Information processing device, information processing method, and program

Abstract

To provide an information processing device capable of increasing objectivity in estimation of an event occurrence time.SOLUTION: An information processing device estimates an event occurrence time based on measurement value information. The measurement value information indicates a measurement value other than at least a frequency among values according to power at a predetermined point in an AC interconnected grid including one or more generators. The event occurrence time is when an event causing a change in a frequency of power according to the measurement value has occurred.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Research and development is being conducted on technology to estimate the system inertia of an AC interconnected system. An AC interconnected system is an AC power system that includes one or more generators and generates electricity synchronously using these one or more generators. The system inertia of an AC interconnected system is the sum of the inertia moments of the rotating bodies (i.e., generator rotors, turbines, etc.) of one or more generators included in the AC interconnected system.

[0003] Here, in an AC interconnection system, the rotors of one or more generators included in the AC interconnection system all rotate synchronously to generate one system frequency. Therefore, when the system inertia of the AC interconnection system changes due to a generator being disconnected in the AC interconnection system, the system frequency fluctuates. The fluctuation of the system frequency may affect the quality of power supply by the AC interconnection system and the stability of the synchronization between the generators. Therefore, from the viewpoint of a stable supply of power by the AC interconnection system, the system inertia of the AC interconnection system is required to always be equal to or greater than a certain value. For these reasons, it is often desired to accurately estimate the system inertia of the AC interconnection system.

[0004] The RoCoF (Rate of Change of Frequency) method is known as a method for estimating the system inertia of an AC interconnected system. The RoCoF method estimates the RoCoF (i.e., the rate of change of the system frequency) for the system frequency when the system frequency fluctuates, and estimates the system inertia of the AC interconnected system based on the estimated RoCoF.

[0005] Here, the kinetic energy of the rotor of each of the one or more generators included in the AC interconnection system can be regarded as the moment of inertia of the rotor of each of the one or more generators included in the AC interconnection system. This is because the kinetic energy of the rotor of each of the one or more generators included in the AC interconnection system can be obtained by multiplying the moment of inertia of the rotor by the square of the angular velocity of the rotor (i.e., the square of the frequency of the rotor). Therefore, in the AC interconnection system, the system inertia of the AC interconnection system can be calculated as the sum of the kinetic energy of the rotor of each of the one or more generators included in the AC interconnection system. Then, such a sum of the kinetic energy can be related to the RoCoF for the system frequency by an oscillation equation obtained by time-differentiating a formula showing the sum of the kinetic energy. The RoCoF method is a method of using this oscillation equation to obtain the system inertia of the AC interconnection system (more precisely, the sum of the kinetic energy that can be regarded as the system inertia) based on the RoCoF. In this specification, since the RoCoF method is known, further detailed description of the RoCoF method is omitted.

[0006] In an AC interconnected system, the system frequency is usually controlled to always keep a value within a certain range while fluctuating slightly over time. Therefore, in order to obtain the RoCoF used to estimate the system inertia of the AC interconnected system by the RoCoF method, the system frequency needs to change significantly to deviate from the range. Therefore, in estimating the system inertia of the AC interconnected system by the RoCoF method, the RoCoF for the system frequency when an event such as a generator being disconnected from the AC interconnected system or a load being disconnected from the AC interconnected system occurs is often used. This is because, in such cases, it is known that the system frequency changes significantly as a result of a large step-like (step function-like) change in the supply power or power consumption in the AC interconnected system.

[0007] However, when the above-mentioned event occurs in the AC interconnection system, the accuracy of estimating the system inertia of the AC interconnection system by the RoCoF method depends on the accuracy of estimating the time when the event occurs in the AC interconnection system. The accuracy of estimating the time depends on the proficiency of the person who estimates the time. For this reason, in the RoCoF method, it is required to establish a method of objectively determining the time, without depending on the proficiency of the person who estimates the time.

[0008] In relation to this, a method is known in which the system inertia of an AC interconnection system is estimated by the RoCoF method using the time when the RoCoF falls below 0.4 [Hz / s] as the time when an event occurs in the AC interconnection system (see Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0009] [Non-Patent Document 1] "Development of a method for estimating system frequency characteristics based on actual measurement results", Report of the Central Research Institute of the Electric Power Industry, Research Report: T94016, May 1995 Summary of the Invention [Problem to be solved by the invention]

[0010] However, there is no reason to justify using the time when the RoCoF falls below 0.4 [Hz / s] as the time when an event occurs in the AC interconnection system other than that the estimation accuracy of the system inertia of the AC interconnection system becomes high empirically. In other words, the method described in the above Non-Patent Document 1 cannot eliminate the subjectivity of the empirical rule, and the objectivity of the estimation of the time when an event occurs in the AC interconnection system is insufficient.

[0011] The present invention has been made in consideration of the above circumstances, and an object of the present invention is to provide an information processing device, an information processing method, and a program that can improve the objectivity of estimating the time when an event occurs. [Means for solving the problem]

[0012] One aspect of the present invention provides measurement value information indicating measured values, excluding at least frequency, of values ​​corresponding to power at a predetermined point in an AC interconnected system including one or more generators. and frequency information indicating a frequency according to the power at the location. The information processing device estimates an event occurrence time at which an event that changes the frequency of the power according to the measured value has occurred based on the measurement result.

[0013] In addition, another aspect of the present invention is a configuration in which, in an information processing device, the measurement value information is information indicating the measurement value for each time, and the information processing device estimates the time of occurrence of the event based on the measurement value information in a target period from a first time before an estimated period in which the event is estimated to have occurred to a second time after the estimated period.

[0014] In addition, another aspect of the present invention may employ a configuration in which an information processing device specifies each of the estimated period, the first time, and the second time in response to a received operation.

[0015] In addition, another aspect of the present invention may employ a configuration in which an information processing device estimates the estimated period, and specifies each of the first time and the second time according to the estimated estimated period.

[0016] In another aspect of the present invention, an information processing device is ,before The event occurrence time may be estimated based on the frequency information during the target period. stomach.

[0017] In addition, another aspect of the present invention may be configured such that an information processing device estimates the event occurrence time by treating each time in the estimation period as a candidate time at which the event occurred, and for each of the time candidates, performing a linear approximation of a waveform of a frequency indicated by the frequency information in a first target period from the first time to the time candidate, a linear approximation of a waveform of the measurement value indicated by the measurement value information in the first target period, a polynomial approximation of a waveform of a frequency indicated by the frequency information in a second target period from the time candidate to the second time, and a linear approximation of a waveform of the measurement value indicated by the measurement value information in the second target period.

[0018] In addition, another aspect of the present invention may be configured such that an information processing device performs, based on a least squares method, a linear approximation of a waveform of a frequency indicated by the frequency information during the first target period, a linear approximation of a waveform of the measurement value indicated by the measurement value information during the first target period, a polynomial approximation of a waveform of a frequency indicated by the frequency information during the second target period, and a linear approximation of a waveform of the measurement value indicated by the measurement value information during the second target period.

[0019] Another aspect of the present invention is measurement value information indicating measurement values, excluding at least frequency, of values ​​corresponding to power at a predetermined point in an AC interconnected system including one or more generators. and frequency information indicating a frequency according to the power at the location. and acquiring the acquired measurement value information. and the acquired frequency information. based on the measured value, estimate an event occurrence time at which an event that changes the frequency of the power according to the measured value has occurred.

[0020] In another aspect of the present invention, a computer is provided that stores measurement value information indicating values ​​corresponding to power at a predetermined point in an AC interconnected system including one or more generators, excluding at least a frequency. and frequency information indicating a frequency according to the power at the location. and acquiring the acquired measurement value information. and the acquired frequency information. and estimating an event occurrence time at which an event that changes the frequency of the power according to the measurement value has occurred based on the measurement result. Effect of the Invention

[0021] According to the present invention, it is possible to improve the objectivity of the estimation of the event occurrence time. [Brief description of the drawings]

[0022] [Figure 1] 1 is a diagram illustrating an example of a configuration of an information processing system 1. [Diagram 2] FIG. 11 is a diagram showing an example of a temporal change in the system frequency at a representative point before and after an event occurrence time. [Diagram 3] FIG. 3 is a diagram in which a fitting function is superimposed on the graph shown in FIG. 2. [Figure 4] FIG. 13 is a diagram showing an example of a temporal change in power at a representative point before and after an event occurrence time. [Diagram 5] FIG. 5 is a diagram in which a fitting function is superimposed on the graph shown in FIG. [Figure 6] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device 20. [Figure 7] FIG. 2 is a diagram illustrating an example of a functional configuration of an information processing device 20. [Figure 8] 11 is a diagram showing an example of a processing flow in which the information processing device 20 estimates an event occurrence time and system inertia. FIG. [Figure 9] FIG. 13 is a diagram showing an example of a graph in which the waveform of the system frequency indicated by the frequency information read out from the storage unit 22 in step S130 and the fitting function for the system frequency derived in step S140 are displayed in an overlapping manner. [Figure 10] FIG. 13 is a diagram showing an example of a graph in which the waveform of power indicated by the power information read from storage unit 22 in step S130 and the fitting function for power derived in step S140 are displayed in an overlapping manner. [Figure 11] 10 is a diagram showing an example of a processing flow in which the information processing device 20 acquires frequency information and power information from the measurement device 10. FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] <Embodiment> Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the following, when the frequency of power measured at a certain point is mentioned, it means the frequency of voltage measured at that point or the frequency of current measured at that point.

[0024] <Configuration of information processing system> First, a configuration of an information processing system 1 according to an embodiment will be described.

[0025] The information processing system 1 estimates the system inertia of an AC interconnection system.

[0026] Here, the AC interconnection system refers to an AC power system that includes one or more generators and generates power synchronously using the one or more generators. For example, the AC power system in the area of ​​Japan consisting of Chubu, Hokuriku, Kansai, Chugoku, Shikoku, and Kyushu (sometimes called the Central and Western system) is an example of an AC interconnection system. The system inertia of an AC interconnection system refers to the sum of the inertia moments of the rotors (i.e., rotors, turbines, etc. of the generators) of one or more generators included in the AC interconnection system. In the following, as an example, a case will be described in which the information processing system 1 estimates the system inertia of the AC interconnection system R shown in FIG. 1.

[0027] The AC interconnection system R includes four generators, generators PP1 to PP4, as one or more generators. In the AC interconnection system R, synchronous power generation is performed by these four generators. In addition, in the AC interconnection system R, the rotors of each of these four generators all rotate synchronously to generate one frequency as a system frequency. In the following, for convenience of explanation, the system frequency generated by the rotors of each of these four generators will be simply referred to as the system frequency. Here, the system frequency is a frequency for the rotation of the rotors, but it is known that it is practically equivalent to the frequency of the power measured at a point where the system frequency is measured among the points included in the AC interconnection system R. For this reason, the system frequency is measured by measuring the frequency of the power. That is, the system frequency is an example of the frequency of the power at each time at a predetermined point in the AC interconnection system. In addition to these four generators, the AC interconnection system R includes other facilities such as a substation. However, in FIG. 1, the other facilities are omitted to simplify the drawing. Moreover, the AC interconnection system R may be configured to include three or less generators, or may be configured to include five or more generators.

[0028] Each of the generators PP1 to PP4 is a generator that supplies electric power by, for example, thermal power generation, hydroelectric power generation, nuclear power generation, solar power generation, geothermal power generation, tidal power generation, etc. Some or all of the generators PP1 to PP4 may be generators that supply electric power by generating the same type of power, or may be generators that supply electric power by generating different types of power.

[0029] Furthermore, the information processing system 1 estimates the system inertia of the AC interconnection system R by a Rate of Change of Frequency (RoCoF) method.

[0030] The RoCoF method is a method for estimating the RoCoF (i.e., the rate of change of the system frequency) for the system frequency when the system frequency fluctuates, and estimating the system inertia of an AC interconnected system based on the estimated RoCoF. In a typical AC interconnected system, the system frequency is usually controlled to always maintain a value within a certain range, even if it fluctuates slightly over time. This is also true for the AC interconnected system R. For this reason, in order to obtain the RoCoF used to estimate the system inertia of the AC interconnected system R by the RoCoF method, the system frequency needs to change so much that it deviates from that range.

[0031] Therefore, in estimating the system inertia of the AC interconnection system R by the RoCoF method, the information processing system 1 uses the RoCoF for the system frequency when an event occurs, such as a generator being disconnected from the AC interconnection system R or a load being disconnected from the AC interconnection system R. This is because it is known that in this case, the system frequency changes significantly as a result of a large step-like (step function-like) change in the supply power or power consumption in the AC interconnection system. In the following, for convenience of explanation, events such as a generator being disconnected from the AC interconnection system R or a load being disconnected from the AC interconnection system R will be simply referred to as an event. Note that when an event occurs, a value according to the power at each point of the AC interconnection system R also changes with the system frequency. The value is, for example, the voltage, current, power itself, phase angle, etc. at each point of the AC interconnection system R, but is not limited to these.

[0032] The information processing system 1 includes a measurement device 10 and an information processing device 20.

[0033] The measurement device 10 is provided at a predetermined representative point in the AC interconnection system R. The representative point may be any point among the points included in the AC interconnection system R, as long as the system frequency can be measured. Here, as described above, the system frequency is measured as the frequency of power measured at a point among the points included in the AC interconnection system R where the system frequency is measured. Therefore, the representative point can be said to be a point at which the power supplied by the AC interconnection system R can be measured. For example, the representative point is a substation or the like included in the AC interconnection system R. Note that, for convenience of explanation, a certain physical quantity X measured by the measurement device 10 at the representative point will be referred to as the physical quantity X of the representative point in the following explanation. The physical quantity X is a system frequency, power, voltage, current, phase angle, or the like.

[0034] The measurement device 10 measures the system frequency at each time of the representative point and a value corresponding to the power at each time of the representative point (for example, voltage, current, phase angle, and power itself). The measurement device 10 is, for example, a PMU (Phasor Measurement Unit). In the following, for convenience of explanation, the measurement values ​​excluding the system frequency among the values ​​measured by the measurement device 10 will be referred to as non-frequency measurement values. Note that the measurement device 10 may be configured to measure the system frequency at each time of the representative point and not to measure the non-frequency measurement values ​​at each time of the representative point. In this case, the information processing device 20 acquires non-frequency measurement value information indicating the non-frequency measurement values ​​at each time of the representative point from another device different from the measurement device 10. In addition, the measurement device 10 may be configured to measure the non-frequency measurement values ​​at each time of the representative point without measuring the system frequency at each time of the representative point. In this case, the information processing device 20 acquires frequency information indicating the system frequency at each time of the representative point from another device different from the measurement device 10.

[0035] The measuring device 10 is communicably connected to an information processing device 20 wirelessly or via wires.

[0036] The information processing device 20 acquires frequency information indicating the system frequency measured by the measuring device 10 at each time from the measuring device 10. The information processing device 20 stores the acquired frequency information at each time. Furthermore, the information processing device 20 acquires non-frequency measurement value information indicating the non-frequency measurement values ​​measured by the measuring device 10 at each time from the measuring device 10 at each time. The information processing device 20 stores the acquired non-frequency measurement value information at each time. Note that when the measuring device 10 does not measure non-frequency measurement values, the information processing device 20 does not acquire non-frequency measurement value information from the measuring device 10.

[0037] Hereinafter, a case will be described in which the non-frequency measurement value is power as an example. For convenience of explanation, non-frequency measurement value information indicating power as the non-frequency measurement value will be referred to as power information.

[0038] The information processing device 20 has, for example, three estimation modes, a first estimation mode, a second estimation mode, and a third estimation mode, as operation modes for estimating the system inertia of the AC interconnection system R. Note that the information processing device 20 may be configured to have another estimation mode instead of some or all of the three estimation modes, or in addition to some or all of the three estimation modes. Also, the information processing device 20 may be configured to have some of the three estimation modes.

[0039] The first estimation mode is an operation mode that, among operation modes for estimating the system inertia of the AC interconnection system R, involves estimation of an event occurrence time based on frequency information acquired from the measurement device 10. Note that the event occurrence time is the time when an event occurs.

[0040] The second estimation mode is an operation mode among operation modes for estimating the system inertia of the AC interconnection system R, which involves estimation of an event occurrence time based on power information (an example of non-frequency measurement value information) acquired from the measurement device 10.

[0041] The third estimation mode is an operating mode among operating modes for estimating the system inertia of the AC interconnection system R, which involves estimating the time when an event occurs based on frequency information acquired from the measurement device 10 and power information acquired from the measurement device 10.

[0042] If the information processing device 20 does not have the second estimation mode and the third estimation mode, the information processing device 20 may be configured not to acquire power information.

[0043] In the first estimation mode, the information processing device 20 estimates the event occurrence time based on frequency information of a period corresponding to the received operation among the frequency information stored in advance. More specifically, in the first estimation mode, the information processing device 20 specifies each of the estimation period, the first time, and the second time according to the received operation. Then, the information processing device 20 estimates the event occurrence time based on frequency information of a target period from the specified first time to the specified second time among frequency information indicating the system frequency for each time at the representative point. Thereby, the information processing device 20 can improve the objectivity of the estimation of the event occurrence time. Here, the estimation period is a period during which the event is estimated to have occurred. The target period is a period during which the event occurrence time is estimated. The first time is a time before the estimation period and is the time when the target period starts. The second time is a time after the estimation period and is the time when the target period ends. The target period may be any period as long as it includes the estimation period. For example, the first time is a time several seconds before the start of the estimation period. This is because the grid frequency at the representative point before the event occurs is kept almost constant. Also, for example, the second time is a time several seconds after the start of the estimation period. This is because fluctuations in the grid frequency at the representative point caused by an event often settle within a few seconds.

[0044] In the first estimation mode, when estimating an event occurrence time, the information processing device 20 derives a fitting function that fits the waveform of the grid frequency (i.e., the change over time of the grid frequency) indicated by the frequency information in the target period by solving an optimization problem. Then, the information processing device 20 estimates the RoCoF for the grid frequency in the estimation period based on the derived fitting function. Then, the information processing device 20 estimates the system inertia of the AC interconnection system R based on the estimated RoCoF and the RoCoF method.

[0045] In the second estimation mode, the information processing device 20 estimates the event occurrence time based on power information for a period corresponding to the received operation among the power information stored in advance. More specifically, in the second estimation mode as well, the information processing device 20 specifies the estimated period, the first time, and the second time according to the received operation. Then, the information processing device 20 estimates the event occurrence time based on power information for a target period from the specified first time to the specified second time among the power information indicating the power at each time at the representative point. In this case as well, the information processing device 20 can improve the objectivity of the estimation of the event occurrence time.

[0046] In the second estimation mode, the information processing device 20 derives a fitting function that fits the waveform of the grid frequency indicated by the frequency information in the target period based on the estimated event occurrence time by solving an optimization problem. Then, the information processing device 20 estimates the RoCoF based on the derived fitting function. Then, the information processing device 20 estimates the system inertia of the AC interconnection system R based on the estimated RoCoF and the RoCoF method.

[0047] In the third estimation mode, the information processing device 20 estimates the event occurrence time based on the frequency information and power information of a period corresponding to the received operation among the frequency information and power information stored in advance. More specifically, in the third estimation mode, the information processing device 20 also specifies the estimation period, the first time, and the second time according to the received operation. Then, the information processing device 20 estimates the event occurrence time based on the frequency information and power information in the target period from the specified first time to the specified second time. In this case, the information processing device 20 can more reliably improve the objectivity of the estimation of the event occurrence time.

[0048] In the third estimation mode, when estimating an event occurrence time, the information processing device 20 derives a fitting function that fits the waveform of the grid frequency (i.e., the change in the grid frequency over time) indicated by the frequency information in the target period by solving an optimization problem. Then, the information processing device 20 estimates the RoCoF for the grid frequency in the aforementioned estimation period based on the derived fitting function. Then, the information processing device 20 estimates the system inertia of the AC interconnection system R based on the estimated RoCoF and the RoCoF method.

[0049] When the first estimation mode is selected as the operation mode, the information processing device 20 estimates the system inertia of the AC interconnection system R based on the acquired frequency information. When the second estimation mode or the third estimation mode is selected as the operation mode, the information processing device 20 estimates the system inertia of the AC interconnection system R based on the acquired frequency information and power information.

[0050] The information processing device 20 is, for example, a workstation, a desktop PC (Personal Computer), a notebook PC, etc. Alternatively, the information processing device 20 may be a tablet PC, a multi-function mobile phone terminal (smartphone), a mobile phone terminal, a PDA (Personal Digital Assistant), etc.

[0051] <Method for estimating event occurrence time and system inertia in the first estimation mode> Hereinafter, a method for estimating an event occurrence time and a system inertia in the first estimation mode will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of a temporal change in the system frequency at a representative point before and after an event occurrence time.

[0052] The horizontal axis of the graph shown in Fig. 2 indicates time. The vertical axis of the graph indicates the system frequency at the representative point. A curve PL1 plotted on the graph is an example of a curve obtained by connecting the system frequencies measured at each time by the measurement device 10 with a straight line in chronological order. In other words, the curve PL1 indicates an example of the waveform of the system frequency at the representative point (i.e., the change over time in the system frequency).

[0053] For example, after an event occurs in the AC interconnection system R, the user of the information processing device 20 determines the above-mentioned estimation period, the first time, and the second time based on a graph as shown in FIG. 2. For this reason, the information processing device 20 displays a graph as shown in FIG. 2 in response to the received operation. At this time, the information processing device 20 receives information indicating a period including the event occurrence time. Then, the information processing device 20 extracts frequency information indicating the system frequency for each time of the representative point in the received period from the frequency information stored in advance. The information processing device 20 generates a graph as shown in FIG. 2 based on the extracted frequency information and displays the generated graph. Note that the information processing device 20 may be configured not to display such a graph. In this case, the graph as shown in FIG. 2 is generated by another device. Note that the determination of the estimation period, the first time, and the second time by the user described above may be performed automatically without user intervention by a function such as machine learning, or semi-automatically with partial user intervention.

[0054] Next, the user of the information processing device 20 determines the estimated period based on the graph displayed on the information processing device 20. More specifically, the user determines the period estimated to include the event occurrence time based on the graph as the estimated period. For example, the user determines the period including the period in which it is visually possible to determine that the system frequency at the representative point has changed to a first predetermined value or more as the estimated period. This is because the system frequency at the representative point changes to the first predetermined value or more before and after the event occurrence time. The first predetermined value is, for example, about 0.2% of the several-second moving average (for example, a 5-second moving average line) of the system frequency at the representative point, but is not limited to this. Note that the method of determining the estimated period may be other methods as long as the method is capable of determining the period including the event occurrence time as the estimated period instead of this. Note that the information processing device 20 may be configured to specify the period estimated to have occurred as the estimated period based on the extracted frequency information and a machine learning model or the like. The period PD1 shown in FIG. 2 is an example of the estimated period determined by the user based on the graph.

[0055] Next, the user of the information processing device 20 determines a time before the determined estimated period as the first time. For example, the user determines a time a first predetermined time before the time when the estimated period starts as the first time. The first predetermined time is, for example, 2 seconds. The time ts1 shown in FIG. 2 is an example of the first time determined by the user based on the estimated period. Note that the first predetermined time may be shorter than 2 seconds or longer than 2 seconds. Alternatively, the first time may be determined in another way. The information processing device 20 may be configured to specify the first time, which is the period during which an event is estimated to have occurred, together with the estimated period, based on the extracted frequency information and a machine learning model, etc.

[0056] Next, the user of the information processing device 20 determines a time after the determined estimated period as the second time. For example, the user determines a time that is a second predetermined time after the time when the estimated period ends as the second time. The second predetermined time is, for example, 2 seconds. The time te1 shown in FIG. 2 is an example of the second time determined by the user based on the estimated period. The second predetermined time may be shorter than 2 seconds or longer than 2 seconds. The second predetermined time may be the same as the first predetermined time or may be different from the first predetermined time. The second time may be determined in another way instead of this. The information processing device 20 may be configured to specify the second time together with the estimated period in which the event is estimated to have occurred based on the extracted frequency information and a machine learning model, etc.

[0057] Here, the period from the first time ts1 to the second time te1 is divided into n-1 equal parts, and time ts1 is set as time t1, and time te1 is set as time t n By setting the time, t i Here, n may be any integer equal to or greater than 1. And i represents any integer included in a closed interval between 1 and n. The period PD2 shown in FIG. 2 is a period from time t1 to time t n This is an example of the period from time ts1 to time te1 (the period from time ts1 to time te1), that is, the aforementioned target period.

[0058] The user of the information processing device 20 inputs the estimation period, the first time, and the second time thus determined to the information processing device 20. That is, the information processing device 20 specifies the estimation period, the first time, and the second time in response to the received operation. The information processing device 20 calculates the target period based on the specified first time and second time. The information processing device 20 extracts frequency information for the calculated target period from frequency information stored in advance. The information processing device 20 estimates a fitting function that fits the waveform of the system frequency at the representative point in the target period based on the extracted frequency information by solving an optimization problem that minimizes an objective function. In the following, as an example, a case will be described in which the information processing device 20 estimates the fitting function by solving the optimization problem using the least squares method in the first estimation mode.

[0059] Here, when the information processing device 20 estimates the fitting function by the least squares method, the information processing device 20 treats the event occurrence time as an unknown (fitting parameter) together with one or more real coefficients included in the fitting function. In the following, for convenience of explanation, the candidate of the event occurrence time is referred to as a time candidate t m m is an integer in the closed interval between 1 and n. m is a time included in the estimation period. The information processing device 20 uses, for example, a polynomial as the fitting function. In the following, as an example, a case where the fitting function is a fifth-order polynomial will be described. In this case, the unknowns when the information processing device 20 estimates the fitting function by the least squares method are six real coefficients included in the fitting function, which is a fifth-order polynomial, and a time candidate t m and . Note that the fitting function may be another function such as an exponential function instead of a polynomial. The fitting function may be a polynomial of degree 4 or less, or a polynomial of degree 5 or more.

[0060] For example, at a certain time tx1 Let time candidate t m If you select , the objective function J to be minimized by the least squares method. f is formulated as the following equations (1) to (3).

[0061]

number

[0062]

number

[0063]

number

[0064] The first term in the brackets on the right side of the above formula (1) is the time when the measurement device 10 i The second term in the brackets on the right side of equation (1) is a fitting function, and is expressed as a fifth-order polynomial as in equation (2) or equation (3) depending on the time. m-1 In the period from the first time to the time candidate t m In equation (2), a0, a1, a2, a3, a4, and a5 are real coefficients (i.e., fitting parameters) included in the fitting function. On the right side of equation (2), the time is the time candidate t m That is, the fitting function expressed by the formula (2) is a straight line. This corresponds to linear approximation of the waveform of the grid frequency indicated by the frequency information in the first target period. On the other hand, the right side of the formula (3) is i Equation (2) and equation (3) depend on the time candidate t mTherefore, the above formulas (1) to (3) can be rewritten as the following formulas (4) and (5).

[0065]

number

[0066]

number

[0067] Then, the minimization problem based on the above equation (4) is expressed as the following equation (6).

[0068]

number

[0069] Moreover, the solution of the above equation (6) is expressed as the following equation (7).

[0070]

number

[0071] The method of deriving the above formula (7) is similar to the general least squares method, and therefore a detailed explanation will be omitted.

[0072] Here, the fitting function obtained by the above formula (7) is x1 This is the function that best fits the waveform of the grid frequency indicated by the frequency information during the target period. However, at time t x1is merely one of the times included in the estimation period and is merely one of the time candidates for the event occurrence time. Therefore, the minimization problem based on the above equation (4) can be defined as the following equation (8) instead of the above equation (6), and the time candidate t m This can be expressed as a minimization problem with unknowns.

[0073]

number

[0074] Here, in the interval [t m1 ,t m2 ] indicates the estimation period, i.e., time t m1 indicates the time when the estimation period begins. Also, time t m2 indicates the time when the estimation period ends. And the interval [t m1 ,t m2 Each time included in t m The time is selected as the

[0075] By solving the minimization problem of the above formula (8), it is possible to estimate a fitting function that best fits the waveform of the grid frequency indicated by the frequency information in the target period, and to obtain a time candidate t m can be estimated as the event occurrence time.

[0076] The RoCoF used in the RoCoF method is a function obtained by differentiating the fitting function obtained in this way once with respect to time, and adding a candidate time t estimated as the event occurrence time to the derivative. m By substituting, we obtain the following equation (9).

[0077]

number

[0078] That is, in the first estimation mode, the information processing device 20 extracts frequency information in the calculated target period, and then estimates a fitting function that best fits the waveform of the grid frequency indicated by the frequency information and estimates the event occurrence time by the least squares method based on the extracted frequency information, each time included in the specified estimation period, and the above formula (8). In other words, in the first estimation mode, the information processing device 20 estimates the event occurrence time by treating each time in the estimation period as a time candidate at which the event occurs and performing linear approximation of the waveform of the grid frequency indicated by the frequency information in the first target period from the first time to the time candidate and polynomial approximation of the waveform of the grid frequency indicated by the frequency information in the second target period from the time candidate to the second time. In such an estimation of the event occurrence time, the information processing device 20 can obtain almost the same estimation result regardless of the selection of the estimation period and the target period as long as the event occurrence time is included in the estimation period and the target period is included in the estimation period (obviously, this does not apply when the functional form of the fitting function is changed each time estimation is performed). Therefore, in the first estimation mode, the information processing device 20 can improve the objectivity of the estimation of the event occurrence time.

[0079] Here, FIG. 3 is a diagram in which a fitting function is superimposed on the graph shown in FIG. 2. In FIG. 3, a function FF1 superimposed on the graph indicates an example of a fitting function estimated by the information processing device 20 in the first estimation mode. A time tv1 shown in FIG. 3 indicates an example of an event occurrence time estimated by the information processing device 20 together with the fitting function. From FIG. 3, it can be seen that in the first estimation mode, the information processing device 20 performs linear approximation of the waveform of the grid frequency indicated by the frequency information in the first target period, which is a period before the event occurrence time. Also, from FIG. 3, it can be seen that in the first estimation mode, the information processing device 20 performs polynomial approximation of the waveform of the grid frequency indicated by the frequency information in the second target period, which is a period after the event occurrence time.

[0080] In the first estimation mode, the information processing device 20 calculates the RoCoF based on the above formula (9), and estimates the system inertia of the AC interconnection system R by the RoCoF method based on the calculated RoCoF. Note that the method by which the information processing device 20 estimates the system inertia by the RoCoF method based on the RoCoF is known, and therefore detailed description thereof will be omitted.

[0081] <Method for estimating event occurrence time and system inertia in the second estimation mode> Hereinafter, a method for estimating an event occurrence time and grid inertia in the second estimation mode will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of a temporal change in power at a representative point before and after an event occurrence time.

[0082] The horizontal axis of the graph shown in Fig. 4 represents time. The vertical axis of the graph represents power at a representative point. Curve PL2 plotted on the graph is an example of a curve obtained by connecting the power measured at each time by the measuring device 10 with a straight line in chronological order. In other words, curve PL2 represents an example of the waveform of the power at the representative point (i.e., the change in the power over time).

[0083] For example, after an event occurs in the AC interconnection system R, the user of the information processing device 20 determines the estimation period, the first time, and the second time based on a graph such as that shown in Fig. 4. Note that the method of determining the estimation period, the first time, and the second time in the second estimation mode may be similar to the method of determining the estimation period, the first time, and the second time in the first estimation mode. Here, a case will be described in which the method of determining the estimation period, the first time, and the second time in the second estimation mode is different from the method of determining the estimation period, the first time, and the second time in the first estimation mode.

[0084] The information processing device 20 displays a graph as shown in FIG. 4 in response to the received operation. At this time, the information processing device 20 receives information indicating a period including the event occurrence time. Then, the information processing device 20 extracts power information (an example of non-frequency measurement value information) indicating power (an example of non-frequency measurement value) at each time of a representative point during the received period from the power information stored in advance. The information processing device 20 generates a graph as shown in FIG. 4 based on the extracted power information, and displays the generated graph. Note that the information processing device 20 may be configured not to display such a graph. In this case, a graph as shown in FIG. 4 is generated by another device.

[0085] Here, when an event occurs in the AC interconnection system R, the non-frequency measurement value of the representative point also changes with the change in the system frequency of the representative point. That is, in this case, the power of the representative point also changes. For this reason, the user of the information processing device 20 can determine, based on the graph displayed on the information processing device 20, a period that is estimated to include the event occurrence time as the estimated period. For example, the user determines, as the estimated period, a period that includes a period that can be visually determined to have changed the power of the representative point to a second predetermined value or more. This is because the power of the representative point changes to a second predetermined value or more before and after the event occurrence time. The second predetermined value is, for example, about 5% of a few-second moving average (for example, a 5-second moving average line) of the power of the representative point, but is not limited to this. Note that the method of determining the estimated period may be other methods as long as the method can determine the period including the event occurrence time as the estimated period. In addition, the information processing device 20 may be configured to specify, as the estimated period, a period that is estimated to have occurred in the event based on the extracted power information and a machine learning model or the like. The period PD3 shown in FIG. 4 is an example of an estimated period determined by the user based on the graph.

[0086] Next, the user of the information processing device 20 determines the first time and the second time based on the determined estimated period. The method of determining the first time and the second time after the estimated period is determined is the same as the method of determining the first time and the second time in the first estimation mode, so detailed description will be omitted. The time ts2 shown in FIG. 4 is an example of the first time determined by the user based on the estimated period. The time te2 shown in FIG. 4 is an example of the second time determined by the user based on the estimated period. Note that the information processing device 20 may be configured to specify the first time and the second time, together with the estimated period, that is the period in which an event is estimated to have occurred, based on the extracted power information and a machine learning model, etc.

[0087] Here, as described above, the period from the first time ts2 to the second time te2 is divided into n-1 equal parts, and time ts2 is set as time t1 and time te2 is set as time t n By setting the time, t i The period PD4 shown in FIG. 4 is from time t1 to time t n This is an example of the period from time ts2 to time te2 (the period from time ts2 to time te2).

[0088] The user of the information processing device 20 inputs the estimated period, the first time, and the second time thus determined to the information processing device 20. That is, the information processing device 20 specifies the estimated period, the first time, and the second time in response to the received operation. The information processing device 20 calculates the target period based on the specified first time and second time. The information processing device 20 extracts power information for the calculated target period from the power information stored in advance. The information processing device 20 estimates a fitting function that fits the waveform of power at the representative point in the target period based on the extracted power information by solving an optimization problem that minimizes an objective function. In the following, as an example, a case will be described in which the information processing device 20 estimates the fitting function by solving the optimization problem using the least squares method in the second estimation mode.

[0089] Here, it is known that when an event occurs in the AC interconnection system R, the power changes like a step function at the representative point. More precisely, it is known that in this case, the power changes like a step function not only at the representative point but also at many points in the AC interconnection system R. Therefore, when estimating the fitting function, the information processing device 20 performs linear approximation of the waveforms of the power before and after the event time. That is, the information processing device 20 uses a constant function as the fitting function. Therefore, for the sake of convenience in the following description, the power before the event occurrence time is referred to as p pre The power after the event occurrence time is p post When the information processing device 20 estimates the fitting function by the least squares method, the information processing device 20 estimates one real coefficient (i.e., p pre ), one real coefficient in the fitting function for the second time period (i.e., p post ), and the event occurrence time are treated as unknowns (fitting parameters). Note that the fitting function may be a polynomial, an exponential function, or another function instead of a constant function.

[0090] For example, each time included in the estimation period is a time candidate t m If we choose as , the objective function J to be minimized by the least squares method is p are formulated as the following equations (10) to (12).

[0091]

number

[0092]

number

[0093]

number

[0094] The first term in the brackets on the right side of the above formula (10) is the time when the measurement device 10 i The measured power is shown in the table. The second term in the brackets on the right side of formula (10) is a fitting function, and is expressed by formula (11) or formula (12) depending on the time. m-1 In the first target period up to time t m From time t n In the second target period from to, the fitting function is expressed by formula (12). Therefore, the above formulas (10) to (12) can be rewritten as the following formulas (13) and (14).

[0095]

number

[0096]

number

[0097] Then, the minimization problem based on the above equation (13) is expressed as the following equation (15).

[0098]

number

[0099] Moreover, the solution of the above equation (15) is expressed as the following equation (16).

[0100]

number

[0101] The method of deriving the above equation (16) is similar to the general least squares method, and therefore a detailed explanation will be omitted.

[0102] Here, as mentioned above, in the interval [t m1 ,t m2 ] indicates the estimation period, i.e., time t m1 indicates the time when the estimation period begins. Also, time t m2 indicates the time when the estimation period ends. And the interval [t m1 ,t m2 Each time included in t m The time is selected as the

[0103] By solving the minimization problem of the above equation (15), it is possible to estimate a fitting function that best fits the waveform of the power indicated by the power information during the target period, and to select a time candidate t m can be estimated as the event occurrence time.

[0104] The RoCoF used in the RoCoF method can be obtained by substituting the event occurrence time into the derivative obtained by differentiating once with respect to time the polynomial obtained by solving the minimization problem of equation (6) into which the event occurrence time estimated in this manner is substituted.

[0105] That is, in the second estimation mode, the information processing device 20 extracts power information for the calculated target period, and then estimates a fitting function that best fits the waveform of power indicated by the power information and estimates the event occurrence time by the least squares method based on the extracted power information, each time included in the determined estimation period, and the above formula (15). In other words, in the second estimation mode, the information processing device 20 estimates the event occurrence time by linearly approximating the waveform of power indicated by the power information for the first target period and the waveform of power indicated by the power information for the second target period based on the least squares method. Even in such an estimation of the event occurrence time, the information processing device 20 can obtain almost the same estimation result regardless of the selection of the estimation period and the target period, as long as the estimation period includes the event occurrence time and the target period includes the estimation period (naturally, this does not apply when the functional form of the fitting function is changed each time estimation is performed). Therefore, the information processing device 20 can improve the objectivity of the estimation of the event occurrence time even in the second estimation mode.

[0106] Here, FIG. 5 is a diagram in which a fitting function is superimposed on the graph shown in FIG. 4. In FIG. 5, a function FF2 superimposed on the graph indicates an example of a fitting function derived by the information processing device 20 in the second estimation mode. A time tv2 shown in FIG. 5 indicates an example of an event occurrence time estimated by the information processing device 20 together with the fitting function. From FIG. 5, it can be seen that in the second estimation mode, the information processing device 20 performs linear approximation of the waveform of power indicated by the power information in the first target period, which is a period before the event occurrence time. Also, from FIG. 5, it can be seen that in the second estimation mode, the information processing device 20 performs polynomial approximation of the waveform of power indicated by the power information in the second target period, which is a period after the event occurrence time.

[0107] Also in the second estimation mode, the information processing device 20 estimates the event occurrence time, and then estimates the RoCoF based on the estimated event occurrence time and the above formulas (1) to (7). Then, the information processing device 20 estimates the system inertia of the AC interconnection system R by the RoCoF method based on the estimated RoCoF.

[0108] <Method of estimating event occurrence time and system inertia in the third estimation mode> Hereinafter, a method for estimating an event occurrence time and system inertia in the third estimation mode will be described.

[0109] The method for estimating the event occurrence time in the third estimation mode is a method in which a function that is a linear combination of the above equations (1) and (10) is used as an objective function to be minimized in an optimization problem for estimating a fitting function and an event occurrence time.

[0110] That is, in the third estimation mode, the information processing device 20 solves the minimization problem expressed by the following equation (17).

[0111]

number

[0112] That is, in the third estimation mode, the information processing device 20 estimates a fitting function that fits the waveform of the grid frequency in the first estimation mode, estimates a fitting function that fits the waveform of the power in the second estimation mode, and estimates the event occurrence time. f , w p Each of w is a weight in the linear combination of the objective functions in equation (17). f , w pare defined by the following formula (18). For convenience of explanation, the fitting function that fits the waveform of the grid frequency in the first estimation mode will be referred to as the first fitting function. For convenience of explanation, the fitting function that fits the waveform of the power in the second estimation mode will be referred to as the fitting function.

[0113]

number

[0114] σ in the above formula (18) f is the variance of the grid frequency indicated by the frequency information during the target period. p is the variance of power indicated by the power information in the target period. The reason for using each of these two variances as weights is that each of the two terms included in the right side of the objective function expressed by the above formula (18) is the squared value of the error. Note that a configuration may be made in which a quantity other than the variance is used as such a weight.

[0115] The user of the information processing device 20 determines the estimated period, the first time, and the second time by the same method as the method of determining the estimated period, the first time, and the second time for the first estimation mode or the second estimation mode. Then, the user of the information processing device 20 inputs the determined estimated period, the first time, and the second time to the information processing device 20. That is, the information processing device 20 specifies the estimated period, the first time, and the second time in response to the received operation. The information processing device 20 calculates the target period based on the specified first time and second time. The information processing device 20 extracts frequency information and power information for the calculated target period from the power information stored in advance. The information processing device 20 solves an optimization problem to minimize the objective function expressed by equation (18) based on the extracted frequency information and power information and the above equations (17) and (18). As a result, the information processing device 20, together with the first fitting function and the second fitting function, calculates a time candidate t when the first fitting function best fits the waveform of the grid frequency indicated by the frequency information in the target period and when the second fitting function best fits the waveform of the power indicated by the power information in the target period. m is estimated as the event occurrence time.

[0116] The method for obtaining the RoCoF used in the RoCoF method may be the method in the first estimation mode or the method in the second estimation mode.

[0117] Also in the third estimation mode, the information processing device 20 calculates the RoCoF based on the above formula (9), and estimates the system inertia of the AC interconnection system R by the RoCoF method based on the calculated RoCoF.

[0118] <Hardware configuration of information processing device> The hardware configuration of the information processing device 20 will be described below with reference to Fig. 6. Fig. 6 is a diagram showing an example of the hardware configuration of the information processing device 20.

[0119] The information processing device 20 includes, for example, a processor 21, a storage unit 22, an input receiving unit 23, a communication unit 24, and a display unit 25. These components are connected to each other via a bus so that they can communicate with each other. The information processing device 20 also communicates with the measurement device 10 and the like via the communication unit 24.

[0120] The processor 21 controls the entire information processing device 20. The processor 21 is, for example, a CPU (Central Processing Unit). The processor 21 may be another processor such as an FPGA (Field-Programmable Gate Array). The processor 21 executes various programs stored in the storage unit 22.

[0121] The storage unit 22 includes, for example, a hard disk drive (HDD), a solid state drive (SSD), an electrically erasable programmable read-only memory (EEPROM), a read-only memory (ROM), a random access memory (RAM), etc. Note that the storage unit 22 may be an external storage device connected via a digital input / output port such as a universal serial bus (USB) instead of being built into the information processing device 20. The storage unit 22 stores various information, various images, various programs, etc. that are processed by the information processing device 20.

[0122] The input receiving unit 23 is an input device such as a keyboard, a mouse, a touch pad, etc. The input receiving unit 23 may be a touch panel that is integrated with the display unit 25.

[0123] The communication unit 24 includes, for example, an antenna, a digital input / output port such as a USB, an Ethernet (registered trademark) port, and the like.

[0124] The display unit 25 is a display device including, for example, a liquid crystal display panel or an organic EL (Electro Luminescence) display panel.

[0125] <Functional configuration of information processing device> The functional configuration of the information processing device 20 will be described below with reference to Fig. 7. Fig. 7 is a diagram showing an example of the functional configuration of the information processing device 20.

[0126] The information processing device 20 includes a storage unit 22, an input receiving unit 23, a communication unit 24, a display unit 25, and a control unit 26.

[0127] The control unit 26 controls the entire information processing device 20. The control unit 26 includes, for example, an acquisition unit 261, an estimation unit 262, a display control unit 263, and a storage control unit 264. These functional units included in the control unit 26 are realized, for example, by the processor 21 executing various programs stored in the storage unit 22. In addition, some or all of the functional units may be hardware functional units such as LSI (Large Scale Integration) and ASIC (Application Specific Integrated Circuit).

[0128] The acquiring unit 261 acquires various types of information. For example, the acquiring unit 261 acquires frequency information from the measurement device 10. Also, for example, the acquiring unit 261 acquires non-frequency measurement value information from the measurement device 10.

[0129] The estimation unit 262 performs various estimations. For example, the estimation unit 262 estimates an event occurrence time. Also, for example, the estimation unit 262 estimates the system inertia of the AC interconnection system R.

[0130] The display control unit 263 generates various images. The display control unit 263 causes the display unit 25 to display the generated images.

[0131] The storage control unit 264 causes the storage unit 22 to store the various pieces of information acquired by the acquisition unit 261 .

[0132] <Processing by the information processing device to estimate the event occurrence time and system inertia> Hereinafter, a process in which the information processing device 20 estimates an event occurrence time and system inertia will be described with reference to FIG. 8. FIG. 8 is a diagram showing an example of a flow of a process in which the information processing device 20 estimates an event occurrence time and system inertia. In the following, as an example, a case will be described in which the information processing device 20 accepts an operation to select any one of the three estimation modes, the first estimation mode to the third estimation mode, as an operation mode at a timing before the processing of step S110 shown in FIG. 8 is performed. In addition, in the following, as an example, a case will be described in which the user of the information processing device 20 determines each of the estimation period, the first time, and the second time at that timing.

[0133] The estimation unit 262 waits until it receives an operation to input the estimated period, the first time, and the second time (step S110).

[0134] When it is determined that the operation of inputting the estimated period, the first time, and the second time has been received (step S110-YES), the estimation unit 262 identifies the estimated period, the first time, and the second time (step S120). In FIG. 8, the process of step S120 is indicated as "period and time identification."

[0135] Next, the estimation unit 262 calculates the target period based on the first time and the second time specified in step S120. Then, the estimation unit 262 extracts and reads out information for the calculated target period from the storage unit 22 according to the operation mode of the information processing device 20 (step S130). For example, when the operation mode of the information processing device 20 is the first estimated mode, the estimation unit 262 extracts and reads out frequency information for the calculated target period from the frequency information stored in the storage unit 22. Also, for example, when the operation mode of the information processing device 20 is the second estimated mode, the estimation unit 262 extracts and reads out power information for the calculated target period from the power information stored in the storage unit 22. Also, for example, when the operation mode of the information processing device 20 is the third estimated mode, the estimation unit 262 extracts and reads out frequency information and power information for the calculated target period from the frequency information and power information stored in the storage unit 22. In FIG. 8, the process of step S130 is indicated as "information reading".

[0136] Next, the estimation unit 262 estimates the event occurrence time by estimating a fitting function using an estimation method for an operation mode corresponding to a previously accepted operation based on the information read from the storage unit 22 in step S130 (step S140). Note that the estimation method has already been described, and therefore will not be described here.

[0137] Next, the estimation unit 262 estimates the RoCoF based on the estimation result in step S140 (step S150). The method of estimating the RoCoF in the process of step S150 has already been described, and therefore will not be described again.

[0138] Next, the estimation unit 262 estimates the system inertia of the AC interconnection system R by the RoCoF method based on the RoCoF estimated in step S150 (step S160).

[0139] Next, the display control unit 263 causes the display unit 25 to display information indicating the system inertia estimated by the estimation unit 262 in step S160 (step S170), and ends the process of the flowchart shown in FIG.

[0140] <Numerical verification of event time estimation> Hereinafter, a description will be given of numerical verification of the estimation of the estimated event time performed by the process of the flowchart shown in Fig. 8. In the following, the numerical verification will be described by taking as an example a case where the information processing device 20 estimates the estimated event time in the third estimation mode.

[0141] Fig. 9 is a diagram showing an example of a graph in which the waveform of the grid frequency indicated by the frequency information read out from the storage unit 22 in step S130 and the fitting function for the grid frequency estimated in step S140 are superimposed. However, in the example shown in Fig. 9, the information processing device 20 derives the fitting function by using a third-order polynomial instead of a fifth-order polynomial.

[0142] The horizontal axis of the graph shown in FIG. 9 indicates the elapsed time from the first time. The vertical axis of the graph indicates the system frequency indicated by the frequency information read from the storage unit 22 in step S130. A curve PL3 plotted on the graph indicates an example of a curve in which the system frequencies at each time in the target period are connected by a straight line in chronological order. A function FF3 on the graph indicates an example of the fitting function. It can be seen from FIG. 9 that the function FF3 is precisely fitted to the curve PL3, i.e., the waveform of the system frequency. That is, in the example shown in FIG. 9, it can be seen that the information processing device 20 precisely estimates the event occurrence time. Note that the fitting function is precisely fitted to the waveform of the system frequency in the first estimation mode as in the third estimation mode. For this reason, the fitting function derived by the first estimation mode is not shown.

[0143] FIG. 10 is a diagram showing an example of a graph in which the waveform of the power indicated by the power information read from the storage unit 22 in step S130 and the fitting function for the power estimated in step S140 are superimposed. The horizontal axis of the graph shown in FIG. 10 indicates the elapsed time from the first time. The vertical axis of the graph indicates the power. A curve PL4 plotted on the graph indicates an example of a curve in which the power at each time in the target period is connected by a straight line in chronological order. A function FF4 on the graph indicates an example of the fitting function. In FIG. 10, it can be seen that the function FF4 is accurately fitted to the curve PL4, i.e., the waveform of the power. That is, in the example shown in FIG. 10, it can be seen that the information processing device 20 accurately estimates the event occurrence time. Note that the fitting function is accurately fitted to the waveform of the power in the second estimation mode as well as in the third estimation mode. For this reason, the fitting function derived by the second estimation mode is not shown.

[0144] <Processing by which the information processing device acquires information from the measuring device> Hereinafter, a process in which the information processing device 20 acquires information from the measurement device 10 will be described with reference to Fig. 11. In the following, as an example, a case in which the information processing device 20 acquires power information together with frequency information from the measurement device 10 will be described. Fig. 11 is a diagram showing an example of a process flow in which the information processing device 20 acquires frequency information and power information from the measurement device 10. For example, when the information processing device 20 receives an operation to start a process of acquiring information from the measurement device 10, the information processing device 20 repeatedly performs the process of the flowchart shown in Fig. 11 every time a predetermined sampling period elapses until the information processing device 20 receives an operation to end the process of acquiring information from the measurement device 10.

[0145] The acquiring unit 261 acquires frequency information and power information from the measurement device 10 (step S210). In FIG. 11, the process of step S210 is shown as "information acquisition". The acquiring unit 261 may be configured to acquire the frequency information and power information from the measurement device 10 by outputting a request to acquire the frequency information and power information to the measurement device 10. The acquiring unit 261 may be configured to acquire, from the measurement device 10, the frequency information and power information output from the measurement device 10.

[0146] Next, the storage control unit 264 stores the frequency information and power information acquired by the acquisition unit 261 in step S210 in the storage unit 22 (step S220), and ends the process.

[0147] The information processing device 20 may be configured to acquire the frequency information and the power information at different times. Also, the information processing device 20 may be configured to store the frequency information and the power information in the storage unit 22 at different times.

[0148] <Modifications of the embodiment> Modifications of the embodiment will be described below.

[0149] The information processing device 20 may be configured to use the time when the estimation period starts as the first time in each of the first to third estimation modes. The information processing device 20 may be configured to use the time when the estimation period ends as the second time in each of the first to third estimation modes. That is, the information processing device 20 may be configured to estimate the event occurrence time based on frequency information in the target period, with the period from the first time before the estimation period to the second time after the estimation period being the period between the target devices.

[0150] When the time when the estimation period starts is used as the first time, in the estimation of the fitting function described above, linear approximation of the waveform of the grid frequency in the period before the event occurrence time is not performed, but polynomial approximation is performed in the period after the event occurrence time.

[0151] On the other hand, when the time when the estimation period ends is used as the second time, in the estimation of the fitting function described above, the period after the event occurrence time becomes shorter compared to when a time after the time when the estimation period ends is used as the second time.

[0152] The information processing device 20 may be configured to estimate an event occurrence time at which an event has occurred based on a system frequency indicated by the frequency information for the target period and a system frequency indicated by the second frequency information for the target period among second frequency information indicating a system frequency for each time at a predetermined second point (a point different from the aforementioned representative point) in the AC interconnection system R. That is, the information processing device 20 may be configured to estimate an event occurrence time at which an event has occurred based on a system frequency measured for each time at each of a plurality of predetermined points in the AC interconnection system R.

[0153] For example, the information processing device 20 estimates an event occurrence time when an event occurs based on a frequency obtained as a result of a weighted average of a system frequency indicated by frequency information in the target period and a system frequency indicated by second frequency information in the target period. At this time, the information processing device 20 estimates a fitting function that fits the waveform of the frequency in the target period as the above-mentioned fitting function. In this case, the information processing device 20 estimates the system inertia of the AC interconnection system R by the RoCoF method using the RoCoF for the frequency. Note that the method of estimating the fitting function is the same as the method described above, and therefore will not be described.

[0154] Here, the information processing device 20 may be configured to estimate the event occurrence time when the event occurs based on another value based on the two system frequencies, instead of the frequency obtained as a result of the weighted average of the system frequency indicated by the frequency information for the target period and the system frequency indicated by the second frequency information for the target period. The other value may be, but is not limited to, an average value not using weights for each time.

[0155] The information processing device 20 may be configured to estimate an event occurrence time when an event occurs based on a value corresponding to a system frequency indicated by frequency information in a target period. The value is, for example, a value of a function indicating a waveform obtained by smoothing the waveform of the system frequency. In this case, the information processing device 20 estimates the system inertia of the AC interconnection system R by the RoCoF method using the RoCoF for the waveform. Even in this case, the estimation method of the fitting function that fits the waveform is the same as the method described above. The value is, for example, a rotation speed calculated based on the system frequency and other quantities. The rotation speed is the rotation speed of a rotor of each generator (for example, each of the generators PP1 to PP4). The other quantities are, for example, power passing through a power transmission line, a transformer, etc., voltage near a representative point, etc., but are not limited thereto. In this case, the information processing device 20 estimates a fitting function that fits the waveform of the system frequency based on the system frequency and the event occurrence time estimated based on the waveform representing the temporal change of the rotation speed, and estimates the RoCoF for the system frequency based on the estimated fitting function. Moreover, the value is, for example, a rotation speed estimated based on a combination of system frequencies measured at multiple points. The rotation speed also refers to the rotation speed of the rotor of each generator (for example, each of generators PP1 to PP4). Instead of these, the value may be another value according to the system frequency.

[0156] The information processing device 20 may be configured to estimate an event occurrence time when an event occurs based on the rotation speed estimated by some method. Any method may be used as the method. That is, the method may be a method of calculation using a system frequency and other quantities, or other methods may be used. In this case, the information processing device 20 estimates the event occurrence time based on a waveform representing a time change in the rotation speed, and also estimates the system inertia of the AC interconnection system R. Note that the method of estimating the system inertia based on a waveform representing a time change in the rotation speed may be a known method or a method to be developed in the future.

[0157] As described above, the information processing device according to the embodiment (information processing device 20 in the example described above) estimates an event occurrence time at which an event has occurred based on frequency information in a target period including an estimated period during which an event that changes the frequency is estimated to have occurred, out of frequency information indicating the frequency of power (system frequency in the example described above) at each time at a predetermined point (representative point in the example described above) in an AC interconnection system (AC interconnection system R in the example described above) including one or more generators (generators PP1 to PP4 in the example described above). This enables the information processing device to improve the objectivity of the estimation of the event occurrence time.

[0158] The information processing device may be configured to estimate the event occurrence time based on frequency information in the target period, the period being from a first time before the estimation period to a second time after the estimation period.

[0159] The information processing device may also be configured to estimate the event occurrence time based on frequency information in the target period, the period being from a first time point before the estimation period to a second time point after the estimation period.

[0160] The information processing device may also be configured to estimate the event occurrence time by treating each time in the estimation period as a candidate time at which an event occurred, and for each time candidate, performing a linear approximation of a waveform of a frequency indicated by frequency information in a first target period from a first time to the time candidate, and a polynomial approximation of a waveform of a frequency indicated by frequency information in a second target period from the time candidate to a second time.

[0161] The information processing device may also be configured to perform linear approximation and polynomial approximation for each time candidate based on the least squares method.

[0162] The information processing device may also be configured to estimate the event occurrence time based on a frequency indicated by the frequency information for the target period and a frequency indicated by the second frequency information for the target period among second frequency information indicating the frequency of power for each time at a predetermined second point in the AC interconnection system.

[0163] The information processing device may also be configured to estimate the event occurrence time based on a frequency obtained as a result of a weighted average of a frequency indicated by frequency information for the target period and a frequency indicated by second frequency information for the target period.

[0164] The information processing device may be configured to estimate the event occurrence time based on a value corresponding to a frequency indicated by frequency information for a target period.

[0165] The information processing device also estimates an event occurrence time at which an event that changes the frequency of the power corresponding to the measurement value has occurred, based on measurement value information (power information in the above-described example) indicating a measurement value (power in the above-described example) excluding at least frequency among values ​​corresponding to the power at a predetermined point in an AC interconnection system including one or more generators. This allows the information processing device to improve the objectivity of the estimation of the event occurrence time.

[0166] In addition, in the information processing device, the measurement value information is information indicating measurement values ​​for each time, and the information processing device may be configured to estimate the time of event occurrence based on measurement value information for a target period from a first time before an estimated period in which the event is estimated to have occurred to a second time after the estimated period.

[0167] In addition, the information processing device may be configured to estimate the event occurrence time based on measurement value information for a target period and frequency information for the target period among frequency information indicating the frequency of power at each time at a predetermined point in the AC interconnection system.

[0168] The information processing device may also be configured to estimate the event occurrence time by treating each time in the estimation period as a candidate time at which the event occurred, and for each time candidate, performing a linear approximation of the waveform of the frequency indicated by the frequency information in the first target period from the first time to the time candidate, a linear approximation of the waveform of the measurement value indicated by the measurement value information in the first target period, a polynomial approximation of the waveform of the frequency indicated by the frequency information in the second target period from the time candidate to the second time, and a linear approximation of the waveform of the measurement value indicated by the measurement value information in the second target period.

[0169] The information processing device may be configured to specify each of the estimated period, the first time, and the second time in response to a received operation.

[0170] Moreover, the information processing device may be configured to estimate the estimated period, and identify each of the first time and the second time according to the estimated period.

[0171] The information processing device may also be configured to perform, based on the least squares method, linear approximation of the waveform of the frequency indicated by the frequency information in the first target period, linear approximation of the waveform of the measurement value indicated by the measurement value information in the first target period, polynomial approximation of the waveform of the frequency indicated by the frequency information in the second target period, and linear approximation of the waveform of the measurement value indicated by the measurement value information in the second target period.

[0172] Although an embodiment of the present invention has been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and may be changed, replaced, deleted, etc. without departing from the gist of the present invention.

[0173] In addition, a program for implementing the functions of any of the components in the above-described device may be recorded in a computer-readable recording medium, and the program may be read and executed by a computer system. Here, the device in question is, for example, the measurement device 10, the information processing device 20, etc. Note that the term "computer system" here includes hardware such as an operating system (OS) and peripheral devices. Furthermore, the term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and compact disks (CDs)-ROMs, and storage devices such as hard disks built into a computer system. Furthermore, the term "computer-readable recording medium" refers to a storage device that holds a program for a certain period of time, such as a volatile memory inside a computer system that becomes a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line.

[0174] The above program may be transmitted from a computer system in which the program is stored in a storage device or the like to another computer system via a transmission medium or by transmission waves in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has a function of transmitting information, such as a network such as the Internet or a communication line such as a telephone line. The above program may be for implementing some of the above functions. Furthermore, the above program may be a so-called differential file or differential program that can implement the above functions in combination with a program already recorded in the computer system. [Explanation of symbols]

[0175] 1...information processing system, 10...measuring device, 20...information processing device, 21...processor, 22...storage unit, 23...input receiving unit, 24...communication unit, 25...display unit, 26...control unit, 261...acquisition unit, 262...estimation unit, 263...display control unit, 264...memory control unit, PP1 to PP4...generators, R...AC interconnection system

Claims

1. based on measurement value information indicating measurement values ​​excluding at least frequency among values ​​corresponding to power at a predetermined point in an AC interconnection system including one or more generators, and frequency information indicating a frequency corresponding to the power at the point, an event occurrence time at which an event that changes the frequency of the power corresponding to the measurement value has occurred; Information processing device.

2. the measurement value information is information indicating the measurement value for each time, the information processing device estimates the event occurrence time based on the measurement value information in a target period from a first time before an estimation period in which the event is estimated to have occurred to a second time after the estimation period, among the measurement value information; The information processing device according to claim 1 .

3. specifying the estimated period, the first time, and the second time in response to the received operation; The information processing device according to claim 2 .

4. estimating the estimated period, and identifying each of the first time and the second time in accordance with the estimated estimated period; The information processing device according to claim 2 .

5. estimating the event occurrence time based on the measurement value information during the target period and the frequency information during the target period; The information processing device according to claim 2 .

6. each time in the estimation period is set as a time candidate at which the event occurred, and for each time candidate, a linear approximation is performed of a waveform of a frequency indicated by the frequency information in a first target period from the first time to the time candidate, a linear approximation of a waveform of the measurement value indicated by the measurement value information in the first target period, a polynomial approximation of a waveform of a frequency indicated by the frequency information in a second target period from the time candidate to the second time, and a linear approximation of a waveform of the measurement value indicated by the measurement value information in the second target period, thereby estimating the event occurrence time; The information processing device according to claim 5 .

7. performing a linear approximation of a waveform of a frequency indicated by the frequency information during the first target period, a linear approximation of a waveform of the measurement value indicated by the measurement value information during the first target period, a polynomial approximation of a waveform of a frequency indicated by the frequency information during the second target period, and a linear approximation of a waveform of the measurement value indicated by the measurement value information during the second target period based on a least squares method; The information processing device according to claim 6.

8. Acquire measurement value information indicating a measurement value, excluding at least a frequency, among values ​​corresponding to power at a predetermined point in an AC interconnection system including one or more generators, and frequency information indicating a frequency corresponding to the power at the point; estimating an event occurrence time at which an event that changes the frequency of power according to the measurement value has occurred based on the acquired measurement value information and the acquired frequency information; Information processing methods.

9. On the computer, acquiring measurement value information indicating a measurement value, excluding at least a frequency, among values ​​corresponding to power at a predetermined point in an AC interconnection system including one or more generators, and frequency information indicating a frequency corresponding to the power at the predetermined point; an event occurrence time at which an event that changes a frequency of power according to the measurement value has occurred is estimated based on the acquired measurement value information and the acquired frequency information; program.

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