Information processing device, information processing method, and information processing program

Abstract

To provide an information processor that accurately estimates a power system state by taking into consideration a difference between a measuring period of a circuit breaker provided with a sensor and another measuring period of a watt-hour meter, and to provide a method and a program therefor.SOLUTION: According to a power distribution system 1, an information processor 2 that estimates a state of the power distribution system in a predetermined period based on each of measured values with sensors of circuit breakers SW1 to SWM each provided with a sensor, and watt-hour meters SM comprises a reception part for receiving a first period as a measuring period of the sensor, and a second period as a measuring period of each of the watt-hour meters; a setting part for setting first weight with respect to the measured values of the sensors, and second weight with respect to the measured values of the watt-hour meters, based on the first period and the second period; and an estimation part for adjusting an influence degree on each of the measured values with respect to the sensors and the watt-hour meters according to the first weight and the second weight and estimating a state of the power distribution system in the predetermined period, based on each of the measured values with the sensors and the watt-hour meters, by using a system model that simulates the power distribution system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Techniques are known for estimating state values ​​such as active power and reactive power at each node of a power distribution system from measurements taken by sensor-equipped switches installed in the distribution system or by smart meters installed at customers in the distribution system (for example, Patent Document 1). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Patent No. 6132994 [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] Incidentally, sensor-equipped switches generally acquire measurement values ​​at intervals of about 1 to 10 minutes. On the other hand, electricity meters generally acquire the cumulative value of a customer's electricity consumption at intervals of about 30 minutes, which is longer than the measurement interval of sensor-equipped switches.

[0005] Therefore, there are times when the sensor-equipped switch acquires a measurement value, but the measurement value from the electricity meter cannot be directly obtained. In such times, the average value of the active power calculated based on the measurement value from the electricity meter is used when estimating the state.

[0006] However, the actual active power of the consumer load at such times may deviate from the average value of active power. Therefore, in such cases, it may not be possible to estimate the state values ​​at each node of the distribution system with the desired accuracy.

[0007] This invention was made in view of these problems, and aims to provide an information processing device that can accurately estimate the state of a power system by taking into account the difference between the measurement cycle of a sensor-equipped switch and the measurement cycle of an energy meter. [Means for solving the problem]

[0008] One invention for achieving the above objective is an information processing device for estimating the state of a power distribution system over a predetermined period of time, based on the respective measured values ​​of a power distribution line and a power distribution meter, in a power distribution system including a power distribution line, a sensor installed at a predetermined position on the power distribution line, and a power meter installed on a load at a predetermined node on the power distribution line, the information processing device comprising: a receiving unit that receives a first period which is the measurement period of the sensor and a second period which is the measurement period of the power distribution meter; a setting unit that sets a first weight for the measured value of the sensor and a second weight for the measured value of the power distribution meter based on the first period and the second period; and an estimation unit that adjusts the respective influence of the measured values ​​on the sensor and the power distribution meter according to the first and second weights, and estimates the state of the power distribution system over the predetermined period of time, based on the respective measured values ​​of the sensor and the power distribution meter, using a system model that simulates the power distribution system. Other features of the present invention will be made clear by the description herein. [Effects of the Invention]

[0009] According to the present invention, it is possible to accurately estimate the state of the power system by taking into account the difference between the measurement cycle of a sensor-equipped switch and the measurement cycle of an energy meter. [Brief explanation of the drawing]

[0010] [Figure 1] This figure shows an example of a power distribution system according to this embodiment. [Figure 2] This diagram illustrates the hardware configuration of the information processing device 2 in this embodiment. [Figure 3] This diagram illustrates the functional blocks of the information processing device 2 in this embodiment. [Figure 4] This figure shows an example of a power distribution system that is the target of estimation in this embodiment. [Figure 5] This is a flowchart illustrating the state estimation process in this embodiment. [Figure 6] This figure shows an example of the reception screen of this embodiment. [Figure 7] This is a flowchart illustrating the state estimation process in this embodiment. [Figure 8] This figure shows an example of the reception screen of this embodiment. [Figure 9] This is a flowchart illustrating the state estimation process in this embodiment. [Modes for carrying out the invention]

[0011] ==Implementation Method== <<Distribution system 1>> Figure 1 shows an example of a power distribution system in which the information processing device 2, described later, performs state estimation. The power distribution system 1 includes a power distribution substation 10, a power distribution line 11, a sensor-equipped switch SWi (i=1~M) installed at a predetermined location on the power distribution line 11, and an energy meter SMj (j=1~N) installed on the load at a predetermined node j (j=1~N) on the power distribution line 11.

[0012] [Distribution Substation 10] The distribution substation 10 transforms the voltage supplied from the transmission line (not shown) and outputs a voltage of 6.6kV to the distribution line 11.

[0013] [Power distribution line 11] The power distribution line 11 originates from the power distribution substation 10 (distribution node) and is connected radially to the power distribution substation 10. In Figure 1, only one power distribution line 11 is shown.

[0014] [node] The nodes are located on the power distribution line 11. In this embodiment, the nodes are aggregated units managed on a pole-mounted transformer (not shown) basis. The power output from the power distribution substation 10 to the power distribution line 11 is supplied to consumers via the nodes.

[0015] Each consumer is a power load. Consumers include equipment that consumes the power supplied from the distribution line 11. Therefore, equipment that consumes the power from the distribution line 11 is connected to the distribution line 11.

[0016] For example, a factory is connected to node 1 as a power-consuming facility. The power output from the power distribution substation 10 to the power distribution line 11 is supplied to the factory via node 1.

[0017] [Sensor-equipped switch (SW)] A sensor-equipped switch SW is a switch equipped with sensors capable of measuring at least the voltage, active power flow, and reactive power flow at the installation point at a fixed period. In this embodiment, the measurement period T1 (hereinafter referred to as "measurement period T1 of sensor-equipped switch SW") measured by all (M) sensor-equipped switches SWi (i=1 to M) is assumed to be 1 minute.

[0018] Furthermore, in this embodiment, the sensor-equipped switch SW measures from 0:01 to 24:00 in a calendar day, with a measurement cycle of 1 minute. The measured values ​​of the sensor-equipped switch SW are output to and stored in the measured value DB20.

[0019] Note that the measurement cycle T1 of the sensor-equipped switch SW does not necessarily have to be 1 minute, and the measurement cycle T1 of all sensor-equipped switch SWs does not have to be the same.

[0020] [Power meter SM] The electricity meter SM is installed on the load at a predetermined node of the distribution line 11. A so-called smart meter can be used as the electricity meter SM.

[0021] The energy meter SM measures the amount of energy consumed by the load at a fixed interval. In this embodiment, the measurement period T2 of the energy meter SM (hereinafter referred to as "measurement period T2 of the energy meter SM," etc.) is assumed to be 30 minutes. In other words, the measurement period T2 of the energy meter SM is longer than the measurement period T1 of the sensor-equipped switch SW. The amount of energy consumed measured by the energy meter SM, converted to energy consumed per unit time, is the active power.

[0022] The electricity meter SM measures energy values ​​in 30-minute intervals from 0:30 to 24:00 within a calendar day. The energy values ​​from the electricity meter SM are output to and stored in the measurement value DB21.

[0023] Furthermore, the measurement cycle T2 of the electricity meter SM does not necessarily have to be 30 minutes, nor does the measurement cycle T2 of all electricity meters SM have to be the same.

[0024] <<Information Processing Device 2>> The information processing device 2 is a device that estimates the state and voltage distribution of the power distribution system 1 over a predetermined period of time, based on the measured values ​​of the sensor-equipped switch SW and the electricity meter SM, respectively, and system equipment information (described later). The information processing device 2 estimates the state values ​​and voltage distribution of the power distribution system 1 over a predetermined period of time using a system model that simulates the power distribution system 1.

[0025] The measured values ​​of the sensor-equipped switch SW are obtained from the measured value DB20 (Figure 1) and stored in the storage device 203 (described later) of the information processing device 2. The measured values ​​of the electricity meter SM are obtained from the measured value DB21 (Figure 1) and stored in the storage device 203. System equipment information is obtained from the system equipment information DB22 (Figure 1) and stored in the storage device 203.

[0026] Furthermore, the status values ​​and voltage distribution of the power distribution system 1 estimated by the information processing device 2 are output to the status value DB23 (Figure 1).

[0027] The following describes the hardware configuration of the information processing device 2, the system model used by the information processing device 2, and the functional blocks of the information processing device 2, in that order.

[0028] <Hardware configuration of information processing device 2> Figure 2 is a diagram illustrating the hardware configuration of an information processing device 2, which is one embodiment of the present invention. The information processing device 2 is a computer having a CPU (Central Processing Unit) 200, memory 201, communication device 202, storage device 203, input device 204, output device 205, and recording medium reader 206.

[0029] [CPU200] The CPU 200 realizes various functions of the information processing device 2 by executing information processing programs stored in the memory 201 and the storage device 203.

[0030] [Memory 201] Memory 201 is, for example, RAM (Random-Access Memory) and is used as a temporary storage area for various programs and data.

[0031] [Communication device 202] The communication device 202 exchanges various programs and data with other computers via the network 5.

[0032] [Storage device 203] The storage device 203 is a non-temporary (e.g., non-volatile) storage device that stores various data executed or processed by the CPU 200.

[0033] The storage device 203 stores the measurement values ​​measured by the sensor-equipped switch SW and the energy meter SM. Details of these measurement values ​​will be described later.

[0034] [Input device 204] The input device 204 is a device that accepts commands and data input from the user and includes input interfaces such as a keyboard and a touch sensor that detects the touch position on a touch panel display.

[0035] [Output device 205] The output device 205 is, for example, a device such as a display or a printer.

[0036] [Recording medium reader 206] The recording medium reader 206 reads various data, such as information processing programs, recorded on the recording medium 3, such as an SD card, DVD, or CD-ROM, and stores them in the storage device 203.

[0037] <Systematic Model> The system model is a model that simulates distribution system 1. The system model is based on a state-space model that takes into account noise in state values ​​and measured values. Here, "state values" are state values ​​that indicate the state of distribution system 1, for example, the active power and reactive power of each node. "Measured values" are measured values ​​from sensor-equipped switch SW and energy meter SM, for example, the active power of the customer's load.

[0038] [State-space model] The information processing device 2 of this embodiment performs dynamic state estimation using the following state space model.

number

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[0039] In these equations (Equations 1 and 2), t is time. x(t) is the state value at time t. w is the noise expected to be included in the state value. x(t) and w are n-dimensional vectors if there are n state values ​​to estimate.

[0040] Furthermore, y(t) is the measured value at time t, and r is the noise expected to be included in the measured value. If the number of obtainable measured values ​​is m, then y(t) and r form an m-dimensional vector.

[0041] Furthermore, U is a system matrix with n x n columns used to predict the state value x(t) at a time Δt later from the state value x(t) at a given time t. F is an operation performed on the state value x(t) to predict the measured value at the same time t from the state value x(t) at a given time t.

[0042] [Status values ​​and measured values] In this embodiment, the state value x(t) and the measured value y(t) are expressed by the following two equations, respectively.

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[0043] In the state value x(t) of Math 3, P j (t) and Q j (t) represents the active power and reactive power of node j, respectively. Here, j is an index specifying the node, ranging from 1 to N, and the same applies below unless otherwise specified.

[0044] In the measured value y(t) in equation 4, P SM j (t) is the active power calculated based on the measurements of the electricity meter SMj installed at the consumer at node j (hereinafter simply referred to as "electricity meter SMj at node j").

[0045] Also, v i (t), p i (t) and q i (t) are the measured values ​​of the sensor-equipped switch SWi, which are voltage, active power flow, and reactive power flow, respectively. Here, i is a subscript specifying the sensor-equipped switch SWi, ranging from 1 to M, but the same applies below unless otherwise specified.

[0046] Here, as described above, the switch SW with a sensor measures the measured value at a measurement period of 1 minute from 0:01 in one calendar day. Let the time when the switch SW with a sensor measures the measured value be t1 (k = 1 to kmax). kmax is 1440.

[0047] Also, as described above, the watt-hour meter SM measures the measured value at a measurement period of 30 minutes from 0:30. The time when the watt-hour meter SM measures the measured value is t k (k = 30, 60, ···).

[0048] That is, for example, at time t k (k = 1 to 29), the switch SW with a sensor performs measurement, but the watt-hour meter SM does not directly perform measurement. Therefore, the measured value P of the watt-hour meter SMj at time t k (k = 1 to 30) SM j (t k )(k = 1 to 30) is the average value of the active power based on the power consumption measured at the measurement time t 30 (0:30) of the watt-hour meter SMj, and all are the same value.

[0049] That is, the measured value P of the watt-hour meter SMj SM j (t k )(k = 1 to 30) is the average value of the active power obtained by dividing the power consumption directly measured by the watt-hour meter SMj at time t 30 by 0.5 hours (30 minutes, which is the measurement period of the watt-hour meter SM). The same applies to other times.

[0050] [Covariance matrix] In the state space models shown in Equation 1 and Equation 2, it is assumed that the noise w predicted to be included in the state value x(t) and the noise r predicted to be included in the measured value y(t) follow a Gaussian distribution with an average value of 0 and a predetermined variance. Based on this premise, the following covariance matrix is defined.

[0051] [Equation]

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[0052] The covariance matrix W in Math 5 is a 2N x 2N square matrix where all elements except the diagonals are zero. The covariance matrix R in Math 6 is a (N+3M) x (N+3M) square matrix where all elements except the diagonals are zero. The components of each covariance matrix will be explained below.

[0053] In Math 5, σ wPj 2 σ is the component in the j row and j column of the covariance matrix W, and represents the variance of noise that is predicted to be included in the active power at node j. wQj 2 This is the (N+j) row and (N+j) column component of the covariance matrix W, and represents the variance of noise that is predicted to be included in the reactive power at node j.

[0054] In Math 6, σ rPSMj 2 This is the component in the j-th row and j-th column of the covariance matrix R, and represents the variance of noise that is expected to be included in the active power calculated based on the measurements of the energy meter SMj at node j.

[0055] Also, σ rvi 2 σ is the (N+i) row and (N+i) column component of the covariance matrix R, and represents the variance of noise expected to be contained in the voltage measured by the sensor-equipped switch SWi. rpi 2 This is the (N+M+i) row (N+M+i) column component of the covariance matrix R, and represents the variance of noise predicted to be included in the active power flow measured by the sensor-equipped switch SWi. rqi 2 This is the (N+2M+i) row (N+2M+i) column component of matrix R, and represents the dispersion of noise expected to be contained in the reactive power flow measured by the sensor-equipped switch SWi.

[0056] As will be explained in more detail later, in the information processing device 2, the components of the covariance matrices W and R are predetermined values.

[0057] The information processing device 2 of this embodiment performs dynamic state estimation using a Kalman filter. The following describes the pre-update, Kalman gain calculation, and post-update within the framework of the Kalman filter.

[0058] TIFF0007852330000007.tif28170

[0059] TIFF0007852330000008.tif13170

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[0060] TIFF0007852330000011.tif20170

[0061] [Calculation of Kalman Gain] Let's explain how to calculate the Kalman gain. Kalman gain K(t k ) is expressed by the following formula.

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[0062] Here, F and Ψ are expressed by the following equations, respectively.

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[0063] TIFF0007852330000015.tif13170

[0064] TIFF0007852330000016.tif20170

[0065] TIFF0007852330000017.tif13170

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[0066] TIFF0007852330000020.tif20170

[0067] TIFF0007852330000021.tif27170

[0068] After the update, time t k The corrected state value and the corrected covariance matrix at time t are obtained again as shown in the following two equations. k These are the predicted values ​​of the state values ​​and the covariance matrix.

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[0069] By repeating the above calculation process, the state over a predetermined period can be estimated.

[0070] [Regarding terminology related to phylogenetic models] Each component of the covariance matrices W and R (Equations 5 and 6) defined above represents the variance of the noise expected to be included in the state value or measured value, but in the following explanation, these will also be referred to as "weights for the state value or measured value".

[0071] In other words, in Math 5, σ wPj 2 This is also referred to as "the weight of active power at node j". wQj 2 This is also called the "weight of reactive power at node j". Here, σ wPj2 and σ wQj 2 When these are grouped together and node j is not distinguished, they are also called "weights for state values."

[0072] Also, in Math 6, σ rPSMj 2 This is also referred to as the "weight of the measurement value from the electricity meter SM." The weight of the measurement value from the electricity meter SM corresponds to the "second weight."

[0073] Also, σ rvi 2 When not distinguishing between sensor-equipped switches SWi (hereinafter the same), this is also referred to as "the weight of the measured voltage of the sensor-equipped switch SW." rpi 2 This is also referred to as "the weight of the measured value of the active power flow of a sensor-equipped switch SW." rqi 2 This is also referred to as "the weight of the measured value of the reactive power flow of a sensor-equipped switch SW". Here, σ rvi 2 , σ rpi 2 and σ rqi 2 This is collectively referred to as "the weight of the measurement value for the sensor-equipped switch SW."

[0074] The weights assigned to the voltage measurement by the sensor-equipped switch SW, the weights assigned to the active power flow measurement, and the weights assigned to the reactive power flow measurement all together correspond to the "first weight."

[0075] [Regarding the influence of the weighting of the measured values] The system model can be described as a model in which the influence of the measured values ​​on the sensor-equipped switch SW and the energy meter SM is adjusted according to the weight given to the measured values ​​on the sensor-equipped switch SW and the energy meter SM, based on the properties of the Kalman filter.

[0076] Here, "degree of influence" refers to the degree of correction applied to the state value during a post-update. The "degree of correction" here is the magnitude of the second term on the right-hand side of equation 12.

[0077] Specifically, one of the factors that influences the magnitude of the second term on the right-hand side of equation 12 is the Kalman gain K (equation 9). As can be seen from equations 10 and 11, the Kalman gain K depends on the covariance matrix R in equation 5. As mentioned above, the covariance matrix W has components that are weights for the measured values ​​of the sensor-equipped switch SW and the energy meter SM.

[0078] More precisely, "influence of measured values" refers to the degree of influence of noise included in the measured values.

[0079] Furthermore, the system model can be described as one in which the influence of the measured values ​​of the sensor-equipped switch SW and the electricity meter SM decreases as the weight given to these values ​​increases. In other words, the greater the weight given to the measured values, the smaller the degree of correction, which is the second term on the right-hand side of equation 12.

[0080] Specifically, from equation 12, we can see that for a given measurement A among the measurements shown in equation 4, the larger the prediction error A (in parentheses in the second term on the right-hand side) in equation 12, the greater the degree of correction caused by the prediction error A tends to be.

[0081] However, the greater the weight given to the measured value A, the smaller the degree of correction caused by the prediction error A will be, due to the properties of the Kalman gain K.

[0082] Therefore, if the reliability of measurement value A is considered low, it is preferable to set a larger weight for measurement value A. This minimizes the degree of correction caused by measurement value A, and prevents unnecessary corrections from being applied to the pre-correction state during subsequent updates.

[0083] On the other hand, if the reliability of measurement value A is considered high, it is preferable to set a small weight for measurement value A. This increases the degree of correction caused by measurement value A, and when updating later, the pre-correction state is corrected to become an even more reliable state.

[0084] Furthermore, the system model used by the information processing device 2 is not limited to the model described above, as long as it is a model that allows for adjustment of the influence of the measured values ​​on the sensor-equipped switch SW and the energy meter SM, respectively, according to the weight given to the measured values ​​of the sensor-equipped switch SW and the energy meter SM.

[0085] [Guidelines for setting weights 1] Based on the above points, guidelines for appropriately setting the weights for measured values ​​in the state estimation of the power distribution system 1 of this embodiment will be explained.

[0086] As described above, in this embodiment, the measured value P of the energy meter SM at time t SM j (t) is the average value of active power calculated based on the measured power consumption at the time when the energy meter SM takes a measurement immediately after time t.

[0087] Therefore, the actual active power of the consumer load at time t is the measured value P from the energy meter SM at time t. SM j It is also possible that it deviates from (t).

[0088] Therefore, it is preferable to perform state estimation after appropriately setting the weights for the measured values ​​of the sensor-equipped switch SW and the measured values ​​of the energy meter SM so that the relative influence of the measured values ​​of the energy meter SM in state estimation is reduced.

[0089] As will be described in detail later, the information processing device 2 of this embodiment is a device that can improve the accuracy of state estimation by reducing the relative influence of the measured values ​​of the energy meter SM in state estimation.

[0090] [Guidelines for setting weights, part 2] The active power P of node j among the state values ​​x. j This is the measured value P of the energy meter SMj at node j among the measured values ​​y. SM jIt is more significantly affected by this than other measurements. Also, the larger the absolute value of the electricity meter SMj measurement, the greater the noise contained in the electricity meter SMj measurement.

[0091] Therefore, it is preferable to consider the reliability of the measurement value of the electricity meter SM to be lower the larger the absolute value of the measurement value of the electricity meter SM. In other words, it is preferable to set a larger weight on the measurement value of the electricity meter SM the larger the absolute value of the measurement value of the electricity meter SM.

[0092] By setting the weights in this way, the larger the absolute value of the measurement from the power meter SMj, the more the predicted state value at node j will be influenced by the measurement from the sensor-equipped switch SW at node j, rather than by the measurement from the power meter SMj.

[0093] In other words, the larger the absolute value of the measurement from the power meter SMj, the greater the contribution of the measurement from the sensor-equipped switch SW to the degree of correction of the state value at node j during the post-update (second term on the right side of equation 12) compared to the measurement from the power meter SMj at node j.

[0094] As a result, when the readings from the energy meter SM are updated, changes in power flow are appropriately reflected in the status values ​​of each node according to the magnitude of the readings from the energy meter SM, improving the responsiveness of the status values.

[0095] One example of a weight given to the measurement values ​​of such an energy meter SMj is the weight shown in the following formula.

number

[0096] In equation 16, all weights for the measured values ​​of the energy meter SMj are based on the common factor σ. SM 2 This includes the measured values ​​P from the power meter SMj for each node. SM j For a quantity corresponding to the absolute value of (t) (in the parentheses on the right side of equation 16), a common factor σ SM2 It is the result of multiplying by .

[0097] Note that the denominator in the right-hand parentheses of equation 16 is the measured value P of the power meter SM for each node. SM j This is the largest absolute value of (t). Therefore, the value in the parentheses on the right side of equation 16 will be less than or equal to 1.

[0098] As will be described in more detail later, the information processing device 2 of this embodiment is also a device that can appropriately reflect changes in power flow in the status values ​​of each node according to the measurement values ​​of the power meter SM, thereby improving the tracking ability of the status values.

[0099] <Functional blocks of the information processing device 2> Figure 3 shows the functional blocks of the information processing device 2. The information processing device 2 includes an acquisition unit 210, an acquisition unit 211, a reception unit 212, a setting unit 213, an estimation unit 214, and an output unit 215.

[0100] [Acquisition unit 210] The acquisition unit 210 refers to the storage device 203 to acquire the configuration of the section to be estimated and the system equipment information of the section. "Configuration of the section" refers to, for example, the number and arrangement of sensor-equipped switches SW and nodes in the section to be estimated. "System equipment information of the section" refers to, for example, the admittance matrix, resistance, reactance, etc. of the section, and is information necessary for state estimation and subsequent power flow calculations to estimate the voltage distribution.

[0101] [Acquisition unit 211] The acquisition unit 211 refers to the storage device 203 and acquires the measured values ​​at each time point within the period subject to state estimation. The measured values ​​here refer to the active power calculated based on the measured values ​​of the power meter, and the measured values ​​of the sensor-equipped switch SW, namely voltage, active power flow, and reactive power flow (Equation 4).

[0102] [Reception Desk 212] The reception unit 212 receives the measurement cycle T1 (corresponding to the "first cycle") of the sensor-equipped switch SW and the measurement cycle T2 (corresponding to the "second cycle") of the energy meter SM. Specifically, the reception unit 212 receives these cycles from the user via the reception screen, but the details will be described later.

[0103] [Settings section 213] The setting unit 213 sets the weight for the measurement value of the sensor-equipped switch SW (corresponding to the "first weight") and the weight for the measurement value of the energy meter SM (corresponding to the "second weight") based on the measurement cycle T1 of the sensor-equipped switch SW and the measurement cycle T2 of the energy meter SM.

[0104] Note that in the following explanation, the weight σ of the measurement value of the sensor-equipped switch SWi is used. rvi 2 , σ rpi 2 and σ rqi 2 These are collectively referred to as "weight W1 for the measured value of the sensor-equipped switch SW". Also, the weight σ for the measured value of the energy meter SMj. rSMj 2 This is collectively referred to as "W2, the weight of the measurement value from the electricity meter SM."

[0105] In this embodiment, the setting unit 213 sets the weight W2 for the measured value of the power meter SM to be larger, or sets the weight W1 for the measured value of the sensor-equipped switch SW to be smaller, when the measurement period T2 of the power meter SM is longer than the measurement period T1 of the sensor-equipped switch SW.

[0106] In other words, the setting unit 213 sets the weight W2 for the measured values ​​of the sensor-equipped switch SW and the measured values ​​of the power meter SM so that when the measurement period T2 of the power meter SM is longer than the measurement period T1 of the sensor-equipped switch SW, the influence of the measured values ​​of the power meter SM is reduced compared to the influence of the measured values ​​of the sensor-equipped switch SW.

[0107] Furthermore, the setting unit 213 sets a larger weight W1 for the measurement value of the sensor-equipped switch SW as the absolute value of the measurement value of the power meter SM increases. For this purpose, the setting unit 213 adopts the weight W1 for the measurement value of the sensor-equipped switch SW shown in Equation 16 above. This will be explained in detail later using a flowchart.

[0108] When the setting unit 213 receives a measurement from the power meter SM, it updates the weight W1 for the measurement value of the sensor-equipped switch SW.

[0109] [Estimation section 214] The estimation unit 214 estimates the state of the power distribution system 1 over a predetermined period of time using a system model that simulates the power distribution system 1. At this time, the estimation unit 214 uses the system model to estimate the state of the power distribution system 1 based on the measured values ​​of the sensor-equipped switch SW and the electricity meter SM.

[0110] Specifically, the estimation unit 214 calculates the already estimated time t k-1 Based on the state of power distribution system 1 (corresponding to the "first state") at time t k-1 A time t later than k Calculate the uncorrected state of power distribution system 1 at (corresponding to "second time") (corresponding to "second state before correction") (Equation 7).

[0111] The estimation unit 214 also calculates time t k The uncorrected covariance matrix D in - (t k Calculate (Math 8).

[0112] The estimation unit 214 also calculates time t k Kalman gain K(t) k Calculate (Math 9).

[0113] The estimation unit 214 also calculates the corrected state (corresponding to the "corrected second state") which is corrected relative to the state before correction, based on the prediction error (Equation 12). The prediction error is, as mentioned above, time t kThe measured value at and the time t predicted from the state before correction. k This is the difference between the measured value and the predicted value.

[0114] The estimation unit 214 also calculates time t k Calculate the corrected covariance matrix in (Equation 13).

[0115] Here, the estimation unit 214 calculates the corrected state such that, based on the properties of the Kalman filter, the larger the weight W2 on the measured values ​​of the sensor-equipped switch SW and the energy meter SM, the smaller the degree of correction based on the prediction error for the sensor-equipped switch SW and the energy meter SM becomes.

[0116] The estimation unit 214 also calculates the voltage distribution of the power distribution system 1 by power flow calculation from the state values ​​that indicate the estimated state of the power distribution system 1.

[0117] [Output section 215] The output unit 215 outputs a status value and voltage distribution indicating the state of the power distribution system 1 estimated by the estimation unit 214.

[0118] <<State estimation process>> The process flow for state estimation in the section to be estimated by the information processing device 2 will be explained. Here, the power distribution system 100 shown in Figure 4 will be used as an example to explain this process. In this example, the number of sensor-equipped switches SW is M is 2, and the number of nodes N is 7.

[0119] In this example as well, the period subject to state estimation will be from 0:01 to 24:00 of a calendar day.

[0120] In this example, only the active power of node j (j=1 to 7) is considered for state estimation, while reactive power is excluded.

[0121] Therefore, the state value x(t) and the measured value y(t) in this example can be expressed by the following two equations.

number

[0122] FIG. 5 is a flowchart for explaining the flow of the state estimation process in the section to be estimated by the information processing apparatus 2. This process includes steps S1 to S8.

[0123] First, in step S1, the acquisition unit 210 acquires the configuration of the section to be the target of state estimation and the system facility information of the section.

[0124] Next, in step S2, the acquisition unit 211 acquires the measurement values (Formula 4) at each time within the period to be the target of state estimation.

[0125] In this example, since the period to be the target of state estimation is from 0:01 to 24:00 of one calendar day, the acquisition unit 211 acquires the measurement values y(t k (k = 1 to kmax)) at time t every one minute starting from 0:01. k Here, kmax is 1440.

[0126] Next, in step S3, the reception unit 212 receives the measurement period T1 of the switch SW with sensor and the measurement period T2 of the watt-hour meter SM.

[0127] FIG. 6 is a reception screen 70 for the reception unit 212 to receive the measurement period T1 of the switch SW with sensor and the measurement period T2 of the watt-hour meter SM. On the reception screen 70, an input window 70a for inputting the measurement period T1 of the switch SW with sensor and an input window 70b for inputting the measurement period T2 of the watt-hour meter SM are arranged.

[0128] FIG. 6 shows a state where "1" indicating 1 minute, which is the measurement period T1 of the switch SW with sensor of the power distribution system 1, is input into the input window 70a, and "30" indicating 30 minutes, which is the measurement period T2 of the watt-hour meter SM, is input into the input window 70b by the user.

[0129] Next, in step S4, the setting unit 213 sets a weight W1 for the measurement value of the switch SW with a sensor and a weight W2 for the measurement value of the power meter SM based on the measurement period T1 of the switch SW with a sensor and the measurement period T2 of the power meter SM.

[0130] Note that in this example, among the weights W1 and W2 (covariance matrix) of the measurement values shown in Equation 6, the weight W1 for the measurement value of the switch SW with a sensor is all σ as shown in the following equation. r 2 It is assumed to be common.

Equation

[0131] Hereinafter, σ r 2 is referred to as "common weight W1 for the measurement value of the switch SW with a sensor" or simply "common weight W1".

[0132] Furthermore, in this example, among the weights W1 and W2 (covariance matrix) of the measurement values shown in Equation 6, the weight W2 for the measurement value of the power meter SM is assumed as shown in the following equation (similar to Equation 14).

Equation

[0133] Hereinafter, σ on the right side of Equation 20 SM 2 is referred to as "common factor for the measurement value of the power meter SM" or simply "common factor".

[0134] That is, in this step S4, the setting unit 213 sets the weight W2 for the measurement value of the switch SW with a sensor to be larger as the absolute value of the measurement value of the power meter SM is larger.

[0135] Step S4 will be described in detail. Step S4 includes steps S41 to S43 (FIG. 7).

[0136] First, in step S41, the setting unit 213 calculates candidate weights W1 and W2 for the measured values ​​based on the measurement period T1 of the sensor-equipped switch SW and the measurement period T2 of the energy meter SM, which are received by the receiving unit 212.

[0137] Specifically, the setting unit 213 uses the common weight σ shown in Equation 19. r 2 (W1) and the common factor σ shown in equation 20 SM 2 Calculate the candidates.

[0138] Here, "candidate" refers to the weight σ r 2 (W1) and the common factor σ SM 2 It is sufficient if it appropriately limits the choices available.

[0139] One example of a candidate is a common weight σ r 2 Given (W1), a common weight σ r 2 The common factor σ is a function of (W1). SM 2 It may also be something that gives a common weight σ. In this case, a common weight σ r 2 As (W1) increases, at least the common factor σ SM 2 It will also increase.

[0140] Another example of a candidate is a common weight σ r 2 Given (W1), a common factor σ SM 2 It may also be a way to limit the range. In this case, a common weight σ r 2 Given that, a common factor σ SM 2 It should be considered as providing a lower limit.

[0141] Next, in step S42, the setting unit 213 receives weights W1 and W2 for the measured values ​​to be used in state estimation from the range of candidate weights W1 and W2 for the measured values ​​calculated in step S41.

[0142] Figure 8 shows that the setting unit 213 has a common weight σ r 2 (W1) and the common factor σ SM 2 This is the reception screen 71 for receiving the information. Note that there is a common weight σ. r 2 (W1) and the common factor σ SM 2 Once these are determined, the weights W1 and W2 for the measured values ​​are identified by equations 19 and 20.

[0143] The reception screen 71 displays the common weight σ r 2 An input window 71a for inputting (W1), and a common factor σ SM 2 An input window 71b for inputting data is provided.

[0144] The reception screen 71 also includes a common weight σ r 2 In response to (W1) being entered into input window 71a, the common factor σ SM 2 A message M is displayed to inform the user of the recommended values. In this example, the common weight σ is used. r 2 As "15" was entered in input window 71a as (W1), the common factor σ SM 2 A message is displayed indicating that the recommended value is 50 or higher.

[0145] Here, the common weight σ r 2 Common factor σ corresponding to SM 2 The recommended value is the value calculated by the setting unit 213 in step S41.

[0146] The user has a common weight σ r 2 (W1) and the common factor σ SM 2 Enter the following and select the OK button. This will set the common weight σ r 2 (W1) and the common factor σ SM 2 The weights W1 and W2 for the measured values ​​are determined, and these weights are identified.

[0147] Next, in step S43, the setting unit 213 sets the weights W1 and W2 for the measured values ​​received in step S42 as initial values. This completes the process in step S4.

[0148] Next, in step S5, the setting unit 213 sets the subscript k, which indicates the time step, to 1.

[0149] Next, in step S6, the estimation unit 214 determines the time t k The state of power distribution system 1 in this location is estimated. Step S6 will be explained in detail below. Step S6 includes steps S61 to S68 (Figure 9).

[0150] First, in step S61, the estimation unit 214 determines the time t k In this step, it is determined whether the measurement value of the electricity meter SM has been updated. If the measurement value of the electricity meter SM has been updated (S61:Y), proceed to step S62. If the measurement value of the electricity meter SM has not been updated (S61:N), proceed to step S63.

[0151] In step S62, the setting unit 213 updates the weights W1 and W2 for the measured values. Specifically, the setting unit 213 applies the updated measured values ​​of the energy meter SM to number 20 and updates the common factors. Then, the process proceeds to step S63.

[0152] Next, in step S63, the estimation unit 214 calculates the already estimated time t k-1 Based on the state of power distribution system 1 at time tk The pre-correction state of power distribution system 1 at time t is calculated (Equation 7). The estimation unit 214 also calculates the state at time t k The uncorrected covariance matrix D in - (t k Calculate (Math 8).

[0153] Next, in step S64, the estimation unit 214 calculates the Kalman gain K(t k Calculate (Math 9).

[0154] Next, in step S65, the estimation unit 214 calculates the corrected state, which is the corrected state relative to the pre-correction state calculated in step S63 (Equation 12). The estimation unit 214 also calculates the time t k Calculate the corrected covariance matrix in (Equation 13).

[0155] Next, in step S66, the estimation unit 214 calculates the voltage distribution of the power distribution system 1 by power flow calculation based on the state values ​​that indicate the estimated state of the power distribution system 1. This completes the process in step S6.

[0156] Next, in step S7, if k is kmax (1440), (S7:Y) proceed to step S9. If k, which represents the time step, is not kmax, (S7:N) proceed to step 8.

[0157] Next, in step S8, add 1 to k and return to step S5.

[0158] Next, in step S9, the output unit 215 outputs a status value and voltage distribution indicating the state of the power distribution system 1 estimated in step S6. This completes the processing of the information processing device 2.

[0159] This processing method allows for improved accuracy of state estimation by adjusting the relative influence of the measurements from the energy meter (SM) in the state estimation. Furthermore, it enables the state values ​​of each node to be appropriately reflected in the state values ​​according to the energy meter (SM) measurements, thereby improving the tracking ability of the state values.

[0160] ==Summary== The information processing device 2 of this embodiment is an information processing device 2 that estimates the state of the power distribution system 1 over a predetermined period based on the respective measured values ​​of the sensor and the power meter SM in a power distribution system 1 including a power distribution line 11, a sensor installed at a predetermined position on the power distribution line 11, and an energy meter SM installed on a load at a predetermined node of the power distribution line 11. The information processing device 2 comprises a reception unit 212 that receives a first period which is the measurement period of the sensor and a second period which is the measurement period of the energy meter SM; a setting unit 213 that sets a weight W1 for the measured value of the sensor and a weight W2 for the measured value of the energy meter SM based on the first period and the second period; and an estimation unit 214 that estimates the state of the power distribution system 1 over a predetermined period based on the respective measured values ​​of the sensor and the energy meter SM, using a system model that simulates the power distribution system 1, and adjusting the respective influence of the measured values ​​on the sensor and the energy meter SM according to the weights W1 and W2.

[0161] With this configuration, it is possible to improve the accuracy of state estimation by adjusting the relative influence of the measurements from the energy meter (SM) in state estimation.

[0162] Furthermore, in the information processing device 2, the system model is such that the influence of the measured values ​​on the sensor and the energy meter SM decreases as the weights W1 and W2 increase. The setting unit 213 sets either weight W2 to be larger or weight W1 to be smaller when the second period is longer than the first period, compared to when the first period is equal to the second period. With this configuration and processing, it is possible to improve the accuracy of state estimation by reducing the relative influence of the measured values ​​of the energy meter SM in state estimation.

[0163] Furthermore, in the information processing device 2, the setting unit 213 sets the weight W2 to be larger the larger the absolute value of the measurement value from the power meter SM. With this configuration, changes in power flow are appropriately reflected in the status values ​​of each node according to the measurement value from the power meter SM, making it possible to improve the tracking ability of the status values.

[0164] Furthermore, in the information processing device 2, the setting unit 213 updates the weight W2 when a measurement is taken from the power meter SM. With this configuration, changes in power flow are more appropriately reflected in the status values ​​of each node according to the measurement values ​​of the power meter SM, and the tracking ability of the status values ​​can be further improved.

[0165] Furthermore, in the information processing device 2, the estimation unit 214 calculates the uncorrected second state, which is the state of the power distribution system 1 at a second time later than the first time, based on the first state, which is the state of the power distribution system 1 at the first estimated time. Based on the prediction error, which is the difference between the measured value at the second time and the predicted value of the measured value at the second time predicted from the uncorrected second state, the corrected second state, which is the state corrected for the uncorrected second state, is calculated. The corrected second state is calculated such that the degree of correction based on the prediction error for the sensor and the power meter SM decreases as the first and weight W2 increase. With this configuration, it is possible to further improve the accuracy of state estimation by more reliably reducing the relative influence of the measured value of the power meter SM in state estimation.

[0166] The information processing method of the embodiment is an information processing method for estimating the state of the power distribution system 1 over a predetermined period based on the respective measured values ​​of the sensor and the power meter SM in a power distribution system 1 which includes a power distribution line 11, a sensor installed at a predetermined position on the power distribution line 11, and an energy meter SM installed on a load at a predetermined node of the power distribution line 11, and includes the steps of: receiving a first period which is the measurement period of the sensor and a second period which is the measurement period of the energy meter SM; setting a weight W1 for the measured value of the sensor and a weight W2 for the measured value of the energy meter SM based on the first period and the second period; adjusting the respective influence of the measured values ​​on the sensor and the energy meter SM according to the weights W1 and W2, and using a system model that simulates the power distribution system 1, estimating the state of the power distribution system 1 over a predetermined period based on the respective measured values ​​of the sensor and the energy meter SM.

[0167] This method makes it possible to improve the accuracy of state estimation by adjusting the relative influence of the measurements from the energy meter (SM) in the state estimation.

[0168] The information processing program of this embodiment is an information processing program that estimates the state of the power distribution system 1 over a predetermined period based on the respective measurement values ​​of the sensor and the power meter SM in a power distribution system 1 including a power distribution line 11, a sensor installed at a predetermined position on the power distribution line 11, and an energy meter SM installed on a load at a predetermined node of the power distribution line 11. The program implements a computer that includes: a reception unit 212 that receives a first period which is the measurement period of the sensor and a second period which is the measurement period of the energy meter SM; a setting unit 213 that sets a weight W1 for the measurement value of the sensor and a weight W2 for the measurement value of the energy meter SM based on the first period and the second period; and an estimation unit 214 that estimates the state of the power distribution system 1 over a predetermined period based on the respective measurement values ​​of the sensor and the energy meter SM, using a system model that simulates the power distribution system 1, and adjusting the respective influence of the measurement values ​​on the sensor and the energy meter SM according to the weights W1 and W2.

[0169] Such a program makes it possible to improve the accuracy of state estimation by adjusting the relative influence of the measurements from the energy meter (SM) in the state estimation. [Explanation of Symbols]

[0170] 1:Power distribution system 10: Power distribution substation 11: Power distribution lines 2: Information Processing Device 200:CPU 201: Memory 202: Communication equipment 203: Storage device 204: Input device 205: Output device 206: Recording medium reader 210: Acquisition Department 211: Acquisition Department 212: Reception Department 213: Settings Section 214: Estimation Department 215: Output section

Claims

1. An information processing device for estimating the state of the power distribution system over a predetermined period of time based on the respective measurement values ​​of the sensors and the energy meters, in a power distribution system including power distribution lines, sensors installed at predetermined locations on the power distribution lines, and energy meters installed on loads at predetermined nodes on the power distribution lines, wherein the information processing device estimates the state of the power distribution system over a predetermined period of time. A receiving unit that receives a first period, which is the measurement period of the sensor, and a second period, which is the measurement period of the electricity meter, A setting unit that sets a first weight for the measurement value of the sensor and a second weight for the measurement value of the energy meter based on the first period and the second period, The system includes an estimation unit that uses a system model to simulate the distribution system and estimates a state value indicating the active power of each of the multiple nodes of the distribution system during a predetermined period, based on the respective measurement values ​​of the sensor and the power meter. The sensor measures the voltage, active power flow, and reactive power flow at the installation location in the first period, The aforementioned power meter measures the amount of power of the load in the second period, The aforementioned system model is This is a state-space model that, upon input of system equipment information of the power distribution system and the respective measurement values ​​of the sensor and the electricity meter, calculates and outputs the state value. This model allows for adjustment of the influence of the sensor and the energy meter measurements on the state value, respectively, according to the first and second weights. Information processing device.

2. An information processing apparatus according to claim 1, The aforementioned system model is one in which the influence of the measured values ​​on the sensor and the energy meter decreases as the weights of the first and second components increase. The setting unit is, When the second period is longer than the first period, compared to when the first period is equal to the second period, Set the second weight mentioned above to a large value, Set the aforementioned first weight to a small value. Information processing device.

3. An information processing apparatus according to claim 2, The setting unit sets the second weight to be larger the larger the absolute value of the measurement value from the electricity meter. Information processing device.

4. An information processing apparatus according to claim 3, The setting unit updates the second weight when the measurement value of the electricity meter is measured. Information processing device.

5. An information processing apparatus according to any one of claims 1 to 4, The estimation unit, Based on the first state, which is the state of the power distribution system at the first estimated time, the uncorrected second state, which is the state of the power distribution system at a second time that is later than the first time, is calculated. Based on the prediction error, which is the difference between the measured value at the second time and the predicted value of the measured value at the second time predicted from the second state before correction, the corrected second state, which is the state corrected from the second state before correction, is calculated. The second corrected state is calculated such that the degree of correction based on the prediction error for the sensor and the energy meter decreases as the first and second weights increase. Information processing device.

6. An information processing method for estimating the state of a power distribution system over a predetermined period of time, based on the respective measurement values ​​of the sensors and the energy meters, in a power distribution system including power distribution lines, sensors installed at predetermined locations on the power distribution lines, and energy meters installed on loads at predetermined nodes on the power distribution lines, wherein the method provides the information for estimating the state of the power distribution system over a predetermined period of time. A step of receiving a first period which is the measurement period of the sensor and a second period which is the measurement period of the electricity meter, A step of setting a first weight for the measurement value of the sensor and a second weight for the measurement value of the energy meter based on the first period and the second period, The process includes the step of using a system model that simulates the power distribution system to estimate a state value indicating the active power of each of the multiple nodes of the power distribution system during a predetermined period, based on the respective measurements from the sensor and the power meter. The sensor measures the voltage, active power flow, and reactive power flow at the installation location in the first period, The aforementioned power meter measures the amount of power of the load in the second period, The aforementioned system model is This is a state-space model that, upon input of system equipment information of the power distribution system and the respective measurement values ​​of the sensor and the electricity meter, calculates and outputs the state value. This model allows for adjustment of the influence of the sensor and the energy meter measurements on the state value, respectively, according to the first and second weights. Information processing methods.

7. An information processing program for estimating the state of the power distribution system over a predetermined period of time based on the respective measurement values ​​of the sensors and the energy meters, in a power distribution system including power distribution lines, sensors installed at predetermined locations on the power distribution lines, and energy meters installed on loads at predetermined nodes on the power distribution lines, wherein the program provides the information processing for estimating the state of the power distribution system over a predetermined period of time. On the computer, A receiving unit that receives a first period, which is the measurement period of the sensor, and a second period, which is the measurement period of the electricity meter, A setting unit that sets a first weight for the measurement value of the sensor and a second weight for the measurement value of the energy meter based on the first period and the second period, An estimation unit is realized that uses a system model that simulates the aforementioned power distribution system and estimates a state value indicating the active power of each of the multiple nodes of the power distribution system during a predetermined period, based on the respective measurement values ​​of the sensor and the power meter. The sensor measures the voltage, active power flow, and reactive power flow at the installation location in the first period, The aforementioned power meter measures the amount of power of the load in the second period, The aforementioned system model is This is a state-space model that, upon input of system equipment information of the power distribution system and the respective measurement values ​​of the sensor and the electricity meter, calculates and outputs the state value. This model allows for adjustment of the influence of the sensor and the energy meter measurements on the state value, respectively, according to the first and second weights. Information processing program.

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