Lightning strike prediction system and lightning strike prediction method

Abstract

A lightning strike prediction system (1) is provided with: a detecting device (10) including a magnetic sensor (11) for detecting a system frequency, which is the frequency of power flowing through a power system; and a processing device (20) for predicting a lightning strike to the power system. The processing device (20) determines whether or not there is a disturbance in the system frequency detected by the magnetic sensor (11), and if there is a disturbance in the system frequency, determines this to be a preindication that lightning will strike the power system within a certain period of time.

Description

Lightning strike prediction system and lightning strike prediction method

[0001] The present disclosure relates to a technique for predicting a sign of a momentary power sag or momentary power outage caused by a lightning strike.

[0002] When lightning strikes a power system (including transmission lines and facilities related to the supply of system power, such as steel towers or utility poles that support the transmission lines), a voltage drop (a momentary drop in voltage) or a momentary power outage (a power outage that lasts for only a few seconds) can occur. A voltage drop or power outage can have a significant impact on social life. For example, if a voltage drop or power outage occurs in a factory that has electrical equipment, the equipment can stop or errors can occur in in-process lots, affecting production. For this reason, it is desirable to predict the occurrence of lightning before it actually strikes the power system and take measures to prevent a voltage drop or power outage.

[0003] Japanese Patent Laid-Open Publication No. 7-151866 (Patent Document 1) discloses a technology that uses a magnetic sensor to detect the position of a thundercloud, calculates the charge distribution within the thundercloud, and predicts locations where lightning is likely to strike based on the calculated charge distribution within the thundercloud.

[0004] Japanese Unexamined Patent Publication No. 7-151866

[0005] Japanese Patent Laid-Open No. 7-151866 (Patent Document 1) discloses a method for detecting the position of a thundercloud using a magnetic sensor, calculating the charge distribution within the thundercloud, and predicting locations where lightning is likely to strike based on the calculated charge distribution within the thundercloud. Therefore, to implement the technology disclosed in Japanese Patent Laid-Open No. 7-151866 (Patent Document 1), a large number of magnetic sensors must be placed over a wide area to detect the position of the thundercloud, which changes from moment to moment, and the charge distribution within the thundercloud must be calculated using the detection results of the large number of magnetic sensors, requiring a massive amount of computational processing.

[0006] Furthermore, if the location where lightning is likely to strike is outside the power grid, the possibility of a voltage drop or power outage occurring is extremely low. Therefore, calculating the charge distribution within a thundercloud as in JP-A-7-151866 (Patent Document 1) in order to take measures against voltage drops or power outages is not desirable from the perspective of cost-effectiveness.

[0007] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to predict in advance that there are signs that a momentary voltage drop or momentary power outage will occur within a certain period of time, using a small number of sensors and with a light calculation load.

[0008] The present disclosure provides a lightning prediction system for predicting lightning strikes on an electric power grid, and includes a detection device having a sensor for detecting a grid frequency, which is the frequency of the electric power flowing through the electric power grid, and a processing device for predicting lightning strikes on the electric power grid. The processing device determines whether or not there is a disturbance in the grid frequency detected by the sensor, and if there is a disturbance in the grid frequency, determines that there is a sign that lightning will strike the electric power grid within a certain time period.

[0009] A lightning prediction method according to the present disclosure is a method for predicting a lightning strike to an electric power grid by a computer, and includes the steps of detecting a grid frequency, which is the frequency of the power flowing through the electric power grid, and predicting a lightning strike to the electric power grid. The step of predicting a lightning strike to the electric power grid includes the steps of determining whether or not there is a disturbance in the grid frequency, and determining, if there is a disturbance in the grid frequency, that there is a sign of a lightning strike to the electric power grid.

[0010] According to the present disclosure, it is possible to predict in advance that there is a sign that a momentary voltage drop or momentary power outage will occur within a certain period of time using a small number of sensors and with a light calculation load.

[0011] 1 is a diagram schematically showing an example of the overall configuration of a lightning strike prediction system. FIG. 2 is a diagram schematically showing an example of the overall configuration of a power system. FIG. 3 is a diagram showing an example of the arrangement of detection devices. FIG. 4 is a diagram showing an example of an output waveform of a detection device (magnetic sensor). FIG. 5 is a diagram showing an example of a processing pattern performed by a detection device. FIG. 6 is a diagram showing an example of a graph of frequency characteristics obtained by fast Fourier transform processing. FIG. 7 is a diagram plotting the peak width around 50 Hz over time in a graph of frequency characteristics obtained by fast Fourier transform. FIG. 8 is a flowchart (part 1) showing an example of the processing procedure of a processing device. FIG. 9 is a flowchart (part 2) showing an example of the processing procedure of a processing device. FIG. 10 is a diagram showing an example of a change in the difference between the system frequency and the reference frequency from normal operation to the occurrence of an instantaneous power outage. FIG. 11 is a flowchart (part 3) showing an example of the processing procedure of a processing device.

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0013] 1 is a diagram schematically illustrating an example of the overall configuration of a lightning strike prediction system 1 according to this embodiment. The lightning strike prediction system 1 is a system for predicting signs of a momentary power sag or momentary power outage caused by a lightning strike.

[0014] The lightning strike prediction system 1 includes a plurality of detection devices 10 and a processing device 20. The lightning strike prediction system 1 further includes a display device 30 and a power supply switching device 40.

[0015] Each detection device 10 includes a magnetic sensor 11, a communication unit 12, and a power supply unit 13. The power supply unit 13 supplies power to the magnetic sensor 11 and the communication unit 12 for operating the magnetic sensor 11 and the communication unit 12. In this embodiment, a case will be described in which the power supply unit 13 is an off-grid battery that is not connected to a power grid. However, the power supply unit 13 may also be an on-grid power supply that receives power from a power grid to secure its power supply.

[0016] The magnetic sensor 11 is placed at a position a predetermined distance away from the transmission line of the power grid. The magnetic sensor 11 detects the system frequency, which is the frequency of the power flowing through the power grid, by detecting a magnetic field generated around the transmission line when the system power flows through the transmission line. The magnetic sensor 11 is realized, for example, by an MI (Magnet-Impedance) sensor, a Hall sensor, an MR (Magneto-Resistive) sensor, a TMR (Tunneling Magneto-Resistive) sensor, or the like.

[0017] The communication unit 12 wirelessly transmits the detection result of the magnetic sensor 11 together with the identification number of the magnetic sensor 11 to the processing device 20 via the network 2. The power transmission line on which the magnetic sensor 11 is installed can be identified from the identification number of the magnetic sensor 11. Note that the communication unit 12 may transmit the detection result of the magnetic sensor 11 to the processing device 20 via a wired connection.

[0018] The processing device 20 is realized by, for example, a server having computer functions, etc. The processing device 20 includes a communication unit 21, a prediction unit 22, and a notification unit 23.

[0019] The communication unit 21 receives the detection results of the magnetic sensor 11 from the detection device 10. The prediction unit 22 includes a CPU (Central Processing Unit) and a memory (neither of which are shown). The prediction unit 22 predicts a lightning strike to the power grid based on the detection results of the magnetic sensor 11 received from the detection device 10.

[0020] When a lightning strike is predicted by the prediction unit 22, the notification unit 23 transmits a lightning strike warning signal to the display device 30 and the power supply switching device 40 via the network 2 to notify them that a lightning strike has been predicted.

[0021] The display device 30 is placed in a position where it can be seen by a user who manages a power consumption location, such as a factory having electrical equipment. When the display device 30 receives a lightning strike prediction signal from the processing device 20 via the network 2, it displays a warning on the screen to notify the manager of the power consumption location or the like that there is a prediction that lightning will strike the power grid within a certain time period.

[0022] The power supply switching device 40 is installed at a power consumption location such as a factory having electrical equipment. When the power supply switching device 40 receives a lightning strike prediction signal from the processing device 20 via the network 2, it automatically switches the power supply of the electrical equipment from the power grid to the emergency power supply.

[0023] 2 is a diagram schematically illustrating an example of the overall configuration of a power system 50. In the power system 50, electricity generated at a power plant 60 (such as a thermal power plant, a hydroelectric power plant, or a nuclear power plant) is transmitted via a power transmission network 70 to power consumption locations 80 (such as homes, shops, small factories, medium-sized factories, or large factories).

[0024] The power transmission network 70 includes a substation (primary substation) 71, a substation (secondary substation) 72, a power transmission network L0 connecting the power plant 60 and the substation 71, a power transmission network (first power transmission network) L1 connecting the substations 71 and 72, and power transmission networks (second power transmission networks) L2 to L4 connecting the substation 71 or the substation 72 with the power consumption location 80. The power transmission networks L0 to L4 include power transmission lines, and the steel towers and utility poles that support the power transmission lines.

[0025] According to the present embodiment, a detection device 10 (magnetic sensor 11) is disposed for each of the power transmission networks L0 to L4. Detecting the constantly changing location of thunderclouds requires sensors to be disposed over a wide area, which further requires extensive computational processing. However, the lightning strike prediction system 1 according to the present embodiment detects not the location of thunderclouds but the presence or absence of signs of lightning striking a fixed object, the power grid. In other words, the lightning strike prediction system 1 according to the present embodiment does not predict atmospheric discharges or lightning strikes to areas other than the power grid, which have no impact on social life, but rather predicts lightning strikes to the power grid, which are the direct cause of voltage sags and power interruptions that have a significant impact on social life. Therefore, as shown in FIG. 2 , it is sufficient to simply place the detection device 10 near the power grid. This reduces the number of detection devices 10 (magnetic sensors 11) compared to detecting the location of thunderclouds and accurately predicting the location of lightning strikes, thereby reducing the computational load.

[0026] 3 is a diagram showing an example of the arrangement of the detection device 10 (magnetic sensor 11) according to the present embodiment. In addition, Fig. 3 illustrates the detection device 10 arranged in a power transmission network L1 connecting substations 71 and 72, and the detection device 10 arranged in a power transmission network L2 connecting the substation 72 and a power consumption location 80 (a large factory in the example shown in Fig. 3).

[0027] The detection device 10 according to this embodiment detects the grid frequency, which is the frequency of the power flowing through the power grid, by using the magnetic sensor 11 to detect the magnetic field generated around the power line when the grid power flows through the power line. Therefore, the detection device 10 according to this embodiment can be placed at a location away from the power line as long as it is within a range where the magnetic field generated around the power line can be detected. If the magnetic sensor 11 has high performance and is placed in an environment with little noise, the grid frequency can be detected by the magnetic sensor 11 even if the location is several meters to several tens of meters away from the power line. Therefore, the detection device 10 can be placed on the ground even if the power line is located several meters or several tens of meters above ground level.

[0028] The electric power company that manages the power transmission network constantly adjusts the system frequency to balance supply and demand and suppress fluctuations in the system frequency at the substation. For example, if a disturbance occurs in the system frequency of the power transmission network L1 connecting the substations 71 and 72 due to the influence of approaching thunderclouds, the disturbance in the system frequency of the power transmission network L1 is suppressed by the substation 72, and the suppressed system power is sent to the power transmission network L2. Therefore, the disturbance in the system frequency of the power transmission network L1 can be detected by the detection device 10 installed on the power transmission network L1, but cannot be detected by the detection device 10 installed on the power transmission network L2 downstream of the substation 72. Furthermore, if lightning actually strikes the power transmission network L1, the substation 72 cannot absorb the effect, and a momentary power drop or momentary power outage may occur at the power consumption location (large factory) 80.

[0029] Furthermore, if a disturbance occurs in the system frequency of the power transmission network L2 connecting the substation 72 and the power consumption location (large factory) 80 due to the influence of approaching thunderclouds to the power transmission network L2, the disturbance in the system frequency of the power transmission network L2 can be detected by the detection device 10 arranged for the power transmission network L2, but cannot be detected by the detection device 10 arranged for the power transmission network L1 upstream of the substation 72. Furthermore, if lightning actually strikes the power transmission network L2, there is a possibility that a momentary drop or momentary interruption will occur at the power consumption location (large factory) 80.

[0030] In consideration of the above, in the lightning strike prediction system 1 according to this embodiment, a detection device 10 (magnetic sensor 11) is provided for each of the power transmission networks L0 to L4. As a result, even if a thundercloud approaches any of the power transmission networks L0 to L4, signs of a momentary sag or momentary power outage (disturbances in the system frequency, which will be described later) can be detected with high accuracy.

[0031] Although Figure 3 shows an example in which one detection device 10 is placed for each of the power transmission networks L0 to L4, in order to improve detection accuracy, two or more detection devices 10 may be placed for each of the power transmission networks L0 to L4.

[0032] <Predicting lightning strikes to power systems> Within a thundercloud, static electricity is generated by friction as ice collides, and when insulation in the air breaks down, a discharge occurs between the positively and negatively charged charges. When lightning strikes a power line, negative charges accumulate at the bottom of the thundercloud, as shown in Figure 3, and their counterparts, positive charges, accumulate on the power line. Lightning strikes a power line when insulation in the air between the thundercloud and the power line breaks down.

[0033] The inventors of the present application analyzed data from past voltage sags and confirmed that when a thundercloud approaches a transmission line, a disturbance occurs in the system frequency of that transmission line. Although the detailed mechanism behind the disturbance in the system frequency of a transmission line approached by a thundercloud has not yet been elucidated, it is thought that the negative charges accumulated at the bottom of the thundercloud and the positive charges that pair with them accumulate in the transmission line, causing a disturbance in the system frequency due to the influence of these positive charges.

[0034] Therefore, the processing device 20 according to this embodiment determines whether or not there is a disturbance in the system frequency detected by the detection device 10 (magnetic sensor 11), and if it is determined that there is a disturbance in the system frequency, determines that there is a sign that a lightning strike to the power system will occur within a certain period of time.

[0035] 4 is a diagram showing an example of the output waveform of the detection device 10 (magnetic sensor 11). For example, if the detection device 10 is installed in eastern Japan, the grid frequency is 50 Hz, and the magnetic sensor 11 detects the state of the grid voltage or grid current, whose phase changes at 50 Hz, as a magnetic change. Therefore, under normal circumstances when no thunderclouds are present near the power grid, the output waveform of the detection device 10 (magnetic sensor 11) is a sine wave corresponding to the grid frequency, as shown in FIG. 4. The following describes an example in which the detection device 10 is installed in eastern Japan.

[0036] Fig. 5 is a diagram showing an example of a processing pattern performed by the detection device 10. As shown in Fig. 5, the detection device 10 intermittently transmits the detection results of the magnetic sensor 11 to the processing device 20. Specifically, the detection device 10 performs a data acquisition process in which the detection results of the magnetic sensor 11 are sampled and stored at one-minute intervals.

[0037] In each data acquisition process, the detection results of the magnetic sensor 11 are acquired every 0.025 milliseconds for one second, and the 40,000 pieces of detection data acquired in one second are transmitted from the communication unit 12 to the processing device 20. When the data acquisition process is not being performed, the detection device 10 is in a sleep state. By performing the data acquisition process intermittently in this manner, the amount of communication data and the power consumption of the detection device 10 can be reduced, thereby extending the operating period of the battery serving as the power supply unit 13. As will be described later, because signs of a lightning strike can be detected early enough to allow measures to be taken against momentary power outages, intermittent data acquisition has little effect on the accuracy of lightning strike prediction.

[0038] 5 is merely an example, and is not intended to be limiting. For example, the data acquisition process may be performed continuously rather than intermittently.

[0039] When the processing device 20 receives the detection result of the magnetic sensor 11 from the detection device 10, it performs a fast Fourier transform (FFT) process on the received detection result to calculate frequency characteristics that indicate the correspondence between the frequency and frequency of the grid power.

[0040] Fig. 6 shows an example of a graph of frequency characteristics obtained by fast Fourier transform processing. The left side of Fig. 6 shows a graph under normal conditions (when there are no thunderclouds near the power system), and the right side of Fig. 6 shows a graph immediately before an instantaneous power outage occurs. In the graph of Fig. 6, the horizontal axis represents frequency, and the vertical axis represents frequency.

[0041] Both during normal operation and immediately before the momentary power outage, the most frequent peak shape was detected at 50 Hz, the system frequency in eastern Japan. However, immediately before the momentary power outage, the base of the 50 Hz peak shape appears to be wider than during normal operation. This indicates that a disturbance (fluctuation) is occurring at the 50 Hz system frequency.

[0042] At each substation, the system frequency is constantly adjusted to stay within the range of 50 Hz ± 0.2 Hz. Therefore, if the frequency characteristics of the waveform shown on the right side of Figure 6 are obtained, it is assumed that this is not a normal state, but that the system frequency is being disturbed by the influence of thunderclouds.

[0043] FIG. 7 is a plot of the peak width over time near 50 Hz in a graph of frequency characteristics obtained by fast Fourier transform. In this specification, the term "peak width" refers to the width of a frequency at which the frequency reaches a reference value that is a predetermined value lower than the peak value in a graph of frequency characteristics obtained by fast Fourier transform. The reference value may be half the peak value (50%), or may be 0.01 to 0.8 times the peak value. In either case, it is desirable to set the reference value so that the peak width fluctuates due to the influence of thunderclouds but does not fluctuate due to the influence of other noises.

[0044] The wider the peak width, the greater the frequency disturbance, and it is assumed that a larger amount of positive charges, which are paired with the negative charges accumulated at the bottom of the thundercloud, are accumulated in the transmission line. Therefore, in this embodiment, the system frequency disturbance is detected based on the peak width. This makes it possible to accurately grasp the fluctuation of the transmission frequency peak even when an external factor occurs, and therefore to detect the system frequency disturbance with high accuracy.

[0045] Because the peak width measurement data itself fluctuates greatly, the inventors of the present application performed filtering on the peak width measurement data to calculate the moving average value of the peak width. As a result, it was observed that the moving average value of the peak width is an almost constant steady value under normal conditions, begins to rise above the steady value approximately one hour before the time of the voltage sag, and reaches its maximum value at the time of the voltage sag. From this observation result, the state in which the peak width begins to rise above the steady value until it reaches its maximum value can be considered a precursor to a voltage sag or momentary power outage.

[0046] Therefore, the processing device 20 of this embodiment determines that there is a disturbance in the system frequency when the magnitude of the moving average value of the peak width exceeds the threshold value Wth, determines that there are signs that lightning will strike the power system within a certain period of time (for example, one hour), and outputs a lightning strike warning signal to notify this fact.

[0047] The threshold value Wth is set to a value between the steady-state value and the maximum value of the moving average value of the peak width, as shown in Fig. 7. The threshold value Wth is set so that the time from when the magnitude of the moving average value of the peak width exceeds the threshold value Wth until when it reaches the maximum value and an instantaneous power outage occurs is at least a time (e.g., about 10 minutes) that allows the power consumption location to take countermeasures against the instantaneous power outage (e.g., switching the power source of the electrical equipment from the power grid to an emergency power source).

[0048] 8 is a flowchart illustrating an example of a processing procedure executed by the processing device 20 when predicting a lightning strike to a power grid. This flowchart is repeatedly executed, for example, every time the processing device 20 receives detection data from the magnetic sensor 11 from the detection device 10.

[0049] The processing device 20 acquires the output (detection result) of the magnetic sensor 11 transmitted from the detection device 10 (step S10).

[0050] Next, the processing device 20 performs fast Fourier transform processing on the output of the magnetic sensor 11 received in step S10 to calculate the frequency characteristic as shown in FIG. 6 (step S11).

[0051] Next, the processing device 20 calculates the above-mentioned peak width from the frequency characteristics calculated in step S11 (step S12).

[0052] Next, the processing device 20 calculates a moving average value of the peak widths within the most recent predetermined period using the peak width calculated in step S12 and peak widths calculated in the past (step S13).

[0053] Next, the processing device 20 determines whether the magnitude of the moving average value of the peak width calculated in step S13 exceeds the threshold value Wth (step S14). If the magnitude of the moving average value of the peak width is less than the threshold value Wth (NO in step S14), it is assumed that there is no disturbance in the system frequency, and therefore the processing device 20 skips the subsequent processing and ends the processing.

[0054] On the other hand, if the magnitude of the moving average value of the peak width exceeds the threshold value Wth (YES in step S14), the processing device 20 determines that there is a disturbance in the system frequency (step S15), and determines that there is a sign that lightning will strike the power system within a certain time period (step S16).The processing device 20 then outputs a lightning strike sign signal to the display device 30, the power supply switching device 40, and the like via the network 2 (email or SNS (Social Networking Service)) to notify that there is a sign that lightning will strike the power system within a certain time period (step S17).

[0055] Upon receiving the lightning strike prediction signal from the processing device 20, the display device 30 displays a warning on its screen, informing users that there is a possibility that lightning will strike the power grid within a certain time period. Users who see this warning can begin taking various preventive measures against a momentary power drop or interruption at least 10 minutes in advance, such as switching to an emergency power source such as a private generator or shutting down electrical equipment normally. This minimizes economic losses due to momentary power drops or interruptions at power consumption locations such as factories.

[0056] Furthermore, the power supply switching device 40, which receives the lightning strike prediction signal from the processing device 20, automatically switches the power supply of the electrical equipment at the power consumption location where a momentary drop or momentary interruption is predicted to occur due to a lightning strike to the power grid from the power grid to the emergency power supply, thereby automatically preventing damage caused by the momentary drop or momentary interruption.

[0057] If the power consumption location is a factory, a device may be installed in the factory that outputs an alarm when a lightning strike prediction signal is received, or a device may be installed that automatically shuts down the electrical equipment normally when a lightning strike prediction signal is received and the current work on the electrical equipment is completed. The lightning strike prediction system 1 does not necessarily have to have the display device 30 or the power supply switching device 40; it can be either one or the other, or it can simply output a lightning strike prediction signal from the notification unit 23 for the purpose of linking with the equipment and systems in the factory.

[0058] As described above, the lightning strike prediction system 1 according to this embodiment does not detect the position of a thundercloud, which changes from moment to moment over a wide area. Instead, the magnetic sensor 11 detects the grid frequency, which is the frequency of the power flowing through the power grid, which is a fixed object. When there is a disturbance in the grid frequency, it is determined that there is a sign that a lightning strike to the power grid will occur within a certain time period. That is, the lightning strike prediction system 1 according to this embodiment can predict the sign of a lightning strike to the power grid, which is a direct cause of a momentary sag or momentary interruption, a certain time (e.g., 10 minutes or more) before the actual occurrence of the momentary sag or momentary interruption. Therefore, compared to detecting the position of a thundercloud and calculating the charge distribution within the thundercloud, it is possible to predict the sign of a momentary sag or momentary interruption within a certain time period with fewer sensors and a lighter computational load.

[0059] <Modification 1> In the above-described embodiment, it is determined that there is a disturbance in the system frequency when the magnitude of the moving average value of the peak width of the system frequency is larger than the threshold value Wth.

[0060] In contrast to this, in the present modified example 1, instead of the magnitude of the moving average value of the peak width, the time rate of change of the moving average value of the peak width (the amount of change per unit time, the slope of the graph of the moving average value shown in FIG. 7 ) is calculated, and when the time rate of change of the moving average value of the peak width exceeds the threshold value Rth, it is determined that there is a disturbance in the system frequency.

[0061] Fig. 9 is a flowchart showing an example of the processing procedure of the processing device 20 according to Modification 1. The flowchart shown in Fig. 9 is obtained by changing step S14 of the flowchart in Fig. 8 described above to step S14A and further adding step S13A.

[0062] The processing device 20 calculates the time change rate of the moving average value of the peak width using the moving average value of the peak width calculated in step S13, the moving average value of the peak width calculated in the previous calculation cycle, and the calculation cycle (step S13A).

[0063] Next, the processing device 20 determines whether the time rate of change of the moving average value of the peak width exceeds a threshold value Rth (step S14A). The threshold value Rth is set to a value corresponding to the gradient between the steady state gradient and the gradient when a precursory phenomenon occurs in the graph of the moving average value of the peak width shown in FIG.

[0064] If the time rate of change of the moving average value of the peak width is less than the threshold value Rth (NO in step S14A), it is assumed that there is no disturbance in the system frequency, and therefore the processing device 20 terminates the processing.

[0065] On the other hand, if the time rate of change of the moving average value of the peak width exceeds the threshold value Rth (YES in step S14A), the processing device 20 determines that there is a disturbance in the system frequency (step S15), determines that there are signs that a lightning strike to the power system will occur within a certain period of time (step S16), and outputs a lightning strike warning signal (step S17).

[0066] As described above, it may be determined that there is a disturbance in the system frequency when the rate of change over time of the moving average value of the peak width exceeds the threshold value Rth.

[0067] <Modification 2> In the above-described embodiment and modification 1, it is determined whether or not there is a disturbance in the system frequency based on the peak width of the system frequency.

[0068] In contrast, in the present modified example 2, the system frequency under normal conditions is used as the reference frequency, and the difference between the system frequency detected by the magnetic sensor 11 and the reference frequency is calculated as the degree of abnormality of the system frequency, and it is determined whether or not there is a disturbance in the system frequency based on the degree of abnormality of the system frequency.

[0069] FIG. 10 is a diagram illustrating an example of changes in the system frequency measured by the magnetic sensor 11 and the system frequency anomaly level (the difference between the system frequency and the reference frequency) from a normal state to an instantaneous power outage. FIG. 10 illustrates the results of calculating the system frequency anomaly level (the difference between the system frequency and the reference frequency) using k-nearest neighbor algorithm, a machine learning method using artificial intelligence (AI). Specifically, the system frequency on a sunny day is used as training data indicating the reference frequency, and the measured system frequency data from a normal state to an instantaneous power outage is compared with the training data. The difference between the measured data and the training data is calculated as the system frequency anomaly level. The measured data may be raw data or may be processed, for example, by a Fourier transform. The training data may be unique data and may be updated periodically.

[0070] 10, the degree of abnormality is a very small value under normal conditions, but 20 minutes before the time of the instantaneous power outage, the degree of abnormality becomes larger than normal, and at the time of the instantaneous power outage, the degree of abnormality becomes very large. Based on this result, in this second modification, a threshold value Dth is set that can detect the degree of abnormality 20 minutes before the time of the instantaneous power outage, and if the degree of abnormality (difference) exceeds the threshold value Dth, it is determined that there is a disturbance in the system frequency.

[0071] Fig. 11 is a flowchart showing an example of the processing procedure of the processing device 20 according to Modification 2. In the flowchart shown in Fig. 11, steps S11 to S14 in the above-mentioned Fig. 8 are replaced with steps S11B and S14B.

[0072] The processing device 20 calculates the degree of abnormality of the system frequency (the difference between the measured value of the system frequency and the reference frequency) by comparing the output of the magnetic sensor 11 received in step S10 with the reference frequency (step S11B).

[0073] Next, the processor 20 determines whether the abnormality degree (difference) calculated in step S11B exceeds a threshold value Dth (step S14B). If the abnormality degree (difference) is less than the threshold value Dth (NO in step S14B), it is assumed that there is no disturbance in the grid frequency, and the processor 20 ends the process.

[0074] On the other hand, if the degree of abnormality (difference) exceeds the threshold value Dth (YES in step S14B), the processing device 20 determines that there is a disturbance in the system frequency (step S15), determines that there are signs that lightning will strike the power system within a certain period of time (step S16), and outputs a lightning strike warning signal (step S17).

[0075] As described above, the difference between the grid frequency detected by the magnetic sensor 11 and the reference frequency may be calculated as the grid frequency anomaly level, and if the grid frequency anomaly level exceeds the threshold value Dth, it may be determined that a grid frequency disturbance has occurred. This makes it possible to predict the occurrence of a momentary power drop or power outage due to a lightning strike at least 10 minutes in advance. During this 10-minute period, countermeasures can be taken in preparation for a momentary power drop or power outage at power consumption locations such as factories.

[0076] As shown in FIG. 10 , the above-described system frequency anomaly degree (the difference between the system frequency and the reference frequency) has a waveform that periodically increases. This is presumably due to the periodic effectiveness of the system frequency adjustment at the substation. Specifically, the anomaly degree is approximately zero while the system frequency adjustment at the substation is effective. The reason why the anomaly degree periodically increases despite the system frequency adjustment at the substation is presumably because the presence of thunderclouds near the transmission line makes it impossible for the system frequency adjustment at the substation to periodically absorb the system frequency disturbances. In consideration of this periodic increase in the system frequency anomaly degree, the number of times the system frequency anomaly degree exceeds a threshold value Dth may be counted, and if the number of times the system frequency anomaly degree exceeds the threshold value Dth, it may be determined that a system frequency disturbance exists.

[0077] <Modification 3> In the above-described embodiment, the system frequency is detected by using the magnetic sensor 11 to detect the magnetic field generated around the power transmission line when the system power flows through the power transmission line.

[0078] However, the system frequency may be detected by detecting the voltage or current of the system power flowing through the transmission line with a voltage sensor or a current sensor, which makes it possible to accurately detect system frequency disturbances and capture precursors of instantaneous sags or instantaneous power outages without using a highly sensitive magnetic sensor.

[0079] However, in order to detect the system frequency of the transmission lines connecting substations using a voltage or current sensor, it is necessary to connect the voltage or current sensor directly to the transmission lines connecting the substations. In reality, this requires the cooperation of the electric power company that manages the transmission lines, and installing the sensors is also costly.

[0080] In contrast, the magnetic sensor 11 can be placed at a location away from the power transmission line, and therefore can inexpensively detect the system frequency without being directly connected to the power transmission line.

[0081] The embodiments and modifications disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present disclosure is defined by the claims, not by the description of the above-mentioned embodiments, and is intended to include all modifications within the meaning and scope of the claims.

[0082] It will be understood by those skilled in the art that the above-described embodiments and their modifications are specific examples of the following aspects.

[0083] (Item 1) A lightning prediction system according to the present disclosure is a system for predicting lightning strikes on an electric power grid, and includes a detection device having a sensor for detecting a grid frequency, which is the frequency of the power flowing through the electric power grid, and a processing device for predicting lightning strikes on the electric power grid. The processing device determines whether there is a disturbance in the grid frequency detected by the sensor, and if there is a disturbance in the grid frequency, determines that there is a sign that lightning will strike the electric power grid within a certain time period.

[0084] The lightning strike prediction system described in paragraph 1 does not detect the position of thunderclouds, which changes over a wide area, but instead uses a sensor to detect the grid frequency, which is the frequency of the power flowing through a fixed power grid, and determines, when there is a disturbance in the grid frequency, that there is a sign that a lightning strike to the power grid will occur within a certain time period. In other words, the lightning strike prediction system described in paragraph 1 can predict the possibility of a lightning strike to the power grid, which is a direct cause of a voltage sag or momentary power interruption, a certain time (e.g., 10 minutes or more) before the actual occurrence of the voltage sag or momentary power interruption. Therefore, compared to detecting the position of a thundercloud and calculating the charge distribution within the thundercloud, it is possible to predict the possibility of a voltage sag or momentary power interruption within a certain time period with fewer sensors and a lighter computational load.

[0085] (2) In the lightning strike prediction system according to the first aspect, the sensor is a magnetic sensor that is placed at a predetermined distance from the power grid.

[0086] According to the lightning strike prediction system described in paragraph 2, the grid frequency is detected by a magnetic sensor located at a predetermined distance from the power grid. Therefore, the grid frequency can be detected without directly connecting the sensor to the power grid.

[0087] (Clause 3) In the lightning prediction system described in paragraph 1, the processing device performs fast Fourier transform processing on the sensor detection results to calculate frequency characteristics that indicate the correspondence between the frequency and frequency of the power flowing through the power system, calculates the frequency width at which the frequency in the calculated frequency characteristics becomes a reference value that is a predetermined value lower than the peak value as the peak width of the system frequency, and determines whether or not there is a disturbance in the system frequency based on the peak width of the system frequency.

[0088] According to the lightning strike prediction system described in paragraph 3, the peak width of the grid frequency is calculated from the frequency characteristics obtained by performing a fast Fourier transform on the detection results of the sensor, and whether or not there is a grid frequency disturbance is determined based on the peak width of the grid frequency. This makes it possible to accurately grasp the fluctuation of the transmission frequency peak even if the detection results of the sensor are affected by external factors. Therefore, it is possible to accurately determine whether or not there is a grid frequency disturbance.

[0089] (4) In the lightning prediction system described in paragraph 3, the processing device calculates a moving average value of the peak width of the system frequency, and determines that there is a disturbance in the system frequency if the magnitude or time rate of change of the calculated moving average value exceeds a threshold value.

[0090] According to the lightning strike prediction system described in paragraph 4, whether or not there is a disturbance in the grid frequency is determined based on the moving average value of the peak width, rather than the instantaneous value of the peak width, which fluctuates greatly. Therefore, whether or not there is a disturbance in the grid frequency can be determined with higher accuracy.

[0091] (5) In the lightning prediction system described in 1, the processing device calculates the difference between the system frequency detected by the sensor and a reference frequency, and determines that there is a disturbance in the system frequency if the calculated difference exceeds the reference value.

[0092] According to the lightning strike prediction system described in paragraph 5, it is possible to determine whether or not there is a disturbance in the grid frequency by a simple process of calculating the difference between the grid frequency and the reference frequency.

[0093] (Item 6) In the lightning strike prediction system described in item 1, the processing device is placed at a location separate from the detection device, and the detection device has a communication unit for transmitting the detection results of the sensor to the processing device.

[0094] According to the lightning strike prediction system described in paragraph 6, the processing device is located at a location remote from the detection device. Therefore, there is no need to install a high-performance processor or large-capacity storage in the detection device, and the processing device (server, etc.) located at a location remote from the detection device can determine in real time whether or not there is a disturbance in the grid frequency.

[0095] (Item 7) In the lightning strike prediction system according to item 1, the detection device intermittently performs a process of transmitting the detection result of the sensor to the processing device.

[0096] According to the lightning strike prediction system described in paragraph 7, the detection device transmits data intermittently, which reduces the amount of communication data and the power consumption of the detection device. For example, if the detection device is battery-powered, the battery power consumption can be reduced and the detection device can continue to operate for a long period of time.

[0097] (Clause 8) In the lightning prediction system described in paragraph 1, the detection device includes a first detection device that detects the system frequency of a first power transmission network connecting substations, and a second detection device that detects the system frequency of a second power transmission network connecting substations and power consumption locations.

[0098] According to the lightning prediction system described in Section 8, disturbances in the system frequency can be detected with high accuracy regardless of whether they occur in the first power transmission network connecting substations or in the second power transmission network connecting substations and power consumption locations.

[0099] (Clause 9) In the lightning prediction system described in paragraph 1, when the processing device determines that there are signs of a lightning strike to the power system, it outputs a lightning strike warning signal to notify the outside that there are signs that a lightning strike to the power system will occur within a certain period of time.

[0100] According to the lightning prediction system described in paragraph 9, a lightning prediction signal is output to the outside a certain time (for example, 10 minutes or more) before the actual occurrence of a momentary power sag or momentary power interruption. This allows a power consumption location that receives the lightning prediction signal to begin taking countermeasures against the momentary power sag or momentary power interruption a certain time before the actual occurrence of the momentary power sag or momentary power interruption. As a result, economic losses at the power consumption location due to the momentary power sag or momentary power interruption can be minimized.

[0101] (10) The lightning strike prediction system according to claim 9, further comprising a switching device that switches the power source of the electrical equipment from the power grid to an emergency power source when a lightning strike prediction signal is received.

[0102] According to the lightning strike prediction system described in paragraph 10, abnormal shutdown of electrical equipment due to a momentary voltage drop or momentary power interruption can be prevented.

[0103] (Clause 11) The lightning prediction system described in clause 9 further includes a display device that, when a lightning warning signal is received, displays a warning on a screen to notify that there are signs that lightning will strike the power grid within a certain period of time.

[0104] According to the lightning strike prediction system described in paragraph 11, it is possible to warn on a screen that there is a sign that lightning will strike the power grid within a certain time period.

[0105] (Item 12) A lightning prediction method according to the present disclosure is a method for predicting a lightning strike to an electric power grid by a computer, the method including the steps of detecting a grid frequency, which is the frequency of power flowing through the electric power grid, and predicting a lightning strike to the electric power grid. The step of predicting a lightning strike to the electric power grid includes the steps of determining whether or not there is a disturbance in the grid frequency, and determining, if there is a disturbance in the grid frequency, that there is a sign of a lightning strike to the electric power grid.

[0106] According to the lightning prediction method described in paragraph 12, similar to the lightning prediction system described in paragraph 1, it is possible to predict in advance that there are signs that a momentary voltage drop or momentary power outage will occur within a certain period of time using a small number of sensors and with a light calculation load.

[0107] 1 Lightning strike prediction system, 2 Network, 10 Detection device, 11 Magnetic sensor, 12, 21 Communication unit, 13 Power supply unit, 20 Processing device, 22 Prediction unit, 23 Notification unit, 30 Display device, 40 Power supply switching device, 50 Power system, 60 Power plant, 70 Power transmission network, 71, 72 Substation, 80 Power consumption location.

Claims

1. A lightning prediction system for predicting lightning strikes on an electric power system, comprising: a detection device having a sensor for detecting a system frequency, which is the frequency of the electricity flowing through the electric power system; and a processing device for predicting lightning strikes on the electric power system, wherein the processing device determines whether or not there is a disturbance in the system frequency detected by the sensor, and if there is a disturbance in the system frequency, determines that there is a sign that lightning will strike the electric power system within a certain time period.

2. The lightning prediction system according to claim 1, wherein the sensor is a magnetic sensor located at a predetermined distance from the power grid.

3. The lightning prediction system of claim 1, wherein the processing device performs a fast Fourier transform on the detection results of the sensor to calculate frequency characteristics that indicate the correspondence between the frequency and frequency of the power flowing through the power system, calculates the frequency width at which the frequency in the calculated frequency characteristics is a reference value that is a predetermined value lower than the peak value as the peak width of the system frequency, and determines whether or not there is a disturbance in the system frequency based on the peak width of the system frequency.

4. The lightning prediction system according to claim 3, wherein the processing device calculates a moving average value of the peak width of the system frequency, and determines that there is a disturbance in the system frequency when the magnitude or time rate of change of the calculated moving average value exceeds a threshold value.

5. The lightning prediction system according to claim 1, wherein the processing device calculates the difference between the system frequency detected by the sensor and a reference frequency, and determines that there is a disturbance in the system frequency if the calculated difference exceeds a reference value.

6. The lightning prediction system according to claim 1, wherein the processing device is located at a location separate from the detection device, and the detection device has a communication unit for transmitting the detection results of the sensor to the processing device.

7. The lightning prediction system according to claim 1, wherein the detection device intermittently transmits the detection results of the sensor to the processing device.

8. The lightning prediction system according to claim 1, wherein the detection device includes: a first detection device that detects the system frequency of a first transmission network connecting substations; and a second detection device that detects the system frequency of a second transmission network connecting substations and power consumption locations.

9. The lightning prediction system of claim 1, wherein the processing device outputs a lightning warning signal to notify an external party that there are signs that lightning will strike the power system within the specified period of time when it determines that there are signs that lightning will strike the power system.

10. The lightning prediction system according to claim 9, further comprising a switching device that switches the power source of the electrical equipment from the power system to an emergency power source when the lightning prediction signal is received.

11. The lightning prediction system of claim 9, further comprising a display device that, when the lightning warning signal is received, displays a warning on a screen to notify that there is a possibility that lightning will strike the power grid within the specified period of time.

12. A lightning prediction method in which a computer predicts lightning strikes on an electric power system, comprising the steps of: detecting a system frequency, which is the frequency of the power flowing through the electric power system; and predicting a lightning strike on the electric power system, wherein the step of predicting a lightning strike on the electric power system comprises the steps of determining whether or not there is a disturbance in the system frequency; and determining, if there is a disturbance in the system frequency, that there is a sign of a lightning strike on the electric power system.

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