Grid-connection Performance Evaluation and Early Warning System and Method Based on Big Data

Abstract

The present invention discloses a grid connection performance evaluation and early warning system and method based on big data, which relates to the technical field of grid connection performance evaluation. The system discloses an equipment data acquisition module, a comprehensive performance evaluation module, a performance prediction module, and a grid connection performance early warning module. By setting the equipment data acquisition module and the comprehensive performance evaluation module, not only the grid connection performance data of grid-connected equipment is collected in real time, but also the operation data of power equipment connected to the grid-connected equipment is collected. The performance of the grid-connected equipment is analyzed deeply and comprehensively on a regular basis to accurately analyze the current performance of the grid-connected equipment itself. By setting the performance prediction module and the grid connection performance early warning module, a simulation operation model of the grid-connected equipment and the power equipment is constructed to simulate and analyze the influence of the current performance of the grid-connected equipment on the operation of the remaining power equipment, thereby establishing a comprehensive and three-dimensional grid-connected equipment performance evaluation and early warning mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of grid connection performance evaluation, and more specifically, to a grid connection performance evaluation and early warning system and method based on big data. Background Art

[0002] With the continuous development of the power system and the improvement of the intelligent level, grid-connected devices play a crucial role in power transmission and distribution. As a bridge connecting power equipment and the power grid, grid-connected devices ensure the efficient and stable exchange of energy and are key elements in building a complete energy conversion and transmission system. They not only need to bear the connection requirements of various types of power equipment such as generators and transformers, but also need to adapt to the complex interactions between different power equipment to ensure the stable operation of the entire power system.

[0003] With the expansion of the power grid scale and the increase in power demand, grid-connected devices need to bear greater loads and pressures, and their performance stability and reliability become important guarantees for the safe operation of the power system.

[0004] Traditionally, the performance evaluation of grid-connected devices mainly relies on manual inspections and regular tests. This method is not only time-consuming and laborious, but also can only evaluate based on the current performance data of grid-connected devices, and cannot deeply analyze the impact of the current performance of grid-connected devices on the operation of other power equipment.

[0005] Therefore, it is particularly important to develop a grid connection performance evaluation and early warning system based on big data. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a grid connection performance evaluation and early warning system and method based on big data.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] A grid connection performance evaluation and early warning system based on big data, including a device data acquisition module, a performance evaluation module, a performance prediction module, and a grid connection performance early warning module;

[0009] The device data acquisition module is used to determine the power equipment connected to the grid-connected device, collect various types of grid connection performance data of the grid-connected device and the operation data of the power equipment in real time, extract features from various types of grid connection performance data of the grid-connected device and the operation data of the power equipment, and obtain the performance characteristics of various types of grid connection performance data of the grid-connected device and the operation characteristics of the power equipment;

[0010] The performance evaluation module obtains the comprehensive performance evaluation value of the grid-connected device every time a performance evaluation period elapses;

[0011] The performance prediction module constructs a grid-connected simulation operation model based on the performance characteristics of various types of grid-connected performance data of grid-connected devices and the operation characteristics of power equipment. The grid-connected simulation operation model conducts a simulation operation for a performance evaluation duration, and then obtains the performance prediction value of the grid-connected device;

[0012] The grid-connected performance warning module obtains the grid-connected performance evaluation warning value based on the comprehensive evaluation value and the performance prediction value of the grid-connected device, and determines whether to send a grid-connected device performance warning signal based on the comparison result between the grid-connected performance evaluation warning value and the grid-connected performance evaluation warning threshold.

[0013] Further, the comprehensive evaluation value of the performance of the grid-connected device is obtained through the following steps: Obtain the performance evaluation value of various types of grid-connected performance data, set the high performance evaluation value and the low performance evaluation value. When the performance evaluation value is greater than or equal to the high performance evaluation value, mark the corresponding type of grid-connected performance data as satisfactory performance data. When the performance evaluation value is less than or equal to the low performance evaluation value, mark the corresponding type of grid-connected performance data as discrepant performance data. When the performance evaluation value is between the high performance evaluation value and the low performance evaluation value, mark the corresponding type of grid-connected performance data as normal performance data. Mark the total number of satisfactory performance data as Bte, mark the total number of discrepant performance data as Mks, sum up the performance evaluation values of the normal performance data and take the average to obtain the normal performance evaluation value Pmd, and use the formula to obtain the comprehensive evaluation value Ustd of the performance of the grid-connected device, where ua is the satisfactory performance coefficient, ub is the discrepant performance coefficient, and uc is the normal performance coefficient.

[0014] Further, the performance evaluation value of various types of grid-connected performance data is obtained through the following steps: Obtain the performance evaluation model of various types of grid-connected performance data, and input the performance characteristics of various types of grid-connected performance data into the corresponding performance evaluation model respectively, and output the performance evaluation value of various types of grid-connected performance data.

[0015] Further, constructing a grid-connected simulation operation model based on the performance characteristics of various types of grid-connected performance data of grid-connected devices and the operation characteristics of power equipment is specifically as follows: Select simulation software, create a grid-connected device ontology and other power equipment ontologies in the simulation software based on the performance characteristics of various types of grid-connected performance data of grid-connected devices and the operation characteristics of power equipment, set parameters for the grid-connected device ontology and the power equipment ontologies, and establish electrical connections between the grid-connected device ontology and each power equipment ontology to construct a grid-connected simulation operation model. The grid-connected simulation operation model can simulate the operation of the grid-connected device ontology and the power equipment ontologies.

[0016] Further, the grid-connected simulation operation model conducts a simulation operation for a performance evaluation duration, and then obtains the predicted value of the performance of the grid-connected equipment. Specifically: divide the performance evaluation duration into m simulation evaluation periods of equal duration, obtain the influence values of the power equipment for each simulation evaluation period, sum up and average the influence values of the power equipment for all simulation evaluation periods to obtain the average equipment influence value Kzy. Arrange all the influence values of the power equipment in the order of the simulation evaluation periods. Sum up the adjacent two influence values of the power equipment after sorting to obtain the continuous equipment influence value. Set the continuous equipment influence threshold. When the continuous equipment influence value is greater than or equal to the continuous equipment influence threshold, increase the continuous influence quantity by one, and mark the continuous influence quantity as Dbp. Calculate the difference between the adjacent two influence values of the power equipment after sorting and take the absolute value to obtain the equipment fluctuation influence value. Set the equipment fluctuation influence threshold. When the equipment fluctuation influence value is greater than or equal to the equipment fluctuation influence threshold, increase the fluctuation influence quantity by one, and mark the fluctuation influence quantity as Tmg. Use the formula to obtain the predicted value of the performance of the grid-connected equipment Astb, where m1 is the equipment influence coefficient, m2 is the continuous influence coefficient, and m3 is the fluctuation influence coefficient.

[0017] Further, the influence value of the power equipment for the simulation evaluation period is obtained through the following steps: collect the operation data of each power equipment body during the simulation evaluation period, extract the features of the operation data of each power equipment body to obtain the operation characteristics of each power equipment body, and then obtain the operation fluctuation value Ecb of each power equipment body, c = 1, 2,..., C, where C is the number of the power equipment body. Set the operation fluctuation coefficient as Fb. Sum up and average the operation fluctuation values of the power equipment bodies of the same equipment type to obtain the equipment fluctuation value of this equipment type. Set the equipment fluctuation threshold. When the equipment fluctuation value is greater than or equal to the equipment fluctuation threshold, increase the number of fluctuating equipment types by one, and mark the number of fluctuating equipment types as Szx. Use the formula to obtain the influence value of the power equipment Lbs for this simulation evaluation period, where e1 is the fluctuating equipment type coefficient.

[0018] Further, the operation fluctuation value of the power equipment body is obtained through the following steps: obtain the equipment type of the power equipment body, obtain the operation fluctuation analysis model of this equipment type, input the operation characteristics of this power equipment body into the operation fluctuation analysis model, and the operation fluctuation analysis model outputs the operation fluctuation value of this power equipment body.

[0019] Further, obtain the grid connection performance evaluation warning value according to the comprehensive evaluation value and the predicted value of the performance of the grid-connected equipment. Specifically: use the formula Obtain the grid connection performance evaluation warning value Rsw, where f1 is the comprehensive evaluation coefficient of performance and f2 is the performance prediction coefficient.

[0020] Furthermore, the grid connection performance evaluation warning method based on big data includes the following steps:

[0021] Step 1: Determine the power equipment connected to the grid-connected equipment, and collect various types of grid connection performance data of the grid-connected equipment and the operation data of the power equipment in real time;

[0022] Step 2: Extract features from various types of grid connection performance data of the grid-connected equipment and the operation data of the power equipment to obtain the performance features of various types of grid connection performance data of the grid-connected equipment and the operation features of the power equipment;

[0023] Step 3: Obtain the comprehensive evaluation value of the performance of the grid-connected equipment every time a performance evaluation period elapses;

[0024] Step 4: Construct a grid connection simulation operation model based on the performance features of various types of grid connection performance data of the grid-connected equipment and the operation features of the power equipment;

[0025] Step 5: Conduct a simulation operation of the grid connection simulation operation model for a performance evaluation period to obtain the performance prediction value of the grid-connected equipment;

[0026] Step 6: Obtain the grid connection performance evaluation warning value according to the comprehensive evaluation value and the performance prediction value of the performance of the grid-connected equipment. Based on the comparison result between the grid connection performance evaluation warning value and the grid connection performance evaluation warning threshold, determine whether to send a grid-connected equipment performance warning signal.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The method of the present invention can accurately analyze the current performance of the grid-connected equipment itself, synchronously simulate and analyze the impact of the current performance of the grid-connected equipment on the operation of other power equipment, and combine the performance of the grid-connected equipment itself and the impact on the operation of the power equipment to meet the dynamic warning of the performance of the grid-connected equipment;

[0029] 2. The system of the present invention, by setting up an equipment data acquisition module and a performance comprehensive evaluation module, not only collects the grid connection performance data of the grid-connected equipment in real time, but also collects the operation data of the power equipment connected to the grid-connected equipment, regularly conducts in-depth and comprehensive analysis of the performance of the grid-connected equipment, sets up a performance prediction module and a grid connection performance warning module, constructs a simulation operation model of the grid-connected equipment and the power equipment, and then establishes a comprehensive and three-dimensional grid-connected equipment performance evaluation warning mechanism. Description of the Drawings

[0030] Figure 1It is a method flow chart of a grid connection performance evaluation and early warning method based on big data;

[0031] Figure 2 It is a system module diagram of a grid connection performance evaluation and early warning system based on big data;

[0032] Figure 3 It is a flow chart for obtaining the operation fluctuation value of the power equipment body. Specific implementation mode

[0033] Example 1: Refer to Figure 1 , a grid connection performance evaluation and early warning method based on big data, includes the following steps:

[0034] Step 1: Determine the power equipment connected to the grid-connected equipment, and collect various types of grid connection performance data of the grid-connected equipment and the operation data of the power equipment in real time.

[0035] Step 2: Extract features from various types of grid connection performance data of the grid-connected equipment and the operation data of the power equipment to obtain the performance characteristics of various types of grid connection performance data of the grid-connected equipment and the operation characteristics of the power equipment.

[0036] Step 3: Every time a performance evaluation period passes, obtain the comprehensive evaluation value of the performance performance of the grid-connected equipment.

[0037] Step 4: Build a grid simulation operation model based on the performance characteristics of various types of grid connection performance data of the grid-connected equipment and the operation characteristics of the power equipment.

[0038] Step 5: The grid simulation operation model conducts simulation operation for a performance evaluation period, and then obtains the predicted value of the performance performance of the grid-connected equipment.

[0039] Step 6: Obtain the grid connection performance evaluation and early warning value according to the comprehensive evaluation value and the predicted value of the performance performance of the grid-connected equipment. Based on the comparison result between the grid connection performance evaluation and early warning value and the grid connection performance evaluation and early warning threshold, determine whether to send a grid-connected equipment performance early warning signal.

[0040] The above method can accurately analyze the current performance of the grid-connected equipment itself, synchronously simulate and analyze the impact of the current performance of the grid-connected equipment on the operation of other power equipment, and dynamically complete the performance early warning of the grid-connected equipment in combination with the self-performance of the grid-connected equipment and the impact on the operation of the power equipment.

[0041] Example 2: Refer to Figures 2 - 3 , a grid connection performance evaluation and early warning system based on big data, includes an equipment data acquisition module, a performance performance comprehensive evaluation module, a performance performance prediction module, and a grid connection performance early warning module.

[0042] Device Data Acquisition Module: Determine the power equipment connected to the grid-connected device (the grid-connected device is a device that connects the power equipment to the power grid for energy exchange. It is mainly used to form a complete energy conversion and transmission system between power equipment such as generators and transformers in the power system and the power grid. Therefore, the grid-connected device needs to be connected to multiple power equipment at the same time, and there are also multiple types of power equipment). Real-time collect various types of grid-connected performance data of the grid-connected device and the operation data of the power equipment, and extract the characteristics of various types of grid-connected performance data of the grid-connected device (the types of grid-connected performance data include active power data, reactive power data, total harmonic distortion data of current, etc. The various types of grid-connected performance data jointly reflect the performance of the grid-connected device) and the operation characteristics of the power equipment to obtain the performance characteristics of various types of grid-connected performance data of the grid-connected device and the operation characteristics of the power equipment.

[0043] Comprehensive Performance Evaluation Module: Set the performance evaluation duration as T asse (T asse is the duration preset by the system. When the grid-connected device is running, the performance evaluation duration loops infinitely). Every time a performance evaluation duration passes, obtain the comprehensive performance evaluation value of the grid-connected device.

[0044] The comprehensive performance evaluation value of the grid-connected device is obtained through the following steps: Obtain the performance evaluation models of various types of grid-connected performance data, input the performance characteristics of various types of grid-connected performance data into the corresponding performance evaluation models respectively, and output the performance evaluation values of various types of grid-connected performance data. Set the high performance evaluation value and the low performance evaluation value (the high performance evaluation value is greater than the low performance evaluation value, and both the high performance evaluation value and the low performance evaluation value are preset values of the system). When the performance evaluation value is greater than or equal to the high performance evaluation value, mark the corresponding type of grid-connected performance data as satisfactory performance data. When the performance evaluation value is less than or equal to the low performance evaluation value, mark the corresponding type of grid-connected performance data as gap performance data. When the performance evaluation value is between the high performance evaluation value and the low performance evaluation value, mark the corresponding type of grid-connected performance data as normal performance data. Mark the total number of satisfactory performance data as Bte, mark the total number of gap performance data as Mks, sum up the performance evaluation values of the normal performance data and take the average to obtain the normal performance evaluation value Pmd, and use the formula to obtain the comprehensive performance evaluation value Ustd of the grid-connected device, where ua is the satisfactory performance coefficient, ub is the gap performance coefficient, uc is the normal performance coefficient, the value of ua is 0.89, the value of ub is 0.81, and the value of uc is 1.25.

[0045] Grid connection performance data of different types have different judgment criteria. Therefore, each type of grid connection performance data corresponds to an independent performance evaluation model. The difference between each performance evaluation model lies in the different training data. Therefore, in this embodiment, only the construction method of the performance evaluation model for one type of grid connection performance data is illustrated by way of example, and the construction methods of the performance evaluation models for various types of grid connection performance data will not be enumerated one by one.

[0046] The construction method of the performance evaluation model for active power data is as follows: Collect n groups of active power data, extract features from each group of active power data to obtain multiple performance features of the active power data, construct a neural network model, use the performance features of the active power data as the training data of the neural network model, assign a performance evaluation value to each performance feature, and the value range of the performance evaluation value is (3.0 - 4.9). The larger the value of the performance evaluation value, the better the performance of the active power; the smaller the value of the performance evaluation value, the worse the performance of the active power. Divide the training data into a 60% training set and a 40% validation set, and perform neural network iterative training on the training set and the validation set. After the training is completed, the performance evaluation model for the active power data is constructed.

[0047] For the performance evaluation model of reactive power data, collect n groups of reactive power data. The larger the value of the performance evaluation value, the better the performance of the reactive power; the smaller the value of the performance evaluation value, the worse the performance of the reactive power.

[0048] Set up a device data acquisition module and a comprehensive performance evaluation module, which not only collect the grid connection performance data of grid-connected devices in real time, but also collect the operation data of power equipment connected to the grid-connected devices, and conduct in-depth and comprehensive analysis of the performance of grid-connected devices regularly to accurately analyze the current performance of grid-connected devices themselves.

[0049] Performance prediction module: Based on the performance characteristics of various types of grid connection performance data of grid-connected devices and the operation characteristics of power equipment, construct a grid connection simulation operation model. The grid connection simulation operation model performs simulation operation for a performance evaluation duration, and then obtains the performance prediction value of the grid-connected device.

[0050] A grid-connected simulation operation model is constructed based on the performance characteristics of various types of grid-connected performance data of grid-connected equipment and the operation characteristics of power equipment. Specifically: Select the MATLAB / Simulink simulation software. Based on the performance characteristics of various types of grid-connected performance data of grid-connected equipment and the operation characteristics of power equipment, create a grid-connected equipment ontology and other power equipment ontologies in the simulation software, set parameters for the grid-connected equipment ontology and the power equipment ontologies, and establish electrical connections between the grid-connected equipment ontology and each power equipment ontology to construct the grid-connected simulation operation model. The grid-connected simulation operation model can simulate the operation of the grid-connected equipment ontology and the power equipment ontologies.

[0051] The grid-connected simulation operation model conducts a simulation operation for a performance evaluation duration to obtain the predicted value of the performance of the grid-connected equipment. Specifically: Divide the performance evaluation duration into m equally long simulation evaluation periods, obtain the power equipment impact values for each simulation evaluation period, sum up and take the average of the power equipment impact values for all simulation evaluation periods to obtain the equipment impact average value Kzy. Arrange all the power equipment impact values in the order of the simulation evaluation periods. Sum up the adjacent two power equipment impact values after sorting to obtain the equipment continuous impact value. Set the equipment continuous impact threshold (the equipment continuous impact threshold is a value preset by the system). When the equipment continuous impact value is greater than or equal to the equipment continuous impact threshold, increase the continuous impact count by one. When the equipment continuous impact value is less than the equipment continuous impact threshold, do not make any treatment and mark the continuous impact count as Dbp. Calculate the difference between the adjacent two power equipment impact values after sorting and take the absolute value to obtain the equipment fluctuation impact value. Set the equipment fluctuation impact threshold (the equipment fluctuation impact threshold is a value preset by the system). When the equipment fluctuation impact value is greater than or equal to the equipment fluctuation impact threshold, increase the fluctuation impact count by one. When the equipment fluctuation impact value is less than the equipment fluctuation impact threshold, do not make any treatment and mark the fluctuation impact count as Tmg. Use the formula to obtain the predicted value of the performance of the grid-connected equipment Astb, where m1 is the equipment impact coefficient, m2 is the continuous impact coefficient, m3 is the fluctuation impact coefficient. The value of m1 is 0.74, the value of m2 is 0.62, and the value of m3 is 0.81.

[0052] The impact value of power equipment during the simulation evaluation period is obtained through the following steps: Collect the operation data of each power equipment body during the simulation evaluation period, extract the characteristics of the operation data of each power equipment body to obtain the operation characteristics of each power equipment body, and then obtain the operation fluctuation value Ecb of each power equipment body, where c = 1, 2, …, C, and C is the number of power equipment bodies. Each power equipment body corresponds to an independent number. Set the operation fluctuation coefficient as Fb, where b = 1, 2, 3, …, b, and F1 < F2 < F3 < … < Fb. Each operation fluctuation coefficient corresponds to an operation fluctuation value within a certain range. The range of the operation fluctuation value includes (0, Ec1], (Ec1, Ec2], …, (Ecb - 1, Ecb]. When Ecb ∈ (0, Ec1], the operation fluctuation coefficient is F1. Sum up and take the average of the operation fluctuation values of the power equipment bodies of the same equipment type to obtain the equipment fluctuation value of this equipment type. Set the equipment fluctuation threshold (the equipment fluctuation threshold is a value preset by the system). When the equipment fluctuation value is greater than or equal to the equipment fluctuation threshold, increase the number of fluctuating equipment types by one. When the equipment fluctuation value is less than the equipment fluctuation threshold, no processing is performed. Mark the number of fluctuating equipment types as Szx. Use the formula to obtain the impact value Lbs of the power equipment during this simulation evaluation period, where e1 is the fluctuating equipment type coefficient, and the value of e1 is 1.97.

[0053] The operation fluctuation value of the power equipment body is obtained through the following steps: Obtain the equipment type of the power equipment body (the types of power equipment bodies include generators, transformers, etc.), obtain the operation fluctuation analysis model of this equipment type, and input the operation characteristics of this power equipment body into the operation fluctuation analysis model. The operation fluctuation analysis model outputs the operation fluctuation value of this power equipment body.

[0054] Each type of power equipment body corresponds to an independent operation fluctuation analysis model. The difference between each operation fluctuation analysis model lies in the different training data. Therefore, in this embodiment, only the construction method of the operation fluctuation analysis model of one type of power equipment body is illustrated as an example, and the construction methods of the operation fluctuation analysis models of each type of power equipment body are not enumerated one by one.

[0055] The construction method of the operation fluctuation analysis model of the generator is as follows: Collect the operation data of x generators, extract the features of the operation data of each group of generators to obtain multiple groups of operation features of the generators, construct a neural network model, use the operation features of the generators as the training data of the neural network model, assign an operation fluctuation value to each performance feature, and the value range of the operation fluctuation value is (0.1 - 0.9). The larger the value of the operation fluctuation value, the more unstable the operation of the generator; the smaller the value of the operation fluctuation value, the more stable the operation of the generator. Divide the training data into a 70% training set and a 30% validation set, and perform neural network iterative training on the training set and the validation set. After the training is completed, the operation fluctuation analysis model of the generator is constructed.

[0056] For the operation fluctuation analysis model of the transformer, collect the operation data of x transformers. The larger the value of the operation fluctuation value, the less stable the operation of the transformer; the smaller the value of the operation fluctuation value, the more stable the operation of the transformer.

[0057] Grid connection performance early warning module: Obtain the grid connection performance evaluation early warning value according to the comprehensive evaluation value and the predicted value of the performance of the grid-connected equipment, set the grid connection performance evaluation early warning threshold (the grid connection performance evaluation early warning threshold is a value preset by the system). When the grid connection performance evaluation early warning value is greater than or equal to the grid connection performance evaluation early warning threshold, send a grid-connected equipment performance early warning signal; when the grid connection performance evaluation early warning value is less than the grid connection performance evaluation early warning threshold, do not process.

[0058] Obtain the grid connection performance evaluation early warning value according to the comprehensive evaluation value and the predicted value of the performance of the grid-connected equipment. Specifically: Use the formula to obtain the grid connection performance evaluation early warning value Rsw, where f1 is the comprehensive evaluation coefficient of the performance, f2 is the predicted coefficient of the performance, the value of f1 is 0.47, and the value of f2 is 0.49.

[0059] Set up a performance prediction module and a grid connection performance early warning module, construct a simulation operation model of the grid-connected equipment and the power equipment, simulate and analyze the influence of the current performance of the grid-connected equipment on the operation of the rest of the power equipment, and then establish a comprehensive and three-dimensional grid-connected equipment performance evaluation early warning mechanism.

[0060] The above formulas are all dimensionless and take their numerical calculations. The formula is obtained by collecting a large amount of data and performing software simulation to get a formula that is closest to the real situation. The preset parameters in the formula are set by technicians in the field according to the actual situation.

[0061] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0062] It should be understood that in various embodiments of the present application, the order numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0063] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0064] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0065] In several embodiments provided by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical, or other forms.

[0066] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, and other various media that can store program codes.

[0067] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. Grid-connected performance evaluation and early warning system based on big data, characterized in that, It includes a device data acquisition module, a comprehensive performance evaluation module, a performance prediction module, and a grid connection performance warning module; The device data acquisition module is used to determine the power equipment connected to the grid-connected device, collect various types of grid connection performance data of the grid-connected device and the operation data of the power equipment in real time, extract features from the various types of grid connection performance data of the grid-connected device and the operation data of the power equipment, and obtain the performance features of the various types of grid connection performance data of the grid-connected device and the operation features of the power equipment; The comprehensive performance evaluation module obtains the comprehensive performance evaluation value of the grid-connected device every performance evaluation period; The performance prediction module constructs a grid-connected simulation operation model based on the performance characteristics of various types of grid-connected performance data of grid-connected devices and the operation characteristics of power equipment. The grid-connected simulation operation model conducts a simulation operation for a performance evaluation duration, divides the performance evaluation duration into m equally long simulation evaluation periods, obtains the power equipment impact values for each simulation evaluation period, sums up and takes the average of the power equipment impact values for all simulation evaluation periods to obtain the equipment impact average value Kzy. Arrange all the power equipment impact values in the order of the simulation evaluation periods, sum up the adjacent two power equipment impact values after sorting to obtain the equipment continuous impact value, set the equipment continuous impact threshold value. When the equipment continuous impact value is greater than or equal to the equipment continuous impact threshold value, increase the continuous impact count by one and mark the continuous impact count as Dbp. Calculate the difference between the adjacent two power equipment impact values after sorting and take the absolute value to obtain the equipment fluctuation impact value, set the equipment fluctuation impact threshold value. When the equipment fluctuation impact value is greater than or equal to the equipment fluctuation impact threshold value, increase the fluctuation impact count by one and mark the fluctuation impact count as Tmg. Use the formula to obtain the performance prediction value Astb of the grid-connected device, where m1 is the equipment impact coefficient, m2 is the continuous impact coefficient, and m3 is the fluctuation impact coefficient; The grid connection performance warning module obtains the grid connection performance evaluation warning value according to the comprehensive performance evaluation value and the performance prediction value of the grid-connected device, and determines whether to send a grid-connected device performance warning signal based on the comparison result between the grid connection performance evaluation warning value and the grid connection performance evaluation warning threshold.

2. The grid connection performance evaluation and early warning system based on big data according to claim 1, characterized in that The comprehensive evaluation value of the performance of grid-connected equipment is obtained through the following steps: Obtain the performance evaluation values of various types of grid-connected performance data, set the high value and low value of performance evaluation. When the performance evaluation value is greater than or equal to the high value of performance evaluation, mark the grid-connected performance data of the corresponding type as satisfactory performance data. When the performance evaluation value is less than or equal to the low value of performance evaluation, mark the grid-connected performance data of the corresponding type as discrepant performance data. When the performance evaluation value is between the high value and low value of performance evaluation, mark the grid-connected performance data of the corresponding type as normal performance data. Mark the total number of satisfactory performance data as Bte, mark the total number of discrepant performance data as Mks, sum up the performance evaluation values of the normal performance data and take the average to obtain the normal performance evaluation value Pmd. Use the formula to obtain the comprehensive evaluation value Ustd of the performance of grid-connected equipment, where ua is the satisfactory performance coefficient, ub is the discrepant performance coefficient, and uc is the normal performance coefficient.

3. The grid connection performance evaluation and early warning system based on big data according to claim 2, characterized in that The performance evaluation value of each type of grid connection performance data is obtained through the following steps: obtain the performance evaluation model of each type of grid connection performance data, input the performance features of each type of grid connection performance data into the corresponding performance evaluation model respectively, and output the performance evaluation value of each type of grid connection performance data.

4. The grid connection performance evaluation and early warning system based on big data according to claim 1, characterized in that, A grid connection simulation operation model is constructed based on the performance features of various types of grid connection performance data of the grid-connected device and the operation features of the power equipment. Specifically: select simulation software, create a grid-connected device ontology and other power equipment ontologies in the simulation software based on the performance features of various types of grid connection performance data of the grid-connected device and the operation features of the power equipment, set parameters for the grid-connected device ontology and the power equipment ontologies, and establish electrical connections between the grid-connected device ontology and each power equipment ontology to construct the grid connection simulation operation model. The grid connection simulation operation model can simulate the operation of the grid-connected device ontology and the power equipment ontologies.

5. The grid connection performance evaluation and early warning system based on big data according to claim 1, characterized in that The influence value of power equipment during the simulation evaluation period is obtained through the following steps: Collect the operation data of each power equipment body during the simulation evaluation period, extract the characteristics of the operation data of each power equipment body to obtain the operation characteristics of each power equipment body, and then obtain the operation fluctuation value Ecb of each power equipment body, where c = 1, 2,..., C, and C is the number of power equipment bodies. Set the operation fluctuation coefficient as Fb, sum up and take the average of the operation fluctuation values of power equipment bodies of the same equipment type to obtain the equipment fluctuation value of this equipment type. Set the equipment fluctuation threshold. When the equipment fluctuation value is greater than or equal to the equipment fluctuation threshold, increase the number of fluctuating equipment types by one, and mark the number of fluctuating equipment types as Szx. Use the formula to obtain the influence value Lbs of power equipment during this simulation evaluation period, where e1 is the coefficient of the fluctuating equipment type.

6. The grid connection performance evaluation and early warning system based on big data according to claim 5, characterized in that The operation fluctuation value of the power equipment ontology is obtained through the following steps: obtain the device type of the power equipment ontology, obtain the operation fluctuation analysis model of this device type, input the operation features of this power equipment ontology into the operation fluctuation analysis model, and the operation fluctuation analysis model outputs the operation fluctuation value of this power equipment ontology.

7. The grid connection performance evaluation and early warning system based on big data according to claim 2, characterized in that, Obtain the grid connection performance evaluation and warning value based on the comprehensive evaluation value and predicted value of the performance of grid-connected equipment. Specifically: Use the formula to obtain the grid connection performance evaluation and warning value Rsw, where f1 is the comprehensive evaluation coefficient of performance and f2 is the predicted coefficient of performance.

8. The grid connection performance evaluation and early warning method based on big data is applied to the grid connection performance evaluation and early warning system based on big data according to any one of claims 1-7, and is characterized in that, It includes the following steps: Step 1: Determine the power equipment connected to the grid-connected device, and collect various types of grid connection performance data of the grid-connected device and the operation data of the power equipment in real time; Step 2: Extract features from the various types of grid connection performance data of the grid-connected device and the operation data of the power equipment to obtain the performance features of the various types of grid connection performance data of the grid-connected device and the operation features of the power equipment; Step 3: Obtain the comprehensive performance evaluation value of the grid-connected device every performance evaluation period; Step 4: Construct a grid connection simulation operation model based on the performance features of various types of grid connection performance data of the grid-connected device and the operation features of the power equipment; Step 5: The grid connection simulation operation model performs a simulation operation for one performance evaluation period, and then obtains the performance prediction value of the grid-connected device; Step 6: Obtain the grid connection performance evaluation warning value based on the comprehensive evaluation value and the predicted value of the performance of the grid-connected device. Determine whether to send a grid-connected device performance warning signal based on the comparison result between the grid connection performance evaluation warning value and the grid connection performance evaluation warning threshold.

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