KR102081218B1 - Digital twin device - Google Patents

Abstract

The digital twin device of the present invention comprises: a setting unit for setting a power related parameter of an analysis target; It may include; generating unit for generating the power management data of the analysis target by using the parameter.

Description

Digital Twin Device {DIGITAL TWIN DEVICE}

The present invention relates to a digital twin device for implementing a real-world energy management system in a virtual world.

To establish a safety policy for an energy management system, it is necessary to test the energy management system. However, it is not practical to test the energy management system directly in the real world for safety reasons.

Thus, there is a need for means to test energy management systems in a safe environment.

Korean Patent Publication No. 1645689 discloses a technology for automatically predicting an outlook for a power market price and a variable profit according to a reduction in power demand. However, the testing of the power usage of each power system in virtual reality has not appeared.

Korea Patent Publication No. 1645689

An object of the present invention is to provide a digital twin device for testing a power usage state of a power system in a virtual environment.

Technical problems to be achieved by the present invention are not limited to the above-mentioned technical problems, and other technical problems not mentioned above will be clearly understood by those skilled in the art from the following description. Could be.

The digital twin device of the present invention comprises: a setting unit for setting a power related parameter of an analysis target; It may include; generating unit for generating the power management data of the analysis target by using the parameter.

The digital twin device of the present invention can test the power usage of the power system in a virtual twin environment.

Power systems are tested using a virtual twin environment, so there are no safety concerns and a variety of tests on power systems. In addition, since future power usage patterns are identified in advance, it is possible to establish future power plans.

1 is a block diagram showing a digital twin device of the present invention.
2 is a schematic diagram showing parameters of an analysis target.
3 is a graph showing an ideal pattern generated in the generation unit.
4 is a graph showing an operation probability model of the consumption means.
5 is a flowchart illustrating a digital twin method of the present invention.
6 is a block diagram showing another digital twin device of the present invention.
7 is a block diagram showing another digital twin device of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this process, the size or shape of the components shown in the drawings may be exaggerated for clarity and convenience of description. In addition, terms that are specifically defined in consideration of the configuration and operation of the present invention may vary depending on the intention or custom of the user or operator. Definitions of these terms should be made based on the contents throughout the specification.

1 is a block diagram showing a digital twin device of the present invention. 2 is a schematic diagram showing parameters of an analysis target.

Nodes that consume or produce electricity in order to establish various power policies, such as the expansion of electric power production facilities, the expansion of electric power storage facilities, the additional electric power lines, the establishment of electric bills, the bidding of electric power markets, and the electric power safety policy. Various tests are required. However, due to safety concerns, it is difficult to perform various tests on the actual analysis target.

For example, assume that the analysis target is a house. There may be A home appliances and B home appliances that consume power in a house. At this time, in order to test the power consumption of the analysis target according to the usage pattern of the A home appliance, the A home appliance should be operated in various usage patterns. However, in the process, the A home appliance may be damaged due to an overload on the A home appliance, or even a fire may be caused by a short circuit. As a result, due to safety concerns, various tests cannot be performed on the actual analyte. Therefore, it is difficult to establish a power policy that requires various tests.

A test environment free from safety problems is required to perform a test to determine whether a pre-designed power policy is executed smoothly and to perform tests necessary for establishing various power policies.

The digital twin device of the present invention is to provide a test environment free from safety issues. The digital twin device of the present invention can build a test environment for analysis in a virtual twin environment. The actual analysis target can be simulated in a virtual twin environment.

The digital twin apparatus shown in FIG. 1 may include setting units 110, 130, and 150 and a generating unit 170.

In order to build the analysis target in the virtual environment, the setting unit may set power related parameters of the analysis target.

The generation unit 170 may generate power management data of the analysis target by using the parameter set in the setting unit.

According to the setting unit and the generating unit 170, an environment similar to the actual analysis target may be established in the program world, and a power safety situation that may occur in the reality may be simulated in the virtual environment.

For example, the setting unit may include a real parameter setting unit 110, a user parameter setting unit 130, and an event parameter setting unit 150.

Analysis targets of homes, factories, buildings, etc. may be provided with a consumption means such as a home appliance that consumes power, and a supply means for supplying power to the consumption means. In this case, the reality parameter setting unit 110 may set the reality parameter for the supply means. The power consumption means may include power consumption means for consuming power positively, such as home appliances, and power generation means for negatively consuming power, such as photovoltaic devices.

For example, the actual parameter setting unit 110 may supply the reference voltage (220V or 110V) of the supply means, the number of distribution board wiring lines, the wiring type, the relay reference value (breaking capacity), the circuit breaker capacity, the transformer impedance, and the distribution boards for each outlet. Actual parameters such as length, line impedance and wiring insulation resistance can be set. The reality parameters are main breaker, branch breaker # 1, relay # 1, outlet # 1, outlet # 3, branch breaker # 2, relay # 2, outlet # 3, branch breaker # 3, relay # 3, outlet # in FIG. 4, each of the outlet # 5 may include an impedance, a reference voltage, and a reference value. In addition, the reality parameter may include the line impedance between the bold points in FIG. 2.

The actual parameter may be provided from an analysis target or may be provided as a default value from a provider equipped with a supply means in the analysis target.

The user parameter setting unit 130 may set a user parameter based on the reality parameter. The user parameter setting unit 130 includes user parameters such as the type of power consumption means connected to the supply means, the power consumption of each power consumption means, the power factor, the location of the outlet connected to each power consumption means, and the wiring connection method of the power consumption means (single phase and three phase). Can be set. User parameters in Figure 2 LCD monitor, PC (force), fluorescent lamp # 1, fluorescent lamp # 2, lamp lamp # 1, air conditioner # 1, fluorescent lamp # 3, refrigerator # 1, oven # 1, refrigerator # 2, mixer # 1, the electronic lens # 1, LCD TV # 1, fluorescent lamp # 4, coffee machine # 1, LCD TV # 2, fluorescent lamp # 5, air conditioner # 2, power consumption, may be connected to the type of outlet.

Table 1 below shows an example of user parameters.

Appliance type Connection type Outlet connection location Power factor Power Consumption PC phase Outlet 2 0.98 500 kW air conditioner 3 phase Outlet 3 0.9 1000 kW

The event parameter setting unit 150 may set the event parameter based on the reality parameter and the user parameter. The event parameter setting unit 150 may set event parameters such as whether to use the consumption means for each set time, the probability of using each of the consumption means, the minimum usage time of the consumption means, the maximum usage time of the consumption means, and the number of times of the consumption means. .

The generation unit 170 may generate power management data of the analysis target using all of the real parameters, the user parameters, and the event parameters. The reality parameter and the user parameter are parameters that are related to the instrument characteristics, and the fluctuations of the event parameters are largely changed according to the user's life pattern and the user's condition.

The event parameter setting unit 150 may include a first setting unit 151 and a second setting unit 152 so that the event parameter converges to a usage pattern of the actual analysis target. The generation unit 170 may include a first generation unit 171 connected to the first setting unit 151 and a second generation unit 172 connected to the second setting unit 152.

The first generator 171 may generate power management data of a corresponding time t. For example, the first generator 171 may generate power management data such as voltage, current, harmonics, resistive leakage current, and capacitive leakage current at a specific time.

The second generator 172 may generate power management data of the corresponding analysis section t to t + n. For example, the second generator 172 may generate power management data such as voltage, current, harmonics, resistive leakage current, capacitive leakage current, etc., every second.

The first setting unit 151 may set an event parameter according to a deterministic method. In detail, the first setting unit 151 may determine whether to use the consumption means for the setting time. The first setting unit 151 may set whether or not each consumption means operates over time.

The first generation unit 171 may determine the event parameter of the set time zone and generate power management data with reference to the real parameter and the user parameter. The first generator 171 may generate power management data corresponding to a deterministically generated scenario.

For example, the first setting unit 151 sets an event parameter indicating that the air conditioner is in use at 9 am, and the first generating unit 171 uses a user parameter of the air conditioner and a reality parameter of a supply means connected to the air conditioner. You can generate power consumption at 9:00 am for analysis.

The second setting unit 152 may set an event parameter according to a stochastic method. In detail, the second setting unit 152 may generate an event occurrence scenario corresponding to an event parameter by probabilistically defining a usage pattern of an analysis target. The second setting unit 152 may generate and input decision elements input to the first setting unit 151 according to a specific pattern and probability. The second setting unit 152 may determine a turn on probability (use probability), a minimum / maximum use time, a minimum turn-on times per day, and the like, for each consumption unit.

The second generator 172 may generate power management data corresponding to a probabilistic generated scenario.

For example, the second setting unit 152 may set the air conditioner use probability to 10% between 9 AM and 9:05 AM. The second generation unit 172 simulates the use of the air conditioner with a 10% probability between 9 am and 9:05 am, and if the air conditioner is determined according to the probability, between the minimum use time and the maximum use time of the air conditioner. Optionally, the air conditioning time can be simulated.

When deterministic methods are applied to analytical objects, it is convenient to simulate specific means. On the other hand, there are too many event parameters that need to be determined for a long simulation. In addition, since the user's pattern may not be the same every day in reality, a probabilistic method of probabilistic modeling event parameters is preferably applied in order to reflect a realistic change in the power usage pattern in a long time simulation.

The first setter 151 and the first generator 171 may simulate a power safety situation in a virtual twin environment by specifying only a situation desired by a user. Therefore, the first setting unit 151 and the first generation unit 171 may be applied to short-term simulation of the analysis target.

The second setting unit 152 may probabilistic model the power usage environment, that is, the event parameter. The second generator 172 may simulate a power safety situation according to various probability combinations in a twin environment. Therefore, in the long-term simulation of the power safety policy, the application / enforcement result of the safety policy can be effectively simulated in various power situations generated by the probability combination.

The generation unit 170 may simulate the power usage pattern of the analysis target by using the parameter. The generation unit 170 may generate power management data according to an ideal pattern corresponding to the simulated power usage pattern.

3 is a graph illustrating an ideal pattern generated by the generation unit 170.

The ideal pattern of FIG. 3 illustrates a power usage pattern of an analysis target of a setting date, and may be generated by the second generation unit 172 by an event parameter set by the second setting unit 152.

The second generation unit 172 may generate power management data such as total power usage of the analysis target, power usage for each time zone, and type of consumption means used for each time zone, using the ideal pattern.

4 is a graph showing an operation probability model of the consumption means.

As illustrated in FIG. 4, the second setting unit 152 may set an operation probability of each time-consuming means, for example, the home appliance A. The second setting unit 152 may set the minimum usage time and the maximum usage time of each of the consumption means once the operation is started (turned on). The horizontal axis of FIG. 4 shows time, and the vertical axis shows the use probability of the household apparatus A. FIG. 10 on the vertical axis represents 100%.

The second generation unit 172 having obtained the operation probability, the minimum use time, and the maximum use time of the consumption means simulates whether each consumption means is turned on according to the operation probability, and uses each turned means turned on according to the operation probability. The time can be determined between the minimum and maximum usage time. The second generator 172 may generate the ideal pattern of FIG. 3 using the determined contents.

5 is a flowchart illustrating a digital twin method of the present invention. The digital twin method illustrated in FIG. 5 may be described as an operation of the digital twin device of FIG. 1.

First, the reality parameter may be set by the reality parameter setting unit 110 (S510). By setting the actual parameters, electrical characteristics such as various distribution boards, wirings, and outlets installed in the analysis target may be constructed in a virtual twin environment driven by a computer.

The user parameter may be set by the user parameter setting unit 130 (S520). By setting the user parameters, electrical characteristics of various home appliances, power generation devices, etc. installed by the user on the analysis target may be built in the virtual twin environment.

The event parameter may be set by the event parameter setting unit 150 (S530). By setting the event parameters, the presence or absence of hourly operation of home appliances, power generation appliances, and the like provided in the analysis target may be constructed in the virtual twin environment. The event parameter may be changed according to the power usage pattern of the user for the analysis target.

The power usage pattern of the analysis target is simulated by the generation unit 170 as shown in FIGS. 3 and 4, and the generation unit 170 may generate power management data using the power usage pattern.

The power management data generated by the generation unit 170 may be used to establish various power policies and safety policies.

The accuracy of various parameters must be ensured in order to obtain power management data that converges to the actual situation.

The default value provided from a business entity equipped with a supply means such as a distribution board and the like to be analyzed may be used as a real parameter. Default values provided from the manufacturer of the consumable means such as a home appliance can be used as the user parameter. However, the actual analysis target may show a difference from the default value due to the aging of the power device and the home appliance. As a result, the power management data generated by the generation unit 170 may exhibit a large difference from the power related measurement data of the actual analysis target.

Deep learning techniques, etc., can be applied so that the simulated power management data converges to the actual measurement data. The problem is that almost no data is required for learning. As a result, they don't know the correct answer, so learning about what's right is wrong.

The digital twin device of the present invention may include correction units 210, 230, 250 for converging the simulated power management data to the actual measurement data.

The calibration unit may inversely generate or correct various parameters by using the power management data or the power-related measurement data of the analysis target. The parameter may include a constant and a variable, wherein the calibration unit may inverse or correct the variable of the parameter such that the power management data converges to the power related measurement data of the analyte.

The generation unit 170 may generate power management data by using the parameters which are generated or corrected in the correction unit.

6 is a block diagram showing another digital twin device of the present invention.

The calibration unit may include an inverse generator 210 which inversely generates a parameter by using power-related measurement data of an analysis target. The inverse generator 210 may include an inverse function of the generation unit 170 that generates an ideal pattern.

The inverse generation unit 210 may determine the real pattern real corresponding to the actual power usage pattern of the analysis target using the power-related measurement data. The inverse generator 210 may inversely generate a parameter using the real pattern real.

According to the inverse generator 210, the analysis may be performed on various analysis targets, and in particular, a user parameter or an event parameter may be generated according to the reality. The reality parameter d, the user parameter u, and the event parameter e, which are inversely generated by the inverse generator 210, are transmitted to the reality parameter setting unit 110, the user parameter setting unit 130, and the event parameter setting unit 150, respectively. Can be. The generating unit 170 may simulate the ideal pattern using each of the inversely generated parameters.

7 is a block diagram showing another digital twin device of the present invention.

The correction unit includes a comparator 250 that compares the ideal pattern ideal output from the generation unit 170 with the actual real pattern real, and a compensator 250 that calculates a parameter correction value using the comparison result of the comparator 230. ) May be included.

The comparison unit 230 may calculate a difference value between the ideal pattern and the real pattern, and the correction unit 250 may calculate a correction value of the parameter applied to the ideal pattern such that the difference value is zero.

For example, the correction unit 250 may generate a correction value Δd of the actual parameter, a correction value Δu of the user parameter, and a correction value Δe of the event parameter.

The reality parameter setting unit 110 may correct the reality parameter using the correction value Δd of the reality parameter.

The user parameter setting unit 130 may correct the user parameter by using the correction value Δu of the user parameter.

The event parameter setting unit 150 may correct the event parameter by using the correction value Δe of the event parameter.

The generating unit 170 for generating the ideal pattern ideal may be feedback controlled by the correction unit 250. The feedback-controlled generation unit 170 may generate an ideal pattern ideal that converges to the real pattern real, and generate accurate power management data that converges to the actual situation using the ideal pattern ideal.

Although embodiments according to the present invention have been described above, these are merely exemplary, and it will be understood by those skilled in the art that various modifications and equivalent embodiments of the present invention are possible therefrom. Therefore, the true technical protection scope of the present invention will be defined by the following claims.

110 ... Real parameter setting section 130 ... User parameter setting section
150 ... Event parameter setting section 151 ... First setting section
152 ... Second setting part 170 ... Generation unit
171 ... first generator 172 ... second generator
210 ... Inverse Generation 230 ... Comparison
250 ... Government

Claims (5)

A setting unit for setting a power related parameter of an analysis target;
A generating unit for generating power management data of the analysis target using the parameter;
A correction unit for inversely generating or correcting the parameter by using the power management data or the power-related measurement data of the analysis target;
Including,
The setting unit sets a reality parameter associated with a supply means installed in the analysis target, a user parameter associated with a consumption means installed in the analysis target, an event parameter associated with a usage pattern of the consumption means,
The setting unit includes a first setting unit and a second setting unit,
The first setting unit determines whether or not to use the consumption means for a predetermined time,
The second setting unit sets a usage pattern of the consumption means by using an operation probability for each time zone, a minimum use time and a maximum use time,
The generating unit includes a first generating unit and a second generating unit,
The first generation unit determines an event parameter of a set time zone and generates the power management data using the reality parameter and the user parameter.
The second generator simulates whether the consumption means is turned on according to the operation probability, and determines a use time of the consumption means turned on according to the operation probability between the minimum use time and the maximum use time, The power management data is generated using the usage time;
The generating unit generates the power management data by using the parameters which are inversely generated or corrected in the correction unit,
The generating unit simulates an ideal pattern using the inversely generated parameter,
The correction unit is provided with an inverse generation unit for inversely generating the parameter by using the power-related measurement data of the analysis target,
The inverse generation unit detects a real pattern corresponding to the actual power usage pattern of the analysis target by using the measured data, and inversely generates the parameter by using the real pattern,
The correction unit includes a comparator for comparing the ideal pattern output from the generation unit with an actual real pattern, and a compensator for calculating a correction value of the parameter using a comparison result of the comparator,
The ideal pattern includes a power usage pattern of the analysis target simulated in the generation unit,
The real pattern is a digital twin device including the actual power usage pattern of the analysis target identified using the power-related measurement data of the analysis target. delete delete delete delete

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