Operation optimization method of prefabricated cabin transformer substation and computer equipment

Abstract

The invention relates to an operation optimization method of a prefabricated cabin transformer substation and computer equipment. The method comprises the following steps: determining environment performance index values in cabins according to obtained environment state data of the cabins in the prefabricated cabin transformer substation; determining a comprehensive performance index value of each cabin according to each environment performance index value in each cabin; determining the overall performance index value of the prefabricated cabin transformer substation according to the comprehensive performance index value of each cabin body; and optimizing the operation of the prefabricated cabin transformer substation according to the overall performance index value of the prefabricated cabin transformer substation, the comprehensive performance index value of each cabin body and each environmental performance index value in each cabin body. By adopting the method, the accuracy of optimizing the operation of the prefabricated cabin transformer substation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of prefabricated cabin substations, and particularly to an operation optimization method and a computer device for a prefabricated cabin substation. Background Art

[0002] With the gradual maturity of the technology of prefabricated cabin substations, prefabricated cabin substations have been widely used in substation construction due to their advantages such as less floor area and short construction period. By optimizing the operation of prefabricated cabin substations, the operation efficiency of prefabricated cabin substations can be improved.

[0003] In traditional substations, the operation status of each operating device in the substation is monitored, and the operation of the substation is optimized according to the monitoring results. For example, by monitoring whether the transformer is overloaded, the load of the transformer is adjusted.

[0004] However, if the operation optimization method of the substation in the related technology is used to optimize the prefabricated cabin substation, there will be a problem of inaccurate optimization. Summary of the Invention

[0005] Based on this, the present application provides an operation optimization method and a computer device for a prefabricated cabin substation, which can improve the accuracy of optimizing the operation of the prefabricated cabin substation.

[0006] In a first aspect, the present application provides an operation optimization method for a prefabricated cabin substation, and the method includes:

[0007] Determine the environmental performance index values in each cabin according to the obtained environmental status data of each cabin in the prefabricated cabin substation;

[0008] Determine the comprehensive performance index value of each cabin according to the environmental performance index values in each cabin;

[0009] Determine the overall performance index value of the prefabricated cabin substation according to the comprehensive performance index values of each cabin;

[0010] Optimize the operation of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin.

[0011] In some embodiments, the environmental status data includes basic environmental data, and the environmental performance index value includes a basic environmental index value; determining the environmental performance index values in each cabin according to the obtained environmental status data of each cabin in the prefabricated cabin substation includes:

[0012] Obtain a preset basic environmental range, where the basic environmental range is the range between the upper limit of the basic environment and the lower limit of the basic environment;

[0013] When each basic environment data is within the basic environment range, obtain the first difference between each basic environment data and the basic environment lower limit, and the second difference between each basic environment data and the basic environment upper limit. Determine an intermediate result based on the first difference and the second difference, and determine the difference between the preset value and the intermediate result as each basic environment index value;

[0014] When each basic environment data is outside the basic environment range, determine the preset value as each basic environment index value.

[0015] In some embodiments, each environmental state data includes at least one noise component data, and the environmental performance index value includes a noise index value; according to the obtained environmental state data of each cabin in the prefabricated substation, determine the environmental performance index values of each cabin, including:

[0016] When each noise component data is less than or equal to the preset noise upper limit, standardize each noise component data to the range between the preset value and 1 to obtain a noise component standardization result, and determine the value obtained by subtracting the noise component standardization result from 1 as the performance index value of each noise component data;

[0017] When each noise component data is greater than the noise upper limit, determine the preset value as the performance index value of each noise component data;

[0018] Determine each noise index value according to the performance index values of at least one noise component data in each environmental state data.

[0019] In some embodiments, the environmental state data includes oxygen content data, and the environmental performance index value includes an oxygen content index value; according to the obtained environmental state data of each cabin in the prefabricated substation, determine the environmental performance index values of each cabin, including:

[0020] Obtain the preset oxygen content range, where the oxygen content range is the range between the oxygen content upper limit and the oxygen content lower limit;

[0021] When each oxygen content data is within the oxygen content range, standardize each oxygen content data to the range between the preset value and 1 to obtain each oxygen content index value;

[0022] When each oxygen content data is greater than the oxygen content upper limit, determine 1 as each oxygen content index value;

[0023] When each oxygen content data is less than the oxygen content lower limit, determine the preset value as each oxygen content index value.

[0024] In some embodiments, the environmental status data includes environmental pollution data, and the environmental performance index value includes the environmental pollution index value; determining the environmental performance index values in each cabin according to the obtained environmental status data of each cabin in the prefabricated substation, including:

[0025] When each environmental pollution data is greater than or equal to the preset upper limit of environmental pollution, set 0 as each environmental pollution index value;

[0026] When each environmental pollution data is less than the upper limit of environmental pollution, perform normalization processing on each environmental pollution data to obtain each environmental pollution index value.

[0027] In some embodiments, determining the comprehensive performance index value of each cabin according to the environmental performance index values in each cabin, including:

[0028] Obtain the type of each cabin, and determine the index weight of each environmental performance index value in each cabin according to the type of each cabin;

[0029] Use the index weight of each environmental performance index value to perform weighted processing on each environmental performance index value in each cabin to obtain the comprehensive performance index value of each cabin.

[0030] In some embodiments, determining the overall performance index value of the prefabricated substation according to the comprehensive performance index value of each cabin, including:

[0031] Obtain the type of each cabin, and determine the cabin weight of each cabin according to the type of each cabin;

[0032] Use the cabin weight of each cabin to perform weighted processing on the comprehensive performance index value of each cabin to obtain the overall performance index value of the prefabricated substation.

[0033] In some embodiments, optimizing the operation of the prefabricated substation according to the overall performance index value of the prefabricated substation, the comprehensive performance index value of each cabin, and the environmental performance index values in each cabin, including:

[0034] When the environmental performance index values in each cabin are less than or equal to the preset environmental performance index thresholds, obtain the adjustment priority of the environmental performance index values in each cabin, and use the adjustment priority to adjust the operation of each cabin until the comprehensive performance index value of each cabin reaches the maximum;

[0035] When the comprehensive performance index values of each cabin are less than or equal to the preset comprehensive performance index thresholds within the preset time period, perform maintenance processing on each cabin;

[0036] When the overall performance index value of the prefabricated cabin substation is less than or equal to the preset overall performance index threshold within the specified duration, maintenance processing is performed on the prefabricated cabin substation.

[0037] In some embodiments, according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin, the operation of the prefabricated cabin substation is optimized, including:

[0038] Construct a performance vector of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin;

[0039] Input the performance vector of the prefabricated cabin substation into a bidirectional long short-term memory neural network to obtain the predicted index value of the prefabricated cabin substation;

[0040] Optimize the operation of the prefabricated cabin substation according to the predicted index value of the prefabricated cabin substation, the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin.

[0041] In a second aspect, the present application provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the above are implemented.

[0042] In the technical solution provided by the embodiments of the present application, since the operation of the prefabricated cabin substation is optimized through the environmental state data of each cabin in the prefabricated cabin substation, the optimization of the operation of the prefabricated cabin substation can be adjusted in real time according to the environmental state data, improving the accuracy of the operation optimization of the prefabricated cabin substation; and, by calculating the comprehensive performance index values of each cabin and calculating the overall performance index value, it is possible to evaluate the quality of the comprehensive operation of each cabin and the overall operation of the prefabricated cabin substation, and then be able to quantitatively evaluate the comprehensive operation of each cabin and the overall operation of the prefabricated cabin substation. Therefore, optimizing the operation of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin can comprehensively evaluate the operation status of the prefabricated cabin substation from local to overall, and further improve the accuracy of optimizing the operation of the prefabricated cabin substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0044] Figure 1 Flow schematic diagram of the operation optimization method for the prefabricated cabin substation provided in the first embodiment;

[0045] Figure 2 Flow schematic diagram of the operation optimization method for the prefabricated cabin substation provided in the second embodiment;

[0046] Figure 3 Flow schematic diagram of the operation optimization method for the prefabricated cabin substation provided in the third embodiment;

[0047] Figure 4 Flow schematic diagram of the operation optimization method for the prefabricated cabin substation provided in the fourth embodiment;

[0048] Figure 5 Flow schematic diagram of the operation optimization method for the prefabricated cabin substation provided in the fifth embodiment;

[0049] Figure 6 Frame schematic diagram of the prefabricated cabin substation state monitoring and optimization device provided in some embodiments;

[0050] Figure 7 Distribution schematic diagram of sensors in one cabin body provided in some embodiments;

[0051] Figure 8 Schematic diagram of the change of various environmental state data over time provided in some embodiments;

[0052] Figure 9 Schematic diagram of the change relationship between the cabin temperature index value and the cabin temperature data provided in some embodiments;

[0053] Figure 10 Schematic diagram of the change relationship between the cabin humidity index value and the cabin humidity data provided in some embodiments;

[0054] Figure 11 Schematic diagram of the change relationship between the noise index value and the noise component data provided in some embodiments;

[0055] Figure 12 Schematic diagram of the change relationship between the oxygen content index value and the oxygen content data provided in some embodiments;

[0056] Figure 13Schematic diagram of the variation relationship between the pollution gas concentration index values and the pollution gas concentration data provided for some embodiments;

[0057] Figure 14 Schematic diagram of the process of the multi-dimensional monitoring and early warning method for prefabricated substation provided for some embodiments;

[0058] Figure 15 Schematic diagram of the process of the comprehensive operation optimization method for prefabricated substation provided for some embodiments;

[0059] Figure 16 Schematic diagram of the structure of a computer device provided for some embodiments. Detailed implementation manners

[0060] Hereinafter, embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and therefore are only examples and cannot be used to limit the protection scope of the present application.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the description of the specification and claims of this application and the above accompanying drawings are intended to cover non-exclusive inclusion.

[0062] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality" is more than two, unless otherwise specifically defined. In the description of the embodiments of the present application, "each" means each or every one of a plurality, unless otherwise specifically defined.

[0063] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0064] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0065] In a traditional substation, equipment failures can be detected by monitoring the operating conditions of the equipment, and defects can be eliminated and potential safety hazards can be removed in a timely manner to ensure the stable operation of the power grid. Exemplarily, transformer oil is an important insulating and cooling medium for transformers. By using an on-line oil chromatograph monitoring device (such as a transformer oil detection device) to monitor the transformer oil, oil chromatograph data can be obtained. By chromatographic analysis instead of the conventional manual oil sampling test, gas data such as hydrogen, acetylene, and hydrocarbons dissolved in the transformer oil can be obtained, and defects can be eliminated and transformer oil replacement can be carried out in a timely manner. However, in the case of a prefabricated cabin substation with a closed operating environment, if the operating optimization method of the substation in the related technology is used to optimize the operation of the prefabricated cabin substation, there will be a problem of incomplete monitoring, so that the obtained monitoring data cannot fully reflect the operating conditions of the prefabricated cabin substation, resulting in inaccurate optimization of the prefabricated cabin substation.

[0066] Based on this, the embodiments of the present application provide an operating optimization method and a computer device for a prefabricated cabin substation, which can improve the optimization accuracy of the prefabricated cabin substation.

[0067] Figure 1 The flow diagram of the operating optimization method for the prefabricated cabin substation provided in the first embodiment is as Figure 1 shown. This method is applied to a computer device, and the method includes:

[0068] S101. Determine the environmental performance index values in each cabin according to the obtained environmental status data of each cabin in the prefabricated cabin substation.

[0069] A prefabricated cabin of a substation refers to a device prefabricated in a factory, which can be directly transported to the substation site for installation and use. It is a form of substation that adopts a prefabricated cabin structure and integrates the main equipment of the substation. Prefabricated cabin substations are usually used in outdoor environments and have the characteristics of standardization, modularization, and intelligence. A prefabricated cabin substation includes multiple cabins. For example, a prefabricated cabin substation includes at least two cabins such as a transformer cabin, a gas insulated switchgear (GIS) cabin, a low-voltage side cabin, and a secondary cabin.

[0070] The computer device can obtain multiple environmental status data in each cabin. For example, the multiple environmental status data include at least two of the following: basic environmental data, at least one noise component data, oxygen content data, and environmental pollution data. Exemplarily, the basic environmental data includes at least one of the following: in-cabin temperature data, in-cabin humidity data, and out-of-cabin humidity data. Exemplarily, the environmental pollution data includes pollution gas concentration data and / or soiling degree data.

[0071] Different status monitoring sensors or devices are installed at different positions inside and outside the cabin to obtain the environmental status data of the cabin.

[0072] Among them, in one cabin, if there are multiple status monitoring sensors or devices monitoring a certain environmental status, the average value of the data monitored by the multiple status monitoring sensors obtained can be used as the detected environmental status data, or the maximum value or median of the data monitored by the multiple status monitoring sensors obtained can be used as the detected environmental status data.

[0073] Among them, the status monitoring sensor or device outside one cabin should be set outside the entire prefabricated substation cabin, that is, the status monitoring sensor is set outdoors, so as to be able to detect the outdoor environmental status data.

[0074] In some embodiments, multiple environmental status data of each cabin can be obtained. Exemplarily, the multiple environmental status data of each cabin can be multiple original status data originally collected by status monitoring sensors or devices. Another example is that multiple originally collected status data can be obtained, and at least one of the processes such as missing value filling, missing value deletion, data error correction, and outlier removal is performed on the multiple original status data to obtain the multiple environmental status data of each cabin.

[0075] In some embodiments, the multiple environmental status data of each cabin can include at least two of the following: in-cabin temperature data, in-cabin humidity data, out-of-cabin humidity data, at least one noise component data, oxygen content data, pollution gas concentration data, and soiling degree data.

[0076] In some embodiments, S101 can include: standardizing the environmental status data of each cabin to each preset environmental status range to obtain the environmental performance index values of each cabin. Among them, different environmental status data are standardized to different preset environmental status ranges. For example, the upper limit and / or lower limit of each corresponding preset environmental status range can be determined according to the importance of each environmental status data. Exemplarily, if the importance of a certain environmental status data is higher, the upper limit and / or lower limit of the corresponding preset environmental status range are also higher. Conversely, if the importance of a certain environmental status data is lower, the upper limit and / or lower limit of the corresponding preset environmental status range are also lower.

[0077] S102. Determine the comprehensive performance index value of each cabin according to the values of each environmental performance index in each cabin.

[0078] In some embodiments, for each cabin, the comprehensive performance index value of each cabin can be determined according to the values of multiple environmental performance indexes in each cabin. For example, the values of multiple environmental performance indexes in each cabin can be summed to obtain the comprehensive performance index value of each cabin. For another example, the values of multiple environmental performance indexes in each cabin can be weighted and summed to obtain the comprehensive performance index value of each cabin.

[0079] S103. Determine the overall performance index value of the prefabricated cabin substation according to the comprehensive performance index values of each cabin.

[0080] In some embodiments, the overall performance index value of the prefabricated cabin substation is determined according to the comprehensive performance index values of multiple cabins in the prefabricated cabin substation. For example, the comprehensive performance index values of multiple cabins in the prefabricated cabin substation can be summed to obtain the overall performance index value of the prefabricated cabin substation. For another example, the comprehensive performance index values of multiple cabins in the prefabricated cabin substation can be weighted and summed to obtain the comprehensive performance index value of each cabin.

[0081] S104. Optimize the operation of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the values of each environmental performance index in each cabin.

[0082] In some embodiments, S104 may include the following steps: when the overall performance index value of the prefabricated cabin substation is greater than or equal to a preset threshold, optimize the operation of the prefabricated cabin substation, including: maintaining the operation of the prefabricated cabin substation.

[0083] In some embodiments, S104 may include the following steps: when the overall performance index value of the prefabricated cabin substation is less than or equal to a preset threshold, obtain the target cabin corresponding to the lowest comprehensive performance index value from the comprehensive performance index values of each cabin, and optimize the operation of the prefabricated cabin substation, including: optimizing the operation of the equipment in the target cabin according to the values of each environmental performance index in the target cabin. After that, when the overall performance index value is greater than or equal to the preset threshold, maintain the operation of the prefabricated cabin substation. When the overall performance index value of the prefabricated cabin substation is less than or equal to the preset threshold, repeat the above steps until the overall performance index value is greater than or equal to the preset threshold.

[0084] In some embodiments, S104 may include the following steps: obtaining the operation status data of each device in the prefabricated substation, and optimizing the operation of the prefabricated substation according to the operation status data of each device, the overall performance index value of the prefabricated substation, the comprehensive performance index value of each cabin, and the environmental performance index value of each cabin. Exemplarily, the operation status data includes at least one of the following: oil chromatogram data, vibration data, load rate data, device temperature data, etc.

[0085] In some embodiments, S104 may include the following steps: determining the operation and maintenance cycle of the prefabricated substation according to the overall performance index value of the prefabricated substation; determining the operation and maintenance cycle of each cabin according to the comprehensive performance index value of each cabin, and maintaining each cabin according to the smaller cycle of the operation and maintenance cycle of the prefabricated substation and the operation and maintenance cycle of each cabin, and the environmental performance index value of each cabin.

[0086] In the technical solution provided by the embodiments of the present application, since the operation of the prefabricated substation is optimized by the environmental status data of each cabin in the prefabricated substation, the optimization of the operation of the prefabricated substation can be adjusted in real time according to the environmental status data, improving the accuracy of the operation optimization of the prefabricated substation; and, by calculating the comprehensive performance index value of each cabin and calculating the overall performance index value, the good or bad of the comprehensive operation of each cabin and the overall operation of the prefabricated substation can be evaluated, and then the comprehensive operation of each cabin and the overall operation of the prefabricated substation can be quantitatively evaluated. Therefore, optimizing the operation of the prefabricated substation according to the overall performance index value of the prefabricated substation, the comprehensive performance index value of each cabin, and the environmental performance index value of each cabin can comprehensively evaluate the operation status of the prefabricated substation from local to whole, and further improve the accuracy of optimizing the operation of the prefabricated substation.

[0087] In some embodiments, the environmental status data includes basic environmental data, and the environmental performance index value includes a basic environmental index value. Exemplarily, S101 may include the following steps: obtaining a preset basic environmental range, where the basic environmental range is a range between a basic environmental upper limit and a basic environmental lower limit; when each basic environmental data is within the basic environmental range, obtaining a first difference between each basic environmental data and the basic environmental lower limit, and a second difference between each basic environmental data and the basic environmental upper limit, determining an intermediate result according to the first difference and the second difference, and determining the difference between a preset value and the intermediate result as each basic environmental index value; when each basic environmental data is outside the basic environmental range, determining the preset value as each basic environmental index value.

[0088] Exemplarily, the basic environmental data may include: cabin temperature data and / or cabin humidity data, and correspondingly, the basic environmental index value includes: cabin temperature index value and / or cabin humidity index value.

[0089] Among them, for the obtained cabin temperature data, the following formula (1) is used to determine the corresponding cabin temperature index value:

[0090]

[0091] Among them, PI t is the cabin temperature index value, and t is the cabin temperature data. For example, the upper limit of the cabin temperature is 30 degrees Celsius, and the lower limit of the cabin temperature is 12 degrees Celsius. Since too high and too low temperatures are both unfavorable to the operation of the prefabricated cabin, the cabin temperature index value obtained by formula (1) can reflect the evaluation level of the cabin temperature.

[0092] Among them, for the obtained cabin humidity data, the following formula (2) is used to determine the corresponding cabin humidity index value:

[0093]

[0094] Among them, PI h represents the cabin humidity index value, and h represents the cabin humidity data. For example, the upper limit of humidity is 88, and the lower limit of humidity is 20. Since too high or too low humidity will affect the operation status of the equipment, the cabin humidity index value obtained by formula (2) can respectively reflect the evaluation level of cabin humidity.

[0095] For example, the humidity data in the embodiments of the present application are relative humidity data unless otherwise specified, and the relative humidity data can be measured by a humidity sensor. Determine, where h a is the absolute humidity (unit: mg / L), h c It is the saturated capacity of water vapor in the air corresponding to the current temperature (unit: mg / L).

[0096] For condensation caused by excessive humidity in the cabin, ventilation measures can be used to reduce the absolute humidity in the room, or the saturated capacity of water vapor can be increased by raising the temperature, thereby reducing the relative humidity and achieving the effect of avoiding condensation in the prefabricated cabin.

[0097] In the technical solution provided in the embodiment of the present application, by obtaining the values ​​of various basic environmental indicators and optimizing the operation of the prefabricated cabin substation according to the values ​​of various basic environmental indicators, the accuracy of the operation optimization of the prefabricated cabin substation can be improved.

[0098] In some embodiments, each environmental status data includes at least one noise component data, and the environmental performance index value includes a noise index value. Exemplarily, S101 may include the following steps: when each noise component data is less than or equal to a preset noise upper limit, normalize each noise component data to a range between a preset value and 1 to obtain a noise component normalization result, and determine the performance index value of each noise component data as the value obtained by subtracting the noise component normalization result from 1; when each noise component data is greater than the noise upper limit, determine the preset value as the performance index value of each noise component data; determine each noise index value according to the performance index value of at least one noise component data in each environmental status data.

[0099] Among them, the environmental status data includes noise data, and the noise data includes at least one noise component data.

[0100] Among them, for the obtained noise data (the original noise sequence data between a preset moment and the current moment, also referred to as the original sequence s(t), and the preset moment and the current moment are separated by a set time period), the following method can be used to decompose the noise data to obtain at least one noise component data:

[0101] First, add a Gaussian white noise sequence with an equal length to the original sequence s(t), and the constructed sequence to be decomposed is: s i (t) = s(t) + ξ i (t); where i = 1, 2, 3,..., n represents the number of times of adding white noise, and ξ i (t) is the white noise sequence added for the i-th time.

[0102] Then, perform n times of repeated decomposition on the sequence after adding noise to obtain the first component IMF1 and the residual component r1(t): r1(t) = s(t) - IMF1; where IMF 1i (t) represents the first component obtained by performing empirical mode decomposition (EMD) on the sequence after adding white noise for the i-th time.

[0103] And so on to obtain the k-th component IMF k and the residual component r k (t): Among them, IMF ki (t) represents the k-th component obtained by EMD decomposition on the sequence after adding white noise for the i-th time.

[0104] When the residual component is a monotonic sequence and cannot be decomposed, end the decomposition operation of the sequence, and the extracted K components and the residual component R are as follows:

[0105] Among them, the K components are the above-mentioned at least one noise component data.

[0106] Taking the noise upper limit of 70 dB as an example, the performance index value of the noise component data is determined by formula (3):

[0107]

[0108] Among them, PI n represents the performance index value of the noise component data, and n represents the noise component data. Exemplarily, in other embodiments, other values can also be adopted for the noise upper limit. In some other embodiments, the noise upper limits corresponding to different time periods within a day are different. For example, the noise upper limit during the day is 70 dB, and the noise upper limit at night is 55 dB.

[0109] In some embodiments, according to the characteristics of the decomposition sequence and the changes in the sequences of different time periods, the abnormal changes of the noise can be discovered, thereby providing a basis for the fault diagnosis of the equipment. The more irregular the sequence obtained by the noise decomposition, the greater the probability of equipment failure, and the prefabricated cabin substation should be repaired in a timely manner.

[0110] In some embodiments, according to the performance index values of at least one noise component data in each environmental state data, each noise index value can be determined, which can be achieved in the following manner: performing one of the processes such as summing, weighted summing, averaging, etc. on the performance index values of at least one noise component data in each environmental state data to obtain each noise index value.

[0111] In the technical solution provided by the embodiments of the present application, by obtaining each noise index value and optimizing the operation of the prefabricated cabin substation according to each noise index value, the accuracy of the operation optimization of the prefabricated cabin substation can be improved.

[0112] In some embodiments, the environmental state data includes oxygen content data, and the environmental performance index value includes an oxygen content index value. Exemplarily, S101 may include the following steps: obtaining a preset oxygen content range, where the oxygen content range is the range between the oxygen content upper limit and the oxygen content lower limit; when each oxygen content data is within the oxygen content range, normalizing each oxygen content data to the range between a preset value and 1 to obtain each oxygen content index value; when each oxygen content data is greater than the oxygen content upper limit, determining 1 as each oxygen content index value; when each oxygen content data is less than the oxygen content lower limit, determining the preset value as each oxygen content index value.

[0113] Taking the oxygen content (unit: volume percentage %) range of 18 to 22 as an example, the oxygen content index value is determined by formula (4):

[0114]

[0115] Among them, ox is the oxygen content data; PI ox is the oxygen content index value.

[0116] In the technical solution provided by the embodiment of the present application, by obtaining each oxygen content index value and optimizing the operation of the prefabricated substation according to each oxygen content index value, the accuracy of the operation optimization of the prefabricated substation can be improved.

[0117] In some embodiments, the environmental status data includes environmental pollution data, and the environmental performance index value includes an environmental pollution index value. Exemplarily, S101 may include the following steps: when each environmental pollution data is greater than or equal to a preset upper limit of environmental pollution, 0 is determined as each environmental pollution index value; when each environmental pollution data is less than the upper limit of environmental pollution, each environmental pollution data is normalized to obtain each environmental pollution index value.

[0118] Among them, the environmental pollution data includes pollution gas concentration data and / or pollution degree data, and the corresponding environmental pollution index value includes a pollution gas concentration index value and / or a pollution degree index value.

[0119] For the obtained pollution gas concentration data (taking SF6 as an example, unit: ppm), the corresponding pollution gas concentration index value can be determined by formula (5):

[0120]

[0121] Among them, X SF6 represents the pollution gas concentration data, and PI SF6 represents the pollution gas concentration index value.

[0122] For the obtained pollution degree data, the corresponding pollution degree index value can be determined by formula (6):

[0123] PI w = f(low, medium, relatively high, high) = (1, 0.75, 0.25, 0) (6);

[0124] Among them, low, medium, relatively high, and high are the obtained pollution degree data respectively. PI w is the pollution degree index value. In some other embodiments, other methods may be used to determine the pollution degree index value, and the embodiments of the present application do not limit this.

[0125] In the technical solution provided by the embodiment of the present application, by obtaining each environmental pollution index value and optimizing the operation of the prefabricated substation according to each environmental pollution index value, the accuracy of the operation optimization of the prefabricated substation can be improved.

[0126] Figure 2 Flow schematic diagram of the operation optimization method for the prefabricated substation provided in the second embodiment, as Figure 2 shown, this method is applied to a computer device, Figure 2 The difference between this embodiment and Figure 1 the embodiment is that S102 may include the steps of S1021 and S1022:

[0127] S1021. Obtain the types of each cabin, and determine the index weights of each environmental performance index value in each cabin according to the types of each cabin.

[0128] Exemplarily, the above-mentioned obtained in-cabin temperature index value PI t , in-cabin humidity index value PI h , noise index value PI n , oxygen content index value PI ox , pollution gas concentration index value PI SF6 , and dirt degree index value PI w . Among them, different cabin types have different weights of environmental state data (i.e., different characteristic index weights). Table 1 shows the index weights of each environmental performance index value of the transformer cabin, GIS cabin, low-voltage side cabin, and secondary cabin. Among them, the index weights of each environmental performance index value include: temperature index weight, humidity index weight, noise index weight, oxygen content index weight, SF6 concentration index weight, and dirt degree index weight.

[0129] Table 1

[0130] Index weight Transformer compartment GIS compartment Low-voltage side compartment Secondary compartment Temperature index weight 30.00％ 25.00％ 25.00％ 28.00％ Humidity index weight 15.00％ 15.00％ 15.00％ 20.00％ Noise index weight 18.00％ 12.00％ 12.00％ 10.00％ Oxygen content index weight 20.00％ 20.00％ 20.00％ 20.00％ <![CDATA[SF6 concentration index weight]]> 0.00％ 21.00％ 17.00％ 0.00％ Degree of pollution index weight 17.00％ 7.00％ 11.00％ 22.00％

[0131] S1022. Use the index weights of each environmental performance index value to perform weighted processing on each environmental performance index value in each cabin to obtain the comprehensive performance index value of each cabin.

[0132] Exemplarily, taking the transformer cabin as an example, the comprehensive performance index value of the transformer cabin can be determined by formula (7).

[0133] PI 变压器舱 =w t PI t +w h PI h +w n PI n +w ox PI 0x +w SF6 PI SF6 +w w PI w (7);

[0134] Among them, PI变压器舱 Represents the comprehensive performance index value of the fixed transformer cabin, w t Is the index value PI of the cabin temperature index t Of the index weight, w h Is the index value PI of the cabin humidity index h Of the index weight, w m Is the index value PI of the noise index n Of the index weight, w ox Is the index value PI of the oxygen content index ox Of the index weight, w SF6 Is the index value PI of the concentration of polluting gases SF6 Of the index weight, w w Is the index value PI of the degree of contamination w Of the index weight. Exemplarily, PI 变压器舱 The index weights used in the calculation can be queried from Table 1. Exemplarily, the comprehensive performance index value PI of the GIS cabin GIS舱 , The comprehensive performance index value PI of the low-voltage side cabin 低压侧舱 , The comprehensive performance index value PI of the secondary cabin 二次舱 Is determined in a similar manner to PI 变压器舱 , And will not be elaborated here.

[0135] In the technical solution provided by the embodiment of the present application, by obtaining the cabin type and determining the index weights of the corresponding environmental performance index values, it is possible to conduct targeted evaluations according to the characteristics of different cabins, making the comprehensive performance index values of each cabin more in line with the actual situation.

[0136] Figure 3 Is a schematic flow diagram of the operation optimization method for the prefabricated substation provided in the third embodiment, as Figure 3 Shown, this method is applied to a computer device, Figure 3 The difference between the embodiment and Figure 1 The embodiment is that S103 may include the steps of S1031 and S1032:

[0137] S1031. Obtain the types of each cabin and determine the cabin weights of each cabin according to the types of each cabin.

[0138] Exemplarily, the cabin weights corresponding to different cabins may be the same or different.

[0139] S1032. Use the cabin weights of each cabin to perform weighted processing on the comprehensive performance index values of each cabin to obtain the overall performance index value of the prefabricated substation.

[0140] Exemplarily, taking the cabin weights of each cabin as 1 as an example, the calculation method of the overall performance index value PI S Is as follows: PI s = PI变压器舱 +PI GIS舱 +PI 低压侧舱 +PI 二次舱 。

[0141] In the technical solution provided by the embodiment of the present application, the comprehensive performance index values of each cabin are weighted by weights to obtain the overall performance index value, providing an intuitive and quantitative overall performance evaluation result for the prefabricated cabin substation. Thus, the overall operation status of the substation can be quickly understood through the overall performance index value, without having to check the detailed indicators of each cabin one by one, improving the decision-making efficiency. In addition, the operation risks faced by different cabins and their sensitivities to the environment are also different. Some cabins may be more easily affected by environmental factors such as temperature and humidity, or their failures may cause more serious consequences. Determining the cabin weights can take these differences into account. For example, due to its energy storage characteristics, the battery cabin may have higher requirements for safety and environmental conditions. Giving an appropriately high weight can more reasonably evaluate its potential impact on the overall performance, thereby improving the accuracy of the determined overall performance index value of the prefabricated cabin substation.

[0142] Figure 4 It is a schematic flowchart of the operation optimization method for the prefabricated cabin substation provided in the fourth embodiment, as Figure 4 shown. This method is applied to a computer device. Figure 4 The difference between this embodiment and Figure 1 the embodiment is that S104 may include the steps of S1041 to S1043:

[0143] S1041. When the environmental performance index values in each cabin are less than or equal to the respective preset environmental performance index thresholds, obtain the adjustment priorities of the environmental performance index values in each cabin, and use the adjustment priorities to adjust the operation of each cabin until the comprehensive performance index values of each cabin reach the maximum.

[0144] Exemplarily, the order of the adjustment priorities of the environmental performance index values from high to low may include: SF6 concentration performance index value, oxygen content performance index value, temperature performance index value, humidity performance index value, noise performance index value, pollution degree performance index value.

[0145] For example, when the temperature performance index value in a cabin is less than or equal to the temperature performance index threshold, if the comprehensive performance index value becomes smaller after adjusting the operation of the cabin, then the operation of the cabin will no longer be adjusted. If the comprehensive performance index value becomes larger after adjusting the operation of the cabin, then the operation of the cabin will continue to be adjusted.

[0146] For another example, when the temperature performance index value in a cabin is less than or equal to the temperature performance index threshold and the SF6 concentration performance index value is less than or equal to the SF6 concentration performance index threshold, the operation of the cabin is preferentially adjusted according to the SF6 concentration performance index value. When the SF6 concentration performance index value is greater than the oxygen content of the SF6 concentration performance index threshold, the operation of the cabin is adjusted according to the SF6 concentration performance index value.

[0147] S1042. When the comprehensive performance index values of all cabins are less than or equal to the respective preset comprehensive performance index thresholds within a preset time period, maintenance processing is performed on all cabins.

[0148] S1043. When the overall performance index value of the prefabricated cabin substation is less than or equal to the preset overall performance index threshold within a specified time period, maintenance processing is performed on the prefabricated cabin substation.

[0149] In the technical solution provided by the embodiment of the present application, by using the adjustment priority, the operation of each cabin is adjusted, so that when the environmental performance index value is less than or equal to the corresponding environmental performance index threshold, the environmental state with a higher priority is preferentially adjusted, realizing the priority execution of key tasks, ensuring that high-priority tasks obtain sufficient resources, and improving the overall optimization efficiency; when the comprehensive performance index values of all cabins are less than or equal to the respective preset comprehensive performance index thresholds within a preset time period, maintenance processing is performed on all cabins. In this way, when the comprehensive performance index value of a certain cabin continuously falls below the preset threshold, the system automatically determines that the cabin is in a potential risk or performance degradation state and triggers the maintenance process, actively intervening before the performance significantly decreases, avoiding system downtime caused by sudden failures, and performing targeted maintenance on the specific cabin with abnormal performance indicators, reducing the waste of resources in non-discriminatory maintenance; when the overall performance index value of the prefabricated cabin substation is less than or equal to the preset overall performance index threshold within a specified time period, maintenance processing is performed on the prefabricated cabin substation. In this way, when the overall performance index value continuously falls below the preset threshold, the system automatically determines that the prefabricated cabin substation is in a potential risk or performance degradation state and triggers the maintenance process, actively intervening before the performance significantly decreases, avoiding system downtime caused by sudden failures, and performing targeted maintenance on the prefabricated cabin substation with abnormal performance indicators, reducing the waste of resources in non-discriminatory maintenance.

[0150] Figure 5 It is a schematic flow chart of the operation optimization method for the prefabricated cabin substation provided in the fifth embodiment, as Figure 5 shown. This method is applied to a computer device. Figure 5 The difference between this embodiment and Figure 1 the embodiment is that S104 may include steps S1044 to S1046:

[0151] S1044. Construct a performance vector of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index value of each cabin, and the environmental performance index value in each cabin.

[0152] Exemplarily, through the environmental state data of each cabin in the prefabricated cabin substation obtained each time, a corresponding performance vector of the prefabricated cabin substation can be obtained. Exemplarily, the computer device can obtain the environmental state data of each cabin once per collection period (such as 1 second, 2 seconds, or 1 minute, etc.), so that the computer device can construct a performance vector per collection period. Also exemplarily, the computer device can determine the maximum value among all the environmental state data of each cabin obtained every specific duration (such as 1 minute, 5 minutes, 10 minutes, or 1 hour, etc.) as the environmental state data of each cabin obtained every specific duration, so that the computer device can construct a performance vector every specific duration.

[0153] S1045. Input the performance vector of the prefabricated cabin substation into a Bi-directional Long Short Term Memory (BiLSTM) neural network to obtain the predicted index value of the prefabricated cabin substation.

[0154] BiLSTM introduces a memory structure to simulate memory behavior and achieve filtering and selection of information. The Bidirectional LSTM (BiLSTM) consists of two parts: a forward neural network and a backward neural network. There are three special structural units, namely, a forget gate, an input gate, and an output gate, in both the forward and backward LSTMs, and the efficiency of the neural network is improved through the gate control unit.

[0155] Among them, the forward neural network is expressed as h t = L f (h t-1 , x t ). Further expanded, each gate unit can be expressed as:

[0156] Forget gate: f t = δ(W f [h t-1 , x t + b f );

[0157] Input gate: i t = δ(W i [h t-1 , x t + b i );

[0158] Memory unit:

[0159] Output gate: ot = δ(W o [h t-1 , x t + b o ); h t = o t tanh(c t );

[0160] L f represents the forward neural network, x t represents the input at time t, h t-1 represents the output value at time t - 1; h t represents the output value at time t, δ is the sigmoid activation function, W f , W i , W o , W c are the weight matrices of the forget gate, input gate, output gate, and memory cell respectively, b f , b i , b o , b c are the biases of the forget gate, input gate, output gate, and memory cell respectively, f t , i t , o t , c t are the values of the forget gate, input gate, output gate, and memory cell at time t respectively, c t-1 is the value of the memory cell at time t - 1, is the generated candidate cell state, tanh is the hyperbolic tangent activation function, o t is the output of the output gate at time t.

[0161] The backward neural network can be expressed as: h' t = L b (h' t+1 , x t ). Where, L b represents the backward neural network, x t represents the input at time t, h' t is the output of the backward neural network at time t, h' t+1 is the output of the backward neural network at time t + 1.

[0162] In this way, the forward neural network obtains the output value of the forward neural network at time t through the output value of the forward neural network at time t - 1, and the backward neural network obtains the output of the backward neural network at time t through the output of the backward neural network at time t + 1. Through o t ' = W ho h t + W ho′ h' t+b can determine the output of the BiLSTM at time t. Among them, h t is the output of the forward neural network at time t, and h′ t is the output of the backward neural network at time t. W ho and W ho′ respectively represent the weight matrices of the forward neural network and the backward neural network. b is the bias of the output layer of the BiLSTM, and o t ′ represents the output of the BiLSTM.

[0163] S1046. Optimize the operation of the prefabricated substation according to the predicted index value of the prefabricated substation, the overall performance index value of the prefabricated substation, the comprehensive performance index value of each cabin, and the environmental performance index value of each environment in each cabin.

[0164] In some embodiments, the warning mechanism of the prefabricated substation is divided into warning based on the current state value and warning based on trend prediction. If the environmental state data of any one cabin is greater than or equal to the product of the preset value (a real number greater than 0 and less than 1, such as 0.8) and the corresponding state threshold, it is a general warning (blue). If the environmental state data of any one cabin is greater than or equal to the corresponding state threshold, it is a yellow warning. After a blue warning or a yellow warning appears, predict the future state data, or determine the predicted index value of the prefabricated substation, and at the same time optimize the operation of the prefabricated substation. For example, start the exhaust device and air conditioner to improve the environment in the prefabricated cabin. Exemplarily, BiLSTM can be used to perform state prediction on the state data of the prefabricated substation. If the current state value exceeds the corresponding state threshold and the predicted state value also exceeds the corresponding state threshold, raise the warning level to a red warning. Otherwise, maintain the yellow warning. In the case of a red warning, an alarm message can be output.

[0165] Exemplarily, based on the multi-dimensional state monitoring indicators of the prefabricated substation, through adjusting solutions such as the operation of the exhaust device and air conditioner, achieve the comprehensive optimization of substation operation and maintenance, improve the utilization rate of substation equipment, improve the operation and maintenance environment in the cabin, and extend the service life of the equipment. Exemplarily, the operation of the prefabricated substation can be adjusted according to the minimum overall performance index value PI s . Among them, PI s =PI 变压器舱 +PI GIS舱 +PI 低压侧舱 +PI 二次舱 . Among them, the overall performance index value PI s reflects the environment and operation state in the prefabricated substation. The higher the overall performance index value PI s , the more suitable the environment in the prefabricated substation is for the stable operation of the substation.

[0166] When adjusting the operation of the prefabricated substation, it is necessary to ensure that the SF6 concentration ≤ 1000 ppm, and the air-conditioning cooling and dehumidification functions cannot be turned on simultaneously.

[0167] The following exemplarily illustrates that according to different prefabricated cabin environmental monitoring indicators, 7 different cabin state scenarios are designed, and different scenarios correspond to different coping schemes (i.e., different optimization methods for optimizing the operation of the prefabricated substation):

[0168] Scenario 1: According to the threshold of the temperature indicator, when the temperature in the prefabricated cabin is too high, the air-conditioning cooling is turned on to adjust the temperature, avoiding the impact of excessive temperature in the cabin on the equipment operation.

[0169] Scenario 2: The humidity in the cabin is too high, and the humidity outside the cabin is greater than that inside the cabin. When the humidity in the cabin is too high, the exhaust device can be turned on to discharge the humid air in the cabin to the outside of the cabin, achieving the effect of reducing the humidity in the cabin. However, at this time, since the humidity outside the cabin is greater than that inside the cabin, turning on the exhaust device cannot effectively reduce the humidity in the cabin. At this time, the exhaust device should be locked first to prevent the humid air in the cabin from flowing into the cabin. At the same time, turn on the air-conditioning dehumidification function to reduce the humidity of the air in the cabin.

[0170] Scenario 3: The humidity in the cabin is too high, and the humidity outside the cabin is less than that inside the cabin. At this time, since the humidity outside the cabin is lower than that inside the cabin, the exhaust device can be turned on to promote the air flow inside and outside the cabin, and better discharge the humid air in the cabin to the outside of the cabin.

[0171] Scenario 4: The noise in the cabin is too high. Excessive noise in the cabin will affect the working environment of the operation and maintenance personnel on the one hand, and on the other hand, excessive noise is also a signal that the equipment may malfunction. At this time, equipment maintenance and fault troubleshooting should be carried out immediately to repair the fault in time, eliminate potential safety hazards, and at the same time reduce the noise in the cabin. In addition, noise reduction processing can also be carried out according to the noise component data.

[0172] Scenario 5: The oxygen content in the cabin is too low. When the oxygen content in the cabin is too low, the exhaust device should be turned on in time to promote air circulation and increase the oxygen concentration in the cabin.

[0173] Scenario 6: The SF6 concentration in the cabin is too high and there is too much dirt. As the equipment is running, the dust in the cabin may gradually increase, which is not conducive to the operation of electrical equipment. Due to the leakage of SF6 gas in GIS or low-voltage distribution equipment, the SF6 concentration in the cabin may rise. Excessive SF6 concentration poses a risk to human health. Therefore, the exhaust device should be turned on as soon as possible to discharge the SF6 in the cabin air to the outside of the cabin to keep the SF6 concentration in the cabin at a low level. When the SF6 concentration in the cabin is too high and other abnormal conditions are detected at the same time, the SF6 concentration in the cabin should be reduced first, and then other abnormal conditions should be dealt with. After the abnormal state is handled, considering the stable operation of the equipment, the SF6 in the equipment should be replenished in time.

[0174] Scenario 7: Too much dirt in the cabin. After the equipment has been running for a long time, the dust in the cabin increases. At this time, the exhaust device can be turned on. Especially for insulators, the accumulation of dust will affect the insulation performance of the insulators and affect the safe operation of the equipment.

[0175] For the three conditions of SF6 concentration being too high, too much dirt, and too low oxygen content, the exhaust device can be turned on to deal with them. Therefore, when the above three conditions occur at the same time, or when one or two of them occur, the exhaust device can be turned on.

[0176] For transformer operation, when the transformer load rate is too high, the substation operation load is large, and the equipment may have obvious heating phenomenon, which is not good for the performance of the transformer. By turning on the exhaust device to speed up the air flow, the air conditioner can be turned on to cool down the temperature of the surrounding environment of the transformer, so as to avoid overheating of the transformer and affect the safe operation of the transformer.

[0177] The present invention combines the characteristics of prefabricated cabin substations to construct a multi-dimensional state monitoring and comprehensive operation optimization system that is more suitable for prefabricated cabin substations in the southern region, provides a solution for monitoring the cabin status of prefabricated cabin substations, and provides support for the safe and reliable operation of prefabricated cabin substations. It provides reference methods and technologies for the various operation and maintenance problems currently existing in prefabricated cabin substations, and provides a reference for the promotion of prefabricated cabin substations and the realization of safe and stable operation of substations.

[0178] In some embodiments, a multi-dimensional condition monitoring and integrated operation optimization system for prefabricated substation cabins applicable to the southern region is provided. Different condition monitoring systems are respectively established for the transformer cabin, GIS cabin, low-voltage side cabin, and secondary cabin, and the condition monitoring variables of each prefabricated cabin are different. Different weights are assigned to different prefabricated cabins to calculate the overall condition value of the prefabricated cabin. The main condition monitoring indicators of the prefabricated cabin include temperature, humidity, noise, oxygen content, SF6 concentration, and degree of contamination. The integration of various condition indicator data provides a reliable data basis for the monitoring of the condition inside the prefabricated substation cabin. Exemplarily, different condition monitoring systems are respectively established for the transformer cabin, GIS cabin, low-voltage side cabin, and secondary cabin, and the condition monitoring variables of each prefabricated cabin are different. Exemplarily, different weights are assigned to different prefabricated cabins to calculate the overall condition value of the prefabricated cabin. There is no SF6 leakage in the transformer cabin and the secondary cabin, so the weight corresponding to the SF6 concentration is 0. The data of different condition monitoring indicators of different prefabricated cabins constitute the data basis for system optimization.

[0179] Figure 6 A schematic framework diagram of a condition monitoring and optimization device for a prefabricated substation provided in some embodiments (i.e., an operation optimization device for a prefabricated substation, applied in a computer device), as Figure 6 shown, the condition monitoring and optimization device for a prefabricated substation can monitor the environmental condition data of the following indicators: temperature indicator, humidity indicator, noise indicator, oxygen content indicator, SF6 concentration indicator, and degree of contamination indicator. Through the environmental condition data of these indicators, through the condition evaluation functions of each indicator (respectively the above formulas (1) to (6)), and the indicator weights, the integrated operation optimization target of the prefabricated substation (i.e., the overall performance indicator value of the prefabricated substation) is determined. According to this integrated operation optimization target of the prefabricated substation, at least one optimization method such as air conditioner start / stop, exhaust device start / stop, and fault repair can be adopted to optimize the operation of the prefabricated substation. Through scenario design and application, the condensation phenomenon can be effectively avoided, the operation environment inside the cabin can be adjusted, and the problem of transformer overheating can be solved. In addition, in the case where the environmental condition data of a certain indicator exceeds the corresponding threshold, a condition warning can be issued.

[0180] In some embodiments, different condition monitoring sensors and devices are installed at different positions inside and outside the prefabricated cabin to obtain the condition information of the prefabricated cabin. Figure 7 A schematic diagram of the distribution of sensors in a cabin body provided in some embodiments, as Figure 7As shown in the figure, temperature sensors are respectively arranged on the inner side of the cabin body and the inner side of the cabin top. Since there are temperature differences at different positions inside the cabin, different sensors are respectively arranged on the inner side of the cabin body and the inner side of the cabin top. According to the temperature differences inside the cabin, the operation optimization of the substation can be better carried out; humidity sensors are arranged at different positions inside and outside the cabin to monitor the humidity difference inside and outside the prefabricated cabin, so as to determine whether to turn on the air conditioner for dehumidification; noise sensors are mainly arranged at different positions inside the cabin near the equipment to monitor the noise generated during the operation of the substation; the oxygen content monitoring device is arranged inside the cabin to monitor the oxygen concentration in the cabin environment; the SF6 gas monitoring device is arranged on the inner side of the cabin top and the bottom of the cabin to facilitate the monitoring of the leakage of SF6 gas inside the prefabricated cabin and the SF6 concentration at different positions; the pollution monitor is arranged on the inner side of the cabin body, the inner side of the cabin top and the bottom of the prefabricated cabin to facilitate the monitoring of the ash accumulation inside the prefabricated cabin, and the transformer oil detection device is arranged inside the cabin to monitor the transformer oil and obtain oil chromatogram data.

[0181] In some embodiments, different state variables are converted into performance index values through specific state evaluation functions. Exemplarily, temperature and humidity are converted into performance index values through quadratic functions, noise and oxygen content are converted into performance index values through linear functions, SF6 concentration is converted into performance index values through quadratic functions, and the degree of pollution is converted into performance index values through mapping functions. Compared with the original state indicators, the performance index values can better reflect the current equipment operation state. The performance index values are used as the objective function for comprehensive system optimization.

[0182] In some embodiments, for the noise inside the cabin, after obtaining the time series data of the noise within a certain period of time, an adaptive complete ensemble empirical mode decomposition time domain analysis is performed on the noise sequence. According to the time domain analysis results, equipment faults are diagnosed and the noise inside the cabin is reduced.

[0183] In some embodiments, an early warning mechanism based on the current state value and trend prediction is established. When the current state value is within the range of ±10% of the threshold, it is a general early warning (blue). When the current state value exceeds the threshold range, it is a yellow early warning. After the blue early warning and yellow early warning occur, the future state value should be predicted, and at the same time, the exhaust device and air conditioner are started to improve the environment inside the prefabricated cabin. The BiLSTM is used to predict the state of the prefabricated cabin. If the current state value exceeds the threshold and the predicted state value also exceeds the threshold, the early warning level is raised to a red early warning, otherwise the yellow early warning is maintained.

[0184] For the condensation phenomenon, the absolute humidity indoors is reduced through ventilation measures, or the saturation capacity of water vapor is increased by raising the temperature, thereby reducing the relative humidity and achieving the effect of avoiding the condensation phenomenon in the prefabricated cabin.

[0185] By the combined operation of a fan and a slightly positive pressure air conditioner, the internal state of the prefabricated cabin such as temperature, humidity, noise, oxygen content, SF6 concentration, and degree of contamination is adjusted.

[0186] In some embodiments, the combined scheduling of a fan and an air conditioner is used to solve the problem of overheating of equipment when the transformer load rate is too high.

[0187] In some embodiments, according to different prefabricated cabin environmental monitoring indicators, 7 different internal state scenarios of the cabin are designed, and different scenarios correspond to different countermeasures. The 7 scenarios cover common abnormal state scenarios of prefabricated cabin substations, providing a method for adjusting the internal state and optimizing the operation of prefabricated cabin substations.

[0188] In some embodiments, the operating state data comes from multiple different monitoring points, which can better reflect the real operating state of the prefabricated cabin. Sensors distributed at different positions collect the operating state data.

[0189] In some embodiments, the state monitoring inside and outside the prefabricated cabin is carried out simultaneously, and the fan and the air conditioner are started and stopped according to the internal and external states of the cabin.

[0190] In some embodiments, a computer-readable storage medium can also be provided, on which prefabricated cabin substation state indicators such as temperature, humidity, noise, oxygen content, SF6 concentration, degree of contamination, and transformer load rate, state evaluation functions, and steps for comprehensive operation optimization of the prefabricated cabin (i.e., the operation optimization method of the prefabricated cabin substation) obtained from sensors and monitoring devices are stored.

[0191] Figure 8 A schematic diagram showing the change of various environmental state data over time for some embodiments is as Figure 8 shown, Figure 8 showing the change rules of temperature (unit: °C), humidity (unit: %), noise (unit: dB), oxygen content (unit: %), SF6 concentration (unit: ppm), and load rate over months. For the environmental state data of a single indicator for each month, the maximum value, average value, or median value of all the environmental state data of the single indicator obtained each month can be determined as Figure 8 the environmental state data of the single indicator for each month shown in Figure 8 For example, the maximum value, average value, or median value of all the temperatures obtained each month can be determined as

[0192] Figure 9 A schematic diagram showing the change relationship between the in-cabin temperature index value and the in-cabin temperature data for some embodiments, Figure 10 A schematic diagram showing the change relationship between the in-cabin humidity index value and the in-cabin humidity data for some embodiments, Figure 11Schematic diagram of the variation relationship between the noise index values and the noise component data provided for some embodiments Figure 12 Schematic diagram of the variation relationship between the oxygen content index values and the oxygen content data provided for some embodiments Figure 13 Schematic diagram of the variation relationship between the pollutant gas concentration index values and the pollutant gas concentration data provided for some embodiments

[0193] Figure 14 Schematic diagram of the process of the multi-dimensional monitoring and early warning method for prefabricated substation cabins provided for some embodiments, as Figure 14 shown, the method includes the following steps:

[0194] S1401. Obtain the status monitoring data of the prefabricated substation cabin

[0195] S1402. Determine whether the status monitoring data is greater than or equal to the corresponding status threshold

[0196] In the case where the status monitoring data is less than the corresponding status threshold, execute S1403; in the case where the status monitoring data is greater than or equal to the corresponding status threshold, execute S1406

[0197] S1403. Determine whether the status monitoring data exceeds the target threshold

[0198] The target threshold is the product of the preset value and the corresponding status threshold

[0199] In the case where the status monitoring data exceeds the target threshold, execute S1404; in the case where the status monitoring data does not exceed the target threshold, execute S1405

[0200] S1404. Issue a blue early warning

[0201] S1405. Do not issue an early warning

[0202] S1406. Use a bidirectional long short-term memory neural network for status prediction

[0203] S1407. Determine whether the predicted value exceeds the corresponding status threshold

[0204] In the case where the predicted value exceeds the corresponding status threshold, execute S1408 and S1410. In the case where the predicted value does not exceed the corresponding status threshold, execute S1409 and S1410

[0205] S1408. Issue a red early warning

[0206] S1409. Issue a yellow early warning

[0207] S1410. Take measures to improve the cabin environment

[0208] Figure 15 Flow schematic diagram of the integrated operation optimization method for prefabricated cabin substations provided for some embodiments, as Figure 15 shown, the method includes the following steps:

[0209] S1501. Obtain monitoring status data. Exemplarily, the following monitoring status data can be obtained: in-cabin temperature data, out-of-cabin humidity data, in-cabin humidity data, noise data, oxygen content data, SF6 concentration data, pollution degree data.

[0210] S1502. Determine the corresponding scenario according to the monitoring status data. Exemplarily, if the in-cabin temperature data is too high, it corresponds to Scenario 1; if the in-cabin humidity is too high and the out-of-cabin humidity is greater than the in-cabin humidity, it corresponds to Scenario 2; if the in-cabin humidity is too high and the out-of-cabin humidity is less than the in-cabin humidity, it corresponds to Scenario 3; if the noise data is too large, it corresponds to Scenario 4; if the oxygen content data is too low, it corresponds to Scenario 5; if the SF6 concentration data is too high, it corresponds to Scenario 6; if the pollution degree data is relatively high, it corresponds to Scenario 7.

[0211] S1503. Determine the countermeasure according to the scenario. Exemplarily, the countermeasure corresponding to Scenario 1 can be air-conditioning refrigeration; the countermeasure corresponding to Scenario 2 can be air-conditioning dehumidification and locking the exhaust device; the countermeasure corresponding to Scenario 3 can be turning on the exhaust device; the countermeasure corresponding to Scenario 4 can be equipment maintenance and fault troubleshooting; the countermeasures corresponding to Scenarios 5, 6, and 7 can be turning on the exhaust device.

[0212] S1504. Determine the countermeasure according to the operating condition of the transformer. Exemplarily, in the case of too high load rate of the transformer, the countermeasures of air-conditioning refrigeration and turning on the exhaust device are taken.

[0213] Based on the same inventive concept, the embodiment of the present application also provides an operation optimization device for a prefabricated cabin substation for implementing the operation optimization method of the prefabricated cabin substation involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the operation optimization device for the prefabricated cabin substation provided below can refer to the limitations on the operation optimization method of the prefabricated cabin substation in the above text, and will not be repeated here.

[0214] In an exemplary embodiment, an operation optimization device for a prefabricated cabin substation is provided. The operation optimization device for the prefabricated cabin substation includes:

[0215] An environmental performance index value determination module, configured to determine the environmental performance index values in each cabin according to the obtained environmental status data of each cabin in the prefabricated cabin substation;

[0216] The comprehensive performance index value determination module is used to determine the comprehensive performance index value of each cabin according to the environmental performance index values in each cabin;

[0217] The overall performance index value determination module is used to determine the overall performance index value of the prefabricated cabin substation according to the comprehensive performance index values of each cabin;

[0218] The optimization module is used to optimize the operation of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin.

[0219] In some embodiments, the environmental status data includes basic environmental data, and the environmental performance index value includes the basic environmental index value; the environmental performance index value determination module may include an acquisition unit and a determination unit. The acquisition unit is used to acquire a preset basic environmental range, where the basic environmental range is the range between the upper limit of the basic environment and the lower limit of the basic environment. The determination unit is used to, when each basic environmental data is within the basic environmental range, acquire the first difference between each basic environmental data and the lower limit of the basic environment, and the second difference between each basic environmental data and the upper limit of the basic environment, determine an intermediate result according to the first difference and the second difference, and determine the difference between the preset value and the intermediate result as each basic environmental index value; the determination unit is further used to, when each basic environmental data is outside the basic environmental range, determine the preset value as each basic environmental index value.

[0220] In some embodiments, each environmental status data includes at least one noise component data, and the environmental performance index value includes a noise index value; the environmental performance index value determination module may include a determination unit, which is used to, when each noise component data is less than or equal to a preset noise upper limit, standardize each noise component data to the range between the preset value and 1 to obtain a noise component standardization result, and determine the value obtained by subtracting the noise component standardization result from 1 as the performance index value of each noise component data; when each noise component data is greater than the noise upper limit, determine the preset value as the performance index value of each noise component data; determine each noise index value according to the performance index values of at least one noise component data in each environmental status data.

[0221] In some embodiments, the environmental status data includes oxygen content data, and the environmental performance index value includes an oxygen content index value; the environmental performance index value determination module may include an acquisition unit and a determination unit. The acquisition unit is configured to acquire a preset oxygen content range, which is a range between an oxygen content upper limit and an oxygen content lower limit; the determination unit is configured to, when each oxygen content data is within the oxygen content range, normalize each oxygen content data to a range between a preset value and 1 to obtain each oxygen content index value; when each oxygen content data is greater than the oxygen content upper limit, determine 1 as each oxygen content index value; and when each oxygen content data is less than the oxygen content lower limit, determine the preset value as each oxygen content index value.

[0222] In some embodiments, the environmental status data includes environmental pollution data, and the environmental performance index value includes an environmental pollution index value; the environmental performance index value determination module may include a determination unit, which is configured to, when each environmental pollution data is greater than or equal to a preset environmental pollution upper limit, determine 0 as each environmental pollution index value; and when each environmental pollution data is less than the environmental pollution upper limit, perform normalization processing on each environmental pollution data to obtain each environmental pollution index value.

[0223] In some embodiments, the comprehensive performance index value determination module includes an acquisition unit and a determination unit. The acquisition unit is configured to acquire the type of each cabin and determine the index weight of each environmental performance index value in each cabin according to the type of each cabin; the determination unit is configured to use the index weight of each environmental performance index value to perform weighted processing on each environmental performance index value in each cabin to obtain the comprehensive performance index value of each cabin.

[0224] In some embodiments, the overall performance index value determination module includes an acquisition unit and a determination unit. The acquisition unit is configured to acquire the type of each cabin and determine the cabin weight of each cabin according to the type of each cabin; the determination unit is configured to use the cabin weight of each cabin to perform weighted processing on the comprehensive performance index value of each cabin to obtain the overall performance index value of the prefabricated cabin substation.

[0225] In some embodiments, the optimization module is further configured to, when each environmental performance index value in each cabin is less than or equal to each preset environmental performance index threshold, acquire the adjustment priority of each environmental performance index value in each cabin, and use the adjustment priority to adjust the operation of each cabin until the comprehensive performance index value of each cabin reaches the maximum; when the comprehensive performance index value of each cabin is less than or equal to each preset comprehensive performance index threshold within a preset time period, perform maintenance processing on each cabin; and when the overall performance index value of the prefabricated cabin substation is less than or equal to the preset overall performance index threshold within a specified time period, perform maintenance processing on the prefabricated cabin substation.

[0226] In some embodiments, the optimization module includes a prediction unit and an optimization unit. The prediction unit is configured to construct a performance vector of the prefabricated substation according to the overall performance index value of the prefabricated substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin; input the performance vector of the prefabricated substation into a bidirectional long short-term memory neural network to obtain the predicted index value of the prefabricated substation. The optimization unit is configured to optimize the operation of the prefabricated substation according to the predicted index value of the prefabricated substation, the overall performance index value of the prefabricated substation, the comprehensive performance index values of each cabin, and the environmental performance index values in each cabin.

[0227] The description of the above device embodiments is similar to that of the above method embodiments and has similar beneficial effects to those of the method embodiments. For the technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0228] Each module in the above operation optimization device of the prefabricated substation can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or stored in the memory of the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0229] In an exemplary embodiment, Figure 16Schematic structural diagram of a computer device provided for some embodiments. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through Wireless Fidelity (WIFI), a mobile cellular network, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an operation optimization method for a prefabricated substation. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0230] Those skilled in the art can understand that Figure 16 the structure shown in

[0231] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0232] In one embodiment, a computer-readable storage medium is provided. When the computer program is executed by the processor, it implements the steps of the method provided in any of the above embodiments.

[0233] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0234] The processor, each functional module, or each functional unit in any embodiment of the present application may include any one or more of the following integrations: general-purpose processor, application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), central processing unit (CPU), graphics processing unit (GPU), embedded neural network processor (NPU), controller, microcontroller, microprocessor, programmable logic device, discrete gate or transistor logic device, discrete hardware component, quantum computing-based data processing logic, artificial intelligence (AI) processor, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0235] The memory or computer-readable storage medium in any embodiment of the present application may include at least one of non-volatile memory and volatile memory. The non-volatile memory includes the integration of one or more of the following: Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Ferromagnetic Random Access Memory (FRAM), Flash Memory, magnetic surface memory, optical disc, Compact Disc Read-Only Memory (CD-ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, volatile memory, etc. The volatile memory includes the integration of one or more of the following: Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc.

[0236] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0237] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An operation optimization method for a prefabricated cabin substation, characterized in that: The method comprises: Determine the environmental performance index values ​​of each cabin in the prefabricated cabin substation according to the acquired environmental status data of each cabin; Determine the comprehensive performance index value of each cabin according to the environmental performance index value of each cabin; Determine the overall performance index value of the prefabricated cabin substation according to the comprehensive performance index value of each cabin; The operation of the prefabricated cabin substation is optimized according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values ​​of each cabin and the environmental performance index values ​​in each cabin.

2. The method according to claim 1, characterized in that The environmental status data includes basic environmental data, and the environmental performance index value includes a basic environmental index value; the environmental performance index value in each cabin is determined according to the environmental status data of each cabin in the prefabricated cabin substation, including: Obtain a preset basic environment range, where the basic environment range is a range between a basic environment upper limit and a basic environment lower limit; In the case where each of the basic environment data is within the basic environment range, obtaining a first difference between each of the basic environment data and the lower limit of the basic environment, and a second difference between each of the basic environment data and the upper limit of the basic environment, determining an intermediate result according to the first difference and the second difference, and determining the difference between a preset value and the intermediate result as each of the basic environment indicator values; When each of the basic environment data is outside the basic environment range, the preset value is determined as each of the basic environment indicator values.

3. The method according to claim 1, characterized in that Each of the environmental status data includes at least one noise component data, and the environmental performance index value includes a noise index value; the environmental performance index value in each of the cabins is determined according to the acquired environmental status data of each of the cabins in the prefabricated cabin substation, including: When each of the noise component data is less than or equal to a preset noise upper limit, each of the noise component data is normalized to a range between a preset value and 1 to obtain a noise component normalization result, and a value obtained by subtracting the noise component normalization result from 1 is determined as a performance indicator value of each of the noise component data; In the case where each of the noise component data is greater than the noise upper limit, determining the preset value as the performance indicator value of each of the noise component data; Each of the noise index values ​​is determined according to the performance index value of the at least one noise component data in each of the environmental state data.

4. The method according to claim 1, characterized in that: The environmental status data includes oxygen content data, and the environmental performance index value includes an oxygen content index value; the environmental performance index value in each cabin is determined according to the environmental status data of each cabin in the prefabricated cabin substation, including: Obtaining a preset oxygen content range, where the oxygen content range is a range between an upper limit of the oxygen content and a lower limit of the oxygen content; When each of the oxygen content data is within the oxygen content range, each of the oxygen content data is standardized to a range between a preset value and 1 to obtain each of the oxygen content index values; When each of the oxygen content data is greater than the oxygen content upper limit, 1 is determined as each of the oxygen content index values; When each of the oxygen content data is less than the oxygen content lower limit, the preset value is determined as each of the oxygen content index values.

5. The method according to claim 1, characterized in that The environmental status data includes environmental pollution data, and the environmental performance index value includes an environmental pollution index value; the environmental performance index value in each cabin is determined according to the environmental status data of each cabin in the prefabricated cabin substation, including: When each of the environmental pollution data is greater than or equal to a preset environmental pollution upper limit, 0 is determined as each of the environmental pollution index values; In the case that each of the environmental pollution data is less than the environmental pollution upper limit, each of the environmental pollution data is normalized to obtain each of the environmental pollution index values.

6. The method according to any one of claims 1 to 5, characterized in that: Determining the comprehensive performance index value of each cabin according to the environmental performance index value of each cabin includes: Acquire the type of each of the cabins, and determine the indicator weight of each of the environmental performance indicator values ​​in each of the cabins according to the type of each of the cabins; The environmental performance index values ​​in each of the cabins are weighted by using the index weights of the environmental performance index values ​​to obtain a comprehensive performance index value for each of the cabins.

7. The method according to any one of claims 1 to 5, characterized in that: Determining the overall performance index value of the prefabricated cabin substation according to the comprehensive performance index value of each cabin body includes: Acquire the type of each of the cabins, and determine the cabin weight of each of the cabins according to the type of each of the cabins; The comprehensive performance index value of each cabin is weighted by using the cabin weight of each cabin to obtain the overall performance index value of the prefabricated cabin substation.

8. The method according to any one of claims 1 to 5, characterized in that: The operation of the prefabricated cabin substation is optimized according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index value of each cabin and the environmental performance index value of each cabin, including: When the environmental performance index values ​​in each of the cabins are less than or equal to the preset environmental performance index thresholds, obtaining the adjustment priority of each of the environmental performance index values ​​in each of the cabins, and using the adjustment priority to adjust the operation of each of the cabins until the comprehensive performance index value of each of the cabins reaches a maximum; When the comprehensive performance index value of each of the cabins is less than or equal to each preset comprehensive performance index threshold value within a preset time period, performing maintenance processing on each of the cabins; When the overall performance index value of the prefabricated cabin substation is less than or equal to a preset overall performance index threshold value within a specified time period, maintenance processing is performed on the prefabricated cabin substation.

9. The method according to any one of claims 1 to 5, characterized in that: The operation of the prefabricated cabin substation is optimized according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index value of each cabin and the environmental performance index value of each cabin, including: Constructing a performance vector of the prefabricated cabin substation according to the overall performance index value of the prefabricated cabin substation, the comprehensive performance index values ​​of each cabin, and the environmental performance index values ​​of each cabin; Inputting the performance vector of the prefabricated cabin substation into a bidirectional long short-term memory neural network to obtain a prediction index value of the prefabricated cabin substation; The operation of the prefabricated cabin substation is optimized according to the predicted index value of the prefabricated cabin substation, the overall performance index value of the prefabricated cabin substation, the comprehensive performance index value of each cabin and the environmental performance index value of each cabin.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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