KR102429359B1 - Main distrubution frame, mdf - Google Patents

Abstract

The present invention relates to an MDF system remotely controlling the power-on or power-off and, more specifically, to an MDF system remotely controlling the power-on or power-off which can turn a panel on or off remotely when responding to an external environment with an earthquake-proof device or the like, detecting a cable which reaches a specific state, and determining a situation as an emergency situation. The MDF system remotely controlling the power-on or power-off comprises: a floor fixing member (10) installed on a lower side portion of a main switchboard of the MDF system; an earthquake-proof spring (20) installed on a central axis portion of the floor fixing member (10); and a fixing nut (30) fixing the floor fixing member (10) and the earthquake-proof spring (20) to a steel support panel. In addition, a polymer elastomer is applied to the earthquake-resistant spring (20). Moreover, a lattice-type panel unit (40) is included which moves in a horizontal or vertical direction by an external force applied to the main switchboard from the outside when an earthquake occurs. Accordingly, the MDF system can protect a communication device from overcurrent flowing into a cable in the MDF system and monitor status of a cable line and device, and remotely turn on or off an alarm or turn on or off an alarm schedule on the panel through predicted information.

Description

MDF system for remote control on or off {MAIN DISTRUBUTION FRAME, MDF}

The present invention relates to an MDF system for remotely controlling on or off, and more particularly, by detecting a cable that has reached a specific state while responding to an external environment with a seismic device, etc. It relates to an MDF system that can remotely control the on or off.

In general, the main wiring board (MDF) is a wiring board that connects external and internal lines. It is installed at the place where the outdoor external wire is introduced into the indoor device, and connects the terminals of the external line facility and the internal exchange facility.

Inside the MDF, the vertical side (V side) is an overseas cable (outer line), and the horizontal side (H side) is installed with a domestic cable (internal line).

In MDF, the cable consists of a line by connecting 'outside terminal (V side) arranged in line number order' and `domestic side terminal (H side) arranged in subscriber number order' by jumper wires.

In MDF, protection devices (lightning bomb-based) such as hot-wire wheels, fuses, and arresters are installed as auxiliary devices on the outer wire side.

As shown in Fig. 1, the integrated wiring board refers to information and communication equipment (optical distribution box, patch panel, terminal block, protector, jumper through, bracket, entry panel, blank panel, etc.) mounted on a RACK (terminal box) and various codes (optical Jumper cords, optical peak tails, patch cords, line cords, etc.) are connected.

However, the conventional integrated wiring board has a general wiring that accommodates all connected equipment by providing a block unit for prior wiring for communication standardization according to the technological development and for expansion, and provides an interface function of various media, so installation and maintenance are easy. It is a single wiring system and it can reduce operation and management costs by constructing advanced wiring, but it is made of a concentrated system box and a lightweight cable made of a specific material, so there is a problem of being vulnerable from short circuit or fire.

Therefore, when such a vulnerable problem occurs, it is necessary to immediately turn on or off the panel remotely depending on the state of the cable.

The present invention has been made to solve the above problems, and in order to protect communication devices from overcurrent flowing into the cable, when an emergency condition such as lightning strike or insulation breakdown occurs, the panel is remotely turned on or off according to the state of the cable at an appropriate time. It aims to provide an MDF system that can do this.

In order to solve the above problems, the present invention provides an MDF system for remotely controlling on or off, a floor fixing member installed on the lower side of the main switchboard, an earthquake-resistant spring installed on the central axis of the floor fixing member, and a support for them made of iron Consists of a fixing nut fixed to the panel, a polymer elastic material is applied to the earthquake-resistant spring, and a grid-type panel part that moves horizontally or vertically by an external force applied to the main switchboard when an earthquake occurs, and an earthquake-resistant shock absorber The spring can absorb horizontal and vertical vibrations caused by earthquakes, protect communication devices from overcurrent flowing into cables, monitor the condition of cable lines and devices, and remotely turn on the panel remotely through predicted information Alternatively, you can schedule an off alarm or an on or off alarm.

The present invention includes a low-power wireless communication method applied BLE 5.0 communication module for securing a cable replacement cycle of an MDF system that remotely controls on or off, and transmits an on or off alarm to an administrator in the case of a cable replacement cycle.

In the MDF system that remotely controls the on or off, smoke can be observed with an image sensor, and seismic vibrations and other mechanical sounds other than smoke can be measured with a sound sensor to turn on or off the panel immediately.

The present invention provides a verification and estimation module for checking, evaluating, and correcting errors in wireless temperature, humidity, smoke, and vibration sensor data by checking, evaluating, and correcting the data. It is verified in real time by a predefined rule that is equally applied to all data regardless of the type, and when an error occurs due to verification and estimation, the panel can be turned on or off immediately.

The main control unit is an accelerated life module that accelerates failure by independently applying a plurality of environmental conditions and load conditions; Including, the accelerated life module includes load condition information applied to the environmental condition module using information such as external temperature and humidity Accelerate failure by independently applying the load condition module using

The present invention provides a prediction module that provides optimization power for maintenance by predicting a future performance degradation trend based on the current state along with a real-time failure detection/diagnosis technology that monitors the real-time current state and takes action. Based on the fault detection and diagnosis results, the module predicts the symptoms of deterioration of the MDF system and internal cables, etc., predicts the on or off time, and delivers it to the manager.

The present invention provides a classification module for fault detection and diagnosis when performing a sequential recovery function in a distribution device; a fuzzy logic in which the classification module calculates information of temperature, humidity, smoke, and vibration sensors using fuzzy logic (Fuzzy Logic, FL) module, fuzzy clustering module that calculates by fuzzy clustering method, and artificial neural network (ANN) module that calculates with statistical learning algorithm diagnose failures and turn the panel on or off immediately can do.

The main function of the MDF as described above is to protect the communication device from the overcurrent flowing into the cable, and to protect the equipment by turning the panel on or off at an appropriate time by monitoring the state of the cable line and the device.

In addition, according to the present invention, it is possible to grasp an accurate emergency situation while exchanging mutual situation information with the manager in real time by interworking between the earthquake-resistant distribution device and the emergency control center and the emergency light, and turn the panel on or off accordingly.

In addition, the present invention can quickly prevent the spread of secondary damage by immediately turning on or off the internal panel of the distributor whose durability is weakened to cut off power when an emergency occurs.

1 is a view showing the structure of an MDF according to the prior art.
2 is a view showing a side perspective view when the panel of the MDF system for remote control on or off according to an embodiment of the present invention is closed and opened.
3 is a view showing a grid-type panel unit and the like moving in a horizontal or vertical direction according to an embodiment of the present invention.
4 is a view showing an on or off device using a voltage/current sensor according to an embodiment of the present invention.
5 is a view showing a sensor for detecting an image and sound according to an embodiment of the present invention.
6 is a diagram illustrating a verification and estimation module according to another embodiment of the present invention.
7 is a view showing an accelerated life module according to another embodiment of the present invention.
8 is a diagram illustrating a prediction module according to another embodiment of the present invention.
9 is a diagram illustrating a classification module according to another embodiment of the present invention.

In order to fully understand the present invention, preferred embodiments of the present invention will be described with reference to the accompanying drawings. Embodiments of the present invention may be modified in various forms, and the scope of the present invention should not be construed as being limited to the embodiments described in detail below. This example is provided to more completely explain the present invention to those of ordinary skill in the art. Accordingly, the shape of elements in the drawings may be exaggerated to emphasize a clearer description. It should be noted that in each drawing, the same member is shown with the same reference numerals in some cases. In addition, detailed descriptions of well-known functions and configurations determined to unnecessarily obscure the gist of the present invention will be omitted.

As shown in FIG. 2, the main functions of the MDF system for remote control on or off made of MDF are to protect communication devices from overcurrent flowing into cables, and to monitor and predict the state of cable lines and devices.

Although not shown in the drawing, it provides an accurate emergency situation identification, guidance and evacuation guidance function and panel on or off function while exchanging mutual situation information in real time by interworking between the earthquake-resistant distribution device and the emergency control center and emergency lights.

At this time, it is important to cut off the power by transmitting the on/off command of the MDF panel. Since it is also important to give an on or off notice to the manager in advance, this will also be described.

The MDF system for remote control on or off according to the present invention is a seismic unit for the main switchboard using a seismic spring 20 that can be easily installed in a switchboard requiring an earthquake resistant device including the main switchboard by modularizing the unit seismic unit. do.

To this end, the present invention consists of an elastic body made of an elastic material in which fastening holes of a predetermined depth are respectively formed in the center toward the inside of the seismic unit for main switchboard, and a plurality of insertion holes are penetrated at positions symmetrical along the edges.

Or as shown in FIG. 3, a support panel 10 made of iron which is fixed on the ground of the main switchboard and supports the main switchboard from the bottom; A plurality of seismic springs 20 fixed on the support panel, a bottom part is inserted into a spring groove to support the vibration of the main switchboard, and an upper part elastically supporting the bottom part of the measurement control panel, and a support part for supporting the earthquake-resistant spring 20 It consists of (30).

A floor fixing member 10 installed on the lower side of the main switchboard, an earthquake-resistant spring 20 installed on the central axis of the floor fixing member 10, and a fixing nut 30 for fixing them to a support panel made of iron. is composed

The seismic spring 20 is coated with an elastic polymer to efficiently absorb horizontal and vertical vibrations caused by an external environment.

Therefore, when an emergency situation occurs, horizontal and vertical vibrations are efficiently absorbed by the grid-type panel unit 40 moving in the horizontal or vertical direction by external force applied to the main switchboard and the earthquake-resistant spring 20 that absorbs the shock external force. can do.

first embodiment

As shown in FIGS. 4 and 5 , in the case of the existing vibration signal data, there is a disadvantage that many sensors must be attached to acquire the data according to the characteristics of the applied system.

For example, when temperature, humidity, smoke, vibration, and current/voltage sensors are attached, issues such as optimizing the number of sensors and installation locations may occur, and data containing a lot of external interference or noise while mechanical systems and facilities are operating It is possible to measure

In order to overcome these disadvantages, the present invention attaches a certain number of temperature, humidity, smoke, vibration, and current/voltage sensors, uses thermal image data, builds an image-based deep learning model, On or off control is possible with the off module 100 .

There is a high risk of a communication device malfunctioning due to smoke and sparks from overcurrent in the MDF system that remotely controls on or off according to the present invention. Smoke can be observed with the image sensor 17, and vibration and other mechanical sounds other than smoke are It can be measured by the sound sensor 16 .

As an embodiment, as shown in FIG. 2, it is a heat or smoke image image inside the MDF system that remotely controls the actual on or off obtained through the image sensor 17. The brighter the yellow, the higher the temperature or the higher the density of smoke. indicates.

And by generating a weak classifier that minimizes the weight error of the thermal or smoke image image, the temperature sensor 11, humidity sensor 12, smoke sensor 13, voltage and current sensor 14 of FIG. 3 for each classifier, etc. Temperature, humidity, smoke, and vibration sensor data weights obtained through

In addition, the strong classifier that can be used receives a vector as input and performs temperature, humidity, smoke, and vibration sensor data classification with the sum of the trained weak classifiers.

For example, a local binary pattern feature used to detect a smoke image inside an MDF system that remotely controls on or off with the above-described AI algorithm is a local texture of smoke or gas such as edges, points, corners, etc. using pixel values. It is used to express features, and it has the advantage of high processing speed, discrimination power, and robustness to changes in lighting with simple operation.

For example, as shown in FIG. 6, after power cut off of the MDF system that remotely controls on or off in case of an emergency, the internal components of the distributor whose durability is weakened are tested to prevent future damage. As the module 51 , the on or off control of the remote MDF system is possible through the on or off module 100 .

Example 1-1

In order to prevent fire due to deterioration of a high-voltage power cable in an MDF system that remotely controls on or off the present invention, the present invention specifically performs model learning through a watchdog module according to the present invention to perform model learning when an abnormality occurs. Switch to the schedule duplication mode so that the local scheduler is operated, so that the local scheduler uses the same schedule as the schedule of the MDF fire prevention system from the previous past time of the transition to the schedule duplication mode to the time of conversion to the schedule duplication mode All schedule control signals and virtual control signals are transmitted to the MDF system so that the inner panels of the MDF operate normally.

That is, a Recurrent Neural Network (RNN) model learning may be performed in a supervised learning method using the schedule control signal and the virtual control signal transmitted in real time.

In addition, it is possible to perform RNN (Recurrent Neural Network) model learning in a supervised learning method using log data measured in real time.

For example, in a supervised learning method, it is possible to create and test multiple models of how to send alarms to managers and perform them with an optimized model.

To this end, an image camera for generating temperature, humidity, smoke, and vibration sensor data may be added to the inside of the MDF.

second embodiment

As shown in FIG. 7, the accelerated load test through the accelerated life module 52 of the internal components of the distributor whose durability is weakened after power cut off in an emergency situation applies harsh experimental conditions so that the product loses performance faster than under normal conditions. The environmental condition module 52-1 and on or off using information such as external temperature and humidity of the MDF system, which is designed to cause one failure and remotely controls various on or off in a manner commonly applied to MDF. The failure can be accelerated by independently applying the load condition module 52-2 using the load condition information applied to the MDF system that remotely controls the .

In addition, real-time learning uses the operation data of the MDF system as it is, and it is a method to check the data in a steady state under the assumption that the new MDF system does not have vibrations due to an emergency for a certain period of time.

In addition, the vibration data-based approach can be divided into a statistical approach and a machine learning approach, and the statistical approach is based on the basic statistical characteristic values of pre-analyzed data.

3rd embodiment

As shown in FIG. 8 , the distribution device receives status information about an emergency situation or a flood situation from a distribution device located in an area where an emergency situation occurs, and power cut-off (on/off) of the distribution device occurs based on the status information In order to predict the usability and lifespan of other electrical distribution devices adjacent to the device, the prediction module 53 receives the corresponding life data through the data module including the average lifespan data of the MDF and multiplies the failure diagnosis probability of the diagnostic unit to estimate the lifespan. It further includes a prediction module 53 for calculating.

The present invention may include an alarm unit that checks the presence or absence of a failure of the MDF and alarms according to the type of failure, a proportional unit that monitors a case where the change in voltage is not proportional to the change in current of the MDF, etc. .

Therefore, the MDF failure diagnosis system through the prediction module 53 uses an exponentially weighted moving average that is more affected by recent data to generate a warning when the temperature, humidity, smoke, and vibration sensor values attached to the MDF are above a certain value. On or off control is possible with the on or off module 100 without being bound by past data.

As the inference and classification method by the classification module 54, a conditional (If-Then) rule may be used simply, and in the case of a complex MDF system encompassing information of temperature, humidity, smoke, and vibration sensors according to the present invention, fuzzy logic Fuzzy Logic (FL) module (54-1) calculated with , Fuzzy Clustering module (54-2) calculated with fuzzy clustering method ANN) module 54-3 or the like may be used.

Example 3-1

The deterioration of the high voltage power cable of the MDF system that remotely controls on or off conventionally is serious, and the prediction module 53 according to the present invention determines that the new observation value is abnormal when the new observation value deviates from the statistical characteristic value of the given data to prevent cable deterioration. predict

The statistical method according to the present invention includes a parametric approach and a non-parametric approach, and the parametric approach is based on basic statistical characteristics of expected vibration data.

In the parametric approach according to the present invention, the parameters related to the probability distribution are calculated as data based on the assumed basic probability distribution, the parameters are calculated using normal driving data, and the probability distribution is given.

By using the probability distribution to detect an abnormal state and predict the remaining useful lie (RUL) of the MDF system that remotely controls on or off, the on or off time can be predicted with the on or off module 100 do.

Example 3-2

As shown in FIG. 8, the nonparametric approach according to the present invention is suitable for cases where the vibration data cannot be characterized by a basic probability distribution or a standard distribution, etc., and histogram comparison and distance to compare the given data with new data. Nearest Neighbor (NN), K-means, and Support Vector Machine (SVM) classification methods, which are methods through calculation, may be used.

The machine learning method according to the present invention is a method of predicting the future by learning a neural network using normal and abnormal data from among temperature, humidity, smoke, and vibration data collected from an MDF system that remotely controls on or off as information.

Using the machine learning approach, vibration data can be classified or clustered into several groups, such as normal and abnormal, and this information can be provided to a prediction algorithm that predicts the useful life of the MDF system.

In other words, learning in which the input data is given a labeled output is called supervised learning, and prediction and soundness can be performed using both normal data and failure data.

In addition, for supervised learning, neural networks or SVM methods can be used, and unsupervised learning is used when there is no pre-classified temperature, humidity, smoke, and vibration data. Normal vibration data can be identified.

As unsupervised learning methods according to the present invention, principal component analysis (PCS), K-means clustering, and fuzzy clustering methods can be used. For the prediction of random signals, a Kalman filter (KF) and a particle filter (Particle Filter, PF) method is also used.

The PoF-based model used in the present invention can be used to optimize product design based on failure modes, mechanisms and effective analysis.

The fault detection and diagnosis method according to the present invention can be divided into two stages: preprocessing and classification. The preprocessing process is a process of extracting or generating characteristic values that can be effectively used in the classification process by using the measured values of temperature, humidity, smoke, and vibration sensors.

The transformation of these characteristic values is one of the methods to extract the failure characteristics, and the classification can classify the cause and location of the failure of the MDF system or parts using an expert system or a pattern recognition method using the data processed in the pre-processing.

Example 3-3

For predictive diagnosis of deterioration of the MDF system, the prediction module 53 may predict the Remaining Useful Life (RUL) of the MDF system being operated or main parts to diagnose the reliability of the MDF system.

RUL (remaining useful life) is information indicating the time to failure, that is, life expectancy by analyzing the characteristics of vibration data that have been observed from the past to the present.

The predictive diagnosis technology of the prediction module 53 according to the present invention is to support state-based maintenance. In addition to the real-time failure detection/diagnosis technology that monitors the real-time current state and takes action, the performance degradation trend in the future based on the current state It is a technology that predicts and provides optimized power for maintenance.

To this end, the remaining life prediction (RUL) module 53-1 predicts the symptoms of deterioration of the MDF system and internal cables, etc. based on the failure detection and diagnosis results of the prediction module 53, and estimates the life of the alarm unit ( 53-3), it is a technology that supports decision-making such as immediate repair or control change of MDF system that remotely controls on or off.

4th embodiment

As shown in FIG. 9 , the present invention uses a classification method by the classification module 54 for fault detection and diagnosis when sequential recovery functions are performed in the distributor.

Classification by the classification module 54 is a process of determining whether a state is normal based on the pre-processed data, and identifying the cause and location of the failure when it is not normal.

The classification process by the classification module 54 can be viewed as an expert system, and the expert system realizes the reasoning process based on human expertise in a specific field using a machine such as a computer, which is based on knowledge base and reasoning. consists of an engine.

As the inference and classification method by the classification module 54, a condition (If-Then) rule may be simply used, and in the case of a complex MDF system encompassing information of temperature, humidity, smoke, and vibration sensors according to the present invention Fuzzy Logic (FL) module (54-1) to calculate by fuzzy logic, Fuzzy Clustering (54-2) module (54-2) to calculate by fuzzy clustering method, and artificial neural network ( to calculate by statistical learning algorithm) Network, ANN) module 54-3 and the like may be used.

The present invention uses a fuzzy K-NN method module 54-4 that connects fuzzy theory to K-NN as a representative classification method using artificial intelligence.

The K-NN decision rule used in the fuzzy K-NN method module 54-4 classifies input patterns of temperature, humidity, smoke, and vibration sensor data by a simple process by classification indicated by the nearest data of the vector. will do

When the K-NN decision rule is used for MDF failure diagnosis, the calculation is simple and very good results can be obtained using a small number of samples.

Therefore, in case of emergency, there is no need for pre-processing of earthquake durability information of internal components of distribution equipment whose durability is weakened after power is cut off, so accurate classification and prediction are possible.

51: verification and estimation module
51-1: Ai Rule Verification Controller
52: accelerated life module
52-1: environmental condition module
52-2: load condition module
53: prediction module
54: classification module
54-1: Fuzzy Logic (FL) module
54-2: Fuzzy Clustering Module
54-3: Artificial Neural Network (ANN) module
54-4: Fuzzy K-NN Method Module

Claims (6)

In the MDF system for remote control on or off,
A floor fixing member 10 installed on the lower part of the main switchboard of the MDF system, an earthquake-resistant spring 20 installed on the central axis of the floor fixing member 10, and a fixing nut for fixing them to a support panel made of iron ( 30) is composed of,
The seismic spring 20 is coated with an elastic polymer and includes a grid-type panel part 40 that moves in a horizontal or vertical direction by an external force applied to the main switchboard when an earthquake occurs,
Protects communication devices from overcurrent flowing into cables in the MDF system, monitors the status of cable lines and devices, and remotely sets on or off alarms or on or off alarms remotely through predicted information,
A verification and estimation module 51 for checking, evaluating, and correcting errors in the wireless temperature, humidity, smoke, and vibration sensor data in the MDF system, and for checking, evaluating, and correcting missing data;
Verification in real time by the verification and estimation module 51, and when an error occurs according to verification and estimation, the panel is immediately turned on or off,
The main control unit 1 includes an accelerated life module 52 that accelerates failure by independently applying a plurality of environmental conditions and load conditions;
The accelerated life module 52 generates life information, and the main control unit 1 selects an optimal material of the integrated line according to the life information,
A prediction module 53 for predicting a future performance degradation trend of the MDF system and providing optimized power for maintenance;
Predict the symptoms of deterioration of the MDF system and internal cable and predict the on or off time accordingly and deliver it to the manager,
When performing a sequential recovery function in the MDF system, a classification module 54 for fault detection and diagnosis;
The classification module 54 is a fuzzy logic (FL) module 54-1 that calculates by fuzzy logic in the case of a complex MDF system encompassing information of temperature, humidity, smoke, and vibration sensors and a fuzzy clustering method. Using the Fuzzy Clustering module (54-2) and the Artificial Neural Network (ANN) module (54-3) that calculates with a statistical learning algorithm, the failure is classified according to a certain criterion and diagnosed according to a specific state. When diagnosed with , the panel is turned on or off immediately,
In order to prevent fire due to the deterioration of the high-voltage power cable in the MDF system, model learning is performed through a watchdog module, and when an error occurs, the local scheduler switches to the schedule duplication mode and operates the local scheduler to prevent MDF fire By using the same schedule as the system's schedule, all schedule control signals and virtual control signals from the previous past time of the transition to the schedule duplication mode to the time of transition to the schedule duplication mode are transmitted to the MDF system, and the MDF inner panel are working normally,
In the MDF system that remotely controls the on or off, smoke can be observed with the image sensor 17, and seismic vibration and other mechanical sounds are measured with the sound sensor 16 to immediately turn on or off the panel,
It is a heat or smoke image image inside the MDF system that remotely controls the actual on or off obtained through the image sensor 17. The brighter the yellow, the higher the temperature or the higher the density of smoke,
MDF system that predicts the symptoms of deterioration of the MDF system and internal cable based on the failure detection and diagnosis results of the prediction module 53 and estimates the lifespan to remotely control on or off through the alarm unit 53-3 MDF system for remote control on or off, characterized in that it comprises a remaining life prediction (RUL) module (53-1) that supports the decision-making of immediate repair or control change of the. The method according to claim 1,
A low-power wireless communication method applied BLE 5.0 communication module for securing the cable replacement cycle of the MDF system that remotely controls the on or off is included, and when the cable replacement cycle arrives, an on or off notice alarm is transmitted to the manager. MDF system for remote control on or off. delete delete delete delete

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