuzzy sets and fuzzy processed.

[0070] Specifically, for example, since the new energy power generation equipment A has been used for a long time and is used in a humid outdoor environment, the aging degree of the new energy power generation equipment, which is an uncertain data, is described by fuzzy mathematics into four levels: normal, a little aged, seriously aged and aged to no use. The new energy power generation equipment A is seriously aged. At this time, it is necessary to select a suitable control method for the new energy power generation equipment through fuzzy processing and fuzzy reasoning of new energy power generation to ensure normal operation and safety and reliability; let the fuzzy set describing the aging degree of the new energy power generation equipment be expressed as: O = {(normal, 0.9), (a little aged, 0.7), (severely aged, 0.5), (aged to no use, 0.3)}; where 0.9, 0.7, 0.5 and 0.3 respectively represent the fuzzy membership function values ​​corresponding to the aging degree of the new energy power generation equipment.

[0071] Among them, the aging degree of the new energy power generation equipment is related to the use time, model, humidity of the use environment and temperature of the use environment of the new energy power generation equipment. For example, if the use time of the new energy power generation equipment is less than or equal to 1 year, then the aging degree of this new energy power generation equipment is normal, and its membership function value is 0.9; for example, if the use time of the new energy power generation equipment is 5 years or longer, and the model of the new energy power generation equipment is a low-end version, and its use environment is very humid and relatively high temperature, then the aging degree of this new energy power generation equipment is serious, and its membership function value is 0.5.

[0072] Install new energy power generation equipment k The aging degree is expressed as o k , New energy power generation equipment k The usage time is denoted as t k , New energy power generation equipment k The model is represented by y k , New energy power generation equipment kThe humidity of the operating environment is expressed as s k , New energy power generation equipment k The ambient temperature of the use is indicated by p k , and its calculation formula is as follows:

[0073] o k =λ*g k (t k ,y k ,s k ,p k )

[0074] In the formula, g k represents the life calculation mapping function of the new energy power generation equipment, and λ represents the reliability coefficient of the life of the new energy power generation equipment.

[0075] The step S2 of visually constructing a knowledge graph of the fuzzified new energy power generation data to generate a fuzzified new energy power generation knowledge graph set includes the following sub-steps:

[0076] Identify key entities from each fuzzy set of fuzzy processed renewable energy power generation data, and extract attribute information related to the corresponding entities;

[0077] Determine the logical associations between the identified key entities, integrate the identified entities and the determined logical associations to form a new energy power generation knowledge graph collection, and use visualization tools to display the knowledge graph objects in a graphical manner.

[0078] It can be understood that, taking the aging degree assessment of new energy power generation equipment A as an example, through fuzzy processing, the aging degree of equipment A can be described into four levels, and the membership function value of its aging degree can be determined according to the actual usage of the equipment, such as usage time, model, humidity and temperature of the usage environment. Through the visualization construction of the knowledge graph, the aging degree of equipment A, related attribute information and the relationship with other equipment can be displayed in a graphical way, which is convenient for operation and maintenance personnel to monitor and manage.

[0079] Through fuzzy processing in this embodiment, uncertain data that cannot be accurately described quantitatively can be processed, which improves the flexibility and adaptability of data processing. Combined with the visualization construction of the knowledge graph, it can comprehensively improve the operation and maintenance management level of the new energy power generation system and improve the reliability and safety of the system.

[0080] S3, establish a knowledge base in the field of renewable energy power generation, and construct a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation.

[0081] Step S3 establishes a knowledge base in the field of renewable energy power generation, and constructs a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation, including the following sub-steps:

[0082] Establishing a knowledge base in the field of renewable energy power generation, wherein the knowledge base stores professional knowledge and operating methods in the field of renewable energy power generation, and extracting trigger conditions and conclusions or operating methods from the professional knowledge and operating methods in the field of renewable energy power generation;

[0083] Use IF-THEN structure to define fuzzy control rules, define the extracted trigger conditions as IF condition items, and define the conclusions or operation methods as THEN conclusion items or instruction items;

[0084] According to the defined fuzzy control rules, a fuzzy vector set is constructed, and the corresponding membership function value is defined for each fuzzy control rule in the fuzzy vector set. All fuzzy control rules are combined to form a fuzzy control rule set for new energy power generation data, including:

[0085] Define a trigger condition set C. Each element C in the trigger condition set i Represents a trigger condition for renewable energy power generation expertise; defines a conclusion set A, where each element A j Represents the conclusion or operation method corresponding to a trigger condition;

[0086] Construct a fuzzy vector set and convert each IF condition item C i and THEN conclusion item or instruction item A j As vectors in the fuzzy vector space, and defining corresponding membership function values, each vector in the fuzzy vector space is associated with its corresponding membership function value;

[0087] Each IF condition item and THEN conclusion item or instruction item and the corresponding membership function value are combined into a fuzzy control rule, and all fuzzy control rules are combined to form a fuzzy control rule set for renewable energy power generation data.

[0088] It should be noted that the IF conditional item can be a single condition or multiple conditions, and the multiple conditions are combined into a logical relationship by AND or OR operations. The conclusion item is the data result or knowledge result of fuzzy reasoning, and the instruction item is the operable instruction result of the new energy power generation equipment of fuzzy reasoning; secondly, the professional knowledge and operating methods in the field of new energy power generation often have certain subjectivity, uncertainty and inconsistency. Before extracting fuzzy control rules from the professional knowledge and operating methods in the field of new energy power generation, it is necessary to first determine whether there is a conflict between these experiences, eliminate the existing conflicts, and adjust the consistency between the experiences; then these regularized experiences are formally represented as a fuzzy vector set according to the IF part definition and the THEN part definition, and the uncertainty of the experience is represented by the membership function value of fuzzy mathematics, and the membership function value is defined and represented by a fuzzy experience rule mapping table.

[0089] S4, based on the fuzzy control rule set and combined with the new energy power generation knowledge graph, obtain the fuzzy reasoning conclusion, defuzzify the fuzzy reasoning conclusion, and perform feedback fuzzy regulation until the expected regulation target of new energy power generation supervision and management is achieved.

[0090] Wherein, step S4 includes the following sub-steps:

[0091] Identify entities, attributes and relationships corresponding to fuzzy control rules in the renewable energy power generation knowledge graph, map the IF condition items of the fuzzy control rules with the entities and relationships of the renewable energy power generation knowledge graph, and associate the THEN conclusion items or instruction items of the fuzzy control rules with the knowledge results or equipment operation instructions in the knowledge graph;

[0092] Construct the inverse mapping of the fuzzy mapping relationship, that is, defuzzify the conclusion after fuzzy reasoning of the fuzzy new energy power generation knowledge graph;

[0093] According to the renewable energy power generation condition items combined with the renewable energy power generation knowledge graph, a fuzzy reasoning conclusion is generated, and the fuzzy reasoning conclusion is defuzzified. The renewable energy power generation equipment executes the defuzzified execution instructions to control the renewable energy power generation equipment and process, and obtains the actual output value;

[0094] Regulate the renewable energy power generation process, compare the regulation results with the expected output value of renewable energy power generation fuzzy control, and judge whether the expected effect is achieved;

[0095] If the actual output of the fuzzy control of the new energy power generation equipment reaches the expected output value, the control process of operating the new energy power generation equipment is set according to the current new energy power generation fuzzy control rule parameter;

[0096] If the actual output of the fuzzy control of the new energy power generation equipment does not reach the expected output value, the current new energy power generation fuzzy control rule parameters are adjusted, the new energy power generation fuzzy control rule parameters are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of the new energy power generation supervision and management is achieved.

[0097] It should be noted that, according to the fuzzy rule mapping table relationship of new energy power generation, reverse mapping is performed to construct the corresponding anti-fuzzy mapping table relationship, forming an inverse mapping between fuzzification processing and anti-fuzzification processing; the fuzzy empirical mapping table relationship of new energy power generation is a kind of encoding of fuzzy mathematical representation, and the reverse mapping to construct the corresponding anti-fuzzy mapping table relationship is a kind of decoding of fuzzy mathematical representation; the fuzzy instruction output after fuzzy reasoning of new energy power generation cannot be recognized and executed by new energy power generation equipment, and must be transformed into a control instruction that can be recognized and executed by new energy power generation equipment through anti-fuzzification processing of new energy power generation; the new energy power generation equipment executes the anti-fuzzified control instruction, obtains the actual output value, regulates the new energy power generation process, and compares its regulation result with the expected output value of the new energy power generation fuzzy control to determine whether the expected effect is achieved; if the actual output of the fuzzy control of the new energy power generation equipment reaches the expected output value, the regulation process of the new energy power generation equipment is set according to the current new energy power generation fuzzy control rule parameter setting; if the actual output of the fuzzy control of the new energy power generation equipment does not reach the expected output value, the current new energy power generation fuzzy control rule parameters are adjusted, the new energy power generation fuzzy control rule parameters are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of the supervision and management of new energy power generation is achieved.

[0098] This embodiment realizes intelligent supervision, management and regulation of the renewable energy power generation process by combining the fuzzy control rule set with the renewable energy power generation knowledge graph. By continuously feeding back and adjusting the control rule parameters, it can ensure the stable operation of the renewable energy power generation system and achieve the expected goals.

[0099] In step S4 of this embodiment, the parameters of the fuzzy control rule for renewable energy power generation are randomly changed, wherein the parameters of the knowledge graph data corresponding to the fuzzy control rule for renewable energy power generation are randomly changed without repetition within their value range according to the random function, and the expression is:

[0100] p ij =random(r j )

[0101] Among them, p ij Represents the jth renewable energy generation fuzzy control rule r j The i-th parameter, random() represents a random function, p ij∈P, P represents the set of unused parameters within the value range; the parameters include the number of equipment, the number of process sections, the number of expert experience rules or the time consumed by the renewable energy power generation process.

[0102] It should be noted that in order to optimize the fuzzy control rules of renewable energy power generation and achieve the expected supervision and management objectives, the method of randomly changing the control rule parameters is adopted. Non-repetitive random adjustments are made within the range of parameter values ​​based on random functions. Through continuous iteration and adjustment of parameters, the fuzzy control rules of renewable energy power generation can be gradually optimized to make them more adaptable to the needs of the actual power generation process.

[0103] Before randomly changing the parameters of the fuzzy control rules for renewable energy generation in step S4, the method further includes:

[0104] Constructing a set of parameters for optimal solutions to be selected, wherein the set of parameters for optimal solutions to be selected contains a subset of parameters that achieve the expected control target of renewable energy power generation supervision and management after each control, and is associated with renewable energy power generation scenarios;

[0105] Before randomly changing the parameters of the fuzzy control rules for renewable energy power generation, a parameter subset of the expected control target that matches the current renewable energy power generation scenario is obtained, and the parameters of the fuzzy rules for renewable energy power generation are adjusted according to the parameter subset of the matched expected control target to determine whether the output of the renewable energy power generation equipment reaches the expected output value;

[0106] If all matched parameter subsets are not satisfied, the parameters of the fuzzy control rules of renewable energy power generation are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of renewable energy power generation supervision and management is achieved. The parameters of the optimized fuzzy control rules of renewable energy power generation are used as parameter subsets, and matched with the current renewable energy power generation scenario and stored in the parameter set to be selected as the optimal solution. If satisfied, the regulation process of the renewable energy power generation equipment is run according to the current parameters of the fuzzy control rules of renewable energy power generation.

[0107] It should be noted that by randomly changing the parameters of the fuzzy control rules and combining feedback fuzzy regulation, the system can better adapt to changes in different renewable energy power generation scenarios, thereby improving the robustness of the system. The optimized fuzzy control rules can more accurately adjust the equipment operating status, reduce equipment failures caused by improper parameter settings, and improve the overall reliability of the system. The set of parameters to be selected provides an effective parameter configuration reference, which enables the optimal power generation strategy to be found more quickly, thereby improving the management and control efficiency of power generation.

[0108] For example, to deal with the power generation process of severely aged new energy power generation equipment, conservative fuzzy control rules are implemented:

[0109] IF the new energy power generation equipment is seriously aged THEN execute conservative fuzzy control instructions for the new energy power generation equipment

[0110] For example, when dealing with the power generation process of new energy power generation equipment encountering thunderstorms, the safety fuzzy control rules of lightning protection are implemented:

[0111] IF the new energy power generation equipment encounters lightning weather THEN execute the safety fuzzy control instructions for lightning protection. Each time the new energy power generation equipment feedback fuzzy regulation is carried out, the rule parameters of new energy power generation are selectively optimized to ensure the consistency and coordination of the management and maintenance of the new energy power generation process knowledge graph, and also to ensure the overall maximum effectiveness of the new energy power generation process knowledge graph.

[0112] like Figure 2 As shown, in a second aspect, the present invention further provides a new energy power generation technology supervision and management system, which is implemented by the new energy power generation technology supervision and management method as described above, and the system includes:

[0113] Data collection module, used to obtain new energy power generation data for supervision and management of new energy power generation technology;

[0114] A knowledge graph construction module is used to perform fuzzification processing on at least two types of renewable energy power generation data, and to perform knowledge graph visualization construction on the fuzzified renewable energy power generation data to generate a fuzzified renewable energy power generation knowledge graph;

[0115] A rule building module is used to establish a knowledge base in the field of renewable energy power generation and to build a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation;

[0116] The reasoning control module is used to obtain fuzzy reasoning conclusions based on the fuzzy control rule set and in combination with the new energy power generation knowledge graph, defuzzify the fuzzy reasoning conclusions, and perform feedback fuzzy regulation until the expected regulation target of new energy power generation supervision and management is achieved.

[0117] It should be noted that this system is a system corresponding to the above-mentioned new energy power generation technology supervision and management method. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of this system and can achieve the same technical effects.

[0118] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system and module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0120] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 devices or units, which can be electrical, mechanical or other forms.

[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0122] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0124] In addition, it should be noted that in the system and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0125] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing system. The computing system can be a well-known general-purpose system. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A new energy power generation technology supervision and management method, characterized in that: The following steps are involved: S1, obtain new energy power generation data for new energy power generation technology supervision and management; S2, fuzzifying at least two kinds of renewable energy power generation data, and visually constructing a knowledge graph for the fuzzified renewable energy power generation data to generate a fuzzified renewable energy power generation knowledge graph; S3, establish a knowledge base in the field of renewable energy power generation, and construct a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation; S4, based on the fuzzy control rule set and combined with the new energy power generation knowledge graph, obtain the fuzzy reasoning conclusion, defuzzify the fuzzy reasoning conclusion, and perform feedback fuzzy regulation until the expected regulation target of new energy power generation supervision and management is achieved.

2. The new energy power generation technology supervision and management method according to claim 1, characterized in that: The new energy power generation data includes health status data of new energy power generation equipment, voltage data of new energy power generation, current data of new energy power generation, uncertain interference data of new energy power generation, sudden failure data of new energy power generation and network emergency processing data of new energy power generation.

3. The new energy power generation technology supervision and management method according to claim 2, characterized in that: The fuzzy processing of at least two kinds of renewable energy power generation data in step S2 includes the following sub-steps: Acquire at least two kinds of renewable energy power generation data, and perform data filtering preprocessing on the renewable energy power generation data; Extract the health status characteristics of the renewable energy power generation equipment, the voltage characteristics of renewable energy power generation, the current characteristics of renewable energy power generation, the interference characteristics of renewable energy power generation, the fault characteristics of renewable energy power generation and the network threat characteristics of renewable energy power generation from the pre-processed data; According to the extracted features, the uncertain fuzzy sets of health status of new energy power generation equipment, the uncertain fuzzy sets of new energy power generation interference, the uncertain fuzzy sets of sudden failures of new energy power generation and the uncertain fuzzy sets of network emergency processing of new energy power generation are constructed respectively; According to the qualitative level and membership relationship of the uncertain characteristic data of new energy power generation data, the membership function value of each fuzzy set is calculated, and the calculation expression is: m km (d k ,f m )=μ k (d k )·m m (f m ) In the formula, μ km (d k , f m ) represents the kth new energy power generation equipment d k The membership function value of the mth uncertain feature data, μ k (d k ) represents the kth new energy power generation equipment d k The probability membership function value, μ m (f m ) represents the membership function value of the mth uncertain feature data.

4. The new energy power generation technology supervision and management method according to claim 3, characterized in that: The step S2 involves visually constructing a knowledge graph of the fuzzified new energy power generation data to generate a fuzzified new energy power generation knowledge graph set, including the following sub-steps: Identify key entities from each fuzzy set of fuzzy processed renewable energy power generation data, and extract attribute information related to the corresponding entities; Determine the logical associations between the identified key entities, integrate the identified entities and the determined logical associations to form a new energy power generation knowledge graph collection, and use visualization tools to display the knowledge graph objects in a graphical manner.

5. The new energy power generation technology supervision and management method according to claim 1, characterized in that: Step S3 establishes a knowledge base in the field of renewable energy power generation, and constructs a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation, including the following sub-steps: Establishing a knowledge base in the field of renewable energy power generation, wherein the knowledge base stores professional knowledge and operating methods in the field of renewable energy power generation, and extracting trigger conditions and conclusions or operating methods from the professional knowledge and operating methods in the field of renewable energy power generation; Use IF-THEN structure to define fuzzy control rules, define the extracted trigger conditions as IF condition items, and define the conclusions or operation methods as THEN conclusion items or instruction items; According to the defined fuzzy control rules, a fuzzy vector set is constructed, and the corresponding membership function value is defined for each fuzzy control rule in the fuzzy vector set, and all the fuzzy control rules are combined to form a fuzzy control rule set for new energy power generation data.

6. The new energy power generation technology supervision and management method according to claim 5, characterized in that: According to the defined fuzzy control rules in step S3, a fuzzy vector set is constructed, and a corresponding membership function value is defined for each fuzzy control rule in the fuzzy vector set, and all fuzzy control rules are combined to form a fuzzy control rule set for new energy power generation data, including the following sub-steps: Define a trigger condition set C. Each element C in the trigger condition set i Represents a trigger condition for renewable energy power generation expertise; defines a conclusion set A, where each element A j Represents the conclusion or operation method corresponding to a trigger condition; Construct a fuzzy vector set and convert each IF condition item C i and THEN conclusion item or instruction item A j As vectors in the fuzzy vector space, and defining corresponding membership function values, each vector in the fuzzy vector space is associated with its corresponding membership function value; Each IF condition item and THEN conclusion item or instruction item and the corresponding membership function value are combined into a fuzzy control rule, and all fuzzy control rules are combined to form a fuzzy control rule set for renewable energy power generation data.

7. The new energy power generation technology supervision and management method according to claim 6, characterized in that: Step S4, according to the fuzzy control rule set, and in combination with the new energy power generation knowledge graph, obtains the fuzzy reasoning conclusion, defuzzifies the fuzzy reasoning conclusion, and performs feedback fuzzy regulation until the expected regulation target of the new energy power generation supervision and management is achieved, including the following sub-steps: Identify entities, attributes and relationships corresponding to fuzzy control rules in the renewable energy power generation knowledge graph, map the IF condition items of the fuzzy control rules with the entities and relationships of the renewable energy power generation knowledge graph, and associate the THEN conclusion items or instruction items of the fuzzy control rules with the knowledge results or equipment operation instructions in the knowledge graph; Construct the inverse mapping of the fuzzy mapping relationship, that is, defuzzify the conclusion after fuzzy reasoning of the fuzzy new energy power generation knowledge graph; According to the renewable energy power generation condition items combined with the renewable energy power generation knowledge graph, a fuzzy reasoning conclusion is generated, and the fuzzy reasoning conclusion is defuzzified. The renewable energy power generation equipment executes the defuzzified execution instructions to control the renewable energy power generation equipment and process, and obtains the actual output value; Regulate the renewable energy power generation process, compare the regulation results with the expected output value of renewable energy power generation fuzzy control, and judge whether the expected effect is achieved; If the actual output of the fuzzy control of the new energy power generation equipment reaches the expected output value, the control process of operating the new energy power generation equipment is set according to the current new energy power generation fuzzy control rule parameter; If the actual output of the fuzzy control of the new energy power generation equipment does not reach the expected output value, the current new energy power generation fuzzy control rule parameters are adjusted, the new energy power generation fuzzy control rule parameters are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of the new energy power generation supervision and management is achieved.

8. The new energy power generation technology supervision and management method according to claim 7, characterized in that: Randomly change the parameters of the fuzzy control rules for renewable energy power generation, where the parameters of the knowledge graph data corresponding to the fuzzy control rules for renewable energy power generation are randomly changed within their value range according to the random function, and the expression is: p ij =random(r j ) Among them, p ij Represents the jth renewable energy generation fuzzy control rule r j The i-th parameter, random() represents a random function, p ij ∈P, P represents the set of unused parameters within the value range; the parameters include the number of equipment, the number of process sections, the number of expert experience rules or the time consumed by the renewable energy power generation process.

9. The new energy power generation technology supervision and management method according to claim 8, characterized in that: Before randomly changing the parameters of the fuzzy control rules for renewable energy generation, it also includes: Constructing a set of parameters for optimal solutions to be selected, wherein the set of parameters for optimal solutions to be selected contains a subset of parameters that achieve the expected control target of renewable energy power generation supervision and management after each control, and is associated with renewable energy power generation scenarios; Before randomly changing the parameters of the fuzzy control rules for renewable energy power generation, a parameter subset of the expected control target that matches the current renewable energy power generation scenario is obtained, and the parameters of the fuzzy rules for renewable energy power generation are adjusted according to the parameter subset of the matched expected control target to determine whether the output of the renewable energy power generation equipment reaches the expected output value; If all matched parameter subsets are not satisfied, the parameters of the fuzzy control rules of renewable energy power generation are randomly changed, and feedback fuzzy regulation is performed until the expected regulation target of renewable energy power generation supervision and management is achieved. The parameters of the optimized fuzzy control rules of renewable energy power generation are used as parameter subsets, and matched with the current renewable energy power generation scenario and stored in the parameter set to be selected as the optimal solution. If satisfied, the regulation process of the renewable energy power generation equipment is run according to the current parameters of the fuzzy control rules of renewable energy power generation.

10. A new energy power generation technology supervision and management system, implemented by the new energy power generation technology supervision and management method according to any one of claims 1 to 9, characterized in that: The system comprises: Data collection module, used to obtain new energy power generation data for new energy power generation technology supervision and management; A knowledge graph construction module is used to perform fuzzification processing on at least two types of renewable energy power generation data, and to perform knowledge graph visualization construction on the fuzzified renewable energy power generation data to generate a fuzzified renewable energy power generation knowledge graph; A rule building module is used to establish a knowledge base in the field of renewable energy power generation and to build a fuzzy control rule set for renewable energy power generation data based on the knowledge base in the field of renewable energy power generation; The reasoning control module is used to obtain fuzzy reasoning conclusions based on the fuzzy control rule set and in combination with the new energy power generation knowledge graph, defuzzify the fuzzy reasoning conclusions, and perform feedback fuzzy regulation until the expected regulation target of new energy power generation supervision and management is achieved.

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