Method and system for determining a characteristic variable

Abstract

The invention describes and claims a method for determining a characteristic quantity for depicting a thermal load of an electrically operated device (9, 11, 19, 21) for a determination point in time by means of a first mathematical model based on a determined value of an ambient temperature of the electrically operated device (9, 11, 19, 21) at the determination point in time and a determined value of a thermal operating parameter of the operating device at the determination point in time, the first mathematical model describing a correlation of a thermal operating parameter of the operating device at a calculation point in time with an ambient temperature of the electrically operated device (9, 11, 19, 21) at the calculation point in time and an electrical load of the operating device (9, 11, 19, 21) at the calculation point in time. The characteristic quantity for the determination point in time is determined as an equivalent electrical load of the electrically operated device (9, 11, 19, 21) at which the first mathematical model predicts a determined value of a thermal operating parameter of the electrically operated device for the determination point in time taking into account the ambient temperature at the determination point in time. Furthermore, a method for network security calculation and a method for operating management of an electrically operated device and corresponding systems are described and claimed.

Description

Technical Field

[0001] This invention relates to a method for determining characteristic parameters that enable the depiction of the thermal load of electrically operated devices at specific points in time. Furthermore, this invention relates to a method for performing network security calculations in a power grid comprising multiple electrically operated devices, a method for operating management of electrically operated devices, a system for determining characteristic parameters, a system for performing network security calculations in a power grid comprising multiple electrically operated devices, and a system for operating management of electrically operated devices. Background Technology

[0002] The reliability, load capacity, and overload capacity of electrical operating devices in a power grid depend critically on the history of these devices and the temperatures they have been subjected to. For example, the aging of solid insulation and the dielectric strength of transformer insulation are largely dependent on the operating temperature of the transformer. Therefore, to ensure reliable operation of electrical operating devices, it is necessary to determine their critical temperatures and, consequently, their thermal load or thermal state.

[0003] For example, the thermal state of the power transformer is described by the top oil temperature or hot spot temperature. If the temperature exceeds a certain limit over a given period of time, the electrical operating components will age too quickly, which may compromise network security or stability.

[0004] Relevant temperatures, such as top oil temperature, can be continuously measured. Alternatively, dynamic models can be used to predict temperatures from other measurements. For example, the hot spot temperature of the transformer can be predicted based on the measured top oil temperature, the measured load on the transformer, and the ambient temperature of the transformer.

[0005] To ensure that the temperature does not exceed the limit during operation of the electrical operating device, a maximum load is assumed when planning the load on the electrical operating device. Under this maximum load, the temperature does not exceed the limit in an assumed equilibrium state. If the maximum load is not exceeded during the operation and management of the electrical operating device, it will not lead to exceeding the critical temperature limit and thus will not cause premature aging of the electrical operating device. The maximum permissible load is also considered when calculating network security, for example, by using the difference between the actual load of the electrical operating device and the maximum permissible load of the operating device as a reserve load, which can be used, for example, in case of undesirable high demand or failure of other electrical operating devices.

[0006] Against this backdrop, the consideration of the thermal state of electrical operating devices is simplified in the control of the power grid, network security calculations, and the control of the operation of electrical components. Summary of the Invention

[0007] The objective of this invention is achieved by the method and system according to the invention. Preferred embodiments are the subject of the dependent claims.

[0008] In a first aspect, the problem upon which the present invention is based is solved by a method for determining characteristic parameters used to depict the thermal load of an electrical operating device at a given time point using a first mathematical model based on the ambient temperature of the operating device at that time point and the calculated values ​​of the thermal operating parameters of the operating device at that time point. The first mathematical model describes the correlation between the thermal operating parameters of the operating device at the calculated time point and the ambient temperature of the electrical operating device at that time point, as well as the electrical load of the operating device at that time point. The characteristic parameters at the given time point are determined as the equivalent electrical load of the electrical operating device, under which the first mathematical model predicts the calculated values ​​of the thermal operating parameters of the electrical operating device at the given time point, taking into account the ambient temperature at that time point.

[0009] In other words, within the scope of this method, characteristic parameters are determined that depict the thermal load of the operating device at a given point in time, and these characteristic parameters can be intuitively used, for example, in network security calculations or in the operational management of the operating device and can be easily integrated into existing processes. Examples of electrical operating devices in a current network include transformers, overhead lines, grounding wires, and power switches.

[0010] To this end, the values ​​of thermal operating parameters for the determined time point are first determined, such as the hot spot temperature or top oil temperature of the transformer. The different possibilities for determining the thermal operating parameters are the subject of preferred embodiments, and the applicability of these parameters depends particularly on the selected time point for which characteristic parameters should be determined. Furthermore, the ambient temperature of the electrical operating device at the determined time point is determined. For example, the ambient temperature can be measured or predicted data can be obtained. Alternatively, a predetermined value of the ambient temperature at the determined time point can also be used. For example, the ambient temperature can be set at the average maximum temperature at the installation location of the operating device at the determined time point. Here, it is particularly preferred that the ambient temperature for the determined time point is set as the value used to calculate the nominal current of the electrical operating device. The values ​​of the thermal operating parameters, the ambient temperature at the determined time point, and, if necessary, additional input parameters form the basis for determining the characteristic parameters by means of a first mathematical model.

[0011] This mathematical model describes the thermal operating parameters, such as the hot spot temperature of a transformer, at a given time point, based on the ambient temperature and electrical load of the electrically operated components at that time point. Additional input parameters can be used if necessary. Therefore, the first mathematical model is a static model, by which additional operating parameters can typically be calculated from multiple input operating parameters of the electrically operated components (here: ambient temperature and electrical load), without considering dynamic effects such as changing ambient temperature or load. Thus, the first mathematical model represents a balanced state. An example of a suitable mathematical model that can be used as the first mathematical model for a transformer is the mathematical model of IEC 60076-7, which describes the hot spot temperature and top oil temperature of a transformer in equilibrium.

[0012] The electrical load of the operating device is determined using a first mathematical model, which predicts previously determined thermal operating parameters while taking into account the known ambient temperature. Therefore, missing input parameters are derived from the known input and output parameters. The electrical load thus determined (under which the first mathematical model predicts the previously determined electrical operating parameters, at least considering the ambient temperature, regardless of the actual electrical load of the electrical operating device) is called the equivalent electrical load.

[0013] Characteristic parameters or equivalent electrical loads can be used advantageously, for example, in controlling electrically operating devices or in network security calculations at specific points in time, instead of actual electrical loads. The equivalent electrical load depicts specific values ​​of thermal operating parameters and ambient temperature. Therefore, it is advantageously considered that, for example, at lower ambient temperatures, higher loads on electrically operating devices are possible without exceeding temperature limits.

[0014] This also differs from traditional calculations that consider dynamic effects by taking into account characteristic parameters. Therefore, the electrical operating device reacts slowly after each change in ambient temperature or load, and does not transition directly from one steady state to another. Thus, in the illustrative change of the operating device's load, the thermal operating parameters do not change abruptly from one value to another, but rather continuously and with a delay. For example, as the load increases, the thermal operating parameters only follow slowly, allowing, for example, the operating device to be utilized significantly more intensely for a short period without exceeding the critical temperature.

[0015] In a preferred embodiment, the thermal operating parameters of the electrical operating device at a predetermined time point are obtained using a second mathematical model. This second mathematical model describes the correlation between the thermal operating parameters of the electrical operating device and the change curves of the ambient temperature, the electrical load, and the initial values ​​of the thermal operating parameters. Here, the values ​​of the thermal operating parameters at the initial time point are used as the initial values. Preferably, the values ​​of the thermal operating parameters at the initial time point are measured at the electrical operating device. The change curve of the ambient temperature describes the change curve of the ambient temperature of the operating device between the initial time point and the predetermined time point, and the change curve of the electrical load describes the change curve of the electrical load between the initial time point and the predetermined time point.

[0016] In other words, in a preferred embodiment, thermal operating parameters are calculated using a second mathematical model, which, unlike the first mathematical model, does not describe a steady state. Instead, the second mathematical model starts from a known starting point in time, taking into account the changes in ambient temperature and the load on the operating devices, for example, by direct measurement or by calculation from other directly measured values. An example that can be used as a second mathematical model for transformers is the dynamic model of IEC 60076-7, which allows for the calculation of the transformer's hot spot temperature and top oil temperature.

[0017] Therefore, it is advantageous to calculate characteristic parameters for future time points from a single measurement of thermal operating parameters. However, it is also conceivable to determine the characteristic parameters for a specific time point in the past relative to the starting time point, so as to be able to estimate, for example, the load that, according to the stability model, causes overload of the electrical operating devices and thus premature aging of the electrical operating devices, which also effectively leads to overload.

[0018] Here, it is further preferred that, within the scope of the method, the variation curve of the characteristic parameter is determined for an evaluation period including multiple evaluation time points in the following manner: for each evaluation time point as a determination time point, the value of the thermal operating parameter for the electrical operating device is obtained by means of the second mathematical model, and based on the value of the thermal operating parameter for the electrical operating device thus obtained, the characteristic parameter for the corresponding evaluation time point as the determination time point is determined by means of the first mathematical model.

[0019] Furthermore, preferably, the variation curve of the characteristic parameter is determined as the expected future variation curve of the characteristic parameter over the evaluation time period, starting from the current time point as the starting time point. Here, the variation curve of the ambient temperature is a prediction of the variation curve of the ambient temperature of the operating device over the evaluation time period, starting from the starting time point, and the variation curve of the electrical load of the operating device is a prediction of the variation curve of the electrical load of the operating device over the evaluation time period, starting from the starting time point.

[0020] Therefore, in the preferred embodiment, the characteristic parameters are determined not only for a single specific point in time, but also for the evaluation period. This is particularly advantageous when calculating future network security or when planning the operation of devices over a future period, because equivalent thermal loads can be considered over the entire time period, and thus dynamic effects can be taken into account in conventional systems.

[0021] In another preferred embodiment of the method, the obtained value of the thermal operating parameter of the operating device is measured at the operating device or calculated from one or more measured operating parameters and / or the measured ambient temperature of the operating device and / or the measured load of the operating device. In other words, in a preferred embodiment, the obtained value of the thermal operating parameter can be directly measured. Alternatively, the value can be calculated using other measurements. Examples from which the measured value of the thermal operating parameter can be obtained are the ambient temperature of the operating device, other measured thermal operating parameters, and measured load.

[0022] Preferably, for a given time point, the equivalent electrical load of the electrical operating device is determined as a characteristic parameter for describing the thermal load of the electrical operating device, wherein the difference between the thermal operating parameter determined for the given time point by means of the first mathematical model and the thermal operating parameter of the electrical operating device at the given time point is less than a predetermined limit value, wherein, in order to reduce the difference, an optimization algorithm is used, and preferably a gradient-based optimization algorithm, such as Newton's method, is used.

[0023] In other words, in the first mathematical model that cannot be analytically solved for electrical load, the equivalent thermal load is determined by means of an iterative approximation method.

[0024] Furthermore, preferably, the limiting characteristic parameter of the electrical operating device at a given time point is determined as an electrical load, and for this electrical load, the first mathematical model predicts the value of the thermal operating parameter of the electrical operating device, taking into account the ambient temperature of the electrical operating device at the given time point. This value corresponds to a limiting value of the thermal operating parameter, which is not allowed to be violated. Preferably, this limiting value depends on the operating mode of the electrical operating device.

[0025] In a preferred embodiment, in addition to the equivalent thermal load, limiting characteristic parameters are additionally determined by calculating, using a first mathematical model, which load, according to the first mathematical model, would cause the allowable limit value of the thermal operating parameters to be reached at the ambient temperature present at a given time point. In this way, the operation and management of the electrically operated device can take into account that lower ambient temperatures allow for higher loads on the operating device until the thermal operating parameters exceed the limit value.

[0026] More preferably, the difference between the limiting characteristic parameter determined for a given time point and the characteristic parameter determined for the given time point is calculated as a reserve characteristic parameter. The reserve characteristic parameter can be advantageously considered in network security calculations because it indicates what additional loads the operating device can accept at the corresponding given time point without thermal operating parameters exceeding the limits and without accelerated aging of the operating device.

[0027] In a preferred embodiment, a different mathematical model is used as a second mathematical model based on the electrical load variation curve of the electrically operated device. Therefore, it is advantageous to use different mathematical models for different load variation curves, as different models can be particularly suitable for describing the development of thermal operating parameters based on the load variation curve.

[0028] In another aspect, the problem upon which the present invention is based is solved by a method for performing network security calculations in a power grid comprising a plurality of electrical operating devices based on expected load variation curves during the plurality of electrical operations, wherein, for at least one of the plurality of electrical operating devices, the expected variation curve of the characteristic parameters of the at least one electrical operating device determined by means of a corresponding embodiment of the method described above is used as the expected load variation curve of the electrical operating device.

[0029] The advantages of the method used for network security calculations correspond to the advantages of the method used herein for determining the characteristic parameters of at least one electrically operated device.

[0030] In another aspect, the problem upon which the present invention is based is solved by a method for the operation management of an electrically operated device, wherein the electrical load of the electrically operated device is adapted at a given time point by taking into account characteristic parameters determined by means of a corresponding implementation of the previously described method for a given time point.

[0031] The advantages of the method for the operation management of electrically operated devices correspond to the advantages of the method used herein for determining the characteristic parameters of at least one electrically operated device.

[0032] In another aspect, the problem upon which the present invention is based is solved by a system for determining characteristic parameters for depicting the thermal load of an electrically operated device at a defined time point, wherein the system includes a data processing device configured to implement a corresponding method according to any of the foregoing embodiments.

[0033] In another aspect, the problem upon which the present invention is based is solved by a system for performing network security calculations in a power grid comprising multiple electrically operated devices, wherein the system includes a data processing device configured to implement a corresponding method according to any of the foregoing embodiments.

[0034] In another aspect, the problem on which the present invention is based is solved by a system for the operation management of electrically operated devices, wherein the system includes a data processing unit configured to implement the corresponding method according to any of the foregoing embodiments.

[0035] The advantages of the various implementations of the system correspond to the advantages of the methods in which the system's data processing unit is configured. Furthermore, the design schemes of the methods shown within the scope of the method description can also be applied to corresponding systems. Attached Figure Description

[0036] Several embodiments of the method for determining characteristic parameters and the system in which the corresponding method can be implemented are described in more detail below with reference to the accompanying drawings. Here, examples are shown...

[0037] Figure 1 A schematic diagram illustrating an embodiment of a portion of a power grid having multiple electrically operated devices is shown.

[0038] Figure 2 A schematic diagram illustrating an embodiment of a data processing apparatus, and

[0039] Figure 3 A schematic diagram illustrating an embodiment of a method for determining characteristic parameters for characterizing the thermal load of an electrically operated device. Detailed Implementation

[0040] Figure 1 First, an embodiment of a power grid or current network in the form of a substation 3 and multiple generators 13 is shown. The substation 3 includes a power input terminal 5, multiple output terminals 7, and multiple electrical operating devices or components 9. Figure 1 Only three output terminals 7 and only three electrical components 9 are shown in the diagram, but the substation 3 may include more or fewer output terminals and electrical operating devices.

[0041] The power input terminal 5 is connected to three generators 13 via an overhead line 11, which is also an electrically operated device. These generators may be, for example, wind power equipment. The substation 3 is connected to the output terminal 7 via... Figure 1 Additional lines (not shown) are connected to loads or appliances (also not shown). Grid 1 may include additional substations, additional generators, and additional appliances.

[0042] In this embodiment, the electrical operating device 9 of the current network 1 is a transformer 14. A monitoring unit 15 is arranged on each transformer 14, which acquires and manages the operating parameters and characteristic numbers of the transformer 14. For example, the monitoring unit 15 acquires the top oil temperature of the transformer 14 using a sensor (not shown) as a thermal operating parameter, and determines the hot spot temperature in the windings of the transformer 14's coils from this top oil temperature using a suitable mathematical model and other operating parameters and characteristic numbers of the transformer 14. The monitoring unit 15 also detects the current flowing through the transformer 14. Furthermore, the monitoring unit 15 retains time-invariant characteristic data, such as the transformer's nominal power and year of manufacture, as well as historical data, such as DGA analysis, startup test, and visual inspection results.

[0043] For each electrically operated device 9, a limit value is defined, which must not be exceeded during operation. These limit values ​​can be stored in the monitoring unit. For example, limit values ​​for top oil temperature and hot spot temperature can be stored.

[0044] Substation 3 also includes multiple power switches 19 and circuit breakers 21, collectively referred to as switches 19 and 21, which together form switching device 17. Switches 19 and 21 are also electrically operated devices.

[0045] Finally, substation 3 and therefore current network 1 includes a central data processing unit, data processing unit, or data processing device 23, which is connected to monitoring unit 15 and also to control unit 25 of substation 3, which specifically controls the positions of switches 19 and 21. Furthermore, the central data processing device 23 is connected to an external data source (not shown), which will be discussed in more detail below. For clarity, the connection of data processing unit 23 is shown in the diagram. Figure 1 Not shown in the image.

[0046] Figure 2 The structure of an exemplary data processing device 23 is schematically shown, as in Figure 1 As shown in the diagram. The data processing unit includes a central computing unit or CPU 27, a communication unit 29, and a memory 31. Furthermore, Figure 2A user interface 33, such as a screen and one or more input devices, is shown. Through this user interface, a user can interact with the central data processing unit 23 and display calculation results to the user. The user interface 33 does not necessarily have to be part of the central data processing unit 23. The central data processing unit 23 can communicate with the control unit 25, the monitoring unit 15, and other data sources (not shown) via the communication unit 29, that is, to receive and send data.

[0047] at last, Figure 3 A flowchart illustrating an embodiment of the method is provided, which uses this method to determine characteristic numbers for electrical operating devices 9, 11, 19, and 21, which describe the thermal load of the operating devices 9, 11, 19, and 21. In the illustrated embodiment, operating device 9 is transformer 14.

[0048] exist Figure 3 The method shown is designed to determine the number of features over a time period, such as 24 or 48 hours. This time period is referred to as the evaluation or prediction period, over which the variation curves of the feature parameters are obtained. The evaluation period typically includes multiple evaluation time points, which may be evenly distributed across the evaluation period in 15-minute intervals, for example. However, this method can also be used to determine the number of features at a single time point, for example, by limiting the evaluation period to that time point.

[0049] As illustrated by the two trapezoids 35, the method steps shown below are performed for all evaluation time points within the prediction period. The method steps can be repeated for each evaluation time point, i.e., performed sequentially. However, some or all of the method steps can also be performed in parallel with each other. The specific order of the steps, and whether a single step is repeated for each evaluation time point, performed in parallel for all evaluation time points, or, if necessary, performed only once for all evaluation time points, is known to those skilled in the art by their general professional knowledge.

[0050] The execution of the method will now be described as if all method steps were repeated for each evaluation time point. The current evaluation time point in which the method is being executed is referred to below as the determination time point.

[0051] In the current embodiment, the ambient temperature of transformer 14 is first determined in the first method step 37. When the determined time point is the current time, the ambient temperature can be measured by means of a temperature sensor in ambient temperature measurement 39. Alternatively, the ambient temperature can also be read from a database, where ambient temperature data is maintained (second step 41). This may be particularly necessary when the determined time point is in the future or the past.

[0052] For example, forecast data or weather reports provided by external service providers can be obtained from ambient temperatures for the future, as well as current temperatures. Ambient temperatures at specific points in the past can be obtained, for example, from a database populated with its own measurements or also provided by external service providers.

[0053] The ambient temperature can also be provided to this method through a monitoring unit 15 arranged on the transformer 14.

[0054] In the third step 43, the thermal operating parameters of transformer 14 at a given time point are determined or obtained. In the current embodiment, the thermal operating parameters are the transformer top oil temperature or hot spot temperature. The thermal operating parameters can be measured, for example, in operating parameter measurement 45, especially when the given time point is the current time point and operating parameters, such as the top oil temperature, can be directly measured. If the hot spot temperature is used as the thermal operating parameter that can only be directly measured, the thermal operating parameters can also alternatively be derived from other measured operating parameters. The hot spot temperature can be calculated, for example, based on the ambient temperature, the measured top oil temperature, and other measurements such as the load on transformer 14.

[0055] Alternatively, the thermal operating parameters can also be calculated using a mathematical model in operating parameter calculation 47. This is particularly relevant for a specific future point in time, meaning that the values ​​of the thermal operating parameters can be calculated using a second mathematical model. The second mathematical model dynamically describes the development of the thermal operating parameters from a known starting point in time, taking into account the development of the ambient temperature and load of the transformer 14. Different second mathematical models can be used based on the load variation curve of the transformer 14. For example, a second mathematical model can particularly well describe sudden load changes, and another second mathematical model can describe uniform load changes over long time periods.

[0056] If the time point is determined to be in the past, the thermal operating parameters can also be calculated using operating parameter calculation 47 or extracted from the database. Finally, the thermal operating parameters can also be determined by the monitoring unit 15 located on the transformer 14 providing the thermal operating parameters when querying this method.

[0057] In step 49, the electrical loads of the electrical operating devices 9, 11, 19, and 21 required for the operating parameter calculation 47 are determined. If the current electrical load is required, it can be determined in load measurement 51. Otherwise, the electrical load can be obtained from a database query 53, either by querying recorded measurements at a specific point in the past or by querying predicted electrical loads for the future. In particular, predicted measurements can also be queried from an external data source.

[0058] The electrical load can also be provided to the method by a monitoring unit 15 arranged on the transformer 14, which performs the steps described above.

[0059] Finally, in step 55, characteristic numbers of the thermal load are determined from the ambient temperature obtained in step 37 and the thermal operating parameters obtained in step 3, as well as the hot spot temperature or top oil temperature of transformer 14. To this end, an equivalent electrical load is determined using a first mathematical model (which describes the correlation between the thermal operating parameters and ambient temperature and electrical load in equilibrium, i.e., in a steady state), in which the first mathematical model predicts the previously determined thermal operating parameters in relation to the ambient temperature.

[0060] To determine the equivalent electrical load, optimization algorithms, such as Newton's method, can be used, where solutions to the mathematical model are iteratively sought. As with all the preceding steps, this process can also be performed in monitoring unit 15.

[0061] In the sixth step 57, an equivalent electrical limit load is determined as a limit characteristic parameter based on the ambient temperature obtained in the first step 37 and one or more limit values ​​known for the thermal operating parameters, wherein the electrical load is determined based on the ambient temperature obtained at a determined time point, and the first mathematical model used for the thermal operating parameters is the predicted limit value of the electrical load.

[0062] Finally, in the final seventh step 59, the difference between the limiting characteristic parameter and the characteristic parameter is determined as the reserve characteristic parameter. This difference indicates, at a given time point, the extent to which the electrical load of transformer 14 can be increased without overloading transformer 14.

[0063] If the above steps are performed over multiple consecutive defined time points, i.e., over a longer evaluation period, then the determined characteristic parameters, limiting characteristic parameters, and reserve characteristic parameters can be summarized as variation curves, which are particularly helpful when evaluating past events or for planning future control of power grid 1 and corresponding electrical auxiliary devices. The method steps described so far are used in this context within the scope of methods for controlling electrically operated devices or methods for network security calculations, wherein, preferably, the calculations are performed for multiple electrically operated devices.

[0064] As described above, the method for calculating the feature parameters can be executed entirely on the monitoring unit 15, which in this case is equipped with a processor or data processing device and forms a system for determining the feature number. Alternatively, distributed computation of the feature number is also conceivable, wherein a portion of the method steps are executed by the monitoring unit 15 and another portion by a central data processing device 23, which may also be part of a cloud.

[0065] The same applies to methods and systems for network security computing and for the operation management of electrically operated devices, wherein the corresponding data processing devices can also be formed through monitoring units, central data processing units, or the cloud or a hybrid thereof.

[0066] Exemplary implementations of the methods and systems enable the intuitive consideration of the thermal load on the electrical operating devices and, in particular, allow the new methods to be easily integrated into existing systems that only consider the electrical load of the electrical operating devices without having to modify the devices themselves.

[0067] List of reference numerals

[0068] 1. Current network, power grid

[0069] 3 substations

[0070] 5 Power input terminals

[0071] 7 Output Terminals

[0072] 9 Electrical operating devices and components

[0073] 11 overhead lines and electrical operating devices

[0074] 13 generator

[0075] 14 Transformers

[0076] 15 monitoring units

[0077] 17 Switching Device

[0078] 19 Power switches and electrical operating devices

[0079] 21 Circuit breakers and electrical operating devices

[0080] 23. Central data processing devices, components, and units

[0081] Control Department of Substation 25

[0082] 27 CPUs

[0083] 29 communication units

[0084] 31 memory

[0085] 33 User Interface

[0086] 35 trapezoid

[0087] 37 First Step

[0088] 39 Ambient Temperature Measurement

[0089] 41 Second step

[0090] 43 Third step

[0091] 45. Measurement of operating parameters

[0092] 47. Calculation of Operating Parameters

[0093] 49. Fourth step

[0094] 51 Load Measurement

[0095] 53 Database Queries

[0096] 55. Fifth step

[0097] 57. Step Six

[0098] 59 Seventh Step

Claims

1. A method for determining characteristic parameters, said characteristic parameters being used to depict the thermal load of electrical operating devices (9, 11, 19, 21) at a predetermined time point using a first mathematical model based on the ambient temperature of the electrical operating devices (9, 11, 19, 21) at that predetermined time point and the calculated values ​​of the thermal operating parameters of the electrical operating devices (9, 11, 19, 21) at that predetermined time point, the first mathematical model describing the correlation between the thermal operating parameters of the electrical operating devices at a predetermined time point and the ambient temperature of the electrical operating devices (9, 11, 19, 21) at that predetermined time point and the electrical load of the electrical operating devices (9, 11, 19, 21) at that predetermined time point. in, The characteristic parameters at the specified time point are determined as the equivalent electrical load of the electrical operating devices (9, 11, 19, 21). Under this equivalent electrical load, the first mathematical model predicts the values ​​of the thermal operating parameters of the electrical operating devices for the specified time point, taking into account the ambient temperature at that time point. Specifically, the thermal operating parameters of the electrical operating devices (9, 11, 19, 21) are obtained at the specified time points using a second mathematical model. This second mathematical model describes the correlation between the thermal operating parameters of the electrical operating devices (9, 11, 19, 21) and the changes in their ambient temperature, electrical load, and the initial values. The initial values ​​for the thermal operating parameters are those used at the starting time point, where the values ​​are measured at the electrical operating devices (9, 11, 19, 21). The ambient temperature variation curve describes the change in ambient temperature of the electrically operated devices (9, 11, 19, 21) between the initial time point and a predetermined time point, and... The electrical load variation curves of the operating devices (9, 11, 19, 21) describe the variation curves of the electrical load of the electrical operating devices (9, 11, 19, 21) between the initial time point and a determined time point. Specifically, for the evaluation period including multiple evaluation time points, the method for determining the change curve of characteristic parameters is as follows: for each evaluation time point that is a determination time point, the value of the thermal operation parameter for the electrical operating device (9, 11, 19, 21) is obtained by means of the second mathematical model, and based on the value of the thermal operation parameter for the electrical operating device (9, 11, 19, 21) obtained in this way, the characteristic parameter for the corresponding evaluation time point is determined by means of the first mathematical model.

2. The method according to claim 1, wherein, The values ​​of the thermal operating parameters at the starting time point were measured at the electrical operating devices (9, 11, 19, 21).

3. The method according to claim 1, wherein, The change curve of the feature parameter is determined as the expected future change curve of the feature parameter over the evaluation time period, starting from the current time point which serves as the starting time point. The ambient temperature change curve is a prediction of the ambient temperature change curve of the electrical operating devices (9, 11, 19, 21) during the evaluation period, starting from the initial time point. The electrical load variation curve of the electrical operating devices (9, 11, 19, 21) is used as a prediction of the electrical load variation curve of the electrical operating devices (9, 11, 19, 21) during the evaluation time period from the starting time point.

4. The method according to claim 1, wherein, The values ​​of the thermal operating parameters of the electrical operating devices (9, 11, 19, 21) are obtained by measuring them at the electrical operating devices (9, 11, 19, 21), or The thermal operating parameters of the electrical operating devices (9, 11, 19, 21) are obtained by calculating one or more measured operating parameters and / or the measured ambient temperature of the electrical operating devices (9, 11, 19, 21) and / or the measured load of the electrical operating devices (9, 11, 19, 21).

5. The method according to any one of claims 1 to 4, wherein, For a given time point, the equivalent electrical load of the electrical operating devices (9, 11, 19, 21) is determined as a characteristic parameter for describing the thermal load of the electrical operating devices (9, 11, 19, 21). Under this equivalent electrical load, the difference between the thermal operating parameters determined for the given time point by means of the first mathematical model and the thermal operating parameters of the electrical operating devices (9, 11, 19, 21) at the given time point is less than a predetermined limit value. An optimization algorithm is used to reduce this difference.

6. The method according to claim 5, wherein, To reduce the difference, an optimization algorithm is used, specifically a gradient-based optimization algorithm.

7. The method according to claim 6, wherein, The gradient-based optimization algorithm is Newton's method.

8. The method according to any one of claims 1 to 4, wherein, For a given time point, the limiting characteristic parameters of the electrical operating devices (9, 11, 19, 21) are determined as the following electrical load: for the electrical load, the first mathematical model predicts the value of the thermal operating parameter of the electrical operating devices (9, 11, 19, 21) taking into account the ambient temperature of the electrical operating devices at the given time point. The value corresponds to the limiting value of the thermal operating parameter, and the operating parameter is not allowed to violate the limiting value.

9. The method according to claim 8, wherein, The limit value is related to the operating mode of the electrical operating devices (9, 11, 19, 21).

10. The method according to claim 5, wherein, As a reserve characteristic parameter, the difference between the limiting characteristic parameter determined for a given time point and the characteristic parameter determined for the given time point is calculated.

11. The method according to any one of claims 1 to 4, wherein, As a second mathematical model, different mathematical models are used based on the electrical load variation curves of the electrical operating devices (9, 11, 19, 21).

12. A method for performing network security calculations in a power grid comprising a plurality of electrical operating devices (9, 11, 19, 21) based on expected load variation curves of the plurality of electrical operating devices (9, 11, 19, 21), wherein, For at least one of the plurality of electrical operating devices (9, 11, 19, 21), the expected change curve of the characteristic parameters of the at least one electrical operating device (9, 11, 19, 21) determined according to claim 3 or according to any one of claims 5 to 10 of claim 3 is used as the expected change curve of the load of the electrical operating device (9, 11, 19, 21).

13. A method for operation management of electrically operated devices (9, 11, 19, 21), wherein, Taking into account the characteristic parameters determined by the method according to any one of claims 1 to 11 for a given time point, the electrical load of the electrical operating device (9, 11, 19, 21) at the given time point is adapted.

14. A system for determining characteristic parameters for describing the thermal load of electrically operated devices (9, 11, 19, 21) at a defined time point, wherein, The system includes a data processing device (23) configured to implement the method according to any one of claims 1 to 11.

15. A system for performing network security calculations in a power grid comprising multiple electrically operated devices (9, 11, 19, 21), wherein, The system includes a data processing device (23) configured to implement the method according to claim 12.

16. A system for operation management of electrically operated devices (9, 11, 19, 21), wherein, The system includes a data processing unit (23) configured to implement the method according to claim 13.

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