KR100959975B1 - Operating Algorithm of the Embedded On-Line Monitoring and Diagnosing Devices - Google Patents

Abstract

In the embedded online power device monitoring and diagnostic apparatus according to the present invention, it is possible to measure partial discharge, leakage current, etc. for the monitoring and diagnosis in many power devices that are scattered in various regions and manage power devices effectively. The control method,

After digitizing the result obtained from the sensor, it processes the signal to extract only the necessary data so that the data can be sent directly to a public network such as the Internet or to a server.

In particular, it is possible to monitor and diagnose many power devices at the same time by using optimal parameter extraction operation algorithm for effective signal processing in the control method of embedded monitoring and diagnostic device and minimizing transmission and improving reliability by optimal signal processing of data. It is characteristic.

Power equipment, diagnostics, monitoring, embedded, parameter, integrated monitoring, measurement correction

Description

Operating Algorithm of the Embedded On-Line Monitoring and Diagnosing Devices}

The present invention relates to a control method of an embedded online power device monitoring and diagnostic apparatus.

Remote power equipment monitoring and diagnosis system refers to a system for remotely monitoring and diagnosing the measurement results made in the field using a communication device. In general, such a system consists of digitizing the measured results in the field terminal device and sending the data to a general-purpose general computer installed nearby as a communication device in the field, where the signal is processed and sent to a server through a public network. have.

In this method, the control process is complicated because the terminal must control the adjustment according to the characteristics of the terminal device or through the computer, and the complexity of using a dual network through the terminal device and the computer until data is transmitted to the server. It has a disadvantage in that the amount of data transmission is large.

In particular, since it is controlled using a general computer, it is difficult to unmanned due to the lack of flexibility in remote control. Because of the large amount of data transmission, the burden of communication is large, making it difficult to apply globally, and it is inferior in reliability and economy.

Recently, in the digital home era, embedded technologies widely used in home appliances are attracting attention, and they are still used in washing machines and refrigerators. For example, a washing machine that controls water, detergents and strengths, etc., depending on the type of laundry, a gas range that operates differently depending on the ingredients used to cook rice, and a refrigerator that automatically reacts to the materials entering the refrigerator or freezer.

The embedded system is also emerging as an essential element in the field of automation, such as factory automation and home automation, and it is expected to expand the related market in the future as it is linked to all areas of human life such as military medical transportation environment. have.

 The present invention is to solve the above problems,

An object of the present invention is to use an embedded online power equipment monitoring and diagnostic apparatus to process the results measured in the field using an optimized signal processing algorithm and to directly send the results to a server or a client through a public network. It is to provide a control method of the monitoring and diagnostic apparatus.

Another object of the present invention is to provide a method of controlling an embedded online power device monitoring and diagnostic device that can minimize the area of failure in case of failure and reliability degradation in a complex dual network using the embedded online power device monitoring and diagnostic device. It is.

The control method of the embedded online power device monitoring and diagnostic apparatus of the present invention can avoid the complexity of the program in the server or client, and can reduce the amount of data transmission, so that the remote control can be performed. Economics can be increased.

The control method of the embedded on-line power equipment monitoring and diagnostic apparatus to achieve the above object is to initialize the power equipment monitoring and diagnostic apparatus, receive initial conditions from the server and operate in the diagnostic mode, and measure the diagnostic data to correct the measured values. A diagnostic operation routine for performing a;

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An optimization processing routine for performing a signal processing after performing the diagnostic operation routine to determine a parameter by comparing the original measurement signal with a signal processing result;

A measurement mode routine driven in the measurement mode by the parameters processed by the optimization processing routine and monitoring and analyzing the measured data;

It is characterized in that it consists of an error processing routine for determining whether the data received from the measurement mode routine is in a normal state and error processing continuously when abnormal data is applied.

The control method of the embedded system of the present invention digitizes the result obtained by the sensor, processes the signal, extracts only the necessary data, and directly sends the data to a server or a monitoring place to a public network such as the Internet, and then performs signal processing. In order to minimize the transmission and improve the reliability, it is possible to monitor and diagnose many power devices at the same time.

In addition, the control method of the embedded on-line power equipment monitoring and diagnostic apparatus of the present invention has the advantage that the remote control can be avoided, and the complexity of the program in the server or client is minimized and the reliability decreases in the complex duplex network. Complete unmanned and integrated monitoring and diagnostics can increase reliability and economics.

Therefore, the method of controlling the embedded on-line power equipment monitoring and diagnostic apparatus according to the present invention measures the partial discharge, leakage current and the like for monitoring and diagnosis in many power devices that are scattered in various regions. It can be configured as a system that can be managed, and it is effective to reduce the failure prevention, maintenance and repair costs of the power equipment.

In order to achieve the above object, the embedded online power device monitoring and diagnostic apparatus according to the present invention will be described in detail with reference to the accompanying drawings.

Fig. 1 shows a construction of a wide area system composed of an embedded online device.

For example, the monitoring and diagnostic devices A1, A2, and A3 for embedded on-line power equipment, each of which has a sensor attached to a specific region, A, and each sensor is attached to another region B. Configured to monitor and diagnose the embedded on-line power equipment (B1), (B2), (B3) to be connected to the server system 10 through a common network, each client (1), (2) Is configured to obtain the monitored and measured results through the server system (10).

That is, by installing several embedded online power equipment monitoring and diagnosis devices in specific areas (A), (B), and (C) in the wide area, the measurement results obtained from the embedded online power equipment monitoring and diagnostic devices through the public network are It transmits to the server and client and shows that the server or client can coordinate the monitoring and diagnostic device for embedded use.

Detailed configuration of the embedded monitoring and diagnostic apparatus (A1), (A2), (A3) is shown in FIG.

2 is a block diagram showing an embedded on-line power device monitoring and diagnostic apparatus.

Embedded monitoring and diagnostic device (A1) is connected to the amplification converter (3) through the sensor (S1).

The embedded monitoring and diagnostic device (A1) is composed of hardware and software, the hardware configuration of the analog-to-digital converter (4) for converting the signal detected by the sensor into a digital signal, the peripheral device and the display device and input and output It consists of a possible input and output unit 5 and a communication device 6 for communicating with a network such as the Internet,

Each memory for executing a software configuration includes an initialization code input unit 11 for initializing at the start of operation of the device, a device driver unit 12 for enabling hardware to be operated by a command of a drive program, and a command of a drive program. It is composed of a memory unit 13, which stores an operating system program for transmitting to the device driver, and a memory unit 14 for storing a drive program for operating the embedded according to the purpose.

The inter-relation between the embedded on-line power device monitoring and diagnostic device and the server is as follows.

(table)

 Embedded system      Delivery direction      server Synchronization of signals
Signal data correction
Signal processing
Measurement and diagnostic mode

← Time and data collection time interval
Calibration value of measuring instrument
Signal processing condition
Mode decision   Data transfer         →    Data storage and display

That is, the server sends the time and acquisition time interval to the embedded online power device monitoring and diagnosis device in order to know the synchronization or timing of the measurement signals of the embedded online power device monitoring and diagnosis device.

In order to calibrate the signal data, the server sends the calibration parameters to the on-line power equipment monitoring and diagnostic device to calibrate the measurement results and linearize them.

Signal processing coefficients are sent and processed based on the results analyzed by the server so that the on-line power equipment monitoring and diagnosis device can perform the signal processing to optimize the data of transmission by filtering only the noise removal and the necessary signals from the measured signals. .

When transmitting the measured and processed data in the embedded online power equipment monitoring and diagnostic device, the measurement time, identification signal of the measuring device, and measurement interval are included in the measurement result data and transmitted to the server.

This relationship is explained with reference to FIG.

3 is a flowchart illustrating an algorithm between an embedded on-line power device monitoring and diagnosis device and a server. Initializing the power device monitoring and diagnosis device, receiving an initial condition from a server, operating in a diagnostic mode, and measuring the diagnostic data to correct measured values. A diagnostic operation routine (RT5) for performing a step, an optimization processing routine (RT10) for performing a signal processing after performing the diagnostic operation routine (RT5), and comparing the original measurement signal with a signal processing result to determine a parameter to be used; The measurement mode routine RT15 driven by the parameters processed by the optimization processing routine RT10 monitors and analyzes the measured data and the data received by the measurement mode routine RT15 are in a normal state. It is composed of an error processing routine (RT20) that judges the application and continuously performs abnormal processing upon application of abnormal data.

When the diagnostic operation routine RT5 executes the start step S1 and initializes the embedded online power equipment monitoring and diagnosis device A1 (step S2), the server is connected to the server through the communication circuit, and the connection is made to the server. Upon recognition, the initial condition transfer of step S3 is performed.

That is, the initial condition transmission transmits the measurement time, measurement time interval, amplifier correction coefficient, and signal processing coefficient, which are initial conditions, to the embedded on-line power equipment monitoring and diagnosis apparatus A1.

The embedded on-line power equipment monitoring and diagnostic apparatus then starts operation (S5) in the diagnostic mode at startup to execute the diagnostic data measurement step (S6) and execute the next measurement value correction step (S7).

Next, the signal processing execution step S11 is executed, and the flow proceeds to step S12 to transmit the original measurement signal and the signal processing result to the server, and compares the transmitted signal at the server (S13) to determine whether it is within the set range. After step S14, the step S15 of adjusting the parameters is performed to obtain an optimized measurement value.

That is, by performing the actual measurement and sending the corrected correction signal and the original signal before processing together to the server to execute the optimization routine (RT10), the server checks whether the signal processing of the original signal is well performed and does not fit within the set range. In step S11, the signal processing parameters are readjusted and sent to the embedded on-line power equipment monitoring and diagnostic apparatus for signal processing, whereby the embedded on-line power equipment monitoring and diagnostic apparatus is operated in the measurement mode (S16). To obtain accurate data, and send the measured value to the server (S17) and send the measured result from the embedded online power equipment monitoring and diagnosis device (A1) to the server and monitor the measurement result from the server. Then, the measurement result monitoring and analysis step (S18) that analyzes is executed.

At this time, the state of the data is monitored to be normal (step S19), and if it is not normal to perform step S5 through step S8 to operate the embedded online power equipment monitoring and diagnosis device A1 in the diagnostic mode, and then After that, proceed to the above process and if the data state is normal, continue to operate the measurement mode, otherwise it is two consecutive times, so in step (S8) to process the embedded on-line power equipment monitoring and diagnostic device abnormally to perform step (S9). Therefore, only the alarm occurrence and the embedded online power device monitoring and diagnosis device are stopped.

That is, in the flow of FIG. 3, the measurement virtual machine is initialized and operated in the diagnostic mode to measure the measurement data, and the parameter setting value is determined through the optimization routine RT10 to be driven as the measurement mode. When data is transmitted and there is an error in the measured data, an error processing routine (RT15) is processed to efficiently detect and diagnose a power device.

When applied to the embedded on-line power device monitoring and diagnostic device as described above for the partial discharge measurement in detail with reference to Figure 4 as follows.

FIG. 4 is an optimization flowchart for partial discharge measurement in the flowchart of FIG. 3.

The same process is performed in steps S1 to S4 as described above, the diagnostic partial discharge data measurement is performed in step S6, and the measurement value correction step S7 for correcting the measured value is executed.

Here, the partial discharge data measured for diagnosis is linearized by the measurement correction.

Next, in the period average value calculating step (S21), the average value of the partial discharge amount corresponding to one period set at the time of measurement is obtained, and step S22 is executed to process a value smaller than this value to 0 to remove white noise and to move the moving average. Are initially averaged by three (step S23).

In this way, since the peak value decreases, the peak value decreases, and in the next step S24, the decrease amount of the peak value is increased by a reduced ratio compared with the original data to correct it.

In step S25, only the peak value and the time axis value are extracted from the pulse waveform and the original measured partial discharge data is transmitted to the server.

That is, in step S25, the amount of data is minimized to increase the transmission speed and the load is prevented from increasing. In step S26, the raw partial discharge data and the signal processing data are transmitted to the servo.

Next, the server is driven in the comparison mode. In step S27, it is compared with the peak value of the original data and the signal processed data to evaluate whether there is a signal redundancy. Partial discharge is measured using the average number used.

However, if it is determined that there is a signal overlap in the comparison mode (S27), the number of data to be averaged at the moving average is increased by two than the previous value, and the above process is performed.

In this way, signal processing can be performed to prevent unnecessary data from being transmitted due to an error.

1 is an implementation diagram of a wide area system composed of an embedded online device;

2 is a block diagram showing an embedded online power device monitoring and diagnostic apparatus,

3 is a flowchart of a parameter and a data transmission algorithm for monitoring and diagnosis between an embedded online power device monitoring and diagnostic apparatus and a server;

FIG. 4 is an optimization flowchart for partial discharge measurement in the flowchart of FIG. 3.

Description of the Related Art

A1, A2, A3: Monitoring and Diagnosis Device 1, 2: Client

10: server 3: amplifier

S1: sensor 4: analog-to-digital converter

5: input / output unit 6: communication device

11: Initialization code input unit NT: Network

RT5, RT10, RT15: routines S1, S2, S3, ...

Claims (5)

delete A diagnostic operation routine (RT5) for initializing a power device monitoring and diagnostic apparatus, receiving initial conditions from a server, operating in a diagnostic mode, and measuring the diagnostic data to correct measured values; An optimization processing routine (RT10) for performing a signal processing after the diagnostic operation routine (RT5) to compare the original measurement signal with a signal processing result to determine a parameter; A measurement mode routine (RT15) driven in the measurement mode by the parameters processed by the optimization processing routine (RT10), for monitoring and analyzing the measured data; Embedded online power device monitoring and diagnostic apparatus, characterized in that consisting of an error processing routine (RT20) to determine whether the data received from the measurement mode routine (RT15) is in a normal state and error processing continuously when abnormal data is applied Control method. 3. The method of claim 2, The optimization process routine (RT10) that determines the parameters A signal processing execution step (S11) for applying a parameter according to the once-measured correction value; A signal processing result server transmission step S12 for transmitting the measurement signal and the original signal processed in the signal processing step S11; A comparison step (S13) for comparing the signal received at the signal processing result server transmission step (S12) with the original signal and the signal processing signal at the server; A determination step S14 for determining whether the value is set within the range of the value set in the comparison step S13; And a readjustment step (S15) to readjust the parameters after the determination in the decision step (S14). The method of claim 2 or 3, The optimization processing routine (RT10) for determining the parameter driven during the partial discharge measurement of the online power equipment is  An average value calculating step (S21) for obtaining an average value of partial discharge amounts corresponding to one set period;  A noise removing step (S22) of removing white noise by processing a value smaller than the value calculated in the average value calculating step (S21) as 0; An averaging step (S23) for averaging three moving averages at the beginning after the noise removal; Step (S24), (S25), (S26) of correcting the peak value by the moving average, extracting only the peak value and the time axis value, and transmitting the peak value and the partial discharge data together to the server. Next, a duplication determination step (S27) for judging whether or not the data received in the step (S26) is duplicated, When it is determined that there is a signal overlap in the duplication determination step (S27) characterized in that it comprises a step (S28) of repeating the previous process by increasing the number of data to be averaged at the time of moving average more than the previous value 2 Embedded online power equipment monitoring and diagnostic device control method. delete

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