CN116488352A - Circuit safety monitoring and early warning method for energy storage power station - Google Patents
Circuit safety monitoring and early warning method for energy storage power station Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 88
- 238000004146 energy storage Methods 0.000 title claims abstract description 57
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- 230000002159 abnormal effect Effects 0.000 claims description 226
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- 238000013528 artificial neural network Methods 0.000 description 2
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- 208000002693 Multiple Abnormalities Diseases 0.000 description 1
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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
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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
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/282—Testing of electronic circuits specially adapted for particular applications not provided for elsewhere
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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
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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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
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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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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the technical field of circuit monitoring, and provides a circuit safety monitoring and early warning method for an energy storage power station, which comprises the following steps: acquiring a battery cluster loop of a first energy storage power station; monitoring the operation data of a battery cluster loop, and acquiring a loop monitoring data set; according to the method, anomaly detection is carried out according to a loop monitoring data set, a first anomaly node, a first anomaly loop where the first anomaly node is located and an anomaly loop belonging to the first anomaly loop are output, anomaly accompanying risks are identified, a plurality of accompanying risk coefficients are obtained and fit, a first anomaly index is output, first early warning information is generated, the technical problem that the circuit monitoring early warning of an energy storage power station is poor in instantaneity is solved, real-time monitoring of the circuit of the energy storage power station is achieved, anomaly conditions can be timely captured, the risk of fault occurrence is reduced, the instantaneity of the circuit monitoring early warning is guaranteed, meanwhile, the anomaly loop belonging to the same genus is accurately found, workers are helped to locate and process the anomaly conditions, and the technical effect of fault processing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of circuit monitoring, in particular to a circuit safety monitoring and early warning method for an energy storage power station.
Background
The circuit of the energy storage power station refers to a circuit system used for storing, transmitting and converting electric energy in the energy storage power station, and comprises a battery pack, an inverter, a charger, a direct current power distribution system, an alternating current power distribution system and the like.
However, since the accuracy of the monitoring data may be limited by the performance and accuracy of the sensor itself, there may be an error, and furthermore, a malfunction or damage of the sensor may cause interruption of the monitoring data, thereby affecting the accuracy and real-time of the early warning.
In summary, the technical problem of poor instantaneity of circuit monitoring and early warning of the energy storage power station exists in the prior art.
Disclosure of Invention
The application aims to solve the technical problem of poor instantaneity of circuit monitoring and early warning of the energy storage power station in the prior art by providing the circuit safety monitoring and early warning method for the energy storage power station.
In view of the above, the present application provides a circuit safety monitoring and early warning method for an energy storage power station.
In a first aspect of the disclosure, a circuit safety monitoring and early warning method for an energy storage power station is provided, wherein the method comprises: acquiring a battery cluster loop of a first energy storage power station; monitoring operation data of the battery cluster loop to obtain a loop monitoring data set; performing abnormality detection according to the loop monitoring data set, and outputting a first abnormal node and a first abnormal loop in which the first abnormal node is located; obtaining the same-genus abnormal loop of the first abnormal loop, wherein the same-genus abnormal loop is a connecting loop comprising the first abnormal node; carrying out abnormality accompanying risk identification according to the first abnormal node and the affiliated abnormal loop to obtain a plurality of accompanying risk coefficients, wherein each accompanying risk coefficient is used for identifying an abnormal risk level of a corresponding abnormal loop; fitting according to the multiple accompanying risk coefficients, and outputting a first abnormality index; and generating first early warning information according to the first abnormality index.
In another aspect of the present disclosure, a circuit safety monitoring and early warning system for an energy storage power station is provided, wherein the system comprises: the battery cluster loop acquisition module is used for acquiring a battery cluster loop of the first energy storage power station; the operation data monitoring module is used for monitoring operation data of the battery cluster loop and acquiring a loop monitoring data set; the abnormality detection module is used for carrying out abnormality detection according to the loop monitoring data set and outputting a first abnormal node and a first abnormal loop where the first abnormal node is located; the same-genus abnormal loop acquisition module is used for acquiring a same-genus abnormal loop of the first abnormal loop, wherein the same-genus abnormal loop is a connection loop comprising the first abnormal node; the abnormality accompanying risk identification module is used for carrying out abnormality accompanying risk identification according to the first abnormality node and the same-genus abnormality loop to obtain a plurality of accompanying risk coefficients, wherein each accompanying risk coefficient is used for identifying an abnormality risk level of a corresponding abnormality loop; the abnormality index output module is used for fitting according to the plurality of accompanying risk coefficients and outputting a first abnormality index; and the early warning information generation module is used for generating first early warning information according to the first abnormality index.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
due to the adoption of a battery cluster loop for acquiring the first energy storage power station; monitoring the operation data of a battery cluster loop, and acquiring a loop monitoring data set; the method comprises the steps of carrying out anomaly detection according to a loop monitoring data set, outputting a first anomaly node, identifying anomaly accompanying risks of the first anomaly loop where the first anomaly node is located and the first anomaly loop which belongs to the anomaly loop, obtaining a plurality of accompanying risk coefficients, fitting, outputting a first anomaly index, generating first early warning information, realizing real-time monitoring of an energy storage power station circuit, capturing anomaly conditions in time, reducing risk of fault occurrence, guaranteeing instantaneity of circuit monitoring early warning, accurately finding out the corresponding anomaly loop, helping workers locate and process the anomaly conditions, and improving the efficiency of fault processing.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a possible circuit safety monitoring and early warning method for an energy storage power station according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a possible process of obtaining a plurality of accompanying risk coefficients in a circuit safety monitoring and early warning method for an energy storage power station according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a possible complexity of outputting multiple loops in a circuit safety monitoring and early warning method for an energy storage power station according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of a circuit safety monitoring and early warning system for an energy storage power station according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a battery cluster loop acquisition module 100, an operation data monitoring module 200, an abnormality detection module 300, an abnormality associated risk identification module 500, an abnormality index output module 600 and an early warning information generation module 700.
Detailed Description
The embodiment of the application provides a circuit safety monitoring and early warning method for an energy storage power station, solves the technical problem of poor instantaneity of circuit monitoring and early warning of the energy storage power station, realizes the technical effects of being capable of monitoring the circuit of the energy storage power station in real time, capturing abnormal conditions in time, reducing the risk of fault occurrence, guaranteeing the instantaneity of circuit monitoring and early warning, accurately finding out the same abnormal loop, helping workers locate and process the abnormal conditions and improving the efficiency of fault processing.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a circuit safety monitoring and early warning method for an energy storage power station, where the method includes:
s10: acquiring a battery cluster loop of a first energy storage power station;
s20: monitoring operation data of the battery cluster loop to obtain a loop monitoring data set;
s30: performing abnormality detection according to the loop monitoring data set, and outputting a first abnormal node and a first abnormal loop in which the first abnormal node is located;
s40: obtaining the same-genus abnormal loop of the first abnormal loop, wherein the same-genus abnormal loop is a connecting loop comprising the first abnormal node;
s50: carrying out abnormality accompanying risk identification according to the first abnormal node and the affiliated abnormal loop to obtain a plurality of accompanying risk coefficients, wherein each accompanying risk coefficient is used for identifying an abnormal risk level of a corresponding abnormal loop;
the energy storage power station is a facility capable of converting electric energy into other forms of energy and converting the energy back into electric energy when needed, stores the electric energy when the load of the electric power system is low, and releases the electric energy when the load is high so as to realize balance adjustment of the electric energy and utilization of peak-to-valley electricity prices, so that dynamic matching is achieved between power generation and power consumption;
in the process of storing redundant electric quantity in the electricity consumption low peak period so as to be released into the power grid again in the electricity consumption high peak period, the energy storage power station needs to monitor and early warn the circuit, timely discovers potential faults or risks and early warns, avoids accidents and ensures the normal operation of the energy storage power station;
the method comprises the steps that a first energy storage power station is a main body with possible circuit safety monitoring requirements, a battery cluster loop of the first energy storage power station is obtained, the battery cluster loop is a circuit loop of all battery clusters in the first energy storage power station, and the battery clusters are composed of a plurality of battery monomers; monitoring the operation data of the battery cluster loop to obtain a loop monitoring data set, wherein the loop monitoring data set is the operation data set of the battery cluster loop and can comprise current data, voltage data and temperature data;
according to the loop monitoring data set, performing abnormality detection, and outputting a first abnormal node, wherein the abnormality detection is used for representing analysis of monitoring data of the battery cluster loop, so that whether the battery cluster loop has an abnormal condition or not can be judged, the abnormal condition can be overload, short circuit, voltage abnormality and the like, and the first abnormal loop where the first abnormal node is located is simply, the first abnormal node is a point for connecting two or more circuit components in the first abnormal loop with the abnormal condition, and commonly, the abnormal node can refer to a battery cell, a circuit component and the like;
finding out other abnormal loops connected with the first abnormal loop to obtain an identical abnormal loop of the first abnormal loop, wherein the identical abnormal loop refers to other abnormal loops connected with the first abnormal loop, and meanwhile, the identical abnormal loop and the first abnormal loop have other abnormal loops with the same abnormal node;
the accompanying risk coefficient is used for representing a coefficient for identifying the abnormal risk level of the abnormal loop, carrying out abnormal accompanying risk identification according to the first abnormal node and the affiliated abnormal loop, determining the accompanying risk associated with the first abnormal node and defining the accompanying risk as a plurality of accompanying risk coefficients, wherein each accompanying risk coefficient is used for identifying the abnormal risk level of the corresponding affiliated abnormal loop;
the method comprises the steps of monitoring and anomaly detecting a battery cluster loop of a first energy storage power station in real time, locating the first anomaly loop and a first anomaly node where the first anomaly loop is located, finding out the same anomaly loop, providing support for quick diagnosis of circuit anomalies, guaranteeing normal operation of the battery cluster loop, and reducing production stagnation and maintenance cost caused by failure of the battery cluster loop.
As shown in fig. 2, step S50 includes the steps of:
s51: acquiring an abnormal grade of the first abnormal node;
s52: analyzing the influence correlation of the energy storage operation of the first abnormal node in the same-genus abnormal loop to obtain a plurality of correlation coefficients;
s53: adjusting the abnormal grade pairs according to the plurality of correlation coefficients, and outputting a plurality of adjusted abnormal grades;
s54: and carrying out abnormality accompanying risk identification according to the abnormality levels to obtain the abnormality accompanying risk coefficients.
Specifically, performing abnormality accompanying risk identification according to the first abnormal node and the same-genus abnormal loop to obtain a plurality of accompanying risk coefficients, including, evaluating a loss degree corresponding to the first abnormal node, and obtaining an abnormality grade of the first abnormal node; the energy storage operation influence correlation is used for representing the influence degree of the abnormal node on the energy storage and operation states of other nodes in the same-genus abnormal loop, the correlation coefficient is used for representing an index for measuring the correlation between the abnormal node and the other nodes in the same-genus abnormal loop (the correlation analysis is the prior art), and the energy storage operation influence correlation of the first abnormal node in the same-genus abnormal loop is analyzed to obtain a plurality of correlation coefficients;
for example, if the battery cluster loop corresponding to the first detection node is overloaded, the rated current of the first detection node is 300mA, the main loop running current of the first detection node is 350mA in the running data monitoring process, the overload of the first detection node is represented, and the correlation coefficient corresponding to the first detection node is 1; if the battery cluster loop corresponding to the first detection node is overloaded, the rated current of the first detection node is 300mA, the main loop running current of the first detection node is 300mA in the running data monitoring process, the first detection node is characterized as running normally, and the correlation coefficient corresponding to the first detection node is 0;
and according to the analysis mode, overload or short circuit or voltage abnormality is taken as a group respectively, the energy storage operation influence correlation of the first abnormal node in the same abnormal loop is analyzed in a grouping way, a plurality of correlation coefficients are obtained through analysis, the plurality of correlation coefficients comprise overload correlation coefficient groups, short circuit correlation coefficient groups and voltage abnormality correlation coefficient groups, and the identification and evaluation of the abnormal nodes in the loop are critical for ensuring the normal operation and the safety of the loop. By using indexes such as a correlation coefficient, the correlation between the abnormal node and other nodes can be analyzed so as to determine the influence degree and risk level of the abnormal node on other nodes in the loop;
carrying out normalization processing on the plurality of correlation coefficients and the abnormal level, taking the plurality of correlation coefficients as weight ratios, carrying out weighted adjustment on normalization processing results of the abnormal level, and outputting a plurality of adjusted abnormal levels, wherein the plurality of abnormal levels are in one-to-one correspondence with the plurality of correlation coefficients;
abnormality accompanying risk identification: evaluating risk levels of a plurality of anomaly levels associated with the first anomaly node (positive correlation between the correlation and the risk levels) by analyzing the correlation between the anomaly loop and the node; the plurality of accompanying risk coefficients are used for representing a quantitative indicator of a risk level associated with the first abnormal node; and carrying out abnormality accompanying risk identification on the same-genus abnormal loop according to the abnormality grades to obtain a plurality of accompanying risk coefficients, wherein the accompanying risk coefficients, the same-genus abnormal loop and the abnormality grades are in one-to-one correspondence, and different factors of the abnormal loop, such as the abnormality grade and the correlation, are comprehensively considered to obtain a more accurate abnormality accompanying risk identification result, thereby being beneficial to early warning potential fault risks in advance and adopting proper measures to reduce the occurrence probability of faults.
Step S51 includes the steps of:
s511: constructing an abnormality accompanying risk identification model, wherein a first loss function is embedded in the abnormality accompanying risk identification model;
s512: performing abnormal loss analysis on each loop according to the first loss function, wherein the abnormal loss analysis comprises abnormal available capacity loss, abnormal temperature rise loss and abnormal instantaneous loss;
s513: calculating by taking the available capacity loss accompanied by the abnormality, the temperature rise loss accompanied by the abnormality and the instantaneous loss accompanied by the abnormality as loss variables, and outputting first loss data;
s514: and acquiring the abnormal grade of the first abnormal node according to the first loss data.
Specifically, obtaining an anomaly level of the first anomaly node, including; the anomaly accompanying risk recognition model is embedded with a first loss function, the anomaly accompanying risk recognition model comprises an input layer, an output layer and a network layer, the network layer is used for training and fitting the first loss function, the anomaly accompanying risk recognition model is embedded with the first loss function, and the first loss function is an operation function used for measuring the difference degree between a predicted value f (x) and a true value Y of the model, for example: the first predicted value corresponding to the predicted value f (x) is 5, the first true value corresponding to the true value Y is 7, the absolute value of the difference between the first predicted value and the first true value at the same time sequence is equal to 2, and the predicted value f (x) is continuously approximated to the true value Y in the process of model convergence training until the preset model precision constraint of the abnormal accompanying risk recognition model is met, and then the abnormal accompanying risk recognition model is obtained;
the abnormal loss of each loop can be any one or more of abnormal accompanying available capacity loss, abnormal accompanying temperature rise loss and abnormal accompanying instantaneous loss, and the abnormal accompanying available capacity loss is used for representing the inconsistency of charge and discharge and can be expressed as the loss amount of the available capacity of the loop under abnormal conditions; the temperature rise loss accompanied by abnormality is used for representing element abnormality, so that the temperature in the circuit rises rapidly, and the loss of the temperature rise of the circuit caused by factors such as overload under abnormal conditions can be represented; the transient loss accompanied by abnormality is used for representing transient data (abrupt) change, and can be represented as transient loss quantity of a loop caused by factors such as transient overvoltage or overcurrent under abnormal conditions; sequentially carrying out available capacity loss analysis accompanied by abnormality, temperature rise loss analysis accompanied by abnormality and instantaneous loss analysis accompanied by abnormality on each loop according to the first loss function;
carrying out weighted summation calculation by taking the available capacity loss accompanied by abnormality, the temperature rise loss accompanied by abnormality and the instantaneous loss accompanied by abnormality of each loop as loss variables to obtain total abnormal loss values of each loop; the total abnormal loss values of all loops can be ordered according to the size, the largest loop total abnormal loss value in the total abnormal loss values of all loops is selected to be set as the abnormal grade of the first abnormal node, the position and degree of occurrence of the abnormality are judged according to the first loss data, so that the abnormal grade of the first abnormal node is determined, meanwhile, after elements or connection relation changes occur in a circuit, data are collected and updated, model updating is carried out, optimization and improvement are carried out on the model in time, and customization setting is carried out on specific application scene requirements.
Step S50 further includes the steps of:
s55: performing loop complexity analysis on the same-genus abnormal loop to obtain a plurality of loop complexity;
s56: dividing the same-genus abnormal loop according to the complexity of the loops to obtain a plurality of loop categories;
s57: building an accompanying risk recognition model according to the loop categories, wherein the accompanying risk recognition model comprises a plurality of recognition branches, and each recognition branch corresponds to one loop category;
s58: and outputting a plurality of accompanying risk coefficients according to the accompanying risk identification model.
Specifically, the method includes performing abnormality accompanying risk identification according to the first abnormal node and the same-genus abnormal loop to obtain a plurality of accompanying risk coefficients, and identifying possible accompanying risk situations by analyzing the abnormal node and the related abnormal loop in the system. In this process, a plurality of accompanying risk factors are obtained for measuring the degree or importance of different risks; performing loop complexity analysis on the same-genus abnormal loop, wherein the loop complexity can include evaluating the complexity of the loop by considering indexes such as a topological structure, a connection mode, the number of components and the like of the loop, so as to obtain a plurality of loop complexities, and better understand the characteristics and the characteristics of the loop;
dividing the same-genus abnormal loop according to the plurality of loop complexity by adopting technologies such as cluster analysis and the like, performing bottom-up condensation hierarchical cluster analysis on the plurality of loop complexity to obtain threshold intervals of the plurality of loop complexity, dividing the same-genus abnormal loop corresponding to the plurality of loop complexity through the threshold intervals of the plurality of loop complexity to obtain a plurality of loop categories,
categorizing the loops by varying degrees of their complexity for better handling;
setting a search character by taking the bp neural network as a model basis and taking a plurality of loop complexity and a plurality of loop types as search contents, and searching experience data in a data storage unit of a circuit safety monitoring and early warning system for an energy storage power station to obtain a plurality of historical loop complexity, a plurality of historical loop types and historical accompanying risk coefficients;
constructing a model for identifying the accompanying risk based on the classification information of the loop: constructing new combination features based on the plurality of historical loop complexity and the plurality of historical loop types by taking the plurality of historical loop complexity as construction data, transmitting the historical associated risk coefficients as identification results into a bp neural network for model convergence learning, constructing and training to obtain the associated risk recognition model, determining the associated risk recognition model, and providing a model foundation for carrying out associated risk recognition; meanwhile, the accompanying risk recognition model comprises a plurality of recognition branches, each recognition branch corresponds to one loop category, the output data of the accompanying risk recognition model is a plurality of accompanying risk coefficients, namely the accompanying risk degree or probability corresponding to each loop category, so that corresponding risk recognition is conducted on loops of different categories.
As shown in fig. 3, step S55 further includes the steps of:
s551: constructing a loop complex analysis module, wherein the loop complex analysis module comprises an index analysis sub-module and a complex calculation sub-module;
s552: acquiring battery cluster configuration information, loop element type information and element connection mode information of each loop in the same abnormal loop;
s553: inputting the battery cluster configuration information, the loop element type information and the element connection mode information into the index analysis submodule to obtain a battery pack complex index, an element complex index and a connection complex index;
s554: inputting the battery pack complexity index, the element complexity index and the connection complexity index into the complex calculation submodule, and outputting a plurality of loop complexity corresponding to each loop of the same-genus abnormal loop.
Specifically, the loop complexity analysis is performed on the same-genus abnormal loop to obtain a plurality of loop complexity, and the loop complexity analysis module is used for performing complexity analysis on the same-genus abnormal loop to evaluate the complexity of the loop, and comprises an index analysis sub-module and a complexity calculation sub-module, and before starting analysis, the loop complexity analysis module needs to acquire battery cluster configuration information, loop element type information and element connection mode information of each loop in the same-genus abnormal loop, so as to provide data support for evaluating the complexity of the loop;
inputting the battery cluster configuration information, the loop element type information and the element connection mode information into the index analysis submodule to obtain a battery pack complex index, an element complex index and a connection complex index: the index analysis submodule can calculate indexes such as capacity, voltage and the like of the battery pack according to the configuration information of the battery clusters, and the complex indexes of the battery pack can reflect the configuration complexity degree among the battery clusters; determining characteristics and performance parameters of the element, such as resistance, inductance and the like, according to the type information of the loop element, wherein the element complexity index can reflect the type complexity of the element in the loop; analyzing the topological structure and the connection mode of the loop according to the element connection mode information, wherein the connection complexity index can reflect the connection mode complexity between the elements;
the complex computing sub-module is used for comprehensively evaluating the complexity of each loop according to the weight relation and the computing method among a plurality of complexity indexes in the loop, the calculated complexity of the loops can represent the complexity of the loop and can be a numerical value or a classification level, the battery pack complexity index, the element complexity index and the connection complexity index are input into the complex computing sub-module, and a plurality of loop complexities corresponding to each loop of the same-genus abnormal loop are output;
and carrying out serial combination on the index analysis submodule and the complex calculation submodule to obtain a loop complex analysis module, and carrying out complexity analysis on the same-genus abnormal loop through the loop complex analysis module so as to obtain a plurality of loop complexity, wherein the complexity information can be used for further risk assessment and decision making so as to reduce the occurrence probability of the accompanying risk.
S60: fitting according to the multiple accompanying risk coefficients, and outputting a first abnormality index;
s70: and generating first early warning information according to the first abnormality index.
Step S60 includes the steps of:
s61: fitting according to the plurality of accompanying risk coefficients, wherein the expression is as follows:, wherein ,/>Fitting regression characterizing a plurality of accompanying risk factors, +.>Loss data characterizing the inconsistency of available capacity in the ith loop; />Loss data characterizing component temperature inconsistencies in the ith loop; />Loss data characterizing transient sharp changes in the ith loop; />Characterizing a residual of the curve regression; n represents the number of loops belonging to the same abnormal loop; />。
Specifically, the plurality of accompanying risk coefficients are substituted into a fitting regression formula to perform operation: fitting according to the plurality of accompanying risk coefficients, wherein the expression is as follows:, wherein ,/>Characterizing abnormality index, by fitting regression of a plurality of accompanying risk coefficients,>loss data characterizing the inconsistency of available capacity in the ith loop; />Loss data characterizing component temperature inconsistencies in the ith loop;loss data characterizing transient sharp changes in the ith loop; />Characterizing a residual of the curve regression; n represents the number of loops belonging to the same abnormal loop; />，/>Characterization->Is not limited to the desired one; />Characterization ofIs a variance of (2); substituting the plurality of accompanying risk coefficients into a computer to obtain a first abnormality index;
and the first abnormality index is used as the early warning content of the first early warning information, the buzzer and the diode are communicated and controlled to be used as the early warning mode of the first early warning information, the first early warning information is obtained, the first early warning information comprises the first early warning information, a buzzer control instruction and a diode control instruction, the first early warning information is used for reminding a technical operator of paying attention to potential problems or problems needing to be noticed or operated, and the safety operation of the energy storage power station is ensured by reminding corresponding staff to take necessary measures.
The embodiment of the application further comprises the steps of:
s81: judging whether the first abnormal node is provided with a first protection device or not;
s82: acquiring a protection monitoring data set according to the information of the first protection device;
s83: performing abnormal protection risk identification on the same-genus abnormal loop according to the protection monitoring data set to obtain a plurality of protection risk coefficients;
s84: fitting the protection risk coefficients and outputting a second abnormality index;
s85: and generating second early warning information according to the second abnormality index.
Specifically, the first protection device is arranged in the main loop, and can be a fuse or a fuse+a breaker or a fuse+an isolating switch for protection, and judges whether the first abnormal node has the first protection device or not; if the first abnormal node has a first protection device, acquiring a protection monitoring data set according to the information of the first protection device, wherein the protection monitoring data set refers to a set of various monitoring parameters and measurement data acquired from the first protection device and is used for analyzing and identifying abnormal protection risks and can comprise real-time information of running states of an electric power system such as current, voltage, frequency, phase difference and the like;
carrying out abnormal protection risk identification on the same-genus abnormal loop according to the protection monitoring data set to obtain a plurality of protection risk coefficients, preferably, setting a current threshold interval and a voltage threshold interval according to the operation requirement of equipment connected in an energy storage power station, and judging that the abnormal protection risk coefficient exists in the system according to the excess when the current or the voltage exceeds the set threshold range, wherein the protection risk coefficient is used for evaluating the response capability and the protection effect of the protection device under the abnormal condition, reflecting the control degree of the protection device on the potential risk, and specifically, can be fault detection rate, misoperation locking rate and the like;
the second abnormality index is a comprehensive index obtained by fitting calculation according to the plurality of protection risk coefficients and is used for measuring the degree of abnormal protection risk, and the second abnormality index can provide a more comprehensive evaluation by comprehensively considering the weights and the association relations of the plurality of protection risk coefficients so as to help judge the degree of abnormal protection risk in the system; and generating second early warning information according to the second abnormality index, wherein the second early warning information is used for reminding a system operator to take corresponding measures so as to prevent faults or accidents. Specifically, the second early warning information may include an alarm notification, an alarm information, an operation suggestion, etc., so as to take measures in time to reduce or eliminate the risk of abnormal protection.
In summary, the circuit safety monitoring and early warning method for the energy storage power station provided by the embodiment of the application has the following technical effects:
1. due to the adoption of a battery cluster loop for acquiring the first energy storage power station; monitoring the operation data of a battery cluster loop, and acquiring a loop monitoring data set; according to the method, the circuit safety monitoring and early warning method for the energy storage power station is provided, the energy storage power station circuit can be monitored in real time, abnormal conditions are timely captured, the risk of fault occurrence is reduced, the timeliness of circuit monitoring and early warning is guaranteed, meanwhile, the same abnormal loop is accurately found, workers are helped to locate and process the abnormal conditions, and the efficiency of fault processing is improved.
2. Judging whether the first abnormal node has a first protection device or not is adopted; according to the information of the first protection device, a protection monitoring data set is obtained, abnormal protection risk identification is carried out on the same abnormal loop, a plurality of protection risk coefficients are obtained and fit, a second abnormal index is output, and second early warning information is generated, so that measures are taken in time to reduce or eliminate the abnormal protection risk.
Example two
Based on the same inventive concept as the circuit safety monitoring and early warning method for the energy storage power station in the foregoing embodiment, as shown in fig. 4, an embodiment of the present application provides a circuit safety monitoring and early warning system for the energy storage power station, where the system includes:
a battery cluster circuit acquisition module 100, configured to acquire a battery cluster circuit of the first energy storage power station;
the operation data monitoring module 200 is configured to monitor operation data of the battery cluster loop, and obtain a loop monitoring data set;
the anomaly detection module 300 is configured to perform anomaly detection according to the loop monitoring data set, and output a first anomaly node and a first anomaly loop in which the first anomaly node is located;
a generic abnormal circuit obtaining module 400, configured to obtain a generic abnormal circuit of the first abnormal circuit, where the generic abnormal circuit is a connection circuit including the first abnormal node;
the abnormality accompanying risk identification module 500 is configured to perform abnormality accompanying risk identification according to the first abnormal node and the affiliated abnormality loop, so as to obtain a plurality of accompanying risk coefficients, where each accompanying risk coefficient is used to identify an abnormality risk level of a corresponding abnormality loop;
the abnormality index output module 600 is configured to perform fitting according to the multiple accompanying risk coefficients, and output a first abnormality index;
the early warning information generating module 700 is configured to generate first early warning information according to the first abnormality index.
Further, the system includes:
the judging module is used for judging whether the first abnormal node is provided with a first protection device or not;
the protection monitoring data set acquisition module is used for acquiring a protection monitoring data set according to the information of the first protection device;
the abnormal protection risk identification module is used for carrying out abnormal protection risk identification on the same-genus abnormal loop according to the protection monitoring data set to obtain a plurality of protection risk coefficients;
the second abnormality index output module is used for fitting the plurality of protection risk coefficients and outputting a second abnormality index;
and the second early warning information generation module is used for generating second early warning information according to the second abnormality index.
Further, the system includes:
the loop complexity analysis module is used for carrying out loop complexity analysis on the same-genus abnormal loop to obtain a plurality of loop complexity;
the loop category obtaining module is used for dividing the same-genus abnormal loop according to the complexity of the loops to obtain a plurality of loop categories;
the accompanying risk recognition model building module is used for building an accompanying risk recognition model according to the loop categories, wherein the accompanying risk recognition model comprises a plurality of recognition branches, and each recognition branch corresponds to one loop category;
and the accompanying risk coefficient output module is used for outputting a plurality of accompanying risk coefficients according to the accompanying risk identification model.
Further, the system includes:
the loop complex analysis module building module is used for building a loop complex analysis module, wherein the loop complex analysis module comprises an index analysis sub-module and a complex calculation sub-module;
the information acquisition module is used for acquiring the battery cluster configuration information, the loop element type information and the element connection mode information of each loop in the same abnormal loop;
the complex index obtaining module is used for inputting the battery cluster configuration information, the loop element type information and the element connection mode information into the index analysis submodule to obtain a complex index of the battery pack, a complex index of the element and a complex connection index;
and the loop complexity output module is used for inputting the battery pack complexity index, the element complexity index and the connection complexity index into the complex calculation sub-module and outputting a plurality of loop complexities corresponding to each loop of the same-genus abnormal loop.
Further, the system includes:
the first abnormal grade acquisition module is used for acquiring the abnormal grade of the first abnormal node;
the correlation coefficient obtaining module is used for analyzing the energy storage operation influence correlation of the first abnormal node in the same-genus abnormal loop respectively to obtain a plurality of correlation coefficients;
the abnormal grade output modules are used for adjusting the abnormal grade pairs according to the correlation coefficients and outputting the adjusted abnormal grades;
and the multiple accompanying risk coefficient obtaining module is used for carrying out abnormality accompanying risk identification according to the multiple abnormality levels to obtain the multiple accompanying risk coefficients.
Further, the system includes:
the abnormality accompanying risk recognition model building module is used for building an abnormality accompanying risk recognition model, wherein a first loss function is embedded in the abnormality accompanying risk recognition model;
the abnormal loss analysis module is used for carrying out abnormal loss analysis on each loop according to the first loss function, wherein the abnormal loss analysis module comprises an abnormal available capacity loss, an abnormal temperature rise loss and an abnormal instantaneous loss;
the first loss data output module is used for calculating by taking the available capacity loss accompanied by the abnormality, the temperature rise loss accompanied by the abnormality and the instantaneous loss accompanied by the abnormality as loss variables and outputting first loss data;
and the second abnormal grade acquisition module is used for acquiring the abnormal grade of the first abnormal node according to the first loss data.
Further, the system includes:
the fitting regression formula module is used for fitting according to the plurality of accompanying risk coefficients, and expressed as follows:, wherein ,/>Fitting regression characterizing a plurality of accompanying risk factors, +.>Loss data characterizing the inconsistency of available capacity in the ith loop; />Loss data characterizing component temperature inconsistencies in the ith loop; />Loss data characterizing transient sharp changes in the ith loop; />Characterizing a residual of the curve regression; n represents the number of loops belonging to the same abnormal loop; />。
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any of the methods to implement embodiments of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. The circuit safety monitoring and early warning method for the energy storage power station is characterized by comprising the following steps of:
acquiring a battery cluster loop of a first energy storage power station;
monitoring operation data of the battery cluster loop to obtain a loop monitoring data set;
performing abnormality detection according to the loop monitoring data set, and outputting a first abnormal node and a first abnormal loop in which the first abnormal node is located;
obtaining the same-genus abnormal loop of the first abnormal loop, wherein the same-genus abnormal loop is a connecting loop comprising the first abnormal node;
carrying out abnormality accompanying risk identification according to the first abnormal node and the affiliated abnormal loop to obtain a plurality of accompanying risk coefficients, wherein each accompanying risk coefficient is used for identifying an abnormal risk level of a corresponding abnormal loop;
fitting according to the multiple accompanying risk coefficients, and outputting a first abnormality index;
and generating first early warning information according to the first abnormality index.
2. The method of claim 1, wherein the method further comprises:
judging whether the first abnormal node is provided with a first protection device or not;
acquiring a protection monitoring data set according to the information of the first protection device;
performing abnormal protection risk identification on the same-genus abnormal loop according to the protection monitoring data set to obtain a plurality of protection risk coefficients;
fitting the protection risk coefficients and outputting a second abnormality index;
and generating second early warning information according to the second abnormality index.
3. The method of claim 1, wherein the method further comprises:
performing loop complexity analysis on the same-genus abnormal loop to obtain a plurality of loop complexity;
dividing the same-genus abnormal loop according to the complexity of the loops to obtain a plurality of loop categories;
building an accompanying risk recognition model according to the loop categories, wherein the accompanying risk recognition model comprises a plurality of recognition branches, and each recognition branch corresponds to one loop category;
and outputting a plurality of accompanying risk coefficients according to the accompanying risk identification model.
4. A method as claimed in claim 3, wherein the method further comprises:
constructing a loop complex analysis module, wherein the loop complex analysis module comprises an index analysis sub-module and a complex calculation sub-module;
acquiring battery cluster configuration information, loop element type information and element connection mode information of each loop in the same abnormal loop;
inputting the battery cluster configuration information, the loop element type information and the element connection mode information into the index analysis submodule to obtain a battery pack complex index, an element complex index and a connection complex index;
inputting the battery pack complexity index, the element complexity index and the connection complexity index into the complex calculation submodule, and outputting a plurality of loop complexity corresponding to each loop of the same-genus abnormal loop.
5. The method of claim 1, wherein the anomaly attendant risk identification is performed based on the first anomaly node and the generic anomaly loop to obtain a plurality of attendant risk coefficients, the method comprising:
acquiring an abnormal grade of the first abnormal node;
analyzing the influence correlation of the energy storage operation of the first abnormal node in the same-genus abnormal loop to obtain a plurality of correlation coefficients;
adjusting the abnormal grade pairs according to the plurality of correlation coefficients, and outputting a plurality of adjusted abnormal grades;
and carrying out abnormality accompanying risk identification according to the abnormality levels to obtain the abnormality accompanying risk coefficients.
6. The method of claim 5, wherein obtaining the anomaly level of the first anomaly node, the method comprising:
constructing an abnormality accompanying risk identification model, wherein a first loss function is embedded in the abnormality accompanying risk identification model;
performing abnormal loss analysis on each loop according to the first loss function, wherein the abnormal loss analysis comprises abnormal available capacity loss, abnormal temperature rise loss and abnormal instantaneous loss;
calculating by taking the available capacity loss accompanied by the abnormality, the temperature rise loss accompanied by the abnormality and the instantaneous loss accompanied by the abnormality as loss variables, and outputting first loss data;
and acquiring the abnormal grade of the first abnormal node according to the first loss data.
7. The method of claim 6, wherein said fitting based on said plurality of attendant risk factors is expressed as follows:
，
wherein ,fitting regression characterizing a plurality of accompanying risk factors, +.>Loss data characterizing the inconsistency of available capacity in the ith loop; />Loss data characterizing component temperature inconsistencies in the ith loop; />Loss data characterizing transient sharp changes in the ith loop; />Characterizing a residual of the curve regression; n represents the number of loops belonging to the same abnormal loop; />。
8. A circuit safety monitoring and early warning system for an energy storage power station, characterized in that the circuit safety monitoring and early warning method for the energy storage power station according to any one of claims 1 to 7 is implemented, and the circuit safety monitoring and early warning system comprises:
the battery cluster loop acquisition module is used for acquiring a battery cluster loop of the first energy storage power station;
the operation data monitoring module is used for monitoring operation data of the battery cluster loop and acquiring a loop monitoring data set;
the abnormality detection module is used for carrying out abnormality detection according to the loop monitoring data set and outputting a first abnormal node and a first abnormal loop where the first abnormal node is located;
the same-genus abnormal loop acquisition module is used for acquiring a same-genus abnormal loop of the first abnormal loop, wherein the same-genus abnormal loop is a connection loop comprising the first abnormal node;
the abnormality accompanying risk identification module is used for carrying out abnormality accompanying risk identification according to the first abnormality node and the same-genus abnormality loop to obtain a plurality of accompanying risk coefficients, wherein each accompanying risk coefficient is used for identifying an abnormality risk level of a corresponding abnormality loop;
the abnormality index output module is used for fitting according to the plurality of accompanying risk coefficients and outputting a first abnormality index;
and the early warning information generation module is used for generating first early warning information according to the first abnormality index.
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