KR102532475B1 - Immunity-reinforced switchgear including a device for comprehensive power quality diagnosis through forecasting and operation method therefor - Google Patents

Abstract

Disclosed are a switchboard with enhanced immunity equipped with a precursor predictive power quality comprehensive diagnosis device for comprehensively evaluating power quality integrity by applying a fuzzy reasoning system and an operation method thereof. According to an embodiment, a switchboard with enhanced immunity having a comprehensive power quality diagnosis device for predicting electric power includes a communication unit for receiving measurement information from one or more sensors installed in the switchboard; a calculation unit for calculating one or more prognostic parameters based on the measurement information; and a prediction unit that predicts the risk of power quality according to the predictive parameter using a fuzzy inference system (FIS).

Description

Immunity-reinforced switchgear including a device for comprehensive power quality diagnosis through forecasting and operation method therefor}

It relates to an immunity-enhanced switchboard equipped with a power quality comprehensive diagnosis device for prediction using a fuzzy reasoning system and an operation method thereof.

In the case of distribution panels, motor control panels, and distribution panels constituting power receiving facilities for private vehicles, when instantaneous voltage drops, surges, and harmonics occur, they affect the operation of various controllers such as magnetic contactors and PLC (Programmable Logic Controller) that control facility loads in an unprotected state. , which may eventually lead to malfunction of the load in operation. Korean Patent Nos. 10-1688274 and 10-2203120 exist as prior art for preventing such a malfunction.

In the case of switchboards equipped with circuit breakers for extra-high voltage and low voltage, a surge absorber is installed to prepare for the occurrence of abnormal voltage in preparation for switching surges generated when opening and closing the circuit breakers. It is not possible to determine the amount of harmonics, which can have a huge impact in loads that require good power quality, such as in data centers.

In addition, when important factors determining power quality occur simultaneously, a device for predicting how severely it affects a load requiring good quality power quality is required, and a management method using this is required.

An object of the present invention is to provide a switchboard with enhanced immunity equipped with a power quality comprehensive diagnosis device for predicting power quality for comprehensively evaluating the integrity of power quality by applying a fuzzy reasoning system and an operation method thereof.

According to one aspect, an immunity-reinforced switchboard equipped with a power quality predictive comprehensive diagnosis device includes a communication unit for receiving measurement information from one or more sensors installed in the switchboard; a calculation unit for calculating one or more prognostic parameters based on the measurement information; and a prediction unit that predicts the risk of power quality according to the predictive parameter using a fuzzy inference system (FIS).

The one or more sensors may be at least one of a voltage sag counter, a surge protector (SPD) counter, and a load side feeder voltage meter.

The calculation unit calculates harmonic distortion and voltage unbalance by using the voltage measurement information of the load-side feeder, and the voltage composed of the voltage axis and the time axis for the measured load-side feeder voltage using the measured voltage measurement information of the load-side feeder. The position on the disturbance scatter plot can be determined.

The calculation unit may calculate a sag severity index based on a load-side feed voltage for a sag duration and a reference voltage according to an ITIC (Informational Technology Industry Council) curve when an instantaneous voltage drop occurs using voltage measurement information on the load-side feeder.

The calculation unit determines which of the two or more regions of the voltage disturbance scatter plot divided based on the ITIC curve corresponds to the position on the determined voltage disturbance scatter plot, and the load-side feeder voltage measured based on the weights assigned to each of the two or more regions corresponds to the corresponding region. The weight can be determined according to the area to be played.

The regions on the voltage disturbance scatter plot include a first region having an effective value of 10% to 70% and a duration of 20 ms to 0.5 s, and a second region having an effective value of 120% to 200% or less and a duration of 8.33 ms to 20 ms. , the third area with an effective value of 10% or less and a duration of 8.33ms to 3s, the fourth area with an effective value of 10% to 90% and a duration of 0.5s to 3s, and an effective value of 110% to 140 %, the duration is 0.5s to 3s, the effective value is 10% or less, the duration is 3s to 60s, the sixth region, the effective value is 10% to 90%, the duration is 3s to 60s , a seventh region having an effective value of 110% to 140%, and an eighth region having a duration of 3s to 60s.

The precursor prediction parameter may include a harmonic distortion rate, a voltage unbalance rate, and a sag severity index.

The prediction unit inputs the harmonic distortion, voltage unbalance, and sag severity index to the fuzzy inference system (FIS), and the fuzzy inference system assigns weights to each of the harmonic distortion, voltage unbalance, and sag severity index according to the determined weights to power the power quality risk can be determined.

The communication unit communicates with an external web server and may transmit the risk of power quality to the user terminal through the external web server.

According to one aspect, a method of operating a switchboard with enhanced immunity equipped with a comprehensive power quality diagnosis device for predicting electric power includes a receiving step of receiving measurement information from one or more sensors installed in the switchboard; a calculation step of calculating one or more prognostic parameters based on the measurement information; and predicting the risk of power quality according to the predictive parameters using a fuzzy inference system (FIS).

A fuzzy reasoning system can be applied to comprehensively evaluate the health of power quality.

1 is a configuration diagram of a comprehensive power quality diagnosis device according to an embodiment.
2 is an exemplary diagram for explaining the operation of a comprehensive power quality diagnosis device according to an embodiment.
3 is an exemplary diagram for explaining a membership function of a fuzzy inference system.
4 is an exemplary diagram for explaining an operating environment of an immunity-reinforced switchboard equipped with a power quality comprehensive diagnosis device for predicting precursors according to an embodiment.
5 is an exemplary diagram of a sequence module circuit according to an example.
6 is a flowchart illustrating a method for diagnosing power quality according to an embodiment.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. In describing the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the terms to be described later are terms defined in consideration of functions in the present invention, which may vary according to the intention or custom of a user or operator. Therefore, the definition should be made based on the contents throughout this specification.

Hereinafter, exemplary embodiments of an immunity-reinforced switchboard equipped with a power quality comprehensive diagnosis device for predicting precursors and an operating method thereof will be described in detail with reference to drawings.

1 is a configuration diagram of a comprehensive power quality diagnosis device according to an embodiment.

According to an embodiment, the comprehensive power quality diagnosis device 100 includes a communication unit 110 that receives measurement information from one or more sensors installed in a switchboard, and a calculation unit 120 that calculates one or more forecasting parameters based on the measurement information. and a prediction unit 130 that predicts the risk of power quality according to the predictive parameter using a fuzzy inference system (FIS).

According to one embodiment, the one or more sensors may be at least one of a voltage sag counter, a surge protector (SPD) counter, and a load side feeder voltage meter. For example, the voltage meter may be an instantaneous voltage drop counter. The communication unit 110 may communicate with at least one of a voltage sag counter, a surge protector (SPD) counter, and a load-side feeder voltage measurer to receive measurement information measured by each sensor.

According to an example, the precursor prediction parameter may include at least one of a harmonic distortion rate, a voltage unbalance rate, and a sag severity index.

According to one example, the communication unit 110 may communicate with an external web server. For example, the communication unit 110 may communicate with an external web server through TCP/IP communication. In this case, the communication unit 110 may transmit the risk of power quality to the user terminal through an external web server. As another example, the communication unit 110 may perform direct communication with the user terminal and transmit the level of risk of power quality to the user terminal.

According to an embodiment, the calculator 120 may calculate harmonic distortion and voltage unbalance by using voltage measurement information about the load-side feeder.

Harmonics are voltages or currents having a frequency that is an integer multiple of the fundamental frequency, and voltages or currents including harmonics have distorted waveforms. In general, odd-order harmonics of the third harmonic or higher appear remarkably, and when harmonic components are included in distribution lines, direct effects such as overload, overheating, and noise of electrical equipment due to excessive inflow of harmonic current may occur. In the case of Korea, the electric utility business sets and operates harmonic permissible standard values in relation to cooperation according to the customer's electricity use.

For example, most harmonic currents are generated in devices using power devices (Power Electronics: SCR, Diode, GTO, IGBT, etc.), and some of them may be caused by transient phenomena. For example, harmonics generated by fluorescent lamps, rotating machines, and transformers are instantaneously generated and are small in size, so there is no major problem. influence is very large.

According to an example, the harmonic distortion may be expressed as total harmonic distortion (THD), and may be defined as in the following equation.

Here, V _k 'is the harmonic voltage after passing through the output filter of the inverter.

If the load power is unbalanced in a state where the power supply voltage is three-phase balanced, unbalance may occur in the receiving voltage. This is because line currents are unbalanced by impedances present in transformers or lines in the process of transferring power from a power receiving end to a load, and as a result, impedance voltage drops become unbalanced. In recent years, many devices that generate harmonics have been installed, and the inductive reactance to harmonics increases to an integer multiple of the commercial frequency, and the capacitive reactance decreases to 1/1 of the commercial frequency. Even when the magnitudes of harmonic currents are different or the phases are different, power supply imbalance due to harmonics may occur.

According to one example, the voltage unbalance ratio may be defined as in the following equation.

The voltage unbalance rate standard is followed depending on the institution, and it is generally recommended to be within 1 to 4%. In IEC, low-voltage systems are set at 2% or less, rotating machines are set at 1% or less, and electrical equipment technical standards set them at 3% or less.

According to an embodiment, the calculation unit 120 may determine a location on a voltage disturbance scatter plot composed of a voltage axis and a time axis for the measured load-side feeder voltage using the measured load-side feeder voltage measurement information.

Referring to FIG. 2 , in the voltage disturbance scatter plot, an x-axis may have a duration and a y-axis may have a voltage value, and the voltage value may be expressed as a ratio of a sag voltage to a normal voltage. For example, the calculation unit 120 may calculate the sag duration and the sag voltage when an instantaneous voltage drop occurs by measuring the load-side feeder voltage, and display the calculated results on a voltage disturbance scatter plot.

According to an embodiment, the calculation unit 120 uses the voltage measurement information of the load-side feeder to determine the load-side feed voltage for a sag duration when an instantaneous voltage drop occurs and a reference voltage according to an ITIC (Informational Technology Industry Council) curve. A sag severity index can be calculated. The instantaneous voltage drop phenomenon refers to a phenomenon in which the voltage drops momentarily to 80 to 40% of the rated voltage for 0.5 to 30 cycles.

According to an example, the sag severity index may be defined as in the following equation.

Here, V is a measured voltage value, and V _ITIC represents a threshold according to the ITIC curve. For example, as shown in FIG. 2 , assuming that the current state is located at point (a) as a result of calculation by the calculator 120, V = 0.8 and V _ITIC = 0.7. Accordingly, the sag severity index may be calculated as 0.67.

According to an embodiment, the calculation unit 120 may determine which region among two or more regions on the voltage disturbance scatter plot divided based on the ITIC curve corresponds to the determined position on the voltage disturbance scatter plot.

For example, as shown in FIG. 2, the regions on the voltage disturbance scatter plot include a first region having an effective value of 10% to 70% and a duration of 20 ms to 0.5 s, an effective value of 120% to 200% or less, and a duration of 20 ms to 0.5 s. A second region of 8.33 ms to 20 ms, a third region with an effective value of 10% or less and a duration of 8.33 ms to 3 s, and a fourth region with an effective value of 10% to 90% and a duration of 0.5 s to 3 s 5th area with an effective value of 110% to 140% and a duration of 0.5s to 3s, 6th area with an effective value of 10% or less and a duration of 3s to 60s, and an effective value of 10% to 90 %, and may include at least one of a seventh region having a duration of 3s to 60s and an eighth region having an effective value of 110% to 140% and a duration of 3s to 60s.

According to an embodiment, the calculator 120 may determine a weight according to a region corresponding to a measured load-side feeder voltage based on weights assigned to each of two or more regions. For example, each of the first to eighth regions may be weighted in the form of a look-up table for each region.

According to an embodiment, the predictor 130 may input harmonic distortion, voltage unbalance, and sag severity index to the fuzzy inference system (FIS).

For example, the calculation unit 120 A/D converts the secondary side voltage of the metering transformer (PT) to display the voltage drop state on the ITIC curve, and calculates the voltage level based on 99 voltage levels and durations per day. The instantaneous voltage drop severity index can be obtained by calculating the medoid. The prediction unit 130 may utilize the severity index calculated by the calculation unit 120 as an input value to the fuzzy inference system. In addition, the prediction unit 130 may calculate harmonic distortion and voltage unbalance in real time and use them as input values to the fuzzy inference system.

According to an example, the fuzzy inference system may operate based on a severity index of 1, a harmonic distortion rate of 3%, and a voltage unbalance rate of 3%. For example, as shown in FIG. 3 , the fuzzy inference system may determine the membership function based on the above reference value.

According to an example, the rules of the fuzzy inference system may be represented as shown in the table below.

momentary voltage drop
Severity Index Harmonic content voltage unbalance factor Output (power quality health index) S S S S M S B M M S S M M B M B S M M M B B M S S S M M B M M S M M M B B B S M M B B B B S S M M M B B M S M M B B B B S B M B B B

For example, a criterion for determining an output value membership function in consideration of a weight may be represented as shown in the table below.

momentary voltage drop
Severity Index Harmonic content voltage unbalance factor weight PQ 0.5 0.3 0.2 One S 0.5 0.3 0.4 1.2 S 0.5 0.3 0.6 1.4 M 0.5 0.5 0.2 1.2 S 0.5 0.5 0.4 1.4 M 0.5 0.5 0.6 1.6 M 0.5 0.7 0.2 1.4 M 0.5 0.7 0.4 1.6 M 0.5 0.7 0.6 1.8 B 0.7 0.3 0.2 1.2 S 0.7 0.3 0.4 1.4 M 0.7 0.3 0.6 1.6 M 0.7 0.5 0.2 1.4 M 0.7 0.5 0.4 1.6 M 0.7 0.5 0.6 1.8 B 0.7 0.7 0.2 1.6 M 0.7 0.7 0.4 1.8 B 0.7 0.7 0.6 2 B 0.9 0.3 0.2 1.4 M 0.9 0.3 0.4 1.6 M 0.9 0.3 0.6 1.8 B 0.9 0.5 0.2 1.6 M 0.9 0.5 0.4 1.8 B 0.9 0.5 0.6 2 B 0.9 0.7 0.2 1.8 B 0.9 0.7 0.4 2 B 0.9 0.7 0.6 2.2 B

For example, when the value of PQ is 1.0 ≤ PQ < 1.4, it is determined that PQ = S, when the value of PQ is 1.4 ≤ PQ < 1.8, PQ = M, and when the value of PQ is 1.8 ≤ PQ < 2.2, PQ = B can

According to an example, the output value of the fuzzy output system in which the input value severity index, harmonic distortion rate, and voltage unbalance rate are classified into Big, Medium, and Small values according to the definition of the membership function can be represented as shown in the table below.

momentary voltage drop
Severity Index Harmonic content voltage unbalance factor input value PQ_index S S S 0, 0, 0 S 1.63 0.5, 1, 1 S 1.92 M 0.5, 1, 3 S 1.92 B 0.5, 1, 5 M 5 M S 0.5, 3, 1 S 1.92 M 0.5, 3, 3 M 5 B 0.5, 3, 5 M 5 B S 0.5, 5, 1 M 5 M 0.5, 5, 3 M 5 B 0.5, 5, 5 B 7.62 M S S 1, 1, 1 S 1.78 M 1, 1, 3 M 5 B 1, 1, 5 M 5 M S 1, 3, 1 M 5 M 1, 3, 3 M 5 B 1, 3, 5 B 7.62 B S 1, 5, 1 M 5 M 1, 5, 3 B 7.86 B 1, 5, 5 B 7.62 B S S 1.5, 1, 1 M 5 M 1.5, 1, 3 M 5 B 1.5, 1, 5 B 7.62 M S 1.5, 3, 1 M 5 M 1.5, 3, 3 B 8.08 B 1.5, 3, 5 B 7.62 B S 1.5, 5, 1 B 7.86 M 1.5, 5, 3 B 7.86 B 1.5, 5, 5 B 7.62 2, 10, 30 B 8.37

Here, the input value represents [severity index, harmonic distortion rate, voltage unbalance rate]. For example, when the severity index is 1, the harmonic distortion rate is 3%, and the voltage unbalance rate is 5%, the input value can be expressed as [1, 3, 5].

As an example, the output of the fuzzy inference system is set to the power quality soundness index, [0; 0; 0] with a minimum value of 1.63, [2; 10; 30], the maximum value is 8.37.

According to an embodiment, the fuzzy inference system may assign weights to each of the harmonic distortion rate, the voltage unbalance rate, and the sag severity index according to the determined weight to determine the degree of risk of power quality. For example, the prediction unit 130 may determine the level of risk of power quality by determining the voltage state as one of normal, warning, and danger levels. For example, in Table 3, S can be indicated as normal, M as warning, and B as danger.

According to one example, the comprehensive power quality diagnosis device may perform communication with an external web server. Referring to FIG. 4 , the comprehensive power quality diagnosis device 100 may receive sensor information measured from the switchboard 10 and communicate with an external web server 20 .

For example, the comprehensive power quality diagnosis device 100 may communicate with the user terminal 30 through the web server 20 . The comprehensive power quality diagnosis device 100 can receive a control signal for controlling the comprehensive power quality diagnosis device from a remote manager through an application that works with the comprehensive power quality diagnosis device installed in the user terminal 30, and measures By delivering risk information in real time, managers can quickly monitor the status of switchboards.

For example, an application linked to a comprehensive power quality diagnosis device can be operated so that the user can remotely monitor the central monitoring room without directly approaching the switchboard or opening the door, and can monitor the current power status and alarm status in real time. It can display the power quality status of a unit plant or plant by outputting it.

For example, an application linked to the comprehensive power quality diagnosis device can download and display essential data from the comprehensive power quality diagnosis device, and display fuzzy reasoning power quality diagnosis results divided into three stages (normal, warning, and dangerous). can In addition, the application linked to the comprehensive power quality diagnosis device can remotely monitor not only the power quality of the panel, but also power and sensor measurement values.

According to one example, the comprehensive power quality diagnosis device 100 may include a digital sequence module.

5 is an exemplary diagram of a sequence module circuit according to an example.

Referring to FIG. 5, the conventional sequence module circuit 500 receives inputs from switches and push buttons for operation based on analog type VCB and ACB blocking, configures a blocking-based operating circuit, assists and displays a series of operations. A large number of auxiliary relays are included for this, and a display lamp and a sequence circuit are configured using this contact point. At this time, the existing sequence circuit directly connected with cables complicates the internal structure of the switchboard, prolongs the construction period during construction, and makes maintenance difficult. For example, the digital sequence module may be installed inside or outside the comprehensive power quality diagnosis device.

According to one example, the digital sequence module can digitally replace auxiliary relays and switches included in conventional analog VCB and ACB panels as shown in FIG. 5, and can be represented as shown in the table below.

input/output Auxiliary relays and switches Existing VCB panel Existing ACB panel digital sequence module input 50/51X O O X 50/51GX O X 67NX O O 27X O O 59X O O 81OX O X 81UX O X 81RX O X 32PX O X 86X O O 64GX X O BS-a for buzzer O O O BS-b for Reset O O O Lamp Test(LT) BS-a O O O Timer (0 to 12 minutes) O O X Setting input keys (up, down, left, right, OK) X X O Print BZ O O O White Lamp O O O 86X (for Lock Out) O O O everyone DC 12V adapter O

For example, when a digital sequence module is applied, monitoring efficiency is increased and real-time control is possible. In addition, the digital sequence module can drastically reduce wiring by replacing many parts of wiring such as auxiliary relays, indicator lamps, changeover switches, CTT, PTT, etc. in the switchboard with programs.

6 is a flowchart illustrating a method for diagnosing power quality according to an embodiment.

According to an embodiment, the comprehensive power quality diagnosis device may receive measurement information from one or more sensors installed in a switchboard (610).

According to an example, the one or more sensors may be at least one of a voltage sag counter, a surge protector (SPD) counter, and a load-side feeder voltage measurer, and the comprehensive power quality diagnosis device receives measurement information measured by each sensor. can do.

According to an embodiment, the comprehensive power quality diagnosis device may calculate one or more precursor prediction parameters based on the measurement information (620).

According to an example, the precursor prediction parameter may include at least one of a harmonic distortion rate, a voltage unbalance rate, and a sag severity index.

According to an embodiment, the comprehensive power quality diagnosis device may predict the risk of power quality according to the predictive parameter using a fuzzy inference system (630). For example, the fuzzy inference system may determine the risk of power quality by assigning weights to each of the harmonic distortion rate, the voltage unbalance rate, and the sag severity index according to the determined weight. For example, the comprehensive power quality diagnosis device may determine the level of risk of power quality by determining the voltage state as one of normal, warning, and danger levels.

Of the embodiment of FIG. 6 , descriptions overlapping with those described with reference to FIGS. 1 to 5 are omitted.

An aspect of the present invention may be implemented as computer readable code on a computer readable recording medium. Codes and code segments implementing the above program can be easily inferred by a computer programmer in the art. A computer-readable recording medium may include all types of recording devices storing data that can be read by a computer system. Examples of computer-readable recording media may include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical disk, and the like. In addition, the computer-readable recording medium may be distributed among computer systems connected through a network, and may be written and executed as computer-readable codes in a distributed manner.

So far, the present invention has been looked at mainly with its preferred embodiments. Those skilled in the art to which the present invention pertains will be able to understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the scope of the present invention should be construed to include various embodiments within the scope equivalent to those described in the claims without being limited to the above-described embodiments.

100: power quality comprehensive diagnosis device
110: communication department
120: calculation unit
130: prediction unit

Claims (10)

a communication unit for receiving measurement information from a voltage sag counter, a surge protector (SPD) counter, and a load-side feeder voltage meter installed in the switchboard;
a calculation unit calculating precursor prediction parameters including a harmonic distortion rate, a voltage unbalance rate, and a sag severity index based on the measurement information; and
A prediction unit for predicting the risk of power quality according to the prognostic prediction parameter using a fuzzy inference system (FIS);
The fuzzy inference system is
Inputs the harmonic distortion factor, voltage unbalance factor, and the load-side feed voltage during the sag duration when an instantaneous voltage drop occurs included in the above measurement information and the sag severity index calculated based on the reference voltage according to the ITIC (Informational Technology Industry Council) curve Any one level of small, medium, or big for harmonic distortion, voltage unbalance, and sag severity indices, respectively, based on the membership function for each of harmonic distortion, voltage unbalance, and sag severity indices. determine the value
Calculate a summed value by assigning a predetermined weight to each of the harmonic distortion rate, voltage unbalance rate, and sag severity index for each level value;
Calculate the risk of power quality according to the summed value,
The weight of the level value of the harmonic distortion is small = 0.3, medium = 0.5, large = 0.7,
The weight of the level value of the voltage unbalance ratio is small = 0.2, medium = 0.4, large = 0.6,
The weight of the sag severity index is small = 0.5, medium = 0.7, large = 0.8,
If the sum of the values is 1.0 or more and less than 1.4, the risk of power quality = small,
If the sum of the values is 1.4 or more and less than 1.8, the risk of power quality = medium,
If the sum of values is greater than or equal to 1.8 and less than 2.2, the risk level of power quality is predicted as follows. delete According to claim 1,
the calculator
An immunity-reinforced switchboard equipped with a predictive power quality comprehensive diagnosis device that determines the position on a voltage disturbance scatter plot composed of a voltage axis and a time axis for the measured load-side feeder voltage using the measured voltage measurement information for the load-side feeder. delete According to claim 3,
the calculator
Determining which region of two or more regions on the voltage disturbance scatter plot divided based on the ITIC curve corresponds to the determined position on the voltage disturbance scatter plot;
An immunity-reinforced switchboard equipped with a power prediction comprehensive power quality diagnosis device that determines weights according to regions to which the measured load-side feeder voltage corresponds based on the weights assigned to each of the two or more regions. According to claim 5,
The area on the voltage disturbance scatter plot is
A first region having an effective value of 10% to 70% and a duration of 20ms to 0.5s;
A second region having an effective value of 120% to 200% or less and a duration of 8.33ms to 20ms;
A third region having an effective value of 10% or less and a duration of 8.33 ms to 3 s;
A fourth region having an effective value of 10% to 90% and a duration of 0.5s to 3s;
A fifth region having an effective value of 110% to 140% and a duration of 0.5s to 3s;
A sixth region having an effective value of 10% or less and a duration of 3s to 60s;
A seventh region having an effective value of 10% to 90% and a duration of 3s to 60s;
An immunity-reinforced switchboard equipped with a foreshadowing predictive power quality comprehensive diagnosis device, including at least one of the eighth regions having an effective value of 110% to 140% and a duration of 3s to 60s. delete According to claim 1,
the communication department
It communicates with an external web server,
An immunity-enhanced switchboard equipped with a foreshadowing prediction power quality comprehensive diagnosis device that transmits the risk level of power quality to a user terminal through the external web server. According to claim 1,
An immunity-reinforced switchboard equipped with a foreshadowing comprehensive power quality diagnosis device, further comprising a digital sequence module installed inside or outside the foreshadowing comprehensive power quality diagnosis device. A receiving step of receiving measurement information from one or more sensors installed in the switchboard;
a calculation step of calculating one or more prognostic parameters based on the measurement information; and
A prediction step of predicting the risk of power quality according to the prognostic prediction parameter using a fuzzy inference system (FIS),
The fuzzy inference system is
Inputs the harmonic distortion factor, voltage unbalance factor, and the load-side feed voltage during the sag duration when an instantaneous voltage drop occurs included in the above measurement information and the sag severity index calculated based on the reference voltage according to the ITIC (Informational Technology Industry Council) curve Any one level of small, medium, or big for harmonic distortion, voltage unbalance, and sag severity indices, respectively, based on membership functions for each of harmonic distortion, voltage unbalance, and sag severity indices. determine the value
Calculate a summed value by assigning a predetermined weight to each of the harmonic distortion rate, voltage unbalance rate, and sag severity index for each level value;
Calculate the risk of power quality according to the summed value,
The weight of the level value of the harmonic distortion is small = 0.3, medium = 0.5, large = 0.7,
The weight of the level value of the voltage unbalance ratio is small = 0.2, medium = 0.4, large = 0.6,
The weight of the sag severity index is small = 0.5, medium = 0.7, large = 0.8,
If the sum of the values is 1.0 or more and less than 1.4, the risk of power quality = small,
If the sum of the values is 1.4 or more and less than 1.8, the risk of power quality = medium,
If the summed value is 1.8 or more and less than 2.2, the risk of power quality = as predicted, a method of operating an immunity-reinforced switchboard equipped with a power quality comprehensive diagnosis device for prediction.

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