Remote alarm device for photovoltaic energy storage power station anomaly monitoring

Abstract

The application discloses a remote alarm device for photovoltaic energy storage power station anomaly monitoring, and relates to the technical field of remote alarm, which comprises: an array information acquisition module for extracting the layout information of photovoltaic modules, inverters and battery groups; a performance attenuation analysis module for identifying device performance degradation areas; a neighborhood attenuation central analysis module for extracting a central value of degradation; a new scheme identification module for dynamically optimizing a remote alarm device layout scheme according to the central value of degradation; and a remote alarm module for updating an alarm device array by using the optimized scheme to realize real-time monitoring and accurate alarm of abnormal areas. The application solves the technical problem that the prior art cannot dynamically adapt according to the actual operation state of the device, affecting the anomaly detection accuracy and fault response efficiency, achieves accurate monitoring and real-time remote alarm of high-risk areas, and improves the technical effects of anomaly detection accuracy and fault response efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote alarm, and particularly relates to a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station. BACKGROUND

[0002] With large-scale deployment and long-term operation of photovoltaic energy storage power stations, gradual degradation of equipment performance and abnormal risks are gradually highlighted. In particular, under complex operating environments, key equipment such as photovoltaic modules, inverters and battery packs is prone to varying degrees of performance degradation.

[0003] Existing remote alarm systems are mostly statically laid out, that is, alarm devices are fixedly laid out according to empirical data and standardized design at the initial stage of construction of the power station. However, as the operation time of the photovoltaic energy storage power station increases, the degree of wear and tear of equipment in each region, the frequency of abnormalities and the distribution of risks gradually change. The traditional alarm system cannot dynamically adapt to the actual operating state of the photovoltaic energy storage power station, cannot timely focus on monitoring the performance degradation region, and leads to a lag in abnormal detection and alarm response, thereby affecting the stable operation of the power station. SUMMARY

[0004] The present application provides a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station, to solve the technical problem that the traditional alarm system in the prior art cannot dynamically adapt to the actual operating state of the equipment, thereby affecting the accuracy of abnormal detection and the efficiency of fault response.

[0005] The application provides a remote alarm device for photovoltaic energy storage power station anomaly monitoring, which comprises an array information acquisition module, an abnormal monitoring data extraction module, a performance attenuation analysis module, a neighborhood attenuation central analysis module, a new scheme identification module and a remote alarm module.

[0006] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0007] The application provides a remote alarm device for photovoltaic energy storage power station anomaly monitoring, relates to the technical field of remote alarm, and extracts layout information and historical abnormal data of photovoltaic modules, inverters and battery groups through an array information acquisition module; a performance attenuation analysis module identifies a performance degradation area; a neighborhood attenuation centralized analysis module extracts a degradation central value; a new scheme identification module dynamically optimizes a remote alarm device layout scheme according to the degradation central value; and a remote alarm module updates an alarm device array by using the optimized scheme, realizes real-time monitoring and accurate alarm of an abnormal area, and solves the technical problem that the conventional alarm system in the prior art cannot dynamically adapt according to the actual operation state of equipment, and the technical problem of affecting abnormal detection accuracy and fault response efficiency. Through dynamic optimization of the layout scheme of the remote alarm device, the area with high performance degradation and abnormal frequency in the photovoltaic energy storage power station is accurately covered, real-time and accurate abnormal monitoring and rapid alarm are realized, and the technical effects of improving the abnormal detection accuracy and fault response efficiency of the system are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0009] Figure 1 The remote alarm device for photovoltaic energy storage power station anomaly monitoring provided by the embodiment of the present application is shown in the structural schematic diagram.

[0010] Figure 2 The flowchart of device performance attenuation distribution neighborhood analysis in the performance attenuation analysis module of the remote alarm device for photovoltaic energy storage power station anomaly monitoring provided by the embodiment of the present application is shown.

[0011] The reference signs are explained as follows: the array information acquisition module 10, the abnormal monitoring data extraction module 20, the performance attenuation analysis module 30, the neighborhood attenuation centralized analysis module 40, the new scheme identification module 50, and the remote alarm module 60. DETAILED DESCRIPTION

[0012] The application provides a remote alarm device for photovoltaic energy storage power station anomaly monitoring, which is used to solve the technical problem that the conventional alarm system in the prior art cannot dynamically adapt according to the actual operation state of equipment, and the technical problem of affecting abnormal detection accuracy and fault response efficiency.

[0013] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0014] It should be noted that the terms in the specification of the present application and the above drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment one, as shown in the present application provides a remote alarm device for abnormal monitoring of a photovoltaic energy storage power station, the device comprises: Figure 1

[0016] An array information acquisition module 10 is configured to acquire photovoltaic component layout array, inverter layout array and battery pack layout array of the target photovoltaic energy storage power station.

[0017] Specifically, the main function of the array information acquisition module 10 of the present application is to acquire the layout array information of various key equipment in the target photovoltaic energy storage power station, including photovoltaic component layout array, inverter layout array and battery pack layout array, to provide basic data support for subsequent abnormal monitoring and equipment performance analysis.

[0018] Firstly, the photovoltaic component layout array refers to the installation arrangement mode of the photovoltaic panel and its spatial distribution in the power station. Photovoltaic components are the core components of photovoltaic power generation systems, which directly affect the power generation efficiency of the power station. Therefore, acquiring the layout information of photovoltaic components not only helps to understand the layout relationship between components, but also reflects the possible performance differences in different areas. For example, the layout angle, orientation of photovoltaic components and the shielding relationship with the surrounding environment (such as buildings, trees, etc.) will affect the power generation effect. The array information acquisition module can scan the physical location of the photovoltaic component through sensors or automated systems, and record its accurate spatial coordinates, direction angle and installation inclination angle and other parameters.

[0019] ​Next, the inverter deployment array is obtained. Inverters are devices that convert the direct current generated by photovoltaic modules into alternating current, playing a crucial role in photovoltaic energy storage power stations. Different models and locations of inverters may have different workloads and decay rates, so accurate acquisition of inverter deployment information is essential for analyzing the degree of equipment degradation and potential failures. The array information acquisition module can ensure that the data of all inverters can be effectively collected according to the installation location, power capacity and role of the inverters in the power station.

[0020] Finally, the battery pack deployment array refers to the deployment method and structure of the battery pack (used to store the electricity generated by photovoltaic power generation). The deployment of the battery pack has a direct impact on the energy storage efficiency and system stability. Factors such as the charging and discharging frequency of the battery and the temperature environment also affect its performance degradation. The array information acquisition module monitors the layout, connection method and battery health status of the battery pack to ensure real-time acquisition of the working state information of the battery pack.

[0021] In actual operation, the array information acquisition module can achieve this goal through various technical means. For example, unmanned aerial vehicles can be used in combination with laser radar technology to scan the positions of photovoltaic modules and inverters, or automatic monitoring systems and sensor networks can be used to collect deployment parameters of each device in real time. These information is transmitted to the back-end processing platform through the data acquisition system, providing accurate data basis for subsequent performance analysis, anomaly monitoring and equipment optimization.

[0022] Through these steps, the array information acquisition module lays the foundation for subsequent performance degradation analysis and optimization of remote alarm schemes, ensuring that photovoltaic energy storage power stations can monitor the operating status of each device in real time during actual operation and provide timely risk warnings.

[0023] The anomaly monitoring data extraction module 20 is configured to extract a photovoltaic module historical anomaly monitoring data set, an inverter historical anomaly monitoring data set and a battery pack historical anomaly monitoring data set from the photovoltaic module deployment array, the inverter deployment array and the battery pack deployment array within a preset historical window, respectively.

[0024] It should be understood that the core function of the anomaly monitoring data extraction module 20 of the present application is to extract historical anomaly monitoring data from each device array of the photovoltaic energy storage power station. These data can reflect the operating status and potential abnormal conditions of each device (photovoltaic module, inverter and battery pack) within a certain time window, providing necessary data support for subsequent performance analysis and equipment health assessment.

[0025] Firstly, the extraction of the photovoltaic module historical anomaly monitoring data set involves real-time collection of operational data for each photovoltaic module. The operational status of photovoltaic modules is influenced by various factors such as environmental temperature, light intensity, shading conditions, etc. Therefore, the anomaly monitoring data extraction module needs to obtain relevant real-time data from the device's sensor network, including voltage, current, power output, and other parameters. At the same time, it needs to combine the device's environmental data, such as light intensity and temperature, to determine whether it has entered an abnormal state. The module will collect and store these data as a photovoltaic module historical anomaly monitoring data set according to the preset historical time window (usually several days, weeks, or months). These data can help the system identify whether the photovoltaic module has faults such as surface damage, wiring problems, or performance degradation.

[0026] Secondly, the extraction of the inverter historical anomaly monitoring data set focuses on the output and working status of the inverter. The main task of the inverter is to convert the direct current generated by the photovoltaic module into alternating current and stabilize the output to the power grid. The anomaly monitoring data extraction module extracts real-time monitoring data from the inverter through the communication interface, including output power, current, voltage, frequency, and other parameters. During operation, the inverter may be abnormal due to load fluctuations, internal faults, or external factors. By analyzing these historical monitoring data, the module can identify the possible fault types of the inverter, such as overload, undervoltage, overtemperature, inverter damage, and other problems. In addition, the working status of the inverter may be affected by environmental temperature, load changes, and other factors, and the extraction of historical data helps to conduct detailed fault analysis and prediction.

[0027] Finally, the extraction of the battery pack historical anomaly monitoring data set involves the state monitoring of the battery pack's charging and discharging. The battery pack is an important part of the photovoltaic energy storage system, responsible for storing the electrical energy generated by the photovoltaic module and releasing it when needed. The performance degradation and failure of the battery are usually closely related to factors such as the number of charging and discharging cycles, temperature, and voltage anomalies. The anomaly monitoring data extraction module extracts real-time monitoring data of the battery from the battery management system (BMS), including battery voltage, temperature, charging / discharging rate, and battery health status information. Through the extraction of these historical data, the module can timely discover potential problems of the battery pack, such as capacity attenuation, overcharging, overdischarging, etc., thereby providing data support for subsequent maintenance and optimization.

[0028] In the process of data extraction, the module will combine the preset historical window period to collect the operation data of each device in batches. The setting of the preset historical window can be adjusted according to the actual demand and the running period of the device to ensure that sufficient data in a certain time span can be obtained. These historical data will be standardized, noise will be removed, and potential abnormal points will be marked to form a corresponding abnormal monitoring data set, which provides accurate input data for subsequent performance degradation analysis, anomaly detection and risk assessment.

[0029] The performance degradation analysis module 30 is configured to perform device performance degradation distribution neighborhood analysis on the photovoltaic module layout array, the inverter layout array and the battery pack layout array based on the photovoltaic module historical abnormal monitoring data set, the inverter historical abnormal monitoring data set and the battery pack historical abnormal monitoring data set, and obtain a photovoltaic module performance degradation distribution neighborhood set, an inverter performance degradation distribution neighborhood set and a battery pack performance degradation distribution neighborhood set.

[0030] Further, as shown in Figure 2 The performance degradation analysis module 30 is further configured to perform the following steps:

[0031] P31: performing abnormal frequency statistics on the photovoltaic modules in the photovoltaic module layout array based on the photovoltaic module historical abnormal monitoring data set to obtain a photovoltaic module abnormal frequency distribution array; P32: randomly extracting Q photovoltaic module abnormal frequencies from the photovoltaic module abnormal frequency distribution array, wherein Q is an integer greater than or equal to 1; P33: performing device performance degradation distribution neighborhood analysis on the photovoltaic module layout array based on the Q photovoltaic module abnormal frequencies and the photovoltaic module abnormal frequency distribution array to obtain the photovoltaic module performance degradation distribution neighborhood set; P34: performing device performance degradation distribution neighborhood analysis on the inverter layout array and the battery pack layout array based on the inverter historical abnormal monitoring data set and the battery pack historical abnormal monitoring data set to obtain the inverter performance degradation distribution neighborhood set and the battery pack performance degradation distribution neighborhood set.

[0032] It should be understood that the main function of the performance degradation analysis module 30 of the present application is to evaluate the performance degradation of each device in the photovoltaic energy storage power station through the analysis of historical abnormal monitoring data, and further to provide data support for subsequent device optimization and fault prediction. The module obtains the performance degradation distribution information of the device by analyzing the abnormal frequency and performance change of the photovoltaic module, the inverter and the battery pack in a certain historical time window, which provides a basis for formulating a reasonable maintenance plan and an alarm scheme.

[0033] Firstly, the module counts the abnormal frequency of all photovoltaic components based on the historical abnormal monitoring data set of the photovoltaic components. The abnormal frequency refers to the number of times that each photovoltaic component appears abnormal within a specified historical time window. The abnormality can be various forms, such as power drop, voltage fluctuation, temperature anomaly, etc. By counting these abnormal frequencies, the module can identify which photovoltaic components exhibit a higher failure frequency, and generate a photovoltaic component abnormal frequency distribution array, i.e., the abnormal frequency summary data of these components.

[0034] Based on the obtained photovoltaic component abnormal frequency distribution array, the module randomly extracts the abnormal frequencies of Q photovoltaic components, where Q is an integer greater than or equal to 1. The randomly extracted Q components represent a part of components that may have potential problems. The purpose of this step is to further analyze the performance degradation trend of the representative component samples selected, and to provide sufficient data for subsequent device analysis.

[0035] Further, based on the extracted abnormal frequencies of the Q photovoltaic components, in combination with the photovoltaic component abnormal frequency distribution array, the module performs performance degradation distribution neighborhood analysis on the photovoltaic component layout array. The performance degradation distribution neighborhood analysis is to identify the more severely degraded device regions by analyzing the performance change trend of the selected components and the surrounding components. The core of neighborhood analysis is to evaluate the spatial performance degradation pattern of the device by investigating the spatial relationship and operating state between photovoltaic components, to reveal which regions of photovoltaic components have already appeared performance degradation, and to predict the possible failure risk. Through this method, a photovoltaic component performance degradation distribution neighborhood set is generated, which contains components and their adjacent components with more severe performance degradation, and becomes the basis for subsequent fault diagnosis and alarm facility optimization.

[0036] In addition to photovoltaic components, inverters and battery packs are also key devices of photovoltaic energy storage power stations. In order to comprehensively evaluate the operating state of the entire system, the module also performs performance degradation distribution neighborhood analysis on the inverter layout array and the battery pack layout array based on the inverter historical abnormal monitoring data set and the battery pack historical abnormal monitoring data set. The performance degradation of inverters is usually closely related to load fluctuations, internal component wear and tear, etc., while the degradation of battery packs is related to charge-discharge times, temperature fluctuations, etc. Through the analysis of these data, the module can identify the more severely degraded inverter and battery pack regions, and generate the corresponding inverter performance degradation distribution neighborhood set and battery pack performance degradation distribution neighborhood set. These information can provide strong support for the device maintenance, performance prediction and early warning system optimization of the power station.

[0037] Further, the step P33 of the embodiment of the present application further comprises:

[0038] P33-1: performing neighborhood center verification on the Q photovoltaic component anomaly frequencies, and if the verification passes, taking the Q photovoltaic component anomaly frequencies as Q initial neighborhood centers; P33-2: calculating Q data difference degrees of each photovoltaic component anomaly frequency in the photovoltaic component anomaly frequency distribution array and the Q initial neighborhood centers respectively, adding each photovoltaic component anomaly frequency into a neighborhood of an initial neighborhood center corresponding to a maximum value of Q data similarity degrees, and obtaining Q initial neighborhoods; P33-3: according to positions of photovoltaic components in the photovoltaic component arrangement array, iteratively updating the Q initial neighborhoods and the Q initial neighborhood centers with a minimum cost function as a target until a preset update stop condition is met, and obtaining Q target neighborhood centers and Q target neighborhoods; wherein the preset update stop condition is that a difference between cost values of two cost functions of adjacent two iterations is less than or equal to a preset cost value difference and / or an iteration number meets a preset iteration number; and P33-4: taking a region formed by photovoltaic components corresponding to a plurality of photovoltaic component anomaly frequencies in each target neighborhood of the Q target neighborhoods as a photovoltaic component performance degradation distribution neighborhood, and obtaining a photovoltaic component performance degradation distribution neighborhood set.

[0039] Further, the step P33-3 of the embodiment of the present application further includes:

[0040] The data difference degree and the photovoltaic component distribution position are comprehensively considered to construct a cost function, wherein the cost function is:

[0041] ; wherein, is a cost value, is a jth photovoltaic component anomaly frequency in an ith initial neighborhood, is a distribution position of a photovoltaic component corresponding to the jth photovoltaic component anomaly frequency in the ith initial neighborhood, is a photovoltaic component anomaly frequency of an ith initial neighborhood center, is a distribution position of a photovoltaic component corresponding to the ith initial neighborhood center, is a weight for balancing the anomaly frequency and the distribution position, is a total number of photovoltaic component anomaly frequencies contained in the ith initial neighborhood.

[0042] Optionally, based on the Q photovoltaic component abnormal frequency, the specific process of the device performance degradation distribution neighborhood analysis of the photovoltaic component layout array in combination with the photovoltaic component abnormal frequency distribution array can be that, first, the neighborhood center of the randomly extracted Q photovoltaic component abnormal frequency is verified. The neighborhood center is the initial reference point of the neighborhood analysis, representing the central position of the abnormal frequency. The neighborhood center verification aims to ensure that the randomly extracted Q photovoltaic component abnormal frequency can reasonably represent the concentrated area of performance degradation. The verification includes data integrity check (excluding missing or abnormal values), statistical distribution analysis (ensuring to cover the main abnormal frequency range and distribute uniformly), similarity verification (cluster analysis and outlier check), and neighborhood coverage verification (ensuring that the neighborhood of the initial point can effectively cover the performance degradation area of the layout array). If the verification is passed, the Q abnormal frequencies are taken as the initial neighborhood center, otherwise, they are re-extracted and verified to ensure the reliability and representativeness of the initial neighborhood center.

[0043] Next, the data difference degree of each photovoltaic component abnormal frequency in the photovoltaic component abnormal frequency distribution array and the Q initial neighborhood centers is calculated. The data difference degree is used to measure the deviation degree of each component abnormal frequency from the initial neighborhood center, which can be calculated by using the Euclidean distance or other similarity algorithms. Each photovoltaic component abnormal frequency is assigned to the neighborhood of the initial neighborhood center corresponding to the maximum data similarity, forming Q initial neighborhoods. For example, the photovoltaic component A has the minimum difference degree with the neighborhood center 1, then A is classified into the neighborhood corresponding to the neighborhood center 1. This classification method ensures that each component is reasonably classified into the most similar neighborhood.

[0044] In order to further optimize the neighborhood division, the module iteratively updates based on the abnormal frequency and distribution position of the photovoltaic component, aiming to minimize the cost function. The expression of the cost function is:

[0045] ; wherein, is the cost value, indicating the cost size of the overall division, is the jth photovoltaic component abnormal frequency in the ith initial neighborhood, is the distribution position of the photovoltaic component corresponding to the jth photovoltaic component abnormal frequency in the ith initial neighborhood, is the photovoltaic component abnormal frequency of the ith initial neighborhood center, is the distribution position of the photovoltaic component corresponding to the ith initial neighborhood center, is the weight of balancing the abnormal frequency and the distribution position, is the total number of photovoltaic component abnormal frequencies contained in the ith initial neighborhood.

[0046] The cost function is optimized by iterative updating, and each iteration recalculates the parameters (abnormal frequency and distribution position) of each neighborhood center and redivides the neighborhood according to the minimum cost principle. This process continues until the preset update stopping condition is met, such as the difference in cost value between adjacent two iterations is less than the preset threshold, or the maximum number of iterations is reached.

[0047] After completing the iterative optimization, the module determines the final Q target neighborhood centers and their corresponding neighborhoods as the performance degradation distribution neighborhood of the photovoltaic module. Specifically, the photovoltaic components corresponding to the multiple photovoltaic component abnormal frequencies contained in each target neighborhood constitute an independent performance degradation area. The collection of these areas forms a set of photovoltaic component performance degradation distribution neighborhoods, which provides a basis for subsequent performance evaluation, fault prediction, and alarm layout optimization.

[0048] The neighborhood degradation central analysis module 40 is configured to perform neighborhood degradation central analysis based on the set of photovoltaic component performance degradation distribution neighborhoods, the set of inverter performance degradation distribution neighborhoods, and the set of battery pack performance degradation distribution neighborhoods, to obtain a set of photovoltaic component neighborhood degradation central values, a set of inverter performance neighborhood degradation central values, and a set of battery pack performance neighborhood degradation central values.

[0049] Further, the neighborhood degradation central analysis module 40 is further configured to perform the following steps:

[0050] P41: Traverse the set of photovoltaic component performance degradation distribution neighborhoods, the set of inverter performance degradation distribution neighborhoods, and the set of battery pack performance degradation distribution neighborhoods to perform edge data cleaning, to obtain a cleaned photovoltaic component performance degradation distribution neighborhood set, a cleaned inverter performance degradation distribution neighborhood set, and a cleaned battery pack performance degradation distribution neighborhood set after removing edge values with small distribution amounts; P42: Perform mean value processing on the cleaned photovoltaic component performance degradation distribution neighborhood set, the cleaned inverter performance degradation distribution neighborhood set, and the cleaned battery pack performance degradation distribution neighborhood set, to obtain the set of photovoltaic component neighborhood degradation central values, the set of inverter performance neighborhood degradation central values, and the set of battery pack performance neighborhood degradation central values.

[0051] Specifically, the main function of the neighborhood degradation central analysis module 40 of the present application is to perform further neighborhood degradation central analysis on photovoltaic components, inverters, and battery packs based on the performance degradation distribution neighborhood sets of these devices, to finally obtain the neighborhood degradation central value sets of photovoltaic components, inverters, and battery packs.

[0052] Firstly, the module traverses the photovoltaic module performance degradation distribution neighborhood set, the inverter performance degradation distribution neighborhood set and the battery pack performance degradation distribution neighborhood set to clean up the edge values with small proportion in the data. The edge values usually refer to the device points with weak performance degradation and small data distribution, which may be generated by sensor error or environmental interference.

[0053] By setting a distribution threshold, these invalid data are removed. For example, in the neighborhood set of photovoltaic modules, if the performance degradation value in a certain area is very low and only contains a small amount of devices, it will be considered as irrelevant data and cleaned up. Similarly, for the neighborhood sets of inverters and battery packs, edge removal is performed according to the data distribution. After cleaning, the cleaned photovoltaic module performance degradation distribution neighborhood set, the cleaned inverter performance degradation distribution neighborhood set and the cleaned battery pack performance degradation distribution neighborhood set are obtained, which removes invalid data and ensures the accuracy of subsequent analysis.

[0054] After cleaning, the module performs mean value processing on the cleaned data set to further extract the neighborhood degradation central value set of each device. Mean value processing is a statistical method aimed at calculating the average level of the performance degradation value of each device in the neighborhood, reflecting the overall performance degradation of the neighborhood. For example, in the photovoltaic module neighborhood, the performance degradation values of all photovoltaic modules are calculated by mean value, and similarly, the performance degradation central values of inverters and battery packs are calculated by the same method to generate the inverter performance neighborhood degradation central value set and the battery pack performance neighborhood degradation central value set. These central values represent the central trend of the performance degradation of each device area, which helps to identify the areas with serious performance degradation and provides data support for subsequent abnormal warning and device maintenance.

[0055] Finally, the neighborhood degradation central analysis module 40 accurately extracts the neighborhood degradation central value set of photovoltaic modules, inverters and battery packs through edge cleaning and mean value calculation. These results can provide accurate decision basis for the operation and maintenance personnel of the photovoltaic energy storage power station, help to locate the device areas with serious performance degradation, and provide strong support for the dynamic optimization of remote alarm facilities and device maintenance strategy.

[0056] The new scheme identification module 50 is used to obtain the remote alarm device layout array of the target photovoltaic energy storage power station, identify the device addition scheme of the remote alarm device layout array based on the neighborhood degradation central value size of the photovoltaic module neighborhood degradation central value set, the inverter performance neighborhood degradation central value set and the battery pack performance neighborhood degradation central value set, and obtain the target device addition scheme.

[0057] Further, the new scheme identification module 50 is further used to perform the following steps:

[0058] P51: based on the photovoltaic module performance degradation distribution neighborhood set, the inverter performance degradation distribution neighborhood set and the battery pack performance degradation distribution neighborhood set, neighborhood matching is performed on the remote alarm device deployment array to obtain a photovoltaic module remote alarm device deployment neighborhood set, an inverter remote alarm device deployment neighborhood set and a battery pack remote alarm device deployment neighborhood set; P52: the distribution quantity and distribution uniformity of the photovoltaic module remote alarm device deployment neighborhood set, the inverter remote alarm device deployment neighborhood set and the battery pack remote alarm device deployment neighborhood set are counted, and after weighted analysis, a photovoltaic module remote alarm device neighborhood deployment coefficient set, an inverter remote alarm device neighborhood deployment coefficient set and a battery pack remote alarm device neighborhood deployment coefficient set are obtained; P53: according to the neighborhood degradation central value size of the photovoltaic module neighborhood degradation central value set, the inverter performance neighborhood degradation central value set and the battery pack performance neighborhood degradation central value set, the photovoltaic module remote alarm device neighborhood deployment coefficient set, the inverter remote alarm device neighborhood deployment coefficient set and the battery pack remote alarm device neighborhood deployment coefficient set are matched and analyzed to obtain a photovoltaic module neighborhood device addition scheme set, an inverter neighborhood device addition scheme set and a battery pack neighborhood device addition scheme set; P54: the photovoltaic module neighborhood device addition scheme set, the inverter neighborhood device addition scheme set and the battery pack neighborhood device addition scheme set are summarized to obtain the target device addition scheme.

[0059] It should be understood that the main function of the addition scheme identification module 50 of the present application is to dynamically identify the device addition scheme based on the neighborhood degradation central value set of the photovoltaic module, the inverter and the battery pack, combined with the current remote alarm device deployment situation, to meet the precise monitoring demand of the device performance degradation area.

[0060] Firstly, the module performs neighborhood matching on the current remote alarm device deployment array based on the photovoltaic module performance degradation distribution neighborhood set, the inverter performance degradation distribution neighborhood set and the battery pack performance degradation distribution neighborhood set.

[0061] In this process, the system one-to-one corresponds the performance degradation neighborhood of the photovoltaic module, the inverter and the battery pack with the remote alarm device deployment area to generate a photovoltaic module remote alarm device deployment neighborhood set, an inverter remote alarm device deployment neighborhood set and a battery pack remote alarm device deployment neighborhood set. This one-to-one matching relationship is associated through device location coordinates and neighborhood performance data, ensuring that each performance degradation area can find the current covered alarm device area, providing a clear data basis for subsequent analysis.

[0062] After completing the neighborhood matching, the module performs detailed statistical analysis on the matched photovoltaic module remote alarm device layout neighborhood set, inverter remote alarm device layout neighborhood set and battery pack remote alarm device layout neighborhood set, focusing on the distribution quantity and distribution uniformity. Among them, the distribution quantity represents the actual layout quantity of the remote alarm device in each neighborhood, reflecting the coverage degree of the alarm device. The distribution uniformity measures the distribution density of the device in each neighborhood, identifying whether the alarm device layout is unbalanced.

[0063] Based on the statistical results, through a weighted analysis method, the distribution quantity and uniformity are comprehensively considered to generate a quantization coefficient for each neighborhood, forming a photovoltaic module remote alarm device neighborhood layout coefficient set, an inverter remote alarm device neighborhood layout coefficient set and a battery pack remote alarm device neighborhood layout coefficient set. These coefficients reflect the pros and cons of the current alarm device layout in each neighborhood, providing data support for the identification of subsequent new schemes.

[0064] Next, based on the neighborhood attenuation central value size of the photovoltaic module neighborhood attenuation central value set, the inverter performance neighborhood attenuation central value set and the battery pack performance neighborhood attenuation central value set, the alarm device layout coefficient set obtained in the previous step is matched and analyzed. The neighborhood attenuation central value represents the severity of device degradation in each performance attenuation neighborhood, and the larger the value, the more obvious the performance attenuation and the higher the risk. The system compares the attenuation central value of each neighborhood with the corresponding layout coefficient to find high-attenuation areas with insufficient monitoring coverage and less device layout. Through matching analysis, the key areas that cannot be fully covered by the current alarm device are identified, and a photovoltaic module neighborhood device addition scheme set, an inverter neighborhood device addition scheme set and a battery pack neighborhood device addition scheme set are generated, clearly indicating the location and type of alarm devices that need to be added.

[0065] Finally, the module summarizes the photovoltaic module neighborhood device addition scheme set, the inverter neighborhood device addition scheme set and the battery pack neighborhood device addition scheme set to form the final target device addition scheme. This scheme covers the addition of alarm devices in areas with serious performance attenuation of photovoltaic modules, inverters and battery packs, including the specific location, quantity and type of device layout. Through this scheme, the remote alarm system can dynamically adapt to the current performance attenuation trend, accurately cover high-risk areas and improve the monitoring accuracy and response capability of the photovoltaic energy storage power station.

[0066] Further, the step P53 of the embodiment of the present application further includes:

[0067] P53-1: A pre-constructed matching analyzer is used to perform one-to-one mapping matching analysis on the photovoltaic module neighborhood attenuation central value set and the photovoltaic module remote alarm device neighborhood layout coefficient set, respectively, to obtain a photovoltaic module neighborhood device addition scheme set; P53-2: The matching analyzer is used to perform matching analysis on the inverter performance neighborhood attenuation central value set and the battery pack performance neighborhood attenuation central value set, and the corresponding inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device neighborhood layout coefficient set, to obtain an inverter neighborhood device addition scheme set and a battery pack neighborhood device addition scheme set.

[0068] In a possible embodiment of the present application, to achieve accurate device addition scheme identification, a pre-constructed matching analyzer can be used to perform one-to-one mapping matching analysis between the attenuation central value set of the photovoltaic module, the inverter and the battery pack, and the neighborhood layout coefficient set of the current remote alarm device. This process ensures accurate correspondence between high attenuation areas and alarm device requirements by constructing an efficient matching mechanism, thereby obtaining an addition scheme set.

[0069] First, a pre-constructed matching analyzer is used as a core tool for performing one-to-one mapping matching analysis. The matching analyzer performs one-to-one matching between the neighborhood attenuation central value set of the photovoltaic module and the photovoltaic module remote alarm device neighborhood layout coefficient set based on an algorithm model. The neighborhood attenuation central value set represents the severity of performance degradation in each neighborhood of the photovoltaic module. The larger the central value, the more obvious the performance degradation of the photovoltaic module in that area, which needs to be monitored. The remote alarm device neighborhood layout coefficient set reflects the layout of the current alarm device in each neighborhood, including the number of devices and the quantitative results of the coverage effect.

[0070] The matching analyzer identifies high attenuation areas in the photovoltaic module neighborhood by comparing the numerical differences and weight priorities of the two sets, and matches the layout of the existing alarm device. For areas with high attenuation central values but low alarm device layout coefficients, the monitoring weak area is identified, and a photovoltaic module neighborhood device addition scheme set is generated. This scheme clearly identifies the device addition needs of the high attenuation area of the photovoltaic module, including the location, number and monitoring range of the added alarm device.

[0071] Then, the matching analyzer further performs matching analysis of the inverter and the battery pack, respectively matches the inverter performance neighborhood decay central value set and the battery pack performance neighborhood decay central value set with the corresponding inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device neighborhood layout coefficient set. The matching degree of the performance decay central value and the layout coefficient of each neighborhood is calculated through the algorithm model to identify the high-decay area with insufficient monitoring. For example, when the decay central value of a certain inverter neighborhood is much higher than that of other areas, but the corresponding layout coefficient is low, it is identified as a new device demand area. Finally, the inverter neighborhood device addition scheme set and the battery pack neighborhood device addition scheme set are generated to determine the specific area and layout scheme of the new alarm device.

[0072] Through the above steps, the performance degradation area of the photovoltaic module, the inverter and the battery pack is accurately matched with the current remote alarm device layout, and the high-decay area with insufficient monitoring is identified. The generated photovoltaic module neighborhood device addition scheme set, inverter neighborhood device addition scheme set and battery pack neighborhood device addition scheme set provide a scientific basis for the dynamic optimization of the remote alarm device, which can ensure that the photovoltaic energy storage power station can realize real-time and accurate monitoring of the key areas, and effectively improve the abnormal detection ability and stability of the system.

[0073] The remote alarm module 60 is used to add devices to the remote alarm device layout array according to the target device addition scheme, obtain an updated remote alarm device layout array, and use the updated remote alarm device layout array to remotely alarm the abnormal monitoring situation of the target photovoltaic energy storage power station.

[0074] Specifically, the main function of the remote alarm module 60 of the present application is to dynamically optimize and add devices to the existing remote alarm device layout array based on the target device addition scheme, and finally form an updated remote alarm device layout array to improve the abnormal monitoring ability of the photovoltaic energy storage power station and realize accurate remote alarm.

[0075] First, the target device addition scheme is received, which specifies the addition position, number and layout strategy of the alarm device in the high-decay area of the photovoltaic module, the inverter and the battery pack. The module optimizes and adjusts the current remote alarm device layout array and adds devices according to the scheme.

[0076] In the process of adding and laying out, the position of the new device is dynamically allocated based on the device layout algorithm and the decay central value and layout coefficient of each area to ensure the uniformity and effectiveness of the distribution of the new device. For example, in the photovoltaic module neighborhood, if the decay central value of a certain area is high and the current alarm device layout coefficient is low, new alarm devices will be preferentially deployed in that area to fill the monitoring gap.

[0077] After the new device is added and laid out, the module will generate an updated remote alarm device layout array. This array not only includes the location information of the new device, but also re-optimizes the layout of the existing alarm devices, forming a more efficient monitoring network. Through dynamic adjustment and device addition, the alarm devices are arranged more scientifically in space, effectively covering the performance degradation areas of photovoltaic modules, inverters and battery packs, solving the problems of uneven monitoring and blind areas.

[0078] Based on the updated alarm device layout array, real-time monitoring and remote alarm of abnormal conditions of the photovoltaic energy storage power station are carried out. When the photovoltaic module, inverter or battery pack is found to have abnormal conditions such as power output degradation, voltage fluctuation, temperature anomaly, etc., the system will immediately trigger the alarm mechanism and remotely transmit the fault information to the operation and maintenance center, providing accurate fault location and diagnosis basis for operation and maintenance personnel.

[0079] In summary, the embodiments of the present application have at least the following technical effects:

[0080] The present application obtains the layout information and historical abnormal data of photovoltaic modules, inverters and battery packs, identifies high degradation areas based on performance degradation distribution neighborhood analysis and degradation central value analysis, combines the existing alarm device layout array, and dynamically optimizes the device layout scheme through the addition scheme to complete the addition and array update, realize accurate monitoring and real-time remote alarm of high-risk areas, and improve the abnormal detection accuracy and fault response efficiency.

[0081] The real-time and accurate abnormal monitoring and rapid alarm are achieved, and the technical effects of improving the fault response capability of the system and the utilization efficiency of the monitoring resources are achieved.

[0082] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. Moreover, the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or may be advantageous.

[0083] The above is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0084] The specification and drawings are, of course, to be regarded in an illustrative rather than a restrictive sense. It is to be understood that any such modifications, variations, combinations or equivalents that fall within the scope of the application are intended to be embraced herein.

Claims

1. A remote alarm device for abnormal monitoring of a photovoltaic energy storage power station, characterized in that, The device comprises: an array information acquisition module, configured to acquire a photovoltaic component layout array, an inverter layout array and a battery pack layout array of a target photovoltaic energy storage power station; an abnormality monitoring data extraction module, configured to extract a photovoltaic component historical abnormality monitoring data set, an inverter historical abnormality monitoring data set and a battery pack historical abnormality monitoring data set of the photovoltaic component layout array, the inverter layout array and the battery pack layout array respectively within a preset historical window; a performance attenuation analysis module, configured to perform equipment performance attenuation distribution neighborhood analysis on the photovoltaic component layout array, the inverter layout array and the battery pack layout array based on the photovoltaic component historical abnormality monitoring data set, the inverter historical abnormality monitoring data set and the battery pack historical abnormality monitoring data set, and obtain a photovoltaic component performance attenuation distribution neighborhood set, an inverter performance attenuation distribution neighborhood set and a battery pack performance attenuation distribution neighborhood set; a neighborhood attenuation central analysis module, configured to perform neighborhood attenuation central analysis based on the photovoltaic component performance attenuation distribution neighborhood set, the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set, and obtain a photovoltaic component neighborhood attenuation central value set, an inverter performance neighborhood attenuation central value set and a battery pack performance neighborhood attenuation central value set; a new scheme identification module, configured to acquire a remote alarm device layout array of the target photovoltaic energy storage power station, perform equipment new scheme identification on the remote alarm device layout array based on neighborhood attenuation central value sizes of the photovoltaic component neighborhood attenuation central value set, the inverter performance neighborhood attenuation central value set and the battery pack performance neighborhood attenuation central value set, and obtain a target equipment new scheme; a remote alarm module, configured to perform equipment new layout on the remote alarm device layout array by using the target equipment new scheme, obtain an updated remote alarm device layout array, and perform remote alarm on an abnormality monitoring condition of the target photovoltaic energy storage power station by using the updated remote alarm device layout array.

2. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 1, wherein, The performance attenuation analysis module is further configured to: perform abnormality frequency statistics on photovoltaic components in the photovoltaic component layout array based on the photovoltaic component historical abnormality monitoring data set, and obtain a photovoltaic component abnormality frequency distribution array; randomly extract Q photovoltaic component abnormality frequencies from the photovoltaic component abnormality frequency distribution array, wherein Q is an integer greater than or equal to 1; perform equipment performance attenuation distribution neighborhood analysis on the photovoltaic component layout array based on the Q photovoltaic component abnormality frequencies and in combination with the photovoltaic component abnormality frequency distribution array, and obtain the photovoltaic component performance attenuation distribution neighborhood set; perform equipment performance attenuation distribution neighborhood analysis on the inverter layout array and the battery pack layout array based on the inverter historical abnormality monitoring data set and the battery pack historical abnormality monitoring data set, and obtain the inverter performance attenuation distribution neighborhood set and the battery pack performance attenuation distribution neighborhood set.

3. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 2, wherein, The performance attenuation analysis module is further configured to: perform neighborhood center verification on the Q photovoltaic component abnormality frequencies, and if the verification is passed, take the Q photovoltaic component abnormality frequencies as Q initial neighborhood centers. respectively, and adding each photovoltaic module abnormality frequency into the initial neighborhood center corresponding to the maximum value of the Q data similarity degrees, to obtain Q initial neighborhoods and Q initial neighborhood centers; According to the positions of the photovoltaic modules in the photovoltaic module arrangement array, iteratively updating the Q initial neighborhoods and the Q initial neighborhood centers to minimize the cost function until a preset update stop condition is met, to obtain Q target neighborhood centers and Q target neighborhoods; regarding the region formed by the photovoltaic modules corresponding to the multiple photovoltaic module abnormality frequencies in each target neighborhood in the Q target neighborhoods as a photovoltaic module performance degradation distribution neighborhood, to obtain a photovoltaic module performance degradation distribution neighborhood set.

4. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 3, wherein, The performance degradation analysis module is further configured to: considering the data difference degrees and the photovoltaic module distribution positions, constructing a cost function, wherein the cost function is: ； wherein, is the value of the generation, is the abnormal frequency of the jth photovoltaic module in the ith initial neighborhood, is the distribution position of the photovoltaic module corresponding to the abnormal frequency of the jth photovoltaic module in the ith initial neighborhood, is the abnormal frequency of the photovoltaic module at the center of the ith initial neighborhood, is the distribution position of the photovoltaic module corresponding to the center of the ith initial neighborhood, is the weight of balancing the abnormal frequency and the distribution position, is the total number of abnormal frequencies of the photovoltaic module contained in the ith initial neighborhood.

5. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 4, wherein, The preset update stop condition is that the difference between the cost values of the cost functions of two adjacent iterations is less than or equal to a preset cost value difference and / or the iteration number satisfies a preset iteration number.

6. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 1, wherein, The neighborhood degradation set analysis module is further configured to: traversing the photovoltaic module performance degradation distribution neighborhood set, the inverter performance degradation distribution neighborhood set, and the battery pack performance degradation distribution neighborhood set to perform edge data cleaning, to obtain a cleaned photovoltaic module performance degradation distribution neighborhood set, a cleaned inverter performance degradation distribution neighborhood set, and a cleaned battery pack performance degradation distribution neighborhood set after removing edge values with small distribution amounts; performing mean value processing on the cleaned photovoltaic module performance degradation distribution neighborhood set, the cleaned inverter performance degradation distribution neighborhood set, and the cleaned battery pack performance degradation distribution neighborhood set, to obtain a photovoltaic module neighborhood degradation central value set, an inverter performance neighborhood degradation central value set, and a battery pack performance neighborhood degradation central value set.

7. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 1, wherein, The new scheme identification module is further configured to: based on the photovoltaic module performance degradation distribution neighborhood set, the inverter performance degradation distribution neighborhood set, and the battery pack performance degradation distribution neighborhood set, performing neighborhood matching on the remote alarm device arrangement array, to obtain a photovoltaic module remote alarm device arrangement neighborhood set, an inverter remote alarm device arrangement neighborhood set, and a battery pack remote alarm device arrangement neighborhood set; statistically analyzing the distribution amounts and the distribution uniformities of the photovoltaic module remote alarm device arrangement neighborhood set, the inverter remote alarm device arrangement neighborhood set, and the battery pack remote alarm device arrangement neighborhood set, to obtain a photovoltaic module remote alarm device neighborhood arrangement coefficient set, an inverter remote alarm device neighborhood arrangement coefficient set, and a battery pack remote alarm device neighborhood arrangement coefficient set after weighted analysis; and According to the size of the neighborhood decay central value set of the photovoltaic module neighborhood decay central value set, the inverter performance neighborhood decay central value set and the battery pack performance neighborhood decay central value set, the photovoltaic module remote alarm device neighborhood layout coefficient set, the inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device neighborhood layout coefficient set are matched and analyzed to obtain the photovoltaic module neighborhood device new scheme set, the inverter neighborhood device new scheme set and the battery pack neighborhood device new scheme set; The photovoltaic module neighborhood device new scheme set, the inverter neighborhood device new scheme set and the battery pack neighborhood device new scheme set are summarized to obtain the target device new scheme.

8. The remote alarm device for abnormal monitoring of photovoltaic energy storage power stations as claimed in claim 7, wherein, The new scheme identification module is further used for: A pre-constructed matching analyzer is used to perform one-to-one mapping matching analysis on the photovoltaic module neighborhood decay central value set and the photovoltaic module remote alarm device neighborhood layout coefficient set respectively by using the matching analyzer to obtain the photovoltaic module neighborhood device new scheme set; The matching analyzer is used to perform matching analysis on the inverter performance neighborhood decay central value set and the battery pack performance neighborhood decay central value set, and on the corresponding inverter remote alarm device neighborhood layout coefficient set and the battery pack remote alarm device neighborhood layout coefficient set to obtain the inverter neighborhood device new scheme set and the battery pack neighborhood device new scheme set.

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