CN116464629B - Intelligent oil pump on-line monitoring system - Google Patents

Intelligent oil pump on-line monitoring system Download PDF

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Publication number
CN116464629B
CN116464629B CN202310722801.8A CN202310722801A CN116464629B CN 116464629 B CN116464629 B CN 116464629B CN 202310722801 A CN202310722801 A CN 202310722801A CN 116464629 B CN116464629 B CN 116464629B
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oil pump
fault
information
signal
alarm
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CN116464629A (en
Inventor
阮博文
张耀浩
陈健昌
庄名富
黄健洪
李春阳
刘凤安
周志红
冯伟明
姚权铨
程娜
梁振豪
李应麟
卢子聪
李朝彬
曾祥明
许怡静
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Foshan Power Supply Bureau of Guangdong Power Grid Corp
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Foshan Power Supply Bureau of Guangdong Power Grid Corp
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
    • F04B51/00—Testing machines, pumps, or pumping installations
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F15—FLUID-PRESSURE ACTUATORS; HYDRAULICS OR PNEUMATICS IN GENERAL
    • F15B—SYSTEMS ACTING BY MEANS OF FLUIDS IN GENERAL; FLUID-PRESSURE ACTUATORS, e.g. SERVOMOTORS; DETAILS OF FLUID-PRESSURE SYSTEMS, NOT OTHERWISE PROVIDED FOR
    • F15B19/00—Testing; Calibrating; Fault detection or monitoring; Simulation or modelling of fluid-pressure systems or apparatus not otherwise provided for
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F15—FLUID-PRESSURE ACTUATORS; HYDRAULICS OR PNEUMATICS IN GENERAL
    • F15B—SYSTEMS ACTING BY MEANS OF FLUIDS IN GENERAL; FLUID-PRESSURE ACTUATORS, e.g. SERVOMOTORS; DETAILS OF FLUID-PRESSURE SYSTEMS, NOT OTHERWISE PROVIDED FOR
    • F15B20/00—Safety arrangements for fluid actuator systems; Applications of safety devices in fluid actuator systems; Emergency measures for fluid actuator systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Fluid Mechanics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Fluid-Pressure Circuits (AREA)

Abstract

The invention provides an intelligent oil pump online monitoring system which comprises an acquisition system, a data storage system, an online monitoring system, an alarm system and a management and maintenance system. The online monitoring system provided by the invention is connected in parallel with the existing hydraulic mechanism, when the oil pump acts, the related switching value signals of the hydraulic mechanism are collected, and the related information is counted, so that the online monitoring of the pumping pressure of the oil pump can be realized, and the transformer operation and maintenance personnel can visually check during daily inspection without additional operation. In addition, the invention integrates the existing data, and the database stores the data by adopting a structure conforming to the daily workflow of the power transformation operation, thereby facilitating the maintenance and the searching of the data. By adopting the management and maintenance system, when the fault occurs again, the system can automatically match the fault condition with the most similar condition for reference of processing personnel. So that the personnel can quickly find the fault. Meanwhile, recorded accident data is convenient for people to learn and browse and grow rapidly.

Description

Intelligent oil pump on-line monitoring system
Technical Field
The invention belongs to the technical field of monitoring of a high-voltage circuit breaker hydraulic mechanism of a transformer substation, and particularly relates to an intelligent oil pump on-line monitoring system.
Background
The opening and closing operation of the circuit breaker is performed through an operating mechanism, and the working performance and the quality of the operating mechanism have great influence on the working performance and the reliability of the circuit breaker. The hydraulic mechanism is widely applied to the operation control of the high-voltage circuit breaker due to the advantages of compact structure, high transmission power, reliable and stable operation and the like. However, hydraulic operating mechanisms often exhibit hydraulic system leaks that result in pressure drops due to poor materials and manufacturing processes used or excessive operation. The oil pump is started to supplement the pressure once the pressure of the hydraulic mechanism cannot be maintained within the operation required range. When the hydraulic system fault continuously exists, the oil pump can be continuously started, and frequent pressing phenomenon is generated. If the oil pump is frequently pressurized, not caused by the control loop fault, but caused by the abnormality of the hydraulic system, the pressure of the mechanism continuously drops, and finally the circuit breaker is blocked and cannot normally act. This may result in an unplanned power outage or an increase in the equipment fault range due to the inability of the circuit breaker to trip again, jeopardizing safe operation of the equipment and the power grid.
At present, an implementation scheme for solving the leakage problem of a high-voltage circuit breaker hydraulic system of 110kV and above of a transformer substation is an intelligent transformer substation. In the scheme, an online monitoring device is added in the design and construction stage of the transformer substation, so that the hydraulic condition can be monitored in real time. However, this solution does not allow a technical retrofit of existing equipment. If the health condition of the hydraulic system is clearly judged through the pressing data recorded by the operating personnel at present in each month, hidden danger can be found in time by shortening the transcription period under the condition of not carrying out technical transformation, but the operation and maintenance workload can be obviously increased.
Disclosure of Invention
In view of the above, the invention aims to solve the problems that when the system of the high-voltage circuit breaker of the transformer substation leaks, the scheme of the existing intelligent transformer substation cannot be applied and the manual operation and maintenance mode can increase the operation and maintenance workload.
In order to solve the technical problems, the invention provides the following technical scheme:
an intelligent oil pump on-line monitoring system, comprising: the system comprises an acquisition system, a data storage system, an online monitoring system, an alarm system and a management maintenance system;
the acquisition system is used for acquiring a switching value signal of the hydraulic mechanism;
the data storage system is provided with a database, a data structure unit is generated in the database for each oil pump monitoring object, basic information and operation information of the oil pump monitoring object are stored in the data structure unit, and the operation information is obtained based on the acquired switching value signal record;
the on-line monitoring system is used for judging according to the operation information of each oil pump monitoring object, determining whether the operation state of each oil pump monitoring object meets the rated condition, if not, generating corresponding fault information and sending out an alarm signal, wherein the rated condition is correspondingly determined by the basic information of each oil pump monitoring object, and the fault information comprises the operation information of the oil pump monitoring object which does not meet the rated condition;
the alarm system is used for sending out corresponding fault alarms according to the alarm signals;
the management maintenance system is used for matching the fault type and the corresponding processing scheme according to the fault information and generating an accident event record.
Further, the switching value signal of the hydraulic mechanism specifically includes:
the motor starting signal, the pressing overtime signal, the closing locking signal, the opening locking signal, the oil pump starting locking signal, the oil pump pressure value, the temperature and the pressing pulse signal.
Further, the operation information comprises the number of times of pumping in 24 hours, and the online monitoring system compares the number of times of pumping in 24 hours with a pumping threshold value, so that judgment is achieved, wherein the online monitoring system adjusts the magnitude of the pumping threshold value through an adjusting coefficient obtained from the management maintenance system so as to adapt to the change of the number of times of normal pumping of the oil pump under different temperature conditions.
Further, the management and maintenance system adjusts the adjustment coefficient in a self-tuning mode based on the temperature, and the self-tuning process of the adjustment coefficient specifically comprises the following steps:
at the current temperature, if fault alarm occurs, the oil pump action alarm is correct, or the oil pump action is not alarm, other alarm actions are false alarm, and the value of the adjusting coefficient is not changed; if the oil pump action alarms and malfunctions, the value of the adjusting coefficient is reduced; if the fault alarm occurs, the oil pump action is not alarmed, and if other alarm actions are correct, the value of the regulating coefficient is increased.
Further, the operation information further includes hydraulic pressure low, hydraulic pressure slope, closing lock, opening lock, pressing timeout and oil pump start lock, based on the operation information, the on-line monitoring system is specifically used for:
comparing the hydraulic pressure with the hydraulic pressure low rated value, if the hydraulic pressure is smaller than the hydraulic pressure low rated value, generating corresponding fault information and sending out an alarm signal;
comparing the hydraulic slope with a hydraulic slope rated value, if the hydraulic slope is not smaller than the hydraulic slope rated value, generating corresponding fault information and sending an alarm signal;
comparing the number of times of pressing with a pressing threshold value, if the number of times of pressing is not smaller than the pressing threshold value, generating corresponding fault information and sending out an alarm signal, wherein the pressing threshold value is the sum of a rated value of the number of times of pressing and an adjusting coefficient;
and comparing the reset time of closing lock, opening lock, pressing overtime and oil pump starting lock with corresponding rated values, and if the corresponding rated values are exceeded, generating corresponding fault information and sending an alarm signal.
Further, the motor starting signal, the pressing timeout signal, the closing locking signal, the opening locking signal and the oil pump starting locking signal record the time information of signal sending and resetting in the database, the oil pump pressure value and the temperature record numerical value information and corresponding time information in the database, and the pressing pulse signal records the time information of pressing pulse sending in the database.
Further, the basic information includes equipment manufacturer, model, operation period, equipment parameters, related work records and accident records.
Further, each data structure unit of the oil pump monitoring object has a corresponding index structure in the database, and the index structure is specifically:
operation and inspection center, transformer substation, voltage class, hydraulic mechanism and data.
Further, the management and maintenance system obtains a plurality of types of typical fault types by adopting a K-mean algorithm based on the historical operation information recorded by the database, and the plurality of types of typical fault types and the corresponding fault treatment schemes are stored in the database.
Further, in the management and maintenance system, before adopting the K-mean algorithm, the historical operation information is processed and converted into a numerical matrix which can be calculated by a computer, specifically:
for the historical operation information recorded by the signal without specific value, the values are set to 0 and 1, when the fault occurs, the signal is set to 1 if the fault occurs, and otherwise, the signal is set to 0;
for the historical operation information recorded by the numerical signal, adopting normalization processing to map all signal values to between [0,1 ];
creating new values according to historical operation information, wherein the new values comprise motor starting, pressing overtime, closing locking, opening locking, oil pump starting locking, pressing times, hydraulic change trend values before 2 hours, motor starting, pressing overtime, closing locking, opening locking, oil pump starting locking, pressing times and hydraulic change trend values before 8 hours, and normalizing;
and adding the processed various numerical values into a numerical matrix to be used as the input of a K-mean algorithm.
In summary, the invention provides an intelligent oil pump online monitoring system which comprises an acquisition system, a data storage system, an online monitoring system, an alarm system and a management and maintenance system. The online monitoring system provided by the invention is connected in parallel with the existing hydraulic mechanism, when the oil pump acts, the related switching value signals of the hydraulic mechanism are collected, and the related information is counted, so that the online monitoring of the pumping pressure of the oil pump can be realized, and the transformer operation and maintenance personnel can visually check during daily inspection without additional operation. In addition, the invention integrates the existing data, and the database stores the data by adopting a structure conforming to the daily workflow of the power transformation operation, thereby facilitating the maintenance and the searching of the data. By adopting the management and maintenance system, when the fault occurs again, the system can automatically match the fault condition with the most similar condition for reference of processing personnel. So that the personnel can quickly find the fault. Meanwhile, recorded accident data is convenient for people to learn and browse and grow rapidly.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of an intelligent oil pump on-line monitoring system according to an embodiment of the present invention;
FIG. 2 is a flow chart of adjusting coefficient self-tuning according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating the structure of a data structure unit according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of alarm logic provided in an embodiment of the present invention;
fig. 5 is a flowchart of alarm judgment provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the embodiments described below are only some embodiments of the present invention, not all embodiments of the present invention. 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.
In the prior art, hydraulic operating mechanisms often exhibit hydraulic system leaks that result in pressure drops due to poor materials and manufacturing processes used or excessive operation. The oil pump is started to supplement the pressure once the pressure of the hydraulic mechanism cannot be maintained within the operation required range. When the hydraulic system fault continuously exists, the oil pump can be continuously started, and frequent pressing phenomenon is generated. In contrast, frequent oil pump start-up can also reflect the potential for anomalies or malfunctions in the hydraulic system.
Consequences that may result from failure of the oil pump include overheating of the motor and even damage. If the oil pump is frequently pressurized, not caused by the control loop fault, but caused by the abnormality of the hydraulic system, the pressure of the mechanism continuously drops, and finally the circuit breaker is blocked and cannot normally act. This may result in an unplanned power outage or an increase in the equipment fault range due to the inability of the circuit breaker to trip again, jeopardizing safe operation of the equipment and the power grid.
However, in the prior art, only slight pressure leakage is usually generated at the initial stage of the hydraulic system failure, and the oil pump is started slightly more than normal, but no obvious phenomenon or alarm signal exists. In addition, because the transformer substation only records the number of times of pressing the breaker mechanism once a month, the operation and maintenance personnel are difficult to discover the abnormality of the hydraulic system in time.
For faults which occur frequently and repeatedly or are processed, alarm information and fault processing information when the faults are not reserved, new personnel want to process the faults and have no thread, and the faults need to be restarted to start searching, so that time and labor are wasted.
Therefore, on the premise of not increasing the workload of the operation and maintenance of the transformer, the invention aims to provide the intelligent oil pump on-line monitoring system which realizes the detection of the intelligent oil pump system with lower equipment transformation cost and risk. Meanwhile, through collecting and analyzing data, the running condition of the hydraulic system can be judged in advance, and the abnormal condition of the hydraulic system can be found and processed in time. The invention can also give out the preliminary judgment of the faults and assist the staff to quickly find the defects, thereby reducing the influence of the faults on equipment and a power grid.
Referring to fig. 1, an embodiment of the present invention provides an intelligent oil pump online monitoring system, including: the system comprises an acquisition system, a data storage system, an online monitoring system, an alarm system and a management and maintenance system.
The following describes each constituent unit of the online monitoring system provided in this embodiment in detail.
In this embodiment, the acquisition system is used to acquire the switching value signal of the hydraulic mechanism.
For monitoring of a high-voltage breaker hydraulic system of a transformer substation, an intelligent transformer substation is added with an online monitoring device in the design and construction stage, so that the hydraulic condition is monitored in real time. This solution does not allow a technical modification of existing equipment. Therefore, in this embodiment, the acquisition system should acquire the switching value information of the hydraulic mechanism in at least one mode of connecting in parallel to the corresponding position of the hydraulic mechanism, and transmit the data to other parts, so as to provide a data source for the subsequent system to perform state detection and diagnosis.
In this embodiment, the data storage system is provided with a database, in which a data structure unit is generated for each oil pump monitoring object, and basic information and operation information of the oil pump monitoring object are stored in the data structure unit, and the operation information is recorded based on the collected switching value signals.
It should be noted that, for the traditional method of clearly judging the health condition of the hydraulic system by the pressing data recorded by the operator at present in each month, in this way, in order to accurately find the fault in time, the transcription period needs to be shortened, which clearly increases the operation and maintenance workload. In this embodiment, the collecting system can obtain relevant information of the hydraulic system in real time or according to requirements, and on the basis, the online monitoring system provided by the embodiment further establishes a database for storing various recorded information so as to realize omnibearing long-term monitoring on the oil pump. The database receives the data from the acquisition system and transmits the data to the on-line monitoring module for real-time display and processing. The management and maintenance system generates new incident record data by inputting or calculating and stores the data in the database. The storage technology used by the database may be Microsoft Access or MySQL.
The database establishes a corresponding data structure unit for each monitored oil pump, and is used for storing some basic information of the oil pump and operation information recorded according to the collected switching value signals.
In this embodiment, the online monitoring system is configured to determine, according to operation information of each oil pump monitoring object, whether an operation state of each oil pump monitoring object meets a rated condition, and if not, generate corresponding fault information and send an alarm signal, where the rated condition is determined by basic information of each oil pump monitoring object, and the fault information includes operation information of the oil pump monitoring object that does not meet the rated condition.
As an important unit for realizing intelligent monitoring of the intelligent oil pump on-line monitoring system provided by the embodiment, the main function of the on-line monitoring system is to judge whether the hydraulic system is normal or not based on the current collected signal condition. It can be understood that the operation information recorded in the database at least includes non-numerical information and numerical information, and based on these information, the online monitoring system can determine whether the number of actions or the change trend of the oil pump satisfies the rated condition, so as to reflect the condition of the hydraulic system.
In this embodiment, the alarm system is configured to send out a corresponding fault alarm according to the alarm signal.
In this embodiment, the management and maintenance system is configured to match the fault type and the corresponding processing scheme according to the fault information, and generate an incident event record.
It should be noted that the management and maintenance system is another important unit for realizing intelligent monitoring for the intelligent oil pump on-line monitoring system. The management and maintenance system receives faults monitored by the on-line monitoring system, various related operation data of the current hydraulic system are contained in the fault information, the management and maintenance system automatically searches similar faults according to the matching degree through the operation data, and according to built-in settings of the system, a possible fault and a processing scheme are provided, and the possible fault and the processing scheme are sent to the on-line monitoring system to be displayed, and meanwhile, a record of an accident event is generated. For staff, the fault can be rapidly positioned according to historical events and prompts of the system, the fault processing time is shortened, and the operation safety of the system is ensured.
The embodiment provides an intelligent oil pump on-line monitoring system, which comprises an acquisition system, a data storage system, an on-line monitoring system, an alarm system and a management maintenance system. The online monitoring system provided by the invention is connected in parallel with the existing hydraulic mechanism, when the oil pump acts, the related switching value signals of the hydraulic mechanism are collected, and the related information is counted, so that the online monitoring of the pumping pressure of the oil pump can be realized, and the transformer operation and maintenance personnel can visually check during daily inspection without additional operation. In addition, the invention integrates the existing data, and the database stores the data by adopting a structure conforming to the daily workflow of the power transformation operation, thereby facilitating the maintenance and the searching of the data. By adopting the management and maintenance system, when the fault occurs again, the system can automatically match the fault condition with the most similar condition for reference of processing personnel. So that the personnel can quickly find the fault. Meanwhile, recorded accident data is convenient for people to learn and browse and grow rapidly.
In one embodiment of the invention, the field switching value signals specifically collected by the collection system comprise information such as motor start, pressing timeout, closing lock, opening lock, oil pump start lock, oil pump pressure value, temperature, pressing pulse and the like. The data switching value information motor is started, the pressing overtime, the closing lock, the opening lock, the oil pump starting lock and the like are obtained through relays connected in corresponding loops. And the temperature, oil pump pressure values are obtained by means of sensors installed in the hydraulic system. The number of pressing times is obtained by connecting an existing hydraulic mechanism pressing time counter in parallel. When the oil pump is pressed, the action pulse is transmitted to the pressing frequency counter of the hydraulic mechanism, and meanwhile, the system also receives the action pulse and records the current time.
Based on the pressing pulse information acquired by the acquisition system, the on-line monitoring system obtains the pressing times of the oil pump in 24 hours by counting the pressing pulse information in a period of time to judge. It should be noted that the number of times the oil pump is pressed is sensitive to the change of temperature. For example, the number of pump strokes may increase significantly at cold air temperatures, especially during the night. Therefore, in order to keep the accuracy of the online monitoring system in judging the information of the pressing pulse, an input adjusting coefficient is added in the judgment, the adjusting coefficient is a temperature-related quantity, the values are different at different temperatures, the initial adjusting coefficient is manually set by an operator, and the initial adjusting coefficient is obtained through analysis of big data of the management and maintenance system in the later stage so as to adjust the pressing threshold value, and the false alarm times are reduced.
For adjustment of the adjustment coefficient, in one embodiment of the present invention, the management and maintenance system adjusts the magnitude of the adjustment coefficient in a self-tuning manner based on the temperature data. Specifically, the data adjustment coefficient output by the management and maintenance system is an array which changes according to the temperature, and the adjustment coefficient is self-set as follows, and the initial value of the adjustment coefficient is set according to the experience value of the operator. The following chart shows the condition that the adjustment coefficient is self-adjusted when fault alarm occurs at a certain temperature. When an alarm occurs, the oil pump pressing alarm action is correct, or the oil pump pressing alarm does not act, and other alarm actions are performed, but the oil pump pressing alarm is checked to be false alarm, and the adjustment coefficient does not need to be changed. If the oil pump action warning malfunction or failure is detected, but the oil pump action warning does not act, the value of the adjusting coefficient is correspondingly adjusted, so that the oil pump action warning is more accurate. And the action of false alarm or missing alarm of the oil pump action is reduced. The flow of the self-tuning of the tuning system is shown in fig. 2.
In one embodiment of the present invention, the operation information recorded in the database specifically includes information such as motor start, timeout of pressing, closing lock, opening lock, oil pump start lock, oil pump pressure value, temperature, pressing time, etc. Further, the motor is started, the pressing time-out, the closing and the opening and the closing are performed, and the oil pump is started and closed, and the time information of signal emission and reset is recorded in the database. The pressing time records the time information sent by the pressing pulse, the pressing is recorded once, and the pressing times in a certain period of time are obtained by counting the pressing times recorded in the certain period of time. In addition, the data structure unit of each oil pump detection object contains basic information of the oil pump equipment, including equipment manufacturer, model, operation period, equipment parameters, related work records and accident records. Wherein the operational years, work records, accident records and the years of collection are gradually increased with the increase of time, and the work records and the accident records are required to be manually input in the management system. The index structure of each unit in the database is shown in fig. 3, namely, the operation and inspection center, the transformer substation, the voltage class, the hydraulic mechanism and the data.
In a further embodiment of the invention, the on-line monitoring system monitors the running state of the oil pump system in real time based on the obtained data, and the key important information of the oil pump system is displayed in front of a user in real time through the processing of a computer. The method comprises the steps of temperature, oil pump pressure value, number of times of pumping in 24 hours, and trend of variation of the oil pump pressure value.
Specifically, the number of times the oil pump is pressurized within 24 hours can be obtained directly from the database. The basic decision is that the oil pump is allowed to start 10 times per day. And the alarm function is started after more than 10 times of strengthening monitoring and attention signals and after more than 20 times of strengthening monitoring and attention signals, at the moment, an audible and visual alarm is sent out on a display interface, and meanwhile, related personnel are notified to process through a short message, and the alarm signals must be manually reset by operating personnel. In cold air temperature, especially at night, the number of times of oil pump pressing may obviously increase, so the determination is added with an adjustment coefficient, the adjustment coefficient is a temperature-related quantity, at different temperatures, the numerical values are different, the initial adjustment coefficient is manually set by an operator, and the later time is obtained through analysis of big data of a management and maintenance system, so as to adjust the pressing threshold value and reduce the number of false alarms. It should be understood that the threshold used for the determination in this embodiment should be determined according to the actual situation, and this embodiment is merely for convenience of illustration.
The change trend of the oil pump pressure value is obtained through oil pump pressure value calculation, the calculated time interval is within the time range of oil pump pressing twice, and the change curvature of time is obtained through polynomial fitting of data. The health degree of the hydraulic mechanism in the current state can be reflected from the side surface by comparing the change trend of the oil pump pressure value of the breaker in the same type in the current state. The change trend of the oil pump pressure value is obviously accelerated, the slope is obviously increased, and when the slope is larger than a preset value, the current situation that the hydraulic mechanism possibly has leakage can be judged, and an alarm system is started immediately to inform relevant personnel of processing.
The on-line monitoring system can show the current running state of the hydraulic system through the graph and the curve. The system comprises all breaker data in operation, and personnel can directly observe various data of the oil pump information system and health conditions of different oil pump systems through visual display and comparison of the data. The system can automatically track the deterioration conditions of different indexes, give a prompt in advance for the condition of accelerating the deterioration of the indexes, facilitate the strengthening monitoring of operators, and simultaneously make preparations in advance, and once the early warning value is reached, the system immediately carries out overhaul on fault equipment. When the system detects a fault, the alarm device is started, and meanwhile, related information is sent to the management and maintenance system, and the management and maintenance system returns related judgment of an accident and generates a record of the accident event.
The logic for discriminating the accident by the system comprehensively adopts a plurality of signals to judge, the fault can be judged by overtime of the hydraulic pressure low and the number of times of pressing, the accident signals are closed, opened, closed and the like, and the alarm is started by adopting the condition that the number of times of pressing exceeds the rated value and is not reset, and the logic is shown in the figure 4.
In one embodiment of the invention, the fault detection report output by the management maintenance system module and the online processing scheme are input by a K-mean algorithm with feature matrix vectors as input. Before the K-mean algorithm is used, the data stored in the database is firstly processed and converted into a numerical matrix which can be calculated by a computer.
For the motor start, the fault signals such as the overtime of pressing, the closing and locking, the opening and locking, the oil pump start and locking and the like have no specific numerical values, the values are set to be 0 and 1, when the fault occurs, the signal is set to be 1, and otherwise, the signal is set to be 0.
Trend of variation in hydraulic pressureThe temperature, the number of times of pressing in 24 hours and the like are numerical values, the sizes of different numerical values are quite different, and normalization processing is adopted for the numerical values, so that all data x are in the same reference system. In the scheme, min-max standardization is adopted to map all data to [0,1]]In between the two,
wherein,,，，respectively, the maximum value, the minimum value and the normalized value of the data.
According to the data in the database, new values are created, including motor start, time-out of pressing, closing lock, opening lock, oil pump start lock and pressing times in 2 hours, hydraulic change trend values before 2 hours, motor start, time-out of pressing, closing lock, opening lock, oil pump start lock and pressing times in 8 hours, hydraulic change trend values before 8 hours are normalized, and the values are added into the input matrix.
Then, K faults with different characterizations are manually selected according to the existing faults, and the values of the faults are taken as centers and recorded as，，，..........，。
Defining a loss function:wherein M is the total number of samples, +.>Represents the ith sample, +.>Is->The cluster to which the cluster belongs.
Next, the following procedure is repeated until J converges.
For each sampleAssign it to the closest center:
where t is the number of steps in the iteration.
For each class center k, the center of the class is recalculated:
；
when the J value converges, the center point and the class of sample division converge simultaneously. As a clustering algorithm, the selection of the initial K value is very important, and may affect the effect of subsequent machine learning, so that different fault samples with typical phenomena should be selected when the sample faults are manually selected for learning at the beginning, otherwise, the final result of the clustering algorithm may be affected.
The system is in one-to-one correspondence with the processing mode of a certain fault, the data are input into a database in advance, and when the most similar fault condition under the current condition is obtained according to the detection data, the system can automatically search out the history fault record which is the most similar to the current fault, and the processing method. The rapid positioning processing of faults is convenient for maintenance personnel.
In addition, the alarm system of the intelligent oil pump is mainly an audible and visual alarm device and a signal transmitting device which are connected to the upper computer. When a fault is received, the processor sends out an action instruction to trigger the loudspeaker to give out sound and alarm and the red light to give out light and alarm, and meanwhile, the GSM module sends an alarm short message to the mobile phone on duty. Therefore, the transformer operator can easily find the abnormal phenomenon of the hydraulic system when inspecting or receiving the short message, and immediately develop corresponding treatment. The audible and visual alarm of the alarm system must be manually reset by an operator to be eliminated. Based on the design, the flow of the intelligent oil pump on-line monitoring system for alarming judgment is shown in fig. 5.
The hardware system of the invention is composed of an acquisition system, transmission hardware, an upper computer, storage hardware and an alarm system. The acquisition system comprises a motor start for acquiring data, a pressing overtime, a closing lock, a separating lock, an oil pump start lock, a relay for pressing pulse data, a hydraulic sensor and a temperature sensor. The wired or wireless data transmission hardware can select GSM or ZigBee by laying cables and optical cables or adopting a wireless transmission mode to upload data to a background machine and a wireless transmission mode. The upper computer is a computer with the computing power of the display card not lower than GTX 950. The storage hardware is a mechanical hard disk. The alarm system comprises an indicator light, a loudspeaker and a GSM module with a short message receiving and transmitting function.
Compared with the prior art, the invention has the following advantages:
1. the intelligent oil pump on-line monitoring device is connected in parallel to the existing hydraulic mechanism pressing frequency counter. When the oil pump is pressurized, the action pulse is transmitted to the hydraulic mechanism pressurizing frequency counter, and meanwhile, the intelligent oil pump on-line monitoring device also receives the action pulse. The number of pressing times within 24 hours is counted, so that the oil pump pressing can be monitored on line, and the transformer operation and maintenance personnel can visually check the oil pump pressing during daily inspection without additional operation.
2. The existing data are integrated, the database stores the data by adopting a structure conforming to the daily workflow of the power transformation operation, and the maintenance and the searching of the data are convenient.
3. The alarm threshold is corrected through temperature, so that the frequent false alarm function caused by frequent pressing due to temperature reduction in winter and night is prevented, and the alarm threshold is adjusted through the automatic learning mode of a machine, so that false alarm and missing alarm are avoided.
4. Through the management maintenance system, the accident happening each time and the data uploaded by the accident hydraulic system at the time are recorded, and the fault condition is learned by using a machine learning mode. When the fault occurs again, the system can automatically match the fault condition with the most similar condition for reference of processing staff. So that the personnel can quickly find the fault. Meanwhile, recorded accident data is convenient for people to learn and browse and grow rapidly.
The intelligent oil pump on-line monitoring system provided by the invention can realize the detection of the pressing and the running health state of the oil pump, can learn and adjust the alarm threshold parameters, provides possible accident reasons and processing schemes of the current accident according to the alarm information when the alarm occurs, and stores records of accident events.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (7)

1.一种智能油泵在线监测系统，其特征在于，包括：采集系统、数据存储系统、在线监测系统、报警系统和管理维护系统；1. An intelligent oil pump online monitoring system, characterized in that it comprises: a data acquisition system, a data storage system, an online monitoring system, an alarm system, and a management and maintenance system; 所述采集系统用于采集液压机构的开关量信号；The acquisition system is used to acquire the switching signals of the hydraulic mechanism; 所述数据存储系统设置有数据库，所述数据库中针对每个油泵监测对象生成一个数据结构单元，所述数据结构单元中存储有所述油泵监测对象的基础信息和运行信息，所述运行信息基于所采集的所述开关量信号记录得到；The data storage system is equipped with a database. A data structure unit is generated in the database for each oil pump monitoring object. The data structure unit stores the basic information and operating information of the oil pump monitoring object. The operating information is obtained based on the collected switch signals. 所述在线监测系统用于根据每个油泵监测对象的所述运行信息进行判断，确定每个油泵监测对象的运行状态是否满足额定条件，若不满足，则生成对应的故障信息并发出告警信号，所述额定条件由每个油泵监测对象的所述基础信息对应确定，所述故障信息包括不满足所述额定条件的油泵监测对象的运行信息；The online monitoring system is used to judge whether the operating status of each oil pump monitoring object meets the rated conditions based on the operating information of each oil pump monitoring object. If it does not meet the rated conditions, the corresponding fault information is generated and an alarm signal is issued. The rated conditions are determined by the basic information of each oil pump monitoring object. The fault information includes the operating information of oil pump monitoring objects that do not meet the rated conditions. 所述报警系统用于根据所述告警信号发出对应的故障报警；The alarm system is used to issue corresponding fault alarms based on the alarm signals; 所述管理维护系统用于根据所述故障信息匹配故障类型和对应的处理方案，并生成一个事故事件记录；The management and maintenance system is used to match the fault type and corresponding handling plan according to the fault information, and generate an incident record; 所述液压机构的开关量信号具体包括：The switching signals of the hydraulic mechanism specifically include: 电机启动信号、打压超时信号、合闸闭锁信号、分闸闭锁信号、油泵启动闭锁信号、油泵压力值、温度和打压脉冲信号；Motor start signal, pressure timeout signal, closing lockout signal, opening lockout signal, oil pump start lockout signal, oil pump pressure value, temperature and pressure pulse signal; 所述运行信息包括油泵24小时内的打压次数，所述在线监测系统将油泵24小时内的打压次数与打压阈值进行比较，从而实现判断，其中，所述在线监测系统通过从所述管理维护系统获取的调节系数来调节所述打压阈值的大小，以适应不同温度条件下油泵正常打压次数的变化；The operating information includes the number of times the oil pump is pressurized within 24 hours. The online monitoring system compares the number of times the oil pump is pressurized within 24 hours with the pressurization threshold to make a judgment. The online monitoring system adjusts the size of the pressurization threshold by obtaining the adjustment coefficient from the management and maintenance system to adapt to the changes in the normal number of pressurizations of the oil pump under different temperature conditions. 所述管理维护系统基于温度采用自整定的方式调整所述调节系数，所述调节系数自整定的过程具体包括：The management and maintenance system adjusts the regulation coefficient based on temperature using a self-tuning method. The self-tuning process of the regulation coefficient specifically includes: 在当前温度下，若发生所述故障报警时，油泵动作告警正确，或油泵动作未告警，其它报警动作误报警，所述调节系数的值不变化；若油泵动作告警误动作，则调低所述调节系数的值；若发生所述故障报警时，油泵动作未告警，其它报警动作正确时，则调高所述调节系数的值。At the current temperature, if the aforementioned fault alarm occurs and the oil pump operation is correctly reported, or if the oil pump operation is not reported but other alarm actions are false alarms, the value of the adjustment coefficient remains unchanged; if the oil pump operation is falsely reported, the value of the adjustment coefficient is lowered; if the aforementioned fault alarm occurs and the oil pump operation is not reported but other alarm actions are correct, the value of the adjustment coefficient is increased. 2.根据权利要求1所述的智能油泵在线监测系统，其特征在于，所述运行信息还包括液压低、液压斜率、合闸闭锁、分闸闭锁、打压超时和油泵启动闭锁，基于所述运行信息，所述在线监测系统具体用于：2. The intelligent oil pump online monitoring system according to claim 1, characterized in that the operating information further includes low hydraulic pressure, hydraulic slope, closing interlock, opening interlock, pressurization timeout, and oil pump start interlock; based on the operating information, the online monitoring system is specifically used for: 将所述液压低与液压低额定值进行比较，若小于所述液压低额定值，则生成对应的故障信息并发出告警信号；The hydraulic pressure is compared with the hydraulic pressure rating. If it is less than the hydraulic pressure rating, a corresponding fault message is generated and an alarm signal is issued. 将所述液压斜率与液压斜率额定值进行比较，若不小于所述液压斜率额定值，则生成对应的故障信息并发出告警信号；The hydraulic slope is compared with the rated value of the hydraulic slope. If it is not less than the rated value of the hydraulic slope, the corresponding fault information is generated and an alarm signal is issued. 将打压次数与所述打压阈值进行比较，若不小于所述打压阈值，则生成对应的故障信息并发出告警信号，所述打压阈值为打压次数额定值与所述调节系数的和；The number of pressure tests is compared with the pressure test threshold. If it is not less than the pressure test threshold, the corresponding fault information is generated and an alarm signal is issued. The pressure test threshold is the sum of the rated number of pressure tests and the adjustment coefficient. 将所述合闸闭锁、分闸闭锁、打压超时和油泵启动闭锁的复归时间与对应额定值进行比较，若超过所述对应额定值，则生成对应的故障信息并发出告警信号。The reset time of the closing interlock, opening interlock, pressure overtime and oil pump start interlock is compared with the corresponding rated value. If the reset time exceeds the corresponding rated value, the corresponding fault information is generated and an alarm signal is issued. 3.根据权利要求1所述的智能油泵在线监测系统，其特征在于，所述电机启动信号、打压超时信号、合闸闭锁信号、分闸闭锁信号、油泵启动闭锁信号在所述数据库中记录的是信号发出以及复归的时间信息，所述油泵压力值和温度在所述数据库中记录的是数值信息和对应的时间信息，所述打压脉冲信号在所述数据库中记录的是打压脉冲发出的时间信息。3. The intelligent oil pump online monitoring system according to claim 1, characterized in that the motor start signal, pressure timeout signal, closing lockout signal, opening lockout signal, and oil pump start lockout signal are recorded in the database as signal issuance and reset time information; the oil pump pressure value and temperature are recorded in the database as numerical information and corresponding time information; and the pressure pulse signal is recorded in the database as pressure pulse issuance time information. 4.根据权利要求1所述的智能油泵在线监测系统，其特征在于，所述基础信息包括设备厂家、型号、运行年限、设备参数、相关的工作记录和事故记录。4. The intelligent oil pump online monitoring system according to claim 1, wherein the basic information includes equipment manufacturer, model, years of operation, equipment parameters, relevant work records and accident records. 5.根据权利要求1所述的智能油泵在线监测系统，其特征在于，每个油泵监测对象的所述数据结构单元在所述数据库中均具有对应的索引结构，所述索引结构具体为：5. The intelligent oil pump online monitoring system according to claim 1, characterized in that each data structure unit of the oil pump monitoring object has a corresponding index structure in the database, wherein the index structure is specifically: 运检中心—变电站—电压等级—液压机构—数据。Operation and maintenance center—substation—voltage level—hydraulic mechanism—data. 6.根据权利要求1所述的智能油泵在线监测系统，其特征在于，所述管理维护系统基于所述数据库所记录的历史运行信息，采用K-mean算法得到若干类典型故障类型，所述若干类典型故障类型和对应的故障处理方案被存储到所述数据库中。6. The intelligent oil pump online monitoring system according to claim 1, characterized in that the management and maintenance system obtains several typical fault types based on the historical operating information recorded in the database using the K-means algorithm, and the several typical fault types and corresponding fault handling schemes are stored in the database. 7.根据权利要求6所述的智能油泵在线监测系统，其特征在于，在所述管理维护系统中，采用K-mean算法之前，将所述历史运行信息进行处理，转换为可供计算机计算的数值矩阵，具体为：7. The intelligent oil pump online monitoring system according to claim 6, characterized in that, before employing the K-means algorithm in the management and maintenance system, the historical operating information is processed and converted into a numerical matrix that can be calculated by a computer, specifically as follows: 对于没有具体数值的信号所记录的历史运行信息，其值定为0和1，在发生故障时，如果出现信号，则为1，否则为0；For historical operational information recorded for signals without specific numerical values, the values are set to 0 and 1. When a fault occurs, if a signal is present, the value is 1; otherwise, it is 0. 对于数值型信号所记录的历史运行信息，采用归一化的处理，将所有信号值映射到[0，1]之间；For historical operational information recorded by numerical signals, normalization is used to map all signal values to the range [0, 1]. 根据所述历史运行信息，创建新的数值，包括2小时内电机启动、打压超时、合闸闭锁、分闸闭锁、油泵启动闭锁、打压次数、2小时前液压变化趋势数值、8小时内电机启动、打压超时、合闸闭锁、分闸闭锁、油泵启动闭锁、打压次数和8小时前液压变化趋势数值，归一化处理；Based on the historical operating information, new values are created, including motor start, pressure timeout, closing lockout, opening lockout, oil pump start lockout, number of pressure tests, hydraulic pressure trend value 2 hours ago, motor start, pressure timeout, closing lockout, opening lockout, oil pump start lockout, number of pressure tests, and hydraulic pressure trend value 8 hours ago within 2 hours, and normalized. 将处理后的各类数值加入到数值矩阵中，作为所述K-mean算法的输入。The processed numerical values are added to the numerical matrix and used as input to the K-means algorithm.
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