CN111812393A - Large-current fault diagnosis system and method based on rough set theory - Google Patents
Large-current fault diagnosis system and method based on rough set theory Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16528—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values using digital techniques or performing arithmetic operations
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- G—PHYSICS
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention relates to the technical field of coal mine power supply equipment management, in particular to a large-current fault diagnosis system and method based on a rough set theory. The system comprises: the multi-sensor module is used for acquiring load data of the underground coal mine power supply system in real time; the control module is used for processing the data collected by the multi-element sensor module and diagnosing whether the coal mine underground power supply system has a large-current fault or not based on a rough set theory; the execution module is used for receiving the instruction sent by the control module to cut off and alarm a fault line when the coal mine underground power supply system has a fault; the communication module is used for realizing data interaction among the control module, the multi-element sensor module and the execution module; and the power supply module is used for supplying power to the multi-element sensor module, the execution module and the communication module. The method is realized based on the system. The invention can better improve the detection efficiency of the large-current fault.
Description
Technical Field
The invention relates to the technical field of coal mine power supply equipment management, in particular to a large-current fault diagnosis system and method based on a rough set theory.
Background
In the coal mine underground power supply system, because underground equipment is arranged at the tail end of the power supply system, the number of motors is large, and the power of the motors is possibly close, the starting current of the underground motor can exceed a normal value, and the working voltage can fluctuate greatly along with the load. The traditional protection prevents protection misoperation by improving a protection fixed value or increasing protection action delay when switching-on is started, but the two methods reduce the protection sensitivity and cannot meet the requirements of the existing increasingly complex power system. Both false operation and false operation of the current protection device can cause serious consequences, and can cause the generator sets which generate electricity in parallel to be disconnected, out of synchronization and stably damaged, even cause the breakdown and the collapse of the whole power system and cause personal risks.
The rough set theory is a newer soft computing method, and can deal with the incompleteness and uncertainty of information. The data set in the domain is described in a rough set using the information table, finding the two most similar to it in all existing databases as the lower approximation and one as the upper approximation. And a decision rule is obtained in the formed decision knowledge expression system according to reduction, so that the circuit with large current fault of the underground coal mine power supply system can be better checked.
Disclosure of Invention
The present invention provides a rough set theory based high current fault diagnosis system that overcomes some or all of the deficiencies of the prior art.
The invention relates to a large-current fault diagnosis system based on rough set theory, which comprises:
the multi-sensor module is used for acquiring load data of the underground coal mine power supply system in real time;
the control module is used for processing the data collected by the multi-element sensor module and diagnosing whether the coal mine underground power supply system has a large-current fault or not based on a rough set theory;
the execution module is used for receiving the instruction sent by the control module to cut off and alarm a fault line when the coal mine underground power supply system has a fault;
the communication module is used for realizing data interaction among the control module, the multi-element sensor module and the execution module; and
and the power supply module is used for supplying power to the multi-element sensor module, the execution module and the communication module.
According to the invention, by utilizing the rough set theory, uncertainty reasoning can be carried out on fault data in the long-distance coal mine underground power supply system, so that a fault protection control rule is obtained. The method and the device can quickly and accurately detect the large-current fault of the long-distance coal mine underground power supply system, and can quickly and accurately remove the fault line, so that the safety level of long-distance power supply is improved.
Preferably, the multi-sensor module comprises a temperature and humidity sensor and an electrical parameter sensor. The multi-sensor module comprises a temperature and humidity sensor and an electrical parameter sensor and is arranged on the power supply line. The temperature and humidity sensor is used for detecting underground real-time temperature and humidity information and assisting in distinguishing underground environment information.
Preferably, the execution module comprises a circuit breaker and an audible and visual alarm. So that the faulty line can be preferably cut off and alarmed in the event of a fault.
The invention also provides a large-current fault diagnosis method based on the rough set theory, which comprises the following steps:
s1, collecting various parameters of the coal mine underground power supply system by adopting a multi-sensor module, and sending the parameters to a control module through a communication module;
s2, the control module processes data collected by the multi-element sensor module based on a rough set theory, and further diagnoses whether a large-current fault occurs in the coal mine underground power supply system; if no fault exists, repeating the steps S1 and S2 in the next sampling period, and if the fault exists, entering the step S3;
and step S3, the control module sends a control instruction to the execution module, so that the fault line is cut off and an alarm is given.
Through the steps S1-S3, a fault decision table can be preferably established through a rough set theory under the condition of no manual participation, and a minimum attribute subset is obtained after the decision table is reduced according to a decision table reduction rule, so that a circuit with a large-current fault of the underground coal mine power supply system is judged.
Preferably, the multi-element sensor module comprises a temperature and humidity sensor and an electrical parameter sensor, wherein the temperature and humidity sensor is used for detecting real-time temperature and humidity information under a coal mine to assist in distinguishing environment information under the coal mine; the electric parameter sensor is used for collecting three-phase current, voltage and power parameters of the underground coal mine power supply system and is used for the control module to establish a decision table.
Preferably, in step S2, the processing of the data collected by the multi-sensor module by the control module includes pre-processing of the data, real-time data processing, and post-processing of the data.
Preferably, the pre-processing of the data comprises engineering conversion processing, filtering processing and table look-up calculation processing of the data, and the post-processing of the data comprises processing of a history storage database.
Preferably, the real-time data processing comprises the steps of,
step S21, establishing a large-current fault decision table according to data collected by the multi-element sensor module based on a rough set theory;
step S22, reducing the large-current fault decision table according to a preset reduction rule, and further obtaining a minimum attribute subset;
and step S23, judging whether a fault occurs and the fault type according to a preset fault analysis rule.
Compared with the prior art, the system and the method of the invention can diagnose the underground heavy current fault in real time without manual operation, thereby greatly improving the detection efficiency of the heavy current fault; the running state of the power supply equipment can be monitored in real time, and technical support is provided for the underground power supply system.
Drawings
Fig. 1 is a block diagram schematically illustrating a large-current fault diagnosis system in embodiment 1;
fig. 2 is a schematic flowchart of a large-current fault diagnosis method in embodiment 1;
fig. 3 is a schematic diagram of the large-current fault diagnosis method in embodiment 1 applied to a feeding cabinet.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Example 1
Referring to fig. 1, the present embodiment provides a large current fault diagnosis system based on rough set theory, which includes:
the multi-sensor module is used for acquiring load data of the underground coal mine power supply system in real time;
the control module is used for processing the data collected by the multi-element sensor module and diagnosing whether the coal mine underground power supply system has a large-current fault or not based on a rough set theory;
the execution module is used for receiving the instruction sent by the control module to cut off and alarm a fault line when the coal mine underground power supply system has a fault;
the communication module is used for realizing data interaction among the control module, the multi-element sensor module and the execution module; and
and the power supply module is used for supplying power to the multi-element sensor module, the execution module and the communication module.
In the embodiment, by using the rough set theory, uncertainty reasoning can be performed on fault data in the long-distance coal mine underground power supply system, so that a fault protection control rule is obtained. The method and the device can quickly and accurately detect the large-current fault of the long-distance coal mine underground power supply system, and can quickly and accurately remove the fault line, so that the safety level of long-distance power supply is improved.
The large-current fault diagnosis system in the embodiment mainly comprises a multi-sensor module, a communication module, a control module and a power supply module, and can detect the load state of the operation of the long-distance power supply system in the underground coal mine in real time by using the multi-sensor module. The high-current fault diagnosis system in the embodiment can establish a high-current fault decision table through signals collected by the multi-element sensor module under the condition of no manual participation, carry out high-current fault diagnosis based on a rough set theory, and timely protect and act when the collected current is larger than a rated action current according to a current protection setting calculation principle, so that technical support can be better provided for high-current fault detection.
In this embodiment, the multi-element sensor module includes a temperature and humidity sensor and an electrical parameter sensor.
The multi-sensor module comprises a temperature and humidity sensor and an electrical parameter sensor and is arranged on the power supply line. The temperature and humidity sensor is used for detecting underground real-time temperature and humidity information and assisting in distinguishing underground environment information. The electric parameter sensor is used for collecting electric parameters and measuring parameters such as three-phase current, voltage, power and the like of the alternating current line in real time. And the data collected by the multi-element sensor module can be sent to the control module through the communication module for processing.
In this embodiment, the communication module can integrate digital quantity and analog quantity output points, and then can be favorable to the transmission of humiture parameter and electrical parameter better. The communication module can be used for presetting a parameter read-write address table, and the multi-element sensor module can complete the interaction of electrical parameter information by reading and writing corresponding addresses through the communication module. After the communication channel between the communication module and the multi-element sensor module is successfully established, the communication module can read parameters at the multi-element sensor module in a fixed reading period and transmit the parameters to the control module.
In this embodiment, the execution module includes a circuit breaker and an audible and visual alarm. So that the faulty line can be preferably cut off and alarmed in the event of a fault.
Based on the system of the embodiment, the embodiment further provides a large-current fault diagnosis method based on the rough set theory, which includes the following steps:
s1, collecting various parameters of the coal mine underground power supply system by adopting a multi-sensor module, and sending the parameters to a control module through a communication module;
s2, the control module processes data collected by the multi-element sensor module based on a rough set theory, and further diagnoses whether a large-current fault occurs in the coal mine underground power supply system; if no fault exists, repeating the steps S1 and S2 in the next sampling period, and if the fault exists, entering the step S3;
and step S3, the control module sends a control instruction to the execution module, so that the fault line is cut off and an alarm is given.
Through the steps S1-S3, a fault decision table can be preferably established through a rough set theory under the condition of no manual participation, and a minimum attribute subset is obtained after the decision table is reduced according to a decision table reduction rule, so that a circuit with a large-current fault of the underground coal mine power supply system is judged.
Wherein, the control module can be established with real-time database, and the system can be according to the sampling time sampling channel signal of setting for.
In the embodiment, the multi-sensor module comprises a temperature and humidity sensor and an electrical parameter sensor, wherein the temperature and humidity sensor is used for detecting real-time temperature and humidity information in the underground coal mine to assist in distinguishing the environment information in the underground coal mine; the electric parameter sensor is used for collecting three-phase current, voltage and power parameters of the underground coal mine power supply system and is used for the control module to establish a decision table.
In this embodiment, in step S2, the processing of the data collected by the multi-sensor module by the control module includes pre-processing, real-time data processing, and post-processing of the data.
In this embodiment, the pre-processing of the data includes an engineering conversion process, a filtering process, and a table lookup calculation process on the data, and the post-processing of the data includes a processing on a history storage database. The data post-processing is to process the historical disk storage database in the configuration module, the main functions include historical curves, historical tables, disk storage data browsing and the like, and in addition, the data post-processing can be connected with an external database, so that the interaction with other systems is facilitated.
In this embodiment, the real-time data processing includes the following steps,
step S21, establishing a large-current fault decision table according to data collected by the multi-element sensor module based on a rough set theory;
step S22, reducing the large-current fault decision table according to a preset reduction rule, and further obtaining a minimum attribute subset;
and step S23, judging whether a fault occurs and the fault type according to a preset fault analysis rule.
The real-time data processing is carried out in the operation and accident processing of the equipment, the data in the real-time database is logically judged according to the operation strategy and the fault decision table of the rough set theory, and whether the fault exists or the fault type is judged, so that the aim of real-time control is fulfilled.
When the method in the embodiment is used for actual processing, firstly, the multi-sensor module samples parameters such as current amplitude, power flow and the like in a cable line in real time and transmits the parameters to the control module through the communication module, the control module judges whether a power supply system fails or not, a large-current fault decision table is established according to the condition attribute set, a minimum attribute subset is obtained after the decision table is reduced according to the decision table reduction rule, the line with the fault in the underground coal mine is judged according to the minimum attribute subset, and the change condition of each phase current in the fault cable before and after the fault is compared to determine the fault type.
Fig. 3 is a schematic diagram illustrating that the method in this embodiment is used to perform large-current fault diagnosis on the power supply area of the feeding cabinet.
In fig. 3, the feeding cabinet is provided with total outgoing lines L1and L2, and outgoing lines of L1and L2 are provided with three loads, namely L11, L12, L13, L21, L22and L23; the multi-sensor module is arranged at the load lines of L11, L12, L13, L21, L22and L23 and used for detecting instantaneous three-phase current, voltage, power and other electrical parameters and temperature and humidity parameters flowing through each load line; the main lines L1and L2 are also provided with circuit breakers as one of the execution modules, and the circuit breakers can receive the control of the main control module to perform switching on and switching off.
Corresponding to step S21, a decision table with { S, L1, L2, L11, L12, L13, L21, L22, L23, Fault } as a set element can be established according to the electrical parameters collected by the multi-sensor module.
Where S is the discourse domain.
Wherein { L1, L2, L11, L12, L13, L21, L22, L23} is a condition attribute set, an attribute value of "1" indicates that the measurement unit current value is out of limit and flows from the bus to the element, an attribute value of "-1" indicates that the measurement current value is out of limit, while the direction of flow in the cable is from the element to the bus, and "0" indicates that the three-phase current value obtained in the cable does not exceed the rated value.
Wherein { Fault } is a decision attribute, and "W-L1" indicates that a large current Fault occurs in the line cable L1. "R" indicates that the feed system has no high current fault.
Corresponding to step S22, the decision table is not listed because the probability of two or more lines failing is very small and its condition attribute is only the superposition of single line failures.
Corresponding to step S23, according to the decision relationship obtained after reduction of the decision table, 9 fault analysis rules are obtained as follows:
rule 1: if (L1 ═ 1) or (L1 ═ 1) Then Fault ═ W-L1 (only if the instantaneous phase current value of the line L1 with non-starting current is out of limit, the line L1 fails);
rule 2: if (L2 is 1) or (L2 is-1) Then Fault is W-L2 (if the instantaneous phase current value of the line L2 with non-starting current is out of limit, the line L2 has a large current Fault);
rule 3: if (L1 ═ 1and L11 ═ 1) or (L1 ═ -1and L11 ═ 1) Then Fault ═ W-L11 (the instantaneous phase current value of the non-starting current in both lines L1and L11 goes beyond the limit, Then the line L11 goes through a large current Fault);
rule 4: if (L1 ═ 1and L12 ═ 1) or (L1 ═ -1and L12 ═ 1) Then Fault ═ W-L114 (the instantaneous phase current value of the non-starting current in both lines L1and L12 is out of limit, Then large current Fault occurs in line L12);
rule 5: if (L1 ═ 1and L13 ═ 1) or (L1 ═ -1and L13 ═ 1) Then Fault ═ W-L13 (the instantaneous phase current value of the non-starting current in both lines L1and L13 goes beyond the limit, Then the line L13 goes through a large current Fault);
rule 6: if (L2 ═ 2and L21 ═ 1) or (L2 ═ -1and L21 ═ 1) Then Fault ═ W-L21 (the instantaneous phase current value of the non-starting current in both lines L2and L21 goes beyond the limit, Then the line L21 goes through a large current Fault);
rule 7: if (L2 ═ 1and L22 ═ 1) or (L2 ═ -1and L22 ═ 1) Then Fault ═ W-L22 (the instantaneous phase current value of the non-starting current in both lines L2and L22 goes beyond the limit, Then the line L22 goes through a large current Fault);
rule 8: if (L2 ═ 1and L23 ═ 1) or (L2 ═ -1and L23 ═ 1) Then Fault ═ W-L23 (the instantaneous phase current value of the non-starting current in both lines L2and L23 goes beyond the limit, Then the line L23 goes through a large current Fault);
rule 9: if (L1and L2and L11and L12and L13and L21and L22and L23 ═ 0) the nfault ═ R (no large current fault occurred).
Compared with the prior art, the system and the method in the embodiment enable the real-time diagnosis of the underground large-current fault detection without manual operation, thereby greatly improving the detection efficiency of the large-current fault; the running state of the power supply equipment can be monitored in real time, and technical support is provided for the underground power supply system.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (8)
1. A large-current fault diagnosis system based on a rough set theory comprises:
the multi-sensor module is used for acquiring load data of the underground coal mine power supply system in real time;
the control module is used for processing the data collected by the multi-element sensor module and diagnosing whether the coal mine underground power supply system has a large-current fault or not based on a rough set theory;
the execution module is used for receiving the instruction sent by the control module to cut off and alarm a fault line when the coal mine underground power supply system has a fault;
the communication module is used for realizing data interaction among the control module, the multi-element sensor module and the execution module; and
and the power supply module is used for supplying power to the multi-element sensor module, the execution module and the communication module.
2. The rough set theory-based high-current fault diagnosis system according to claim 1, wherein: the multi-element sensor module comprises a temperature and humidity sensor and an electrical parameter sensor.
3. The rough set theory-based high-current fault diagnosis system according to claim 1, wherein: the execution module comprises a circuit breaker and an audible and visual alarm.
4. The large-current fault diagnosis method based on the rough set theory comprises the following steps of:
s1, collecting various parameters of the coal mine underground power supply system by adopting a multi-sensor module, and sending the parameters to a control module through a communication module;
s2, the control module processes data collected by the multi-element sensor module based on a rough set theory, and further diagnoses whether a large-current fault occurs in the coal mine underground power supply system; if no fault exists, repeating the steps S1 and S2 in the next sampling period, and if the fault exists, entering the step S3;
and step S3, the control module sends a control instruction to the execution module, so that the fault line is cut off and an alarm is given.
5. The rough set theory-based large-current fault diagnosis method according to claim 4, wherein: the multi-sensor module comprises a temperature and humidity sensor and an electrical parameter sensor, wherein the temperature and humidity sensor is used for detecting real-time temperature and humidity information under a coal mine to assist in distinguishing environment information under the coal mine; the electric parameter sensor is used for collecting three-phase current, voltage and power parameters of the underground coal mine power supply system and is used for the control module to establish a decision table.
6. The rough set theory-based large-current fault diagnosis method according to claim 5, wherein: in step S2, the processing of the data collected by the multi-sensor module by the control module includes pre-processing, real-time data processing, and post-processing of the data.
7. The rough set theory-based large-current fault diagnosis method according to claim 6, wherein: the pre-processing of the data comprises the engineering conversion processing, the filtering processing and the table look-up calculation processing of the data, and the post-processing of the data comprises the processing of a historical storage database.
8. The rough set theory-based large-current fault diagnosis method according to claim 7, wherein: the real-time data processing comprises the following steps,
step S21, establishing a large-current fault decision table according to data collected by the multi-element sensor module based on a rough set theory;
step S22, reducing the large-current fault decision table according to a preset reduction rule, and further obtaining a minimum attribute subset;
and step S23, judging whether a fault occurs and the fault type according to a preset fault analysis rule.
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| CN103941079A (en) * | 2014-04-16 | 2014-07-23 | 华北电力大学 | On-line monitoring and fault diagnosis system for power distribution network PT |
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Application publication date: 20201023 |