JP6144611B2 - Distribution system state estimation device, state estimation method, and state estimation program - Google Patents

Description

  The present invention relates to a technique for estimating a state of a power distribution system.

  In response to demands for maintaining voltage when installing distributed power sources in power distribution systems and for labor saving in new installations and maintenance of power company facilities, there is an increasing need for voltage and current management that grasps the actual situation of power distribution systems. The power system state estimation technology has been developed mainly in the case where measured values of nodes such as substations can be obtained sufficiently (redundantly) in a high-voltage power transmission system. For example, Patent Document 1 discloses a method of assigning weights to observed values of tidal currents observed at a plurality of points in an electric power system and estimating the state of the system by a weighted least square method.

  On the other hand, Patent Document 2 discloses a technique for estimating the current in each section in the feeder from the distribution current of the distribution substation as a method for estimating the power flow state of the distribution system for which sufficient measurement information cannot be obtained. This is a method of measuring the delivery current of the distribution system, allocating it as each section current based on the load characteristic database of each section, and estimating the load current and power in each section.

  Patent Document 3 discloses a technique for accurately estimating a power flow state of a distribution system from observed values of a limited voltage / current of the distribution system.

  Furthermore, Patent Document 4 discloses a technique for improving the estimation accuracy by correcting the inherent measurement error included in each voltage / current measuring device when information on the voltage / current at each point in the distribution system is obtained. It is shown.

Japanese Patent Publication No. 7-16289 JP 2003-79071 A JP 2006-87177 A JP 2008-154418 A

  Even if the state of the power distribution system is estimated using measurement errors determined for each type of sensor provided in the power distribution system, the accuracy of the estimation can be improved due to variations in the measurement error for each individual sensor. There are cases where it is not possible.

  In order to solve the above problems, a distribution system state estimation device according to an aspect of the present invention includes a storage unit, a weight coefficient calculation unit, an estimated value calculation unit, and an individual error calculation unit. The storage unit includes a measured value of a state including a voltage and a line power flow measured by a plurality of sensors in the distribution system in a plurality of time sections, a system configuration data indicating an impedance and a connection state of the line in the distribution system, and a distribution system. The maximum error of the sensor or load at each node is stored. The weighting factor calculation unit calculates a weighting factor for each node based on the maximum error. The estimated value calculation unit determines a first initial value based on the measured value for each of the multiple time sections, and calculates a first estimated value of the state by power flow calculation using the system configuration data and the first initial value. The second initial value is calculated on the basis of the first estimated value, the first initial value, and the weighting coefficient, and the second estimated value of the state is calculated by power flow calculation using the system configuration data and the second initial value. The individual error calculation unit calculates the steady-state error and the random error of each of the plurality of sensors based on the measured value of the multiple time section and the second estimated value of the multiple time section.

  According to one aspect of the present invention, it is possible to estimate an individual error of a sensor provided in a power distribution system.

The structure of the electric power grid | system system of Example 1 of this invention is shown. An example of a measured value is shown. An example of the maximum error database 13 is shown. An example of the maximum measurement error rate of a sensor is shown. An example of the rated value of a sensor is shown. An example of the weight coefficient database 15 is shown. An example of a branch database is shown. An example of a correction amount upper and lower limit database is shown. An example of the sensitivity coefficient database of a sending voltage is shown. An example of the sensitivity coefficient database of an active power load is shown. An example of the sensitivity coefficient database of a reactive power load is shown. The calculation method of a sensitivity coefficient is shown. A voltage-current distribution estimation process is shown. An example of the first initial value is shown. An example of the voltage-current distribution database 18 is shown. The sensor individual error estimation process is shown. An example of the distribution of the voltage deviation of the cross section in multiple time in a certain sensor is shown. An example of the steady-state error database 20 is shown. An example of the random error database 21 is shown. The structure of the state estimation apparatus 10b of Example 2 is shown. An example of the corrected measurement value database 23 is shown. An example of the correction weight coefficient database 25 is shown. The structure of the state estimation apparatus 10c of Example 3 is shown. The sensor individual error update determination process is shown. An example of the distribution of the correction deviation of the voltage of a certain sensor is shown. An example of the sensor individual error update log database 29 is shown. The structure of the state estimation apparatus 10d of Example 4 is shown. An example of a display screen is shown. An example of the random error database 21 of Example 5 is shown. The structure of the state estimation apparatus 10f of Example 6 is shown. The sensor combination designation process is shown. An example of the sensor combination database 32 is shown. An abnormal sensor identification process is shown. An example of the abnormality sensor database 34 is shown. The structure of the electric power grid | system system of Example 7 is shown. The structure of the sensor switch 160 is shown. The sensor switch on / off determination process is shown. An example of the measured value of Example 7 is shown. An example of the stationary error database 20 of Example 7 is shown. An example of the random error database 21 of Example 7 is shown.

  Embodiments of the present invention will be described below with reference to the drawings.

  In this example, from the measured values of multiple time sections such as voltage and current from the sensors installed in the power system, together with the distribution of the voltage, current, active power, reactive power of the power system, for each individual sensor An example of a power system that includes a state estimation device that estimates errors by dividing them into steady errors and random errors will be described.

  FIG. 1 shows a configuration of a power system of a first embodiment of the present invention. The power system of the present embodiment includes a power system 100 and a state estimation device 10. The power system 100 is, for example, a power distribution system. The power system 100 includes a substation 120, a node 110 representing a bus or a power pole, a branch 130 representing a distribution line connecting the substation 120 and several nodes 110, and a customer load 140 connected to the node 110. The sensor 150 is provided on the branch 130. In the following description, the alphabet may be omitted when it is not necessary to distinguish the elements by the alphabet of symbols.

  The sensor 150 measures the voltage at the node 110, the current passing through the branch 130, the active power, and the reactive power. The sensor 150 sends a measurement value to the state estimation device 10 via the communication network 200.

  Next, the configuration of the state estimation device 10 will be described.

  The state estimation apparatus 10 includes a measured value receiving unit 11, a measured value database (DB) 12, a maximum error database 13, a weighting factor calculation unit 14, a weighting factor database 15, an equipment database 16, a voltage / current distribution estimating unit 17, and a voltage / current distribution. The database 18, the sensor individual error estimation unit 19, the steady error database 20, and the random error database 21 are configured.

  The state estimation device 10 may be a computer including a memory and a microprocessor, for example. The memory stores a state estimation program and data for executing the processing of the state estimation device 10. The microprocessor is connected to the memory and executes the processing of the state estimation device 10 according to the state estimation program in the memory. The state estimation program may be stored in a computer-readable recording medium and read by a computer.

  Next, each element of the state estimation device 10 will be described in detail.

  The measurement value receiving unit 11 receives the measurement value of the sensor 150 via the communication network 200.

  The measurement value database 12 stores a table based on the measurement values received by the measurement value receiving unit 11. FIG. 2 shows an example of measured values. The measured value corresponds to the measured value of the node state measured by the sensor, the measurement time that is the time of the measurement, the node ID that indicates the node where the sensor is provided, and the sensor ID that indicates the sensor for each sensor. It is attached. The measured value of the node state includes the three phases A, B, and C, or the voltage, current, active power, and reactive power between the phases. The three-phase measurement values are converted into three-phase average values or representative phase values by the measurement value receiving unit 11 and stored as the measurement value database 12. Thereby, the measurement value database 12 stores the measurement values at a plurality of measurement times (time sections). The measurement value database 12 may store three-phase measurement values or may store converted measurement values.

  The maximum error database 13 stores a maximum error including a maximum measurement error determined for each sensor model and a maximum apportioning error of the load. FIG. 3 shows an example of the maximum error database 13. The maximum error database 13 is a table in which a node, a sensor ID, and a maximum error are associated with each node. When a sensor is provided in a node, the maximum measurement error of the node includes the maximum measurement error of the sensor voltage, current, active power, and reactive power, and the active power component and reactive power component of the maximum apportionment error of the load. . When the sensor is not provided in the node, the active power component and reactive power component of the maximum apportioning error of the load are included. When the maximum measurement error rate e of the sensor and the rated value Mrat of the sensor are given instead of the maximum measurement error ε of the sensor, the maximum measurement error ε may be calculated as in Expression (1).

  FIG. 4 shows an example of the maximum measurement error rate of the sensor. Instead of the maximum measurement error, the maximum measurement error rate of voltage, current, active power, and reactive power is given in advance as sensor specifications for each sensor. FIG. 5 shows an example of the rated value of the sensor. For each sensor, rated values of voltage, current, active power, and reactive power are given in advance as sensor specifications. The maximum apportioning error is preferably set based on the type of load such as factory or photovoltaic power generation. The weighting factor calculation unit 14 calculates the weighting factor W as shown in Expression (2) using the maximum error ε in the maximum error database 13.

  The weighting factor database 15 stores the weighting factor calculated by the weighting factor calculation unit 14. FIG. 6 shows an example of the weight coefficient database 15. The weighting factor database 15 is a table in which the voltage, current, active power, reactive power weighting factor, node, and sensor ID are associated with each sensor.

  The equipment database 16 stores equipment data of the power system 100. The equipment database 16 includes a branch database, a correction amount upper / lower limit database, and a sensitivity coefficient database.

  FIG. 7 shows an example of a branch database. The branch database is a table in which the branch 130 is associated with the distribution line impedance. Distribution line impedance is represented by resistance and reactance, for example.

  FIG. 8 shows an example of the correction amount upper and lower limit database. The correction amount upper and lower limit database is a table in which the upper and lower limit rates of the correction amount are associated with the nodes for the transmission voltage of the substation 120 and the active power component and reactive power component of the load 140, respectively. The upper and lower limit rates are preferably determined based on the upper limit value of the load based on the maximum measurement error of the sensor and the contract power of the customer. For example, the upper limit value of the load is determined on condition that the load does not exceed the contract power.

  The sensitivity coefficient database stores sensitivity coefficients indicating the amount of change in voltage, current, and active power of each node or each branch when a certain variable of a node state of a certain node is changed. FIG. 9 shows an example of the sensitivity coefficient database of the transmission voltage. The sent-out voltage sensitivity coefficient database shows the sensitivity coefficient α of the voltage, current, active power, and reactive power at each node when the sent voltage V of a certain node is changed. The sensitivity coefficient database of the transmission voltage has a table for each node for changing the transmission voltage V. FIG. 10 shows an example of the sensitivity coefficient database of the active power load. The sensitivity coefficient database of the active power load indicates the sensitivity coefficient β of the voltage, current, active power, and reactive power of each node when the active power component Lp of the load 140 is changed. The active power load sensitivity coefficient database has a table for each node that changes the active power load Lp. FIG. 11 shows an example of a sensitivity coefficient database of reactive power load. The reactive power load sensitivity coefficient database indicates the voltage, current, active power, and reactive power sensitivity coefficient γ of each node when the reactive power component Lq of the load 140 is changed. The reactive power load sensitivity coefficient database has a table for each node for changing the reactive power load Lq.

  A method for calculating the sensitivity coefficient will be described. FIG. 12 shows a method for calculating the sensitivity coefficient. Here, i is a number indicating a node from which a measured voltage value is obtained, i is a number indicating a node from which I, P, and Q measured values indicating power flow are obtained, and k is a variable of the optimization problem. Let j be a number indicating a node having ΔV, ΔLp, and ΔLq. The sensitivity coefficients α, β, and γ are the voltage changes ΔVij of the node ij and the branch kj when the transmission voltage ΔVj of the substation 120 of the node j and the active power component ΔLpj and the reactive power component ΔLqj of the load 140 are changed, respectively. It is an index showing a load current change ΔIkj, an active power change ΔPkj, and a load reactive power change ΔQkj. ΔVij can be grasped by performing a tidal calculation by slightly changing the transmission voltage Vj from the initial condition and obtaining a deviation between the tidal current calculation result under the initial condition and the tidal current calculation result slightly changed. ΔIkj, ΔPkj, and ΔQkj can also be grasped by the same method. Here, changes in ΔVij, ΔIkj, ΔPkj, and ΔQkj with respect to changes in the transmission voltage ΔVj are αVij, αIkj, αPkj, and αQkj, respectively. Changes in ΔVij, ΔIkj, ΔPkj, and ΔQkj with respect to a change in the active power component ΔLpj of the load are assumed to be βVij, βIkj, βPkj, and βQkj, respectively. Changes in ΔVij, ΔIkj, ΔPkj, and ΔQkj with respect to a change in reactive power component ΔLqj of the load are denoted by γVij, γIkj, γPkj, and γQkj, respectively. Calculation of these sensitivity coefficients is expressed by equations (3) to (14).

  The voltage / current distribution estimator 17 uses the measured values in the measured value database 12, the weighting factors in the weighting factor database 15, and the various facility data in the facility database 16, and the voltage and current of each node and each branch in a certain time section. , Voltage / current distribution estimation processing for estimating active power, reactive power, and load.

  The flow of the voltage / current distribution estimation process will be described below. FIG. 13 shows a voltage / current distribution estimation process.

  In step S <b> 11, the voltage / current distribution estimation unit 17 reads the measurement value database 12. Specifically, the measured values of the voltage, current, active power, and reactive power of each node and each branch in a certain time section are read. In step S <b> 12, the voltage / current distribution estimation unit 17 reads the weighting coefficient database 15. Specifically, the weighting coefficient of each node and each branch is read. In step S <b> 13, the voltage / current distribution estimation unit 17 reads the equipment database 16. Specifically, the distribution line impedance in the branch database, the transmission voltage of the substation 120 in the correction amount upper and lower limit database, the upper and lower limit rates of the correction amount of the active power component and the reactive power component of the load 140, and the sensitivity coefficient database The sensitivity coefficient in is read.

  In step S14, the voltage / current distribution estimation unit 17 determines and stores the first initial value. FIG. 14 shows an example of the first initial value. This figure shows the first initial values of the transmission voltage V, the active power component Lp of the load, and the reactive power component Lq. The voltage / current distribution estimation unit 17 uses the voltage of the node 110a of the substation 120 in the measurement value database 12 as the first initial value of the transmission voltage V. The voltage here was an average value of three phases. Further, the voltage / current distribution estimation unit 17 calculates the active power and reactive power of the load in the sensor section from the measured values of the active power and reactive power in the measurement value database 12, and apportions them based on the contract power of each node. Thus, the calculated values of the active power component Lp and reactive power component Lq of the load are calculated and used as the first initial values of the loads Lp and Lq.

  In step S15, the voltage / current distribution estimation unit 17 calculates upper and lower limit values of the correction amounts of the transmission voltage V, the load active power Lp, and the reactive power Lq. The formula for calculating the upper and lower limit values is shown in Formula (15). Here, x is the upper and lower limit rates of the voltage, the active power component of the load, and the reactive power component shown in the correction amount upper and lower limit database.

  In step S16, the voltage / current distribution estimation unit 17 performs power flow calculation using the measurement value database 12, the weighting factor database 15, the equipment database 16, and the first initial value data obtained in steps S11 to S14. The voltage / current distribution estimator 17 estimates the voltage, current, active power, and reactive power of each node and each branch by power flow calculation to obtain a first estimated value.

  In step S <b> 17, the voltage / current distribution estimation unit 17 calculates a deviation between the first estimated value and the measured value of the voltage, current, active power, and reactive power of each node and each branch. Specifically, a deviation between the measured value read in step S11 and the first estimated value obtained by the tidal current calculation calculated in step S16 is calculated. For example, for a voltage at a certain node i, a deviation ΔVi = Vi′−Vi is obtained from a deviation ΔVi between the measured value Vi ′ and the first estimated value Vi. The current, active / reactive power, etc. are similarly calculated.

  In step S18, the voltage / current distribution estimation unit 17 calculates the correction amount of the transmission voltage V, the active power component Lp of the load, and the reactive power component Lq. Here, the voltage / current distribution estimation unit 17 minimizes the voltage, current, active power, reactive power deviation, and load deviation of each node and each branch. The calculation method of the correction amount is shown below.

  The optimization calculation for calculating the send voltage and the load correction amount is formulated as a quadratic programming method. First, the objective function f will be described. The objective function f is defined as the sum of voltage, current, active power, reactive power deviation, and load deviation multiplied by a weighting coefficient, and is defined by equation (16).

  Here, Ax is a weighting factor. yVi, yIk, yPk, and yQk are intermediate variables that define the deviation between the measured value of the current I, voltage V, active power P, and reactive power Q and the first estimated value, respectively, and yLpj and yLqj are the loads Lp, It is an intermediate variable that defines the deviation between the calculated value of Lq and the first estimated value, and is represented by Expression (17). The objective function f need not use Lp and Lq for the load.

  These equations are treated as equality constraints in the quadratic programming problem. Here, the deviation between the measured value of the voltage V, current I, active power P, reactive power Q and the first estimated value is given as an initial value, and the deviation between the calculated values of the loads Lp and Lq and the first estimated value is the initial value. Given as a value. Here, ΔLpj represents the correction amount of the active power component of the load connected to the node j, ΔLqj represents the correction amount of the reactive power component, and ΔVj represents the correction amount of the transmission voltage of the substation 120. ΔLpj, ΔLqj, and ΔVj are variables for the optimization problem.

  Next, the voltage / current distribution estimation unit 17 uses the upper and lower limit values of the correction amounts of the transmission voltage, the active power component Lp and the reactive power component Lq calculated in step S15 as inequality constraints. Next, the voltage / current distribution estimation unit 17 uses the values read in step S13 as the sensitivity coefficients α, β, and γ.

  By correcting the quadratic programming problem formulated as described above and optimizing calculation that minimizes the objective function f, it is possible to obtain correction amounts of the transmission voltage V, the active power Lp of the load, and the reactive power Lq.

  In step S19, the voltage / current distribution estimation unit 17 determines the transmission voltage V, the active power component Lp of the load, and the effective voltage component Lp of the load, based on the correction amounts of the active voltage component Lp and reactive power component Lq of the load calculated in step S18. The first initial value of the reactive power component Lq is corrected to the second initial value. The voltage / current distribution estimating unit 17 calculates the second estimated values of the voltage, current, active power, and reactive power of each node and each branch by performing the power flow calculation again using the second initial value.

  Thus, the voltage / current distribution estimation process ends. By this process, the voltage, current, active power, and reactive power of each node and each branch of the power system in a certain time section can be estimated with high accuracy. Based on the first estimated value, the first initial value, and the weighting factor, the first initial value can be corrected, and a second initial value with higher accuracy than the first initial value can be calculated. The correction amount of the first initial value can be calculated by the optimization calculation of the objective variable using the deviation and the weighting coefficient of the first estimated value and the first initial value. The first initial value includes the calculated load value, and the first initial value is corrected to calculate the second initial value, so that the accuracy of state estimation can be improved. Further, the second estimated value with higher accuracy than the first estimated value can be calculated by the tidal current calculation using the second initial value.

  The voltage / current distribution database 18 stores the second estimated values of the voltage, current, active power, and reactive power of each node and each branch estimated by the voltage / current distribution estimation unit 17. FIG. 15 shows an example of the voltage / current distribution database 18. The voltage / current distribution database 18 is a table in which each second estimated value, node, sensor ID, and time are associated with each other. By performing the voltage / current distribution estimation process on each of the plurality of time sections stored in the measurement value database 12, the voltage / current distribution database 18 stores the second estimated values of the plurality of time sections.

  The sensor individual error estimator 19 uses the second estimated value of the voltage / current distribution database 18 and the measured value of the measured value database 12 to calculate sensor individual error information that is an error for each sensor individual. Process. The sensor individual error information includes a stationary error and a random error.

  The flow of the sensor individual error estimation process will be described below. FIG. 16 shows sensor individual error estimation processing.

  In step S <b> 21, the sensor individual error estimation unit 19 reads the measurement value database 12. Specifically, the sensor individual error estimator 19 reads the measured values of the voltage, current, active power, and reactive power of the cross section for a plurality of times. In step S <b> 22, the sensor individual error estimation unit 19 reads the voltage / current distribution database 18. Specifically, the second estimated values of the voltage, current, active power, and reactive power in a plurality of time sections are read. In step S23, the sensor individual error estimator 19 calculates a deviation between the measured value of each time section and the second estimated value.

  In step S24, the sensor individual error estimation unit 19 estimates a steady error for each sensor. In step S25, the sensor individual error estimation unit 19 estimates a random error for each sensor. FIG. 17 shows an example of a distribution of deviations of a plurality of time sections in a certain sensor. In this figure, the horizontal axis represents the deviation between the measured voltage value and the second estimated value, and the vertical axis represents the frequency. In step S24, the sensor individual error estimator 19 calculates an average value of the deviations of the cross sections for a plurality of times from the deviation calculated in step S23, and sets the average value μ as a steady error. In step S25, the sensor individual error estimator 19 calculates the standard deviation σ of the deviation of the plurality of time sections from the deviation calculated in step S23, and sets the standard deviation as a random error. The steady-state error is caused by an error in the transformation ratio when a transformer is used for the sensor. Further, the random error is caused by temperature characteristics when a capacitor is used for the sensor, a shift in communication timing of measured values, and the like.

  According to the above sensor individual error estimation process, the error for each sensor individual is grasped by dividing it into a steady-state error and a random error from the measured values and second estimated values of voltage, current, active power and reactive power in multiple time sections. can do.

  The steady error database 20 stores the steady error estimated by the sensor individual error estimator 19. FIG. 18 shows an example of the steady error database 20. The steady error database 20 is a table in which a node, a sensor ID, and a steady error of voltage, current, active power, and reactive power are associated with each node in which a sensor is provided.

  The random error database 21 stores the random error estimated by the sensor individual error estimation unit 19. FIG. 19 shows an example of the random error database 21. The random error database 21 is a table in which a node, a sensor ID, and a random error of voltage, current, active power, and reactive power are associated with each node provided with a sensor.

  According to the present embodiment, it is possible to estimate an error for each individual sensor, not an error determined for each sensor type.

  In the present embodiment, in addition to the function of the first embodiment, a function for correcting a measurement value using a steady error, a function for correcting a weighting coefficient using a random error, and a power using a corrected measurement value and a corrected weighting coefficient. An example of a state estimation device having a function of estimating distribution of system voltage, current, active power, and reactive power will be described.

  The power system of the present embodiment includes a state estimation device 10b instead of the state estimation device 10 of the first embodiment. FIG. 20 illustrates a configuration of the state estimation device 10b according to the second embodiment. In addition to the elements of the state estimation device 10, the state estimation device 10b according to the present embodiment includes a corrected measurement value calculation unit 22, a correction measurement value database 23, a correction weight coefficient calculation unit 24, a correction weight coefficient database 25, and a correction voltage / current distribution estimation. Unit 26 and correction voltage / current distribution database 27. Among the elements of the state estimation device 10b, elements having the same reference numerals as the elements of the state estimation device 10 are assumed to have the same function and description thereof is omitted.

  The corrected measured value calculation unit 22 corrects the measured value using the measured value in the measured value database 12 and the steady error of the individual sensor in the steady error database 20 to obtain a corrected measured value. A formula for calculating the corrected measurement value is shown in Formula (18).

  The corrected measured value database 23 stores the corrected measured value calculated by the corrected measured value calculation unit 22. FIG. 21 shows an example of the corrected measurement value database 23. The corrected measurement value database 23 is a table in which corrected measurement values of time, node, sensor ID, voltage, current, active power, and reactive power are associated with each node provided with a sensor. The corrected measurement value here is an average value of three phases.

  The correction weight coefficient calculation unit 24 calculates a correction weight coefficient using the random error of the individual sensor in the random error database 21. The calculation of the correction weighting coefficient is the same as the processing of the weighting coefficient calculation unit 14 described in the first embodiment, but a random error is used instead of the maximum error ε.

  The correction weight coefficient database 25 stores the corrected measurement value calculated by the correction weight coefficient calculation unit 24. FIG. 22 shows an example of the correction weight coefficient database 25. The correction weighting coefficient database 25 is a table in which correction times coefficients of time, sensor ID, voltage, current, active power, and reactive power are associated with each node provided with a sensor.

  The correction voltage / current distribution estimation unit 26 uses the corrected measurement value in the correction measurement value database 23, the correction weight coefficient in the correction weight coefficient database 25, and the weight coefficient in the weight coefficient database 15 to Estimate the voltage, current, active power, and reactive power of each branch. The process of the correction voltage / current distribution estimation unit 26 is the same as the voltage / current distribution estimation process of the first embodiment. However, the corrected voltage / current distribution estimating unit 26 determines the third initial value instead of the first initial value by using the corrected measured value in the corrected measured value database 23 instead of the measured value in the measured value database 12, and The third estimated value is calculated instead of the first estimated value by the power flow calculation using the three initial values. Further, the correction voltage / current distribution estimation unit 26 uses the correction weighting factor of the correction weighting factor database 25 instead of the weighting factor of the weighting factor database 15 as the weighting factor of voltage, current, active power, and reactive power. The weighting factor of the weighting factor database 15 is used as the weighting factor, the correction amount is calculated by performing optimization calculation, and the third initial value is corrected by the correction amount, whereby the fourth initial value is used instead of the second initial value. Calculate the value. Further, the corrected voltage / current distribution estimating unit 26 calculates a corrected estimated value instead of the second estimated value by power flow calculation using the fourth initial value.

  The corrected voltage / current distribution database 27 stores corrected estimated values of the voltage, current, active power, and reactive power of each note and each branch of the power system estimated by the corrected voltage / current distribution estimation unit 26. The configuration of the corrected voltage / current distribution database 27 is the same as that of the voltage / current distribution database 18 of the first embodiment.

  According to the present embodiment, a corrected measurement value is calculated by correcting the measurement value using a steady error, a correction weighting factor is calculated using a random error, and a state is estimated using the corrected measurement value and the correction weighting factor. By performing the above, it is possible to improve the estimation accuracy of the distribution of the voltage, current, active power, and reactive power of the power system. The accuracy of state estimation can be improved by correcting the measurement value based on the steady-state error and performing the power flow calculation using the corrected measurement value. Further, it is possible to improve the accuracy of state estimation by correcting the weighting factor based on the random error, correcting the initial value based on the corrected weighting factor, and performing the power flow calculation using the corrected initial value. it can.

  In this embodiment, an example of a state estimation apparatus having a function of updating the steady error database 20 and the random error database 21 in addition to the functions of the second embodiment will be described.

  The power system of the present embodiment has a state estimation device 10b instead of the state estimation device 10b of the second embodiment. FIG. 23 illustrates a configuration of the state estimation device 10c according to the third embodiment. The state estimation device 10c includes a sensor individual error update determination unit 28 and a sensor individual error update log database 29 in addition to the elements of the state estimation device 10b. Of the elements of the state estimation device 10c, elements having the same reference numerals as those of the state estimation device 10b have the same functions, and the description thereof is omitted.

  The sensor individual error update determination unit 28 includes correction measurement values of voltage, current, active power, and reactive power stored in the correction measurement value database 23, and voltage, current, active power stored in the correction voltage / current distribution database 27, Based on the corrected estimated value of reactive power, a sensor individual error update determination process is performed to send a trigger for recalculating steady error and random error to the sensor individual error estimator 19. In addition, the sensor individual error update determination unit 28 stores a table in the sensor individual error update log database 29 in which the time when the update of the sensor individual error information is determined and the presence / absence of the update are associated with each other.

  FIG. 24 shows sensor individual error update determination processing. The sensor individual error update determination unit 28 executes sensor individual error update determination processing for each sensor.

  In step S31, the corrected measured values of the voltage, current, active power, and reactive power of the cross section for a plurality of times are read from the corrected measured value database 23. The multiple time section here means a measurement period from the time section of the previous sensor individual error update determination process to the time section of the current sensor individual error update determination process. The corrected measurement value of the cross section for a plurality of hours means a corrected measurement value obtained by correcting the measurement value measured during this measurement period. In this embodiment, the multi-time cross section means this measurement period unless otherwise specified.

  In step S <b> 32, the sensor individual error update determination unit 28 reads the correction estimated values of the voltage, current, active power, and reactive power of a plurality of time sections from the correction voltage / current distribution database 27. In step S <b> 33, the sensor individual error update determination unit 28 calculates a deviation between the corrected measured value and the corrected estimated value for each time section for each of the voltage, current, active power, and reactive power to obtain a corrected deviation.

  In step S <b> 34, the sensor individual error update determination unit 28 calculates the average value of the correction deviations of the plurality of time sections. Hereinafter, μV, μI, μP, and μQ respectively indicate an average value of voltage correction deviation, an average value of current correction deviation, an average value of correction deviation of active power, and an average value of correction deviation of reactive power.

  In steps S35 to S38, the sensor individual error update determination unit 28 determines whether or not the sensor individual error information is updated. In steps S35, S36, S37 and S38, the correction deviation average values μV, μI, μP and μQ are compared with a predetermined allowable range, and if any of the average values deviates from the allowable range, the update is performed. Judge as necessary. For example, the absolute value of the average value of the correction deviation is preset, and the sensor individual error update determination unit 28 updates the absolute value of any one of μV, μI, μP, and μQ that exceeds the allowable value. Is determined to be necessary.

  FIG. 25 shows an example of the distribution of correction deviation of the voltage of a certain sensor. In this figure, the horizontal axis represents the voltage correction deviation, and the vertical axis represents the frequency. In this example, the average value μV of the voltage correction deviation is 15 V and the allowable range is ± 10 V. Since the average value μV deviates from the allowable range, it is determined that the sensor individual error information needs to be updated. An allowable value indicating the size of the upper and lower limits of the allowable range is set in advance in the range from 0 to the maximum measurement error for each of voltage, current, active power, and reactive power. As the allowable value is closer to 0, the accuracy of the estimated value of the sensor individual error information is improved, but the number of updates of the sensor individual error information is increased. For this reason, it is desirable to set the allowable value to a value close to 0 within a range in which the number of updates can be allowed while operating the state estimation device.

  If any one of the average correction deviation values μV, μI, μP, and μQ deviates from the allowable range in steps S35 to S38, in step S39, the sensor individual error update determination unit 28 determines that updating is necessary, A trigger for recalculating the sensor individual error information is sent to the sensor individual error estimator 19. If none of the average correction deviation values μV, μI, μP, and μQ deviates from the allowable range in steps S35 to S38, the sensor individual error update determination unit 28 shifts the processing to step S310.

  In step S <b> 310, the sensor individual error update determination unit 28 stores, in the sensor individual error update log database 29, a table in which the time when the sensor individual error update determination process is performed and the determination result (update presence / absence) are associated with each other. FIG. 26 shows an example of the sensor individual error update log database 29. The sensor individual error update log database 29 associates the date (year, month, day) and time when the sensor individual error update processing is performed with the presence / absence of update of the sensor individual error information.

  It is desirable to start the sensor individual error update determination process described above at the timing of every fixed period such as every month. The reason for every fixed period is that the sensor individual error information changes due to aging. Further, the sensor individual error estimation unit 18 may periodically recalculate the sensor individual error information.

  According to the present embodiment, it is possible to determine the timing for recalculating the sensor individual error information using the corrected measurement value of the multiple time section and the corrected estimated value of the multiple time section. Thereby, the sensor individual error information can be updated in accordance with the change in the sensor individual error information. Further, by performing state estimation using the updated sensor individual error information, it is possible to improve the accuracy of state estimation.

  In this embodiment, an example in which a display device is provided in addition to the configuration of the second embodiment will be described.

  The power system of the present embodiment includes a state estimation device 10d instead of the state estimation device 10b of the second embodiment, and further includes a display device 400. FIG. 27 illustrates a configuration of the state estimation device 10d according to the fourth embodiment. The state estimation device 10d includes a communication unit 30 in addition to the elements of the state estimation device 10b. The display device 400 is connected to the communication unit 30 via the communication network 300. Of the elements of the state estimation device 10d, the elements denoted by the same reference numerals as those of the state estimation device 10b have the same functions and will not be described.

  The communication unit 30 includes a maximum measurement error in the maximum error database 13, a measurement value in the measurement value database 12, a random error in the random error database 21, a corrected measurement value in the correction measurement value database 23, a voltage in the correction voltage / current distribution database 27, The estimated values of the current, active power, and reactive power distribution are sent to the display device 400 outside the state estimation device 10 via the communication network 300. The display device 400 may be directly connected to the state estimation device 10.

  The display device 400 displays data sent from the communication unit 30 via the communication network 300 on the screen. FIG. 28 shows an example of the display screen. This display screen is a display related to the voltage distribution of the power system 100. The vertical axis of the display screen represents voltage, and the horizontal axis represents the line length of the power system. A circular point 511 represents a measured value, a diamond-shaped point 512 represents a corrected measured value, a solid curve 513 represents a corrected estimated value, a broken line bar 514 represents the maximum measurement error width, and a solid line 515 represents a random error. Represents the width of.

  According to the present embodiment, by providing the display device 400, the operator of the power system can measure the sensor measurement value, the sensor correction measurement value, the maximum measurement error, the random error, the voltage, the current, and the active power. Any of the corrected estimated values such as reactive power can be grasped from the display screen. Further, by displaying these distributions with respect to the position of the node, the operator can grasp the state of the entire power system 100.

  In the present embodiment, an example of a state estimation apparatus having a function of storing each of a plurality of predetermined periods in association with sensor individual error information in addition to the function of the second embodiment will be described. Each of the plurality of periods is a part of a predetermined cycle, such as a season of the year, a day of the week, a time zone of the day, or the like.

  The configuration of the state estimation apparatus 10b of the present embodiment is the same as that of the second embodiment, but the configurations of the steady error database 20 and the random error database 21 are different.

  FIG. 29 shows an example of the random error database 21 of the fifth embodiment. In addition to the random error database 21 of the second embodiment, the random error database 21 of the present embodiment includes an item representing the period of measurement values used for estimation. The period is specified by the season, day of the week, time zone, and the like. That is, the random error database 21 according to the present embodiment is a table in which the seasons, days of the week, and time zones are associated with random errors. Depending on the length of the period, a temperature change that is one of the sensor error factors may appear as a steady error or a random error.

  The steady error database 20 of the present embodiment also includes a period in addition to the steady error database 20 of the second embodiment. Since the configuration of the steady error database 20 is the same as that of the random error database 21, detailed description thereof is omitted.

  The corrected measurement value calculation unit 22 reads the steady-state error of the period corresponding to the target multiple time section from the steady-state error database 20 and calculates the corrected measurement value. Similarly, the correction weight coefficient calculation unit 24 reads a random error in a period corresponding to a target multi-time section from the random error database 21 and calculates a correction weight coefficient.

  In this way, by grasping the steady-state error and random error by time zone, it is possible to grasp the steady-state error and random error change due to the temperature characteristics of the sensor components, and to detect the steady-state error and random error of the sensor with higher accuracy. I can grasp it. Further, by improving the accuracy of the steady error and the random error, it is possible to improve the accuracy of the corrected measurement value calculation unit, the correction weight coefficient calculation unit, and the correction voltage current distribution estimation unit that use the steady error and the random error.

  In the present embodiment, an example of a state estimation device having a function of specifying a sensor showing an abnormal tendency in addition to the function of the first embodiment will be described.

  The power system of the present embodiment includes a state estimation device 10f instead of the state estimation device 10 of the first embodiment. FIG. 30 illustrates a configuration of the state estimation device 10f according to the sixth embodiment. The state estimation device 10 f includes a sensor combination designation unit 31, a sensor combination database 32, an abnormal sensor specification unit 33, and an abnormal sensor database 34 in addition to the elements of the state estimation device 10. Of the elements of the state estimation device 10f, elements having the same reference numerals as the elements of the state estimation device 10 have the same functions and will not be described.

  The sensor combination designation unit 31 performs a sensor combination designation process for designating a combination of sensors used for calculation of sensor individual error information from all the sensors using the measurement values of the measurement value database 12.

  FIG. 31 shows a sensor combination designation process.

  In step S41, the sensor combination designation unit 31 reads the measurement value database 12. Specifically, the sensor ID and the measured values of voltage, current, active power, and reactive power are read from the measured value database 12.

  In step S42, the sensor combination designating unit 31 determines a sensor combination. Specifically, a sensor combination used for calculation of sensor individual error information is designated as a sensor combination case among all sensors. For example, one sensor combination case is a combination that does not use one sensor among all the sensors but uses another sensor.

  The sensor combination database 32 stores combinations of sensors divided into cases by the sensor combination specifying unit 31. FIG. 32 shows an example of the sensor combination database 32. The sensor combination database 32 includes, for each sensor combination case, a table including items of a sensor combination case number (No) indicating the sensor combination case, a sensor ID of the sensor included in the sensor combination case, and whether or not the sensor can be used. It is. In the item of availability of the sensor, “possible” indicates that the measured value of the sensor is used, and “not permitted” indicates that the measured value of the sensor is not used. In this example, the sensor combination case number “1” indicates that the measured values of the sensors with the sensor IDs “130b” and “130c” are used and the measured values of the sensor with the sensor ID “130a” are not used.

  After the sensor combination designation processing, the voltage / current distribution estimation unit 17 and the sensor individual error estimation unit 19 read the sensor measurement values corresponding to the sensor combination case from the measurement value database 12 in the sensor combination case, The same voltage / current distribution estimation processing and sensor individual error estimation processing as those in the first embodiment are performed.

  The abnormal sensor specifying unit 33 uses the steady error in the steady error database 20, the random error in the random error database 21, and the sensor ID of the sensor used for each sensor combination case in the sensor combination database 32 to indicate an abnormal tendency. An abnormal sensor specifying process for specifying the indicated sensor is performed.

  FIG. 33 shows the abnormality sensor specifying process.

  In step S51, the abnormal sensor specifying unit 33 reads the sensor combination database 32. Specifically, from the sensor combination database 32, the sensor ID of one sensor combination case and the availability of the sensor for each sensor ID are read in the order of the sensor combination case number. In step S <b> 52, the abnormality sensor specifying unit 33 reads the maximum error database 13. Specifically, the maximum measurement error of the voltage, current, active power, and reactive power of the sensor to be used in the maximum error database 13 is read.

  In step S53, the abnormality sensor specifying unit 33 reads the steady error database 20. Specifically, the node, sensor ID, and steady error of the sensor to be used in the steady error database 20 are read. In step S54, the abnormality sensor specifying unit 33 reads the random error database 21. Specifically, the node, sensor ID, and random error of the sensor to be used in the random error database 21 are read.

  In step S55, the abnormal sensor specifying unit 33 calculates, for each sensor, a total value of a steady error and a random error as a sensor individual error for each of voltage, current, active power, and reactive power.

  In step S56, the abnormal sensor specifying unit 33 compares the individual sensor error and the maximum measurement error for each of the voltage, current, active power, and reactive power for each sensor. If sensor individual error> maximum measurement error, the abnormal sensor specifying unit 33 determines that a sensor showing an abnormal tendency is included in the symmetrical sensor combination case. The abnormality sensor specifying unit 33 may determine using either a steady error or a random error instead of the sensor individual error.

  In step S57, the abnormal sensor specifying unit 33 determines whether or not the processes of S51 to S56 have been completed for all the sensor combination cases. If the processes for all the sensor combination cases have not been completed, the abnormal sensor specifying unit 33 shifts the process to S51 and performs the process for the next sensor combination case. When the processing for all the sensor combination cases is completed, in step S58, the abnormality sensor specifying unit 33 specifies the abnormality sensor based on the determination results of the abnormality sensors for all the sensor combination cases.

  For example, in the example of the sensor combination database 32 described above, it is determined that the sensor combination case number “1” does not include the abnormal sensor, and the sensor combination case numbers “2” and “3” determine that the abnormal sensor is included. In such a case, the abnormality sensor specifying unit 33 specifies the sensor ID “130a” as an abnormality sensor. In other words, the abnormal sensor specifying unit 33 specifies a sensor that is not used in the sensor combination case that is determined not to include the abnormal sensor as an abnormal sensor.

  The abnormal sensor database 34 stores the result of the abnormal sensor specifying process. FIG. 34 shows an example of the abnormality sensor database 34. The abnormality sensor database 34 is a table in which sensor IDs of sensors indicating abnormal trends identified by the abnormality sensor identification unit 33 are associated with their determination times.

  According to the present embodiment, it is possible to characterize a sensor that exhibits an abnormal tendency by calculating a sensor individual error for each sensor combination case and determining the sensor individual error.

  In this embodiment, an example of a state estimation device having a function of determining the on / off state of a switch using a steady error and a random error of an individual sensor in addition to the functions of the first embodiment will be described.

  FIG. 35 shows the configuration of the power system of the seventh embodiment. Compared with the power system of the first embodiment, the power system of the present embodiment has a power system 100 g instead of the power system 100 and a state estimation device 10 g instead of the state estimation device 10. Compared with the power system 100, the power system 100 g includes a sensor switch 160 that is a switch having a measurement function instead of the sensor 150. Compared to the state estimation device 10, the state estimation device 10 g includes a sensor switch on / off determination unit 35 in addition to the configuration of the state estimation device 10. Among the elements of the power system of the seventh embodiment, elements having the same reference numerals as those of the power system of the first embodiment are assumed to have the same function and the description thereof is omitted.

  Next, the configuration of the sensor switch 160 will be described. FIG. 36 shows the configuration of the sensor switch 160. The sensor switch 160 includes sensors 161 and 162 and a switch 163. The sensors 161 and 162 are connected to the transmission device 164. The transmission device 164 is connected to the communication network 200.

  Next, each configuration of the sensor switch 160 will be described.

  The sensors 161 and 162 have the same function as the sensor 150. The sensor 161 is a sensor installed on the substation 120 side (S side) of the sensor switch 160. The sensor 162 is a sensor installed on the terminal side (L side) of the sensor switch 160.

  The switch 163 is a power device that connects or cuts off a distribution line connected to the sensor switch 160. For example, the sensor switch 160c in the power system 100g connects or disconnects between the branches 150b and 150c. In this embodiment, the state where the distribution line connected to the sensor switch 160 is connected is defined as “ON”, and the state where the distribution line is blocked is defined as “OFF”.

  The transmission device 164 transmits the measurement values of the sensors 161 and 162 to the state estimation device 10 via the communication network 200.

  Next, the sensor switch on / off determination unit 35 will be described. The sensor switch on / off determination unit 35 uses a measurement value in the measurement value database 12, a steady error in the steady error database 20, and a random error in the random error database 21 to determine the on / off state of the sensor switch 160. Switch on / off judgment processing is performed.

  FIG. 37 shows a sensor switch on / off determination process.

  In step S61, the sensor switch on / off determination unit 35 reads the voltage measurement values VS and VL of the sensors 161 (S side) and 162 (L side) from the measurement value database 12. FIG. 38 shows an example of measured values in Example 7. The measured values of the present embodiment are obtained by measuring, for each sensor in the sensor switch 160, the three phases A, B, and C, or the voltage between each phase, current, active power, reactive power, measurement time, node, FIG. 6 is a table in which sensor switch IDs are associated with each other. In the present embodiment, the three-phase measurement values are converted into three-phase average values by the measurement value receiving unit 11 and stored as the measurement value database 12.

  In step S62, the sensor switch on / off determination unit 35 reads the steady-state errors μS and μL of the measured values of the voltages of the sensors 161 (S side) and the sensor 162 (L side) from the steady-state error database 20, respectively. FIG. 39 shows an example of the steady error database 20 of the seventh embodiment. The steady-state error database 20 of the present embodiment is a table in which the steady-state errors of voltage, current, active power, and reactive power, node ID, and sensor switch ID are associated with each sensor in the sensor switch 160. is there.

  In step S63, the sensor switch on / off determination unit 35 reads the random errors σS and σL of the measured voltage values of the sensor 161 (S side) and the sensor 162 (L side) from the random error database 21, respectively. FIG. 40 shows an example of the random error database 21 of the seventh embodiment. The random error database 21 according to the present embodiment associates an estimated value of each random error of voltage, current, active power, and reactive power, a node ID, and a sensor switch ID for each sensor in the sensor switch 160. It is a table.

  In step S64, the sensor switch on / off determination unit 35 uses the measured values VS and VL of the voltages of the sensors 161 (S side) and the sensor 162 (L side) to calculate the measured voltage difference ΔVSL as shown in Expression (19). calculate. Here, the measured voltage difference ΔVSL is an absolute value.

  In step S65, the sensor switch on / off determination unit 35 uses the steady-state errors μS and μL and the random errors σS and σL of the voltage measurement values of the sensor 161 (S side) and the sensor 162 (L side) to obtain the equation (20). Then, the individual sensor errors εS and εL are calculated as shown in Equation (21).

  In step S66, the sensor switch on / off determination unit 35 uses the individual sensor errors εS and εL of the sensor 161 (S side) and the sensor 162 (L side), and the voltage sensor individual difference εSL as shown in Expression (22). Calculate

  In step S67, the sensor switch on / off determination unit 35 determines whether or not the voltage measurement difference ΔVSL is larger than the voltage sensor individual difference εSL. As a result of the determination, when the voltage measurement difference ΔVSL is larger than the voltage sensor individual difference εSL, the sensor switch on / off determination unit 35 shifts the processing to step S68. If it is smaller, the sensor switch on / off determination unit 35 causes the process to proceed to step S69.

  In step S68, the sensor switch on / off determination unit 35 determines that the sensor switch 160 is in the “ON” state. In step S69, the sensor switch on / off determination unit 35 determines that the sensor switch 160 is in the “off” state. In step S68 and step S69, the sensor switch on / off determination unit 35 sends the determination result to the voltage / current distribution estimation unit 17.

  According to the sensor switch on / off determination process, the on / off state of the sensor switch 160 can be determined.

  Next, the voltage / current distribution estimation process of the present embodiment will be described. The voltage / current distribution estimation process of the present embodiment is obtained by adding a process for determining the configuration of the distribution system to the voltage / current distribution estimation process of the first embodiment. For example, the case where the sensor switch on / off determination unit 35 determines that the sensor switches 160a and 160b are “on” and the 160c is “off” among the sensor switches 160a, 160b, and 160c will be described.

  In this example, the voltage / current distribution estimator 17 determines each node from the node 110a representing the substation 120 to the node 110e to which the sensor switch 160c is connected and each branch from the branches 150a to 150d connecting them. It is determined that the distribution system is the target of the estimation process, and the voltage / current distribution estimation process similar to that of the first embodiment is performed for the target distribution system.

  According to the present embodiment, the function of determining the on / off state of the switch based on the estimated values of the steady state error and the random error of the individual sensor is provided, thereby determining the configuration of the distribution system and determining the state estimation target. It becomes possible.

  Terms used in one embodiment of the present invention will be described. The storage unit corresponds to the measurement value database 12, the maximum error database 13, the equipment database 16, the steady error database 20, the random error database 21, the corrected measurement value database 23, the correction weight coefficient database 25, and the like. The weighting factor calculation unit corresponds to the weighting factor calculation unit 14 and the like. The estimated value calculator corresponds to the voltage / current distribution estimator 17, the corrected voltage / current distribution estimator 26, the corrected measured value calculator 22, the correction weight coefficient calculator 24, and the like. The individual error calculating unit corresponds to the sensor individual error estimating unit 19, the abnormal sensor specifying unit 33, and the like. The display control unit corresponds to the communication unit 30 and the like. The update determination unit corresponds to the sensor individual error update determination unit 28 and the like. The open / close state determination unit corresponds to the sensor switch on / off determination unit 35 and the like. The system configuration data corresponds to the equipment database 16 or the like. The switches correspond to the sensor switch 160, the switch 163, and the like.

  The present invention is not limited to the above embodiments, and can be modified in various other forms without departing from the spirit of the present invention.

10, 10b, 10c, 10d, 10f, 10g: State estimation device 11: Measurement value receiving unit 12: Measurement value database 13: Maximum error database 14: Weighting factor calculation unit 15: Weighting factor database 16: Facility database 17: Voltage current Distribution estimation unit 18: Voltage / current distribution database 19: Sensor individual error estimation unit 20: Steady error database 21: Random error database 22: Correction measurement value calculation unit 23: Correction measurement value database 24: Correction weight coefficient calculation unit 25: Correction weight Coefficient database 26: correction voltage current distribution estimation unit 27: correction voltage current distribution database 28: sensor individual error update determination unit 29: sensor individual error update log database 30: communication unit 31: sensor combination designation unit 32: sensor combination database 33: Abnormal sensor Unit 34: Abnormal sensor database 35: Sensor switch on / off determination unit 100, 100g: Power system 110: Node 120: Substation 130: Branch 140: Load 150: Sensor 160: Sensor switch 161: Sensor (S side) 162 : Sensor (L side) 163: Switch 164: Transmission device 200: Communication network 300: Communication network 400: Display device

Claims (12)

Measurement values of the state including the voltage and line power flow measured by a plurality of sensors in the distribution system in a multi-time section, system configuration data indicating the impedance and connection state of the line in the distribution system, and in the distribution system A storage unit for storing a maximum error of a sensor or a load in each node;
For each node, a weighting factor calculation unit that calculates a weighting factor based on the maximum error;
For each of the multiple time sections, determine a first initial value based on the measured value, and by calculating the power flow using the system configuration data and the first initial value, calculate a first estimated value of the state, A second initial value is calculated based on the first estimated value, the first initial value, and the weighting factor, and the second estimation of the state is performed by power flow calculation using the system configuration data and the second initial value. An estimated value calculation unit for calculating a value;
Based on the measurement value of the multiple time section and the second estimated value of the multiple time section, an individual error calculation unit that calculates a steady state error and a random error of each of the plurality of sensors;
An apparatus for estimating a state of a power distribution system. The estimated value calculation unit calculates an amount of correction of the first initial value by optimization calculation of an objective function using the first estimated value, a deviation of the first initial value, and the weighting factor, and the first initial value Calculating the second initial value based on the value and the correction amount;
The state estimation apparatus according to claim 1. The estimated value calculating unit calculates a calculated value of a load of each node based on the measured value, and determines the first initial value including the measured value and the calculated value;
The state estimation apparatus according to claim 2. The estimated value calculation unit calculates a corrected measurement value by correcting the measurement value based on the steady-state error, determines a third initial value based on the correction measurement value, and determines the system configuration data and the third Calculating a third estimate of the condition by tidal current calculations using initial values;
The state estimation apparatus according to claim 3. The estimated value calculating unit calculates a corrected weighting coefficient by correcting the weighting coefficient based on the random error, and calculates a deviation of the third estimated value and the third initial value, the weighting coefficient, and the third initial value. A fourth initial value is calculated based on the value, and a corrected estimated value of the state is calculated by power flow calculation using the system configuration data and the fourth initial value.
The state estimation apparatus according to claim 4. A display control unit for causing the display device to display any one of the measurement value, the stationary error, the random error, the corrected measurement value, and the correction estimated value;
The state estimation apparatus according to claim 5. The storage unit stores the stationary error and the random error,
An update determination unit that determines whether to update the steady-state error and the random error based on a corrected measurement value and a correction estimated value obtained from a measurement value of a multi-time cross section after the multi-time cross section;
The state estimation apparatus according to claim 5. The storage unit stores a steady error and a random error obtained from each measurement value of a plurality of periods,
The estimated value calculation unit calculates a corrected measurement value and a correction weight coefficient based on a steady-state error and a random error in a period corresponding to the measurement value,
The state estimation apparatus according to claim 5. The individual error calculation unit detects a sensor having an abnormal tendency from the plurality of sensors by using at least one of the steady error and the random error and the maximum error.
The state estimation apparatus according to claim 5. An open / close state determination unit that determines the open / close state of the switch based on the steady-state error, the random error, and the measured values of the state of both ends of the switch;
The estimated value calculation unit corrects the system configuration data based on the open / close state, and calculates the estimated value of the state using the corrected system configuration data.
The state estimation apparatus according to claim 5. Measurement values of the state including the voltage and line power flow measured by a plurality of sensors in the distribution system in a multi-time section, system configuration data indicating the impedance and connection state of the line in the distribution system, and in the distribution system Memorize the maximum error of the sensor or load at each node,
For each node, calculate a weighting factor based on the maximum error,
For each of the multiple time sections, determine a first initial value based on the measured value, and by calculating the power flow using the system configuration data and the first initial value, calculate a first estimated value of the state, A second initial value is calculated based on the first estimated value, the first initial value, and the weighting factor, and the second estimation of the state is performed by power flow calculation using the system configuration data and the second initial value. Calculate the value,
Based on the measured value of the multi-time cross section and the second estimated value of the multi-time cross section, to calculate the steady state error and random error of each of the plurality of sensors,
A state estimation method comprising: Measurement values of the state including the voltage and line power flow measured by a plurality of sensors in the distribution system in a multi-time section, system configuration data indicating the impedance and connection state of the line in the distribution system, and in the distribution system Memorize the maximum error of the sensor or load at each node,
For each node, calculate a weighting factor based on the maximum error,
For each of the multiple time sections, determine a first initial value based on the measured value, and by calculating the power flow using the system configuration data and the first initial value, calculate a first estimated value of the state, A second initial value is calculated based on the first estimated value, the first initial value, and the weighting factor, and the second estimation of the state is performed by power flow calculation using the system configuration data and the second initial value. Calculate the value,
Based on the measured value of the multi-time cross section and the second estimated value of the multi-time cross section, to calculate the steady state error and random error of each of the plurality of sensors,
A state estimation program that causes a computer to execute this.

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