Power distribution network ice melting device key point distribution method and system based on historical icing data

Abstract

The invention relates to the technical field of power distribution network ice melting device key point distribution, in particular to a power distribution network ice melting device key point distribution method and system based on historical icing data. Acquiring first-level data; dividing an icing area, and predicting an equipment risk level; carrying out primary maintenance according to the icing area and the equipment risk level, and carrying out secondary risk level division; and carrying out ice melting device point distribution according to the risk level of the secondary division. By combining the historical icing data and the real-time meteorological conditions to carry out accurate evaluation and risk grading on the equipment, the method can effectively identify the easy icing area and the high-risk equipment. The accurate identification method can provide timely protection measures for the power distribution network equipment, and especially in an easy icing area, through reasonable arrangement of the ice melting device, equipment faults or shutdown caused by ice accumulation can be effectively prevented, and long-term stable operation of the equipment is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of key point distribution of power distribution network ice melting device, and particularly relates to a key point distribution method and system of power distribution network ice melting device based on historical icing data. BACKGROUND

[0002] In the operation and management of modern power distribution networks, meteorological disasters have an increasingly significant impact on electrical equipment, especially under snow and ice weather conditions. Ice covering has a particularly prominent impact on electrical facilities. Ice covering not only directly causes faults in power distribution network equipment, but also can cause long-term unstable operation of equipment, and even large-scale shutdown. Therefore, how to ensure the safe and reliable operation of electrical facilities in the face of extreme weather conditions has become an important issue in the construction and management of power distribution networks.

[0003] Currently, the main measures for power distribution networks include upgrading of protective facilities for equipment, intelligent monitoring and dispatching of equipment, and emergency response mechanisms for special weather conditions. However, existing technical methods still have certain limitations, especially in high-risk areas where ice covering occurs, there is a lack of systematic monitoring, early warning, and targeted measures, which cannot achieve accurate management and targeted distribution of equipment, resulting in frequent equipment failures and difficulty in taking effective remedial measures in a timely manner.

[0004] The key point distribution method of power distribution network ice melting device based on historical icing data has emerged as the times require. This method makes full use of historical meteorological data, icing event data, and operation history data of transformers and lines, and combines modern intelligent control technology to conduct a comprehensive risk assessment of the power distribution network, thereby achieving dynamic monitoring, fault prediction, and emergency response of equipment. According to the risk levels of different regions and equipment, the method can accurately arrange ice melting devices to minimize the risk of equipment failure and shutdown caused by ice accumulation, and improve the safety and stability of power distribution networks under extreme weather conditions. SUMMARY

[0005] In view of the problems existing in the prior art, the present application is proposed.

[0006] Therefore, the present application provides a key point distribution method of power distribution network ice melting device based on historical icing data, which solves the problem that the existing ice melting device arrangement method often has unreasonable distribution points and inaccurate deployment, resulting in the possibility that some areas of equipment may not be effectively protected, or the configuration of ice melting devices is excessive, causing resource waste.

[0007] In a first aspect, the present application provides a key point distribution method of power distribution network ice melting device based on historical icing data, comprising:

[0008] Real-time data acquisition is performed to obtain primary data;

[0009] Divide icing area and predict equipment risk level;

[0010] According to the icing area and the equipment risk level, carry out primary maintenance and secondary division of risk level;

[0011] According to the secondary division of risk level, carry out ice melting device point distribution.

[0012] As a preferred scheme of the power distribution network ice melting device key point distribution method based on historical icing data, wherein: the division of the icing area comprises,

[0013] The first-level data is input into the first-level model to obtain the environmental state index of the area;

[0014] The environmental state index is compared with a preset threshold value, and the icing area is divided according to the comparison result.

[0015] As a preferred scheme of the power distribution network ice melting device key point distribution method based on historical icing data, wherein: the prediction of the equipment risk level comprises,

[0016] The first-level data is input into the second-level model to obtain second-level prediction data;

[0017] The second-level prediction data is compared with a preset risk threshold value to obtain the equipment risk level.

[0018] As a preferred scheme of the power distribution network ice melting device key point distribution method based on historical icing data, wherein: the primary maintenance according to the icing area and the equipment risk level, and the secondary division of risk level comprises,

[0019] Risk assessment is carried out based on the icing area division result and the equipment risk result;

[0020] According to the risk assessment result, the equipment of the area is maintained;

[0021] According to the primary maintenance result, the equipment risk level is secondarily divided.

[0022] As a preferred scheme of the power distribution network ice melting device key point distribution method based on historical icing data, wherein: the ice melting device point distribution according to the secondary division of risk level comprises,

[0023] According to the division of the icing area and the secondary division result of the equipment risk level, heating devices and whether to configure backup interfaces are configured;

[0024] The control mode of the heating device configured according to different equipment risk levels is formulated.

[0025] As a preferred scheme of the power distribution network ice-melting device key point distribution method based on historical icing data, wherein: the comparison of the secondary prediction data with the preset risk threshold to obtain the device risk level comprises,

[0026] When the environmental state index is greater than or equal to the preset threshold, the area is determined as an icing-prone area.

[0027] When the environmental state index is less than the preset threshold, the area is determined as a non-icing-prone area.

[0028] As a preferred scheme of the power distribution network ice-melting device key point distribution method based on historical icing data, wherein: the comparison of the secondary prediction data with the preset risk threshold to obtain the device risk level comprises,

[0029] When the calculation result is less than or equal to the first preset risk threshold, the level is determined as a low-risk device.

[0030] When the calculation result is greater than the first preset risk threshold and less than or equal to the second preset risk threshold, the level is determined as a medium-risk device.

[0031] When the calculation result is greater than the second preset risk threshold, the level is determined as a high-risk device.

[0032] In a second aspect, the application provides a power distribution network ice-melting device key point distribution system based on historical icing data, comprising: a collection module, a calculation module, a maintenance module, and a distribution output module.

[0033] The collection module acquires primary data.

[0034] The calculation module divides icing areas and predicts device risk levels.

[0035] The maintenance module performs primary maintenance according to the icing areas and the device risk levels, and performs secondary division of risk levels.

[0036] The distribution output module performs ice-melting device distribution according to the secondary division of risk levels.

[0037] In a third aspect, the application provides an electronic device, comprising:

[0038] a memory and a processor.

[0039] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to implement the steps of the power distribution network ice-melting device key point distribution method based on historical icing data.

[0040] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the method for key point positioning of power distribution network ice-melting device based on historical icing data.

[0041] The present application has the following advantages: By combining historical icing data and real-time weather conditions to accurately assess and risk grade equipment, the present application can effectively identify icing-prone areas and high-risk equipment. This accurate identification method can provide timely protection measures for power distribution network equipment, especially in icing-prone areas, through reasonable ice-melting device arrangement, which can effectively prevent equipment failure or shutdown caused by ice accumulation and ensure long-term stable operation of equipment.

[0042] By comprehensively assessing and grading key equipment such as transformers and lines, differentiated management strategies are implemented for low-risk, medium-risk and high-risk equipment. Low-risk equipment can be maintained and monitored regularly to reduce unnecessary intervention; medium and high-risk equipment can be subjected to emergency measures such as load adjustment, equipment inspection and redundant power activation to avoid cascading reactions caused by equipment failure. In addition, the rational arrangement of ice-melting devices enables efficient allocation of resources, reducing excessive investment and resource waste. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0044] Figure 1 The method for key point positioning of power distribution network ice-melting device based on historical icing data provided by an embodiment of the present application is shown in the flowchart. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0046] Embodiment 1

[0047] Reference Figure 1 For the first embodiment of the present application, the embodiment provides a method for key point positioning of power distribution network ice-melting device based on historical icing data, as shown in Figure 1 the flowchart.

[0048] S1: Real-time data acquisition to obtain primary data;

[0049] S2: Divide the icing area and predict the equipment risk level;

[0050] S3: According to the icing area and the equipment risk level, carry out the first maintenance, and carry out the secondary division of the risk level;

[0051] S4: According to the risk level of the secondary division, carry out the ice melting device distribution.

[0052] It should be pointed out that the existing power distribution network anti-icing scheme generally has a series of technical problems such as rough regional risk identification, unclear equipment classification, lack of targeted device configuration, low intelligent level of control strategy, and lack of feedback evolution ability of the system, which is difficult to meet the comprehensive requirements of accurate protection, economic configuration and dynamic response of power equipment under the current trend of climate extreme.

[0053] Therefore, in view of the existing problems, through the closed loop process composed of S1 to S4, the identification of the icing prone area in the power distribution network, the dynamic classification and evaluation of the equipment risk, and the differentiated distribution and configuration of the ice melting device can be independently completed, thereby effectively solving the problems of inaccurate regional identification, rough equipment risk identification, and unreasonable protection resource configuration in the prior art.

[0054] Embodiment 2, with reference to Figure 1 For an embodiment of the present application, based on the above embodiment, a power distribution network ice melting device key distribution method based on historical icing data is provided.

[0055] In the embodiments of the present application, the real-time data acquisition in step S1 is performed by establishing a data interface with the provincial / municipal meteorological bureau, the power dispatching center and the historical operation and maintenance archive system, collecting multi-year historical data resources, pre-processing the real-time data, and forming primary data.

[0056] In an optional implementation, the real-time data acquisition in step S1 can also be performed by deploying various intelligent sensing terminals in the power distribution network, such as meteorological monitoring nodes, line temperature sensors, transformer load detection units, environmental humidity monitoring devices, etc. to collect current state data. This method is suitable for subsequent model dynamic updating and real-time response strategy formulation.

[0057] In another optional implementation, the real-time data acquisition in step S1 can also be performed by accessing remote sensing image data and open meteorological database.

[0058] In the embodiments of the present application, the step of dividing the icing area in step S2 comprises the following steps A1-A2: and predicting the operating level of the transformer comprises the following steps B1-B2:

[0059] A1: inputting the first-level data into a first-level model to obtain an environmental state index of the area;

[0060] A2: judging the environmental state index against a preset threshold, and dividing the icing area according to the judging result.

[0061] B1: inputting the first-level data into a second-level model to obtain second-level prediction data;

[0062] B2: comparing the second-level prediction data against a preset risk threshold to obtain a device risk level.

[0063] In step A1, the first-level model is represented as:

[0064] ICE = (T-T t ) × H t × P f

[0065] wherein T represents the actual temperature, T t represents the freezing point temperature, H t represents the humidity threshold, P f represents the probability of the precipitation being freezing rain, and ICE represents the weather index.

[0066] In an optional embodiment, the first-level model can also be a comprehensive icing index model based on wind cooling weighting. In a low-temperature environment, the greater the wind speed, the faster the temperature of the device surface drops, and the more likely icing is triggered. A wind cooling enhancement factor is introduced to improve the physical rationality of the environmental modeling.

[0067] In another optional embodiment, the first-level model can also be a dynamic icing risk model based on gradient change rate. Compared with the static temperature judgment, this model is suitable for capturing the risk of “sudden icing events”, reflecting the device icing risk triggered by rapid temperature drop and sudden humidity increase, and is suitable for predicting dynamic area identification.

[0068] In step A2, the environmental state index is judged against a preset threshold, and the icing area is divided according to the judging result. The specific implementation is as follows:

[0069] When ICE≥0, it is determined that the area is an icing-prone area. When the actual temperature is close to or below the freezing point, and the humidity is high and the probability of freezing rain is high, it is determined that the area is an icing-prone area. In such areas, the equipment is exposed to extreme weather and is prone to icing or freezing rain, which can cause equipment damage or malfunction. When ICE<0, it is determined that the area is not an icing-prone area. When the actual temperature is higher than the freezing point, or the humidity is low and the probability of freezing rain is low, it is determined that the area is not an icing-prone area. In these areas, the climate conditions are unlikely to cause icing, so the risk is low and the equipment is less affected.

[0070] It should be noted that the transformer is one of the most important equipment in the power distribution network, and its operating state directly affects the safety and reliability of the power grid. Predicting the operating level of the transformer helps to identify potential risks in advance and take appropriate preventive measures to ensure that the transformer can operate stably under extreme weather conditions, thereby avoiding equipment failure and power grid outage events.

[0071] Specifically, the secondary model in step B1 is represented as:

[0072] R t = w1·R l + w2·R f + w3·R a + w4·R w

[0073] wherein R l represents the load risk value of the transformer, R f represents the failure risk value of the transformer, R a represents the aging risk value of the transformer, R w represents the risk value of the weather condition, w1, w2, w3, w4 represent the weight coefficients, and R t represents the comprehensive risk value.

[0074] Further, in step B2, the device risk level is obtained by comparing the secondary prediction data with the preset risk threshold. The specific implementation is as follows:

[0075] When R t ≤ X1, it is determined that the level is a low-risk device, indicating that the load, failure, aging, and weather condition of the transformer are within a safe range, and the transformer is expected to operate stably under extreme weather conditions without additional intervention. t When X1< R t ≤ X2, it is determined that the level is a medium-risk device, indicating that the transformer has some risk, which may be due to the load being close to overload, the equipment being aged, or the weather condition being harsh. Monitoring should be strengthened and preventive measures should be taken in advance, such as regular inspection of the transformer state, load scheduling, etc.When X2 is high, the risk level is high, indicating that the transformer has a high risk and may face failure or overload, especially in adverse weather conditions. Immediate measures need to be taken, such as temporarily disabling the transformer, increasing backup equipment, strengthening maintenance, etc., to avoid failure or accidents.

[0076] In an optional embodiment, the secondary model can also be a fuzzy mathematical method, which evaluates the influence of various risk factors on the equipment operating state through a fuzzy membership function and performs fuzzy weight weighting. It is suitable for situations where risk data is difficult to accurately quantify or subjective experience is required, to improve the fault tolerance and robustness of risk assessment.

[0077] In another optional embodiment, the secondary model can also be a Bayesian network probability model, which constructs a Bayesian network structure between the transformer operating state and multiple risk factors, calculates the joint probability of abnormality, and estimates the overall risk level.

[0078] In the embodiments of the present application, the primary maintenance in step S3 according to the icing area and the transformer operating state includes the following steps C1-C3:

[0079] C1: Risk assessment based on the icing area division result and the equipment risk result;

[0080] C2: Corresponding primary maintenance of the equipment in the area according to the risk assessment result;

[0081] C3: Secondary division of the equipment risk level according to the primary maintenance result.

[0082] The primary maintenance includes, when the low-risk equipment is located in the icing-prone area, performing dynamic monitoring of the load, current and temperature, determining whether the risk level needs to be upgraded according to the dynamic monitoring result, if the detection data is abnormal, the current risk level is maintained, if the detection data is abnormal, the risk level is upgraded according to the abnormal data interval, the physical equipment is detected regularly, and the ice melting device interface is reserved, if the physical equipment detection is abnormal, the current risk level is maintained, if the physical equipment detection is abnormal, the risk level is upgraded according to the abnormal situation;

[0083] For low-risk equipment, the operating state of these equipment is usually stable, but dynamic monitoring of load, current and temperature is still needed to discover potential problems in time. Although low-risk equipment has no obvious failure risk, environmental factors such as ice and snow, temperature changes, etc. may cause fluctuations in the operating state of the equipment. By dynamically monitoring the load, current and temperature, abnormalities in the operation of the equipment can be discovered in time, such as high load, high temperature, etc., to determine whether the risk level needs to be upgraded. For example, when the load, current or temperature of the equipment exceeds the normal range, it may indicate that the equipment is facing an overload or other problems, and the risk level needs to be upgraded.

[0084] When the medium-risk equipment is located in the icing-prone area, it is determined whether the overload is met, and if the load is detected to reach the critical risk interval, intelligent load adjustment is performed, and it is determined whether the adjusted load is overloaded, if the adjusted load is in the critical risk interval, the medium-risk equipment notification is maintained, and the on-site maintenance personnel are notified for manual intervention, if the adjusted load returns to the normal interval, physical equipment detection is performed, if the physical equipment detection determines that there is no damage, the medium-risk equipment is reduced to low-risk equipment, if the physical equipment detection determines that there is an abnormality, the on-site maintenance personnel are notified for manual intervention, and the medium-risk notification is maintained;

[0085] At this stage, attention is focused on whether the equipment is close to the critical risk interval, that is, whether it is possible to overload. Therefore, the adjustment of the equipment load is the primary task. Through intelligent load adjustment, the risk of equipment overload is reduced, and if the adjusted load is still in the critical interval, the medium-risk state is maintained and the on-site maintenance personnel are notified for manual intervention. At this stage, load adjustment is the core, and the monitoring of current and temperature relies more on the adjusted load condition to determine whether to continue adjustment.

[0086] When the high-risk equipment is located in the icing-prone area, intelligent load adjustment is performed, redundant power supply and backup equipment are enabled, and manual intervention is required, manual comprehensive maintenance of physical equipment is performed, and equipment is checked, through comprehensive evaluation of physical facilities, load recovery situation and meteorological influence, preliminary risk reduction is implemented;

[0087] High-risk equipment often faces greater pressure, and direct monitoring methods are no longer limited to simple monitoring of temperature, current and load, but shift to more systematic protection measures such as redundant power supply activation, backup equipment activation, etc. At this time, the equipment may have reached the failure critical point, and the monitoring of temperature and current is more for data support for emergency response rather than daily early warning means. Therefore, the activation of redundant power supply and backup equipment, manual intervention and comprehensive equipment inspection become the main monitoring strategies, and the specific data of current and temperature become secondary.

[0088] The physical protection facilities include the installation of anti-freezing devices and reinforced shells.

[0089] The first maintenance also includes, in non-icing-prone areas, although the meteorological risk is small, there is still a risk of equipment aging and load overload. For low-risk equipment, maintain regular monitoring and maintenance, enhance the protection facilities of the equipment, and ensure long-term efficient operation of the equipment. For medium-risk equipment, more frequent load scheduling and equipment detection are performed, and protective covers are installed to prevent external factors from affecting the equipment. For high-risk equipment, take load reduction measures, assess whether the equipment needs to be replaced, and configure redundant power supply and backup equipment to ensure that the equipment can be quickly switched when there is a problem.

[0090] The one-time maintenance strategy ensures timely and effective maintenance of low-risk, medium-risk and high-risk equipment under different environmental conditions by combining dynamic monitoring, intelligent scheduling and manual intervention. Different maintenance strategies are developed for icing-prone areas and non-icing-prone areas based on their different meteorological characteristics to improve the safety and stability of equipment operation. Through multi-dimensional data analysis and calculation of load, temperature, weather, etc., the risk can be accurately predicted and the equipment operation state can be adjusted in time, greatly reducing the risk of equipment failure and power grid outage.

[0091] In the embodiments of the present application, the ice melting device is distributed according to the risk level of secondary division in step S4, including the following steps D1-D2:

[0092] D1: According to the secondary division result of the equipment risk level calculated by the first model, configure the heating device and whether to configure the standby interface;

[0093] D2: Formulate the control mode according to the heating device configured according to different equipment risk levels.

[0094] The specific implementation of steps D1-D2 is as follows:

[0095] According to the division of icing area and the secondary division result of equipment risk level, configure the heating device and whether to configure the standby interface;

[0096] Preferably, the main protection of low-risk equipment is focused on the exposed parts of transformer shell, cable joints and other equipment, which are most susceptible to cold weather. For low-risk equipment, simple electric heating tape or electric heating pad is used for protection. The heating tape can provide enough heat in a short time to prevent the shell, joints and other parts from being affected by low temperature or ice accumulation. When installing, the electric heating tape needs to be laid along the contact surface of the transformer shell and the cable joint to ensure that its coverage area is sufficient enough to provide uniform heat. In addition, the equipment design needs to reserve the ice melting device interface to facilitate the installation of more powerful ice melting devices in the later stage of sudden weather events.

[0097] For medium risk equipment, besides the transformer shell and cable joints, other components that may be affected, such as cable sealing rings, terminal connections, etc., also need to be concerned. For medium risk equipment, electric heating belts and temperature control heating systems need to be installed. Electric heating belts can provide stable heating effect, while temperature control systems can automatically adjust the working state of the heating belt by monitoring the surface temperature of the equipment in real time, avoiding excessive heating or insufficient heating. In addition, temperature sensors are installed at key positions (such as transformer shells, cable joints, etc.) to monitor the temperature in real time and adjust the ice melting intensity. At important positions such as joints, cable ports, and sealing places, redundant ice melting systems are configured to ensure that the main ice melting device can still maintain normal operation of the equipment when it fails, further improving the safety of the equipment.

[0098] For high risk equipment, a more complete and powerful ice melting system is preferred. These devices need comprehensive protection measures, including electric heating devices (covering transformer shells, joints, and all other possible frozen parts), heating shells (adding heating shells to transformers and external components to ensure that the temperature of the entire device is maintained within a safe range in snowy weather), and redundant power supply systems (to ensure continuous power supply to the ice melting device to prevent power failure from causing ice melting failure). Through an intelligent control system, real-time weather data (such as temperature, humidity, precipitation, etc.) is used as the basis for adjusting the ice melting device, and the heating intensity of the ice melting device is dynamically adjusted. The system can automatically calculate and adjust the ice melting demand based on weather forecasts or real-time data to ensure that the ice melting intensity matches the actual environmental conditions. For example, when the outside temperature is below a certain threshold, the system will increase the heating intensity; when the temperature returns, the heating amount will automatically decrease to avoid wasting energy. In non-icing areas, low-risk equipment is protected by reserved interfaces and simple electric heating belts, medium-risk equipment adjusts the ice melting system according to load and weather conditions, and reserves spare device interfaces to ensure that the equipment can still operate stably in extreme weather. For high-risk equipment, although the icing risk is low, redundant ice melting devices, temperature monitoring sensors, and intelligent control systems still need to be installed to deal with occasional low-temperature weather; similar to icing areas, low-risk equipment in non-icing areas has key parts such as transformer shells and cable joints. Due to the lower meteorological risk in non-icing areas, low-risk equipment only needs to reserve ice melting device interfaces for future quick installation. If the temperature of the exposed part of the device decreases to a certain critical value, a simple electric heating belt can be used to provide necessary protection.

[0099] For medium-risk equipment, in addition to the transformer shell and cable joints, the internal components such as the terminal blocks and cable connectors should also be protected. The ice melting system is adjusted based on the actual load and weather conditions. A temperature control system is installed and through intelligent devices, real-time data such as temperature changes, load changes, etc. are used to automatically adjust the working state of the ice melting system. For example, when the temperature drops below the set threshold, the ice melting device is automatically activated and the heating intensity is adjusted; when the temperature recovers, the system automatically shuts down or reduces the heating. This process is completed through an intelligent temperature control system. To deal with the risk of equipment operation abnormalities in extreme weather conditions, a backup ice melting device interface is reserved. Once the temperature is too low or other meteorological factors change, the backup ice melting equipment can be quickly deployed to ensure stable operation of the equipment.

[0100] For high-risk equipment, although the meteorological risk in non-icing areas is low, redundant ice melting devices are still needed to protect the equipment. Redundant devices can provide additional protection when the main device fails, ensuring that the equipment can operate stably even in extreme low-temperature weather.

[0101] For high-risk equipment, although the meteorological risk in non-icing areas is low, redundant ice melting devices are still needed to protect the equipment. Redundant devices can provide additional protection when the main device fails, ensuring that the equipment can operate stably even in extreme low-temperature weather.

[0102] For high-risk equipment, although the meteorological risk in non-icing areas is low, redundant ice melting devices are still needed to protect the equipment. Redundant devices can provide additional protection when the main device fails, ensuring that the equipment can operate stably even in extreme low-temperature weather.

[0103] Furthermore, in all ice melting devices, the intelligent control system plays a crucial role. Through real-time weather monitoring systems and device state feedback mechanisms, combined with meteorological data, load changes, device operating status, etc., the heating intensity of the ice melting system is automatically adjusted. This ensures that the ice melting device is always in the most suitable state for equipment operation, ensuring that the equipment is not affected by freezing and avoiding energy waste.

[0104] For example, when the temperature drops below -5°C, the system automatically adjusts the heating intensity of the ice melting device; when the temperature rises to 0°C, the system automatically reduces the heating intensity or even shuts down the ice melting device.

[0105] In an optional embodiment, the ice-melting device is distributed according to the risk level of the secondary division in step S4, and the ice-melting device can also be distributed based on a centralized intelligent ice-melting control scheme for the icing area. The specific implementation is as follows:

[0106] A regional centralized intelligent ice-melting control device is used to uniformly control multiple devices: an integrated intelligent control cabinet is deployed in an icing-prone area, and multiple devices (transformers, cable joints, distribution boxes, etc.) in the area are uniformly heated and managed; the control cabinet is internally provided with a remote communication module, a meteorological data acquisition module, and a programmable controller (PLC), which analyzes real-time meteorological data to determine whether to start or stop the ice-melting device of the downstream device; for low-risk devices, only when the temperature is lower than the set value and accompanied by snowfall conditions, the simple electric heating belt is started; the medium-risk device is automatically started when the temperature and humidity jointly meet the set threshold, and the power is adjusted according to the temperature feedback of the device surface; the high-risk device actively enters the heating mode at the initial stage of system startup, and the backup power supply is started to ensure the continuous operation of the heating device. It is suitable for scenarios such as substation peripheral devices and mountainous distributed power supply areas, and can improve the centralized operation and maintenance efficiency and response speed.

[0107] In another optional embodiment, the ice-melting device is distributed according to the risk level of the secondary division in step S4, and the ice-melting device can also be distributed based on an adaptive ice-melting strategy of the device health state and fault history. The specific implementation is as follows:

[0108] A device health score model is constructed through historical operation data, combined with the load, current, and voltage fluctuation of the SCADA system to determine the current device health level; the device is divided into three categories: healthy, sub-healthy, and high-risk fault, and combined with the risk level to form a more detailed configuration strategy:

[0109] The healthy device (low risk) uses a passive reserved ice-melting interface, and only arranges the heating device quickly when the weather is abnormal; the sub-healthy device (medium risk) is long-term configured with a low-power heating belt and a temperature controller, and adjusts the operation according to the temperature and load changes; the high-risk fault device (high risk) is configured with a heating system working in all-weather mode, and is equipped with a dual-power supply system and a remote abnormal alarm function;

[0110] The device state is uploaded to the platform in real time, and the ice-melting strategy parameters are automatically corrected by fusing meteorological prediction and device state change trend to realize adaptive operation; this strategy is suitable for large and medium-sized industrial load areas and areas with many old and dilapidated devices, and can improve the precision control ability and system safety.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

[0112] Embodiment 3

[0113] In the third embodiment of the present application, the embodiment provides a power distribution network ice-melting device key point distribution system based on historical icing data, and has the characteristics that it comprises a collection module, a calculation module, a maintenance module and a distribution output module.

[0114] The collection module: real-time collection of data to obtain primary data;

[0115] The calculation module: division of icing areas and prediction of equipment risk levels;

[0116] The maintenance module: one-time maintenance according to the icing areas and the equipment risk levels, and secondary division of risk levels;

[0117] The distribution output module: ice-melting device distribution according to the secondary division of risk levels.

[0118] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products, which are stored in a storage medium and include a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk and various program code storage media.

[0119] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In this context, a "computer-readable medium" can be any means that can store the program for use by or in connection with the instruction execution system, apparatus, or device.

[0120] The computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer.

[0121] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following techniques, which are well known in the art of making integrated circuits, can be used alone or in any combination to implement the application: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0122] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced, without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of claims of the present application.

Claims

1. A method for keying ice-melting device locations in a distribution network based on historical ice coverage data, characterized by: include, Collect data in real time and obtain primary data; Divide icing areas and predict equipment risk levels; Carry out primary maintenance based on the icing area and equipment risk level, and perform secondary risk level classification; Ice melting devices are deployed according to the secondary risk levels.

2. The method for deploying ice-melting devices in distribution networks based on historical ice coverage data according to claim 1, wherein: The division of ice-covered areas includes: Input the first-level data into the first-level model to obtain the first-level model calculation results of the region; The environmental status index is compared with the preset threshold, and the ice-covered area is divided according to the judgment result.

3. The method for key deployment of ice melting devices in a distribution network based on historical ice coverage data according to claim 2, characterized in that: The predicted equipment risk levels include: Input the primary data into the secondary model to obtain the secondary model prediction results; The equipment risk level is obtained by comparing the secondary prediction data with the preset risk threshold.

4. The method for key deployment of ice melting devices in a distribution network based on historical ice coverage data according to claim 3, characterized in that: The said primary maintenance is carried out according to the icing area and equipment risk level, and the secondary risk level is divided into: Conduct risk assessment based on ice area division results and equipment risk results; Carry out one-time maintenance of equipment in the corresponding area according to the risk assessment results; The equipment risk level is divided into two levels according to the results of the first maintenance.

5. The method for key deployment of ice-melting devices in a distribution network based on historical ice coverage data according to claim 4, characterized in that: The ice melting device placement according to the secondary risk level includes: Based on the secondary classification of equipment risk levels calculated by the first-level model, configure a heating device and whether to configure a backup interface; Control modes are developed for heating devices configured according to different equipment risk levels.

6. The method for key deployment of ice melting devices in a distribution network based on historical ice coverage data according to claim 5, characterized in that: The equipment risk level obtained by comparing the secondary prediction data with the preset risk threshold includes: When the environmental status index is greater than or equal to the preset threshold, it is judged as an area prone to icing; When the environmental status index is less than the preset threshold, it is judged as a non-icing-prone area.

7. The method for key deployment of ice melting devices in a distribution network based on historical ice coverage data according to claim 6, characterized in that: The equipment risk level obtained by comparing the secondary prediction data with the preset risk threshold includes: When the calculated result is less than or equal to the first preset risk threshold, the device is judged to be a low-risk device; When the calculated result is greater than the first preset risk threshold and the calculated result is less than or equal to the second preset risk threshold, the device is judged to be of medium risk level; When the calculated result is greater than the second preset risk threshold, the device is judged to be a high-risk device.

8. A system for the method for deploying ice melting devices in a distribution network based on historical ice coverage data according to any one of claims 1 to 7, characterized in that: It includes acquisition module, calculation module, maintenance module and point distribution output module.

9. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for focusing on the distribution network ice melting device based on historical ice coverage data as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method for key deployment of ice-melting devices in a distribution network based on historical ice coverage data as described in any one of claims 1 to 7.

Images