CN116455085A - Intelligent monitoring system of battery energy storage power station - Google Patents
Intelligent monitoring system of battery energy storage power station Download PDFInfo
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- CN116455085A CN116455085A CN202310718244.2A CN202310718244A CN116455085A CN 116455085 A CN116455085 A CN 116455085A CN 202310718244 A CN202310718244 A CN 202310718244A CN 116455085 A CN116455085 A CN 116455085A
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C3/00—Fire prevention, containment or extinguishing specially adapted for particular objects or places
- A62C3/16—Fire prevention, containment or extinguishing specially adapted for particular objects or places in electrical installations, e.g. cableways
-
- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C37/00—Control of fire-fighting equipment
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
- G01B7/18—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge using change in resistance
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K11/00—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00
- G01K11/32—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres
- G01K11/3206—Measuring temperature based upon physical or chemical changes not covered by groups G01K3/00, G01K5/00, G01K7/00 or G01K9/00 using changes in transmittance, scattering or luminescence in optical fibres at discrete locations in the fibre, e.g. using Bragg scattering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/482—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for several batteries or cells simultaneously or sequentially
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
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Abstract
The invention relates to the technical field of battery energy storage, and discloses an intelligent monitoring system of a battery energy storage power station, which is used for realizing intelligent monitoring of the battery energy storage power station and improving the accuracy of monitoring operation and maintenance. The system comprises: the system comprises a battery cell state monitoring module, a module temperature monitoring module and a power station fire alarm module; the cell state monitoring module is used for: acquiring battery core body parameters and monitoring parameters of a battery energy storage power station to monitor battery core states, and outputting a plurality of battery core state detection results corresponding to each battery core state analysis model; the module temperature monitoring module is used for: acquiring the array type module temperature of the battery energy storage power station for module temperature early warning analysis, and generating a module temperature early warning analysis result; the power station fire alarm module is used for: and acquiring energy storage power station working condition data of the battery energy storage power station to perform power station fire alarm analysis, and obtaining a power station fire alarm analysis result.
Description
Technical Field
The invention relates to the technical field of battery energy storage, in particular to an intelligent monitoring system of a battery energy storage power station.
Background
The lithium ion battery consists of a positive electrode, a negative electrode, a diaphragm and electrolyte, wherein the positive electrode of the main stream product is usually made of nickel-manganese-cobalt ternary materials or lithium iron phosphate, and the negative electrode is mostly made of carbon materials such as graphite. The lithium ion battery has the advantages of high energy density, no memory effect, quick charge and discharge, quick response speed and the like, and is widely applied to new energy power generation side allocation and storage and user side energy storage projects such as wind power photovoltaic and the like.
At present, in order to ensure the operation safety of the battery energy storage power station, the temperature of each battery in the energy storage power station needs to be measured, and the traditional measurement mode is to attach a patch type temperature sensor to the surface of the battery, but because the number of the batteries in the energy storage power station is large, the consumption of the temperature sensor can reach thousands to tens of thousands, so that the cost is high, the effective management is difficult, meanwhile, the temperature of a battery case can only be detected, the temperature and the deformation of an electric core cannot be monitored, and the limitation is large.
Disclosure of Invention
The invention provides an intelligent monitoring system of a battery energy storage power station, which is used for realizing intelligent monitoring of the battery energy storage power station and improving the accuracy of monitoring operation and maintenance.
The first aspect of the invention provides an intelligent monitoring system of a battery energy storage power station, which comprises: the system comprises a battery cell state monitoring module, a module temperature monitoring module and a power station fire alarm module;
the battery cell state monitoring module is used for: acquiring a battery core body parameter and a monitoring parameter of a battery energy storage power station, and carrying out information fusion on the battery core body parameter and the monitoring parameter to obtain parameter fusion data; and respectively carrying out cell state monitoring on the parameter fusion data through a plurality of preset cell state analysis models, and outputting a plurality of cell state detection results corresponding to each cell state analysis model;
The module temperature monitoring module is used for: acquiring the array module temperature of the battery energy storage power station based on a preset fiber grating array temperature sensor; inputting the array module temperature into a preset module temperature analysis model for module temperature early warning analysis, and generating a module temperature early warning analysis result;
the power station fire alarm module is used for: and acquiring the working condition data of the energy storage power station of the battery energy storage power station, inputting the working condition data of the energy storage power station into a preset power station fire alarm model for power station fire alarm analysis, and obtaining a power station fire alarm analysis result.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the battery cell state monitoring module is specifically configured to:
based on a preset fiber bragg grating cell sensor, obtaining a cell body parameter of a battery energy storage power station, wherein the cell body parameter comprises: cell size data, cell mechanism data, usage time data, production time data, and frequency of occurrence of unexpected conditions;
acquiring the temperature of a battery core of the battery energy storage power station by adopting a probe thermometer, acquiring the deformation of the battery core of the battery energy storage power station by implanting a patch type strain gauge into the battery core, and taking the temperature of the battery core and the deformation of the battery core as monitoring parameters;
Information fusion is carried out on the battery cell body parameters and the monitoring parameters, and parameter fusion data are obtained;
inputting the parameter fusion data into a plurality of preset cell state analysis models, wherein the cell state analysis models comprise: the battery cell life assessment model, the battery cell charge and discharge state analysis model and the battery cell thermal runaway analysis model;
and processing the parameter fusion data through the plurality of cell state analysis models to obtain a plurality of cell state detection results corresponding to each cell state analysis model.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the module temperature monitoring module is specifically configured to:
arranging a preset fiber grating array temperature sensor and a module temperature sensing optical cable in the battery energy storage power station;
monitoring a module temperature field of the battery energy storage power station through the fiber bragg grating array temperature sensor and the module temperature sensing optical cable to obtain an array module temperature;
and inputting the array type module temperature into a preset module temperature analysis model for module temperature early warning analysis, and generating a module temperature early warning analysis result, wherein the module temperature early warning analysis result comprises overtemperature early warning and temperature rise rate early warning.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the power station fire alarm module is specifically configured to:
arranging the fiber bragg grating array temperature sensor and a preset fire-fighting temperature sensing optical cable in the battery energy storage power station;
acquiring a fire control temperature field of the battery energy storage power station through the fiber bragg grating array temperature sensor and the fire control temperature sensing optical cable to obtain working condition data of the energy storage power station;
and inputting the working condition data of the energy storage power station into a preset power station fire alarm model to perform power station fire alarm analysis, so as to obtain a power station fire alarm analysis result.
With reference to the first aspect, in a fourth implementation manner of the first aspect of the present invention, the battery energy storage power station intelligent monitoring system further includes: the intelligent operation and maintenance system comprises a power station environment monitoring module, an equipment safety linkage module and an intelligent operation and maintenance module; the power station environment monitoring module comprises: the device comprises a humidity monitoring unit, a soaking monitoring unit, a vibration monitoring unit and an electromagnetic monitoring unit; the equipment safety linkage module comprises: a fire-fighting facility control unit, a drainage facility control unit and a temperature control facility control unit.
With reference to the first aspect, in a fifth implementation manner of the first aspect of the present invention, the power station environment monitoring module is configured to:
Acquiring environmental temperature data, soaking data, vibration data and electromagnetic data of the battery energy storage power station based on a preset environmental sensor group;
inputting the environmental temperature data into a preset environmental temperature alarm model for environmental temperature alarm analysis to obtain an environmental temperature alarm result;
inputting the flooding data into a preset flooding alarm model for flooding analysis to obtain a flooding analysis result;
inputting the vibration data into a preset vibration alarm model for vibration analysis to obtain a vibration analysis result;
and inputting the electromagnetic data into a preset electromagnetic alarm model for electromagnetic analysis to obtain an electromagnetic analysis result.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the device safety linkage module is configured to:
fire control facilities in the battery energy storage power station are controlled through a preset fire control facility control model, and when abnormal fire control data of the power station occur, a command is issued through the intelligent monitoring system of the energy storage power station to trigger the fire control facilities to carry out safety linkage;
the drainage facility control is carried out on the drainage facility in the battery energy storage power station through a preset drainage facility control model, and when the water immersion data of the power station is abnormal, a command is issued through the intelligent monitoring system of the energy storage power station to trigger the drainage facility to carry out safety linkage;
And controlling the temperature control facilities in the battery energy storage power station through a preset temperature control facility control model, and when the temperature data of the power station battery core and the module are abnormal, issuing a command through the intelligent monitoring system of the energy storage power station to trigger the temperature control facilities to carry out safe linkage.
With reference to the first aspect, in a seventh implementation manner of the first aspect of the present invention, the intelligent operation and maintenance module is configured to:
and carrying out operation and maintenance analysis on the battery energy storage power station according to the sensing data set and the technical parameter set of the battery energy storage power station to obtain target operation and maintenance information, wherein the target operation and maintenance information comprises operation and maintenance frequency, operation and maintenance position and operation and maintenance depth.
In the technical scheme provided by the invention, the system comprises: the system comprises a battery cell state monitoring module, a module temperature monitoring module and a power station fire alarm module; the cell state monitoring module is used for: acquiring battery core body parameters and monitoring parameters of a battery energy storage power station to monitor battery core states, and outputting a plurality of battery core state detection results corresponding to each battery core state analysis model; the module temperature monitoring module is used for: acquiring the array type module temperature of the battery energy storage power station for module temperature early warning analysis, and generating a module temperature early warning analysis result; the power station fire alarm module is used for: the method comprises the steps of acquiring working condition data of the energy storage power station of the battery energy storage power station to carry out fire alarm analysis of the power station, obtaining fire alarm analysis results of the power station, adopting an array type module temperature monitoring unit and an array type container temperature monitoring unit which are composed of a fiber bragg grating demodulator and a plurality of fiber bragg grating array temperature sensors, effectively reducing the use quantity of the temperature sensors in the battery energy storage power station, carrying out classified management on the monitored data more easily, carrying out effective monitoring on the temperature and deformation of the battery cells through the use of a probe thermometer and a patch type strain gauge, and accordingly timely acquiring the state of each battery cell, facilitating targeted maintenance of the power station and the battery module, further realizing intelligent monitoring of the battery energy storage power station and improving the accuracy of monitoring operation and maintenance of the battery energy storage power station.
Drawings
FIG. 1 is a schematic diagram of an embodiment of an intelligent monitoring system for a battery energy storage power station according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a Bragg grating measurement point according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides an intelligent monitoring system of a battery energy storage power station, which is used for realizing intelligent monitoring of the battery energy storage power station and improving the accuracy of monitoring operation and maintenance. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention is described below with reference to fig. 1, where an embodiment of a battery energy storage power station intelligent monitoring system in an embodiment of the present invention includes:
the system comprises a battery cell state monitoring module 101, a module temperature monitoring module 102 and a power station fire alarm module 103;
it can be understood that the execution body of the invention can be an intelligent monitoring device of a battery energy storage power station, and can also be a terminal or a server, and the execution body is not limited in the specific description. The embodiment of the invention is described by taking a server as an execution main body as an example.
The cell state monitoring module 101 is configured to: acquiring a battery core body parameter and a monitoring parameter of a battery energy storage power station, and carrying out information fusion on the battery core body parameter and the monitoring parameter to obtain parameter fusion data; the parameter fusion data are respectively subjected to cell state monitoring through a plurality of preset cell state analysis models, and a plurality of cell state detection results corresponding to each cell state analysis model are output;
in this embodiment, the parameter fusion data is obtained by acquiring the parameters of the battery core body and the monitoring parameters of the battery energy storage power station and fusing the parameters. Meanwhile, the module also uses a plurality of preset cell state analysis models to monitor the cell state of the parameter fusion data and outputs a plurality of cell state detection results corresponding to each cell state analysis model. The purpose of doing so is to discover the abnormity of the state of the battery cell in time, and avoid the loss of the whole battery pack caused by the failure of a single battery cell.
The module temperature monitoring module 102 is configured to: acquiring the array module temperature of a battery energy storage power station based on a preset fiber grating array temperature sensor; inputting the array type module temperature into a preset module temperature analysis model for module temperature early warning analysis, and generating a module temperature early warning analysis result;
in this embodiment, the array module temperature of the battery energy storage power station is obtained based on a preset fiber grating array temperature sensor. And then inputting the obtained array module temperature into a preset module temperature analysis model for temperature early warning analysis, and generating a module temperature early warning analysis result. Therefore, when the temperature of a certain module is found to be abnormal, the system can give an alarm and process in time so as to avoid faults caused by overheating.
The station fire alarm module 103 is configured to: and acquiring the working condition data of the energy storage power station of the battery energy storage power station, inputting the working condition data of the energy storage power station into a preset power station fire alarm model for power station fire alarm analysis, and obtaining a power station fire alarm analysis result.
The power station fire alarm module is used for monitoring working condition data of the battery energy storage power station, and inputting the obtained data into a preset power station fire alarm model for analysis so as to obtain a power station fire alarm analysis result. The purpose of doing so is to prevent safety accidents such as fire disasters of the battery energy storage power station and ensure the operation safety of the battery energy storage power station. Firstly, the system adopts various sensors and analysis models, and can comprehensively and accurately monitor and analyze various parameters of the battery energy storage power station, so that the risk of faults or accidents of the battery energy storage power station is effectively reduced. And secondly, the system has high intelligent and automatic degree, can realize remote monitoring, early warning and control, and improves the management efficiency and response speed of the battery energy storage power station.
The cell state monitoring module 101 is specifically configured to:
based on energy storage battery body performance, acquire battery energy storage power station's electric core body parameter, wherein, electric core body parameter includes: cell size data, cell mechanism data, usage time data, production time data, and frequency of occurrence of unexpected conditions;
acquiring the temperature of a battery core of the battery energy storage power station by adopting a probe thermometer, acquiring the deformation of the battery core of the battery energy storage power station by implanting a patch type strain gauge into the battery core, and taking the temperature of the battery core and the deformation of the battery core as monitoring parameters;
information fusion is carried out on the parameters of the battery cell body and the monitoring parameters, and parameter fusion data are obtained;
inputting the parameter fusion data into a plurality of preset cell state analysis models, wherein the cell state analysis models comprise: the battery cell life assessment model, the battery cell charge and discharge state analysis model and the battery cell thermal runaway analysis model;
and processing the parameter fusion data through a plurality of cell state analysis models to obtain a plurality of cell state detection results corresponding to each cell state analysis model.
It should be noted that, through energy storage battery body performance, acquire battery cell body parameter of battery energy storage power station, the sensor is including implanting the inside probe thermometer of electric core and the surface mounted strain gauge on electric core surface, and the probe thermometer is used for monitoring electric core temperature, and the surface mounted strain gauge is used for monitoring electric core deformation volume, and wherein, electric core body parameter includes: cell size data, cell mechanism data, time of use data, time of production data, and frequency of occurrence of accidents. The parameters can reflect the service conditions, the residual life and other important information of the battery cell, thereby helping operation and maintenance personnel to maintain and manage in time. The use of the probe thermometer and the patch type strain gauge can obtain the temperature and deformation of the battery core of the battery energy storage power station. The probe thermometer is adopted to monitor the temperature change of the battery cell in real time under the condition that the normal operation of the battery cell is not affected; the cell deformation can be monitored by implanting the cell through the patch type strain gauge, so that the state of the cell is further evaluated. And carrying out information fusion on the battery cell body parameters and the monitoring parameters to obtain parameter fusion data. The purpose of this is to comprehensively consider the relationships between the various parameters, thereby more accurately assessing the cell state. And inputting the parameter fusion data into a plurality of preset cell state analysis models. Wherein, a plurality of electric core state analysis models include: cell life assessment model, cell charge-discharge state analysis model and cell thermal runaway analysis model. The models can evaluate the service life, the charge and discharge state, the thermal runaway risk and other conditions of the battery cell according to different scenes and requirements, and output corresponding detection results to operation and maintenance personnel for reference, thereby being beneficial to improving the safe and stable operation level of the battery energy storage power station. The battery cell state analysis model generally consists of a plurality of sub-models, and mainly comprises a battery cell service life assessment model, a battery cell charge and discharge state analysis model, a battery cell thermal runaway analysis model and the like. Wherein, electric core life assessment model: the model is mainly used to evaluate the remaining life of the battery. The method generally predicts the time point when the battery possibly fails according to the factors such as the service time, the cycle times, the load condition, the historical temperature, the historical strain and the like of the battery, thereby reminding a user or a manager of replacement maintenance. Cell charge and discharge state analysis model: the model aims at evaluating the charge and discharge states of the battery cells. The model can evaluate the charge and discharge states of the battery cells according to the parameters of the battery cells, such as voltage, capacity, internal resistance and the like, and give corresponding state detection results. Cell thermal runaway analysis model: the model is mainly used for evaluating the temperature change trend of the battery and evaluating the possible thermal runaway risk of the battery. By analyzing the temperature and deformation data of the battery and combining a related algorithm, whether the battery can generate a thermal runaway phenomenon can be predicted, and operation and maintenance personnel can be helped to take corresponding measures in time. The battery life assessment model predicts the life of the battery mainly according to historical data, wherein the historical data comprises the service time, the working state, the cycle number, the historical temperature, the historical strain and the like of the battery. Based on an empirical model, a statistical method or an artificial neural network and other methods are generally adopted to build the model, and the battery failure time point is predicted. When building an empirical model, it is first necessary to accurately collect and record the usage of the battery, including the parameter changes during charge and discharge and the operating state of the battery. Then, based on these data, a corresponding model is constructed by statistical methods such as regression analysis, survival analysis, kalman filtering, and the like. For example, in making an assessment of cell life, the remaining life of a cell can be considered as a random process that varies over time. In this process, if the cell has failed, its lifetime will be marked as 0 and will not be considered further. From the first day the cell is put into operation, data about the cell, such as voltage, temperature, load, etc., of the cell is collected every day. By using the data, a cell failure rate curve can be obtained by using a survival analysis method. In practical application, the prediction accuracy based on the empirical model is low, but the calculation amount is small, the modeling is simple, and the method is often adopted in some practical application scenes. Meanwhile, the prediction result based on the experience model can be comprehensively evaluated by combining the analysis result based on the physical model, so that the prediction precision is improved. The battery cell charge and discharge state analysis model is a mathematical model for evaluating the charge and discharge state of a battery. The method evaluates the charge and discharge states of the battery cells by monitoring parameters such as the voltage, the capacity, the internal resistance and the like of the battery cells and combining some characteristic indexes reflecting the working states of the battery cells, such as cut-off voltage, SOC (State of Charge), SOH (State of Health) and the like. In practical applications, the battery cell charge and discharge state analysis model is generally modeled by adopting various algorithms and techniques. Among them, machine learning methods such as support vector machines, neural networks, bayesian classifiers, decision trees, etc. are often used to improve accuracy and stability of models. In addition, in order to more accurately evaluate the charge and discharge states of the battery cells, the battery cell charge and discharge state analysis model also needs to consider a plurality of factors, such as circuit topology, temperature variation, charge and discharge rate, and the like. Based on the factors, the battery cell charge and discharge state analysis model can further predict the service life of the battery cell, avoid excessive charge and discharge and prolong the service life of the battery. The cell thermal runaway analysis model is a mathematical model for assessing the risk of thermal runaway that may occur in a battery. The method can predict whether the battery can generate overheat phenomenon in the battery by monitoring parameter data such as temperature change trend, deformation and the like of the battery, thereby timely taking corresponding safety measures. In practice, the cell thermal runaway analysis model is typically modeled in conjunction with a number of algorithms and techniques. The method can be used for predicting the temperature distribution of the battery cell and the occurrence probability of thermal runaway by using methods such as finite element analysis, thermal conduction theory and thermal model. In addition, the physical characteristics of chemical reactions, electrochemical reactions, etc. inside the battery are also important factors affecting thermal runaway, and these factors need to be taken into consideration.
The module temperature monitoring module 102 is specifically configured to:
arranging a preset fiber grating array temperature sensor and a module temperature sensing optical cable in a battery energy storage power station;
monitoring a module temperature field of the battery energy storage power station through a fiber bragg grating array temperature sensor and a module temperature sensing optical cable to obtain an array module temperature;
inputting the array type module temperature into a preset module temperature analysis model for module temperature early warning analysis, and generating a module temperature early warning analysis result, wherein the module temperature early warning analysis result comprises overtemperature early warning and temperature rising rate early warning.
Specifically, in the battery energy storage power station, a plurality of fiber bragg grating array temperature sensors and module temperature sensing optical cables are preset and are arranged on a battery module, as shown in fig. 2, fig. 2 is a schematic diagram of bragg grating measuring points, wherein the array module temperature monitoring and the array container temperature monitoring are composed of a fiber bragg grating demodulator and a plurality of fiber bragg grating array temperature sensors, and the fiber bragg grating array temperature sensors are distributed and inscribed with a plurality of bragg grating measuring points. And monitoring the module temperature field of the battery energy storage power station through the fiber bragg grating array temperature sensors and the module temperature sensing optical cable to obtain array module temperature data. The array module temperature data is input into a preset module temperature analysis model for module temperature early warning analysis, and the model can be modeled by adopting algorithms such as machine learning and the like and is analyzed by combining historical data and real-time monitoring data. And generating a module temperature early warning analysis result, wherein the module temperature early warning analysis result comprises an overtemperature early warning and a temperature rise rate early warning. The overtemperature early warning means that if the temperature of the battery module is higher than a certain set value, an overtemperature early warning signal is sent out; the early warning of the temperature rise rate refers to that if the temperature change speed of the battery module is too high, a corresponding early warning signal of the temperature rise rate is sent out. The module temperature analysis model is a mathematical model for monitoring and analyzing the module temperature in a battery energy storage power station. The module temperature data can be input, and the module temperature can be predicted and analyzed by using algorithms such as machine learning, so that the early warning and the optimal control of the module temperature can be realized. A two-layer threshold cycle network (Double Threshold Recurrent Neural Network, DTRNN) is one of the common models. The DTRNN can capture time-dependent and nonlinear relations of time-series data, and thus is suitable for predicting a battery module temperature change process. The DTRNN model is typically composed of two neuron layers: an input layer and a hidden layer. The input layer receives the historical temperature data and sends the historical temperature data into the hidden layer for processing. The hidden layer adopts a threshold unit structure to help the network find key information and transmit the information to the output layer. The output layer is used for predicting the future module temperature. In the DTRNN model, two threshold values, an upper threshold and a lower threshold, are set for distinguishing between normal and abnormal conditions. When the predicted result exceeds the upper limit/lower limit threshold, a corresponding early warning signal is sent to prompt that the temperature of the module is abnormal. In addition to DTRNN, there are many other model of module temperature analysis, such as kalman filtering, support vector machines, etc. The models can be selected and customized according to actual scenes, so that accuracy of module temperature prediction is improved, and early warning and control are effectively realized.
The power station fire alarm module 103 is specifically configured to:
arranging a fiber bragg grating array temperature sensor and a preset fire-fighting temperature sensing optical cable in a battery energy storage power station;
acquiring a fire control temperature field of the battery energy storage power station through a fiber bragg grating array temperature sensor and a fire control temperature sensing optical cable to obtain working condition data of the energy storage power station;
and inputting the working condition data of the energy storage power station into a preset power station fire alarm model to perform power station fire alarm analysis, so as to obtain a power station fire alarm analysis result.
Specifically, in the battery energy storage power station, a plurality of fiber bragg grating array temperature sensors and fire-fighting temperature sensing optical cables are preset and are arranged on a battery module. And acquiring a fire-fighting temperature field of the battery energy storage power station through the fiber bragg grating array temperature sensors and the fire-fighting temperature sensing optical cable to obtain working condition data of the energy storage power station. And inputting the working condition data of the energy storage power station into a preset power station fire alarm model to perform power station fire alarm analysis. The model can be modeled by adopting algorithms such as machine learning and the like, and is analyzed by combining historical data and real-time monitoring data. And obtaining a power station fire alarm analysis result according to the analysis result of the power station fire alarm model. The result will include information on: temperature trend changes: the battery energy storage power station may have temperature change trends at different positions, and the fire alarm analysis results can reflect the trends and provide references for management staff. Overtemperature early warning: if the temperature of a part of areas of the battery energy storage power station exceeds a set safety range, the fire alarm model can send out corresponding early warning signals. Hot spot discovery: hot spot areas, i.e., areas of abnormal temperature and potential fire initiation, may exist in battery energy storage power stations. The fire alarm analysis result can help the manager to find the hot spots and take corresponding measures in time. In a word, the fiber bragg grating array temperature sensor and the fire-fighting temperature sensing optical cable are used for fire-fighting monitoring of the battery energy storage power station, so that management staff can be helped to timely master the working state of the battery energy storage power station, possible fire accidents are predicted, corresponding safety measures are timely taken, and safe and stable operation of the battery energy storage power station is guaranteed. Meanwhile, the power station fire alarm model provides more accurate monitoring data and operability for management staff, and plays a positive role in promoting the scientization, the intellectualization and the refinement of fire control work. The power station fire alarm model is a mathematical model for monitoring and analyzing the fire risk of the battery energy storage power station. The method can predict and analyze the fire risk of the power station by inputting fire monitoring data such as a fiber bragg grating temperature sensor and the like and utilizing algorithms such as machine learning and the like, and generate corresponding fire alarm analysis results. The residual network (Residual Networks, resNet) is one of the common models. ResNet is a deep neural network, and is used for solving the problem of gradient disappearance/explosion in the neural network training process and improving the accuracy and stability of the model. The following is the calculation process of ResNet in the power station fire alarm model: input layer: fire control monitoring data such as a fiber grating temperature sensor are input into the ResNet model. Feature extraction layer: the convolutional neural network is used for extracting the characteristics of the fire monitoring data, helping to find key information and transmitting the information to the next layer. Residual layer: consists of a plurality of residual blocks. Each residual block comprises two convolution layers and a jump connection layer and is used for adding input data and residual values so as to avoid the problem of gradient disappearance/explosion and improve the accuracy of the model. Global average pooling layer: and carrying out average pooling on the feature images output by the residual error layer to obtain statistical information of the whole data set. Output layer: and further processing and analyzing by using algorithms such as a classifier and the like according to the output result of the global average pooling layer to generate a corresponding fire alarm analysis result. These results may include over-temperature warnings, hot spot findings, temperature trend changes, etc.
The battery energy storage power station intelligent monitoring system still includes: a power station environment monitoring module 104, an equipment safety linkage module 105 and an intelligent operation and maintenance module 106; the plant environment monitoring module 104 includes: the device comprises a humidity monitoring unit, a soaking monitoring unit, a vibration monitoring unit and an electromagnetic monitoring unit; the device safety linkage module 105 includes: a fire-fighting facility control unit, a drainage facility control unit and a temperature control facility control unit.
In this embodiment, the power station environment monitoring module: the environment around the battery energy storage power station is monitored through sensors such as a humidity monitoring unit, a soaking monitoring unit, a vibration monitoring unit, an electromagnetic monitoring unit and the like. The sensors can help management personnel to timely master the change condition of the surrounding environment of the power station, predict the possible safety risk and take corresponding safety measures. And the equipment safety linkage module is used for: the equipment such as the fire-fighting equipment control unit, the drainage equipment control unit, the temperature control equipment control unit and the like are used for realizing interconnection and intercommunication among the equipment. For example, the fire protection device control unit may automatically trigger the fire protection device to limit the spread of fire when a fire or other security incident is detected. The drainage facility control unit can be responsible for controlling liquid drainage in the power station, so that accidents caused by liquid leakage are avoided. The temperature control facility control unit can be responsible for controlling the air temperature in the power station, and ensures that the temperature is in a safe range. The intelligent operation and maintenance module: and the running state of the battery energy storage power station is monitored and analyzed in real time through technologies such as data acquisition, analysis and excavation. The module can help management personnel to know the running condition of the power station in time, predict possible faults, coordinate maintenance work and improve the reliability and stability of the power station.
The power station environment monitoring module 104 is configured to:
acquiring environmental temperature data, soaking data, vibration data and electromagnetic data of a battery energy storage power station based on a preset environmental sensor group;
inputting the environmental temperature data into a preset environmental temperature alarm model for environmental temperature alarm analysis to obtain an environmental temperature alarm result;
inputting the flooding data into a preset flooding alarm model for flooding analysis to obtain a flooding analysis result;
inputting the vibration data into a preset vibration alarm model for vibration analysis to obtain a vibration analysis result;
and inputting the electromagnetic data into a preset electromagnetic alarm model for electromagnetic analysis to obtain an electromagnetic analysis result.
Specifically, some environmental sensor groups including a temperature sensor, a soaking sensor, a vibration sensor, an electromagnetic sensor and the like are preset, and are used for acquiring temperature data, soaking data, vibration data and electromagnetic data of the surrounding environment of the battery energy storage power station. And inputting the acquired environmental temperature data into a preset environmental temperature alarm model for analysis. The alarm model may be modeled using machine learning or other algorithms and analyzed in combination with historical data and real-time monitoring data. By analyzing the data, the model can predict the possible abnormal temperature condition and generate a corresponding environment temperature alarm result so as to prompt the manager to take safety measures in time. And inputting the acquired flooding data into a preset flooding alarm model for analysis. The model may also be modeled using machine learning or other algorithms and analyzed in combination with historical data and real-time monitoring data. By analyzing the data, the model can predict the possible risk of water immersion such as liquid leakage and the like, and generate corresponding water immersion analysis results so as to prompt management personnel to take safety measures in time. And inputting the acquired vibration data into a preset vibration alarm model for analysis. The model may also be modeled using machine learning or other algorithms and analyzed in combination with historical data and real-time monitoring data. By analyzing the data, the model can predict the possible vibration risks of equipment abnormal conditions, mechanical faults and the like, and generate corresponding vibration analysis results so as to prompt management personnel to take safety measures in time. And inputting the acquired electromagnetic data into a preset electromagnetic alarm model for analysis. The model may also be modeled using machine learning or other algorithms and analyzed in combination with historical data and real-time monitoring data. By analyzing the data, the model can predict electromagnetic risks such as electromagnetic interference, signal interference and the like which possibly occur, and generate corresponding electromagnetic analysis results so as to prompt management personnel to take security measures in time.
The device security linkage module 105 is configured to:
fire control facilities in the battery energy storage power station are controlled through a preset fire control facility control model, and when abnormal fire control data of the power station occur, an intelligent monitoring system of the energy storage power station issues a command to trigger the fire control facilities to carry out safe linkage;
the drainage facility control method comprises the steps that drainage facilities in a battery energy storage power station are controlled through a preset drainage facility control model, and when the water immersion data of the power station are abnormal, a command is issued through an intelligent monitoring system of the energy storage power station to trigger the drainage facilities to carry out safe linkage;
and (3) performing temperature control facility control on temperature control facilities in the battery energy storage power station through a preset temperature control facility control model, and when abnormal temperature data of a power station cell and a module occurs, issuing a command through an intelligent monitoring system of the energy storage power station to trigger the temperature control facilities to perform safety linkage.
Specifically, in order to realize control of fire fighting facilities, the server can monitor each fire fighting facility in the battery energy storage power station through a preset fire fighting facility control model. The model can determine the optimal control strategy by analyzing the functional characteristics and the use scene of the fire protection facility, and preset the optimal control strategy in the intelligent monitoring system of the energy storage power station. When the fire data of the power station is abnormal, such as a fire disaster, the intelligent monitoring system of the energy storage power station can automatically detect the abnormal condition and issue a command to trigger the fire protection facilities to carry out safe linkage, so that the occurrence of accidents such as the fire disaster is effectively avoided. In order to achieve control of the drain, each drain in the battery energy storage power station may be monitored by a preset drain control model. The model can determine the optimal control strategy by analyzing the functional characteristics and the use scene of the drainage facility, and the optimal control strategy is preset in the intelligent monitoring system of the energy storage power station. When the power station submergence data is abnormal, for example, due to sewage leakage, natural disasters and other reasons, the liquid level in the power station is too high, the intelligent monitoring system of the energy storage power station can automatically detect abnormal conditions and issue commands to trigger the drainage facility to carry out safe linkage, so that smooth drainage in the power station is ensured, and various safety problems caused by unsmooth drainage are avoided. In order to achieve control of the temperature control facilities, each temperature control facility in the battery energy storage power station can be monitored through a preset temperature control facility control model. The model can determine the optimal control strategy by analyzing the functional characteristics and the use scene of the temperature control facility, and the optimal control strategy is preset in the intelligent monitoring system of the energy storage power station. When the temperature data of the power station battery core and the module are abnormal, for example, the temperature in the power station is too high due to the reason of overhigh temperature or short circuit of a circuit, and the intelligent monitoring system of the energy storage power station can automatically detect abnormal conditions and issue commands to trigger a temperature control facility to carry out safe linkage, so that the stability and safety of the temperature in the power station are ensured.
The intelligent operation and maintenance module 106 is configured to:
and carrying out operation and maintenance analysis on the battery energy storage power station according to the sensing data set and the technical parameter set of the battery energy storage power station to obtain target operation and maintenance information, wherein the target operation and maintenance information comprises operation and maintenance frequency, operation and maintenance position and operation and maintenance depth.
Specifically, the server collects data: firstly, data of various sensors in a battery energy storage power station, such as temperature, humidity, voltage and the like, are required to be collected, and technical parameter sets of the battery energy storage power station, such as capacity, charge-discharge efficiency and the like, are acquired. Data preprocessing: preprocessing the acquired data, such as removing abnormal values, filling missing values, reducing noise and the like, so as to ensure the accuracy and the integrity of the data. And (3) operation and maintenance analysis: based on the preprocessed data, operation and maintenance analysis such as cluster analysis, anomaly detection, time series analysis, and the like are performed using a machine learning or statistical analysis method. Through operation and maintenance analysis, abnormal conditions in the battery energy storage power station, such as overheating, overload and other problems, can be identified, and target operation and maintenance information such as operation and maintenance frequency, position, depth and the like required by each abnormality can be calculated. Outputting target operation and maintenance information: and outputting the operation and maintenance analysis result as target operation and maintenance information, wherein the target operation and maintenance information comprises operation and maintenance frequency, position, depth and the like. The operation and maintenance personnel can operate and maintain the battery energy storage power station according to the information so as to ensure the normal and stable operation of the battery energy storage power station.
In an embodiment of the present invention, a system includes: the system comprises a battery cell state monitoring module, a module temperature monitoring module and a power station fire alarm module; the cell state monitoring module is used for: acquiring battery core body parameters and monitoring parameters of a battery energy storage power station to monitor battery core states, and outputting a plurality of battery core state detection results corresponding to each battery core state analysis model; the module temperature monitoring module is used for: acquiring the array type module temperature of the battery energy storage power station for module temperature early warning analysis, and generating a module temperature early warning analysis result; the power station fire alarm module is used for: the method comprises the steps of acquiring working condition data of the energy storage power station of the battery energy storage power station to carry out fire alarm analysis of the power station, obtaining fire alarm analysis results of the power station, adopting an array type module temperature monitoring unit and an array type container temperature monitoring unit which are composed of a fiber bragg grating demodulator and a plurality of fiber bragg grating array temperature sensors, effectively reducing the use quantity of the temperature sensors in the battery energy storage power station, carrying out classified management on the monitored data more easily, carrying out effective monitoring on the temperature and deformation of the battery cells through the use of a probe thermometer and a patch type strain gauge, and accordingly timely acquiring the state of each battery cell, facilitating targeted maintenance of the power station and the battery module, further realizing intelligent monitoring of the battery energy storage power station and improving the accuracy of monitoring operation and maintenance of the battery energy storage power station.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The utility model provides a battery energy storage power station intelligent monitoring system which characterized in that, battery energy storage power station intelligent monitoring system includes: the system comprises a battery cell state monitoring module, a module temperature monitoring module and a power station fire alarm module;
the battery cell state monitoring module is used for: acquiring a battery core body parameter and a monitoring parameter of a battery energy storage power station, and carrying out information fusion on the battery core body parameter and the monitoring parameter to obtain parameter fusion data; and respectively carrying out cell state monitoring on the parameter fusion data through a plurality of preset cell state analysis models, and outputting a plurality of cell state detection results corresponding to each cell state analysis model;
The module temperature monitoring module is used for: acquiring the array module temperature of the battery energy storage power station based on a preset fiber grating array temperature sensor; inputting the array module temperature into a preset module temperature analysis model for module temperature early warning analysis, and generating a module temperature early warning analysis result;
the power station fire alarm module is used for: and acquiring the working condition data of the energy storage power station of the battery energy storage power station, inputting the working condition data of the energy storage power station into a preset power station fire alarm model for power station fire alarm analysis, and obtaining a power station fire alarm analysis result.
2. The intelligent monitoring system of a battery energy storage power station according to claim 1, wherein the cell state monitoring module is specifically configured to:
based on energy storage battery body performance, acquire battery energy storage power station's electric core body parameter, wherein, electric core body parameter includes: cell size data, cell mechanism data, usage time data, production time data, and frequency of occurrence of unexpected conditions;
acquiring the temperature of a battery core of the battery energy storage power station by adopting a probe thermometer, acquiring the deformation of the battery core of the battery energy storage power station by implanting a patch type strain gauge into the battery core, and taking the temperature of the battery core and the deformation of the battery core as monitoring parameters;
Information fusion is carried out on the battery cell body parameters and the monitoring parameters, and parameter fusion data are obtained;
inputting the parameter fusion data into a plurality of preset cell state analysis models, wherein the cell state analysis models comprise: the battery cell life assessment model, the battery cell charge and discharge state analysis model and the battery cell thermal runaway analysis model;
and processing the parameter fusion data through the plurality of cell state analysis models to obtain a plurality of cell state detection results corresponding to each cell state analysis model.
3. The intelligent monitoring system of a battery energy storage power station of claim 1, wherein the module temperature monitoring module is specifically configured to:
arranging a preset fiber grating array temperature sensor and a module temperature sensing optical cable in the battery energy storage power station;
monitoring a module temperature field of the battery energy storage power station through the fiber bragg grating array temperature sensor and the module temperature sensing optical cable to obtain an array module temperature;
and inputting the array type module temperature into a preset module temperature analysis model for module temperature early warning analysis, and generating a module temperature early warning analysis result, wherein the module temperature early warning analysis result comprises overtemperature early warning and temperature rise rate early warning.
4. The battery energy storage power station intelligent monitoring system of claim 1, wherein the power station fire alarm module is specifically configured to:
arranging the fiber bragg grating array temperature sensor and a preset fire-fighting temperature sensing optical cable in the battery energy storage power station;
acquiring a fire control temperature field of the battery energy storage power station through the fiber bragg grating array temperature sensor and the fire control temperature sensing optical cable to obtain working condition data of the energy storage power station;
and inputting the working condition data of the energy storage power station into a preset power station fire alarm model to perform power station fire alarm analysis, so as to obtain a power station fire alarm analysis result.
5. The battery energy storage power station intelligent monitoring system of claim 1, further comprising: the intelligent operation and maintenance system comprises a power station environment monitoring module, an equipment safety linkage module and an intelligent operation and maintenance module; the power station environment monitoring module comprises: the device comprises a humidity monitoring unit, a soaking monitoring unit, a vibration monitoring unit and an electromagnetic monitoring unit; the equipment safety linkage module comprises: a fire-fighting facility control unit, a drainage facility control unit and a temperature control facility control unit.
6. The battery energy storage power station intelligent monitoring system of claim 5, wherein the power station environment monitoring module is configured to:
Acquiring environmental temperature data, soaking data, vibration data and electromagnetic data of the battery energy storage power station based on a preset environmental sensor group;
inputting the environmental temperature data into a preset environmental temperature alarm model for environmental temperature alarm analysis to obtain an environmental temperature alarm result;
inputting the flooding data into a preset flooding alarm model for flooding analysis to obtain a flooding analysis result;
inputting the vibration data into a preset vibration alarm model for vibration analysis to obtain a vibration analysis result;
and inputting the electromagnetic data into a preset electromagnetic alarm model for electromagnetic analysis to obtain an electromagnetic analysis result.
7. The intelligent monitoring system of a battery energy storage power station of claim 5, wherein the device safety linkage module is configured to:
fire control facilities in the battery energy storage power station are controlled through a preset fire control facility control model, and when abnormal fire control data of the power station occur, a command is issued through the intelligent monitoring system of the energy storage power station to trigger the fire control facilities to carry out safety linkage;
the drainage facility control is carried out on the drainage facility in the battery energy storage power station through a preset drainage facility control model, and when the water immersion data of the power station is abnormal, a command is issued through the intelligent monitoring system of the energy storage power station to trigger the drainage facility to carry out safety linkage;
And controlling the temperature control facilities in the battery energy storage power station through a preset temperature control facility control model, and when the temperature data of the power station battery core and the module are abnormal, issuing a command through the intelligent monitoring system of the energy storage power station to trigger the temperature control facilities to carry out safe linkage.
8. The intelligent monitoring system of claim 5, wherein the intelligent operation and maintenance module is configured to:
and carrying out operation and maintenance analysis on the battery energy storage power station according to the sensing data set and the technical parameter set of the battery energy storage power station to obtain target operation and maintenance information, wherein the target operation and maintenance information comprises operation and maintenance frequency, operation and maintenance position and operation and maintenance depth.
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