Substation low-carbon operation and maintenance platform based on digitization

Abstract

The invention discloses a transformer substation low-carbon operation and maintenance platform based on digitization, and relates to the technical field of transformer substation low-carbon operation and maintenance, and the transformer substation low-carbon operation and maintenance platform comprises the following steps: monitoring and obtaining equipment operation carbon emission data and waste treatment carbon emission data of a transformer substation; transmitting the equipment operation carbon emission data and the waste treatment carbon emission data to a carbon emission hidden danger prediction model established based on a neural network, and generating a carbon emission hidden danger prediction coefficient; obtaining a carbon emission hidden danger risk type based on a preset carbon emission risk coefficient, carrying out reduction and emission reduction measures on the transformer substation, and absorbing the carbon emission through an adsorption device; and collecting the absorbed residual carbon emission in the transformer substation, carrying out data analysis, obtaining the carbon emission absorption capability of the adsorption device, and carrying out corresponding adjustment. Through digital low-carbon operation and maintenance, the problems that comprehensive collection and analysis of real-time data are difficult to achieve, and low-carbon operation and maintenance of the transformer substation are difficult to achieve intelligently are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon operation and maintenance of substations, and more specifically, to a digital-based low-carbon operation and maintenance platform for substations. Background Art

[0002] Digital substation operation and maintenance is the process of managing and maintaining substations using digital technologies and tools. It improves the efficiency, reliability and safety of substation operation and maintenance by integrating various digital devices, sensors, data analysis and intelligent technologies, while reducing maintenance costs and environmental impacts. Digital substation operation and maintenance improves the management and maintenance level of substations by integrating advanced digital technologies, making them more intelligent, efficient and environmentally friendly.

[0003] For example, the invention patent with announcement number: CN116993187A announces a method and device for monitoring the operation and maintenance quality of a substation. The method and device scan and collect the operation and maintenance information and fault response plans of various types of equipment and upload them to a first database, compare the deviations between the operation and maintenance information and the fault response plans in the first database to achieve defect trend prediction and prejudgment, summarize and classify the operation and maintenance information, form corresponding links between corresponding problems and response plans, and retrieve the operation and maintenance information from the first database to the second database; the beneficial effects achieved by the present invention are: summarizing and classifying the collected operation and maintenance information through a data generation module, and uploading corresponding problems and response plans, and monitoring the operation and maintenance information in real time through a substation operation and maintenance quality monitoring device, which saves time and effort compared to manual judgment operations, and when problems occur, can generate response plans while issuing problem warnings, which helps to respond quickly and deal with the problems.

[0004] For example, the invention patent with announcement number: CN217642306U announces an operation and maintenance control device for a digital substation. In the field of power equipment operation and maintenance technology, it includes a base, a box body is provided on the base, a plurality of partitions are fixed in the box body, and the box body is divided into a high-voltage chamber, a control chamber and a low-voltage chamber in sequence by the partitions. A first transformer and a second transformer are provided in the high-voltage chamber and the low-voltage chamber respectively, a high-voltage cabinet and a low-voltage cabinet are provided in the two partitions respectively, an operation and maintenance control cabinet is provided in the control chamber, a temperature and humidity sensor is provided on the side wall of the operation and maintenance control cabinet, a top box is provided on the box body, a detection mechanism connected to the operation and maintenance control cabinet is provided in the top box, and detection probes connected to the first transformer, the second transformer, the high-voltage cabinet and the low-voltage cabinet are provided on the side wall of the detection mechanism respectively, and a heat dissipation mechanism is provided between the box body and the top box, which has the effect of facilitating on-site operation and maintenance.

[0005] The above disclosed technical solutions have at least the following technical problems: Traditional substation operation and maintenance management usually relies on manual operation and traditional monitoring systems, which makes it difficult to achieve comprehensive collection and analysis of real-time data, and it is also difficult to optimize the operation and maintenance process to reduce carbon emissions. With the development of digital technology, intelligent and automated operation and maintenance management has gradually become mainstream, but the application of existing technologies in low-carbon and environmental protection is still insufficient. There is a lack of effective digital low-carbon operation and maintenance platforms to promote the green development of substations. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a digital-based low-carbon operation and maintenance platform for substations, which solves the problem of difficulty in achieving comprehensive collection and analysis of real-time data and intelligently realizing low-carbon operation and maintenance of substations through digital low-carbon operation and maintenance.

[0007] To achieve the above object, the present invention provides the following technical solutions: A low-carbon operation and maintenance platform for substations based on digitization comprises the following steps: monitoring and acquiring equipment operation carbon emission data and waste treatment carbon emission data of substations; transmitting the equipment operation carbon emission data and waste treatment carbon emission data to a carbon emission hidden danger prediction model established based on a neural network to generate a carbon emission hidden danger prediction coefficient; acquiring a carbon emission hidden danger risk type based on a preset carbon emission risk coefficient according to the carbon emission hidden danger prediction coefficient, taking emission reduction measures for the substation according to the carbon emission hidden danger risk type and absorbing carbon emissions through an adsorption device; collecting the carbon emissions remaining in the substation after absorption, performing data analysis, acquiring the carbon emission absorption capacity of the adsorption device and making corresponding adjustments.

[0008] In a preferred embodiment, the equipment operation carbon emission data includes an equipment loss impact coefficient and an energy consumption impact coefficient; the waste treatment carbon emission data includes a waste treatment impact coefficient.

[0009] In a preferred embodiment, the equipment loss influence coefficient is specifically obtained by the following method: by applying the rated voltage to the coil on either side of the transformer and measuring the no-load current of the transformer when the other coils are open, and performing no-load experiments multiple times to calculate and take the average value as the no-load loss coefficient of the transformer; applying a sinusoidal wave voltage with a frequency equal to the rated frequency to the winding on one side of the transformer, short-circuiting the winding on the other side of the transformer, adjusting the voltage so that the current is equal to the rated current of the winding, and the power loss measured by the transformer tester is the load loss coefficient of the transformer; by monitoring and obtaining the loss coefficients of other equipment, and combining the no-load loss coefficient and load loss coefficient of the transformer based on the equipment power loss calculation formula to obtain the equipment power loss coefficient; according to the equipment power loss coefficient and the carbon emissions generated per unit of electricity, the equipment loss influence coefficient is calculated based on the preset equipment loss carbon emission formula.

[0010] In a preferred embodiment, the energy consumption impact coefficient is specifically obtained by the following method: real-time monitoring and obtaining the power shortage caused by the consumption of new energy in the process of power supply of the substation, so as to use high-carbon energy to supplement the energy consumption of electricity, and the high-carbon energy refers to coal, natural gas and oil; obtaining the energy consumption weights and corresponding carbon emission factors of coal, natural gas and oil in the process of power supply of the substation through a large amount of historical data; obtaining the energy consumption impact coefficient based on the preset energy consumption and carbon emission calculation formula according to the energy consumption and the consumption proportion of each high-carbon energy and the carbon emission factor.

[0011] In a preferred embodiment, the waste treatment impact coefficient is specifically obtained by the following method: identifying and collecting the types of waste and the quantity of each type of waste, the types of waste including cables, insulating materials and other equipment components; obtaining the carbon content of each type of waste based on the strength of the spectral lines emitted by the type of waste when excited at high temperature; monitoring and obtaining first data of each waste treatment method for different types of waste, the first data including the treatment benefit and treatment working time of each waste treatment method; and obtaining the waste treatment impact coefficient by calculating the quantity and carbon content of the waste in combination with the first data based on a preset waste treatment carbon emission calculation formula.

[0012] In a preferred embodiment, the carbon emission hidden danger estimation coefficient is specifically obtained by the following method: historical carbon emission data is collected, wherein the historical carbon emission data includes historical energy carbon emissions, historical waste carbon emissions and historical equipment loss carbon emissions, and the data is cleaned and preprocessed; the preprocessed data is trained using an LSTM model, and the model parameters are adjusted to enable the model to learn the changing pattern of carbon emissions and the carbon emission impact weights; the equipment operation carbon emission data and the waste treatment carbon emission data are input into the trained carbon emission hidden danger estimation model to generate a carbon emission hidden danger estimation coefficient.

[0013] In a preferred embodiment, the carbon emission hidden danger risk type is obtained based on the preset carbon emission risk coefficient according to the carbon emission hidden danger estimation coefficient, specifically: the carbon emission hidden danger estimation coefficient is compared with the preset carbon emission first-level risk coefficient and the carbon emission second-level risk coefficient to generate three risk levels; when it is at the first risk level, the platform will remind the staff to reduce unnecessary energy waste in the operation and management process to reduce carbon emissions; when it is at the second risk level, the platform will remind the operation and status of the equipment to reduce energy waste and carbon emission release caused by improper equipment and handling; when it is at the third risk level, the platform will turn on the substation's adsorption device to absorb carbon emissions, thereby reducing carbon emissions and achieving low-carbon operation and maintenance.

[0014] In a preferred embodiment, the carbon emission absorption capacity of the adsorption device is obtained and adjusted accordingly, specifically: collecting the carbon emissions remaining in the substation after absorption; collecting and obtaining the flow rate of carbon emissions absorbed by the adsorption device within G hours and the carbon concentration in the gas; obtaining the absorption efficiency of the adsorption device based on the efficiency calculation formula according to the absorbed carbon emission flow rate and the carbon concentration in the gas combined with the carbon emissions remaining in the substation, and judging the carbon emission absorption capacity; for the adsorption device whose absorption efficiency does not reach 80%, it is necessary to replace and adjust its filter core in time to ensure that its working efficiency is up to standard and meets the standard of reducing carbon emissions.

[0015] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through digital technology, the carbon emission data of substations can be accurately acquired and analyzed, which effectively solves the problem of difficulty in achieving comprehensive collection and analysis of real-time data in existing technologies.

[0016] 2. Through low-carbon operation and maintenance technology, intelligent emission reduction measures can be taken to achieve low-carbon operation and maintenance of substations, optimize resource utilization, reduce energy consumption and operating costs, and promote environmental protection and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of a digital-based low-carbon operation and maintenance platform for substations provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, Figure 1 A schematic diagram of a digital substation low-carbon operation and maintenance platform structure provided in an embodiment of the present application includes the following steps: S1, by monitoring and obtaining the carbon emission data of substation equipment operation and waste treatment.

[0020] In this example, the substation, as the basic unit of the power system, undertakes important tasks such as voltage conversion, power distribution, and regional power grid connection. The carbon emission impact data of the substation can be divided into equipment operation carbon emission data and waste treatment carbon emission data. The equipment operation carbon emission data includes equipment loss impact coefficient and energy consumption impact coefficient. Among them, the equipment loss impact coefficient refers to the carbon emissions caused by the increase in power generation due to the loss of power transmission and transformation equipment such as transformers in the substation during the operation of the substation. The energy consumption impact coefficient refers to the process of power generation in the substation, which first relies on new energy or renewable energy such as wind energy, hydropower, and solar energy as the energy material of the power plant. However, due to improper treatment or inadequate combustion of new energy or renewable energy, the power supply and demand of the substation cannot keep up. At this time, it is necessary to obtain electricity from high-carbon energy such as coal power and oil power through combustion, so the carbon emissions during its operation will increase significantly.

[0021] Among them, analyzing the impact coefficient of equipment loss has the following effects on the low-carbon operation and maintenance of substations: Identify the main sources of loss: By analyzing the equipment loss impact coefficient, the main loss equipment in the substation (such as transformers, switchgear, etc.) can be identified, and then corresponding optimization measures can be taken, such as adjusting the operating mode and improving equipment performance.

[0022] Optimize operating parameters: According to the loss coefficient data, adjust the operating parameters of the equipment (such as load, temperature, etc.) to ensure that the equipment operates in the best condition to reduce energy consumption.

[0023] Reduce energy consumption: Equipment losses are directly related to electricity consumption. Optimizing losses can significantly reduce energy use and thus reduce carbon emissions.

[0024] Promoting the use of clean energy: By reducing the consumption of traditional energy, space can be made for the introduction and use of clean energy, thereby promoting the transformation of substations to low-carbon ones.

[0025] Reduce operating costs: By reducing equipment losses, substations can significantly reduce power consumption and maintenance costs, and improve overall economic benefits.

[0026] Extend equipment life: Reducing equipment wear and tear can reduce its load, extend the service life of the equipment, and reduce replacement costs due to equipment aging.

[0027] Promoting equipment intelligence: Analyzing the impact coefficient of equipment loss will help promote the intelligent management of substation equipment, use advanced monitoring and control technologies to monitor equipment status in real time, optimize operation and maintenance strategies, and promote low-carbon development.

[0028] Strengthen management decision-making: Data analysis can provide scientific basis for management to formulate more effective low-carbon operation and maintenance policies and goals, thereby promoting substations to move towards sustainable development goals.

[0029] Implement precise operation and maintenance: Based on the analysis of the equipment loss impact coefficient, the substation can adopt a more precise maintenance strategy and conduct targeted inspections and maintenance to ensure that the equipment is always in the best operating state.

[0030] Dynamically adjust maintenance plans: By real-time monitoring of equipment wear and tear, dynamically adjust maintenance plans to respond to various situations that arise during equipment operation and improve maintenance efficiency.

[0031] The specific method for obtaining the equipment loss influence coefficient is as follows: By applying rated voltage to the coil on either side of the transformer and leaving the other coils open, the no-load current of the transformer is measured, and the no-load test is performed multiple times to calculate the average value as the no-load loss coefficient of the transformer. A sine wave voltage with a frequency equal to the rated frequency is applied to one winding of the transformer. The other winding of the transformer is short-circuited. The voltage is adjusted so that the current is equal to the rated current of the winding. The power loss measured by the transformer tester is the load loss factor of the transformer. By monitoring and obtaining the loss coefficients of other equipment, and combining the no-load loss coefficient and load loss coefficient of the transformer, the power loss coefficient of the equipment is obtained based on the equipment power loss calculation formula; The equipment loss impact coefficient is calculated based on the preset equipment loss carbon emission formula according to the equipment's power loss coefficient and the carbon emissions generated per unit of electricity.

[0032] The specific calculation formula of the no-load damage coefficient of the transformer is as follows:

[0033] The specific calculation formula of the power loss coefficient of the equipment is as follows:

[0034] The specific calculation formula of the equipment loss influence coefficient is as follows:

[0035] In the formula, is the equipment loss influence coefficient, Q is the carbon emission generated per unit of electricity, is the power loss coefficient, t is the working time, is the thermal efficiency of the device, For the heat generated, is the no-load damage coefficient, is the experiment from the 1st to the nth time, where =1,2,3...,n, where n is an integer, and is the applied voltage and measured current from the 1st to the nth experiment, N is the number of experiments, is the load loss factor, is the loss coefficient of other equipment.

[0036] It should be noted that during the operation of the substation, the carbon emissions generated by transformer equipment losses account for the largest proportion. Among the transformer operating losses, the acquisition of no-load losses and load losses plays a significant role in calculating the transformer losses. In addition to the transformer losses, the losses of other equipment in the equipment will also increase the power damage, thereby increasing the carbon emissions caused by equipment losses.

[0037] Among them, analyzing the energy consumption impact coefficient has the following effects on the low-carbon operation and maintenance of substations: Identify the main sources of energy consumption: By analyzing the energy consumption impact coefficient, the main sources of energy consumption in substation operation can be identified. For example, the energy consumption characteristics of substation equipment (such as transformers, switchgear, cooling systems, etc.) under different load conditions. Knowing this information can help operators focus on optimizing the operation mode and maintenance strategy of high-energy-consuming equipment.

[0038] Optimize equipment operation: Analyzing the impact coefficient can help formulate a reasonable equipment operation strategy. For example, through load forecasting and optimized scheduling, ensure that the equipment works within the optimal operating range and reduce unnecessary energy consumption. At the same time, use the energy management system (EMS) to monitor the real-time operating status of the equipment, adjust its operating mode in time, and reduce energy consumption.

[0039] Formulate energy-saving improvement measures: Based on the analysis of the energy consumption impact coefficient, specific energy-saving improvement measures can be formulated. This includes updating equipment (such as high-efficiency transformers, smart switches), improving insulation materials, optimizing cooling systems, etc. By comparing the energy consumption data before and after the improvement, the effectiveness of the measures can be evaluated.

[0040] Enhance system flexibility and reliability: By analyzing the impact coefficient of energy consumption, the adaptability of substations to renewable energy access can be improved. Effective coordination of multiple energy sources can be achieved, system operation can be optimized, and the flexibility and reliability of power supply can be improved, thereby promoting the realization of low-carbon operation and maintenance goals.

[0041] Promote intelligent operation and maintenance: Using advanced data analysis and artificial intelligence technology, it is possible to monitor and analyze the energy consumption of substations in real time. Through data mining, potential energy-saving opportunities and optimization space for equipment operation can be discovered, achieving more intelligent and efficient operation and maintenance management.

[0042] Improve economic benefits: Low-carbon operation and maintenance not only helps protect the environment, but also reduces operating costs. By effectively managing energy consumption and reducing unnecessary expenses, the economic benefits of substations can be improved. For example, electricity bills can be reduced and the service life of equipment can be extended.

[0043] The specific method for obtaining the energy consumption impact coefficient is as follows: Real-time monitoring and acquisition of insufficient electricity consumption caused by consumption of new energy in the power supply process of the substation, so as to use high-carbon energy to supplement the energy consumption of electricity consumption, wherein the high-carbon energy refers to coal, natural gas and oil; Obtain the energy consumption weights and corresponding carbon emission factors of coal, natural gas and oil in the power supply process of substations through a large amount of historical data; The energy consumption impact coefficient is obtained based on the preset energy consumption and carbon emission calculation formula according to the energy consumption, the consumption proportion of each high-carbon energy and the carbon emission factor.

[0044] The specific calculation formula of the energy consumption impact coefficient is as follows:

[0045] In the formula, is the energy consumption impact coefficient, is the consumption of high-carbon energy. , , are the weights of coal, natural gas and oil in the energy consumption process, , , are the carbon emission factors of coal, natural gas and oil respectively.

[0046] In this example, the waste treatment carbon emission data includes the waste treatment impact coefficient, which means that the substation will generate waste during equipment replacement and maintenance, especially the treatment of cables and insulation materials, which will cause additional carbon emissions during the treatment process.

[0047] Among them, analyzing waste treatment carbon emission data has the following effects on the low-carbon operation and maintenance of substations: Identify carbon emission sources and optimize treatment processes: Waste treatment is one of the important sources of carbon emissions during substation operation and maintenance. By analyzing carbon emission data, it is possible to identify which waste treatment links are the main sources of carbon emissions, such as the treatment of waste equipment, insulating oil, transformer components, etc. After identifying these high-carbon emission sources, the waste treatment process can be optimized in a targeted manner, and more environmentally friendly technologies or treatment methods can be selected to reduce unnecessary carbon emissions.

[0048] Establish a carbon footprint monitoring mechanism: Analyzing carbon emission data from waste treatment can help substations establish a carbon footprint monitoring mechanism. During the operation and maintenance process, regularly monitor the carbon emissions from waste treatment and compare them with the established low-carbon goals to facilitate timely adjustment of operation and maintenance strategies. For example, monitor the carbon emissions from waste treatment in different time periods of the substation to evaluate the carbon impact of different operations and ensure that the national or industry low-carbon policy requirements are met.

[0049] Improve the resource utilization rate of waste: Through the analysis of carbon emission data, the environmental benefits of waste resource utilization can be more accurately evaluated. For example, some wastes such as waste transformer materials or insulating oil can reduce the carbon emissions of direct treatment through recycling or remanufacturing. Based on the feedback of carbon emission data, substations can promote waste regeneration and recycling, improve resource utilization, reduce primary energy and material consumption, and thus reduce carbon emissions.

[0050] Support decision-making and policy compliance: The analysis of carbon emission data can provide reliable data support for the low-carbon operation and maintenance decision-making of substations. For example, when selecting a waste treatment outsourcing company, you can evaluate the carbon emission performance of its treatment technology and choose partners that are more in line with low-carbon standards. In addition, waste treatment carbon emission data can also help substations meet local and national carbon emission policy requirements, avoid potential legal or policy risks, and ensure the legality and compliance of substation operation and maintenance.

[0051] Promoting technological innovation and improvement: By analyzing the carbon emission data of waste treatment, we can find the shortcomings of current treatment technology and promote technological innovation. For example, if a certain waste treatment method produces high carbon emissions, data analysis can prompt substations to introduce or develop more efficient and low-carbon treatment technologies, such as pyrolysis, cooling recovery and other advanced waste treatment technologies, to further reduce carbon emissions.

[0052] Social responsibility and sustainable development: The transparency and continuous improvement of carbon emission data for waste treatment also help substations fulfill their social responsibilities and enhance their public image. Through clear carbon emission analysis, substations can show the public and stakeholders their efforts in low-carbon operation and maintenance and environmental protection, and enhance the company's image of sustainable development. In addition, this will help to cope with more stringent environmental regulations and standards that may emerge in the future.

[0053] The waste treatment impact coefficient is specifically obtained as follows: Identify and collect the types of waste, including cables, insulation materials and other equipment parts, and the quantities of each type of waste; The carbon content of each type of waste is obtained based on the intensity of the spectral line emitted by the type of waste when excited at high temperature; Monitoring different types of waste and obtaining first data of each waste treatment method, wherein the first data includes treatment benefits and treatment working time of each waste treatment method; The waste treatment impact coefficient is calculated based on the quantity and carbon content of the waste in combination with the first data based on a preset waste treatment carbon emission calculation formula.

[0054] The specific calculation formula of the waste treatment impact coefficient is as follows:

[0055] In the formula, Y is the waste treatment impact coefficient, is the carbon content of the mth type of waste, The treatment efficiency of the jth treatment method for the mth type of waste is: is the processing time of the jth processing method, is the quantity of waste of type m.

[0056] It should be noted that for different types of waste, its structural materials are different. Some waste needs to be incinerated, some needs to be mechanically crushed, and some needs to be recycled, so the treatment methods are also different.

[0057] S2, transmitting the equipment operation carbon emission data and waste treatment carbon emission data to a carbon emission potential risk estimation model established based on a neural network to generate a carbon emission potential risk estimation coefficient.

[0058] In this example, the carbon emission potential risk estimation coefficient is obtained in the following specific method: Collect historical carbon emission data, including historical energy carbon emissions, historical waste carbon emissions, and historical equipment loss carbon emissions, and clean and pre-process the data; Use the LSTM model to train the preprocessed data and adjust the model parameters to learn the changing patterns of carbon emissions and the impact weights of carbon emissions; The equipment operation carbon emission data and waste treatment carbon emission data are input into the trained carbon emission hidden danger estimation model to generate the carbon emission hidden danger estimation coefficient.

[0059] The specific calculation formula of the carbon emission hidden danger estimation coefficient is as follows:

[0060] In the formula, C is the carbon emission potential estimation coefficient, is the energy consumption impact coefficient, Y is the waste treatment impact coefficient, is the equipment loss influence coefficient, Train weights for energy consumption impact coefficients, is the weight of the equipment loss influence coefficient, Y is the weight of the waste treatment impact coefficient.

[0061] S3, obtaining the carbon emission hidden danger risk type based on the preset carbon emission risk coefficient according to the carbon emission hidden danger estimation coefficient, taking emission reduction measures for the substation according to the carbon emission hidden danger risk type and absorbing the carbon emissions through the adsorption device.

[0062] The method of obtaining the carbon emission hidden danger risk type based on the preset carbon emission risk coefficient according to the carbon emission hidden danger estimation coefficient is specifically: The estimated coefficient of carbon emission hidden dangers is compared with the preset first-level carbon emission risk coefficient and the second-level carbon emission risk coefficient to generate three risk levels. Different emission reduction measures are taken according to different risk levels to reduce the carbon emissions of the substation and achieve low-carbon operation and maintenance of the substation. When it is at the first-level risk level, the platform will remind the staff to reduce unnecessary energy waste to reduce carbon emissions through intelligent scheduling and optimization of operation strategies during the operation and management process. When it is at the second-level risk level, the platform will remind the operation and status of the equipment, and ask the staff to check the aging, damage and waste disposal of the equipment, and reduce the waste of energy and the release of carbon emissions caused by improper equipment and handling. When it is at the third-level risk level, the platform will turn on the adsorption device of the substation to absorb carbon emissions, thereby reducing carbon emissions and achieving low-carbon operation and maintenance.

[0063] S4, collects the carbon emissions remaining in the substation after absorption, performs data analysis, obtains the carbon emission absorption capacity of the adsorption device and makes corresponding adjustments.

[0064] In this example, the carbon emission absorption capacity of the adsorption device is obtained and adjusted accordingly, specifically: Collect the carbon emissions remaining in the substation after absorption; Collect and obtain the flow rate of carbon emissions absorbed by the adsorption device within G hours and the carbon concentration in the gas; The absorption efficiency of the adsorption device is obtained based on the efficiency calculation formula according to the flow rate of absorbed carbon emissions and the carbon concentration in the gas combined with the residual carbon emissions in the substation, and the carbon emission absorption capacity is determined; For adsorption devices whose absorption efficiency does not reach 80%, it is necessary to replace and adjust the filter core in time to ensure that its working efficiency meets the standard and achieves the standard of reducing carbon emissions.

[0065] The specific calculation formula for the absorption efficiency of the device is as follows:

[0066] In the formula, is the device absorption efficiency, is the flow of carbon emissions absorbed, is the carbon concentration in the gas, G is the absorption time of the adsorption device, and S is the remaining carbon emissions in the power station.

[0067] When the absorption efficiency of the adsorption device does not reach 80%, the platform will remind the staff to adjust the adsorption device in time, including the maintenance of the adsorption device and the timely replacement of the filter core and filter net, to ensure the normal operation of the adsorption device, thereby reducing the carbon emissions of the substation and realizing low-carbon operation and maintenance of the substation.

[0068] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0069] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0070] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0071] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0072] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0073] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital-based low-carbon operation and maintenance platform for substations, characterized in that: The steps include: By monitoring and obtaining carbon emission data of substation equipment operation and waste treatment; Transmitting the equipment operation carbon emission data and waste treatment carbon emission data to a carbon emission potential risk estimation model established based on a neural network to generate a carbon emission potential risk estimation coefficient; According to the carbon emission hidden danger estimation coefficient, the carbon emission hidden danger risk type is obtained based on the preset carbon emission risk coefficient, and emission reduction measures are taken for the substation according to the carbon emission hidden danger risk type, and the carbon emissions are absorbed by the adsorption device; The carbon emissions remaining in the substation after absorption are collected, and data analysis is performed to obtain the carbon emission absorption capacity of the adsorption device and make corresponding adjustments.

2. According to claim 1, a digital-based low-carbon operation and maintenance platform for substations is characterized in that: The equipment operation carbon emission data includes the equipment loss impact coefficient and the energy consumption impact coefficient; the waste treatment carbon emission data includes the waste treatment impact coefficient.

3. A digital-based low-carbon operation and maintenance platform for substations according to claim 2, characterized in that: The specific method for obtaining the equipment loss influence coefficient is as follows: By applying rated voltage to the coil on either side of the transformer and leaving the other coils open, the no-load current of the transformer is measured, and the no-load test is performed multiple times to calculate the average value as the no-load loss coefficient of the transformer. A sine wave voltage with a frequency equal to the rated frequency is applied to one winding of the transformer. The other winding of the transformer is short-circuited. The voltage is adjusted so that the current is equal to the rated current of the winding. The power loss measured by the transformer tester is the load loss factor of the transformer. By monitoring and obtaining the loss coefficients of other equipment, and combining the no-load loss coefficient and load loss coefficient of the transformer, the power loss coefficient of the equipment is obtained based on the equipment power loss calculation formula; The equipment loss impact coefficient is calculated based on the preset equipment loss carbon emission formula according to the equipment's power loss coefficient and the carbon emissions generated per unit of electricity.

4. A digital-based low-carbon operation and maintenance platform for substations according to claim 2, characterized in that: The specific method for obtaining the energy consumption impact coefficient is as follows: Real-time monitoring and acquisition of insufficient electricity consumption caused by consumption of new energy in the power supply process of the substation, so as to use high-carbon energy to supplement the energy consumption of electricity consumption, wherein the high-carbon energy refers to coal, natural gas and oil; Obtain the energy consumption weights and corresponding carbon emission factors of coal, natural gas and oil in the power supply process of substations through a large amount of historical data; The energy consumption impact coefficient is obtained based on the preset energy consumption and carbon emission calculation formula according to the energy consumption, the consumption proportion of each high-carbon energy and the carbon emission factor.

5. A digital-based low-carbon operation and maintenance platform for substations according to claim 2, characterized in that: The waste treatment impact coefficient is specifically obtained as follows: Identify and collect the types of waste, including cables, insulation materials and other equipment parts, and the quantities of each type of waste; The carbon content of each type of waste is obtained based on the intensity of the spectral line emitted by the type of waste when excited at high temperature; Monitoring different types of waste and obtaining first data of each waste treatment method, wherein the first data includes treatment benefits and treatment working time of each waste treatment method; The waste treatment impact coefficient is calculated based on the quantity and carbon content of the waste in combination with the first data based on a preset waste treatment carbon emission calculation formula.

6. A digital-based low-carbon operation and maintenance platform for substations according to claim 1, characterized in that: The specific method for obtaining the carbon emission hidden danger estimation coefficient is as follows: Collect historical carbon emission data, including historical energy carbon emissions, historical waste carbon emissions, and historical equipment loss carbon emissions, and clean and pre-process the data; Use the LSTM model to train the preprocessed data and adjust the model parameters to learn the changing patterns of carbon emissions and the impact weights of carbon emissions; The equipment operation carbon emission data and waste treatment carbon emission data are input into the trained carbon emission hidden danger estimation model to generate the carbon emission hidden danger estimation coefficient.

7. A digital-based low-carbon operation and maintenance platform for substations according to claim 1, characterized in that: The method of obtaining the carbon emission hidden danger risk type based on the preset carbon emission risk coefficient according to the carbon emission hidden danger estimation coefficient is specifically: Compare the estimated coefficient of carbon emission hidden dangers with the preset first-level carbon emission risk coefficient and second-level carbon emission risk coefficient to generate three risk levels; When the risk level reaches level 1, the platform will remind staff to reduce unnecessary energy waste during operation and management to reduce carbon emissions; When the risk level reaches the second level, the platform will remind the operation and status of the equipment to reduce the energy waste and carbon emission caused by improper equipment and handling; When the risk level reaches level three, the platform will turn on the substation's adsorption device to absorb carbon emissions, thereby reducing carbon emissions and achieving low-carbon operation and maintenance.

8. A digital-based low-carbon operation and maintenance platform for substations according to claim 1, characterized in that: The obtaining of the carbon emission absorption capacity of the adsorption device and making corresponding adjustments are specifically as follows: Collect the carbon emissions remaining in the substation after absorption; Collect and obtain the flow rate of carbon emissions absorbed by the adsorption device within G hours and the carbon concentration in the gas; The absorption efficiency of the adsorption device is obtained based on the efficiency calculation formula according to the flow rate of absorbed carbon emissions and the carbon concentration in the gas combined with the residual carbon emissions in the substation, and the carbon emission absorption capacity is determined; For adsorption devices whose absorption efficiency does not reach 80%, it is necessary to replace and adjust the filter core in time to ensure that its working efficiency meets the standard and achieves the standard of reducing carbon emissions.

9. A digital-based low-carbon operation and maintenance platform for substations according to claim 3, characterized in that: The specific calculation formula of the no-load damage coefficient of the transformer is as follows: The specific calculation formula of the power loss coefficient of the equipment is as follows: The specific calculation formula of the equipment loss influence coefficient is as follows: In the formula, is the equipment loss influence coefficient, Q is the carbon emission generated per unit of electricity, is the power loss coefficient, t is the working time, is the thermal efficiency of the device, For the heat generated, is the no-load damage coefficient, is the experiment from the 1st to the nth time, where =1,2,3...,n, where n is an integer, and is the applied voltage and measured current from the 1st to the nth experiment, N is the number of experiments, is the load loss factor, is the loss coefficient of other equipment.

10. A digital-based low-carbon operation and maintenance platform for substations according to claim 8, characterized in that: The specific calculation formula for the absorption efficiency of the device is as follows: In the formula, is the device absorption efficiency, is the flow of carbon emissions absorbed, is the carbon concentration in the gas, G is the absorption time of the adsorption device, and S is the remaining carbon emissions in the power station.

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