Equipment shutdown diagnosis method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of equipment diagnosis, and discloses an equipment shutdown diagnosis method and device, electronic equipment and a storage medium, which are used for improving the accuracy of an equipment shutdown diagnosis result. The method comprises the steps of obtaining power generation data of a new energy power station; judging whether the new energy power station is in an overall power limiting scene according to the power generation data; if the new energy power station is not in the overall power limiting scene, performing power limiting identification on each device in the new energy power station through a preset partial power limiting feature model to obtain a plurality of non-power-limiting shutdown devices; and real-time shutdown diagnosis is carried out on the multiple pieces of non-power-limit shutdown equipment, and an equipment shutdown diagnosis result is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of equipment diagnosis, and in particular to an equipment shutdown diagnosis method, device, electronic device and storage medium. Background Art

[0002] Renewable energy power stations usually implement power restrictions to respond to grid dispatch instructions, ensure stable grid operation, or adjust power generation when there is excess power generation and the grid's acceptance capacity is limited. When a power station is power-restricted, the inverter will shut down or the inverter power will be limited to a lower power generation level. When the inverter is at a lower operating level, the downstream equipment connected to the inverter will shut down.

[0003] At present, most new energy power stations monitor power generation equipment through intelligent operation and maintenance systems to monitor whether each device in the new energy power station is shut down. However, the existing equipment shutdown diagnosis results are often mixed with equipment that is shut down due to power restrictions. Such equipment should not be triggered to trigger a shutdown alarm, resulting in inaccurate equipment shutdown diagnosis results. Summary of the invention

[0004] The present application provides an equipment shutdown diagnosis method, device, electronic device and storage medium, which are used to determine whether a new energy power station is in an overall power-limiting scenario. If the new energy power station is not in an overall power-limiting scenario, a partial power-limiting scenario is identified for the new energy power station through a preset partial power-limiting feature model, and multiple non-power-limited shutdown devices are obtained and shutdown diagnosis is performed to obtain equipment shutdown diagnosis results, thereby solving the problem of inaccurate equipment shutdown diagnosis results and achieving the effect of reducing invalid shutdown warnings.

[0005] A first aspect of an embodiment of the present application provides an equipment shutdown diagnosis method, including: obtaining power generation data of a new energy power station; judging whether the new energy power station is in an overall power restriction scenario based on the power generation data; if the new energy power station is not in an overall power restriction scenario, performing power restriction identification on each device in the new energy power station through a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices; performing real-time shutdown diagnosis on multiple non-power restriction shutdown devices to obtain equipment shutdown diagnosis results.

[0006] The second aspect of an embodiment of the present application provides an equipment shutdown diagnosis device, including: an acquisition module, used to acquire power generation data of a new energy power station; a judgment module, used to judge whether the new energy power station is in an overall power limit scenario based on the power generation data; an identification module, used to identify the power limit of each device in the new energy power station through a preset partial power limit feature model if the new energy power station is not in an overall power limit scenario, and obtain multiple non-power limit shutdown devices; a diagnosis module, used to perform real-time shutdown diagnosis on multiple non-power limit shutdown devices to obtain equipment shutdown diagnosis results.

[0007] A third aspect of an embodiment of the present application provides an electronic device, comprising: a memory and at least one processor, wherein instructions are stored in the memory; and at least one processor calls the instructions in the memory so that the electronic device executes the device shutdown diagnosis method of the first aspect mentioned above.

[0008] A fourth aspect of the present application provides a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is run on a computer, the computer executes the above-mentioned equipment shutdown diagnosis method.

[0009] In the technical solution provided in the embodiment of the present application, the power generation data of the new energy power station is obtained; based on the power generation data, it is determined whether the new energy power station is in an overall power restriction scenario; if the new energy power station is not in an overall power restriction scenario, the power restriction of each device in the new energy power station is identified through a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices; real-time shutdown diagnosis is performed on multiple non-power restriction shutdown devices to obtain equipment shutdown diagnosis results. In the embodiment of the present application, it is first determined whether the new energy power station is in an overall power restriction scenario. If the new energy power station is not in an overall power restriction scenario, the partial power restriction scenario is identified for the new energy power station through a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices and perform shutdown diagnosis to obtain equipment shutdown diagnosis results, which solves the problem of inaccurate equipment shutdown diagnosis results and achieves the effect of reducing invalid shutdown warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a schematic diagram of an embodiment of a device shutdown diagnosis method in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a power generation capacity curve of a new energy power station in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of voltage fluctuation of an inverter in an embodiment of the present application;

[0013] Figure 4 This is another schematic diagram of an embodiment of the device shutdown diagnosis method in the embodiment of the present application;

[0014] Figure 5 A schematic diagram of a device shutdown diagnosis apparatus in an embodiment of the present application;

[0015] Figure 6 A schematic diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The present application provides an equipment shutdown diagnosis method, device, electronic device and storage medium, which are used to determine whether a new energy power station is in an overall power-limiting scenario. If the new energy power station is not in an overall power-limiting scenario, a partial power-limiting scenario is identified for the new energy power station through a preset partial power-limiting feature model, and multiple non-power-limited shutdown devices are obtained and shutdown diagnosis is performed to obtain equipment shutdown diagnosis results, thereby solving the problem of inaccurate equipment shutdown diagnosis results and achieving the effect of reducing invalid shutdown warnings.

[0017] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] See also Figure 1 , a flow chart of a device shutdown diagnosis method provided in an embodiment of the present application, specifically comprising:

[0019] 101. Obtain power generation data of new energy power stations.

[0020] The equipment shutdown diagnosis device obtains power generation data of the new energy power station, and the power generation data at least includes the power generation of each inverter.

[0021] It should be noted that the new energy power station can be a photovoltaic power station, a wind power station, or other clean energy power stations. The photovoltaic power station is used as an example for explanation in the embodiment of the present application and subsequent embodiments.

[0022] It is understandable that when the new energy power station is a photovoltaic power station, the new energy power station includes multiple devices, which can be physical devices, such as inverters, junction boxes, strings or components; devices can also be virtual integrated concept devices, such as power stations, collectors, and arrays. When searching for the upper and lower relationships between devices, you can choose one to judge. For example, when the diagnosed shutdown device is an overall array inverter, its upper device is the collector and its lower device is the inverter; similarly, when the diagnosed shutdown device is an inverter, its upper device is the array and its lower device is the junction box.

[0023] The executor of the embodiment of the present application may be an equipment shutdown diagnostic device, or other controllers with processing functions. For ease of description, the embodiment of the present application and subsequent embodiments are described using the equipment shutdown diagnostic device as an example.

[0024] 102. Determine whether the new energy power station is in an overall power restriction scenario based on power generation data.

[0025] The equipment shutdown diagnosis device determines whether the new energy power station is in an overall power restriction scenario based on the power generation data.

[0026] It should be noted that the power generation capacity of the new energy power station at the current moment can be used to identify the overall power restriction scenario. When the power generation capacity is lower than the target value, it is confirmed whether the new energy power station is in the overall power restriction scenario. All equipment under the new energy power station does not need to enter the partial power restriction feature model for identification. The equipment includes power stations, collectors, arrays, inverters, junction boxes and strings, etc. Among them, the power generation capacity refers to the ratio between the power generation power of the power station and the installed capacity, that is, the actual equivalent utilization hours at the current moment, and the target value is the theoretical equivalent utilization hours that the new energy power station should achieve at the current moment.

[0027] It is understandable that when a new energy power station is in an overall power-rationing scenario, all equipment is shielded and continuously monitored, that is, there is no need for shutdown diagnosis, until the new energy power station is no longer in an overall power-rationing scenario, and then all equipment is judged one by one whether power is rationed.

[0028] When the new energy power station is in the overall power restriction scenario, the overall power generation of the new energy power station is low, and the power (i.e. power generation capacity) of the new energy power station obviously violates the characteristics of power generation, such as Figure 2 As shown, when the inverter is power-limited, the power generation capacity of the new energy power station changes suddenly and remains unchanged for a certain period of time.

[0029] 103. If the new energy power station is not in an overall power-limiting scenario, power-limiting identification is performed on each device in the new energy power station through a preset partial power-limiting feature model to obtain multiple non-power-limiting shutdown devices.

[0030] If the new energy power station is not in an overall power-limiting scenario, the equipment shutdown diagnosis device identifies power limits on each device in the new energy power station through a preset partial power-limiting feature model to obtain multiple non-power-limiting shutdown devices.

[0031] It is understandable that the equipment shutdown diagnosis device identifies the power-limited equipment through the pre-built partial power-limited feature model and fault tree tracing strategy, and then shields the equipment that is shut down due to power-limited, thereby obtaining multiple non-power-limited shutdown equipment. The voltage of the power-limited equipment will fluctuate significantly due to power-limited, for example Figure 3As shown, the voltage of the power-limited equipment will drop sharply and be limited to a certain level, and the voltage will obviously rise above the normal operating voltage. The greater the power limitation, the closer the power is to 0, and the closer the voltage is to the open circuit voltage.

[0032] 104. Perform real-time shutdown diagnosis on multiple non-power-limited shutdown devices to obtain equipment shutdown diagnosis results.

[0033] The equipment shutdown diagnosis device performs real-time shutdown diagnosis on multiple non-power-limited shutdown equipment to obtain equipment shutdown diagnosis results.

[0034] The shutdown fault diagnosis is performed on the shutdown equipment after being filtered by the partial power-limiting characteristic model. Specifically, the diagnosis can be performed through time series data, that is, the shutdown characteristic model of voltage, current, and power is established according to the mechanism analysis after the equipment is shut down, and the power generation data of the equipment is diagnosed in real time. When the power generation data meets the shutdown diagnosis characteristic model, a shutdown alarm is issued. The cause of the shutdown alarm is obtained by the equipment's own remote signaling system, such as power grid fault, DC side fault, equipment internal fault, etc.

[0035] In the embodiment of the present application, it is first determined whether the new energy power station is in an overall power restriction scenario. If the new energy power station is not in an overall power restriction scenario, a partial power restriction scenario is identified for the new energy power station through a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices, and shutdown diagnosis is performed on the multiple non-power restriction shutdown devices to obtain equipment shutdown diagnosis results, thereby excluding equipment that is shut down due to power restriction and obtaining equipment that is shut down due to faults. The problem of inaccurate equipment shutdown diagnosis results is solved, and the effect of reducing invalid shutdown warnings is achieved.

[0036] See also Figure 4 , another flow chart of the device shutdown diagnosis method provided in the embodiment of the present application, specifically includes:

[0037] 401. Obtain power generation data of the new energy power station.

[0038] The equipment shutdown diagnosis device obtains power generation data of the new energy power station, and the power generation data at least includes the power generation of each inverter.

[0039] It should be noted that the new energy power station can be a photovoltaic power station, a wind power station, or other clean energy power stations. The photovoltaic power station is used as an example for explanation in the embodiment of the present application and subsequent embodiments.

[0040] It is understandable that when the new energy power station is a photovoltaic power station, the new energy power station includes multiple devices, which can be physical devices, such as inverters, junction boxes, strings or components; devices can also be virtual integrated concept devices, such as power stations, collectors, and arrays. When searching for the upper and lower relationships between devices, you can choose one to judge. For example, when the diagnosed shutdown device is an overall array inverter, its upper device is the collector and its lower device is the inverter; similarly, when the diagnosed shutdown device is an inverter, its upper device is the array and its lower device is the junction box.

[0041] 402. Determine whether the new energy power station is in an overall power restriction scenario based on power generation data.

[0042] In a feasible implementation, it specifically includes: the equipment shutdown diagnostic device analyzes the power generation data to obtain the equivalent utilization hours and the fluctuation rate of the equivalent utilization hours; the equipment shutdown diagnostic device determines whether a first condition is met based on the equivalent utilization hours and the fluctuation rate of the equivalent utilization hours, the first condition being that the equivalent utilization hours are less than a first threshold and the fluctuation rate of the equivalent utilization hours is greater than a second threshold; if the first condition is met, the equipment shutdown diagnostic device determines that the new energy power station is in an overall power restriction scenario; if the first condition is not met, the equipment shutdown diagnostic device determines that the new energy power station is not in an overall power restriction scenario.

[0043] It should be noted that in this embodiment, both the equivalent utilization hours and the volatility of the equivalent utilization hours are considered. Assuming that the equivalent utilization hours are H1 and the volatility of the equivalent utilization hours is V1, then H1 must be satisfied at the same time.<M1,V1> M2, where M1 is the first threshold, which is the preset minimum equivalent utilization hours, and M2 is the second threshold, which is the preset minimum percentage. Only when all conditions are met at the same time can it be confirmed that the equipment in the entire power station is subject to power restriction.

[0044] In a feasible implementation, it specifically includes: the equipment shutdown diagnostic device analyzes the power generation data to obtain the equivalent utilization hours; the equipment shutdown diagnostic device determines whether the second condition is met based on the equivalent utilization hours, the second condition is that the equivalent utilization hours are less than the product of a third threshold and a first coefficient, and the third threshold is the equivalent utilization hours corresponding to a preset model inverter; if the second condition is met, the equipment shutdown diagnostic device determines that the new energy power station is in an overall power restriction scenario; if the second condition is not met, the equipment shutdown diagnostic device determines that the new energy power station is not in an overall power restriction scenario.

[0045] It should be noted that in this embodiment, only the equivalent utilization hours are considered. Assume that the equivalent utilization hours are H1, and then it is necessary to satisfy H1 < K * M3, where M3 is the third threshold, which is the average equivalent utilization hours corresponding to multiple pre-determined sample machines. The sample machines can be set by the power station configuration information or represented by the inverters ranked among the top m in terms of the power generation hours of the power station on consecutive ideal days.

[0046] 403. If the new energy power station is not in an overall power curtailment scenario, the power curtailment identification of each device in the new energy power station is performed through a preset partial power curtailment feature model to obtain multiple non-power curtailment shutdown devices.

[0047] Specifically, the device shutdown diagnosis device analyzes the power generation data to obtain the power generation parameters of each device. The power generation parameters include voltage, current, and power. The device shutdown diagnosis device identifies multiple first-level power-curtailed devices through a preset partial power curtailment feature model for the power generation parameters of each device. The device shutdown diagnosis device identifies the upper and lower level devices of the multiple first-level power-curtailed devices through a preset fault tree chain tracing strategy to obtain multiple second-level power-curtailed devices. The device shutdown diagnosis device excludes the multiple initially screened power-curtailed devices and the multiple power-curtailed upper and lower level devices from all the devices in the station to obtain multiple non-power curtailment shutdown devices.

[0048] The fault tree tracing strategy traces the root cause of the device failure based on the organizational tree relationship and the genetic characteristics of the operation data stream between devices to avoid ineffective shutdown alarms. Among them, the organizational tree relationship refers to the upper and lower level relationship of devices, and the genetic characteristics of the operation data stream mean that when the upper level device is power-curtailed, the lower level device also has corresponding characteristics. For example, the trend changes of the voltage of the inverter and the voltage of the busbar box are basically the same during the power-curtailed period and show a rising phenomenon.

[0049] In a feasible embodiment, the device shutdown diagnosis device identifies the upper and lower level devices of the multiple first-level power-curtailed devices through a preset fault tree chain tracing strategy to obtain multiple second-level power-curtailed devices, including:

[0050] The equipment shutdown diagnosis device obtains the organization tree relationship of each first-level power-restricted device through a preset fault tree chain tracing strategy, and the organization tree relationship is used to indicate the upper and lower devices of the first-level power-restricted device; the equipment shutdown diagnosis device determines whether the voltage and power change trend of each first-level power-restricted device is consistent with that of the corresponding upper device; if the voltage and power change trend of any upper device is consistent with that of the first-level power-restricted device, the equipment shutdown diagnosis device determines that any upper device is a second-level power-restricted device; if the voltage and power change trend of each first-level power-restricted device is inconsistent with that of the corresponding upper device, the equipment shutdown diagnosis device determines whether each first-level power-restricted device has an operating data flow inheritance feature, and the operating data flow inheritance feature is used to indicate that the lower device corresponding to the first-level power-restricted device has a power-restriction feature; if any first-level power-restricted device has an operating data flow inheritance feature, the equipment shutdown diagnosis device determines that multiple lower devices of any first-level power-restricted device are second-level power-restricted devices.

[0051] It is understandable that the fault tree tracing strategy is applicable to all equipment in the entire power station, including virtual integrated concept equipment such as power stations and collectors, as well as physical equipment such as arrays, inverters, combiner boxes, strings or components. The judgment logic of the fault tree tracing strategy is: if the upper-level equipment is power-restricted, the lower-level equipment will not alarm. For example, when the first-level power-restricted equipment is an inverter, the lower-level combiner boxes and strings will not alarm, and the combiner boxes and strings are determined to be second-level power-restricted equipment. For example, there are 4 combiner boxes at the lower level of the 1# inverter, namely 1-1, 1-2, 1-3, and 1-4 combiner boxes. If the 1# inverter is in a power-restricted state, then the shutdown of the 1-1, 1-2, 1-3, and 1-4 combiner boxes does not require an alarm.

[0052] For another example, when the primary power-restricted device is a combiner box, then the voltage and power change trend of the corresponding inverter at the upper level is consistent with the voltage and power change trend of the combiner box. If they are consistent, it means that the inverter also meets the power restriction characteristics, and there is no need to shut down the inverter for diagnosis, and the corresponding inverter is determined as the secondary power-restricted device. For example, there are 4 combiner boxes at the lower level of the 1# inverter, namely 1-1, 1-2, 1-3, and 1-4 combiner boxes. When the 1-1 combiner box is in the power-restricted state, if the voltage and power change trend of the 1# inverter is consistent with the voltage and power change trend of the 1-1 combiner box, it indicates that the 1# inverter is also in the power-restricted state, and no shutdown diagnosis or alarm is required.

[0053] Optionally, before step 401, the method further includes:

[0054] Acquire historical power generation data of the new energy power station, wherein the historical power generation data includes voltage data and power data; construct a power-limiting feature based on the voltage data and the power data, wherein the power-limiting feature is used to indicate that a sudden change occurs in the power data volatility and the volatility is less than a fluctuation threshold for a period of time; train an initial model based on the constructed power-limiting feature to obtain a partial power-limiting feature model.

[0055] It should be noted that it is necessary to analyze the power-limiting characteristics of power generation data, construct the power-limiting characteristics of voltage and power trend changes, and train the model based on the power-limiting characteristics to obtain a partial power-limiting characteristic model. The power-limiting characteristics may also include other parameters, such as current.

[0056] 404. Perform real-time shutdown diagnosis on multiple non-power-limited shutdown devices to obtain device shutdown diagnosis results.

[0057] The equipment shutdown diagnosis device performs real-time shutdown diagnosis on multiple non-power-limited shutdown equipment to obtain equipment shutdown diagnosis results.

[0058] The shutdown fault diagnosis is performed on the shutdown equipment after being filtered by the partial power-limiting characteristic model. Specifically, the diagnosis can be performed through time series data, that is, the shutdown characteristic model of voltage, current, and power is established according to the mechanism analysis after the equipment is shut down, and the power generation data of the equipment is diagnosed in real time. When the power generation data meets the shutdown diagnosis characteristic model, a shutdown alarm is issued. The cause of the shutdown alarm is obtained by the equipment's own remote signaling system, such as power grid fault, DC side fault, equipment internal fault, etc.

[0059] 405. Determine each non-power-restricted shutdown device and the shutdown reason based on the equipment shutdown diagnosis result.

[0060] The equipment shutdown diagnosis device eliminates the equipment that is shut down due to power restriction, obtains various non-power restriction shutdown equipment, and then obtains the shutdown reason through the remote signaling system of each equipment itself. The shutdown reason can be a power grid fault, a DC side fault or an internal equipment fault.

[0061] 406. Determine a target operation and maintenance strategy in a preset strategy library based on each non-power-limited shutdown device and the shutdown reason.

[0062] Among them, the preset strategy library can be obtained through technical association equipment user manuals or expert experience libraries such as knowledge graphs, or relevant operation and maintenance strategies can be automatically generated through AI models.

[0063] 407. Score each non-power-limited shutdown device from a preset plurality of dimensions using a preset scoring algorithm to generate an evaluation report, wherein the evaluation report is used to reflect the horizontal and vertical levels of each non-power-limited shutdown device in each dimension.

[0064] Among them, multiple dimensions include conversion efficiency, failure rate, operating status and power generation capacity. The evaluation algorithm can be evaluated by the double base point method (Technique for order preference by Similarity to ideal solution, TOPSIS), or other evaluation algorithms, which are not limited here. The evaluation report can obtain the horizontal and vertical levels of each dimension of the target non-limited power equipment during its life cycle. The horizontal level refers to the score value of the target non-limited power equipment and other non-limited equipment in the same dimension, and the vertical level refers to the score value of the target non-limited power equipment in different dimensions.

[0065] In the embodiment of the present application, it is first determined whether the new energy power station is in an overall power restriction scenario. If the new energy power station is not in an overall power restriction scenario, a partial power restriction scenario is identified for the new energy power station through a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices, and shutdown diagnosis is performed on the multiple non-power restriction shutdown devices to obtain equipment shutdown diagnosis results, and a target operation and maintenance strategy and an evaluation report are generated for the equipment shutdown diagnosis results, thereby shielding equipment that is shut down due to power restriction, improving the accuracy of identifying equipment that is shut down due to faults, reducing invalid shutdown alarms, reducing alarm work orders, and increasing the reliability of operation and maintenance decisions, thereby increasing power generation.

[0066] The above describes the device shutdown diagnosis method in the embodiment of the present application. The following describes the device shutdown diagnosis device in the embodiment of the present application. Figure 5 , an embodiment of the device shutdown diagnosis device in the embodiment of the present application includes:

[0067] The first acquisition module 501 is used to acquire power generation data of the new energy power station;

[0068] A judgment module 502 is used to judge whether the new energy power station is in an overall power restriction scenario according to the power generation data;

[0069] The identification module 503 is used to identify the power limit of each device in the new energy power station by using a preset partial power limit feature model if the new energy power station is not in the overall power limit scenario, and obtain multiple non-power limit shutdown devices;

[0070] The diagnosis module 504 is used to perform real-time shutdown diagnosis on the plurality of non-power-limited shutdown devices to obtain device shutdown diagnosis results.

[0071] In a feasible implementation manner, the determination module 502 is specifically configured to:

[0072] Analyzing the power generation data to obtain equivalent utilization hours and fluctuation rate of the equivalent utilization hours;

[0073] Based on the equivalent utilization hours and the fluctuation rate of the equivalent utilization hours, determining whether a first condition is met, the first condition being that the equivalent utilization hours are less than a first threshold and the fluctuation rate of the equivalent utilization hours are greater than a second threshold;

[0074] If the first condition is met, it is determined that the new energy power station is in an overall power restriction scenario;

[0075] If the first condition is not met, it is determined that the new energy power station is not in an overall power restriction scenario.

[0076] In a feasible implementation manner, the determination module 502 is further configured to:

[0077] Analyzing the power generation data to obtain equivalent utilization hours;

[0078] Based on the equivalent utilization hours, judging whether a second condition is met, the second condition being that the equivalent utilization hours are less than a product of a third threshold and a first coefficient, the third threshold being an equivalent utilization hour corresponding to a preset model inverter;

[0079] If the second condition is met, it is determined that the new energy power station is in an overall power restriction scenario;

[0080] If the second condition is not met, it is determined that the new energy power station is not in an overall power restriction scenario.

[0081] In a feasible implementation manner, the identification module 503 includes:

[0082] The parsing unit 5031 is used to parse the power generation data to obtain power generation parameters of each device, where the power generation parameters include voltage, current and power;

[0083] A first identification unit 5032 is used to identify the power generation parameters of each device through a preset partial power restriction feature model to obtain a plurality of first-level power-restricted devices;

[0084] The second identification unit 5033 is used to identify the upper and lower level devices of the plurality of primary power-restricted devices through a preset fault tree chain tracing strategy to obtain a plurality of secondary power-restricted devices;

[0085] The elimination unit 5034 is used to eliminate the multiple preliminarily screened power-restricted devices and the multiple power-restricted upper and lower level devices from all the station devices to obtain multiple non-power-restricted shutdown devices.

[0086] In a feasible implementation manner, the second identification unit 5033 is specifically used for:

[0087] The organization tree relationship of each first-level power-restricted device is obtained through a preset fault tree chain tracing strategy, wherein the organization tree relationship is used to indicate the upper-level device and the lower-level device of the first-level power-restricted device;

[0088] Determine whether the voltage and power change trends of each first-level power-restricted device are consistent with those of the corresponding upper-level device;

[0089] If the voltage and power variation trends of any upper-level device are consistent with those of the first-level power-restricted device, then the upper-level device is determined to be a second-level power-restricted device;

[0090] If the voltage and power change trends of each first-level power-restricted device are inconsistent with those of the corresponding upper-level device, it is determined whether each first-level power-restricted device has an operation data flow inheritance feature, and the operation data flow inheritance feature is used to indicate that the lower-level device corresponding to the first-level power-restricted device has a power restriction feature;

[0091] If any first-level power-restricted device has the operation data flow inheritance feature, it is determined that multiple subordinate devices of any first-level power-restricted device are second-level power-restricted devices.

[0092] In a feasible implementation manner, the equipment shutdown diagnosis device further includes:

[0093] The second acquisition module 505 is used to acquire historical power generation data of the new energy power station, where the historical power generation data includes voltage data and power data;

[0094] A construction module 506, configured to construct a power-limiting feature according to the voltage data and the power data, wherein the power-limiting feature is used to indicate that a sudden change occurs in the power data fluctuation rate and the fluctuation rate is less than a fluctuation threshold for a period of time;

[0095] The training module 507 is used to train the initial model based on the constructed power-limiting feature to obtain a partial power-limiting feature model.

[0096] In a feasible implementation manner, the equipment shutdown diagnosis device further includes:

[0097] A first determination module 508, configured to determine each non-power-limited shutdown device and the shutdown reason based on the device shutdown diagnosis result;

[0098] A second determination module 509 is used to determine a target operation and maintenance strategy in a preset strategy library based on each non-power-limited shutdown device and the shutdown reason;

[0099] The generating module 510 is used to score each non-power-limited shutdown device from a preset plurality of dimensions using a preset scoring algorithm to generate an evaluation report, wherein the evaluation report is used to reflect the horizontal and vertical levels of each non-power-limited shutdown device in each dimension.

[0100] In the embodiment of the present application, it is first determined whether the new energy power station is in an overall power restriction scenario. If the new energy power station is not in an overall power restriction scenario, a partial power restriction scenario is identified for the new energy power station through a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices, and shutdown diagnosis is performed on the multiple non-power restriction shutdown devices to obtain equipment shutdown diagnosis results, and a target operation and maintenance strategy and an evaluation report are generated for the equipment shutdown diagnosis results, thereby shielding equipment that is shut down due to power restriction, improving the accuracy of identifying equipment that is shut down due to faults, reducing invalid shutdown alarms, reducing alarm work orders, and increasing the reliability of operation and maintenance decisions, thereby increasing power generation.

[0101] See also Figure 6 As shown, the electronic device includes a processor 600 and a memory 601 , wherein the memory 601 stores machine executable instructions that can be executed by the processor 600 , and the processor 600 executes the machine executable instructions to implement the above-mentioned device shutdown diagnosis method.

[0102] Further, Figure 6 The electronic device shown further includes a bus 602 and a communication interface 603 , and the processor 600 , the communication interface 603 and the memory 601 are connected via the bus 602 .

[0103] The memory 601 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), for example, at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 603 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 602 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0104] The processor 600 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 600. The above processor 600 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 601 , and the processor 600 reads the information in the memory 601 and completes the method steps of the above-mentioned embodiment in combination with its hardware.

[0105] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the device shutdown diagnosis method, which specifically include:

[0106] Obtain the power generation data of the new energy power station; determine whether the new energy power station is in an overall power restriction scenario based on the power generation data; if the new energy power station is not in an overall power restriction scenario, then use a preset partial power restriction feature model to identify the power restrictions of each device in the new energy power station, and obtain multiple non-power restriction shutdown devices; perform real-time shutdown diagnosis on multiple non-power restriction shutdown devices to obtain equipment shutdown diagnosis results. In the embodiment of the present application, first determine whether the new energy power station is in an overall power restriction scenario. If the new energy power station is not in an overall power restriction scenario, then use a preset partial power restriction feature model to identify the partial power restriction scenario of the new energy power station, obtain multiple non-power restriction shutdown devices and perform shutdown diagnosis to obtain equipment shutdown diagnosis results, which solves the problem of inaccurate equipment shutdown diagnosis results and achieves the effect of reducing invalid shutdown warnings.

[0107] The above-mentioned judging whether the new energy power station is in an overall power restriction scenario based on the power generation data includes: parsing the power generation data to obtain the equivalent utilization hours and the fluctuation rate of the equivalent utilization hours; judging whether the first condition is met based on the equivalent utilization hours and the fluctuation rate of the equivalent utilization hours, the first condition being that the equivalent utilization hours are less than the first threshold and the fluctuation rate of the equivalent utilization hours is greater than the second threshold; if the first condition is met, it is determined that the new energy power station is in an overall power restriction scenario; if the first condition is not met, it is determined that the new energy power station is not in an overall power restriction scenario.

[0108] The above-mentioned judging whether the new energy power station is in an overall power restriction scenario based on the power generation data includes: parsing the power generation data to obtain the equivalent utilization hours; judging whether the second condition is met based on the equivalent utilization hours, the second condition is that the equivalent utilization hours are less than the product of the third threshold and the first coefficient, and the third threshold is the equivalent utilization hours corresponding to the preset model inverter; if the second condition is met, it is determined that the new energy power station is in an overall power restriction scenario; if the second condition is not met, it is determined that the new energy power station is not in an overall power restriction scenario.

[0109] The above-mentioned method uses a preset partial power restriction feature model to identify power restrictions on various devices in the new energy power station to obtain multiple non-power restriction shutdown devices, including: parsing power generation data to obtain power generation parameters of various devices, and the power generation parameters include voltage, current and power; identifying the power generation parameters of various devices through a preset partial power restriction feature model to obtain multiple first-level power-restricted devices; identifying the upper and lower level devices of multiple first-level power-restricted devices through a preset fault tree chain tracing strategy to obtain multiple second-level power-restricted devices; eliminating multiple initially screened power-restricted devices and multiple power-restricted upper and lower level devices in the entire station equipment to obtain multiple non-power restriction shutdown devices.

[0110] The above-mentioned method identifies the upper and lower devices of multiple first-level power-restricted devices through a preset fault tree chain tracing strategy to obtain multiple second-level power-restricted devices, including: obtaining the organization tree relationship of each first-level power-restricted device through a preset fault tree chain tracing strategy, and the organization tree relationship is used to indicate the upper and lower devices of the first-level power-restricted device; judging whether the voltage and power change trend of each first-level power-restricted device is consistent with that of the corresponding upper device; if the voltage and power change trend of any upper device is consistent with that of the first-level power-restricted device, then determining that any upper device is a second-level power-restricted device; if the voltage and power change trend of each first-level power-restricted device is inconsistent with that of the corresponding upper device, then judging whether each first-level power-restricted device has an operating data flow inheritance feature, and the operating data flow inheritance feature is used to indicate that the lower device corresponding to the first-level power-restricted device has a power-restriction feature; if any first-level power-restricted device has an operating data flow inheritance feature, then determining that multiple lower devices of any first-level power-restricted device are second-level power-restricted devices.

[0111] Before the above-mentioned acquisition of the power generation data of the new energy power station, it also includes: acquiring the historical power generation data of the new energy power station, the historical power generation data includes voltage data and power data; constructing a power restriction feature based on the voltage data and the power data, the power restriction feature is used to indicate that the power data volatility has suddenly changed and the volatility has been less than the volatility threshold for a period of time; training the initial model based on the constructed power restriction feature to obtain a partial power restriction feature model.

[0112] After the above-mentioned real-time shutdown diagnosis of multiple non-power-limited shutdown devices is performed to obtain the equipment shutdown diagnosis results, it also includes: determining each non-power-limited shutdown device and the shutdown reason based on the equipment shutdown diagnosis result; determining the target operation and maintenance strategy in a preset strategy library based on each non-power-limited shutdown device and the shutdown reason; scoring each non-power-limited shutdown device from preset multiple dimensions through a preset scoring algorithm, and generating an evaluation report, which is used to reflect the horizontal and vertical levels of each non-power-limited shutdown device in each dimension.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0114] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0115] In the description of the present application, the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0116] Finally, it should be noted that the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that any person skilled in the art who is familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for diagnosing equipment downtime, characterized in that: include: Obtain power generation data of new energy power stations; Determining whether the new energy power station is in an overall power restriction scenario according to the power generation data; If the new energy power station is not in an overall power-limiting scenario, power-limiting identification is performed on each device in the new energy power station through a preset partial power-limiting feature model to obtain a plurality of non-power-limiting shutdown devices; Perform real-time shutdown diagnosis on the plurality of non-power-limited shutdown devices to obtain device shutdown diagnosis results.

2. The equipment shutdown diagnosis method according to claim 1, characterized in that: The determining, according to the power generation data, whether the new energy power station is in an overall power restriction scenario includes: Analyzing the power generation data to obtain equivalent utilization hours and fluctuation rate of the equivalent utilization hours; Based on the equivalent utilization hours and the fluctuation rate of the equivalent utilization hours, determining whether a first condition is met, the first condition being that the equivalent utilization hours are less than a first threshold and the fluctuation rate of the equivalent utilization hours are greater than a second threshold; If the first condition is met, it is determined that the new energy power station is in an overall power restriction scenario; If the first condition is not met, it is determined that the new energy power station is not in an overall power restriction scenario.

3. The equipment shutdown diagnosis method according to claim 1, characterized in that: The determining, according to the power generation data, whether the new energy power station is in an overall power restriction scenario includes: Analyzing the power generation data to obtain equivalent utilization hours; Based on the equivalent utilization hours, judging whether a second condition is met, the second condition being that the equivalent utilization hours are less than a product of a third threshold and a first coefficient, the third threshold being an equivalent utilization hour corresponding to a preset model inverter; If the second condition is met, it is determined that the new energy power station is in an overall power restriction scenario; If the second condition is not met, it is determined that the new energy power station is not in an overall power restriction scenario.

4. The equipment shutdown diagnosis method according to claim 1, characterized in that: The power restriction of each device in the new energy power station is identified by using a preset partial power restriction feature model to obtain multiple non-power restriction shutdown devices, including: Analyzing the power generation data to obtain power generation parameters of each device, wherein the power generation parameters include voltage, current and power; Identify the power generation parameters of each device through a preset partial power restriction feature model to obtain a plurality of first-level power-restricted devices; Identify the upper and lower level devices of the plurality of first-level power-restricted devices through a preset fault tree chain tracing strategy to obtain a plurality of second-level power-restricted devices; The plurality of initially screened power-restricted devices and the plurality of upper and lower level power-restricted devices are eliminated from all the station devices to obtain a plurality of non-power-restricted shutdown devices.

5. The equipment shutdown diagnosis method according to claim 4, characterized in that: The method of identifying the upper and lower level devices of the plurality of first-level power-restricted devices by using a preset fault tree chain tracing strategy to obtain a plurality of second-level power-restricted devices includes: The organization tree relationship of each first-level power-restricted device is obtained through a preset fault tree chain tracing strategy, wherein the organization tree relationship is used to indicate the upper-level device and the lower-level device of the first-level power-restricted device; Determine whether the voltage and power change trends of each first-level power-restricted device are consistent with those of the corresponding upper-level device; If the voltage and power variation trends of any upper-level device are consistent with those of the first-level power-restricted device, then the upper-level device is determined to be a second-level power-restricted device; If the voltage and power change trends of each first-level power-restricted device are inconsistent with those of the corresponding upper-level device, it is determined whether each first-level power-restricted device has an operation data flow inheritance feature, and the operation data flow inheritance feature is used to indicate that the lower-level device corresponding to the first-level power-restricted device has a power restriction feature; If any first-level power-restricted device has the operation data flow inheritance feature, it is determined that multiple subordinate devices of any first-level power-restricted device are second-level power-restricted devices.

6. The equipment shutdown diagnosis method according to claim 1, characterized in that: Before obtaining the power generation data of the new energy power station, the method further includes: Acquire historical power generation data of the new energy power station, wherein the historical power generation data includes voltage data and power data; Constructing a power-limiting feature according to the voltage data and the power data, wherein the power-limiting feature is used to indicate that a sudden change occurs in the power data fluctuation rate and the fluctuation rate is less than a fluctuation threshold for a period of time; The initial model is trained based on the constructed power-limiting feature to obtain a partial power-limiting feature model.

7. The equipment shutdown diagnosis method according to any one of claims 1 to 6, characterized in that: After performing real-time shutdown diagnosis on the plurality of non-power-limited shutdown devices to obtain device shutdown diagnosis results, the method further includes: Determine each non-power-limited shutdown device and the shutdown reason based on the equipment shutdown diagnosis result; Determine the target operation and maintenance strategy in the preset strategy library based on each non-power-limited shutdown equipment and shutdown reasons; The non-power-limited shutdown devices are scored from preset multiple dimensions using a preset scoring algorithm to generate an evaluation report, which is used to reflect the horizontal and vertical levels of each non-power-limited shutdown device in each dimension.

8. A device for diagnosing equipment shutdown, characterized in that: include: An acquisition module is used to obtain power generation data of new energy power stations; A judgment module, used to judge whether the new energy power station is in an overall power restriction scenario according to the power generation data; An identification module, for identifying power restrictions on each device in the new energy power station by using a preset partial power restriction feature model if the new energy power station is not in an overall power restriction scenario, and obtaining a plurality of non-power restriction shutdown devices; The diagnosis module is used to perform real-time shutdown diagnosis on the plurality of non-power-limited shutdown devices to obtain device shutdown diagnosis results.

9. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor calls the instructions in the memory to enable the electronic device to execute the device shutdown diagnosis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a processor, the equipment shutdown diagnosis method according to any one of claims 1 to 7 is implemented.

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