Diagnostic device and diagnostic method

Abstract

To streamline maintenance management of multiple power generation devices.SOLUTION: An arithmetic unit 102 is configured to identify an abnormality value indicative of a degree of abnormality at each of diagnostic points of multiple wind power generation devices 20 based on a physical quantity, identify a maximum abnormality point having the greatest abnormal value from among all the diagnostic points, and generate and output abnormality information including the maximum abnormality point.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to diagnostic devices and methods. [Background technology]

[0002] For example, Japanese Patent Application Laid-Open Publication No. 2006-342766 discloses a technique for diagnosing the operating state of a wind turbine generator. This diagnosing device stores the operational history of the wind turbine generator that it has acquired and preset maintenance conditions for the wind turbine generator. The diagnosing device then determines whether or not maintenance is necessary by determining whether or not the operational history has reached the operating volume that requires maintenance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-342766 Summary of the Invention [Problem to be solved by the invention]

[0004] However, a configuration is conceivable in which multiple wind turbine generators are monitored simultaneously. However, conventional diagnostic devices have been limited to monitoring only one wind turbine generator. This can lead to a problem in that it is not possible to improve the efficiency of maintenance management of multiple wind turbine generators.

[0005] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to improve the efficiency of maintenance management of multiple power generation devices. [Means for solving the problem]

[0006] The diagnostic device of the present disclosure is a diagnostic device for multiple power generation devices. Each of the multiple power generation devices is equipped with a sensor that detects a physical quantity of at least one diagnostic location of the power generation device. The diagnostic device includes a calculation device and an interface that acquires the physical quantity from the sensor installed in each of the multiple power generation devices. The calculation device identifies an abnormal value indicating the degree of abnormality of each diagnostic location of all of the multiple power generation devices based on the physical quantity. The calculation device identifies a maximum abnormality location, which has the maximum abnormal value, from all diagnostic locations. The calculation device generates abnormality information including the maximum abnormality location. The calculation device outputs the abnormality information.

[0007] The diagnostic method disclosed herein is a method for diagnosing a plurality of power generation devices. Each of the plurality of power generation devices is provided with a sensor for detecting a physical quantity of at least one diagnostic location of the power generation device. The diagnostic method includes acquiring the physical quantity from the sensor provided in each of the plurality of power generation devices. The diagnostic method includes identifying, for each diagnostic location of all of the plurality of power generation devices, an abnormal value indicating the degree of abnormality of the diagnostic location based on the physical quantity. The diagnostic method includes identifying, from all diagnostic locations, a maximum abnormal location where the abnormal value is the maximum abnormal value. The diagnostic method includes generating abnormality information including the maximum abnormal location. The diagnostic method includes outputting the abnormality information. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to improve the efficiency of maintenance management of multiple power generation devices. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram illustrating an example of a configuration of a management system according to an embodiment of the present invention; [Figure 2] FIG. 10 is a diagram illustrating an example of an input screen. [Figure 3] FIG. 10 is a diagram showing an example of a result screen. [Figure 4] FIG. 10 is a diagram illustrating an example of a first DB. [Figure 5] FIG. 10 is a diagram illustrating an example of a second DB. [Figure 6] FIG. 2 is a functional block diagram of the diagnostic device. [Figure 7] FIG. 2 is a diagram for explaining the calculation content of a processing unit. [Figure 8] 3 is a flowchart showing main processing of the diagnostic device. [Figure 9] FIG. 10 is a diagram illustrating an example of another result screen. [Figure 10] FIG. 10 is a diagram illustrating an example of another result screen. [Figure 11] FIG. 10 is a diagram illustrating an example of another result screen. [Figure 12] FIG. 10 is a diagram illustrating an example of another result screen. [Figure 13] FIG. 10 is a diagram illustrating an example of another result screen. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following drawings, the same or corresponding parts are designated by the same reference numerals, and description thereof will not be repeated.

[0011] First Embodiment [Management system configuration example] 1 is a diagram showing an example of the configuration of a management system 10 according to the present embodiment. The management system 10 according to the present disclosure is a system to which a condition monitoring system (CMS) is applied. The management system 10 includes M (M is an integer equal to or greater than 1) wind power generation units 45, a diagnostic device 100, a user terminal 50, a maintenance server 70, and a network NW. A collection device 30, which will be described later, the diagnostic device 100, the user terminal 50, and the maintenance server 70 are capable of communicating with each other via the network NW.

[0012] For example, the M power generation units are related to one another. More specifically, the wind power generation devices of the M wind power generation units are related to one another. Here, "related" means that the M wind power generation devices have the same configuration and that the M wind power generation devices form a wind farm WF. Therefore, the external environments (temperature, humidity, wind volume, etc.) of the M power generation units are similar. In other words, the wind farm WF forms a group of wind power generation devices. Note that, as a modified example, the M wind power generation devices do not have to have the same configuration.

[0013] As a modified example, the term "related" may be a concept that includes at least one of the M wind turbine generators having the same start time of operation and the M wind turbine generators being the same model. The M wind turbine generators correspond to the "plurality of wind turbine generators" in this disclosure. As a modified example, the M power generation units may not be related to each other.

[0014] 1 shows one wind farm, but the diagnostic device 100 manages Q (Q is an integer equal to or greater than 1) wind farms. Specifically, the diagnostic device 100 collectively diagnoses all of the wind power generation equipment belonging to each of the Q wind farms.

[0015] The wind power generation unit 45 includes a wind power generation device 20, a collection device 30, and N sensors Sn (n=1,...,N, N is an integer equal to or greater than 1). The N sensors Sn include, for example, a vibration sensor and a temperature sensor.

[0016] The wind turbine generator 20 is a device that receives wind power and generates power. Each of the sensors Sn detects a physical quantity corresponding to the sensor at a diagnostic location of the wind turbine generator 20. In this way, each of the M wind turbine generators 20 is provided with a sensor Sn that detects a physical quantity at at least one diagnostic location of the wind turbine generator 20. In the example of FIG. 1, N sensors are provided in all of the M wind turbine generators, but the number of sensors may differ among the M wind turbine generators.

[0017] Each of the Q wind farms is assigned a wind farm ID (identification). The wind farm ID is also referred to as a "WFID" or a "wind power generation device group ID." Each of the M wind power generation devices is assigned a wind power generation device ID. Each of the sensors installed in the M wind power generation devices is assigned a sensor ID. The sensor ID is also the "diagnosis location ID of the location diagnosed by the sensor with that sensor ID."

[0018] If the sensor is a vibration sensor, the physical quantity is a vibration value, and if the sensor is a temperature sensor, the physical quantity is a temperature. The diagnosis points are, for example, the bearings, the gearbox, and the generator of the wind power generation device 20.

[0019] The vibration value is expressed by, for example, any one of the displacement, velocity, and acceleration of the predetermined location. The physical quantity detected by the sensor Sn and the sensor ID of the sensor are associated with each other and output to the collection device 30 as time-series data.

[0020] The time series data collected by the collection device 30 is output to the diagnostic device 100 with the wind power generation device ID of the wind power generation device 20 corresponding to the collection device 30 associated with the time series data. The memory 104 of the diagnostic device 100 stores the wind power generation device ID and the time series data in association with each other. In this way, the memory 104 of the diagnostic device 100 collectively stores the time series data of all the wind power generation devices 20. Note that, in the example of FIG. 1, a configuration is disclosed in which the diagnostic device 100 collectively stores the time series data of M wind power generation devices 20, but the time series data may also be stored in a device (server) separate from the diagnostic device 100.

[0021] The diagnostic device 100 uses time-series data to diagnose M wind turbine generators 20. Diagnosis of the wind turbine generators 20 includes a process of determining whether or not an abnormality exists in the wind turbine generators 20, and, when an abnormality in the wind turbine generators 20 is detected, a process of identifying the location of the abnormality.

[0022] If, as a result of the diagnostic processing by the diagnostic device 100, the diagnostic device 100 detects an abnormality in the wind power generation device 20, an operator (not shown) performs maintenance on the wind power generation device 20 to inspect and repair the abnormality.

[0023] The diagnostic device 100 includes an arithmetic unit 102, a memory 104, and an interface 106. The arithmetic unit 102 executes various processes and calculations. The memory 104 includes a read-only memory (ROM) and a random access memory (RAM). The components are interconnected by a data bus.

[0024] The arithmetic device 102 is configured with a central processing unit (CPU), a field-programmable gate array (FPGA), a graphics processing unit (GPU), etc. The arithmetic device 102 may be configured with at least one of a CPU, an FPGA, and a GPU. The arithmetic device 102 may also be configured with processing circuitry. The arithmetic device 102 is also referred to as "at least one processor" or "arithmetic circuitry."

[0025] The memory 104 includes a volatile storage area (e.g., a working area) that temporarily stores program code, work memory, etc. when the arithmetic device 102 executes any program. For example, the memory 104 includes a RAM (Random Access Memory) and a ROM (Read Only Memory).

[0026] The ROM stores programs executed by the arithmetic unit 102. The RAM temporarily stores data generated by the execution of programs in the arithmetic unit 102. The RAM can function as a temporary data memory used as a working area.

[0027] The interface 106 is configured to communicate with devices external to the diagnostic device 100 (such as the collection device 30, the user terminal 50, and the maintenance server 70).

[0028] The user terminal 50 is a terminal device owned by a user A. "User A" is typically a person who owns M wind power generation units 45, such as a power generation company. The user terminal 50 is typically a mobile terminal that can be carried by the user A. The user terminal 50 may also be a dedicated computer terminal.

[0029] The display unit of the user terminal 50 displays an input screen (see FIG. 2, which will be described later) and a result screen (see FIG. 3, which will be described later) for the input screen, which will be described later.

[0030] The maintenance server 70 is a server that stores maintenance information related to the maintenance of the wind turbine generator 20. The maintenance information includes, for example, the schedule of the workers who will perform the maintenance, the number of replacement parts in stock for the parts to be maintained, and the estimated cost of the maintenance.

[0031] As described above, the diagnostic device 100 collectively monitors M wind turbine generators. Here, for example, there are cases where user A wants to know which of the M wind turbine generators is the most abnormal. In other situations, user A may want to know the ranking of abnormal values ​​of the M wind turbine generators. For example, user A may want to know information indicating which of the M wind turbine generators is the most abnormal and which is the second most abnormal. By recognizing such information, user A can determine the priority of maintenance of the wind turbine generators 20, and can achieve efficient maintenance management of the M wind turbine generators 20.

[0032] Therefore, in order to realize such efficient maintenance management, the diagnostic device 100 displays abnormality information on the user terminal 50. The abnormality information is information including the most abnormal location (most abnormal location) among all the diagnostic locations of the M wind turbine generators 20. The abnormality information may also include information regarding the ranking of abnormal values ​​for all the diagnostic locations of the multiple power generators. The abnormal value for each diagnostic location is a value indicating the degree of abnormality of the diagnostic location.

[0033] [Input screen] 2 is an example of an input screen 300 displayed by the user terminal 50. When the user performs a predetermined display operation on the user terminal 50, the user terminal 50 displays the input screen 300 of FIG.

[0034] The input screen 300 includes a character image 301, a wind farm ID input image 302, and a diagnosis period input image 303.

[0035] Text image 301 is a text image that reads "Shows abnormality information for multiple wind power generation devices in your wind farm." Wind farm ID input image 302 includes a text image that prompts the user to input a wind farm ID, and an input area for the wind farm ID. Diagnosis period input image 303 includes a text image that prompts the user to input a diagnosis period, which will be described later, and an input area for the diagnosis period.

[0036] 2, when the user performs a predetermined transmission operation, the input information is transmitted to diagnostic device 100. The transmission operation is, for example, a user operation on a transmission button (not shown) displayed on user terminal 50.

[0037] Diagnostic device 100 executes the calculation process described below using the input information transmitted from user terminal 50. Diagnostic device 100 then transmits result screen data indicating the calculation results to user terminal 50. User terminal 50 displays a result screen based on the result screen data.

[0038] [Result screen] 3 is an example of a result screen 400. The result screen 400 includes a text image 401 and anomaly information 430. The text image 401 is a text image that reads, "The ranking of abnormal values ​​(anomaly value ranking) for your wind power generation equipment is as follows."

[0039] The abnormality information 430 is information including a maximum abnormality location that is the maximum abnormal value among the abnormal values ​​for all diagnosis locations. The abnormality information 430 includes device ranking information 412, abnormality location information 413, maintenance type information 414, estimated cost information 415, and start time information 416. In the example of FIG. 3, the M wind turbine generators are five wind turbine generators (i.e., an example where M=5). The five wind turbine generators are referred to as wind turbine generator A1, wind turbine generator A2, wind turbine generator A3, wind turbine generator A4, and wind turbine generator A5. In this example, it is assumed that each of the five wind turbine generators is equipped with the same number of sensors, N. In other words, 5×N sensors are arranged in one wind farm. In other words, the diagnosis device 100 will diagnose 5×N diagnosis locations.

[0040] The device ranking information 412 is information indicating the type and ranking of each wind turbine generator. The device ranking information 412 in the example of Fig. 3 indicates, for example, that the wind turbine generator with the highest abnormal value (i.e., the wind turbine generator with the highest degree of abnormality) is wind turbine generator A5. The device ranking information 412 also indicates that the wind turbine generator with the second highest abnormal value is wind turbine generator A3. The device ranking information 412 corresponds to "information indicating the ranking of abnormal values" in this disclosure.

[0041] The abnormality location information 413 is information indicating the abnormality location for each wind turbine generator. In the example of Fig. 3, it is shown that the abnormality locations for wind turbine generator A5 are the bearing device and the gearbox. It is also shown that the abnormality location for wind turbine generator A3 is the bearing device. It is also shown that there are no abnormality locations for the other wind turbine generators.

[0042] The abnormality location information 413 is also information that indicates the ranking of abnormal values ​​for at least one of all the diagnosis locations. In the example of Fig. 3, the abnormality location information 413 indicates the ranking of abnormal values ​​for three diagnosis locations out of all (5 x N) diagnosis locations. The three diagnosis locations are the bearing device of wind turbine generator A5, the gearbox of wind turbine generator A5, and the bearing device of wind turbine generator A3.

[0043] 3, the abnormality value increases from the top down. That is, in the example of FIG. 3, the abnormality value ranking for each of the three diagnosis locations is shown to be that the first ranked abnormal location is the bearing device of wind turbine generator A5, the second ranked abnormal location is the gearbox of wind turbine generator A5, and the third ranked abnormal location is the bearing device of wind turbine generator A3. The bearing device of wind turbine generator A5, which is the first ranked abnormal location, has the highest abnormal value. The bearing device of wind turbine generator A5 corresponds to an example of the "most abnormal location" of the present disclosure.

[0044] The maintenance type information 414 is information that indicates the type of maintenance required for each abnormal location. In the example of Fig. 3, the maintenance type "bearing replacement" is indicated for the bearing device, which is the abnormal location of wind turbine generator A5. Furthermore, the maintenance type "gearbox replacement" is indicated for the gearbox, which is the abnormal location of wind turbine generator A5. Furthermore, the maintenance type "bearing replacement" is indicated for the bearing device, which is the abnormal location of wind turbine generator A3.

[0045] 3, the maintenance type information 414 indicates that the degree of necessity of maintenance increases from the top down. That is, in the example of FIG. 3, the ranking of each of the three maintenance tasks is, first, bearing replacement of wind turbine generator A5, second, gearbox replacement of wind turbine generator A5, and third, bearing replacement of wind turbine generator A3. The maintenance type information 414 corresponds to "information indicating the ranking of the degree of necessity of maintenance" in the present disclosure. Note that the higher the degree of abnormality of the diagnosed location (abnormal location), the higher the degree of necessity of maintenance.

[0046] The estimate information 415 is information that indicates, for each maintenance type, the cost (estimate) required for the maintenance indicated by the maintenance type. In the example of Fig. 3, the estimated maintenance cost for replacing the bearing device of wind turbine generator A5 is shown to be L1 yen. The estimated maintenance cost for replacing the gearbox of wind turbine generator A5 is shown to be L2 yen. The estimated maintenance cost for replacing the bearing device of wind turbine generator A3 is shown to be L3 yen.

[0047] The start time information 416 is information that indicates when the maintenance indicated by the maintenance type can start (or is possible to start). The start time information 416 is information identified from the maintenance status (see FIG. 5, described later). The maintenance status includes, for example, the inventory of parts required for maintenance (e.g., new bearings), the schedule of the maintenance worker, and the maintenance lead time (the period required for maintenance).

[0048] [Database (DB) provided by diagnostic equipment] Next, the first DB (Data Base) and second DB included in the diagnostic device 100 will be described. Fig. 4 is a diagram showing an example of the first DB. In the first DB, a wind farm ID is associated with a wind turbine generator ID, a sensor ID, and a user terminal ID. Note that in Fig. 4 and other figures, the wind farm ID is indicated as "WFID." In the present disclosure, an ID may be used as a reference symbol for the item to which the ID is assigned. For example, a wind turbine generator with a wind turbine generator ID of A1 is also referred to as "wind turbine generator A1" (see also Fig. 3).

[0049] In the example of Fig. 4, wind farm W1 includes five wind power generators, wind power generators A1 to A5. Furthermore, wind power generator A1 is equipped with N sensors, sensors B1 to BN. Furthermore, a sensor ID is also referred to as a diagnosis location ID, which is the ID of a diagnosis location where a sensor indicated by the sensor ID is installed. A user terminal U1 is associated with wind farm W1.

[0050] It should be noted that wind farm IDs and user terminal IDs do not necessarily have to be associated one-to-one; for example, a representative user terminal ID may be associated with a plurality of wind farm IDs.

[0051] 5 is a diagram showing an example of the second DB. In the second DB, a maintenance ID, a maintenance estimate, and a maintenance status are defined in association with each abnormality ID.

[0052] For example, an example is shown in which maintenance M1, estimated maintenance cost: N1 (yen), and maintenance status R1 are associated with abnormality C1. That is, abnormality C1 is associated with performing maintenance M1 on the wind turbine generator 20 in order to repair abnormality C1. Furthermore, abnormality C1 is associated with an estimated cost (cost) of N1 yen required for maintenance M1. Furthermore, abnormality C1 is associated with the current or future status of maintenance M1 being R1. As described above, the maintenance status includes the inventory of parts required for maintenance (for example, new bearings), the schedule of the maintenance worker, and the maintenance lead time (the period required for maintenance).

[0053] [Functional block diagram of diagnostic device 100] 6 is a functional block diagram of diagnostic device 100. Diagnostic device 100 has a receiving unit 112, a processing unit 114, a transmitting unit 116, and memory 104. Receiving unit 112 and transmitting unit 116 correspond to interface 106 in FIG. 1. Processing unit 114 corresponds to arithmetic unit 102 in FIG. 1.

[0054] The memory 104 stores a first DB 141 (see FIG. 4), a second DB 142 (see FIG. 5), and reference physical quantities 151 for all diagnosis locations. The reference physical quantities 151 will be described later.

[0055] The receiving unit 112 acquires input information entered from the user terminal 50 into the input screen 300 (see FIG. 2). As explained in FIG. 2, the input information includes the wind farm ID (information entered into the wind farm ID input image 302) and the diagnosis period (information entered into the diagnosis period input image 303). The input information acquired by the receiving unit 112 is output to the processing unit 114. In the following explanation, it is assumed that the input wind farm ID is "W1". That is, in the example of FIG. 4, it is assumed that the number of wind power generation devices is five and the number of sensors (number of diagnosis points) is 5×N as mentioned above.

[0056] When the processing unit 114 receives the input information, it acquires (extracts) from the time-series data the latest (most recent) physical quantities of all diagnosis locations of the wind power generation unit included in the wind farm ID included in the input information. The latest physical quantities correspond to the "physical quantities" in the present disclosure. The latest physical quantities are physical quantities acquired by the interface 106. Note that when the input information is input, the processing unit 114 may request the latest physical quantities from all collection devices and acquire the latest physical quantities.

[0057] Then, the processing unit 114 identifies abnormal values ​​for all diagnosis locations of the plurality of wind turbine generators based on the physical quantities. Here, the plurality of wind turbine generators are, for example, all wind turbine generators (the above-mentioned five wind turbine generators) included in the wind farm indicated by the wind farm ID input by the user. The physical quantities are the physical quantities of all diagnosis locations of the plurality of wind turbine generators (the above-mentioned 5×N diagnosis locations). In other words, the physical quantities of all diagnosis locations are physical quantities detected by all sensors of the plurality of wind turbine generators.

[0058] Next, a method for identifying (calculating) abnormal values ​​will be described. As described above, all sensors in a wind turbine generator include, for example, vibration sensors and temperature sensors. Therefore, the physical quantities of the 5×N diagnosis locations include various types of physical quantities, such as vibration values ​​and temperatures.

[0059] Therefore, the processing unit 114 calculates the absolute values ​​of the differences between the latest physical quantities (5×N latest physical quantities) at all diagnosis locations and the reference physical quantities corresponding to the diagnosis locations. In other words, the processing unit 114 calculates the absolute values ​​of the differences between the latest physical quantities at all diagnosis locations and the reference physical quantities at all diagnosis locations. The processing unit 114 calculates the absolute values ​​of the differences (hereinafter also referred to as "absolute differences"), for example, by the following equation (1):

[0060] Absolute difference = |Latest physical quantity - Reference physical quantity corresponding to the latest physical quantity| (1) In equation (1), |X| indicates the absolute value of X. The processing unit 114 calculates 5×N absolute difference values ​​by calculating equation (1) for all diagnosis points. Note that equation (1) is also disclosed in *1 of FIG. 7, which will be described later.

[0061] Furthermore, the processing unit 114 determines whether the abnormal value is greater than a predetermined threshold value, and then identifies the diagnosis location corresponding to the abnormal value greater than the threshold value (excessively large abnormal value) as the abnormal location.

[0062] The reference physical quantity is a physical quantity detected at a predetermined time in the past. The predetermined time in the past may be any time. For example, the predetermined time is a time going back to the diagnosis period input by the user to the diagnosis period input image 303 (see FIG. 2). For example, if the user inputs information of "3 years" as the diagnosis period, the predetermined time is a time three years ago from the present time.

[0063] The absolute difference calculated by Equation (1) is the amount of change in the physical quantity used to calculate the absolute difference from the predetermined timing. Generally, the absolute difference (amount of change) of a normal diagnostic location tends to be small. However, the absolute difference (amount of change) of an abnormal diagnostic location tends to be large.

[0064] Therefore, the processing unit 114 identifies the absolute difference values ​​for all (5×N) diagnosis locations as abnormal values ​​for the diagnosis locations. Furthermore, the processing unit 114 may perform a predetermined calculation on the absolute difference values ​​and determine the results of the predetermined calculation as abnormal values ​​for the diagnosis locations. The predetermined calculation may include, for example, a normalization process according to the type of sensor (type of physical quantity). Note that the method for calculating the abnormal value is not limited to the above-described formula (1), and a method using at least one of another calculation formula, a predetermined function, and AI (artificial intelligence) may be adopted. Furthermore, the abnormal value may be a value obtained by dividing the latest physical quantity by a reference physical quantity. For example, the abnormal value may be determined as the latest physical quantity / reference physical quantity.

[0065] Fig. 7 is a diagram for explaining the calculation contents of the processing unit 114. The example of Fig. 7 shows a case where wind farm W1 is input as input information. The descriptions of the wind power generation device ID and the sensor ID in the example of Fig. 7 are the same as those in Fig. 4.

[0066] As described above and shown in Fig. 7, the processing unit 114 detects abnormal values ​​(for each diagnosis point ID) at all diagnosis points of all (five) wind turbine generators 20 included in the wind farm specified by the wind farm ID input by the user. In the example of Fig. 7, an abnormal value E1 is calculated for diagnosis point B1, and an abnormal value E10 is calculated for diagnosis point B10. Abnormal values ​​are also calculated for the other diagnosis points.

[0067] Furthermore, the processing unit 114 identifies the abnormality ID of the abnormality that occurred in the identified abnormality location. For example, the processing unit 114 uses the diagnosis location ID that is the abnormality location and the diagnosis location ID to identify the abnormality ID using a predetermined database (not shown). In the example of Fig. 7, the abnormality ID of the diagnosis location ID is shown to be C1.

[0068] Furthermore, the processing unit 114 identifies abnormal values ​​for each of the multiple wind turbine generators 20 (hereinafter also referred to as "device abnormal values") based on the abnormal values ​​of all the diagnosis points of the wind turbine generator. For example, the processing unit 114 calculates the abnormal value of the wind turbine generator 20 by executing a predetermined process on the abnormal values ​​of all the diagnosis points of the (single) wind turbine generator 20. The predetermined process is, for example, an addition process. In other words, the processing unit 114 calculates the device abnormal value of one wind turbine generator 20 by adding up the abnormal values ​​of all the diagnosis points of the wind turbine generator 20.

[0069] 7, "F1" is calculated as the device abnormal value for wind turbine generator A1. Furthermore, "F5" is calculated as the device abnormal value for wind turbine generator A5. Device abnormal values ​​F2 to F4 are also calculated for the other wind turbine generators A2 to A4, respectively.

[0070] Then, the processing unit 114 compares the magnitudes of all (five) device abnormal values ​​and assigns a rank to all device abnormal values. In this way, the processing unit 114 generates device ranking information 412 (see FIG. 3). Assume that F1, F2, F3, F4, and F5 are device abnormal values ​​of wind turbine generator A1, wind turbine generator A2, wind turbine generator A3, wind turbine generator A4, and wind turbine generator A5, respectively. In this case, the example of device ranking information 412 in FIG. 3 is F5>F3>F2>F1>F4.

[0071] Furthermore, the processing unit 114 generates abnormality location information 413 based on the abnormal value for each diagnosis location ID. The processing unit 114 identifies the abnormality location with the maximum abnormal value as the maximum abnormality location (see the parentheses in FIG. 3).

[0072] 5, the processing unit 114 generates the maintenance type indicated by the maintenance ID corresponding to the abnormality ID in Fig. 7 as maintenance type information 414. Furthermore, the processing unit 114 generates the maintenance estimate corresponding to this maintenance ID as estimate amount information 415 by referring to the second DB 142 in Fig. 5. Furthermore, the processing unit 114 generates start time information 416 based on the maintenance status corresponding to the abnormality ID in Fig. 7 by referring to the second DB 142 in Fig. 5.

[0073] Then, the processing unit 114 generates image data of a result screen (result screen data) based on the calculation result (generated information). The transmission unit 116 refers to the first DB 141 and transmits the result screen data to the user terminal 50 corresponding to the input wind farm ID. Then, the user terminal 50 displays a result screen (FIG. 3) according to the result screen data.

[0074] [flowchart] Fig. 8 is a flowchart showing the main processing of diagnostic device 100. The flowchart of Fig. 8 starts when user terminal 50 transmits to diagnostic device 100 input information input on the input screen of Fig. 2.

[0075] First, in step S2, the diagnostic device 100 acquires input information from the user terminal 50 and the latest physical quantities from the collection device 30. Next, in step S4, the diagnostic device 100 calculates abnormal values ​​for all diagnostic points of the M wind turbine generators 20 (see the column of abnormal values ​​in FIG. 7).

[0076] Next, in step S6, diagnostic device 100 compares the magnitude of the abnormal values ​​of all diagnostic locations to identify the location with the greatest abnormality, etc. Next, in step S8, diagnostic device 100 calculates the abnormal values ​​(device abnormal values ​​in the example of FIG. 7) for each of the M wind turbine generators.

[0077] Next, in step S10, the diagnostic device 100 determines whether maintenance is necessary. For example, if the diagnostic device 100 has identified (diagnosed) an abnormality with an abnormality ID associated with the maintenance ID in the second DB (see FIG. 5), the result of step S10 is YES. On the other hand, if the diagnostic device 100 has not identified an abnormality with an abnormality ID associated with the maintenance ID, the result of step S10 is NO.

[0078] If the determination in step S10 is YES, in step S12, diagnostic device 100 refers to the second DB to identify the estimated maintenance cost and the start time of maintenance. Then, the process proceeds to step S14. On the other hand, if the determination in step S10 is NO, the process proceeds to step S14.

[0079] In step S14, the diagnostic device 100 generates the abnormality information 430. Next, in step S16, image data of the abnormality information 430 (result screen data in FIG. 6) is transmitted to the user terminal 50.

[0080] [Summary] (1) As described above, the diagnostic device 100 identifies (step S4) abnormal values ​​(see FIG. 7) for each of the diagnosis locations (5×N locations) of the multiple (M locations) wind turbine generators 20. Next, the diagnostic device 100 compares all the abnormal values ​​to identify the maximum abnormal location where the abnormal value is the maximum abnormal value (step S6).

[0081] Next, in step S14, diagnostic device 100 generates anomaly information 430 including the most abnormal location. Next, in step S16, diagnostic device 100 executes processing for notifying user A of the anomaly information. With this configuration, it is possible to make the user aware of the most abnormal location, which is the largest abnormal value among all the diagnosed locations of the multiple power generation devices. Therefore, the user can recognize the anomaly information including the most abnormal location of the multiple power generation devices, and the efficiency of maintenance management of the multiple power generation devices can be improved.

[0082] (2) Furthermore, as shown in Fig. 3, the abnormality information 430 includes apparatus ranking information 412 indicating the ranking of the apparatus abnormality degree for each of the plurality of wind turbine generators 20. With this configuration, the diagnostic device 100 can allow the user A to recognize the ranking of the apparatus abnormality degree for each of the plurality of wind turbine generators.

[0083] (3) Furthermore, as shown in Fig. 3, abnormality information 430 includes maintenance type information 414 that indicates the order of the degree of necessity of maintenance. With this configuration, diagnostic device 100 can allow user A to recognize the order of the degree of necessity of maintenance.

[0084] 3, the abnormality information 430 includes estimated cost information 415 indicating the estimated cost of the maintenance and start time information 416 indicating the start time of the maintenance. With this configuration, the diagnostic device 100 can allow the user A to recognize the estimated cost of the maintenance and the start time of the maintenance.

[0085] (5) Furthermore, as shown in *1 in Fig. 7, the diagnostic device 100 calculates the absolute difference value as the abnormal value for each diagnostic location (see also the above formula (1)). This allows the diagnostic device 100 to calculate the amount of change in the physical quantity as the abnormal value, and to calculate the abnormal value with high accuracy.

[0086] (6) The reference physical quantity used in calculating the absolute difference value is a physical quantity acquired by the diagnostic device 100 at a predetermined timing. In this embodiment, the predetermined timing is a timing designated by the user (the diagnostic period input in the diagnostic period input image 303 in FIG. 2, a retroactive timing), as shown at *2 in FIG. 7. With this configuration, the diagnostic device 100 can calculate the amount of change in the physical quantity from the predetermined timing designated by the user as an abnormal value.

[0087] Second Embodiment In the second embodiment, a configuration different from the first embodiment will be described.

[0088] (1) Fig. 9 is a diagram illustrating an example of another anomaly information 430A. The anomaly information 430A includes urgency information 420 instead of the start time information 416 in Fig. 3. As a modification, the anomaly information 430A may include both the start time information 416 and the urgency information 420.

[0089] The urgency information 420 is information indicating the urgency (priority) of maintenance to be performed on the wind turbine generator 20. As a modified example, the urgency information 420 may be information indicating the order of necessity of maintenance required for the diagnosis location. With regard to the urgency information 420, the processing unit 114 determines the urgency of maintenance for the maintenance ID corresponding to the abnormality ID of the abnormal value according to the magnitude of the abnormal value. For example, the processing unit 114 determines the urgency of maintenance for the maintenance ID corresponding to the abnormality ID where the abnormal value is greater than a threshold (the abnormal value is excessively large) so that the urgency is high. Furthermore, the processing unit 114 may determine the urgency of maintenance for the maintenance ID corresponding to the abnormality ID of the abnormal value to be higher the larger the abnormal value. Furthermore, the condition that the abnormal value is greater than a threshold corresponds to an example of an "urgency condition" in the present disclosure.

[0090] The example of Fig. 9 shows that the abnormal values ​​of the bearing device and the gearbox of wind turbine generator A5 are excessively high. Therefore, the urgency of maintenance, namely, replacing the bearings and the gearbox of wind turbine generator A5, is specified to be high. Furthermore, in the example of Fig. 9, recommendation information 421 is added to the urgency information 420. The recommendation information 421 is information that recommends that maintenance be performed with priority when an emergency condition indicating that maintenance is urgent is met. The recommendation information 421 is not necessary.

[0091] Generally, when multiple maintenance tasks are presented on the results screen, it is difficult for user A to recognize which of the multiple maintenance tasks should be performed as a priority. This configuration allows the user to recognize the most urgent maintenance tasks (maintenance priority). Furthermore, user A can recognize that maintenance tasks with recommended information 421 should be performed as a top priority. Furthermore, by recognizing the maintenance priority, user A can understand the maintenance costs required for the entire wind farm. Therefore, by user A planning long-term maintenance, the operating availability rate of the wind power generation equipment can be improved. Furthermore, by knowing the quantity of items required for maintenance, user A can improve the efficiency of maintenance management of the wind power generation equipment.

[0092] (2) Fig. 10 is a diagram for explaining an example of another result screen 400. The result screen in Fig. 10 includes currently generated anomaly information 430 and previously generated anomaly information 432.

[0093] 10, the wind turbine generator A2 is shown as the wind turbine generator with the largest (first) device abnormality value in the previous abnormality information 432. Furthermore, the replacement of the gearbox is presented as necessary maintenance for this wind turbine generator A2, and the cost of this maintenance is presented as 11 yen.

[0094] Then, suppose that the worker replaces the gearbox of wind turbine generator A2. As a result, the ranking of the equipment abnormality degree of wind turbine generator A2 has changed from first to third in the current abnormality information 430. Therefore, user A can recognize that by replacing the gearbox of wind turbine generator A2, the equipment abnormality value of wind turbine generator A2 has improved, and the ranking of the equipment abnormality value of wind turbine generator A2 has improved from first to third.

[0095] Next, a method for generating the result screen 400 shown in Fig. 10 will be described using the dashed lines in Fig. 6 and the dashed lines in Fig. 8. Every time the processing unit 114 (see Fig. 6) generates anomaly information, it stores the anomaly information in the memory 104 as previous anomaly information 152 (see the dashed lines in Fig. 6). When the processing unit 114 generates next anomaly information, it acquires the previous anomaly information 152 (step S20 shown by the dashed lines in Fig. 8). Then, the processing unit 114 transmits result screen data including the generated anomaly information and the previous anomaly information 152 to the user terminal 50. The user terminal 50 displays a result screen (see Fig. 10) based on the result screen data.

[0096] With this configuration, user A can compare the anomaly information created this time with the anomaly information created last time.

[0097] (3) The abnormality information 430 in Fig. 3 has been described as being information indicating the order of abnormal values ​​of the wind power generation equipment. However, information indicating the order of abnormal values ​​of the diagnosis points may be displayed as the abnormality information 430.

[0098] Fig. 11 is a diagram for explaining other abnormality information 430B. In the example of Fig. 11, the abnormality information 430B includes location ranking information 431 instead of the device ranking information 412 of Fig. 3. The location ranking information 431 is information indicating the ranking of abnormal values ​​of diagnosed locations (abnormal locations).

[0099] 11, the type of maintenance for the abnormal location, the estimated maintenance cost, and the start time of maintenance are associated with each abnormal location. Note that the processing unit 114 generates the location ranking information 431 using the ranking of the abnormal values ​​(see FIG. 7) of all the diagnosis locations.

[0100] With this configuration, user A can recognize the ranking of abnormal values ​​for each diagnostic location and the priority of maintenance.

[0101] (4) Fig. 12 is a diagram illustrating an example of other abnormality information 430C. The abnormality information 430C is a screen in which feasibility information 442 is added to the abnormality information 430 of Fig. 3. The feasibility information 442 is information indicating whether each of a plurality of wind turbine generators is operable. In the example of Fig. 12, it has been determined that wind turbine generator A5 is inoperable, and it has been determined that wind turbine generator A3 should perform restricted operation.

[0102] The processing unit 114 generates the propriety information 442 using the ranking of the device abnormality value (see FIG. 7) of the wind power generation equipment. For example, a wind power generation equipment whose device abnormality value is excessively large (the device abnormality value is larger than the threshold value) is determined to be inoperable.

[0103] With this configuration, the user can be made aware of whether each wind turbine generator is operable or not.

[0104] (5) Fig. 13 is a diagram illustrating an example of other abnormality information 430D. The abnormality information 430D is a screen in which lifespan information 443 is added to the abnormality information 430 in Fig. 3. The lifespan information 443 is information indicating the ranking of the estimated remaining lifespan of each of a plurality of wind turbine generators.

[0105] In the example of Fig. 13, the estimated remaining lifespan is V5>V4>V3>V2>V1. That is, in the example of Fig. 13, the remaining lifespan V1 of the wind turbine generator A5 is the shortest, and the remaining lifespan V5 of the wind turbine generator A4 is the longest.

[0106] The remaining life of a wind turbine generator is calculated, for example, based on an abnormal value of the wind turbine generator. The diagnostic device 100 calculates the remaining life of the wind turbine generator by applying the abnormal value of the wind turbine generator to a life model. The life model is a model that outputs the remaining life of the wind turbine generator such that the larger the abnormal value of the wind turbine generator, the shorter the remaining life of the wind turbine generator.

[0107] With this configuration, the diagnostic device 100 can allow the user to recognize the ranking of the remaining life spans of a plurality of wind turbine generators.

[0108] (6) In the first embodiment, the predetermined timing of the reference physical quantity is a timing designated by the user. However, the predetermined timing may be another timing. For example, the predetermined timing may be a timing during a period when the wind power generation device is operating normally (see *2 in FIG. 7). More specifically, the predetermined timing may be a timing when the wind power generation device starts operating.

[0109] With this configuration, the diagnostic device 100 can calculate the amount of change from the physical quantity when the power generation device is operating normally as the abnormal value of the diagnostic location. Therefore, the diagnostic device 100 can calculate the abnormal value with high accuracy without requiring the user to specify a predetermined timing.

[0110] (7) In the above example, an embodiment has been described in which the power generation device is a wind power generation device. However, the power generation device may be another type of power generation device. For example, the power generation device may be a solar power generation device.

[0111] (8) In the above example, a single wind farm is configured from multiple wind turbines. However, multiple wind turbines do not necessarily have to configure a single wind farm. For example, a configuration may be adopted in which at least two of the multiple wind turbines are installed in separate locations. If such a configuration is adopted, for example, wind farm A may have M turbines (M is an integer greater than or equal to 1) and wind farm B may have N turbines (N is an integer greater than or equal to 1).

[0112] (9) In the above example, the display of the ranking of the equipment abnormal values ​​of the wind power generation equipment and the ranking of the abnormal locations of the wind power generation equipment were described. However, other items may be displayed. For example, the diagnostic device 100 may be configured to display only the most abnormal location (see FIG. 3) on the user terminal 50. Furthermore, the diagnostic device 100 may be configured to display only the type of maintenance that should be performed on the most abnormal location.

[0113] (10) In the above embodiment, an example was disclosed in which the anomaly information was transmitted to the user terminal 50 as the "notification process for notifying the user of the anomaly information." However, the notification process may be any process for notifying the user of the anomaly information. For example, the notification process may be a process for displaying the anomaly information on a display device of the diagnostic device 100. The diagnostic device 100 may then transmit the anomaly information to the user terminal 50 in response to an input from an administrator of the diagnostic device 100 or regardless of the input.

[0114] (11) The abnormality information may include at least one of information indicating the ranking of the device abnormality values ​​of the wind turbine generator, information indicating the ranking of the abnormality values ​​of the abnormal location, and information indicating the ranking of the degree of necessity of maintenance. The at least one piece of information may include only a predetermined number of rankings (for example, only the fifth place) and not include other rankings (for example, rankings sixth and below).

[0115] (12) A configuration may also be adopted in which some of the above-described processes performed by diagnostic device 100 are performed by another device. In this disclosure, when such a configuration is adopted, the diagnostic device and the other device are collectively referred to as the diagnostic device.

[0116] [Note] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the description of the above embodiments, and is intended to include all modifications within the meaning and scope of the claims.

[0117] (Supplementary Note 1) The diagnostic device of the present disclosure is a diagnostic device for multiple power generation devices. Each of the multiple power generation devices is equipped with a sensor that detects a physical quantity of at least one diagnostic location of the power generation device. The diagnostic device includes a calculation device and an interface that acquires the physical quantity from the sensor installed in each of the multiple power generation devices. The calculation device identifies an abnormal value indicating the degree of abnormality of each diagnostic location of all of the multiple power generation devices based on the physical quantity. The calculation device identifies a maximum abnormality location from all diagnostic locations, where the abnormal value is the maximum abnormal value. The calculation device generates abnormality information including the maximum abnormality location. The calculation device outputs the abnormality information.

[0118] With this configuration, the diagnostic device can output the most abnormal point, which is the point with the greatest abnormal value among all the diagnostic points of the multiple power generation devices, thereby improving the efficiency of maintenance management of the multiple wind turbine power generation devices.

[0119] (Supplementary Note 2) The diagnostic device according to Supplementary Note 1, wherein the computing device identifies a ranking of the abnormal values ​​for each of all diagnostic locations, and the abnormality information includes information indicating the ranking.

[0120] With this configuration, the diagnostic device can output the ranking of abnormal values ​​for each diagnostic location of all of the multiple power generation devices.

[0121] (Appendix 3) A diagnostic device as described in Appendix 1 or Appendix 2, wherein the computing device identifies abnormal values ​​for each of a plurality of power generation devices based on abnormal values ​​at at least one diagnostic point of the power generation devices, and the abnormality information includes information indicating the ranking of the abnormal values ​​for each of the plurality of power generation devices.

[0122] With this configuration, the diagnostic device can output the ranking of abnormal values ​​for each of a plurality of power generation devices.

[0123] (Appendix 4) A diagnostic device as described in Appendix 3, wherein the calculation device determines whether each of a plurality of power generation devices is operable based on abnormal values ​​of the power generation devices, and the abnormality information includes information indicating whether each of the plurality of power generation devices is operable.

[0124] With this configuration, the diagnostic device can output whether or not each of the plurality of power generation devices is operable.

[0125] (Appendix 5) A diagnostic device according to any one of appendices 1 to 4, wherein the computing device determines the remaining life of each of a plurality of power generation devices based on an abnormal value of at least one diagnostic point of the power generation devices, and the abnormality information includes information indicating the ranking of the remaining life of each of the plurality of power generation devices.

[0126] With this configuration, the diagnostic device can output the ranking of remaining life spans of a plurality of power generation devices.

[0127] (Appendix 6) A diagnostic device according to any one of appendices 1 to 5, wherein the calculation device identifies the degree of necessity of maintenance for each diagnostic location according to the abnormal values ​​for each diagnostic location, and the abnormality information includes information indicating the ranking of the degree of necessity of maintenance.

[0128] With this configuration, the diagnostic device can output the degree of necessity of maintenance.

[0129] (Appendix 7) A diagnostic device as described in Appendix 6, wherein the abnormality information includes recommendation information that recommends that the maintenance be performed as a priority when the degree of necessity of the maintenance satisfies an emergency condition indicating that the maintenance is urgent.

[0130] With this configuration, the diagnostic device can output recommendation information that recommends that maintenance that satisfies the emergency conditions be performed with priority.

[0131] (Appendix 8) The diagnostic device according to appendix 6 or appendix 7, wherein the abnormality information includes at least one of an estimated cost of maintenance and a start time of the maintenance.

[0132] With this configuration, the diagnostic device can output at least one of an estimated cost of maintenance and a start time for the maintenance.

[0133] (Supplementary Note 9) A diagnostic device according to any one of Supplementary Note 1 to Supplementary Note 8, wherein the arithmetic device includes a memory that stores reference physical quantities at predetermined timings in the past for each of all diagnostic locations, the arithmetic device calculates absolute values ​​of differences between the physical quantities acquired by the interfaces at all diagnostic locations and the reference physical quantities corresponding to the diagnostic locations, and identifies abnormal values ​​for each of all diagnostic locations based on the absolute values ​​at all diagnostic locations.

[0134] With this configuration, by subtracting the reference physical quantity corresponding to the diagnosis location from the physical quantity acquired by the interface, it is possible to identify an abnormal value that reflects the amount of change in the physical quantity, thereby improving the accuracy of the abnormal value identification.

[0135] (Supplementary Note 10) The diagnostic device according to Supplementary Note 9, wherein the predetermined timing is a timing designated by a user.

[0136] With this configuration, the reference physical quantity can be used at a timing desired by the user.

[0137] (Supplementary Note 11) The diagnostic device according to Supplementary Note 9, wherein the predetermined timing is a timing during a period in which the power generation device is operating normally.

[0138] With this configuration, it is possible to identify the amount of change from the physical quantity when the power generation device is operating normally.

[0139] (Appendix 12) The diagnostic device according to any one of Appendices 1 to 11, further comprising a memory that stores abnormality information each time the abnormality information is generated, and the abnormality information includes previously generated abnormality information stored in the memory.

[0140] According to this configuration, the anomaly information created this time and the anomaly information created last time can be output.

[0141] (Supplementary Note 13) The diagnostic device according to any one of Supplementary Note 1 to Supplementary Note 12, wherein the arithmetic device outputs the abnormality information to user terminals of users of the plurality of power generation devices.

[0142] With this configuration, the user can be made aware of the abnormality information.

[0143] (Supplementary Note 14) The diagnostic device according to any one of Supplementary Notes 1 to 13, wherein the power generation device is a wind power generation device.

[0144] With this configuration, the diagnostic device can output abnormality information relating to the ranking of abnormal values ​​for each diagnostic point in all of the multiple wind turbine generators. [Explanation of symbols]

[0145] 10 Management system, 20 Wind power generation equipment, 30 Collection device, 45 Wind power generation unit, 50 User terminal, 70 Maintenance server, 100 Diagnosis device, 102 Arithmetic unit, 104 Memory, 106 Interface, 112 Receiving unit, 114 Processing unit, 116 Transmitting unit, 118 Storage unit, 151 Reference physical quantity, 432 Previous abnormality information, 300 Input screen, 301, 401 Character image, 302 Wind farm ID input image, 303 Diagnosis period input image, 400 Result screen, 412 Equipment ranking information, 413 Abnormality location information, 414 Maintenance type information, 415 Estimated amount information, 416 Start time information, 420 Urgency information, 421 Recommendation information, 430 Abnormality information, 431 Location ranking information, 442 Possibility information, 443 Lifespan information.

Claims

1. A diagnostic device for a plurality of power generation units, a sensor for detecting a physical quantity of at least one diagnostic location of each of the plurality of power generation devices is installed in each of the power generation devices; The diagnostic device comprises: A computing device; an interface for acquiring physical quantities from sensors installed in each of the plurality of power generation devices; The computing device identifying an abnormality value indicating the degree of abnormality at each diagnosis point of all of the plurality of power generation devices based on the physical quantity; Identifying a maximum abnormality point where the abnormal value is the maximum abnormal value from all of the diagnosis points; generating abnormality information including the maximum abnormality location; A diagnostic device that outputs the abnormality information.

2. the computing device identifies a ranking of abnormal values ​​for each of all of the diagnostic locations; The diagnostic device according to claim 1 , wherein the abnormality information includes information indicating the rank.

3. the computing device identifies an abnormal value for each of the plurality of power generation units based on an abnormal value at at least one diagnostic point of the power generation unit; The diagnostic device according to claim 1 or 2, wherein the abnormality information includes information indicating a ranking of abnormal values ​​for each of the plurality of power generation units.

4. the computing device determines whether each of the plurality of power generation units is operable based on an abnormal value of the power generation unit; The diagnostic device according to claim 3 , wherein the abnormality information includes information indicating whether each of the plurality of power generation units is operable.

5. the computing device specifies a remaining life of each of the plurality of power generation devices based on an abnormal value of at least one diagnostic point of the power generation device; 3. The diagnostic device according to claim 1, wherein the abnormality information includes information indicating a ranking of remaining life spans for each of the plurality of power generation devices.

6. The computing device Identifying the degree of necessity of maintenance for each of the diagnosis locations according to the abnormal values ​​for each of the diagnosis locations; 3. The diagnostic device according to claim 1, wherein the abnormality information includes information indicating a ranking of the degree of necessity of the maintenance.

7. The diagnostic device according to claim 6 , wherein the abnormality information includes at least one of an estimated cost of the maintenance and a start time of the maintenance.

8. The computing device a memory for storing a reference physical quantity at a predetermined timing in the past for each of the diagnosis locations; The computing device calculating absolute values ​​of differences between the physical quantities acquired by the interface at all of the diagnosis locations and the reference physical quantities corresponding to the diagnosis locations; The diagnostic device according to claim 1 , wherein the abnormal value is identified for each of the diagnosis locations based on the absolute values ​​at all of the diagnosis locations.

9. The diagnostic device according to claim 8 , wherein the predetermined timing is a timing designated by a user.

10. the diagnostic device further includes a memory that stores the abnormality information each time the abnormality information is generated; 3. The diagnostic device according to claim 1, wherein the abnormality information includes the previously generated abnormality information stored in the memory.

11. The diagnostic device according to claim 1 or 2, wherein the arithmetic device outputs the abnormality information to a user terminal of a user of the plurality of power generation devices.

12. The diagnostic device according to claim 1 or 2, wherein the power generation device is a wind power generation device.

13. 1. A method for diagnosing a plurality of power generation units, comprising: a sensor for detecting a physical quantity of at least one diagnostic location of each of the plurality of power generation devices is installed in each of the power generation devices; The diagnostic method comprises: acquiring a physical quantity from a sensor installed in each of the plurality of power generation devices; identifying an abnormality value indicating the degree of abnormality of each diagnosis point of all diagnosis points of the plurality of power generation devices based on the physical quantity; Identifying a maximum abnormality point where the abnormal value is a maximum abnormal value from all of the diagnosis points; generating abnormality information including the maximum abnormality location; and outputting the abnormality information.

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