Intelligent monitoring method, system, electronic equipment and storage medium for new energy power station

Abstract

The present application relates to a new energy power station intelligent monitoring method, system, electronic device and storage medium, which are applied to the field of power station monitoring technology, and the method includes: obtaining monitoring data, equipment connection information and power supply tasks of the power station; performing abnormal analysis on the monitoring data based on the association between the monitoring data, obtaining abnormal monitoring data, and determining the power equipment corresponding to the abnormal monitoring data as abnormal equipment; constructing a simulation operation model of the power station based on the equipment connection information; if there is a connection relationship between multiple abnormal equipment, determining abnormal information based on the simulation operation model and the abnormal monitoring data, and the abnormal information includes the abnormal source and the abnormal cause; evaluating the life loss of the power equipment based on the operating time and operating environment of the power equipment to obtain an evaluation result; formulating a power supply adjustment strategy based on the abnormal information, the evaluation result and the power supply task. The present application has the effect of improving the monitoring efficiency of the power station.

Description

Technical Field

[0001] The present application relates to the technical field of power station monitoring, and in particular to a method, system, electronic equipment and storage medium for intelligent monitoring of a new energy power station. Background Art

[0002] With the rapid development and widespread application of new energy technologies, the construction scale of new energy power stations has continued to expand, and the number of power equipment has also increased. However, this has also brought about the complexity and difficulty of power station operation and maintenance. Traditional power station monitoring methods often rely on manual inspections and regular equipment inspections, which makes it difficult to detect and handle equipment anomalies in a timely manner. The efficiency of power station monitoring is low, which affects the stable operation of the power station and the reliability of power supply. Summary of the invention

[0003] In order to improve the efficiency of power station monitoring, the present application provides a new energy power station intelligent monitoring method, system, electronic equipment and storage medium.

[0004] In the first aspect, the present application provides a new energy power station intelligent monitoring method, which adopts the following technical solution:

[0005] A new energy power station intelligent monitoring method, comprising:

[0006] Acquire monitoring data, equipment connection information, and power supply tasks of the power station, wherein the monitoring data includes status data, string data, video data, and audio data;

[0007] Performing abnormal analysis on the monitoring data based on the association between the monitoring data to obtain abnormal monitoring data, and determining the power equipment corresponding to the abnormal monitoring data as an abnormal equipment;

[0008] Building a simulation operation model of the power station based on the device connection information;

[0009] If a plurality of the abnormal devices are connected to each other, abnormal information is determined based on the simulation operation model and the abnormal monitoring data, wherein the abnormal information includes an abnormal source and an abnormal cause;

[0010] Performing a life loss assessment on the electric power equipment based on the operating time and operating environment of the electric power equipment to obtain an assessment result;

[0011] A power supply adjustment strategy is formulated based on the abnormal information, the evaluation result and the power supply task.

[0012] By adopting the above technical scheme, the monitoring data is analyzed for abnormalities through the association between the monitoring data to obtain abnormal monitoring data, thereby improving the reliability of the abnormal monitoring data. When determining the abnormal information, the connection relationship between the abnormal equipment is taken into consideration, and simulation is performed through the simulation operation model to improve the reliability of the abnormal information. By evaluating the life of the power equipment and jointly formulating the power supply adjustment strategy based on the abnormal information, the evaluation results and the power supply task, the reliability of the power supply adjustment strategy is improved, thereby improving the efficiency of power station monitoring according to the power supply adjustment strategy.

[0013] Optionally, performing abnormal analysis on the monitoring data based on the association between the monitoring data to obtain abnormal monitoring data includes:

[0014] Comparing the state data with standard data to obtain state abnormality data;

[0015] The discrete rate of the string data is calculated based on a discrete rate formula, wherein the discrete rate formula includes: discrete rate = standard deviation of string data / average value of string data×100%;

[0016] Determining the string data whose discrete rate exceeds the standard discrete rate as abnormal string data;

[0017] Performing image recognition on the video data to obtain visual abnormality data;

[0018] Performing speech recognition on the audio data to obtain abnormal sound data;

[0019] Extracting feature data from the state data, the group string data, the video data, and the audio data;

[0020] The feature data are subjected to feature fusion based on preset rules to obtain a comprehensive feature vector;

[0021] Inputting the comprehensive feature vector into a preset anomaly detection model to perform anomaly judgment and obtain an anomaly vector;

[0022] Abnormal monitoring data is determined based on the state abnormality data, the string abnormality data, the visual abnormality data, the sound abnormality data, and the abnormality vector.

[0023] By adopting the above technical scheme, anomaly analysis is performed on different monitoring data through various analysis methods to obtain a variety of abnormal data, thereby improving the comprehensiveness of the abnormal data. By extracting feature data from various monitoring data and performing anomaly detection on the fused feature data through a preset anomaly detection model, the anomaly detection is performed while fully considering the correlation between the monitoring data, thereby further improving the reliability of the abnormal data.

[0024] Optionally, the determining the abnormal information based on the simulation operation model and the abnormal monitoring data includes:

[0025] Acquire historical abnormality information, each of which includes a historical abnormality cause, a historical abnormality source, and at least one historical abnormal device;

[0026] Determining the historical abnormal information of multiple historical abnormal devices as linkage abnormal information;

[0027] Analyze the linkage abnormality information to obtain the abnormality type and device type corresponding to each linkage abnormality;

[0028] Determining a candidate anomaly type based on the anomaly monitoring data;

[0029] Determine a candidate linkage anomaly based on the candidate anomaly type and a device type corresponding to the abnormal device;

[0030] The candidate linkage anomaly is simulated by the simulation operation model to obtain simulated monitoring data;

[0031] Determine a current linkage anomaly based on the simulated monitoring data and the monitoring data;

[0032] Determine the source and cause of the abnormality based on the current linkage abnormality;

[0033] Abnormal information is determined based on the abnormal source, the abnormal cause and the abnormal monitoring data.

[0034] By adopting the above technical solution, when determining abnormal information, the influence of the connection relationship between abnormal devices on the abnormality is taken into account, so that the source and cause of the abnormality can be found more quickly, that is, the efficiency of abnormal location is improved, and then the efficiency of abnormal monitoring is improved.

[0035] Optionally, the operating environment includes the hottest point temperature of the winding, and the evaluation result includes the remaining life of the equipment. The life loss evaluation of the electric equipment based on the operating time and operating environment of the electric equipment to obtain the evaluation result includes:

[0036] The remaining life of the device is calculated based on the life loss formula, the operating time and the hottest point temperature of the winding, and the life loss formula includes: remaining life of the device = design life - operating time × V0, , where θc is the hottest point temperature of the winding.

[0037] Optionally, formulating a power supply adjustment strategy based on the abnormal information, the evaluation result, and the power supply task includes:

[0038] Determine the equipment to be maintained based on the abnormal information and the evaluation result;

[0039] Determining the power supply task of the equipment to be maintained as a task to be adjusted;

[0040] Obtaining the priority, occupied load and power quality requirements of the task to be adjusted;

[0041] Sorting the tasks to be adjusted according to the priorities and the occupied loads to obtain a first sorting result;

[0042] Calculate a first remaining capacity and a second remaining capacity of each power device, wherein the first remaining capacity is the maximum load size that can be connected to the power device, and the second remaining capacity is the maximum load size that can be connected under a preset power quality;

[0043] A power supply adjustment strategy is determined based on the first remaining capacity, the second remaining capacity, the first sorting result, and the power quality requirement.

[0044] By adopting the above technical scheme, when allocating power equipment to the task to be adjusted, the priority, occupied load and power quality requirements of the task to be adjusted are fully taken into consideration, thereby improving the reliability of equipment allocation, and also taking into account the first remaining capacity and the second remaining capacity of the power equipment, thereby improving the power quality of the power equipment allocated to the task to be adjusted.

[0045] Optionally, the calculating the first remaining capacity and the second remaining capacity of each power device includes:

[0046] Obtain the rated capacity and current load of each power device;

[0047] calculating the first remaining capacity based on the rated capacity and the current load;

[0048] Obtain historical operating data of power equipment;

[0049] Analyze the historical operation data to obtain the load capacity corresponding to the preset power quality;

[0050] A second remaining capacity is calculated based on the load capacity and the current load.

[0051] Optionally, the determining a power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result, and the power quality requirement includes:

[0052] Allocate adjustment equipment to the tasks to be adjusted in sequence according to the first sorting result;

[0053] The step of allocating an adjustment device to the task to be adjusted includes:

[0054] Calculating a first difference between each of the second remaining capacities and the occupied load;

[0055] If there is a first difference value greater than 0, the power equipment corresponding to the smallest first difference value greater than 0 that meets the power quality requirement is determined as the adjustment equipment for the task to be adjusted;

[0056] If there is no first difference greater than 0, then calculating the second difference between the first remaining capacity and the occupied load; determining the power equipment that meets the power quality requirements, has the largest second difference and the highest equipment efficiency as the adjustment equipment for the task to be adjusted;

[0057] The first remaining capacity and the second remaining capacity of the adjustment device are updated based on the occupancy load.

[0058] By adopting the above technical solution, when allocating adjustment equipment for the task to be adjusted, the second remaining capacity is preferentially allocated, thereby improving the reliability of the power quality of the adjustment equipment.

[0059] In the second aspect, the present application provides a new energy power station intelligent monitoring system, which adopts the following technical solutions:

[0060] An intelligent monitoring system for a new energy power station, comprising:

[0061] A power station data acquisition module, used to acquire monitoring data, equipment connection information and power supply tasks of the power station, wherein the monitoring data includes status data, string data, video data and audio data;

[0062] an abnormal data determination module, configured to perform abnormal analysis on the monitoring data based on the association between the monitoring data, obtain abnormal monitoring data, and determine the power equipment corresponding to the abnormal monitoring data as an abnormal equipment;

[0063] A power model building module, used to build a simulation operation model of the power station based on the equipment distribution information and the equipment connection information;

[0064] An abnormal information determination module, configured to determine abnormal information based on the simulation operation model and the abnormal monitoring data if a plurality of the abnormal devices are connected to each other, wherein the abnormal information includes an abnormal source and an abnormal cause;

[0065] A life loss assessment module, used to assess the life loss of the power equipment based on the operating time and operating environment of the power equipment to obtain an assessment result;

[0066] The adjustment strategy determination module is used to formulate a power supply adjustment strategy based on the abnormal information, the evaluation result and the power supply task.

[0067] By adopting the above technical scheme, the monitoring data is analyzed for abnormalities through the association between the monitoring data to obtain abnormal monitoring data, thereby improving the reliability of the abnormal monitoring data. When determining the abnormal information, the connection relationship between the abnormal equipment is taken into consideration, and simulation is performed through the simulation operation model to improve the reliability of the abnormal information. By evaluating the life of the power equipment and jointly formulating the power supply adjustment strategy based on the abnormal information, the evaluation results and the power supply task, the reliability of the power supply adjustment strategy is improved, thereby improving the efficiency of power station monitoring according to the power supply adjustment strategy.

[0068] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0069] An electronic device comprises a processor, wherein the processor is coupled to a memory;

[0070] The memory stores a computer program that can be loaded by the processor and executes the new energy power station intelligent monitoring method described in any one of the first aspects.

[0071] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0072] A computer-readable storage medium stores a computer program that can be loaded by a processor and execute the new energy power station intelligent monitoring method described in any one of the first aspects.

[0073] In summary, the present application includes at least one of the following beneficial technical effects:

[0074] 1. By analyzing the abnormality of the monitoring data through the association between the monitoring data, the abnormal monitoring data is obtained, which improves the reliability of the abnormal monitoring data. When determining the abnormal information, the connection relationship between the abnormal devices is taken into account, and the simulation is performed through the simulation operation model to improve the reliability of the abnormal information. By evaluating the life of the power equipment and jointly formulating the power supply adjustment strategy based on the abnormal information, the evaluation results and the power supply task, the reliability of the power supply adjustment strategy is improved, thereby improving the efficiency of power station monitoring according to the power supply adjustment strategy;

[0075] 2. Through a variety of analysis methods, different monitoring data are analyzed for anomalies, and a variety of abnormal data are obtained, which improves the comprehensiveness of the abnormal data. By extracting feature data from various monitoring data and performing anomaly detection on the fused feature data through a preset anomaly detection model, the association between the monitoring data is fully considered for anomaly detection, which further improves the reliability of the abnormal data. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a flow chart of a new energy power station intelligent monitoring method provided in an embodiment of the present application.

[0077] Figure 2 It is a structural block diagram of an intelligent monitoring system for a new energy power station provided in an embodiment of the present application.

[0078] Figure 3 It is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0079] The present application is further described in detail below in conjunction with the accompanying drawings.

[0080] The embodiment of the present application provides a new energy power station intelligent monitoring method, which can be executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, etc., but is not limited thereto.

[0081] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0082] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0083] like Figure 1 As shown, a new energy power station intelligent monitoring method, the main process of the method is described as follows (steps S101 to S106):

[0084] Step S101: Acquire monitoring data, equipment connection information, and power supply tasks of the power station.

[0085] Among them, the monitoring data includes status data, string data, video data and audio data. The status data includes but is not limited to current, voltage, power and temperature. The string data is the power generation rate data of the photovoltaic string. The video data is the video of the power equipment. The audio data is the audio of the power equipment during operation. The equipment connection information is the connection relationship between various power equipment. The power supply tasks are various power generation tasks or power transmission tasks of the power plant.

[0086] There are various monitoring devices installed on the power equipment, which can obtain status data from various monitoring devices, and obtain equipment connection information and power supply tasks from the staff.

[0087] Step S102: performing abnormal analysis on the monitoring data based on the association between the monitoring data to obtain abnormal monitoring data, and determining the power equipment corresponding to the abnormal monitoring data as abnormal equipment.

[0088] Specifically, based on the association between the monitoring data, the monitoring data is analyzed for abnormalities to obtain abnormal monitoring data, including: comparing the state data with the standard data to obtain the state abnormal data; calculating the discrete rate of the string data based on the discrete rate formula, the discrete rate formula includes: discrete rate = standard deviation of the string data / average value of the string data × 100%; determining the string data whose discrete rate exceeds the standard discrete rate as string abnormal data; performing image recognition on the video data to obtain visual abnormal data; performing speech recognition on the audio data to obtain sound abnormal data; extracting feature data from the state data, string data, video data and audio data; performing feature fusion on the feature data based on preset rules to obtain a comprehensive feature vector; inputting the comprehensive feature vector into a preset abnormality detection model for abnormality judgment to obtain an abnormal vector; determining the abnormal monitoring data based on the state abnormal data, string abnormal data, visual abnormal data, sound abnormal data and abnormal vector.

[0089] In this embodiment, standard data corresponding to each state data is obtained from the database, and the state data that does not meet the standard data is determined as state abnormal data; the standard deviation and the average value of the string data are calculated respectively (the calculation method is not repeated here), and the standard deviation of the string data and the average value of the string data are calculated by the discrete rate formula to obtain the discrete rate of the string data. The lower the discrete rate of the string data, the higher the consistency and the more stable the power generation situation; the higher the discrete rate of the string data, the worse the consistency, and at this time there is usually a faulty unit, the standard discrete rate is obtained from the database, and the string data whose discrete rate exceeds the standard discrete rate is determined as string abnormal data; the video data is image recognized by the preset image recognition model to obtain visual abnormal data; the audio data is voice recognized by the preset audio recognition model to obtain sound abnormal data.

[0090] Feature extraction rules and preset rules are obtained from the database. The feature extraction rules are rules for extracting feature data from monitoring data. Feature data in state data, string data, video data and audio data are extracted according to the feature extraction rules. The preset rules are rules for fusing feature data into feature vectors, such as weighted average. The extracted feature data are feature-fused according to the preset rules to obtain a comprehensive feature vector. The preset anomaly detection model is a pre-trained model that can perform anomaly detection on the feature vector, that is, anomaly detection is performed based on the correlation between the monitoring data. The comprehensive feature vector is input into the preset anomaly detection model for anomaly judgment to obtain anomaly vectors. The state abnormal data, string abnormal data, visual abnormal data, sound abnormal data and abnormal vectors are collectively determined as abnormal monitoring data.

[0091] Step S103: construct a simulation operation model of the power station based on the equipment connection information.

[0092] The device connection information is input into a power simulation tool to construct a simulation operation model of the power station, wherein the power simulation tool includes but is not limited to BPA, DSP, PSASP, PSSE, EMTP / ATP, PSCAD and DIGSILENT, which are not specifically limited here.

[0093] Step S104: If there is a connection relationship between the multiple abnormal devices, determine the abnormal information based on the simulation operation model and the abnormal monitoring data.

[0094] The abnormal information includes the abnormal source and the abnormal cause. The abnormal source is the power equipment that causes the monitoring data of other power equipment to be abnormal.

[0095] Specifically, determining abnormal information based on a simulation operation model and abnormal monitoring data includes: obtaining historical abnormal information, each historical abnormal information including a historical abnormal cause, a historical abnormal source and at least one historical abnormal device; determining historical abnormal information with multiple historical abnormal devices as linkage abnormal information; analyzing the linkage abnormal information to obtain an abnormal type and a device type corresponding to each linkage abnormality; determining a candidate abnormal type based on the abnormal monitoring data; determining a candidate linkage abnormality based on the candidate abnormal type and the device type corresponding to the abnormal device; simulating the candidate linkage abnormality through a simulation operation model to obtain simulated monitoring data; determining a current linkage abnormality based on the simulated monitoring data and the monitoring data; determining an abnormal source and an abnormal cause based on the current linkage abnormality; determining abnormal information based on the abnormal source, the abnormal cause and the abnormal monitoring data.

[0096] In this embodiment, if multiple abnormal devices have a connection relationship, historical abnormal information is obtained from the database, and the historical abnormal information of multiple historical abnormal devices is determined as linkage abnormal information. The abnormal type and device type corresponding to the linkage abnormality are searched from the linkage abnormal information. Each linkage abnormality information corresponds to a linkage abnormality. Each linkage abnormality may correspond to multiple linkage abnormalities. One linkage abnormality includes multiple power devices (i.e., multiple device types), multiple abnormal data (i.e., multiple abnormal types), an abnormal source (i.e., the power device at the source) and at least one abnormal cause. Multiple abnormal devices with a connection relationship and the abnormal monitoring data corresponding to the abnormal devices are determined as an abnormal combination, and the abnormal group is The abnormal type of the abnormal monitoring data in the combination is determined as the candidate abnormal type, the device type of the abnormal device in the abnormal combination is determined as the candidate device type, the linkage abnormalities that are the same as all the candidate abnormal types corresponding to the abnormal combination and all the candidate device types corresponding to the abnormal combination are determined as candidate linkage abnormalities, the historical abnormal information corresponding to the candidate linkage abnormalities is simulated through the simulation operation model to obtain simulated monitoring data, the candidate linkage abnormality corresponding to the simulated monitoring data with the smallest difference with the monitoring data is determined as the current linkage abnormality, the historical abnormal cause and the historical abnormal source corresponding to the current linkage abnormality are determined as the abnormal cause and the abnormal source, and the abnormal source, the abnormal cause and the abnormal monitoring data are jointly determined as abnormal information.

[0097] If there is no connection relationship between the abnormal devices, the abnormal information includes the abnormal device and abnormal monitoring data.

[0098] Step S105: Evaluate the life loss of the power equipment based on the operating time and operating environment of the power equipment to obtain an evaluation result.

[0099] Specifically, the operating environment includes the hottest point temperature of the winding, and the evaluation result includes the remaining life of the equipment. The life loss of the power equipment is evaluated based on the operating time and operating environment of the power equipment, and the evaluation result is obtained, including: the remaining life of the equipment is calculated based on the life loss formula, the operating time and the hottest point temperature of the winding. The life loss formula includes: remaining life of the equipment = design life - operating time × V0, V0 is the equipment life loss rate, and the calculation method of V0 is: , where θc is the hottest point temperature of the winding.

[0100] The operating environment will affect the life of the power equipment. In this embodiment, the remaining life of the equipment can be calculated by the life loss formula. When the remaining life of the equipment is short, the staff can be reminded in time to perform maintenance or replacement, thereby reducing the occurrence of failures.

[0101] Step S106: formulate a power supply adjustment strategy based on the abnormal information, the evaluation results and the power supply task.

[0102] Specifically, a power supply adjustment strategy is formulated based on abnormal information, evaluation results and power supply tasks, including: determining the equipment to be maintained based on the abnormal information and evaluation results; determining the power supply task of the equipment to be maintained as the task to be adjusted; obtaining the priority, occupied load and power quality requirements of the task to be adjusted; sorting the tasks to be adjusted according to the priority and occupied load to obtain a first sorting result; calculating the first remaining capacity and the second remaining capacity of each power equipment, the first remaining capacity being the size of the maximum load that can be connected to the power equipment, and the second remaining capacity being the size of the maximum load that can be connected under preset power quality; determining the power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result and the power quality requirements.

[0103] In this embodiment, the power equipment corresponding to the abnormal information and the power equipment whose remaining life is less than the preset time in the evaluation results are determined as equipment to be maintained, and the power supply task of the equipment to be maintained is determined as the task to be adjusted, the priority, occupied load and power quality requirements of the task to be adjusted are obtained from the database, and the tasks to be adjusted are sorted from high to low according to the priority. If two or more tasks to be adjusted have the same priority, they are sorted from high to low according to the occupied load to obtain a first sorting result; the first remaining capacity that can be connected to each power equipment at most and the second remaining capacity that can be connected at most under the preset power quality are calculated, and the power supply adjustment strategy is determined according to the first remaining capacity, the second remaining capacity, the first sorting result and the power quality requirements.

[0104] Specifically, calculating the first remaining capacity and the second remaining capacity of each power equipment includes: obtaining the rated capacity and current load of each power equipment; calculating the first remaining capacity based on the rated capacity and the current load; obtaining the historical operation data of the power equipment; analyzing the historical operation data to obtain the load capacity corresponding to the preset power quality; and calculating the second remaining capacity based on the load capacity and the current load.

[0105] In this embodiment, the rated capacity and current load of each power equipment are obtained from the database, and the first remaining capacity = rated capacity - current load; the historical operation data of the power equipment is obtained from the database, and the historical operation data is analyzed by a data analysis tool to obtain the power quality of each power equipment under different loads, and store them in the database, so as to obtain the maximum load capacity that the power equipment can bear when the power quality is not lower than the preset power quality, and the second remaining capacity = load capacity - current load, wherein the data analysis tool can be EXCEL, Python, or SQL, which is not specifically limited here.

[0106] Specifically, a power supply adjustment strategy is determined based on the first remaining capacity, the second remaining capacity, the first sorting result and the power quality requirements, including: allocating adjustment equipment to the tasks to be adjusted in sequence according to the first sorting result; allocating adjustment equipment to the tasks to be adjusted, including: calculating the first difference between each second remaining capacity and the occupied load; if there is a first difference greater than 0, then the power equipment corresponding to the smallest first difference greater than 0 that meets the power quality requirements is determined as the adjustment equipment for the task to be adjusted; if there is no first difference greater than 0, then calculating the second difference between the first remaining capacity and the occupied load; determining the power equipment that meets the power quality requirements, has the largest second difference and has the highest equipment efficiency as the adjustment equipment for the task to be adjusted; and updating the first remaining capacity and the second remaining capacity of the adjustment equipment based on the occupied load.

[0107] In this embodiment, adjustment equipment is assigned to each task to be adjusted in turn according to the first sorting result; when an adjustment equipment is assigned to a task to be adjusted, the first difference between each second remaining capacity and the occupied load of the task to be adjusted is calculated, and the first difference = second remaining capacity - occupied load; if there is a first difference greater than 0, the power equipment corresponding to the first difference greater than 0 is determined as a candidate equipment, and the access load after the candidate equipment is connected to the task to be adjusted is calculated, and the access load = the current load of the candidate equipment + the occupied load of the task to be adjusted. The database stores the power quality of each power equipment under different load conditions, and the power quality of the candidate equipment after the candidate equipment is connected to the task to be adjusted is obtained from the database according to the access load, and the candidate equipment whose power quality after connecting to the task to be adjusted meets the power quality requirements and whose first difference is the smallest is determined as the adjustment equipment of the task to be adjusted.

[0108] If there is no first difference greater than 0, then calculate the second difference between each first remaining capacity and the occupied load, the second difference = first remaining capacity - occupied load, and determine the power equipment whose power quality after connecting to the task to be adjusted meets the power quality requirements, the second difference is the largest and the equipment efficiency is the highest as the adjustment equipment for the task to be adjusted, and the power supply adjustment strategy is to adjust the power supply according to the correspondence between the task to be adjusted and the adjustment equipment; update the first remaining capacity and the second remaining capacity of the adjustment equipment, the updated first remaining capacity = the original first remaining capacity - the occupied load of the task to be adjusted, and the updated second remaining capacity = the original second remaining capacity - the occupied load of the task to be adjusted.

[0109] Figure 2 A structural block diagram of a new energy power station intelligent monitoring system 200 provided in an embodiment of the present application.

[0110] like Figure 2 As shown, the new energy power station intelligent monitoring system 200 mainly includes:

[0111] The power station data acquisition module 201 is used to acquire the monitoring data, equipment connection information and power supply tasks of the power station. The monitoring data includes status data, string data, video data and audio data;

[0112] The abnormal data determination module 202 is used to perform abnormal analysis on the monitoring data based on the association between the monitoring data, obtain abnormal monitoring data, and determine the power equipment corresponding to the abnormal monitoring data as abnormal equipment;

[0113] The power model building module 203 is used to build a simulation operation model of the power station based on the equipment distribution information and the equipment connection information;

[0114] The abnormal information determination module 204 is used to determine the abnormal information based on the simulation operation model and the abnormal monitoring data if there is a connection relationship between the multiple abnormal devices, and the abnormal information includes the abnormal source and the abnormal cause;

[0115] The life loss assessment module 205 is used to assess the life loss of the power equipment based on the operating time and operating environment of the power equipment to obtain an assessment result;

[0116] The adjustment strategy determination module 206 is used to formulate a power supply adjustment strategy based on the abnormal information, the evaluation results and the power supply task.

[0117] As an optional implementation of this embodiment, the abnormal data determination module 202 is also specifically used to perform abnormal analysis on the monitoring data based on the association between the monitoring data to obtain abnormal monitoring data, including: comparing the state data with the standard data to obtain state abnormal data; calculating the discrete rate of the string data based on the discrete rate formula, the discrete rate formula includes: discrete rate = standard deviation of the string data / average value of the string data × 100%; determining the string data whose discrete rate exceeds the standard discrete rate as string abnormal data; performing image recognition on the video data to obtain visual abnormal data; performing voice recognition on the audio data to obtain sound abnormal data; extracting feature data from the state data, string data, video data and audio data; performing feature fusion on the feature data based on preset rules to obtain a comprehensive feature vector; inputting the comprehensive feature vector into a preset abnormal detection model for abnormal judgment to obtain an abnormal vector; determining the abnormal monitoring data based on the state abnormal data, string abnormal data, visual abnormal data, sound abnormal data and abnormal vector.

[0118] As an optional implementation of this embodiment, the abnormal information determination module 204 is also specifically used to determine abnormal information based on the simulation operation model and abnormal monitoring data, including: obtaining historical abnormal information, each historical abnormal information including a historical abnormal cause, a historical abnormal source and at least one historical abnormal device; determining historical abnormal information with multiple historical abnormal devices as linkage abnormal information; analyzing the linkage abnormal information to obtain the abnormal type and device type corresponding to each linkage abnormality; determining candidate abnormality types based on the abnormal monitoring data; determining candidate linkage abnormalities based on the candidate abnormality types and the device type corresponding to the abnormal device; simulating the candidate linkage abnormalities through the simulation operation model to obtain simulated monitoring data; determining the current linkage abnormality based on the simulated monitoring data and the monitoring data; determining the abnormal source and the abnormal cause based on the current linkage abnormality; determining abnormal information based on the abnormal source, the abnormal cause and the abnormal monitoring data.

[0119] As an optional implementation of this embodiment, the operating environment includes the hottest point temperature of the winding, and the evaluation result includes the remaining life of the equipment. The life loss evaluation module 205 is also specifically used to evaluate the life loss of the power equipment based on the operating time and the operating environment of the power equipment, and obtain the evaluation result, including: calculating the remaining life of the equipment based on the life loss formula, the operating time and the hottest point temperature of the winding, and the life loss formula includes: remaining life of the equipment = design life - operating time × V0, , where θc is the hottest point temperature of the winding.

[0120] As an optional implementation of this embodiment, the adjustment strategy determination module 206 is also specifically used to formulate a power supply adjustment strategy based on abnormal information, evaluation results and power supply tasks, including: determining the equipment to be maintained based on the abnormal information and evaluation results; determining the power supply task of the equipment to be maintained as the task to be adjusted; obtaining the priority, occupied load and power quality requirements of the task to be adjusted; sorting the tasks to be adjusted according to the priority and occupied load to obtain a first sorting result; calculating the first remaining capacity and the second remaining capacity of each power equipment, the first remaining capacity being the size of the maximum load that can be connected to the power equipment, and the second remaining capacity being the size of the maximum load that can be connected under preset power quality; determining the power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result and the power quality requirements.

[0121] As an optional implementation of this embodiment, the adjustment strategy determination module 206 is also specifically used to calculate the first remaining capacity and the second remaining capacity of each power equipment, including: obtaining the rated capacity and current load of each power equipment; calculating the first remaining capacity based on the rated capacity and the current load; obtaining the historical operation data of the power equipment; analyzing the historical operation data to obtain the load capacity corresponding to the preset power quality; and calculating the second remaining capacity based on the load capacity and the current load.

[0122] As an optional implementation mode of this embodiment, the adjustment strategy determination module 206 is also specifically used to determine the power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result and the power quality requirements, including: allocating adjustment equipment to the tasks to be adjusted in sequence according to the first sorting result; allocating adjustment equipment to the tasks to be adjusted, including: calculating the first difference between each second remaining capacity and the occupied load; if there is a first difference greater than 0, then the power equipment corresponding to the smallest first difference greater than 0 that meets the power quality requirements is determined as the adjustment equipment for the task to be adjusted; if there is no first difference greater than 0, then calculating the second difference between the first remaining capacity and the occupied load; determining the power equipment that meets the power quality requirements, has the largest second difference and has the highest equipment efficiency as the adjustment equipment for the task to be adjusted; and updating the first remaining capacity and the second remaining capacity of the adjustment equipment based on the occupied load.

[0123] In one example, the module in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0124] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0126] Figure 3 A structural block diagram of an electronic device 300 provided in an embodiment of the present application.

[0127] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include an information input / information output (I / O) interface 303 , one or more of a communication component 304 , and a communication bus 305 .

[0128] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps of the above-mentioned intelligent monitoring method for a new energy power station; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method used to operate on the electronic device 300, and data related to the application. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), programmable read-only memory (Programmable Read-Only Memory, PROM), read-only memory (Read-Only Memory, ROM), magnetic memory, flash memory, magnetic disk or optical disk. One or more.

[0129] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0130] The electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the intelligent monitoring method for new energy power stations given in the above embodiment.

[0131] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0132] The electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and may also be servers, etc.

[0133] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned new energy power station intelligent monitoring method are implemented.

[0134] The computer-readable storage medium may include: 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, and other media that can store program codes.

[0135] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0136] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A new energy power station intelligent monitoring method, characterized in that: include: Acquire monitoring data, equipment connection information, and power supply tasks of the power station, wherein the monitoring data includes status data, string data, video data, and audio data; Performing abnormal analysis on the monitoring data based on the association between the monitoring data to obtain abnormal monitoring data, and determining the power equipment corresponding to the abnormal monitoring data as an abnormal equipment; Building a simulation operation model of the power station based on the device connection information; If a plurality of the abnormal devices are connected to each other, abnormal information is determined based on the simulation operation model and the abnormal monitoring data, wherein the abnormal information includes an abnormal source and an abnormal cause; Performing a life loss assessment on the electric power equipment based on the operating time and operating environment of the electric power equipment to obtain an assessment result; Formulate a power supply adjustment strategy based on the abnormal information, the evaluation result and the power supply task; The formulating a power supply adjustment strategy based on the abnormal information, the evaluation result and the power supply task includes: Determine the equipment to be maintained based on the abnormal information and the evaluation result; Determining the power supply task of the equipment to be maintained as a task to be adjusted; Obtaining the priority, occupied load and power quality requirements of the task to be adjusted; Sorting the tasks to be adjusted according to the priorities and the occupied loads to obtain a first sorting result; Calculate a first remaining capacity and a second remaining capacity of each power device, wherein the first remaining capacity is the maximum load size that can be connected to the power device, and the second remaining capacity is the maximum load size that can be connected under a preset power quality; Determine a power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result, and the power quality requirement; The calculating the first remaining capacity and the second remaining capacity of each power device includes: Obtain the rated capacity and current load of each power device; calculating the first remaining capacity based on the rated capacity and the current load; Obtain historical operating data of power equipment; Analyze the historical operation data to obtain the load capacity corresponding to the preset power quality; calculating a second remaining capacity based on the load capacity and the current load; The determining of the power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result, and the power quality requirement includes: Allocate adjustment equipment to the tasks to be adjusted in sequence according to the first sorting result; The step of allocating an adjustment device to the task to be adjusted includes: calculating a first difference between each of the second remaining capacities and the occupied load; If there is a first difference greater than 0, the power equipment corresponding to the smallest first difference greater than 0 that meets the power quality requirement is determined as the adjustment equipment for the task to be adjusted; If there is no first difference greater than 0, then calculating the second difference between the first remaining capacity and the occupied load; determining the power equipment that meets the power quality requirements, has the largest second difference and the highest equipment efficiency as the adjustment equipment for the task to be adjusted; The first remaining capacity and the second remaining capacity of the adjustment device are updated based on the occupancy load.

2. The method according to claim 1, characterized in that The performing abnormal analysis on the monitoring data based on the association between the monitoring data to obtain abnormal monitoring data includes: Comparing the state data with standard data to obtain state abnormality data; The discrete rate of the string data is calculated based on a discrete rate formula, wherein the discrete rate formula includes: discrete rate = standard deviation of string data / average value of string data×100%; Determining the string data whose discrete rate exceeds the standard discrete rate as abnormal string data; Performing image recognition on the video data to obtain visual abnormality data; Performing speech recognition on the audio data to obtain abnormal sound data; Extracting feature data from the state data, the group string data, the video data, and the audio data; The feature data are subjected to feature fusion based on preset rules to obtain a comprehensive feature vector; Inputting the comprehensive feature vector into a preset anomaly detection model to perform anomaly judgment and obtain an anomaly vector; Abnormal monitoring data is determined based on the state abnormality data, the string abnormality data, the visual abnormality data, the sound abnormality data, and the abnormality vector.

3. The method according to claim 1, characterized in that The determining of abnormal information based on the simulation operation model and the abnormal monitoring data includes: Acquire historical abnormality information, each of which includes a historical abnormality cause, a historical abnormality source, and at least one historical abnormal device; Determining the historical abnormal information of multiple historical abnormal devices as linkage abnormal information; Analyze the linkage abnormality information to obtain the abnormality type and device type corresponding to each linkage abnormality; Determining a candidate anomaly type based on the anomaly monitoring data; Determine a candidate linkage anomaly based on the candidate anomaly type and a device type corresponding to the abnormal device; The candidate linkage anomaly is simulated by the simulation operation model to obtain simulated monitoring data; Determine a current linkage anomaly based on the simulated monitoring data and the monitoring data; Determine the source and cause of the abnormality based on the current linkage abnormality; Abnormal information is determined based on the abnormal source, the abnormal cause and the abnormal monitoring data.

4. The method according to claim 1, characterized in that: The operating environment includes the hottest point temperature of the winding, and the evaluation result includes the remaining life of the equipment. The life loss evaluation of the power equipment based on the operating time and operating environment of the power equipment is performed to obtain the evaluation result, including: The remaining life of the device is calculated based on the life loss formula, the operating time and the hottest point temperature of the winding, and the life loss formula includes: remaining life of the device = design life - operating time × V0, , where θc is the hottest point temperature of the winding.

5. An intelligent monitoring system for a new energy power station, characterized in that: include: A power station data acquisition module, used to acquire monitoring data, equipment connection information and power supply tasks of the power station, wherein the monitoring data includes status data, string data, video data and audio data; an abnormal data determination module, configured to perform abnormal analysis on the monitoring data based on the association between the monitoring data, obtain abnormal monitoring data, and determine the power equipment corresponding to the abnormal monitoring data as an abnormal equipment; A power model building module, used to build a simulation operation model of a power station based on the equipment distribution information and the equipment connection information; An abnormal information determination module, configured to determine abnormal information based on the simulation operation model and the abnormal monitoring data if a plurality of the abnormal devices are connected to each other, wherein the abnormal information includes an abnormal source and an abnormal cause; A life loss assessment module, used to assess the life loss of the power equipment based on the operating time and operating environment of the power equipment to obtain an assessment result; An adjustment strategy determination module, configured to formulate a power supply adjustment strategy based on the abnormal information, the evaluation result and the power supply task; The adjustment strategy determination module is specifically used to formulate a power supply adjustment strategy based on the abnormal information, the evaluation result and the power supply task, including: Determine the equipment to be maintained based on the abnormal information and the evaluation result; Determining the power supply task of the equipment to be maintained as a task to be adjusted; Obtaining the priority, occupied load and power quality requirements of the task to be adjusted; Sorting the tasks to be adjusted according to the priorities and the occupied loads to obtain a first sorting result; Calculate a first remaining capacity and a second remaining capacity of each power device, wherein the first remaining capacity is the maximum load size that can be connected to the power device, and the second remaining capacity is the maximum load size that can be connected under a preset power quality; Determine a power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result, and the power quality requirement; The adjustment strategy determination module is specifically used for calculating the first remaining capacity and the second remaining capacity of each power device, including: Obtain the rated capacity and current load of each power device; calculating the first remaining capacity based on the rated capacity and the current load; Obtain historical operating data of power equipment; Analyze the historical operation data to obtain the load capacity corresponding to the preset power quality; calculating a second remaining capacity based on the load capacity and the current load; The adjustment strategy determination module is specifically used to determine the power supply adjustment strategy based on the first remaining capacity, the second remaining capacity, the first sorting result and the power quality requirement, including: Allocate adjustment equipment to the tasks to be adjusted in sequence according to the first sorting result; The adjustment strategy determination module is specifically used for allocating adjustment equipment to the task to be adjusted, including: calculating a first difference between each of the second remaining capacities and the occupied load; If there is a first difference greater than 0, the power equipment corresponding to the smallest first difference greater than 0 that meets the power quality requirement is determined as the adjustment equipment for the task to be adjusted; If there is no first difference greater than 0, then calculating the second difference between the first remaining capacity and the occupied load; determining the power equipment that meets the power quality requirements, has the largest second difference and the highest equipment efficiency as the adjustment equipment for the task to be adjusted; The first remaining capacity and the second remaining capacity of the adjustment device are updated based on the occupancy load.

6. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, which, when executed on a computer, causes the computer to execute the method according to any one of claims 1 to 4.

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