CN116404758B - Intelligent seal cabinet power distribution monitoring and early warning control system - Google Patents
Intelligent seal cabinet power distribution monitoring and early warning control system Download PDFInfo
- Publication number
- CN116404758B CN116404758B CN202310663922.XA CN202310663922A CN116404758B CN 116404758 B CN116404758 B CN 116404758B CN 202310663922 A CN202310663922 A CN 202310663922A CN 116404758 B CN116404758 B CN 116404758B
- Authority
- CN
- China
- Prior art keywords
- value
- power distribution
- preset
- coefficient
- signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 61
- 238000012502 risk assessment Methods 0.000 claims abstract description 60
- 230000007613 environmental effect Effects 0.000 claims abstract description 43
- 239000000779 smoke Substances 0.000 claims abstract description 31
- 238000007781 pre-processing Methods 0.000 claims abstract description 27
- 238000009423 ventilation Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims description 75
- 238000012423 maintenance Methods 0.000 claims description 56
- 230000002159 abnormal effect Effects 0.000 claims description 35
- 239000002245 particle Substances 0.000 claims description 23
- 238000000034 method Methods 0.000 claims description 16
- 238000010586 diagram Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000002265 prevention Effects 0.000 claims description 3
- 238000005286 illumination Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 abstract description 7
- 230000033228 biological regulation Effects 0.000 abstract description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000007175 bidirectional communication Effects 0.000 description 3
- 230000006854 communication Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 239000010960 cold rolled steel Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000009931 harmful effect Effects 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
Classifications
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02B—BOARDS, SUBSTATIONS OR SWITCHING ARRANGEMENTS FOR THE SUPPLY OR DISTRIBUTION OF ELECTRIC POWER
- H02B1/00—Frameworks, boards, panels, desks, casings; Details of substations or switching arrangements
- H02B1/56—Cooling; Ventilation
- H02B1/565—Cooling; Ventilation for cabinets
-
- 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
- H02J13/00001—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 the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- 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
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Human Computer Interaction (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention relates to the technical field of power distribution monitoring and early warning control, in particular to an intelligent seal cabinet power distribution monitoring and early warning control system which comprises a server, a risk assessment unit, a compensation analysis unit, an early warning unit, a self-checking feedback unit and a preprocessing unit; according to the invention, the fault risk level evaluation analysis is carried out on the power distribution equipment in high-load operation so as to make early warning operation corresponding to the fault risk level, so that the fault risk of the power distribution equipment in high-load operation is reduced, meanwhile, the intelligent regulation and control of the power distribution equipment are realized, the influence condition of environmental data on the risk element is judged and set through two dimensions of a temperature salient value and a smoke salient value, the analysis is more comprehensive, and the ventilation mechanism is reasonably controlled to ventilate the power distribution equipment so as to reduce the influence of the environmental data on the risk element, thereby reducing the fault risk probability of the power distribution equipment.
Description
Technical Field
The invention relates to the technical field of power distribution monitoring and early warning control, in particular to an intelligent seal cabinet power distribution monitoring and early warning control system.
Background
The power distribution cabinet is a final-stage device of a common power distribution system, the power distribution cabinet is a cabinet which is formed by processing steel materials and used for protecting components from working normally, the manufacturing materials of the power distribution cabinet are generally divided into two types, namely a hot rolled steel plate and a cold rolled steel plate, the components in the power distribution cabinet can generate heat in the power distribution cabinet in the use process, the temperature in the power distribution cabinet is increased, the normal use and the service life of the components in the power distribution cabinet are easily affected when the temperature is too high, the power distribution cabinet is provided with a heat dissipation structure, and the power distribution is a link which is directly connected with a user in a power system and distributes electric energy to the user, wherein the power distribution device of the seal cabinet is required to be monitored, pre-warned and controlled;
at present, the appearance of intelligent operation and maintenance system has solved the fortune dimension hidden danger of seal cabinet distribution control early warning to a certain extent, but the operation and maintenance system among the prior art can't reduce the risk probability in the high-load operation of distribution equipment, and then cause serious harmful effects to the distribution equipment in the operation, very easily cause distribution equipment trouble, reduce its work efficiency, and to the interference problem that the environmental data that exists produced distribution equipment and inside electrical component, can't timely make the reply early warning and handle, and then there is the problem of increasing distribution equipment's trouble risk probability.
Disclosure of Invention
The invention aims to provide an intelligent seal cabinet power distribution monitoring early warning control system which solves the technical defects, and is characterized in that fault risk grade evaluation analysis is carried out on power distribution equipment in high-load operation to make early warning operation corresponding to the fault risk grade, so that the fault risk of the power distribution equipment in the high-load operation is reduced, meanwhile, intelligent regulation and control of the power distribution equipment are realized, in-depth comparison analysis is carried out on risk elements in the power distribution equipment, the influence condition of environmental data on the risk elements is judged through two dimensions of a temperature salient value and a smoke salient value, the analysis is more comprehensive, and ventilation treatment is carried out on the power distribution equipment by a ventilation mechanism is reasonably controlled, so that the influence of the environmental data on the risk elements is reduced, and the fault risk probability of the power distribution equipment is reduced.
The aim of the invention can be achieved by the following technical scheme:
an intelligent seal cabinet power distribution monitoring and early warning control system comprises a server, a risk assessment unit, a compensation analysis unit, an early warning unit, a self-checking feedback unit and a preprocessing unit;
when the server generates a supervision instruction, the supervision instruction is sent to the risk assessment unit, the risk assessment unit immediately collects operation data of the power distribution equipment when receiving the supervision instruction, analyzes the operation data to obtain a normal signal and an abnormal signal, sends the normal signal to the compensation analysis unit through the server, sends the abnormal signal to the early warning unit through the server, and immediately carries out yellow light illumination display on an alarm lamp on the power distribution equipment corresponding to the abnormal signal after receiving the abnormal signal by the early warning unit, and simultaneously starts a preset abnormal early warning scheme;
the compensation analysis unit immediately collects a historical maintenance list of the power distribution equipment after receiving the normal signal, analyzes the historical maintenance list and sends a maintenance processing coefficient W to the risk assessment unit through the server;
when the risk assessment unit generates a normal signal, judging the fault risk level condition of the power distribution equipment, analyzing the voltage record oscillogram and the running temperature of each electric element to obtain a primary processing signal, a secondary processing signal and a tertiary processing signal, and sending the primary processing signal, the secondary processing signal and the tertiary processing signal to the preprocessing unit through the server, wherein the processing degrees corresponding to the primary processing signal, the secondary processing signal and the tertiary processing signal are sequentially reduced;
when the self-checking feedback unit receives the precaution signal, the self-checking feedback unit immediately retrieves data from the risk assessment unit and analyzes the data to obtain an environment management signal, and sends the environment management signal to the preprocessing unit, and the preprocessing unit immediately controls the ventilation mechanism to ventilate the power distribution equipment after receiving the environment management signal.
Preferably, the operation data analysis process of the risk assessment unit is as follows:
the method comprises the steps of collecting time length from the starting working time to the ending working time of equipment, marking the time length as a time threshold, collecting operation data of the equipment matched with the time threshold, wherein the operation data comprise zero sequence overcurrent values and operation voltage values of the power distribution equipment, dividing the time threshold into o sub-time nodes, wherein o is a natural number larger than zero, obtaining the operation voltage values of the power distribution equipment in each sub-time node, further obtaining average operation voltage of the power distribution equipment in the time threshold according to the operation voltage values, and analyzing and comparing the average operation voltage and the zero sequence overcurrent values with preset average operation voltage threshold and zero sequence overcurrent value threshold:
if the average running voltage is smaller than a preset average running voltage threshold value and the zero sequence overcurrent value is smaller than a zero sequence overcurrent value threshold value, generating a normal signal;
if the average running voltage is greater than or equal to a preset average running voltage threshold value or the zero sequence overcurrent value is greater than or equal to a zero sequence overcurrent value threshold value, an abnormal signal is generated.
Preferably, the analysis process of the historical maintenance list by the compensation analysis unit is as follows:
collecting the time length from the beginning of the equipment to the current time and marking the time length as analysisThe method comprises the steps of obtaining a historical maintenance list of matched equipment at the same time, obtaining the time from the historical maintenance list to the time from the first maintenance time of the power distribution equipment in the analysis time to the last maintenance end time, obtaining the maintenance times of the power distribution equipment in the analysis time, obtaining a maintenance frequency value WP of the matched equipment in the analysis time, obtaining the time of each maintenance of the power distribution equipment in the analysis time from the historical maintenance list, obtaining the total maintenance time ZC of the power distribution equipment in the analysis time, and passing through a formulaAnd obtaining a maintenance processing coefficient, wherein a1 and a2 are preset proportional coefficients of a maintenance frequency value and a total maintenance duration respectively, a3 is a preset deviation correction coefficient, a1, a2 and a3 are natural numbers larger than zero, and W is the maintenance processing coefficient.
Preferably, the risk assessment unit generates the normal signal by the following analysis process:
step one: acquiring a voltage record waveform diagram of power distribution equipment in a time threshold, acquiring the total operation duration of paired equipment corresponding to the voltage record waveform diagram with the voltage greater than or equal to a preset voltage threshold, marking the total operation duration as load duration FC, dividing the load duration FC into i sub-time nodes, wherein i is a natural number greater than zero, acquiring the operation power of the power distribution equipment in each sub-time node, constructing an operation power set A, performing difference calculation on a subset in the set A and a preset rated operation power, constructing a difference set B, acquiring the sum of all subsets of the set B, and marking the sum as an exceeding power value CB;
step two: acquiring the operation temperature of each electric element in the load time period, comparing the operation temperature of each electric element with a preset electric element operation temperature threshold value recorded and stored in the electric element, acquiring the number of electric elements and the corresponding operation temperature which are larger than or equal to the preset electric element operation temperature threshold value, constructing a set C according to the number, acquiring a maximum subset and a minimum subset in the set C, marking the difference between the maximum subset and the minimum subset as a maximum temperature span value WK, and comparing the maximum temperature span value WK with a preset maximum temperature span value threshold value recorded and stored in the electric element: if the maximum temperature span value WK is greater than or equal to the preset maximum temperature span value, a prevention signal is generated;
step three: obtaining a risk assessment coefficient F through a formula, and comparing the risk assessment coefficient F with a preset risk assessment coefficient interval in the risk assessment coefficient F for analysis:
if the risk assessment coefficient F is larger than the maximum value in the preset risk assessment coefficient interval, generating a first-stage processing signal;
if the risk assessment coefficient F is located in the preset risk assessment coefficient interval, generating a secondary processing signal;
and if the risk assessment coefficient F is smaller than the minimum value in the preset risk assessment coefficient interval, generating a three-level processing signal.
Preferably, the self-checking feedback unit retrieves data from the risk assessment unit and performs the analysis process as follows:
immediately calling a set C from a risk assessment unit, acquiring areas corresponding to electric elements corresponding to all subsets in the set C, marking the areas as abnormal areas, wherein g is a natural number larger than zero, and acquiring environment data of the abnormal areas, wherein the environment data comprises an environment temperature value and an environment smoke particle concentration;
acquiring an environment temperature value and an environment smoke particle concentration corresponding to the electric elements in each abnormal area in the load duration, marking the electric elements in the abnormal area as risk elements, acquiring a standard temperature value and a standard smoke particle concentration value of the risk elements recorded and stored in a normal operation state, marking a difference value between the environment temperature value and the standard temperature value in the normal operation state as a temperature highlighting value TXg, marking a difference value between the environment smoke particle concentration and the standard smoke particle concentration value in the normal operation state as a smoke highlighting value YWg, and performing a formulaObtaining environment salient coefficients of each abnormal region, wherein c1 and c2 are preset proportion factors of a temperature salient value and a smoke salient value respectivelyEach of the sub-numbers c1 and c2 is a number greater than zero, c1+c2=2.7329, and hg is an environmental emphasis coefficient of each abnormal region.
Preferably, the environment highlighting coefficient Hg analysis process of the self-checking feedback unit is as follows:
comparing the environment highlighting coefficient Hg with a preset environment highlighting coefficient minimum value Hgmin which is recorded and stored in the environment highlighting coefficient Hg: if the environmental salient coefficient Hg is smaller than a preset minimum value Hgmin of the environmental salient coefficient, judging that the environmental temperature value and the environmental smoke particle concentration are not fault risk influence parameters of the risk element, and generating no signal; if the environmental salient coefficient Hg is larger than or equal to a preset minimum value Hgmin of the environmental salient coefficient, judging that the environmental temperature value and the environmental smoke particle concentration are fault risk influence parameters of the risk element, and generating an environmental management signal.
Preferably, the preprocessing unit immediately makes a preset early warning scheme corresponding to the processing signal after receiving the first-stage processing signal, the second-stage processing signal and the third-stage processing signal, namely, the preprocessing unit immediately makes a preset first-stage early warning scheme corresponding to the first-stage processing signal after receiving the first-stage processing signal, the preprocessing unit immediately makes a preset second-stage early warning scheme corresponding to the second-stage processing signal after receiving the second-stage processing signal, and the preprocessing unit immediately makes a preset third-stage early warning scheme corresponding to the third-stage processing signal after receiving the third-stage processing signal.
The beneficial effects of the invention are as follows:
the invention carries out fault risk level evaluation analysis on the power distribution equipment in high-load operation, carries out comprehensive analysis in a symbol calibration and formulation mode to carry out early warning operation corresponding to the fault risk level, is beneficial to reducing the fault risk of the power distribution equipment in high-load operation, simultaneously realizes intelligent regulation and control on the power distribution equipment, carries out deep analysis on a historical maintenance list, evaluates the operation quality of the power distribution equipment, and carries out data compensation and support on the fault risk level of the power distribution equipment according to the analysis result so as to ensure the accuracy of fault risk level evaluation;
the invention further carries out in-depth comparison analysis on the risk elements in the power distribution equipment, judges the influence condition of environmental data on the risk elements through two dimensions of the temperature salient value and the smoke salient value, analyzes more comprehensively, enlarges the analysis dimension, is favorable for more accurately and reasonably analyzing the data, judges whether the environmental temperature value and the environmental smoke particle concentration are fault risk influence parameters of the risk elements, and further controls the ventilation mechanism to ventilate the power distribution equipment so as to reduce the influence of the environmental data on the risk elements, thereby reducing the fault risk probability of the power distribution equipment.
Drawings
The invention is further described below with reference to the accompanying drawings;
FIG. 1 is a block flow diagram of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the invention discloses an intelligent seal cabinet power distribution monitoring and early warning control system, which comprises a server, a risk assessment unit, a compensation analysis unit, an early warning unit, a self-checking feedback unit and a preprocessing unit, wherein the server is in bidirectional communication connection with the risk assessment unit, the server is in bidirectional communication connection with the compensation analysis unit, the server is in bidirectional communication connection with the self-checking feedback unit, the risk assessment unit is in unidirectional communication connection with the preprocessing unit, the self-checking feedback unit is in unidirectional communication connection with the preprocessing unit, and the server is in unidirectional communication connection with the early warning unit;
when the server generates a supervision instruction, the supervision instruction is sent to the risk assessment unit, the risk assessment unit immediately collects operation data of the power distribution equipment when receiving the supervision instruction, judges the operation condition of the matched equipment and the fault risk level condition of the power distribution equipment in high-load operation, and then makes a reasonable emergency early warning scheme for the power distribution equipment according to the fault risk level condition so as to reduce the fault risk level in the operation of the power distribution equipment, and meanwhile, the power distribution equipment is guaranteed to work normally, and the specific analysis process is as follows:
acquiring the duration from the starting working time to the ending working time of the equipment, marking the duration as a time threshold, acquiring the running data of the equipment matched with the time threshold, wherein the running data comprises zero sequence overcurrent value and running voltage value of the power distribution equipment, dividing the time threshold into o sub-time nodes, o is a natural number larger than zero, acquiring the running voltage value of the power distribution equipment in each sub-time node, further acquiring the average running voltage of the power distribution equipment in the time threshold, analyzing and comparing the average running voltage and the zero sequence overcurrent value with the preset average running voltage threshold and the zero sequence overcurrent value in the power distribution equipment, wherein the average running voltage can show the running stability of the equipment, the larger the average voltage is, the larger the load of the power distribution equipment is, the larger the possibility of risk fault of the power distribution equipment is, if the average running voltage is smaller than the preset average running voltage threshold, the zero sequence overcurrent value is smaller than the zero sequence overcurrent value, generating a normal signal, transmitting the normal signal to a compensation analysis unit through a server, and if the average running voltage is larger than or the zero sequence overcurrent value is larger than the preset average running voltage threshold, generating the average running voltage and the zero sequence overcurrent value, and transmitting the average running voltage and the zero sequence overcurrent value is larger than the preset running voltage threshold to a preset power supply, and the power supply is used for immediately starting a warning lamp after the power supply is in a corresponding to the power supply, and warning unit is started up to a warning lamp, and warning a warning device is used for warning the abnormal, and warning device is used for warning that is used for warning the abnormal equipment, and has been started immediately when the abnormal;
the compensation analysis unit immediately collects a historical maintenance list of the power distribution equipment after receiving the normal signal, analyzes the historical maintenance list, evaluates the operation quality of the power distribution equipment according to the historical maintenance list, and performs data compensation and support on the fault risk level of the power distribution equipment according to the analysis result to ensure the accuracy of the fault risk level evaluation, wherein the specific analysis process is as follows:
acquiring a time length from the starting time of the equipment to the current time, marking the time length as an analysis time length, acquiring a historical maintenance list of the matched equipment, acquiring the time length from the first maintenance time of the power distribution equipment in the analysis time length to the last maintenance end time, acquiring the maintenance times of the power distribution equipment in the analysis time length, acquiring a maintenance frequency value of the matched equipment in the analysis time length, wherein the maintenance frequency value is denoted by WP, acquiring the time length of each maintenance of the power distribution equipment in the analysis time length from the historical maintenance list, further acquiring the total maintenance time length of the power distribution equipment in the analysis time length, and the total maintenance time length is denoted by ZC, and passing through a formulaObtaining a maintenance processing coefficient, wherein a1 and a2 are respectively preset proportion coefficients of a maintenance frequency value and total maintenance duration, a3 is a preset deviation correction coefficient, a1, a2 and a3 are natural numbers larger than zero, W is the maintenance processing coefficient, the maintenance processing coefficient W is sent to a risk assessment unit through a server, data support is facilitated to fault risk assessment of power distribution equipment through the maintenance processing coefficient W, data rigor and effectiveness are improved, and an obtained result is more accurate.
Example 2
When a normal signal is generated by the risk assessment unit, acquiring a voltage record waveform diagram of power distribution equipment in a time threshold value, acquiring the total operation duration of paired equipment corresponding to a part with a voltage greater than or equal to a preset voltage threshold value according to the voltage record waveform diagram, marking the paired equipment as load duration FC, dividing the load duration FC into i sub-time nodes, i being a natural number greater than zero, acquiring the operation power of the power distribution equipment in each sub-time node, constructing an operation power set A, carrying out difference calculation on subsets in the set A and preset rated operation power, acquiring the sum of all subsets in the set B according to the constructed difference set B, marking the sum as an exceeding power value, and marking the sum as a CB.
In addition, the operation temperature of each electric element in the load duration is obtained, the operation temperature of each electric element is compared with a preset electric element operation temperature threshold value recorded and stored in the electric element, the number of electric elements and the corresponding operation temperature of the electric elements which are larger than or equal to the preset electric element operation temperature threshold value are obtained, a set C is constructed according to the number of electric elements, a maximum subset and a minimum subset in the set C are obtained, the difference value between the maximum subset and the minimum subset is marked as a maximum temperature span value WK, and the maximum temperature span value WK is compared with a preset maximum temperature span value threshold value recorded and stored in the electric element: if the maximum temperature span value WK is larger than or equal to the preset maximum temperature span value, a prevention signal is generated and generated to the self-checking feedback unit through the server;
through the formulaObtaining a risk assessment coefficient, wherein b1 and b2 are preset weight coefficients of an exceeding power value and a maximum temperature span value respectively, b3 is a preset interference correction coefficient, b1, b2 and b3 are natural numbers larger than zero, b1+b2= 1.2846, b2+b3=1.367, F is a risk assessment coefficient, and the risk assessment coefficient F is compared with an internal preset risk assessment coefficient interval thereof for analysis:
if the risk assessment coefficient F is larger than the maximum value in the preset risk assessment coefficient interval, generating a first-stage processing signal;
if the risk assessment coefficient F is located in the preset risk assessment coefficient interval, generating a secondary processing signal;
if the risk assessment coefficient F is smaller than the minimum value in the preset risk assessment coefficient interval, a three-level processing signal is generated, the first-level processing signal, the second-level processing signal and the three-level processing signal are sent to a preprocessing unit through a server, the processing degrees corresponding to the first-level processing signal, the second-level processing signal and the three-level processing signal are sequentially reduced, the preprocessing unit immediately makes a preset early warning scheme corresponding to the processing signals after receiving the first-level processing signal, namely the preprocessing unit immediately makes a preset first-level early warning scheme corresponding to the first-level processing signal after receiving the first-level processing signal, the preprocessing unit immediately makes a preset second-level early warning scheme corresponding to the second-level processing signal after receiving the second-level processing signal, and the preprocessing unit immediately makes a preset third-level early warning scheme corresponding to the third-level processing signal after receiving the third-level processing signal, so that the fault risk of the power distribution equipment in high-load operation is reduced, the normal operation of the power distribution equipment is facilitated, and intelligent regulation and control of the power distribution equipment is realized.
Example 3
When the self-checking feedback unit receives the precaution signal, the self-checking feedback unit immediately invokes the set C from the risk assessment unit, acquires the areas corresponding to the electrical elements corresponding to all subsets in the set C, marks the areas as abnormal areas, the marks g and g are natural numbers larger than zero, acquires environment data of the abnormal areas, and analyzes the environment data, wherein the environment data comprises environment temperature values and environment smoke particle concentration, and the specific analysis process is as follows:
acquiring an environment temperature value and an environment smoke particle concentration corresponding to the electric elements in each abnormal area in the load duration, simultaneously marking the electric elements in the abnormal area as risk elements, simultaneously acquiring a standard temperature value and a standard smoke particle concentration value of the risk elements recorded and stored in a normal operation state, marking a difference value between the environment temperature value and the standard temperature value in the normal operation state as a temperature salient value, marking TXg, marking a difference value between the environment smoke particle concentration and the standard smoke particle concentration value in the normal operation state as a smoke salient value, marking YWg, and passing through a formulaObtaining the environmental prominence of each abnormal regionThe method comprises the steps of (1) and (2) respectively representing preset scale factors of a temperature salient value and a smoke salient value, wherein c1 and c2 are numbers larger than zero, c1+c2=2.7329, hg is an environment salient coefficient of each abnormal region, hg is a numerical value reflecting the probability of electric faults in each abnormal region, the higher the numerical value of the environment salient coefficient Hg is, the higher the probability of electric faults in each abnormal region is, and comparing the environment salient coefficient Hg with a preset environment salient coefficient minimum value Hgmin which the environment salient coefficient Hg is recorded and stored in the environment salient coefficient Hg:
if the environmental salient coefficient Hg is smaller than a preset minimum value Hgmin of the environmental salient coefficient, judging that the environmental temperature value and the environmental smoke particle concentration are not fault risk influence parameters of the risk element, and generating no signal; if the environmental salient coefficient Hg is greater than or equal to a preset minimum value Hgmin of the environmental salient coefficient, the environmental temperature value and the environmental smoke particle concentration are judged to be fault risk influence parameters of the risk element, an environmental management signal is generated at the same time and sent to the preprocessing unit, and the preprocessing unit immediately controls the ventilation mechanism to ventilate the power distribution equipment after receiving the environmental management signal so as to reduce the influence of environmental data on the risk element and further reduce the fault risk of the power distribution equipment.
In summary, the invention carries out fault risk level evaluation analysis on the power distribution equipment in high-load operation, carries out comprehensive analysis in a symbol calibration and formulation mode to carry out early warning operation corresponding to the fault risk level, is beneficial to reducing the fault risk of the power distribution equipment in high-load operation, simultaneously realizes intelligent regulation and control on the power distribution equipment, carries out deep analysis on a historical maintenance list, evaluates the operation quality of the power distribution equipment, and carries out data compensation and support on the fault risk level of the power distribution equipment according to the analysis result so as to ensure the accuracy of fault risk level evaluation;
in addition, through carrying out in-depth comparison analysis to the risk component inside the distribution equipment, and judge and establish the influence condition of environmental data to the risk component through two dimensions of temperature salient value and smog salient value, the analysis is more comprehensive, and with this enlarged analysis dimension, help more accurate, reasonable data analysis, judge whether environmental temperature value and environment smog particle concentration are the trouble risk influence parameter of risk component, and then control ventilation mechanism carries out ventilation treatment to the distribution equipment, in order to reduce the influence of environmental data to the risk component, thereby reduce the trouble risk probability of distribution equipment.
The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.
Claims (4)
1. The intelligent seal cabinet power distribution monitoring and early warning control system is characterized by comprising a server, a risk assessment unit, a compensation analysis unit, an early warning unit, a self-checking feedback unit and a preprocessing unit;
when the server generates a supervision instruction, the supervision instruction is sent to the risk assessment unit, the risk assessment unit immediately collects operation data of the power distribution equipment when receiving the supervision instruction, analyzes the operation data to obtain a normal signal and an abnormal signal, sends the normal signal to the compensation analysis unit through the server, sends the abnormal signal to the early warning unit through the server, and immediately carries out yellow light illumination display on an alarm lamp on the power distribution equipment corresponding to the abnormal signal after receiving the abnormal signal by the early warning unit, and simultaneously starts a preset abnormal early warning scheme;
the compensation analysis unit immediately collects a historical maintenance list of the power distribution equipment after receiving the normal signal, analyzes the historical maintenance list and sends a maintenance processing coefficient W to the risk assessment unit through the server;
when the risk assessment unit generates a normal signal, judging the fault risk level condition of the power distribution equipment, analyzing the voltage record oscillogram and the running temperature of each electric element to obtain a primary processing signal, a secondary processing signal and a tertiary processing signal, and sending the primary processing signal, the secondary processing signal and the tertiary processing signal to the preprocessing unit through the server, wherein the processing degrees corresponding to the primary processing signal, the secondary processing signal and the tertiary processing signal are sequentially reduced;
when the self-checking feedback unit receives the precaution signal, the self-checking feedback unit immediately invokes data from the risk assessment unit and analyzes the data to obtain an environment management signal, and sends the environment management signal to the preprocessing unit, and the preprocessing unit immediately controls the ventilation mechanism to ventilate the power distribution equipment after receiving the environment management signal;
the compensation analysis unit analyzes the historical maintenance list as follows:
acquiring a time length from the starting time of the equipment to the current time, marking the time length as an analysis time length, acquiring a historical maintenance list of the matched equipment, acquiring the time length from the first maintenance time of the power distribution equipment in the analysis time length to the last maintenance end time, acquiring the maintenance times of the power distribution equipment in the analysis time length, acquiring a maintenance frequency value WP of the matched equipment in the analysis time length, acquiring the time length of each maintenance of the power distribution equipment in the analysis time length from the historical maintenance list, further acquiring the total maintenance time length ZC of the power distribution equipment in the analysis time length, and passing through a formulaObtaining a maintenance processing coefficient, wherein a1 and a2 are preset proportional coefficients of a maintenance frequency value and a total maintenance duration respectively, a3 is a preset deviation correction coefficient, a1, a2 and a3 are natural numbers larger than zero, and W is the maintenance processing coefficient;
the analysis process when the risk assessment unit generates a normal signal is as follows:
step one: acquiring a voltage record waveform diagram of power distribution equipment in a time threshold, acquiring the total operation duration of paired equipment corresponding to the voltage record waveform diagram with the voltage greater than or equal to a preset voltage threshold, marking the total operation duration as load duration FC, dividing the load duration FC into i sub-time nodes, wherein i is a natural number greater than zero, acquiring the operation power of the power distribution equipment in each sub-time node, constructing an operation power set A, performing difference calculation on a subset in the set A and a preset rated operation power, constructing a difference set B, acquiring the sum of all subsets of the set B, and marking the sum as an exceeding power value CB;
step two: acquiring the operation temperature of each electric element in the load time period, comparing the operation temperature of each electric element with a preset electric element operation temperature threshold value recorded and stored in the electric element, acquiring the number of electric elements and the corresponding operation temperature which are larger than or equal to the preset electric element operation temperature threshold value, constructing a set C according to the number, acquiring a maximum subset and a minimum subset in the set C, marking the difference between the maximum subset and the minimum subset as a maximum temperature span value WK, and comparing the maximum temperature span value WK with a preset maximum temperature span value threshold value recorded and stored in the electric element: if the maximum temperature span value WK is greater than or equal to the preset maximum temperature span value, a prevention signal is generated and sent to a self-checking feedback unit;
step three: obtaining a risk assessment coefficient F through a formula, and comparing the risk assessment coefficient F with a preset risk assessment coefficient interval in the risk assessment coefficient F for analysis:
if the risk assessment coefficient F is larger than the maximum value in the preset risk assessment coefficient interval, generating a first-stage processing signal;
if the risk assessment coefficient F is located in the preset risk assessment coefficient interval, generating a secondary processing signal;
if the risk assessment coefficient F is smaller than the minimum value in the preset risk assessment coefficient interval, generating a three-level processing signal;
the self-checking feedback unit is used for calling data from the risk assessment unit and analyzing the data as follows:
immediately calling a set C from a risk assessment unit, acquiring areas corresponding to electric elements corresponding to all subsets in the set C, marking the areas as abnormal areas, wherein g is a natural number larger than zero, and acquiring environment data of the abnormal areas, wherein the environment data comprises an environment temperature value and an environment smoke particle concentration;
acquiring an environment temperature value and an environment smoke particle concentration corresponding to the electric elements in each abnormal area in the load duration, marking the electric elements in the abnormal area as risk elements, acquiring a standard temperature value and a standard smoke particle concentration value of the risk elements recorded and stored in a normal operation state, marking a difference value between the environment temperature value and the standard temperature value in the normal operation state as a temperature highlighting value TXg, marking a difference value between the environment smoke particle concentration and the standard smoke particle concentration value in the normal operation state as a smoke highlighting value YWg, and performing a formulaAnd obtaining the environment salient coefficients of each abnormal region, wherein c1 and c2 are preset scale factors of a temperature salient value and a smoke salient value respectively, c1 and c2 are numbers larger than zero, c1+c2=2.7329, and Hg is the environment salient coefficient of each abnormal region.
2. The intelligent seal cabinet power distribution monitoring and early warning control system according to claim 1, wherein the operation data analysis process of the risk assessment unit is as follows:
the method comprises the steps of collecting time length from the starting working time to the ending working time of equipment, marking the time length as a time threshold, collecting operation data of the equipment matched with the time threshold, wherein the operation data comprise zero sequence overcurrent values and operation voltage values of the power distribution equipment, dividing the time threshold into o sub-time nodes, wherein o is a natural number larger than zero, obtaining the operation voltage values of the power distribution equipment in each sub-time node, further obtaining average operation voltage of the power distribution equipment in the time threshold according to the operation voltage values, and analyzing and comparing the average operation voltage and the zero sequence overcurrent values with preset average operation voltage threshold and zero sequence overcurrent value threshold:
if the average running voltage is smaller than a preset average running voltage threshold value and the zero sequence overcurrent value is smaller than a zero sequence overcurrent value threshold value, generating a normal signal;
if the average running voltage is greater than or equal to a preset average running voltage threshold value or the zero sequence overcurrent value is greater than or equal to a zero sequence overcurrent value threshold value, an abnormal signal is generated.
3. The intelligent seal cabinet power distribution monitoring and early warning control system according to claim 1, wherein the environment salient coefficient Hg analysis process of the self-checking feedback unit is as follows:
comparing the environment highlighting coefficient Hg with a preset environment highlighting coefficient minimum value Hgmin which is recorded and stored in the environment highlighting coefficient Hg: if the environmental salient coefficient Hg is smaller than a preset minimum value Hgmin of the environmental salient coefficient, judging that the environmental temperature value and the environmental smoke particle concentration are not fault risk influence parameters of the risk element, and generating no signal; if the environmental salient coefficient Hg is larger than or equal to a preset minimum value Hgmin of the environmental salient coefficient, judging that the environmental temperature value and the environmental smoke particle concentration are fault risk influence parameters of the risk element, and generating an environmental management signal.
4. The intelligent seal cabinet power distribution monitoring and early warning control system according to claim 1, wherein the preprocessing unit immediately makes a preset early warning scheme corresponding to the processing signal after receiving the first-stage processing signal, the second-stage processing signal and the third-stage processing signal, namely, the preprocessing unit immediately makes a preset first-stage early warning scheme corresponding to the first-stage processing signal after receiving the first-stage processing signal, the preprocessing unit immediately makes a preset second-stage early warning scheme corresponding to the second-stage processing signal after receiving the second-stage processing signal, and the preprocessing unit immediately makes a preset third-stage early warning scheme corresponding to the third-stage processing signal after receiving the third-stage processing signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310663922.XA CN116404758B (en) | 2023-06-07 | 2023-06-07 | Intelligent seal cabinet power distribution monitoring and early warning control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310663922.XA CN116404758B (en) | 2023-06-07 | 2023-06-07 | Intelligent seal cabinet power distribution monitoring and early warning control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116404758A CN116404758A (en) | 2023-07-07 |
| CN116404758B true CN116404758B (en) | 2023-08-22 |
Family
ID=87012627
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310663922.XA Active CN116404758B (en) | 2023-06-07 | 2023-06-07 | Intelligent seal cabinet power distribution monitoring and early warning control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116404758B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116594346B (en) * | 2023-07-18 | 2023-10-20 | 山东恒圣石墨科技有限公司 | Intelligent control system and control method for graphite heater |
| CN116651534B (en) * | 2023-07-31 | 2023-10-20 | 延边大学 | Separated noise-reducing and vibration-absorbing optical platform for patch clamp experiment |
| CN117057513B (en) * | 2023-10-11 | 2024-01-12 | 山东建筑大学 | Intelligent park is with control management system based on internet |
| CN117408514A (en) * | 2023-10-25 | 2024-01-16 | 国网江苏省电力有限公司宿迁供电分公司 | Intelligent operation and maintenance transformer substation monitoring and early warning system and method based on multi-parameter sensor |
| CN117382468B (en) * | 2023-10-31 | 2024-05-14 | 珠海康晋电气股份有限公司 | Charging pile operation control system based on edge calculation |
| CN117437384B (en) * | 2023-11-14 | 2024-05-03 | 北京裕佳科技有限公司 | Multi-angle and multi-height based image recognition device and method |
| CN117614991B (en) * | 2023-11-30 | 2024-05-03 | 北京康洁宏创科技有限公司 | Intelligent seal cabinet based on remote control |
| CN117691750B (en) * | 2024-01-30 | 2024-04-09 | 深圳市森通电力科技有限公司 | Intelligent power distribution monitoring method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115789856A (en) * | 2022-12-09 | 2023-03-14 | 安徽省凯杰机械制造有限公司 | On-line monitoring control system for ventilation equipment |
| CN116308285A (en) * | 2023-03-22 | 2023-06-23 | 齐犇科技集团有限公司 | Intelligent terminal safety supervision system based on data processing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101942647B1 (en) * | 2017-04-04 | 2019-01-25 | 한국전력공사 | Apparatus for asset management of power transformers |
-
2023
- 2023-06-07 CN CN202310663922.XA patent/CN116404758B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115789856A (en) * | 2022-12-09 | 2023-03-14 | 安徽省凯杰机械制造有限公司 | On-line monitoring control system for ventilation equipment |
| CN116308285A (en) * | 2023-03-22 | 2023-06-23 | 齐犇科技集团有限公司 | Intelligent terminal safety supervision system based on data processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116404758A (en) | 2023-07-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116404758B (en) | Intelligent seal cabinet power distribution monitoring and early warning control system | |
| CN114430199B (en) | Cubical switchboard operation supervisory systems based on big data | |
| CN116483010A (en) | Power control cabinet safe operation supervision early warning system | |
| CN116660672B (en) | Power grid equipment fault diagnosis method and system based on big data | |
| CN113420992A (en) | Power system network risk assessment method and system | |
| CN109656913B (en) | Data acquisition abnormity recruitment method based on Internet of things | |
| CN111428984A (en) | Risk early warning and analysis method of intelligent safety electricity utilization management system | |
| CN107085153A (en) | A kind of multiplexing electric abnormality fire alarm method and system | |
| CN113223263A (en) | Electrical fire monitoring system based on Internet of things | |
| CN107589318A (en) | The method for detecting abnormality and device of inverter in photovoltaic plant | |
| CN104820884A (en) | Power network dispatching real-time data inspection method combined with characteristics of power system | |
| CN112328961A (en) | On-line monitoring device quality evaluation system based on fault tree and Bayesian network | |
| CN116844708A (en) | Medical equipment fault prediction method and system based on artificial intelligence | |
| CN116187759A (en) | Cable line operation risk assessment method and system | |
| CN115951606A (en) | Intelligent factory production environment early warning processing method | |
| CN117110768B (en) | Automatic printer safety precaution system based on data analysis | |
| CN117491787B (en) | Electromagnetic interference detection system of chip production equipment | |
| CN117764559A (en) | Electrical cabinet operation and maintenance overhaul supervision and early warning system based on data analysis | |
| CN110750760B (en) | Abnormal theoretical line loss detection method based on situation awareness and control diagram | |
| CN109633333B (en) | Provincial and above power grid various load shedding amount conflict detection and coordination check method | |
| CN116465059A (en) | Energy-saving air conditioner operation supervision system based on data analysis | |
| CN111581802A (en) | Method and system for calculating real-time comprehensive fault rate of power distribution equipment | |
| CN110889649A (en) | Intelligent power distribution monitoring management system with high reliability | |
| CN102509154B (en) | Dynamic adjustment method for infrared temperature measuring period of transformer station equipment | |
| CN111273075B (en) | Distribution network automatic current setting alarm method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |