CN115933503A - Intelligent adjustment control method and system for power generation equipment - Google Patents
Intelligent adjustment control method and system for power generation equipment Download PDFInfo
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- CN115933503A CN115933503A CN202310224886.7A CN202310224886A CN115933503A CN 115933503 A CN115933503 A CN 115933503A CN 202310224886 A CN202310224886 A CN 202310224886A CN 115933503 A CN115933503 A CN 115933503A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention provides an intelligent regulation control method and system of power generation equipment, and relates to the technical field of intelligent data processing.
Description
Technical Field
The invention relates to the technical field of intelligent data processing, in particular to an intelligent regulation control method and system for power generation equipment.
Background
With the rapid development of science and technology, power generation equipment is involved in a plurality of fields such as production, national defense, science and technology and becomes necessary demand equipment, but in the operation process of the power generation equipment, the power generation equipment is influenced by a plurality of uncontrollable external factors and internal factors, so that energy loss is caused to a certain degree, and resource waste is caused. In order to ensure the reasonable utilization of energy, the real-time loss direction and loss value need to be subjected to targeted adjustment control.
At present, mainly based on equipment operation time and load carry out the equipment utilization and calculate, and then confirm equipment operation loss, the loss is higher through carrying out the live patrol and examine of equipment operation, confirms the loss source, does the pertinence regulation and control, mainly relies on professional technical personnel to go on, has certain subjectivity and manpower loss, needs to optimize the adjustment.
In the prior art, the operation loss analysis method of the power generation equipment is traditional and is not intelligent enough, so that the loss analysis efficiency is low, the accuracy is not enough, certain limitation is caused on subsequent regulation and control, and the regulation and control effect is influenced.
Disclosure of Invention
The invention provides an intelligent regulation and control method and system for power generation equipment, which are used for solving the technical problems that the operation loss analysis method of the power generation equipment in the prior art is more traditional and is not intelligent enough, so that the loss analysis efficiency is low, the accuracy is not enough, certain limitation is caused on subsequent regulation and control, and the regulation and control effect is influenced.
In view of the above problems, the present invention provides an intelligent regulation control method and system for power generation equipment.
In a first aspect, the present invention provides a method for intelligent regulation control of a power generation device, the method comprising:
acquiring power generation attribute information of first power generation equipment;
acquiring a power generation monitoring index set based on the power generation attribute information;
the power generation monitoring module is used for carrying out real-time power generation monitoring according to the power generation monitoring index set to obtain a power generation monitoring data set;
inputting the power generation monitoring data set into a power generation loss identification model, and obtaining a loss data set according to the power generation loss identification model, wherein the power generation loss identification model comprises equipment loss and energy storage loss;
performing loss fluctuation analysis according to the loss data set to obtain a loss fluctuation interval;
inputting the loss fluctuation interval into an energy-saving conversion module for analysis to obtain energy-saving control parameters;
and controlling the first power generation equipment according to the energy-saving control parameter.
In a second aspect, the present invention provides an intelligent regulation control system for a power generation plant, the system comprising:
the information acquisition module is used for acquiring the power generation attribute information of the first power generation equipment;
the index acquisition module is used for acquiring a power generation monitoring index set based on the power generation attribute information;
the index monitoring module is used for carrying out real-time power generation monitoring according to the power generation monitoring index set by the power generation monitoring module to obtain a power generation monitoring data set;
the loss data acquisition module is used for inputting the power generation monitoring data set into a power generation loss identification model and obtaining a loss data set according to the power generation loss identification model, wherein the power generation loss identification model comprises equipment loss and energy storage loss;
the loss fluctuation analysis module is used for carrying out loss fluctuation analysis according to the loss data set to obtain a loss fluctuation interval;
the parameter acquisition module is used for inputting the loss fluctuation interval into the energy-saving conversion module for analysis to acquire energy-saving control parameters;
and the equipment control module is used for controlling the first power generation equipment by using the energy-saving control parameter.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
the embodiment of the invention provides an intelligent regulation control method of power generation equipment, which comprises the steps of obtaining power generation attribute information of first power generation equipment; acquiring a power generation monitoring index set based on the power generation attribute information, performing real-time power generation monitoring based on the power generation monitoring module to obtain a power generation monitoring data set, and inputting the power generation monitoring data set into a power generation loss identification model to obtain a loss data set, wherein the power generation loss identification model comprises equipment loss and energy storage loss; the loss data set is subjected to loss fluctuation analysis to obtain a loss fluctuation interval, the loss fluctuation interval is input into the energy-saving conversion module for analysis, energy-saving control parameters are obtained to control the first power generation equipment, the technical problems that the operation loss analysis method of the power generation equipment in the prior art is traditional and not intelligent enough, the loss analysis efficiency is low, the accuracy is not enough, certain limitation is caused to subsequent regulation and control, the regulation and control effect is affected are solved, targeted processing of monitoring data is carried out based on multidimensional monitoring indexes, modeling is carried out for evaluation and prediction, intelligent, efficient and accurate analysis of the operation loss of the equipment is realized, and the regulation and control effect of the equipment is guaranteed.
Drawings
FIG. 1 is a flow chart of an intelligent regulation control method for a power generation device according to the present invention;
FIG. 2 is a schematic diagram illustrating a power generation monitoring index set acquisition process in an intelligent regulation control method for a power generation plant according to the present invention;
FIG. 3 is a schematic diagram illustrating a flow of acquiring a loss fluctuation interval in an intelligent regulation control method for a power generation device according to the present invention;
fig. 4 is a schematic structural diagram of an intelligent regulation control system of a power generation device provided by the invention.
Description of reference numerals: the device comprises an information acquisition module 11, an index acquisition module 12, an index monitoring module 13, a loss data acquisition module 14, a loss fluctuation analysis module 15, a parameter acquisition module 16 and a device control module 17.
Detailed Description
The invention provides an intelligent regulation and control method and system of power generation equipment, wherein a power generation monitoring index set is obtained based on power generation attribute information of first power generation equipment, a power generation monitoring data set is obtained by performing real-time power generation monitoring based on a power generation monitoring module, the power generation monitoring data set is input into a power generation loss identification model to obtain a loss data set, loss fluctuation analysis is performed to obtain a loss fluctuation interval, an energy-saving control parameter is input into an energy-saving conversion module to obtain an energy-saving control parameter, and the first power generation equipment is controlled.
Example one
As shown in fig. 1, the present invention provides an intelligent regulation control method for a power generation device, the method is applied to an energy saving management system of the power generation device, the system is connected with a power generation monitoring module in a communication manner, and the method includes:
step S100: acquiring power generation attribute information of first power generation equipment;
specifically, the equipment required by the power generation equipment necessity is influenced by various uncontrollable external factors and internal factors in the operation process to cause energy loss to a certain degree, and in order to realize the maximum utilization of energy, the method for the only adjustment control of the power generation equipment provided by the invention is applied to the energy-saving management system of the power generation equipment. Specifically, the first generator is a target generator to be subjected to loss analysis and evaluation, and power generation attribute analysis is performed on the first generator, that is, a power generation source, such as water power, wind power, solar energy, and the like. Due to the difference of the power generation attributes, the specific analysis indexes are different from the monitoring direction, the targeted evaluation is facilitated, and the adaptability and the accuracy of the follow-up adjustment control are improved.
Step S200: acquiring a power generation monitoring index set based on the power generation attribute information;
further, as shown in fig. 2, step S200 of the present invention further includes:
step S210: determining power generation raw material information, power generation environment information and power generation power information based on the power generation attribute information;
step S220: performing index analysis according to the power generation raw material information, the power generation environment information and the power generation power information to obtain a raw material monitoring index, an environment monitoring index and a power monitoring index;
step S230: and performing matrixing on the raw material monitoring index, the environment monitoring index and the power monitoring index to generate the power generation monitoring index set.
Specifically, based on the power generation attribute information, determining an index direction to be evaluated, and acquiring the power generation raw material information, the power generation environment information, and the power generation power information, for example, when the power generation attribute information is wind power generation, the power generation raw material information is wind energy; the power generation environment information is a wind power plant which is mostly a wind energy integration area; the power generation power information is influenced by the power generation equipment and the real-time wind energy. Then based on the power generation raw material information, taking the raw material attribute, the raw material content and the like as the raw material monitoring indexes; based on the power generation environment information, taking environmental external factors such as wind power size, direction, climate, terrain and the like as the environmental monitoring indexes; and based on the generated power information, taking voltage, current, equipment parameters, real-time equipment temperature and the like as the power monitoring indexes to obtain multidimensional monitoring index parameters. Similarly, when the power generation attribute information is solar power generation, the corresponding power generation raw material is solar energy, the power generation environment information is a solar power plant, most of the power generation environment information is an area with strong illumination, and the power generation power information is influenced by power generation equipment and a real-time environment, including illumination intensity, illumination direction, coverage area and the like. And generating equipment with different generating attributes is different from the corresponding generating raw material information, the generating environment information and the generating power information, and specific analysis processing is carried out on attribute difference. Preferably, corresponding power generation demand information is respectively determined according to different power generation attributes, the power generation demand information and the power generation demand information are mapped and correspond to form an attribute information list, before information monitoring and acquisition are carried out, associated information can be directly extracted based on the attribute information list, and the information acquisition direction can be rapidly determined. The raw material monitoring index, the environment monitoring index and the power monitoring index are used as information sources, due to the diversification and complexity of data, the data processing is complicated and is matrixed, so that the regular and ordered data are improved, the data identification and extraction are convenient, and the index parameter integration is carried out to generate the power generation monitoring index set.
Further, step S230 of the present invention further includes:
step S231: analyzing an energy-saving management system of the power generation equipment to obtain preset matrix parameters, wherein the preset matrix parameters comprise a row index quantity interval and a column index quantity interval;
step S232: generating a first matrix constraint condition according to the row index quantity interval of the preset matrix parameters;
step S233: generating a second matrix constraint condition according to the column index quantity interval of the preset matrix parameter;
step S234: and generating a power generation monitoring index set according to the first matrix constraint condition and the second matrix constraint condition.
Further, analyzing the energy-saving management system of the power generation device to obtain a preset matrix parameter, step S231 of the present invention further includes:
step S2311: connecting a system terminal of an energy-saving management system of the power generation equipment to obtain a mapping database of server load-calculation efficiency;
step S2312: identifying by the mapping database of server load-calculation efficiency, identifying load data corresponding to preset calculation efficiency, and outputting identification load data;
step S2313: and generating the preset matrix parameters according to the identification load data.
Specifically, the raw material monitoring index, the environment monitoring index and the power monitoring index are subjected to matrixing processing, specifically, the energy-saving management system of the power generation equipment is used as a master control system for operation regulation and control, is connected with a system terminal of the energy-saving management system of the power generation equipment, determines the calculation efficiency of a plurality of server loads, and performs mapping correspondence on the server loads and the system terminal to generate the mapping database of the server loads and the calculation efficiency. The method comprises the steps of obtaining preset computing efficiency, namely a configured computing efficiency value meeting analysis requirements, evaluating the preset computing efficiency as a limiting factor, wherein the preset computing efficiency is not a fixed value, dynamically adjusting based on actual requirements, traversing a mapping database of server load-computing efficiency, extracting server loads matched with the preset computing efficiency based on the mapping relation between the preset computing efficiency and the computing efficiency, collecting matched server loads, determining load parameters including relevant performance, indexes, models and the like, identifying the server loads so as to identify and distinguish data, and obtaining identification load data. And configuring the preset matrix parameters based on the identification load data, wherein the preset matrix parameters comprise the row index number interval and the column index number interval, namely the limited number of the index parameters.
Specifically, an index dimension is used as the row index number interval, the row index number interval based on the preset matrix parameter is used, for example, the current multi-dimensional index to be evaluated includes the raw material monitoring index, the environment monitoring index and the power monitoring index, and a quantity value 3 is used as the first matrix constraint condition; and limiting a plurality of index parameters contained in the multidimensional monitoring index to a column index number interval of the preset matrix parameter, for example, limiting the column index number to 6, using the column index number as the second matrix constraint condition, preferably, performing decreasing ordering on the index parameters corresponding to all the dimension indexes based on index parameter weights, wherein the index parameter weights are in direct proportion to parameter influence degrees, and the sequence order can be further improved. The first matrix constraint condition and the second matrix constraint condition are not constant values, and can be dynamically adjusted based on actual requirements, for example, as the index dimension is expanded, the number of corresponding row indexes is increased; the number of the column indexes is controlled based on actual monitoring requirements, including but not limited to 6, and a plurality of index parameters meeting the second matrix constraint condition and having higher parameter weights are respectively determined for the multi-dimensional monitoring indexes. Based on the first matrix constraint condition and the second matrix constraint condition, performing multi-dimensional index parameter arrangement, where the column index parameters are arranged in a weight decreasing manner, for example, a 3 × 6 index matrix may be generated as the power generation monitoring index set.
Step S300: the power generation monitoring module is used for carrying out real-time power generation monitoring according to the power generation monitoring index set to obtain a power generation monitoring data set;
step S400: inputting the power generation monitoring data set into a power generation loss identification model, and obtaining a loss data set according to the power generation loss identification model, wherein the power generation loss identification model comprises equipment loss and energy storage loss;
specifically, the power generation monitoring module is a functional module for acquiring and storing power generation data in real time, the power generation monitoring index set is used as an acquisition direction, index data of a power generation device in an operation live state are determined, mapping correspondence and arrangement are carried out by combining the power generation monitoring index set, a monitoring data matrix is determined, the monitoring data matrix is identified based on real-time acquisition time, the power generation monitoring data set is generated, and the power generation monitoring data set is to-be-evaluated source data to be subjected to power generation loss analysis.
Further, the equipment loss and the energy storage loss are used as loss evaluation directions, the power generation loss identification model is constructed and comprises a plurality of sub-function models, namely an equipment loss identification submodel and an energy storage loss identification submodel, the power generation monitoring data set is input into the power generation loss identification model, energy consumption analysis is respectively carried out on the basis of the equipment loss identification submodel and the energy storage loss identification submodel, directional energy consumption analysis results are determined and comprise a raw material loss data set, a heating loss data set, a device loss data set and an energy storage loss data set, the loss data sets are integrated, and the loss data sets are generated and are equipment losses to be optimized, controlled and adjusted, and basic data sources are provided for follow-up energy consumption fluctuation analysis.
Further, the power generation monitoring data set is input into a power generation loss identification model, a loss data set is obtained according to the power generation loss identification model, and the applying step S400 further includes:
step S410: inputting the power generation monitoring data set into the power generation loss identification model, wherein the power generation loss identification model comprises an equipment loss identification submodel, and the equipment loss identification submodel comprises a raw material loss identification layer, a heating loss identification layer and a device loss identification layer;
step S420: outputting a raw material loss data set, a heating loss data set and a device loss data set according to the loss identification model;
step S430: and outputting the loss data set by using the raw material loss data set, the heating loss data set and the device loss data set.
Further, step S420 of the present invention further includes:
step S421: inputting the power generation monitoring data set into the power generation loss identification model, wherein the power generation loss identification model comprises an energy storage loss identification submodel;
step S422: acquiring an energy storage loss data set according to the energy storage loss identification submodel;
step S423: adding the energy storage loss data set to the loss data set for output.
Specifically, a main body framework of the power generation loss recognition model is constructed by training a neural network, the equipment loss recognition submodel and the energy storage loss recognition submodel are embedded in the main body framework, the execution functions of the models are different, and the modeling process and the operation mechanism of the models are similar. The power generation loss identification model comprises a plurality of functional layers, a raw material loss identification layer, a heating loss identification layer and a device loss identification layer, and is used for carrying out multi-dimensional loss identification analysis.
And further, inputting the power generation monitoring data into the equipment loss identification submodel in the power generation loss identification model for analyzing the running loss of the equipment. Identifying raw material monitoring data based on the raw material loss identification layer, and performing loss detection analysis to determine the raw material loss data set; extracting environment monitoring data and equipment operation monitoring data based on the heating loss identification layer, and performing heating loss detection analysis to determine the heating loss data set; and extracting power monitoring data and equipment operation data based on the device loss identification layer, detecting and analyzing device loss, namely analyzing the loss of the equipment, and determining the device loss data set. And the targeted index data identification and loss analysis are performed by layer-by-layer analysis, so that the analysis efficiency and accuracy are improved.
And inputting the power generation detection data into the energy storage loss identification submodel in the power generation loss identification model for analyzing the energy storage loss of the equipment. And generating the energy storage loss data set by carrying out data identification and model detection analysis. The raw material loss data set, the heating loss data set and the device loss data set are integrated and normalized to serve as an equipment loss data set, the equipment loss data set and the energy storage loss data set are subjected to attribution integration, loss directional identification is carried out, the loss data set is generated and output in a model mode, data loss analysis is carried out through the built model, and objectivity and accuracy of a loss analysis result can be effectively guaranteed.
Step S500: performing loss fluctuation analysis according to the loss data set to obtain a loss fluctuation interval;
further, as shown in fig. 3, a loss fluctuation analysis is performed according to the loss data set to obtain a loss fluctuation interval, and step S500 of the present invention further includes:
step S510: performing curve drawing on the loss data set to obtain a first loss curve, a second loss curve and a third loss curve;
step S520: performing linear discrete value identification on the first loss curve, the second loss curve and the third loss curve to obtain a primary identification data set;
step S530: carrying out secondary discrete value identification according to the primary identification data set to obtain a secondary identification data set;
step S540: and obtaining the loss fluctuation interval according to the secondary identification data set.
Specifically, the power generation equipment operation energy consumption analysis is carried out according to the power generation loss identification model, and the loss data set is obtained. And respectively carrying out index parameter loss fluctuation analysis on the loss data set based on multi-dimensional indexes, and taking the positioned discrete value loss fluctuation data as the loss fluctuation interval by carrying out data discrete evaluation.
Specifically, based on the raw material loss dataset, the heating loss dataset and the device loss dataset, performing equipment operation loss fluctuation interval analysis, taking index parameters and loss data as coordinate axes, constructing a coordinate system, constructing the first loss curve based on the raw material loss dataset, for example, an abscissa can be a plurality of index parameters of the raw material detection index, performing loss data positioning of the index parameters in an ordinate, determining a plurality of index loss coordinate points, and generating the first loss curve; and respectively constructing the second loss curve and the third loss curve according to the heating loss data set and the device loss data set based on the curve construction mode. Further, linear analysis is carried out on the basis of the first loss curve, a corresponding linear trend is determined, a plurality of coordinate points distributed outside the linear trend in the first loss curve are determined, and the coordinate points are used as linear discrete values to be identified; and similarly, respectively determining corresponding linear trends of the second loss curve and the third loss curve, extracting discrete coordinate points, performing integrated attribution on the discrete coordinate points corresponding to the identified multiple loss curves, performing curve identification, and generating the primary identification data set. The primary identifier is a plurality of loss data in which loss fluctuation exists.
Further, performing discrete analysis again on the basis of the primary identification data set, extracting the primary identification data set of the first loss curve, performing linear trend analysis again on the primary identification data set, determining a discrete coordinate point outside the linear trend on the basis, and taking the discrete coordinate point as a discrete value for identification; and similarly, respectively carrying out discrete value analysis again on the primary identification data sets corresponding to the second loss curve and the third loss curve, and carrying out discrete value integration to generate the secondary identification data set. And taking a data fluctuation interval corresponding to the secondary identification data set as the loss fluctuation interval. The secondary identification data set is data with a large discrete value, data except the secondary expression data set in the primary identification data set is ignored, and due to the fact that data fluctuation is small, the corresponding adjustment effect is small, the data can be properly ignored, and data processing efficiency is improved.
Step S600: inputting the loss fluctuation interval into an energy-saving conversion module for analysis to obtain energy-saving control parameters;
step S700: and controlling the first power generation equipment according to the energy-saving control parameter.
Specifically, the energy-saving loss module is a processing module for data conversion, illustratively, a loss conversion database is constructed, the loss conversion database comprises a plurality of sample loss fluctuation intervals and corresponding sample energy-saving control parameters, the sample loss fluctuation intervals and the corresponding sample energy-saving control parameters are mapped and associated, sample data acquisition can be performed based on historical loss conversion records, and the energy-saving conversion module is trained and optimized based on the loss conversion database. The loss fluctuation interval is input into the energy-saving conversion module, data matching mapping is carried out through traversing the loss conversion database, control parameters corresponding to matching data are used as the energy-saving control parameters, the energy-saving control parameters are control parameters for carrying out loss adjustment optimization on power generation equipment, the control parameters are matched with the real-time running state of the power generation equipment, the first power generation equipment is controlled based on the energy-saving control parameters, the energy loss in the equipment running process can be effectively reduced, and the maximum utilization of energy is realized.
Example two
Based on the same inventive concept as the intelligent regulation control method of the power generation equipment in the foregoing embodiment, as shown in fig. 4, the present invention provides an intelligent regulation control system of the power generation equipment, the system including:
the information acquisition module 11 is used for acquiring power generation attribute information of the first power generation equipment;
an index obtaining module 12, where the index obtaining module 12 is configured to obtain a power generation monitoring index set based on the power generation attribute information;
the index monitoring module 13 is configured to perform real-time power generation monitoring according to the power generation monitoring index set by using the power generation monitoring module to obtain a power generation monitoring data set;
the loss data acquisition module 14 is configured to input the power generation monitoring data set into a power generation loss identification model, and obtain a loss data set according to the power generation loss identification model, where the power generation loss identification model includes equipment loss and energy storage loss;
the loss fluctuation analysis module 15 is configured to perform loss fluctuation analysis according to the loss data set to obtain a loss fluctuation interval;
the parameter obtaining module 16, where the parameter obtaining module 16 is configured to input the loss fluctuation interval into an energy saving conversion module for analysis, and obtain an energy saving control parameter;
and the equipment control module 17, wherein the equipment control module 17 is configured to control the first power generation equipment by using the energy saving control parameter.
Further, the system further comprises:
the parameter information acquisition module is used for determining power generation raw material information, power generation environment information and power generation power information based on the power generation attribute information;
the monitoring index acquisition module is used for performing index analysis according to the power generation raw material information, the power generation environment information and the power generation power information to acquire a raw material monitoring index, an environment monitoring index and a power monitoring index;
and the index set generating module is used for performing matrixing on the raw material monitoring index, the environment monitoring index and the power monitoring index to generate the power generation monitoring index set.
Further, the system further comprises:
the matrix parameter acquisition module is used for analyzing an energy-saving management system of the power generation equipment to obtain preset matrix parameters, wherein the preset matrix parameters comprise a row index number interval and a column index number interval;
the first matrix constraint condition generation module is used for generating a first matrix constraint condition according to the row index quantity interval of the preset matrix parameter;
the second matrix constraint condition generation module is used for generating a second matrix constraint condition according to the interval of the number of the column indexes of the preset matrix parameters;
and the monitoring index set generating module is used for generating a power generation monitoring index set according to the first matrix constraint condition and the second matrix constraint condition.
Further, the system further comprises:
the database acquisition module is used for connecting a system terminal of an energy-saving management system of the power generation equipment to obtain a mapping database of server load-calculation efficiency;
the data identification module is used for identifying the mapping database of the server load-calculation efficiency, identifying load data corresponding to preset calculation efficiency and outputting identification load data;
and the matrix parameter generation module is used for generating the preset matrix parameters according to the identification load data.
Further, the system further comprises:
the data input module is used for inputting the power generation monitoring data set into the power generation loss identification model, wherein the power generation loss identification model comprises an equipment loss identification submodel, and the equipment loss identification submodel comprises a raw material loss identification layer, a heating loss identification layer and a device loss identification layer;
the model analysis module is used for outputting a raw material loss data set, a heating loss data set and a device loss data set according to the loss identification model;
a loss data set output module to output the loss data set with the feedstock loss data set, the heating loss data set, and the device loss data set.
Further, the system further comprises:
a monitoring data set input module for inputting the power generation monitoring data set into the power generation loss identification model, wherein the power generation loss identification model comprises an energy storage loss identification submodel;
the sub-model analysis module is used for identifying a sub-model according to the energy storage loss and acquiring an energy storage loss data set;
and the data adding module is used for adding the energy storage loss data set to the loss data set for outputting.
Further, the system further comprises:
the curve drawing module is used for drawing a curve according to the loss data set to obtain a first loss curve, a second loss curve and a third loss curve;
a primary identification data set acquisition module, configured to perform linear discrete value identification on the first loss curve, the second loss curve, and the third loss curve to obtain a primary identification data set;
the secondary identification data set acquisition module is used for carrying out secondary discrete value identification according to the primary identification data set to obtain a secondary identification data set;
and the loss fluctuation interval acquisition module is used for acquiring the loss fluctuation interval according to the secondary identification data set.
In the present specification, through the foregoing detailed description of the intelligent regulation control method for power generation equipment, it will be apparent to those skilled in the art that the intelligent regulation control method and system for power generation equipment in the present embodiment are disclosed.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An intelligent regulation control method for power generation equipment is applied to an energy-saving management system of the power generation equipment, the system is in communication connection with a power generation monitoring module, and the method comprises the following steps:
acquiring power generation attribute information of first power generation equipment;
acquiring a power generation monitoring index set based on the power generation attribute information;
carrying out real-time power generation monitoring according to the power generation monitoring index set by using the power generation monitoring module to obtain a power generation monitoring data set;
inputting the power generation monitoring data set into a power generation loss identification model, and obtaining a loss data set according to the power generation loss identification model, wherein the power generation loss identification model comprises equipment loss and energy storage loss;
performing loss fluctuation analysis according to the loss data set to obtain a loss fluctuation interval;
inputting the loss fluctuation interval into an energy-saving conversion module for analysis to obtain energy-saving control parameters;
and controlling the first power generation equipment according to the energy-saving control parameter.
2. The method of claim 1, wherein the method further comprises:
determining power generation raw material information, power generation environment information and power generation power information based on the power generation attribute information;
performing index analysis according to the power generation raw material information, the power generation environment information and the power generation power information to obtain a raw material monitoring index, an environment monitoring index and a power monitoring index;
and performing matrixing on the raw material monitoring index, the environment monitoring index and the power monitoring index to generate the power generation monitoring index set.
3. The method of claim 2, wherein the method further comprises:
analyzing an energy-saving management system of the power generation equipment to obtain preset matrix parameters, wherein the preset matrix parameters comprise a row index quantity interval and a column index quantity interval;
generating a first matrix constraint condition according to the row index quantity interval of the preset matrix parameters;
generating a second matrix constraint condition according to the column index quantity interval of the preset matrix parameter;
and generating a power generation monitoring index set according to the first matrix constraint condition and the second matrix constraint condition.
4. The method of claim 3, wherein the energy conservation management system of the power generation equipment is analyzed to obtain the preset matrix parameters, the method further comprising:
connecting a system terminal of an energy-saving management system of the power generation equipment to obtain a mapping database of server load-calculation efficiency;
identifying by the mapping database of server load-calculation efficiency, identifying load data corresponding to preset calculation efficiency, and outputting identification load data;
and generating the preset matrix parameters according to the identification load data.
5. The method of claim 2, wherein the power generation monitoring data set is input into a power generation loss identification model, and a loss data set is derived from the power generation loss identification model, the method further comprising:
inputting the power generation monitoring data set into the power generation loss identification model, wherein the power generation loss identification model comprises an equipment loss identification submodel, and the equipment loss identification submodel comprises a raw material loss identification layer, a heating loss identification layer and a device loss identification layer;
outputting a raw material loss data set, a heating loss data set and a device loss data set according to the loss identification model;
and outputting the loss data set by using the raw material loss data set, the heating loss data set and the device loss data set.
6. The method of claim 5, wherein the method further comprises:
inputting the power generation monitoring data set into the power generation loss identification model, wherein the power generation loss identification model comprises an energy storage loss identification submodel;
acquiring an energy storage loss data set according to the energy storage loss identification submodel;
adding the energy storage loss data set to the loss data set for output.
7. The method of claim 1, wherein a loss ripple analysis is performed from the loss data set to obtain a loss ripple interval, the method further comprising:
performing curve drawing on the loss data set to obtain a first loss curve, a second loss curve and a third loss curve;
performing linear discrete value identification on the first loss curve, the second loss curve and the third loss curve to obtain a primary identification data set;
performing secondary discrete value identification according to the primary identification data set to obtain a secondary identification data set;
and obtaining the loss fluctuation interval according to the secondary identification data set.
8. An intelligent regulation control system of power generation equipment, characterized in that, the system is connected with power generation monitoring module communication, the system includes:
the information acquisition module is used for acquiring the power generation attribute information of the first power generation equipment;
the index acquisition module is used for acquiring a power generation monitoring index set based on the power generation attribute information;
the index monitoring module is used for carrying out real-time power generation monitoring according to the power generation monitoring index set by the power generation monitoring module to obtain a power generation monitoring data set;
the loss data acquisition module is used for inputting the power generation monitoring data set into a power generation loss identification model and obtaining a loss data set according to the power generation loss identification model, wherein the power generation loss identification model comprises equipment loss and energy storage loss;
the loss fluctuation analysis module is used for carrying out loss fluctuation analysis according to the loss data set to obtain a loss fluctuation interval;
the parameter acquisition module is used for inputting the loss fluctuation interval into the energy-saving conversion module for analysis to acquire energy-saving control parameters;
and the equipment control module is used for controlling the first power generation equipment by using the energy-saving control parameter.
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