Online monitoring and early warning method and system for new energy power equipment

Abstract

The invention relates to the technical field of electric power, and provides an online monitoring and early warning method and system for new energy power equipment, and the method comprises the steps: S1, obtaining a data collection module for detecting the operation parameters of the new energy power equipment; s2, fault early warning and monitoring; s3, abnormal early warning monitoring: when any parameter value in the operation parameters exceeds a preset abnormal threshold but is smaller than a preset safety threshold, acquiring information of risk new energy power equipment associated with target equipment; and S4, taking the operation parameter information of the target equipment and the risk new energy power equipment as the input of a prediction model, and predicting the operation state of the target equipment. According to the technical scheme, early warning can be performed in time when a safety accident occurs; and performing anomaly prediction by using a prediction model of a machine learning algorithm based on an artificial neural network, thereby improving the reliability and efficiency of equipment operation.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an online monitoring and early warning method and system for new energy electric power equipment. Background Art

[0002] With the continuous advancement of new energy systems, the power supply structure and grid structure are also constantly adjusted and deepened. Large-scale new energy bases use ultra-high voltage direct current to transmit power to load centers over long distances, becoming the main way for my country to balance resources across regions.

[0003] However, compared with the traditional power system, the new energy system is very different in terms of power supply structure and grid structure, and the operating characteristics of the system have also changed greatly. The grid connection of renewable energy such as solar energy and light energy has increased the uncertainty of the power generation side of the power grid; the expansion of load scale and the complexity of types have increased the uncertainty of the load side of the power grid. For example, electric vehicles are both grid loads and can play a regulatory role in charging and discharging the grid; therefore, the development of new energy has brought new security risks to the power grid system, and it is necessary to conduct timely early warning and judgment of risks in the operation of the power system. Summary of the invention

[0004] The present invention aims to provide an online monitoring and early warning method and system for new energy power equipment to solve the above-mentioned technical problems.

[0005] The present invention provides an online monitoring and early warning method for new energy power equipment, comprising the steps of: S1, obtaining a data acquisition module for detecting operating parameters of new energy power equipment, wherein the operating parameters include power information and environmental information; the power information includes multiple of the voltage, current, power and frequency of the new energy power equipment, and the environmental information includes multiple of the light, temperature, image, humidity and wind speed information of the new energy power equipment; S2, fault warning monitoring, when any parameter value in the operating parameters exceeds a preset safety threshold, a first warning signal is sent to the management party or background of the new energy power equipment corresponding to the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; the first warning signal includes the parameter value exceeding the safety threshold, the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold, and the information of the new energy power equipment corresponding to the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; S3, abnormal early warning monitoring, when any parameter value in the operating parameters exceeds the preset abnormal threshold but is less than the preset safety threshold, the new energy power equipment corresponding to the location information of the data acquisition module corresponding to the parameter value exceeding the abnormal threshold is taken as the target equipment, and the information of the risk new energy power equipment associated with the target equipment is obtained; S4, using the operating parameter information of the target device and the risky new energy power equipment as the input of the prediction model of the trained machine learning algorithm based on artificial neural network, predicting the operating status of the target device and determining whether the target device has any abnormality.

[0006] Furthermore, the training steps of the machine learning algorithm model based on artificial neural network include: Build an initial model for an artificial neural network-based machine learning algorithm; The initial model is trained and parameters are optimized using an actual training data set to obtain the prediction model.

[0007] Furthermore, The training data set includes positive samples and negative samples, the positive samples are the results when the true value of the operating value is abnormal, and the negative samples are the results when the true value of the operating value is not abnormal; each of the positive samples and the negative samples also includes the operating parameters of the target equipment and the operating parameters of the risky new energy power equipment.

[0008] Furthermore, each of the positive sample and the negative sample also includes operating parameters of the target equipment and operating parameters of the risky new energy power equipment within a T time period before and after the operating value, and the T time period is one of the time segment lengths of 30 seconds to 48 hours.

[0009] Furthermore, after step S4, the following steps are further included: S5, sending a second warning signal to the management party or background of the target device; the second warning signal includes the parameter value exceeding the abnormal threshold, the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold, and the information of the target device.

[0010] Furthermore, after step S5, the method further includes: S61, obtaining abnormal result confirmation information fed back by the management party or the backend; S62, taking the operating parameters of the target device, the operating parameters of the risky new energy power equipment and the abnormal result confirmation information as correction training samples of the prediction model, and performing correction training on the prediction model.

[0011] Furthermore, the step S4 includes converting and encoding the operating parameter information of the target device and the risky new energy power equipment to make it conform to the input of the training model given to the machine learning algorithm; when the operating parameter information of the target device and the risky new energy power equipment is missing or empty, the corresponding default value is filled.

[0012] The present invention also provides an online monitoring and early warning system for new energy power equipment, comprising: A data acquisition module is used to detect the operating parameters of the new energy power equipment, wherein the operating parameters include power information and environmental information; the power information includes multiple of the voltage, current, power and frequency of the new energy power equipment, and the environmental information includes multiple of the light, temperature, image, humidity and wind speed information of the new energy power equipment; A backend server comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the online monitoring and early warning method for new energy power equipment as described in any one of the above items are implemented.

[0013] Furthermore, the data acquisition module adopts a distributed structure, and the online monitoring and early warning system of the new energy power equipment also includes multiple distributed network nodes, each of which is matched with the data acquisition module, and is used to receive the data collected by the data acquisition module in the corresponding area and transmit it to the background server.

[0014] The present invention provides an online monitoring and early warning method and system for new energy power equipment, which includes the following steps: S1, obtaining a data acquisition module for detecting operating parameters of the new energy power equipment, wherein the operating parameters include power information and environmental information; S2, fault early warning monitoring, when any parameter value in the operating parameters exceeds a preset safety threshold, a first early warning signal is sent to the management party or background of the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; S3, abnormal early warning monitoring, when any parameter value in the operating parameters exceeds a preset abnormal threshold but is less than the preset safety threshold, the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the abnormal threshold is taken as the target device, and information of the risk new energy power equipment associated with the target device is obtained; S4, the operating parameter information of the target device and the risk new energy power equipment is used as the input of a prediction model of a trained machine learning algorithm based on an artificial neural network, the operating state of the target device is predicted, and it is determined whether the target device has an abnormality. Through the above technical scheme, by setting up a data acquisition module, the power information of new energy power equipment and the environmental information that has a greater impact on the equipment operation status can be fully collected, thereby ensuring the integrity of the monitoring information; first, a safety warning is carried out, and a timely warning can be given in case of sudden safety accidents; then, the prediction model based on the machine learning algorithm of the artificial neural network is used to make an abnormal prediction, which can help to achieve effective monitoring and prediction of the performance of new energy power equipment as a whole, thereby improving the reliability and efficiency of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of an online monitoring and early warning method for new energy power equipment in one embodiment of the present invention.

[0016] Figure 2 It is a schematic diagram of the hardware structure of a system running an online monitoring and early warning method for new energy power equipment in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0020] The present invention will now be described in further detail with reference to the accompanying drawings.

[0021] Please combine Figure 1 and Figure 2 , Figure 1 FIG. 1 is a flow chart of an online monitoring and early warning method for new energy power equipment in one embodiment of the present invention. Figure 2 This is a schematic diagram of the framework structure of the online monitoring and early warning system for new energy power equipment.

[0022] In this embodiment, the online monitoring and early warning method of new energy power equipment includes the following steps: S1, obtain the data acquisition module for detecting the operating parameters of the new energy power equipment, the operating parameters include power information and environmental information; the power information includes multiple voltage, current, power and frequency of the new energy power equipment, and the environmental information includes multiple scores of the light, temperature, image, humidity and wind speed information of the new energy power equipment.

[0023] The data acquisition module may include an ammeter, a voltmeter, a temperature sensor, a humidity sensor, a meteorological sensor (wind speed, humidity, etc.), a camera, a partial discharge sensor, etc. It may also include an image camera, an infrared thermal imaging camera for detecting infrared thermal imaging, etc. Those skilled in the art may set and select according to their needs.

[0024] S2, fault warning monitoring, when any parameter value among the operating parameters exceeds a preset safety threshold, a first warning signal is sent to the management party or background of the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; the first warning signal includes the parameter value exceeding the safety threshold, the position information of the data acquisition module corresponding to the parameter value exceeding the safety threshold, and the information of the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the safety threshold.

[0025] S3, abnormal warning monitoring, when any parameter value in the operating parameters exceeds the preset abnormal threshold but is less than the preset safety threshold, the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the abnormal threshold is taken as the target device, and the information of the risk new energy power equipment associated with the target device is obtained.

[0026] S4, using the operating parameter information of the target device and the risky new energy power equipment as the input of the prediction model of the trained machine learning algorithm based on artificial neural network, predicting the operating status of the target device and determining whether the target device has any abnormality.

[0027] Furthermore, the training steps of the machine learning algorithm model based on artificial neural network include: Build an initial model for an artificial neural network-based machine learning algorithm; The initial model is trained and parameters are optimized using an actual training data set to obtain the prediction model.

[0028] Furthermore, The training data set includes positive samples and negative samples, the positive samples are the results when the true value of the operating value is abnormal, and the negative samples are the results when the true value of the operating value is not abnormal; each of the positive samples and the negative samples also includes the operating parameters of the target equipment and the operating parameters of the risky new energy power equipment.

[0029] Furthermore, each of the positive sample and the negative sample also includes operating parameters of the target equipment and operating parameters of the risky new energy power equipment within a T time period before and after the operating value, and the T time period is one of the time segment lengths of 30 seconds to 48 hours.

[0030] Furthermore, after step S4, the following steps are further included: S5, sending a second warning signal to the management party or background of the target device; the second warning signal includes the parameter value exceeding the abnormal threshold, the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold, and the information of the target device.

[0031] Furthermore, after step S5, the method further includes: S61, obtaining abnormal result confirmation information fed back by the management party or the backend; S62, taking the operating parameters of the target device, the operating parameters of the risky new energy power equipment and the abnormal result confirmation information as correction training samples of the prediction model, and performing correction training on the prediction model.

[0032] Furthermore, the step S4 includes converting and encoding the operating parameter information of the target device and the risky new energy power equipment to make it conform to the input of the training model given to the machine learning algorithm; when the operating parameter information of the target device and the risky new energy power equipment is missing or empty, the corresponding default value is filled.

[0033] The present invention also provides an online monitoring and early warning system for new energy electric power equipment, comprising a background server and a data acquisition module.

[0034] The data acquisition module is used to detect the operating parameters of the new energy power equipment, and the operating parameters include power information and environmental information; the power information includes multiple voltage, current, power and frequency of the new energy power equipment, and the environmental information includes multiple scores of the light, temperature, image, humidity and wind speed information of the new energy power equipment.

[0035] The online monitoring and early warning system for the new energy power equipment is established and operated on the basis of a computer system (backend server), and specifically includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 63, the steps of the online monitoring and early warning method for the new energy power equipment as described in any one of the above items are implemented.

[0036] Furthermore, the data acquisition module adopts a distributed structure, and the online monitoring and early warning system of the new energy power equipment also includes multiple distributed network nodes, each of which is matched with the data acquisition module, and is used to receive the data collected by the data acquisition module in the corresponding area and transmit it to the background server.

[0037] This embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the online monitoring and early warning method for new energy power equipment as described in any one of the above items.

[0038] The present invention provides an online monitoring and early warning method and system for new energy power equipment, which includes the following steps: S1, obtaining a data acquisition module for detecting operating parameters of the new energy power equipment, wherein the operating parameters include power information and environmental information; S2, fault early warning monitoring, when any parameter value in the operating parameters exceeds a preset safety threshold, a first early warning signal is sent to the management party or background of the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; S3, abnormal early warning monitoring, when any parameter value in the operating parameters exceeds a preset abnormal threshold but is less than the preset safety threshold, the new energy power equipment corresponding to the position information of the data acquisition module corresponding to the parameter value exceeding the abnormal threshold is taken as the target device, and information of the risk new energy power equipment associated with the target device is obtained; S4, the operating parameter information of the target device and the risk new energy power equipment is used as the input of a prediction model of a trained machine learning algorithm based on an artificial neural network, the operating state of the target device is predicted, and it is determined whether the target device has an abnormality. Through the above technical scheme, by setting up a data acquisition module, the power information of new energy power equipment and the environmental information that has a greater impact on the equipment operation status can be fully collected, thereby ensuring the integrity of the monitoring information; first, a safety warning is carried out, and a timely warning can be given in case of sudden safety accidents; then, the prediction model based on the machine learning algorithm of the artificial neural network is used to make an abnormal prediction, which can help to achieve effective monitoring and prediction of the performance of new energy power equipment as a whole, thereby improving the reliability and efficiency of equipment operation.

[0039] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0040] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0041] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0042] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0043] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0044] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0045] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0046] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0047] In the description of this specification, the description with reference to the terms "an embodiment", "another embodiment", "other embodiments", or "first embodiment to Xth embodiment" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, method steps or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0048] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0049] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An online monitoring and early warning method for new energy power equipment, characterized in that: Includes steps: S1, obtaining a data acquisition module for detecting operating parameters of new energy power equipment, wherein the operating parameters include power information and environmental information; the power information includes multiple of the voltage, current, power and frequency of the new energy power equipment, and the environmental information includes multiple of the light, temperature, image, humidity and wind speed information of the new energy power equipment; S2, fault warning monitoring, when any parameter value in the operating parameters exceeds a preset safety threshold, a first warning signal is sent to the management party or background of the new energy power equipment corresponding to the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; the first warning signal includes the parameter value exceeding the safety threshold, the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold, and the information of the new energy power equipment corresponding to the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold; S3, abnormal early warning monitoring, when any parameter value in the operating parameters exceeds the preset abnormal threshold but is less than the preset safety threshold, the new energy power equipment corresponding to the location information of the data acquisition module corresponding to the parameter value exceeding the abnormal threshold is taken as the target equipment, and the information of the risk new energy power equipment associated with the target equipment is obtained; S4, using the operating parameter information of the target device and the risky new energy power equipment as the input of the prediction model of the trained machine learning algorithm based on artificial neural network, predicting the operating status of the target device and determining whether the target device has any abnormality.

2. The online monitoring and early warning method for new energy power equipment according to claim 1 is characterized in that: The training steps of the machine learning algorithm model based on artificial neural network include: Build an initial model for an artificial neural network-based machine learning algorithm; The initial model is trained and parameters are optimized using an actual training data set to obtain the prediction model.

3. The online monitoring and early warning method for new energy power equipment according to claim 2 is characterized in that: The training data set includes positive samples and negative samples, the positive samples are results in which the true value of the operating value is abnormal, and the negative samples are results in which the true value of the operating value is not abnormal; each of the positive samples and the negative samples also includes the operating parameters of the target equipment and the operating parameters of the risky new energy power equipment.

4. The online monitoring and early warning method for new energy power equipment according to claim 3 is characterized in that: Each of the positive sample and the negative sample also includes the operating parameters of the target equipment and the operating parameters of the risky new energy power equipment within the T time period before and after the operating value, and the T time period is one of the time segment lengths of 30 seconds to 48 hours.

5. The online monitoring and early warning method for new energy power equipment according to claim 2 is characterized in that: After step S4, the following steps are also included: S5, sending a second warning signal to the management party or background of the target device; the second warning signal includes the parameter value exceeding the abnormal threshold, the location information of the data acquisition module corresponding to the parameter value exceeding the safety threshold, and the information of the target device.

6. The online monitoring and early warning method for new energy power equipment according to claim 5 is characterized in that: After step S5, the method further includes: S61, obtaining abnormal result confirmation information fed back by the management party or the backend; S62, taking the operating parameters of the target device, the operating parameters of the risky new energy power equipment and the abnormal result confirmation information as correction training samples of the prediction model, and performing correction training on the prediction model.

7. The online monitoring and early warning method for new energy power equipment according to any one of claims 1 to 6, characterized in that: The step S4 includes converting and encoding the operating parameter information of the target device and the risky new energy power equipment to make it conform to the input of the training model given to the machine learning algorithm; when the operating parameter information of the target device and the risky new energy power equipment is missing or empty, the corresponding default value is filled.

8. An online monitoring and early warning system for new energy power equipment, characterized in that: include: A data acquisition module, used to detect the operating parameters of the new energy power equipment, the operating parameters including power information and environmental information; The power information includes multiple of the voltage, current, power and frequency of the new energy power equipment, and the environmental information includes multiple of the light, temperature, image, humidity and wind speed information of the new energy power equipment; A backend server comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the online monitoring and early warning method for new energy power equipment as described in any one of claims 1 to 7 are implemented.

9. The online monitoring and early warning system for new energy power equipment according to claim 8 is characterized in that: The data acquisition module adopts a distributed structure, and the online monitoring and early warning system of the new energy power equipment also includes multiple distributed network nodes, each of which is matched with the data acquisition module and is used to receive the data collected by the data acquisition module in the corresponding area and transmit it to the background server.

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