A method and system for diagnosing status of a generator set

Abstract

The present invention provides a generator set state diagnosis method and system, belonging to the technical field of power measurement diagnosis, including: obtaining real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes multiple operation sub-data; generating a first combination and a second combination based on the real-time operation data and the real-time operation control instructions, wherein the first combination includes multiple first sub-combinations, and the second combination includes multiple second sub-combinations; determining first associated data based on the first combination, and at the same time, determining second associated data based on the second combination; determining the state diagnosis result of the generator set based on the first associated data and the second associated data. The accuracy and real-time performance of the state diagnosis of the generator set can be improved, the precise monitoring of the operating state of the generator set and early fault identification can be achieved, and the stability and safety of the system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric quantity measurement diagnosis, and in particular to a generator set state diagnosis method and system. Background Art

[0002] Generators are an important part of the power system. With the development of technology, the detection of the status of generator sets is becoming more and more important. At present, the status diagnosis method often sets sensors on the generator and related equipment to determine whether a fault occurs, and collects information sent by the sensors to determine whether the generator and related equipment are in operation. However, this status diagnosis method can only determine whether the equipment is in operation, and the specific operation status of the equipment cannot be determined. Therefore, there is a problem of inaccurate diagnosis of the operating status of the generator equipment.

[0003] Therefore, the present invention provides a generator set status diagnosis method and system. Summary of the invention

[0004] The present invention provides a generator set status diagnosis method and system, which are used to generate a first combination and a second combination according to acquired real-time operation data and real-time operation control instructions, analyze the first combination to determine first related data, analyze the second combination to determine second related data, and determine the status diagnosis result of the generator set according to the first related data and the second related data. The accuracy and real-time performance of the status diagnosis of the generator set can be improved, the precise monitoring of the operating status of the generator set and early fault identification can be achieved, and the stability and safety of the system can be improved.

[0005] In one aspect, the present invention provides a method for diagnosing a generator set status, comprising:

[0006] 101: Acquire real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes a plurality of operation sub-data;

[0007] 102: Generate a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, wherein the first combination includes a plurality of first sub-combinations, and the second combination includes a plurality of second sub-combinations;

[0008] 103: Determine first associated data based on the first combination, and simultaneously determine second associated data based on the second combination;

[0009] 104: Determine a status diagnosis result of the generator set based on the first associated data and the second associated data.

[0010] According to a generator set status diagnosis method provided by the present invention, real-time operation data and real-time operation control instructions of the generator set during operation at the current moment are obtained, including:

[0011] Collecting first data of all types of physical parameters of the generator set within a specified time period under the same conditions, wherein the first data includes a plurality of first sub-data;

[0012] Sorting each first sub-data in the first data according to the order of collection time within a specified time period, and determining the sorting result of each first sub-data as the corresponding second sub-data;

[0013] Determine the running sub-data of the first sub-data based on each first sub-data and the corresponding second sub-data;

[0014] Real-time operation data is determined based on all the operation sub-data.

[0015] According to a generator set status diagnosis method provided by the present invention, the physical parameters include: voltage parameters, current parameters, power parameters, rotor speed parameters, coolant temperature parameters, coolant flow parameters, generator set vibration parameters and load parameters.

[0016] According to a generator set status diagnosis method provided by the present invention, a first combination and a second combination are generated based on the real-time operation data and the real-time operation control instruction, including:

[0017] Acquire historical operation data and historical control instructions of the generator set, and generate preset first grouping parameters and preset second grouping parameters of all types of physical parameters based on the historical operation data and historical control instructions;

[0018] When the number of the acquired real-time operation control instructions is no more than one, generating a first combination based on the real-time operation data and a preset first grouping parameter, and generating the second combination based on a preset second grouping parameter, the real-time operation data, and the real-time operation control instruction;

[0019] When the number of the real-time operation control instructions obtained is more than one, determine the time range and instruction label of each real-time operation control instruction, divide the real-time operation data into time operation data based on the time range of each real-time operation control instruction, determine the instruction label of the real-time operation control instruction as the instruction label of the divided time operation data, generate a first combination based on all the time operation data, the instruction label of each time operation data and a preset first grouping parameter, and generate a second combination based on a preset second grouping parameter, the real-time operation data, all real-time operation control instructions and the corresponding instruction labels, wherein the time operation data includes multiple time sub-data.

[0020] According to a generator set status diagnosis method provided by the present invention, a preset first group parameter and a preset second group parameter of all types of physical parameters are generated based on historical operation data and historical control instructions, including:

[0021] determining a plurality of target physical parameters based on all types of physical parameters of the generator set;

[0022] determining a plurality of target control instructions based on all historical instructions appearing in the historical control instructions;

[0023] Performing principal component analysis on historical operation data and historical control instructions to determine a first principal component parameter corresponding to each target physical parameter, and at the same time, determining a second principal component parameter for each target control instruction;

[0024] generating a preset first sub-grouping parameter of each target physical parameter based on each target physical parameter and the corresponding first principal component parameter, and determining a preset first grouping parameter based on all the preset first sub-grouping parameters;

[0025] A preset second sub-grouping parameter of each target control instruction is generated based on each target control instruction and the corresponding second principal component parameter, and a preset second grouping parameter is determined based on all the preset second sub-grouping parameters.

[0026] According to a generator set status diagnosis method provided by the present invention, first associated data is determined based on the first combination, and second associated data is determined based on the second combination, including:

[0027] Determine a sub-feature vector of each first sub-combination in the first combination based on all running sub-data of each first sub-combination in the first combination;

[0028] Based on all running sub-data and all sub-feature vectors of every two first sub-combinations in the first combination, calculating the corresponding first correlation coefficient;

[0029] in, represents the first correlation coefficient between the ath first subcombination and the bth first subcombination in the first combination, denote the first sub-correlation coefficient and the second sub-correlation coefficient of the a-th first sub-combination and the b-th first sub-combination in the first combination, respectively. They represent the ath first sub-combination and the bth first sub-combination in the first combination respectively, N1 represents the number of time points in a specified time period, aN2 represents the number of running sub-data at the i-th time point in the ath first sub-combination in the first combination, bN2 represents the number of running sub-data at the i-th time point in the bth first sub-combination in the first combination, represents the data value of the jth running sub-data at the i-th time point in the a-th first sub-combination in the first combination, represents the data value of the jth running sub-data at the i-th time point in the b-th first sub-combination in the first combination, represents the average data value of the j-th running sub-data at all time points in the a-th first sub-group and the average data value of the j-th running sub-data at all time points in the b-th first sub-group in the first combination, They represent the sub-feature vector of the a-th first sub-combination and the sub-feature vector of the b-th first sub-combination in the first combination, respectively. They represent the modulus of the sub-feature vector of the a-th first sub-combination and the modulus of the sub-feature vector of the b-th first sub-combination in the first combination, respectively. Respectively represent the weight of the first sub-correlation coefficient and the weight of the second sub-correlation coefficient;

[0030] calculating a second correlation coefficient for each second sub-combination based on the second combination;

[0031] in, , N3 represents the number of real-time operation control instructions obtained, represents the second correlation coefficient of the kth second sub-combination in the second combination, The second sub-correlation coefficient of the second sub-combination when the number of acquired real-time operation control instructions is not more than one, represents the second sub-correlation coefficient of the kth second sub-combination when the number of acquired real-time operation control instructions is more than one, kN4 represents the number of operation sub-data of the kth second sub-combination in the second combination, It represents the attenuation factor of the pth real-time operation control instruction within a specified time period. represents the first time delay factor, represents the influence weight of the p-th real-time operation control instruction on the z-th operation sub-data, It represents the delay impact value of the p-th real-time operation control instruction on the z-th operation sub-data. represents the second time delay factor of the real-time operation control instruction to the zth operation sub-data, represents the influence weight of the mth running sub-data on the zth running sub-data, It represents the delay impact value of the mth running sub-data on the zth running sub-data. represents the comprehensive delay impact value of all operation sub-data except the z-th operation sub-data in the second combination, KN5 represents the number of real-time operation control instructions of the k-th second sub-combination in the second combination, Indicates the time length of the time range of the pth real-time operation control instruction, represents the kth preset second grouping parameter, Indicates the pth real-time operation control instruction, The time operation data representing the time range of the p-th real-time operation control instruction, represents a functional relationship between the p-th real-time operation control instruction and the time operation data within the time range of the p-th real-time operation control instruction;

[0032] The first correlation data is determined based on all the first correlation coefficients of the first combination, and the second correlation data is determined based on all the second correlation coefficients of the second combination.

[0033] According to a generator set status diagnosis method provided by the present invention, a status diagnosis result of the generator set is determined based on first associated data and second associated data, including:

[0034] Comparing the first associated data with the first standard associated data, and at the same time, comparing the second associated data with the second standard associated data, to determine a comparison result;

[0035] When the comparison result is that there is no abnormality, determining that the status diagnosis result is that there is no abnormality;

[0036] When the comparison result is that an abnormality exists, a status diagnosis result is determined based on the first combination, the first associated data, the second combination, and the second associated data.

[0037] On the other hand, the present invention also provides a generator set status diagnosis system, comprising:

[0038] Acquisition module: acquires real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes a plurality of operation sub-data;

[0039] A generating module: generating a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, wherein the first combination includes a plurality of first sub-combinations and the second combination includes a plurality of second sub-combinations;

[0040] Determining module: determining first associated data based on the first combination, and determining second associated data based on the second combination;

[0041] Diagnostic module: determines a status diagnosis result of the generator set based on the first associated data and the second associated data.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] Generate a first combination and a second combination based on the acquired real-time operation data and real-time operation control instructions, analyze the first combination to determine the first associated data, analyze the second combination to determine the second associated data, and determine the status diagnosis result of the generator set based on the first associated data and the second associated data. This can improve the accuracy and real-time performance of the status diagnosis of the generator set, achieve precise monitoring of the operating status of the generator set and early fault identification, and improve the stability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a flow chart of a generator set status diagnosis method provided by an embodiment of the present invention.

[0046] Figure 2 It is a structural schematic diagram of a generator set status diagnosis system provided by an embodiment of the present invention.

[0047] Figure 3 It is a structural schematic diagram of a generator set status diagnosis device in a hardware operating environment involved in a generator set status diagnosis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Embodiment 1:

[0050] The embodiment of the present invention provides a method for diagnosing the status of a generator set, such as Figure 1 and Figure 3 As shown, including:

[0051] 101: Acquire real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes a plurality of operation sub-data;

[0052] 102: Generate a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, wherein the first combination includes a plurality of first sub-combinations, and the second combination includes a plurality of second sub-combinations;

[0053] 103: Determine first associated data based on the first combination, and simultaneously determine second associated data based on the second combination;

[0054] 104: Determine a status diagnosis result of the generator set based on the first associated data and the second associated data.

[0055] In this embodiment, the operation sub-data correspond to the physical parameters one by one.

[0056] In this embodiment, the real-time operation data and the real-time operation control instructions refer to the data collected and processed during the operation of the generator set within a specified time period at the current moment.

[0057] In this embodiment, the real-time operation control instructions may include start instructions, stop instructions, load adjustment instructions, excitation control instructions, cooling adjustment instructions, etc. For example: the operation control instruction is a cooling adjustment instruction, which may specifically be an instruction to adjust the first cooling parameter to the second cooling parameter.

[0058] In this embodiment, the first sub-combination includes at least two operating sub-data, for example: flow data and temperature data of the coolant of the generator, wherein the flow data and temperature data of the coolant of the generator are not obtained by direct adjustment, but are achieved by adjusting the speed of the pump. Adjusting the speed of the pump will eventually cause both the flow data and the temperature data to change. The first correlation coefficient of the corresponding two operating sub-data is calculated through the flow data and the temperature data, and the first correlation data is determined based on all the first correlation coefficients of all the first sub-combinations in the first combination.

[0059] In this embodiment, if Figure 3 As shown, the generator set status diagnosis device may include: a processor 1001, such as a central processing unit, a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005, wherein the communication bus 1002 is used to realize the connection and communication between these components; the interactive device 1003 may include a display screen, an input unit such as a keyboard, and the interactive device 1003 may also be connected to the communication bus through a standard wired interface and a wireless interface; the network interface 1004 may optionally include a standard wired interface and a wireless interface; the memory 1005 may be a high-speed random access memory, or a stable non-volatile memory, such as a disk memory, and the memory 1005 may optionally be a storage device independent of the aforementioned processor 1001, wherein, Figure 3 The structure shown in the figure does not constitute a limitation on the generator set status diagnosis device, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0060] In this embodiment, if Figure 3 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a generator set status diagnosis program.

[0061] In this embodiment, Figure 3In the generator set status diagnosis device shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the generator set status diagnosis device of the present invention can be set in the generator set status diagnosis device, and the generator set status diagnosis device calls the generator set status diagnosis program stored in the memory 1005 through the processor 1001, and executes the generator set status diagnosis method provided by the embodiment of the present invention.

[0062] The beneficial effects of the above technical solution are as follows: a first combination and a second combination are generated according to the acquired real-time operation data and real-time operation control instructions, the first combination is analyzed to determine the first associated data, the second combination is analyzed to determine the second associated data, and the status diagnosis result of the generator set is determined according to the first associated data and the second associated data. This can improve the accuracy and real-time performance of the status diagnosis of the generator set, realize accurate monitoring of the operating status of the generator set and early fault identification, and improve the stability and safety of the system.

[0063] Embodiment 2:

[0064] The embodiment of the present invention provides a generator set status diagnosis method, which obtains real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, including:

[0065] Collecting first data of all types of physical parameters of the generator set within a specified time period under the same conditions, wherein the first data includes a plurality of first sub-data;

[0066] Sorting each first sub-data in the first data according to the order of collection time within a specified time period, and determining the sorting result of each first sub-data as the corresponding second sub-data;

[0067] Determine the running sub-data of the first sub-data based on each first sub-data and the corresponding second sub-data;

[0068] Real-time operation data is determined based on all the operation sub-data.

[0069] In this embodiment, the same conditions mean that the collected frequencies are the same, the collected phases are the same, and the setting parameters of the generators are the same.

[0070] In this embodiment, the specified time period indicates a period of time between the current time and after the current time.

[0071] In this embodiment, each type of physical parameter corresponds to a first sub-data, and each first sub-data corresponds to a second sub-data and an operation sub-data.

[0072] In this embodiment, the data of each physical parameter (each first sub-data) is arranged in the time sequence of collection to form an ordered time series. The sorted data is called the second sub-data, that is, the data set arranged in time sequence.

[0073] In this embodiment, in order to ensure that the first data is collected under the same conditions, the first sub-data corresponding to different types of physical parameters of the generator set within a specified time period are simultaneously collected to obtain multiple first sub-data to ensure that they are collected at the same time.

[0074] In this embodiment, the data are arranged in the order of collection to ensure the consistency of the data, thereby improving the accuracy of the real-time operation data.

[0075] The beneficial effect of the above technical solution is: obtaining the real-time operation data and real-time operation control instructions of the generator set during operation at the current moment can provide data basis for generating the first combination and the second combination.

[0076] Embodiment 3:

[0077] An embodiment of the present invention provides a generator set status diagnosis method, wherein the physical parameters include: voltage parameters, current parameters, power parameters, rotor speed parameters, coolant temperature parameters, coolant flow parameters, generator set vibration parameters and load parameters.

[0078] In this embodiment, the voltage parameter refers to the voltage value when the generator set is output or running. The voltage represents the potential energy difference of the movement of charges in the circuit, usually in volts (V). The voltage parameter is used to measure the voltage level and stability of the generator output.

[0079] In this embodiment, the current parameter refers to the current size when the generator set is output or running. The current is the rate of charge flow, usually in amperes (A). The current parameter is used to evaluate the current output of the generator set during operation.

[0080] In this embodiment, the power parameter refers to the rate at which the electrical energy is generated by the generator set, usually in watts (W) or kilowatts (kW). Power can be divided into active power (the part that actually does work) and reactive power (the part that does not do work but affects the efficiency of the system). The power parameter measures the actual energy output of the generator set.

[0081] In this embodiment, the rotor speed parameter refers to the number of rotations per minute of the rotor inside the generator set (usually the part that converts mechanical energy into electrical energy), usually in revolutions per minute (RPM). The speed parameter directly affects the power generation efficiency and stability of the generator.

[0082] In this embodiment, the temperature parameter of the coolant refers to the temperature of the coolant used to cool the generator set, which is usually measured in degrees Celsius (°C) or degrees Fahrenheit (°F). The temperature parameter of the coolant is used to monitor whether the generator set is overheated and ensure that it operates within a safe temperature range.

[0083] In this embodiment, the coolant flow parameter refers to the flow rate of the coolant passing through the generator set per unit time, usually in liters per minute (L / min) or cubic meters per hour (m³ / h). The coolant flow parameter is used to evaluate the effectiveness of the cooling system to ensure that the generator set is adequately cooled.

[0084] In this embodiment, the vibration parameter of the generator set refers to the amplitude of the mechanical vibration generated by the generator set during operation, usually in units of acceleration or displacement (mm / s², m / s², etc.). The vibration parameter can be used to detect the mechanical state and abnormalities of the equipment, such as imbalance, looseness or failure.

[0085] In this embodiment, the load parameter refers to the power load that the generator set bears during operation, usually in kilowatts (kW) or megawatts (MW). The load parameter reflects the output power demand of the generator set and helps determine whether there is an overload or load instability.

[0086] The beneficial effects of the above technical solution are: determining all types of physical parameters can improve the accuracy of real-time operation data.

[0087] Embodiment 4:

[0088] An embodiment of the present invention provides a generator set status diagnosis method, which generates a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, including:

[0089] Acquire historical operation data and historical control instructions of the generator set, and generate preset first grouping parameters and preset second grouping parameters of all types of physical parameters based on the historical operation data and historical control instructions;

[0090] When the number of the acquired real-time operation control instructions is no more than one, generating a first combination based on the real-time operation data and a preset first grouping parameter, and generating the second combination based on a preset second grouping parameter, the real-time operation data, and the real-time operation control instruction;

[0091] When the number of the real-time operation control instructions obtained is more than one, determine the time range and instruction label of each real-time operation control instruction, divide the real-time operation data into time operation data based on the time range of each real-time operation control instruction, determine the instruction label of the real-time operation control instruction as the instruction label of the divided time operation data, generate a first combination based on all the time operation data, the instruction label of each time operation data and a preset first grouping parameter, and generate a second combination based on a preset second grouping parameter, the real-time operation data, all real-time operation control instructions and the corresponding instruction labels, wherein the time operation data includes multiple time sub-data.

[0092] In this embodiment, the historical operation data and the historical control instructions represent the historical data records and the historical instruction records before the current moment.

[0093] In this embodiment, the time range represents the execution time interval corresponding to the real-time operation control instruction.

[0094] In this embodiment, the instruction tag represents the classification or identification of the corresponding real-time control instruction.

[0095] In this embodiment, when the number of the acquired real-time operation control instructions is more than one, each real-time operation control instruction corresponds to a time operation data, and each time sub-data in the time operation data corresponds to a type of physical parameter.

[0096] In this embodiment, the collected real-time operation data is generated according to the number of real-time operation control instructions in accordance with different generation methods to obtain a first combination and a second combination.

[0097] In this embodiment, for example: load data, voltage data and current data can be a first sub-combination; coolant temperature data and engine efficiency data can be a first sub-combination; fuel flow data and combustion efficiency data can be a first sub-combination; ambient temperature, coolant temperature and coolant flow can be a first sub-combination.

[0098] In this embodiment, when the number of real-time operation control instructions obtained is one, it corresponds to a second combination. The first combination is matched and analyzed only based on the current real-time data and preset parameters. The second combination is generated using the preset second grouping parameters, real-time operation data and a unique real-time control instruction. The second combination not only considers the current real-time data, but also the impact of the real-time control instruction.

[0099] In this embodiment, when the number of real-time operation control instructions obtained is more than one, the number of second combinations is determined according to the number of real-time operation control instructions, and the time range and instruction label are determined for each real-time operation control instruction. Based on the time range of each real-time control instruction, the entire real-time operation data is divided into corresponding multiple time operation data, and a corresponding instruction label is assigned to each time operation data. The first combination is generated using all the time operation data, the instruction label of each time operation data and the preset first grouping parameter. The second combination is generated based on the preset second grouping parameter, all the real-time operation data, all the real-time control instructions and their corresponding instruction labels, and the influence of all instructions is comprehensively considered.

[0100] The beneficial effects of the above technical solution are as follows: by generating the first combination and the second combination according to the real-time operation data and the real-time operation control instructions, multi-dimensional combination data can be generated, which provides a data basis for determining the first related data, the second related data and performing diagnosis, so as to further improve the system's comprehensive understanding of the operating status of the generator set and the diagnostic accuracy.

[0101] Embodiment 5:

[0102] An embodiment of the present invention provides a generator set status diagnosis method, which generates preset first group parameters and preset second group parameters of all types of physical parameters based on historical operation data and historical control instructions, including:

[0103] determining a plurality of target physical parameters based on all types of physical parameters of the generator set;

[0104] determining a plurality of target control instructions based on all historical instructions appearing in the historical control instructions;

[0105] Performing principal component analysis on historical operation data and historical control instructions to determine a first principal component parameter corresponding to each target physical parameter, and at the same time, determining a second principal component parameter for each target control instruction;

[0106] Generate a preset first sub-grouping parameter of each target physical parameter based on each target physical parameter and the corresponding first principal component parameter, and determine a preset first grouping parameter based on all preset first sub-grouping parameters;

[0107] A preset second sub-grouping parameter of each target control instruction is generated based on each target control instruction and the corresponding second principal component parameter, and a preset second grouping parameter is determined based on all the preset second sub-grouping parameters.

[0108] In this embodiment, the preset first sub-grouping parameter represents a grouping template of the corresponding target physical parameter, and the preset second sub-grouping parameter represents a grouping template of the corresponding target control instruction.

[0109] In this embodiment, other physical parameters associated with the target physical parameter are determined by principal component analysis, and the contribution rate between each target physical parameter and the other physical parameters is specifically calculated. Since the higher the contribution rate, the stronger the relationship between the corresponding principal component and the target physical parameter, the physical parameter corresponding to the contribution rate greater than the preset contribution rate threshold is selected as the first principal component parameter of the target physical parameter. The first principal component parameter of the target physical parameter can also be determined based on indicators such as load parameters and eigenvalue parameters in the principal component analysis.

[0110] In this embodiment, each target physical parameter corresponds to a preset first sub-group parameter, and each target control instruction corresponds to a preset second sub-group parameter.

[0111] In this embodiment, a preset first sub-grouping parameter correlated with the target physical parameter can be constructed through principal component analysis, so that the corresponding associated data can be calculated in subsequent steps. In addition, the preset second grouping parameter is generated based on the historical real-time operation control instructions and the second principal component parameters, and the impact of the real-time operation control instructions on the physical parameter data can also be determined, so as to determine whether the changes in the real-time operation data during the operation are correct, thereby improving the accuracy of the status diagnosis results of the generator set.

[0112] The beneficial effects of the above technical solution are: generating preset first grouping parameters and preset second grouping parameters for all types of physical parameters based on historical operating data and historical control instructions, which can provide a grouping template for generating multi-dimensional combination data to determine the first combination and the second combination, thereby improving the system's comprehensive understanding of the operating status of the generator set and the diagnostic accuracy.

[0113] Embodiment 6:

[0114] An embodiment of the present invention provides a generator set status diagnosis method, which determines first associated data based on the first combination, and determines second associated data based on the second combination, including:

[0115] Determine a sub-feature vector of each first sub-combination in the first combination based on all running sub-data of each first sub-combination in the first combination;

[0116] Based on all running sub-data and all sub-feature vectors of every two first sub-combinations in the first combination, calculating the corresponding first correlation coefficient;

[0117] in, represents the first correlation coefficient between the ath first subcombination and the bth first subcombination in the first combination, denote the first sub-correlation coefficient and the second sub-correlation coefficient of the a-th first sub-combination and the b-th first sub-combination in the first combination, respectively. They represent the ath first sub-combination and the bth first sub-combination in the first combination respectively, N1 represents the number of time points in a specified time period, aN2 represents the number of running sub-data at the i-th time point in the ath first sub-combination in the first combination, bN2 represents the number of running sub-data at the i-th time point in the bth first sub-combination in the first combination, represents the data value of the jth running sub-data at the i-th time point in the a-th first sub-combination in the first combination, represents the data value of the jth running sub-data at the i-th time point in the b-th first sub-combination in the first combination, represents the average data value of the j-th running sub-data at all time points in the a-th first sub-group and the average data value of the j-th running sub-data at all time points in the b-th first sub-group in the first combination, They represent the sub-feature vector of the a-th first sub-combination and the sub-feature vector of the b-th first sub-combination in the first combination, respectively. They represent the modulus of the sub-feature vector of the a-th first sub-combination and the modulus of the sub-feature vector of the b-th first sub-combination in the first combination, respectively. Respectively represent the weight of the first sub-correlation coefficient and the weight of the second sub-correlation coefficient;

[0118] calculating a second correlation coefficient for each second sub-combination based on the second combination;

[0119] in, , N3 represents the number of real-time operation control instructions obtained, represents the second correlation coefficient of the kth second sub-combination in the second combination, The second sub-correlation coefficient of the second sub-combination when the number of acquired real-time operation control instructions is not more than one, represents the second sub-correlation coefficient of the kth second sub-combination when the number of acquired real-time operation control instructions is more than one, kN4 represents the number of operation sub-data of the kth second sub-combination in the second combination, It represents the attenuation factor of the pth real-time operation control instruction within a specified time period. represents the first time delay factor, represents the influence weight of the p-th real-time operation control instruction on the z-th operation sub-data, It represents the delay impact value of the p-th real-time operation control instruction on the z-th operation sub-data. represents the second time delay factor of the real-time operation control instruction to the zth operation sub-data, represents the influence weight of the mth running sub-data on the zth running sub-data, It represents the delay impact value of the mth running sub-data on the zth running sub-data. represents the comprehensive delay impact value of all operation sub-data except the z-th operation sub-data in the second combination, KN5 represents the number of real-time operation control instructions of the k-th second sub-combination in the second combination, Indicates the time length of the time range of the pth real-time operation control instruction, represents the kth preset second grouping parameter, Indicates the pth real-time operation control instruction, The time operation data representing the time range of the p-th real-time operation control instruction, represents a functional relationship between the p-th real-time operation control instruction and the time operation data within the time range of the p-th real-time operation control instruction;

[0120] The first correlation data is determined based on all the first correlation coefficients of the first combination, and the second correlation data is determined based on all the second correlation coefficients of the second combination.

[0121] In this embodiment, the first correlation coefficient represents the correlation result of the corresponding two first sub-combinations in the first combination.

[0122] In this embodiment, for example: the sub-feature vector of the first sub-combination of load data, voltage data and current data can be instantaneous output power, load efficiency, and power factor; the sub-feature vector of the first sub-combination of coolant temperature data and engine efficiency data can be engine thermal efficiency, engine cooling system status, and engine performance; the sub-feature vector of the first sub-combination of fuel flow data and combustion efficiency data can be fuel efficiency and carbon emission characteristics; the sub-feature vector of the first sub-combination of ambient temperature, coolant temperature and coolant flow can be cooling efficiency, thermal management performance, and generator set stability.

[0123] In this embodiment, the functional relationship It can be a linear combination, a product form, a nonlinear combination, etc.

[0124] In this embodiment, Represents the correlation result between the p-th real-time operation control instruction in the k-th second sub-combination in the second combination and the time operation data within the time range of the real-time operation control instruction.

[0125] In this embodiment, the second correlation coefficient represents the correlation result of all real-time operation control instructions of the corresponding second sub-combination in the second combination and the time operation data within the time range of the real-time operation control instructions, wherein the number of real-time operation control instructions in the second sub-combination is different, and the corresponding second correlation coefficient is calculated in a different way.

[0126] The beneficial effects of the above technical solution are as follows: by determining the first associated data according to the first combination and, at the same time, determining the second associated data according to the second combination, parallel processing and associated analysis of multi-source information can be achieved, thereby enhancing the diagnostic accuracy and efficiency of the operating status of the generator set and improving the stability and intelligent management level of the system.

[0127] Embodiment 7:

[0128] An embodiment of the present invention provides a generator set state diagnosis method, which determines a state diagnosis result of the generator set based on first associated data and second associated data, including:

[0129] Comparing the first associated data with the first standard associated data, and at the same time, comparing the second associated data with the second standard associated data, to determine a comparison result;

[0130] When the comparison result is that there is no abnormality, determining that the status diagnosis result is that there is no abnormality;

[0131] When the comparison result is that an abnormality exists, a status diagnosis result is determined based on the first combination, the first associated data, the second combination, and the second associated data.

[0132] In this embodiment, when the comparison result is that an abnormality exists, and the number of the acquired real-time operation control instructions is more than one, the cause of the abnormality is determined according to the first combination, the first associated data, the second combination, the second associated data, the instruction tags of all real-time operation control instructions, and the time range, and the state diagnosis result is determined according to the cause of the abnormality and the comparison result;

[0133] In this embodiment, when the comparison result is abnormal, and the number of the acquired real-time operation control instructions is no more than one, the status diagnosis result is determined according to the first combination, the first associated data, the second combination and the second associated data.

[0134] In this embodiment, in addition to determining which parameters are abnormal, the comparison results can also reflect whether the execution of the real-time operation control instructions is complete and accurate. For example, when the number of real-time operation control instructions obtained is more than one, when the associated data is normal during the execution of the first operation control instruction, it can be determined that the first operation control instruction did not cause the associated data to be abnormal in the state of the generator set, and when the associated data is abnormal during the execution of the second operation control instruction, it can be determined that the second operation control instruction caused the abnormal state of the generator set. In addition, when all the associated data corresponding to all the operation control instructions are abnormal, it can be identified that the cause of the abnormality is not related to the operation control instruction.

[0135] The beneficial effects of the above technical solution are as follows: by determining the status diagnosis result of the generator set according to the first associated data and the second associated data, the accuracy and real-time performance of the status diagnosis of the generator set can be improved, accurate monitoring of the operating status of the generator set and early fault identification can be achieved, and the stability and safety of the system can be improved.

[0136] Embodiment 8:

[0137] The embodiment of the present invention provides a generator set status diagnosis system, such as Figure 2 As shown, including:

[0138] Acquisition module: acquires real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes a plurality of operation sub-data;

[0139] A generating module: generating a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, wherein the first combination includes a plurality of first sub-combinations and the second combination includes a plurality of second sub-combinations;

[0140] Determining module: determining first associated data based on the first combination, and determining second associated data based on the second combination;

[0141] Diagnostic module: determines a status diagnosis result of the generator set based on the first associated data and the second associated data.

[0142] The beneficial effects of the above technical solution are as follows: a first combination and a second combination are generated according to the acquired real-time operation data and real-time operation control instructions, the first combination is analyzed to determine the first associated data, the second combination is analyzed to determine the second associated data, and the status diagnosis result of the generator set is determined according to the first associated data and the second associated data. This can improve the accuracy and real-time performance of the status diagnosis of the generator set, realize accurate monitoring of the operating status of the generator set and early fault identification, and improve the stability and safety of the system.

[0143] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0144] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for diagnosing the status of a generator set, characterized in that: include: 101: Acquire real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes a plurality of operation sub-data; 102: Generate a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, wherein the first combination includes a plurality of first sub-combinations, and the second combination includes a plurality of second sub-combinations; 103: Determine first associated data based on the first combination, and simultaneously determine second associated data based on the second combination; 104: Determine a status diagnosis result of the generator set based on the first associated data and the second associated data; The step of determining first associated data based on the first combination and determining second associated data based on the second combination includes: Determine a sub-feature vector of each first sub-combination in the first combination based on all running sub-data of each first sub-combination in the first combination; Based on all running sub-data and all sub-feature vectors of every two first sub-combinations in the first combination, calculating the corresponding first correlation coefficient; in, represents the first correlation coefficient between the ath first subcombination and the bth first subcombination in the first combination, denote the first sub-correlation coefficient and the second sub-correlation coefficient of the a-th first sub-combination and the b-th first sub-combination in the first combination, respectively. They represent the ath first sub-combination and the bth first sub-combination in the first combination respectively, N1 represents the number of time points in a specified time period, aN2 represents the number of running sub-data at the i-th time point in the ath first sub-combination in the first combination, bN2 represents the number of running sub-data at the i-th time point in the bth first sub-combination in the first combination, represents the data value of the jth running sub-data at the i-th time point in the a-th first sub-combination in the first combination, represents the data value of the jth running sub-data at the i-th time point in the b-th first sub-combination in the first combination, represents the average data value of the j-th running sub-data at all time points in the a-th first sub-group and the average data value of the j-th running sub-data at all time points in the b-th first sub-group in the first combination, They represent the sub-feature vector of the a-th first sub-combination and the sub-feature vector of the b-th first sub-combination in the first combination, respectively. They represent the modulus of the sub-feature vector of the a-th first sub-combination and the modulus of the sub-feature vector of the b-th first sub-combination in the first combination, respectively. Respectively represent the weight of the first sub-correlation coefficient and the weight of the second sub-correlation coefficient; calculating a second correlation coefficient for each second sub-combination based on the second combination; in, , N3 represents the number of real-time operation control instructions obtained, represents the second correlation coefficient of the kth second sub-combination in the second combination, The second sub-correlation coefficient of the second sub-combination when the number of acquired real-time operation control instructions is not more than one, represents the second sub-correlation coefficient of the kth second sub-combination when the number of acquired real-time operation control instructions is more than one, kN4 represents the number of operation sub-data of the kth second sub-combination in the second combination, It represents the attenuation factor of the pth real-time operation control instruction within a specified time period. represents the first time delay factor, represents the influence weight of the p-th real-time operation control instruction on the z-th operation sub-data, It represents the delay impact value of the p-th real-time operation control instruction on the z-th operation sub-data. represents the second time delay factor of the real-time operation control instruction to the zth operation sub-data, represents the influence weight of the mth running sub-data on the zth running sub-data, It represents the delay impact value of the mth running sub-data on the zth running sub-data. represents the comprehensive delay impact value of all operation sub-data except the z-th operation sub-data in the second combination, KN5 represents the number of real-time operation control instructions of the k-th second sub-combination in the second combination, Indicates the time length of the time range of the pth real-time operation control instruction, represents the kth preset second grouping parameter, Indicates the pth real-time operation control instruction, The time operation data representing the time range of the p-th real-time operation control instruction, represents a functional relationship between the p-th real-time operation control instruction and the time operation data within the time range of the p-th real-time operation control instruction; The first correlation data is determined based on all the first correlation coefficients of the first combination, and the second correlation data is determined based on all the second correlation coefficients of the second combination.

2. A generator set status diagnosis method according to claim 1, characterized in that: Obtain the real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, including: Collecting first data of all types of physical parameters of the generator set within a specified time period under the same conditions, wherein the first data includes a plurality of first sub-data; Sorting each first sub-data in the first data according to the order of collection time within a specified time period, and determining the sorting result of each first sub-data as the corresponding second sub-data; Determine the running sub-data of the first sub-data based on each first sub-data and the corresponding second sub-data; Real-time operation data is determined based on all the operation sub-data.

3. A generator set status diagnosis method according to claim 2, characterized in that: The physical parameters include: voltage parameters, current parameters, power parameters, rotor speed parameters, coolant temperature parameters, coolant flow parameters, generator set vibration parameters and load parameters.

4. A generator set status diagnosis method according to claim 1, characterized in that: Generating a first combination and a second combination based on the real-time operation data and the real-time operation control instruction includes: Acquire historical operation data and historical control instructions of the generator set, and generate preset first grouping parameters and preset second grouping parameters of all types of physical parameters based on the historical operation data and historical control instructions; When the number of the acquired real-time operation control instructions is no more than one, generating a first combination based on the real-time operation data and a preset first grouping parameter, and generating the second combination based on a preset second grouping parameter, the real-time operation data, and the real-time operation control instruction; When the number of the real-time operation control instructions obtained is more than one, determine the time range and instruction label of each real-time operation control instruction, divide the real-time operation data into time operation data based on the time range of each real-time operation control instruction, determine the instruction label of the real-time operation control instruction as the instruction label of the divided time operation data, generate a first combination based on all the time operation data, the instruction label of each time operation data and a preset first grouping parameter, and generate a second combination based on a preset second grouping parameter, the real-time operation data, all real-time operation control instructions and corresponding instruction labels, wherein the time operation data includes multiple time sub-data.

5. A generator set status diagnosis method according to claim 4, characterized in that: Generate preset first group parameters and preset second group parameters of all types of physical parameters based on historical operation data and historical control instructions, including: determining a plurality of target physical parameters based on all types of physical parameters of the generator set; determining a plurality of target control instructions based on all historical instructions appearing in the historical control instructions; Performing principal component analysis on historical operation data and historical control instructions to determine a first principal component parameter corresponding to each target physical parameter, and at the same time, determining a second principal component parameter for each target control instruction; generating a preset first sub-grouping parameter of each target physical parameter based on each target physical parameter and the corresponding first principal component parameter, and determining a preset first grouping parameter based on all the preset first sub-grouping parameters; A preset second sub-grouping parameter of each target control instruction is generated based on each target control instruction and the corresponding second principal component parameter, and a preset second grouping parameter is determined based on all the preset second sub-grouping parameters.

6. A generator set status diagnosis method according to claim 1, characterized in that: Determining a state diagnosis result of the generator set based on the first associated data and the second associated data includes: Comparing the first associated data with the first standard associated data, and at the same time, comparing the second associated data with the second standard associated data, to determine a comparison result; When the comparison result is that there is no abnormality, determining that the status diagnosis result is that there is no abnormality; When the comparison result is that an abnormality exists, a status diagnosis result is determined based on the first combination, the first associated data, the second combination, and the second associated data.

7. A generator set status diagnosis system, used to implement any one of claims 1 to 6 of the generator set status diagnosis method, characterized in that: include: Acquisition module: acquires real-time operation data and real-time operation control instructions of the generator set during operation at the current moment, wherein the real-time operation data includes a plurality of operation sub-data; A generating module: generating a first combination and a second combination based on the real-time operation data and the real-time operation control instruction, wherein the first combination includes a plurality of first sub-combinations and the second combination includes a plurality of second sub-combinations; Determining module: determining first associated data based on the first combination, and determining second associated data based on the second combination; Diagnostic module: determines a status diagnosis result of the generator set based on the first associated data and the second associated data.

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