Information processing method, information processing system, and computer program

Abstract

To provide a technique suitable for processing using multiple sets of measurement data.SOLUTION: An information processing method disclosed herein involves having a computer perform processing for acquiring measurement data of multiple power storage elements in time series, and generating compressed data by performing compression processing on difference data between the acquired measurement data of each power storage element and reference measurement data.SELECTED DRAWING: Figure 5

Description

[Technical field]

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

[0002] Energy storage elements are widely used in uninterruptible power supplies, DC power supplies, etc. In addition, the use of energy storage elements in large-scale systems that store electricity generated by renewable energy or existing power generation systems is expanding. In large-scale systems, multiple energy storage elements are used.

[0003] For stable operation of a system that uses storage elements, it is important to understand the state and lifespan of the storage elements. Regarding methods for diagnosing the state and predicting the lifespan of storage elements, various methods have been proposed that use measurement data such as voltage, current, and temperature observed when the storage elements are charged and discharged, and efforts are being made to improve accuracy (for example, see Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2013-003115 A Summary of the Invention [Problem to be solved by the invention]

[0005] A large-scale energy storage system is constructed using a large number of energy storage elements. For example, in a large-scale photovoltaic power generation system called a mega solar power generation system, an extremely large number of energy storage elements, such as several million, are installed, and this number is far greater than the number of energy storage elements used, for example, for powering a vehicle or for auxiliary machinery. In such a large-scale energy storage system, if data is measured for each energy storage element at a predetermined interval, the amount of measurement data obtained will be enormous. It is not easy to compile and accurately grasp the enormous amount of measurement data, and furthermore, if various processes are performed on the entirety of the enormous amount of measurement data, a considerable amount of processing time is required. A technology suitable for processing using multiple measurement data is desired.

[0006] An object of the present disclosure is to provide a technique suitable for processing using multiple pieces of measurement data. [Means for solving the problem]

[0007] An information processing method according to one embodiment of the present disclosure includes a computer executing a process of acquiring time-series measurement data for a plurality of energy storage elements, and generating compressed data by performing a compression process on differential data between the acquired measurement data for each energy storage element and reference measurement data. Effect of the Invention

[0008] According to the present disclosure, it is possible to provide a technique suitable for processing using a plurality of measurement data. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram showing an overview of a remote monitoring system according to an embodiment of the present invention. [Diagram 2] 1 is a block diagram showing a configuration example of a power generation system; [Diagram 3] 1 is a block diagram showing an example of the configuration of an information processing device; [Figure 4] FIG. 2 is a block diagram showing a configuration example of a terminal device. [Diagram 5] FIG. 2 is a functional block diagram showing a configuration example of an information processing device. [Figure 6]FIG. 4 is a diagram illustrating an example of compressed data. [Figure 7] FIG. 4 is a schematic diagram showing an example of a screen displayed on a display unit of a terminal device. [Figure 8] 11 is a flowchart illustrating an example of a processing procedure executed by an information processing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] (1) An information processing method according to one embodiment of the present disclosure includes a computer executing a process of acquiring time-series measurement data for a plurality of energy storage elements, and generating compressed data by performing a compression process on differential data between the acquired measurement data for each energy storage element and a reference measurement data.

[0011] According to the information processing method described in (1) above, measurement data acquired from a plurality of storage elements provided in a large-scale energy storage system can be converted into compressed data. In this specification, compression processing means lossy compression, not lossless compression that can be compressed and decompressed. By compressing the measurement data to reduce the information of the original data, a huge amount of measurement data can be converted into an easy-to-handle form and provided as data suitable for subsequent processing. By generating compressed data, it is possible to reduce unnecessary noise and improve the accuracy of subsequent data analysis. By compressing the difference data between the measurement data and the reference measurement data, rather than compressing the measurement data itself, it is possible to reflect the deviation from the reference state in the compressed data, and it is possible to appropriately grasp the behavior of each storage element in the group.

[0012] (2) In the information processing method described above in (1), an abnormality in the storage element may be detected based on the generated compressed data.

[0013] According to the information processing method described in (2) above, the interpretability of anomaly detection can be improved by using compressed data obtained by compressing measurement data. By detecting anomalies based on measurement data as relative values ​​with respect to a reference state, false detections can be reduced and anomalies can be detected with high accuracy. By using compressed data, an increase in the processing load associated with a large-scale energy storage system can be suppressed, leading to efficient execution of anomaly detection.

[0014] (3) In the information processing method described in (1) or (2) above, distribution information that visualizes the distribution of the generated compressed data may be output.

[0015] According to the information processing method described in (3) above, the distribution status of the compressed data can be visualized and presented, and the user can visually and clearly recognize the distribution of the compressed data. By presenting the compressed information, the state of each storage element can be easily grasped at a glance.

[0016] (4) In the information processing method described in any one of (1) to (3) above, the compressed data may be classified into normal clusters and abnormal clusters using a clustering technique, and an abnormality in the storage element may be detected based on the classification results.

[0017] According to the information processing method described in (4) above, anomaly detection can be easily and accurately performed using a clustering method. By using a clustering method, which is a type of machine learning method, anomalies can be detected from features based on the obtained measurement data, reducing the dependency on individuals in anomaly detection. By using compressed data, the load of clustering can be reduced and accuracy can be improved compared to when the measurement data itself is used. By using an unsupervised learning algorithm, there is no need to prepare training data, and unknown anomalies can be recognized with high accuracy.

[0018] (5) In the information processing method described in (4) above, the normal clusters and the abnormal clusters may be superimposed in a visually distinguishable manner on distribution information that visualizes the distribution of the compressed data and output.

[0019] According to the information processing method described in (5) above, the distribution state of compressed data and the anomaly detection result can be associated and presented, allowing the user to visually clearly recognize the information. By visualizing and presenting the anomaly detection result, the explainability of the anomaly detection is improved.

[0020] (6) In the information processing method described in any one of (1) to (5) above, the differential data may be generated by calculating the difference between the average or median of the measurement data of the multiple storage elements at the same time point as the reference measurement data and the measurement data of each storage element.

[0021] According to the information processing method described in (6) above, reference measurement data can be calculated based on the measurement data of a plurality of storage elements. By using a relative value with respect to the reference measurement data that takes into account the actual state of a plurality of storage elements, the accuracy of subsequent processing such as anomaly detection can be improved. Aberrant storage elements that exhibit behavior that deviates from the group can be detected with high accuracy.

[0022] (7) In the information processing method according to any one of (1) to (6) above, the compression process may be performed using principal component analysis, UMAP, t-SNE, or an autoencoder.

[0023] According to the information processing method described in (7) above, the dimensions of the measurement data can be easily and accurately reduced using a machine learning technique. Since the data can be compressed while retaining the original features, compressed data that appropriately reflects the behavior of each storage element can be generated. By using an unsupervised learning algorithm, there is no need to prepare training data, and the characteristics of the measurement data that change in a diverse manner can be accurately recognized.

[0024] (8) In the information processing method according to any one of (1) to (7) above, the measurement data may include at least one of a voltage, a temperature, and a State of Charge (SOC) of the power storage element.

[0025] According to the information processing method described in (8) above, by using data on voltage, temperature, SOC or a combination thereof, which are sensitive to changes and well reflect the internal state of the storage element, among other measurement data, it is possible to provide data that properly represents the state of the storage element.

[0026] (9) In the information processing method described in any one of (2) to (8) above, an abnormality in the storage element may be further detected using a storage element simulator that estimates measurement data of the storage element based on an internal state of the storage element.

[0027] According to the information processing method described in (9) above, in addition to the anomaly detection by the above-mentioned method, anomaly detection can be performed using a storage element simulator, so that multifaceted judgment is possible. Since the measurement data of a storage element depends on the internal state of the storage element, false detection can be reduced by using a simulator that can take into account the internal state of the storage element in anomaly detection. However, since it is generally difficult for a simulator to handle internal states that engineers do not anticipate, the reliability of anomaly detection for both expected and unknown anomalies can be increased by combining anomaly detection processing using a machine learning method with a method for detecting anomalies by focusing on the features of the measurement data itself to be detected.

[0028] (10) An information processing system according to one embodiment of the present disclosure includes an acquisition unit that acquires time-series measurement data for a plurality of storage elements, and a generation unit that generates compressed data by performing a compression process on differential data between the acquired measurement data for each storage element and reference measurement data.

[0029] (11) A computer program according to one embodiment of the present disclosure causes a computer to acquire time-series measurement data for a plurality of energy storage elements, and to perform a compression process on differential data between the acquired measurement data for each energy storage element and reference measurement data to generate compressed data.

[0030] Hereinafter, the present disclosure will be specifically described with reference to the drawings showing the embodiments thereof.

[0031] (First embodiment) FIG. 1 shows an overview of a remote monitoring system 100 according to the present embodiment. Information relating to the energy storage elements included in a power generation system 200 in the remote monitoring system 100 can be accessed remotely. The remote monitoring system 100 is an example of an information processing system. The remote monitoring system 100 includes an information processing device 50 as a main device. The information processing device 50 collects information from the power generation system 200 that is the subject of remote monitoring. The information processing device 50 is connected to a network N1 such as the Internet. In addition, a terminal device 60 and the power generation system 200 are connected to the network N1.

[0032] The information processing device 50 and the terminal device 60 are not limited to being separate devices, and may be, for example, a single processing device that serves as the information processing device 50 and the terminal device 60. The information processing device 50 and / or the terminal device 60 may be integrated into any one of the power generation systems 200. The number of power generation systems 200 may be one or three or more.

[0033] FIG. 2 is a block diagram showing a configuration example of a power generation system 200. Illustration of power generation devices such as a solar power generation system and a wind power generation system is omitted. The power generation system 200 includes a communication device 10, a domain management device 30, and a power storage unit (domain) 40. The server device 20 is connected to the communication device 10 via a network N2. The power storage unit 40 may include a plurality of banks 41. For example, the power storage unit 40 is housed in a battery panel and used in a thermal power generation system, a mega solar power generation system, a wind power generation system, an uninterruptible power supply (UPS), a stabilized power supply system for railways, and the like. A configuration including the communication device 10, the domain management device 30, and the power storage unit 40 is called a power storage system. The power storage system may include a power conditioner (not shown). The power storage unit 40 is not limited to industrial use, and may be for home use.

[0034] The business operator designs, introduces, operates and maintains a power storage system including a communication device 10, a domain management device 30 and a power storage unit 40, and can remotely monitor the power storage system using a remote monitoring system 100.

[0035] The communication device 10 includes a control unit 11, a storage unit 12, a first communication unit 13, and a second communication unit 14. The control unit 11 is composed of a CPU (Central Processing Unit) and the like, and controls the entire communication device 10 using built-in memories such as a ROM (Read Only Memory) and a RAM (Random Access Memory).

[0036] The storage unit 12 includes a non-volatile storage device such as a flash memory, etc. The storage unit 12 can store required information, for example, information obtained by processing by the control unit 11.

[0037] The first communication unit 13 includes a communication interface that realizes communication with the domain management device 30 or the battery management device 44. The control unit 11 can communicate with the domain management device 30 through the first communication unit 13.

[0038] The second communication unit 14 includes a communication interface that realizes communication via the network N2. The control unit 11 can communicate with the server device 20 through the second communication unit 14.

[0039] The domain management device 30 uses a predetermined communication interface to transmit and receive information to and from each bank 41. The storage unit 12 can store the measurement data acquired via the domain management device 30.

[0040] The server device 20 can collect measurement data of the power storage system from the communication device 10. The measurement data includes measurement values ​​such as current, voltage, and temperature of each power storage element in the power storage system. The server device 20 may store the collected measurement data by dividing it into groups for each power storage element. The server device 20 can transmit the measurement data to the information processing device 50 via the networks N2 and N1. Note that the networks N1 and N2 may be a single communication network.

[0041] The bank 41 is made up of a plurality of power storage modules connected in series, and includes a battery management unit (BMU) 44, a plurality of power storage modules 42, and a measurement board (CMU: Cell Management Unit) 43 provided in each power storage module 42.

[0042] The storage module 42 has a plurality of storage cells connected in series. The power generation system 200 is a large-scale storage system including a large number of storage cells, for example, more than one million storage cells. The storage cells are an example of a storage element. The storage elements are preferably rechargeable, such as secondary batteries, such as lead-acid batteries and lithium-ion batteries, or capacitors. Some of the storage elements may be non-rechargeable primary batteries. The storage element may refer to the storage module 42, the bank 41, or a domain in which the banks 41 are connected in parallel.

[0043] The measurement board 43 acquires measurement data related to each power storage cell of the power storage module 42. The measurement data can be repeatedly acquired at an appropriate period, such as every 0.1 seconds, 0.5 seconds, or 1 second.

[0044] The battery management device 44 can communicate with the measurement board 43 with a communication function via serial communication, and can acquire measurement data detected by the measurement board 43. The battery management device 44 can transmit and receive information to and from the domain management device 30. The domain management device 30 aggregates measurement data from the battery management devices 44 of the banks belonging to the domain. The domain management device 30 outputs the aggregated measurement data to the communication device 10. In this way, the communication device 10 can acquire and store the measurement data of the power storage unit 40 via the domain management device 30.

[0045] The communications device 10 transmits, at a predetermined timing (e.g., at a fixed period or when the amount of data satisfies a predetermined condition, etc.), measurement data stored since the previous timing to the server device 20. The communications device 10 transmits the measurement data in association with identification information of the power storage cell (e.g., a cell ID). The communications device 10 may transmit all the measurement data obtained via the domain management device 30, or may transmit measurement data thinned out at a predetermined rate.

[0046] The information processing device 50 acquires measurement data of the power storage cells provided in the power generation system 200 through the server device 20, and monitors the state of the power generation system 200 based on the acquired measurement data. The information processing device 50 converts the acquired measurement data into an easy-to-handle format using a machine learning technique, and performs anomaly detection of the power storage cells using the converted data. The information processing device 50 presents the results of various analysis processes to the user through the terminal device 60. In this specification, machine learning refers to machine learning in a broad sense, including multivariate analysis such as clustering and principal component analysis.

[0047] 3 is a block diagram showing a configuration example of an information processing device 50. The information processing device 50 is a computer capable of various information processing and transmitting and receiving information, such as a server computer, a personal computer, a quantum computer, etc. The information processing device 50 may be a multi-computer consisting of multiple computers, or may be a virtual machine virtually constructed by software. The information processing device 50 includes a control unit 51, a storage unit 52, and a communication unit 53.

[0048] The control unit 51 is an arithmetic circuit including a CPU, a GPU (Graphics Processing Unit), a ROM, a RAM, etc. The CPU or GPU included in the control unit 51 executes various computer programs stored in the ROM or the storage unit 52, and controls the operation of each of the above-mentioned hardware units. The control unit 51 may include functions such as a timer that measures the elapsed time from when an instruction to start measurement is given to when an instruction to end measurement is given, a counter that counts numbers, and a clock that outputs date and time information.

[0049] The storage unit 52 includes a non-volatile storage device such as a flash memory, a hard disk drive, etc. The storage unit 52 stores various computer programs and data referenced by the control unit 51. The storage unit 52 may be an external storage device connected to the information processing device 50.

[0050] The storage unit 52 of this embodiment stores a program 521 for causing a computer to execute processing using measurement data, and a measurement DB (Data Base) 522 as data necessary for executing this program 521.

[0051] The measurement DB 522 is a database that stores the measurement data received from the power generation system 200. As described above, the measurement data includes measurement values ​​such as the current, voltage, and temperature of the storage cell in the power generation system 200. The measurement data includes data when the storage cell is charged or discharged. The measurement data may include not only the measurement values ​​of the measurement board 43 such as the current, voltage, and temperature, but also a calculated value (e.g., SOC) calculated using the measurement values. The measurement DB 522 stores records in which, for example, an ID for identifying the measurement data is used as a key to link information such as identification information of the storage cell, the measurement date and time, and the measurement value. The measurement DB 522 may further store a result of anomaly detection. Every time the control unit 51 receives measurement data transmitted from the server device 20, the control unit 51 stores the received measurement data in the measurement DB 522 in chronological order.

[0052] A computer program (program product) including the program 521 may be provided by a non-transitory recording medium 5A on which the computer program is readably recorded. The recording medium 5A is a portable memory such as a CD-ROM, a USB memory, or a Secure Digital (SD) card. The control unit 51 reads a desired computer program from the recording medium 5A using a reading device (not shown) and stores the read computer program in the storage unit 52. Alternatively, the computer program may be provided by communication. The program 521 may be a single computer program or may be composed of multiple computer programs, and may be executed on a single computer or multiple computers interconnected by a communication network.

[0053] The communication unit 53 includes a communication interface that realizes communication via the network N1. The control unit 51 receives measurement data transmitted from the power generation system 200 through the communication unit 53. The control unit 51 transmits various processing results to an external device such as a terminal device 60 through the communication unit 53.

[0054] The information processing device 50 may include a display unit that displays various information, an operation unit that accepts user operations, and the like.

[0055] 4 is a block diagram showing a configuration example of the terminal device 60. The terminal device 60 is a computer capable of various information processing and transmitting and receiving information, such as a personal computer, a smartphone, or a tablet terminal. The terminal device 60 is used by a user such as a manager, a maintenance person, or an operator of the storage battery system of the power generation system 200. The terminal device 60 includes a control unit 61, a storage unit 62, a communication unit 63, a display unit 64, an operation unit 65, and the like.

[0056] The control unit 61 is an arithmetic circuit including a CPU, a ROM, a RAM, etc. The CPU or GPU included in the control unit 61 executes various computer programs stored in the ROM or the storage unit 62, and controls the operation of each of the above-mentioned hardware components.

[0057] The storage unit 62 includes a non-volatile storage device such as a flash memory, a hard disk drive, etc. The storage unit 62 stores various computer programs and data referenced by the control unit 61. The control unit 61 causes the display unit 64 to display various processing results provided by the information processing device 50 based on the computer programs stored in the storage unit 62.

[0058] The communication unit 63 includes a communication interface that realizes communication via the network N1. The control unit 61 transmits and receives information to and from the information processing device 50 through the communication unit 63.

[0059] The display unit 64 includes a display device such as a liquid crystal display or an organic electro-luminescent display (OLED). The display unit 64 displays information to be notified to the user in accordance with an instruction from the control unit 61. The display unit 64 may be interpreted as a notification unit and may be a means for notifying the user by other means such as sound.

[0060] The operation unit 65 is an interface that accepts user operations. The operation unit 65 includes, for example, a keyboard, a touch panel device with a built-in display, a speaker, a microphone, etc. The operation unit 65 accepts operation input from the user and sends a control signal to the control unit 61 according to the operation content.

[0061] 5 is a functional block diagram showing a configuration example of an information processing device 50. A control unit 51 of the information processing device 50 realizes each function of an acquisition unit 511, a first generation unit 512, a second generation unit 513, an anomaly detection unit 514, and an output unit 515 by reading and executing a program 521 stored in a storage unit 52. Each functional unit of the information processing device 50 may be realized by software, hardware, or a combination thereof.

[0062] The acquiring unit 511 acquires time-series measurement data for a predetermined period of time set in advance for a plurality of power storage cells in the power generation system 200. Identification information of the power storage cell is associated with the measurement data. The acquiring unit 511 may acquire the measurement data transmitted from the server device 20 via the communication unit 53, or may acquire the measurement data by reading out the measurement data stored in the measurement DB 522. The predetermined period of time may be, for example, one hour, one day, one month, etc.

[0063] The measurement data includes one or more data selected from the voltage, temperature, SOC, current, and power of the power storage cell. From the viewpoint of improving the accuracy of anomaly detection, the measurement data preferably includes at least one of the voltage, temperature, and SOC, and more preferably includes the voltage. In this embodiment, the measurement data is obtained as the voltage data of the power storage cell.

[0064] The acquiring unit 511 may acquire measurement data from all the storage cells in the power generation system 200, but the processing load can be reduced by selecting a predetermined number of storage cells from among all the storage cells as the storage cells from which measurement data is acquired. The storage cells from which data is acquired may be selected in advance according to a predetermined rule or manually. The storage cells from which data is acquired are preferably storage cells that represent the load or temperature in each unit when the power generation system 200 is divided into specific units (for example, banks). The number of storage cells from which data is acquired can be determined taking into consideration the total number of storage cells in the power generation system 200.

[0065] The first generating unit 512 generates differential data representing the difference between the voltage value of each storage cell and a reference voltage value, based on the voltage data of each storage cell acquired by the acquiring unit 511. The first generating unit 512 calculates the difference between the voltage value of the storage cell at the same point in time (same measurement time) and the reference voltage value for each measurement time, thereby obtaining time-series differential data. The reference voltage value is, for example, the average or median of the voltage values ​​of all the storage cells at the same measurement time. The differential data is high-dimensional data having a number of data items according to the number of times the voltage values ​​are measured.

[0066] The second generating unit 513 compresses the difference data generated by the first generating unit 512 using a predetermined compression method to generate compressed data. The compression method may be a dimensionality compression method, such as Principal Component Analysis (PCA), UMAP (Uniform Manifold Approximation and Projection), t-SNE (t-distributed Stochastic Neighbor Embedding), autoencoder, or the like. The dimensionality compression method is preferably principal component analysis. The second generating unit 513 generates low-dimensional data by reducing the dimension of the difference data through compression processing. When the compressed data is presented to the user using distribution information, which will be described later, the compressed data is preferably data reduced to two or three dimensions.

[0067] Fig. 6 is a diagram showing an example of compressed data. Fig. 6 shows an example in which measurement data for a predetermined period of time in each storage cell is compressed into two dimensions using principal component analysis. In Fig. 6, the compressed data for each storage cell is mapped onto a two-dimensional coordinate system with the horizontal axis representing the first principal component and the vertical axis representing the second principal component. As conceptually shown in Fig. 6, voltage data for a predetermined period of time corresponding to a certain storage cell is converted into compressed data shown by one plot.

[0068] When performing dimensionality reduction by principal component analysis, the second generating unit 513 may determine principal components to be used based on the cumulative contribution rate of each component. The second generating unit 513 may compress the difference data to a predetermined number of dimensions by extracting a preset number of principal components in descending order of cumulative contribution rate. The second generating unit 513 may extract principal components whose cumulative contribution rate is equal to or greater than a preset threshold value.

[0069] It is preferable that one compressed data is generated based on the difference data for a predetermined period that is the unit of acquisition of the measurement data, but if the predetermined period is relatively long, the predetermined period may be divided into preset time units and compressed data may be generated for each time unit. The unit of generation of the compressed data may be appropriately set according to the purpose of the analysis process using the measurement data.

[0070] The second generating unit 513 may perform compression processing by combining a plurality of compression methods. The second generating unit 513 may perform, for example, principal component analysis and then further compress the dimensions by UMAP.

[0071] The anomaly detection unit 514 detects an anomaly in the power storage cell based on the compressed data of each power storage cell generated by the second generation unit 513. The anomaly detection unit 514 of this embodiment performs anomaly detection using k-means, which is one of the clustering methods.

[0072] K-means is one of unsupervised machine learning methods, and automatically classifies each compressed data into a number of clusters designated in advance. In this embodiment, as shown in Fig. 6, by designating the number of clusters as three, each compressed data is classified into one normal cluster and the remaining (two) abnormal clusters. In Fig. 6, the clusters surrounded by solid lines are normal clusters, and the clusters surrounded by dashed lines are abnormal clusters.

[0073] In k-means, the distance (e.g., Euclidean distance, Mahalanobis distance, etc.) between the compressed data and the center of each cluster is calculated, and the compressed data is classified into the cluster with the smallest calculated distance. The anomaly detection unit 514 can regard the cluster having the largest number of pieces of compressed data among the multiple clusters generated as a normal cluster, and regard the remaining clusters as abnormal clusters. The anomaly detection unit 514 identifies an abnormal storage cell based on the clustering result of the compressed data of each storage cell.

[0074] In the above, anomaly detection is performed using k-means, but the anomaly detection method is not limited to this as long as it is possible to identify the presence or absence of anomalies in the compressed data of each storage cell. The anomaly detection unit 514 may use methods such as hierarchical clustering, k-nearest neighbors, an autoregressive model, a neural network, and a support vector machine. The anomaly detection unit 514 may use a combination of multiple anomaly detection methods.

[0075] The output unit 515 outputs screen information indicating the compressed data generated by the second generation unit 513 and the abnormality detection result by the abnormality detection unit 514 to the terminal device 60. The control unit 61 of the terminal device 60 displays a screen indicating the compressed data and the abnormality detection result on the display unit 64 based on the screen information transmitted through the output unit 515.

[0076] 7 is a schematic diagram showing an example of a screen 640 displayed on the display unit 64 of the terminal device 60. The screen 640 includes a result display section 641 that displays information related to a storage cell detected as abnormal, and a data display section 642 that displays compressed data of each storage cell.

[0077] The output unit 515 receives the abnormality detection result by the abnormality detection unit 514, and causes the result display unit 641 to display the power storage cell detected as abnormal in a visually distinguishable form. In Fig. 7, the result display unit 641 displays the cell ID of the power storage cell detected as abnormal, and an illustration showing the arrangement position of the power storage cell within the power generation system 200.

[0078] The output unit 515 receives the compressed data from the second generation unit 513, and generates distribution information that visualizes the distribution of the received compressed data. The output unit 515 displays the generated distribution information on the data display unit 642. The distribution information is, for example, a distribution diagram in which the compressed data of each storage cell is mapped on an n-dimensional coordinate system with the first axis (horizontal axis) representing the first principal component, the second axis (vertical axis) representing the second principal component, the n-th axis representing the n-th principal component, and so on. FIG. 7 shows a two-dimensional distribution diagram.

[0079] Information visibly indicating the clustering result or the anomaly detection result by the anomaly detection unit 514 may be superimposed on the distribution map. In Fig. 7, the clustering result is visualized by displaying the compressed data on the distribution map using markers with different colors or shading (different shading in Fig. 7) for each cluster. In addition, the anomaly detection result is visually indicated by displaying a solid line dividing the normal cluster area from the abnormal cluster area on the distribution map.

[0080] The markers indicating the respective compressed data on the distribution map may be configured to be selectable. For example, when the output unit 515 receives a selection of any marker on the distribution map via the terminal device 60, the output unit 515 reads out the identification information of the energy storage cell corresponding to the received marker, the measurement data before compression, and the like, based on the information stored in the storage unit 52. As shown in Fig. 7, the output unit 515 displays the read-out identification information and measurement data in a pop-up format or in a separate window.

[0081] The information processing device 50 may be configured to perform operations up to the generation of compressed data without including the anomaly detection unit 514. In this case, only the compressed data is output as a processing result. Anomaly detection may be performed by a user based on the compressed data provided through the terminal device 60.

[0082] 8 is a flowchart showing an example of a processing procedure executed by the information processing device 50. The processing in each of the following flowcharts is executed by the control unit 51 in accordance with the program 521 stored in the storage unit 52 of the information processing device 50. The control unit 51 periodically executes the processing procedure shown below.

[0083] The control unit 51 of the information processing device 50 acquires time-series measurement data for a predetermined period of time for each of the multiple power storage cells included in the power generation system 200 (step S11). The measurement data is, for example, voltage data.

[0084] The control unit 51 generates differential data for each storage cell by calculating the difference between the voltage value of each storage cell and a reference voltage value based on the acquired voltage data of each storage cell (step S12). The differential data is time-series data of voltage differences. The control unit 51 obtains the reference voltage value for each measurement time, for example, by calculating the average or median of the voltage values ​​of all storage cells at the same measurement time. The control unit 51 generates differential data for the target storage cell by calculating the difference between the reference voltage value at each measurement time and the voltage value of the target storage cell for which differential data is to be generated. Differential data is generated for each of the multiple storage cells.

[0085] In the above, a case where the average value or median value of the measurement data of all the storage cells is used as an example of the reference value (reference voltage value in this embodiment) has been described. However, it is not necessary to be limited to this, and a reference value may be set in advance (a separate experimental value is used), an updated reference value obtained by appropriately learning and updating the reference value may be used, a value obtained by correcting the reference value using a definition formula, an average value or median value of measurement data from a plurality of storage cells randomly selected by a computer, or an average value or median value of a plurality of representative storage cells selected in advance may be used. Alternatively, an ideal reference value calculated by a simulation may be used as the reference value. These reference values ​​may be selected according to the situation and the scene.

[0086] The control unit 51 performs compression processing on the generated difference data by principal component analysis to generate compressed data for each power storage cell (step S13). The control unit 51 generates compressed data that has been dimensionally compressed to two dimensions by, for example, principal component analysis.

[0087] The control unit 51 performs anomaly detection using a clustering method based on the compressed data of each storage cell generated by the second generation unit 513 (step S14). The control unit 51 classifies the compressed data of each storage cell into a normal cluster and an abnormal cluster. Based on the classification result, the control unit 51 detects a storage cell corresponding to compressed data belonging to an abnormal cluster as an abnormal storage cell.

[0088] The control unit 51 generates a screen including a distribution map showing the distribution of the compressed data of each storage cell and an abnormality detection result (step S15). The control unit 51 generates a screen including a distribution map in which the compressed data of each storage cell is mapped on a two-dimensional coordinate system and information indicating the storage elements detected as abnormal. The generation of the distribution map may be performed simultaneously with the generation of the compressed data or the clustering process. The control unit 51 visually displays the clustering result on the distribution map and also displays normal clusters and abnormal clusters in a distinguishable manner.

[0089] The control unit 51 outputs a screen including the generated distribution map and the abnormality detection result to the terminal device 60 (step S16), and causes it to be displayed on the display unit 64 of the terminal device 60. The control unit 51 ends the series of processes.

[0090] Second embodiment The information processing device 50 of the second embodiment performs anomaly detection using a power storage cell (power storage element) simulator as anomaly detection processing, in addition to anomaly detection using the machine learning technique described in the first embodiment. The following mainly describes the above-mentioned differences.

[0091] The energy storage cell simulator means a simulator constructed to simulate the behavior of an energy storage cell. The energy storage cell simulator can output measurement data of the energy storage cell based on the internal state quantity of the energy storage cell. The internal state quantity given to the energy storage cell simulator may include, for example, the SOC, internal temperature, positive electrode capacity, negative electrode capacity, and capacity balance deviation of the energy storage cell. The capacity balance deviation means a difference in capacity between the positive electrode and the negative electrode of the energy storage cell, at which charged ions can reversibly move in and out of the electrodes.

[0092] The energy storage cell simulator may further include a usage history of the energy storage cell as an input element. The usage history means information indicating a usage pattern (how the energy storage cell is used). The usage history may include, for example, information indicating a change in the power or current (load) of the energy storage cell over a predetermined period of time, information indicating a change in the environmental temperature over a predetermined period of time, etc.

[0093] In the following, for ease of explanation, a storage cell that is the target of the anomaly detection process is referred to as a target cell, and a cell that is a comparison standard when performing anomaly detection is referred to as a representative cell. The representative cell is selected from the storage cells in the power generation system 200, for example, taking into consideration the arrangement in the power generation system 200 and measurement data such as voltage, current, and temperature.

[0094] The information processing device 50 of the second embodiment executes anomaly detection using a power storage cell simulator in addition to anomaly detection using a machine learning technique in step S14 of the first embodiment.

[0095] The abnormality detection method using the energy storage cell simulator is not limited, but an example is shown below. The information processing device 50 obtains the usage history of each of the representative cell and the target cell based on measurement data including the voltage, current, and temperature of the representative cell and the target cell. The information processing device 50 obtains the internal state quantity corresponding to the acquired measurement data and usage history for each of the representative cell and the target cell using the energy storage cell simulator. The information processing device 50 sets a provisional value of the internal state quantity, and searches for an optimal value of the internal state quantity based on the provisional value of the set internal state quantity and the specified usage history so that the measurement data output from the energy storage cell simulator approximates the actual measurement data. The information processing device 50 searches for the optimal value of the internal state quantity using a known optimization method such as a genetic algorithm, a Nelder-Mead method, a gradient method, etc.

[0096] The information processing device 50 detects an abnormality in the target cell by determining whether the difference between the internal state quantity of the obtained representative cell and the internal state quantity of the target cell is less than a preset threshold. If the difference in the internal state quantity is less than the threshold, the information processing device 50 determines that the target cell is normal, and if the difference in the internal state quantity is equal to or greater than the threshold, the information processing device 50 determines that the target cell is abnormal.

[0097] In the above-described embodiment, an example has been described in which each process in the above flowchart is executed by the information processing device 50. Alternatively, a part or all of the above-described processes may be executed by another processing entity, such as the domain management device 30, the server device 20, or the terminal device 60.

[0098] In the above-mentioned embodiment, the remote monitoring system as shown in Fig. 1 has been described, but the information processing method, the information processing system, and the computer program may be applied to a moving body (such as an automobile, a train, an airplane, a ship, etc.) Also, for example, other than the remote system as shown in Fig. 1, all the systems may be mounted (for example, all the systems are mounted on a moving body) to complete the embodiment.

[0099] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. The sequences shown in each embodiment are not limited, and the order of each process may be changed and multiple processes may be executed in parallel, as long as there is no contradiction. The subject of each process is not limited, and the process of each device may be executed by another device, as long as there is no contradiction.

[0100] The matters described in each embodiment can be combined with each other. In addition, the independent claims and dependent claims described in the claims can be combined with each other in any and all combinations regardless of the citation format. Furthermore, the claims use a format in which a claim cites two or more other claims (multiple claim format), but this is not limited to this. A multiple claim (multi-multi claim) that cites at least one multiple claim may also be used. [Explanation of symbols]

[0101] 100 Remote monitoring system (information processing system) 200 Power Generation System 10. Communication Devices 11 Control section 12 Storage section 13 First Communications Department 14 2nd Communications Department 20 Server device 30 Domain Management Device 40 Energy Storage Unit 41 Bank 42 Energy storage module 43 Measurement Board 44 Battery management device 50 Information processing device 51 Control section 52 Storage section 53 Communications Department 521 Program 522 Measurement DB 5A Recording Media 60 Terminal Equipment 61 Control section 62 Memory section 63 Communications Department 64 Display section 65 Operation section

Claims

1. Acquiring time-series measurement data for a plurality of energy storage elements; Compression processing is performed on the difference data between the acquired measurement data of each storage element and the reference measurement data to generate compressed data. An information processing method in which processing is performed by a computer.

2. Detecting an abnormality in the storage element based on the generated compressed data The information processing method according to claim 1 .

3. Output distribution information that visualizes the distribution of the generated compressed data 3. The information processing method according to claim 1.

4. Classifying the compressed data into normal clusters and abnormal clusters using a clustering technique; Detecting an abnormality in the storage element based on a classification result 3. The information processing method according to claim 1.

5. The normal clusters and the abnormal clusters are superimposed on distribution information obtained by visualizing the distribution of the compressed data in a visually distinguishable manner and output. The information processing method according to claim 4.

6. The difference between an average value or a median value of the measurement data of the plurality of storage elements at the same time point as the reference measurement data and the measurement data of each storage element is calculated to generate the difference data.

3. The information processing method according to claim 1.

7. The compression process is performed using principal component analysis, UMAP, t-SNE, or an autoencoder.

3. The information processing method according to claim 1.

8. The measurement data includes at least one of a voltage, a temperature, and an SOC of the storage element.

3. The information processing method according to claim 1.

9. An abnormality in the energy storage element is further detected using an energy storage element simulator that estimates measurement data of the energy storage element based on an internal state of the energy storage element. The information processing method according to claim 2 .

10. An acquisition unit that acquires time-series measurement data of a plurality of energy storage elements; a generating unit that generates compressed data by performing a compression process on differential data between the acquired measurement data of each storage element and the reference measurement data; Information processing system.

11. Acquiring time-series measurement data for a plurality of energy storage elements; Compression processing is performed on the difference data between the acquired measurement data of each storage element and the reference measurement data to generate compressed data. A computer program that causes a computer to carry out processing.

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