Multi-line collaborative direct-current deicing method, system and equipment

Abstract

The invention provides a multi-line collaborative direct-current ice melting method, system and device, and relates to the technical field of power transmission and distribution of power systems, and the method comprises the steps: obtaining real-time environment data and line icing thickness data corresponding to an ice melting line based on a line sensor network; constructing an ice melting index calculation model, calculating a line ice melting index and judging a real-time icing state; obtaining the moving distance and cost of ice melting equipment corresponding to the ice melting line, and calculating a comprehensive evaluation index and determining a line ice melting sequence in combination with the line ice melting index; constructing an energy consumption distribution optimization function, obtaining real-time electrical parameters of the ice-melting lines, and determining line ice-melting energy consumption of each ice-melting line in combination with the line icing thickness data; based on the line ice-melting sequence and the line ice-melting energy consumption of each ice-melting line, a multi-line collaborative ice-melting strategy is determined, so that the overall ice-melting efficiency can be improved, the total ice-melting time and energy consumption are reduced, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system transmission and distribution, and particularly to a multi-line collaborative DC de-icing method, system and equipment. Background Art

[0002] Under harsh weather conditions, the icing phenomenon of power lines seriously threatens the safe and stable operation of the power grid.

[0003] Traditional manual de-icing methods are often inefficient and risky. Although the automated de-icing method for a single line has been improved, in the case of multiple lines being iced simultaneously, there is a lack of an effective collaborative control strategy. Currently, the de-icing method of one line after another is usually adopted, ignoring the differences in the icing conditions and de-icing requirements between different lines, and not considering the collaborative effect between multiple lines, which leads to low overall de-icing efficiency, long time consumption and high energy consumption.

[0004] Therefore, it is necessary to provide a multi-line collaborative DC de-icing method, system and equipment to solve the above technical problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a multi-line collaborative DC de-icing method, system and equipment, which are used to solve the problems that in the existing multi-line de-icing process, there is a lack of an effective coordination mechanism, resulting in low overall de-icing efficiency, inability to fully utilize limited resources to quickly eliminate the icing on the lines, increasing the total de-icing time and energy consumption.

[0006] A multi-line collaborative DC de-icing method provided by the present invention, the de-icing method includes: Based on the line sensor network, obtaining the real-time environmental data and the line icing thickness data corresponding to the de-icing line; Constructing a de-icing index calculation model, calculating the line de-icing index according to the real-time environmental data and the line icing thickness data, and judging the real-time icing state; Obtaining the moving distance and cost of the de-icing equipment corresponding to the de-icing line, combining with the line de-icing index, calculating a comprehensive evaluation index and determining the line de-icing sequence; Constructing an energy consumption allocation optimization function, obtaining the real-time electrical parameters of the de-icing line, and combining with the line icing thickness data, determining the line de-icing energy consumption of each de-icing line; Based on the line de-icing sequence and the line de-icing energy consumption of each de-icing line, determining a multi-line collaborative de-icing strategy.

[0007] Preferably, the obtaining the real-time environmental data and the line icing thickness data corresponding to the de-icing line based on the line sensor network specifically includes: The line sensor network includes a temperature sensor, a humidity sensor, a wind speed sensor, and an ice thickness measurement sensor; The temperature sensor, humidity sensor, and wind speed sensor are respectively used to measure the real-time temperature data, real-time humidity data, and real-time wind speed data of the environment where the ice melting line is located, and summarize the real-time temperature data, real-time humidity data, and real-time wind speed data to obtain the real-time environment data; The ice thickness measurement sensor is used to measure the line ice thickness data of the ice melting line.

[0008] Preferably, obtain an ice thickness threshold, a preset judgment time, and a growth rate threshold; If the line ice thickness data of multiple ice melting lines exceeds the ice thickness threshold, or within the preset judgment time, the thickness growth rate of all the line ice thickness data exceeds the growth rate threshold, then multi-line ice melting operation is required, and the calculation process of the line ice melting index is automatically started.

[0009] Preferably, associate the line numbers of the ice melting lines with the corresponding real-time environment data and line ice thickness data; Obtain the historical temperature data, historical humidity data, historical wind speed data, first ice thickness threshold, and second ice thickness threshold corresponding to the ice melting line, and based on the ice melting index calculation model, calculate the line ice melting index as follows by combining the real-time environment data and the line ice thickness data: In the formula, RBZS represents the line ice melting index; respectively represent the temperature weight, humidity weight, and wind speed weight; respectively represent the temperature influence factor, humidity influence factor, and wind speed influence factor; represents the ice thickness correction coefficient; respectively represent the real-time temperature data, real-time humidity data, and real-time wind speed data; respectively represent the average values of the historical temperature data, historical humidity data, and historical wind speed data; respectively represent the maximum values of the historical temperature data, historical humidity data, and historical wind speed data; respectively represent the minimum values of the historical temperature data, historical humidity data, and historical wind speed data; represents the line ice thickness data; respectively represent the first correction coefficient, second correction coefficient, and third correction coefficient; respectively represent the first ice thickness threshold and the second ice thickness threshold.

[0010] Preferably, obtain the first ice melting index threshold and the second ice melting index threshold corresponding to the ice melting line, and determine the real-time icing state according to the line ice melting index: In the formula, FBZT represents the real-time icing state; RBZS represents the line ice melting index; represents the first ice melting index threshold; represents the second ice melting index threshold.

[0011] Preferably, obtain the moving distance and cost of the ice melting equipment corresponding to the ice melting line, combine the line ice melting index, calculate the comprehensive evaluation index and determine the line ice melting sequence, specifically including: The expression of the comprehensive evaluation index is as follows: In the formula, represents the comprehensive evaluation index of the i-th ice melting line; respectively represent the weights corresponding to the line ice melting index, the moving distance of the ice melting equipment, and the moving cost of the ice melting equipment; respectively represent the line ice melting index, the moving distance of the ice melting equipment, and the moving cost of the ice melting equipment of the i-th ice melting line; respectively represent the maximum moving distance and the maximum moving cost of the ice melting equipment for all ice melting lines; Arrange the comprehensive evaluation indexes from large to small, and the obtained arrangement order is the line ice melting sequence.

[0012] Preferably, construct the energy consumption allocation optimization function, obtain the real-time electrical parameters of the ice melting line, and combine the line icing thickness data to determine the line ice melting energy consumption of each ice melting line, specifically including: Obtain the real-time electrical parameters of the ice melting line, that is, the line resistance and the line length; Obtain the current ice melting coefficient, and combine the line icing thickness data to calculate the minimum line ice melting power as follows: In the formula, represents the minimum line ice melting power of the i-th ice melting line; represents the line length of the i-th ice melting line; represents the line icing thickness data of the i-th ice melting line; represents the current ice melting coefficient; The expression of the energy consumption allocation optimization function is as follows: In the formula, min represents the minimum value operation; n represents the number of ice melting lines; denote the ice melting current of the i-th ice melting line; denote the line resistance of the i-th ice melting line; respectively denote the minimum allowable current and the maximum allowable current of the i-th ice melting line; T denotes the ice melting time; Solve the energy consumption allocation optimization function based on the particle swarm optimization algorithm to determine the line ice melting energy consumption of each ice melting line.

[0013] A multi-line collaborative DC ice melting method further includes obtaining future environmental data of the ice melting line based on a meteorological data interface; Construct an ice thickness prediction model and predict future ice thickness data of the ice melting line according to the future environmental data; If the difference between the future ice thickness data and the line ice thickness data exceeds the maximum thickness growth threshold, trigger an emergency ice melting alarm mechanism.

[0014] A multi-line collaborative DC ice melting system, the ice melting system includes: A data acquisition module for obtaining real-time environmental data and line ice thickness data corresponding to the ice melting line based on a line sensor network; An index calculation module for constructing an ice melting index calculation model, calculating a line ice melting index according to the real-time environmental data and the line ice thickness data, and judging the real-time icing state; An order determination module for obtaining the moving distance and cost of the ice melting equipment corresponding to the ice melting line, combining the line ice melting index, calculating a comprehensive evaluation index, and determining the line ice melting order; An energy consumption determination module for constructing an energy consumption allocation optimization function, obtaining the real-time electrical parameters of the ice melting line, and combining the line ice thickness data to determine the line ice melting energy consumption of each ice melting line; A strategy generation module for determining a multi-line collaborative ice melting strategy based on the line ice melting order and the line ice melting energy consumption of each ice melting line.

[0015] A multi-line collaborative DC ice melting device includes a memory and a processor, and a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a multi-line collaborative DC ice melting method as described above.

[0016] Compared with the related technology, a multi-line collaborative DC ice melting method, system and device provided by the present invention have the following beneficial effects: The present invention can obtain real-time environmental data and line ice coating thickness data corresponding to the de-icing line based on a line sensor network; construct a de-icing index calculation model, calculate the line de-icing index according to the real-time environmental data and the line ice coating thickness data, and judge the real-time ice coating state; obtain the moving distance and cost of the de-icing equipment corresponding to the de-icing line, combine the line de-icing index, calculate a comprehensive evaluation index, and determine the line de-icing sequence; construct an energy consumption allocation optimization function, obtain the real-time electrical parameters of the de-icing line, and combine the line ice coating thickness data to determine the line de-icing energy consumption of each de-icing line; based on the line de-icing sequence and the line de-icing energy consumption of each de-icing line, determine a multi-line collaborative de-icing strategy, thereby improving the overall de-icing efficiency, reducing the total de-icing time and energy consumption, enhancing the system's ability to cope with extreme weather conditions, improving the energy utilization efficiency, and solving the problem of the lack of an effective coordination mechanism in the existing multi-line de-icing process.

[0017] By adopting a multi-line collaborative de-icing strategy, the present invention can optimize the de-icing order and power configuration, significantly shorten the overall de-icing cycle, reduce the total de-icing energy consumption, and achieve more efficient resource allocation. By introducing an energy consumption allocation optimization function, the present invention can minimize the total de-icing energy consumption while maintaining efficient de-icing work, achieving the effect of energy conservation and emission reduction. Through the ice coating thickness prediction model, the present invention can predict the future ice coating thickness of the line, perceive potential de-icing crises in advance and make corresponding adjustments, greatly improving the emergency response speed and ensuring that the power grid can quickly return to normal operation under bad weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a multi-line collaborative DC de-icing method provided by an embodiment of the present invention; Figure 2 It is a flowchart of a line sensor network for obtaining de-icing line data provided by an embodiment of the present invention; Figure 3 It is a system block diagram of a multi-line collaborative DC de-icing system provided by an embodiment of the present invention; Figure 4 It is a schematic hardware structure diagram of a multi-line collaborative DC de-icing device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] AsFigure 1 As shown, it is a flowchart of a multi-line collaborative DC de-icing method provided by an embodiment of the present invention. Figure 1 The execution subject of the method shown can be a software and / or hardware device. The execution subject of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. Among them, user equipment can include, but is not limited to, computers, smart phones, personal digital assistants (Personal Digital Assistant, abbreviated as: PDA), and the above-mentioned electronic equipment, etc. Network equipment can include, but is not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Among them, cloud computing is a type of distributed computing, which is composed of a group of loosely coupled computers to form a super virtual computer. This embodiment does not limit this. It includes steps S1 to S5, specifically as follows: S1, based on the line sensor network, obtain the real-time environmental data and line ice thickness data corresponding to the de-icing line; Among them, the line sensor network refers to a network composed of multiple sensors distributed on the de-icing line. These sensors can monitor and collect the surrounding environmental data and ice thickness data of the de-icing line in real time. Specifically, the real-time environmental data can be temperature, humidity, wind speed, etc. The line ice thickness data reflects the actual thickness of the ice layer on the line and is an important basis for subsequent de-icing decisions.

[0021] S2, construct a de-icing index calculation model, and calculate the line de-icing index and judge the real-time icing state according to the real-time environmental data and the line ice thickness data; It can be understood that the de-icing index calculation model refers to a model that calculates the line de-icing index according to the obtained real-time environmental data and line ice thickness data. The line de-icing index refers to an index used to measure the influence degree of environmental factors and the icing condition of the line itself on the de-icing operation. Then, according to the calculated de-icing index, the real-time icing state of the line can be judged.

[0022] S3, obtain the moving distance and cost of the de-icing equipment corresponding to the de-icing line, combine the line de-icing index, calculate the comprehensive evaluation index and determine the line de-icing order; It should be noted that the moving distance of the de-icing equipment refers to the distance that the de-icing equipment moves from its current location to the line where de-icing operation needs to be carried out, and the moving cost of the de-icing equipment can be the cost in aspects such as transportation and redeployment of the de-icing equipment. The comprehensive evaluation index refers to an index that quantitatively evaluates the de-icing priority of the line and the rationality of resource allocation. The line de-icing order refers to the order of de-icing operations for multiple lines.

[0023] S4. Construct an optimized function for energy consumption allocation, obtain the real-time electrical parameters of the ice melting line, and determine the line ice melting energy consumption of each ice melting line in combination with the line ice thickness data. Among them, the optimized function for energy consumption allocation refers to a function for allocating energy consumption to each line. The line ice melting energy consumption refers to the energy consumption required for the line during the ice melting process.

[0024] S5. Based on the line ice melting sequence and the line ice melting energy consumption of each ice melting line, determine the multi-line collaborative ice melting strategy.

[0025] In practical applications, a multi-line collaborative ice melting strategy can be formulated based on the line ice melting sequence and the line ice melting energy consumption of each ice melting line, so as to ensure that multiple lines can carry out ice melting operations efficiently and orderly, realize the reasonable allocation and utilization of ice melting resources, and ensure the rapid restoration of the power system to normal operation.

[0026] Exemplarily, in an actual power grid, when it is monitored that lines A, B, and C are simultaneously covered with ice, the ice melting index of line A can be calculated as 80, line B as 60, and line C as 70 according to temperature, humidity, wind speed, and the ice thickness of each line. Then, in combination with the moving distance and cost of the ice melting equipment, the final ice melting sequence is determined as A, C, B.

[0027] During the ice melting process, the ice melting current of line A can be dynamically adjusted to 500 A, line C to 400 A, and line B to 300 A according to parameters such as the resistance and length of each line. At the same time, the ice covering changes of each line can be monitored in real time. If it is found that the ice melting speed of line A is slow, the current of this line can be appropriately increased to 550 A.

[0028] In addition, taking line B as an example, it can be determined through the meteorological data interface that the humidity in the area where line B is located will increase significantly within the next 2 hours, and the ice covering may worsen. At this time, an emergency response mechanism is immediately triggered.

[0029] Furthermore, the ice melting priority of line B can be re-evaluated and adjusted, and the required ice melting power can be recalculated. The ice melting current is adjusted to 400 A, and at the same time, the ice melting plans of other lines are adjusted until the ice melting of all lines is completed.

[0030] As Figure 2 shown, it is a flowchart of the line sensor network provided by the embodiment of the present invention for obtaining ice melting line data. Based on the line sensor network, obtaining the real-time environmental data and line ice thickness data corresponding to the ice melting line specifically includes: The line sensor network includes a temperature sensor, a humidity sensor, a wind speed sensor, and an ice thickness measurement sensor. The temperature sensor, humidity sensor, and wind speed sensor are respectively used to measure the real-time temperature data, real-time humidity data, and real-time wind speed data of the environment where the ice melting line is located, and summarize the real-time temperature data, real-time humidity data, and real-time wind speed data to obtain the real-time environment data; The ice coating thickness measurement sensor is used to measure the ice coating thickness data of the ice melting line.

[0031] In practical applications, through the collaborative work of various sensors, the environmental data and ice coating thickness data of the ice melting line can be collected in real time. Specifically, the temperature sensor, humidity sensor, and wind speed sensor are respectively used to measure the real-time temperature data, real-time humidity data, and real-time wind speed data of the environment where the ice melting line is located. The ice coating thickness measurement sensor is used to directly measure the ice coating thickness of the line, so as to avoid misjudgment of the ice coating condition of the line caused by the delay and error of manual measurement.

[0032] Obtain the ice coating thickness threshold, preset judgment time, and growth rate threshold; If the ice coating thickness data of multiple ice melting lines exceed the ice coating thickness threshold, or within the preset judgment time, the thickness growth rate of all the ice coating thickness data exceeds the growth rate threshold, then multi-line ice melting operation is required, and the calculation process of the line ice melting index is automatically started.

[0033] It can be understood that by obtaining the ice coating thickness threshold, preset judgment time, and growth rate threshold, the ice coating condition of the line can be monitored in real time and quickly judged. Once the ice coating thickness of multiple ice melting lines exceeds the threshold, or within the preset time, the ice coating thickness growth rate of all lines is too fast, then multi-line ice melting operation is required, and the calculation process of the line ice melting index is automatically started.

[0034] Through the above method, serious ice coating accidents can be avoided, the ability of the power grid to cope with ice coating disasters can be greatly improved, the possibility of faults can be reduced, the safe and stable operation of the power grid can be maintained, and the waste of resources caused by blind ice melting can be avoided.

[0035] Associate the line number of the ice melting line with the corresponding real-time environment data and ice coating thickness data; Obtain the historical temperature data, historical humidity data, historical wind speed data, first ice coating thickness threshold, and second ice coating thickness threshold corresponding to the ice melting line, and based on the ice melting index calculation model, calculate the line ice melting index as follows by combining the real-time environment data and the ice coating thickness data: In the formula, RBZS represents the line ice melting index; respectively represent the temperature weight, humidity weight, and wind speed weight; respectively represent the temperature influence factor, humidity influence factor, and wind speed influence factor; represents the icing thickness correction coefficient; respectively represent the real-time temperature data, real-time humidity data, and real-time wind speed data; respectively represent the average values of historical temperature data, historical humidity data, and historical wind speed data; respectively represent the maximum values of historical temperature data, historical humidity data, and historical wind speed data; respectively represent the minimum values of historical temperature data, historical humidity data, and historical wind speed data; represents the line icing thickness data; respectively represent the first correction coefficient, second correction coefficient, and third correction coefficient; respectively represent the first icing thickness threshold and second icing thickness threshold.

[0036] It should be noted that the line number of each line can be associated with the environmental data and icing thickness. As shown in Table 1, it is the association table of the line number of line A provided by the embodiment of the present invention and the corresponding real-time environmental data and line icing thickness data. Among them, the real-time temperature data of line A is -4°C; the real-time humidity data here is the relative humidity, and the corresponding value is 85%; the real-time wind speed data is 10 m / s; the line icing thickness data is 21 mm.

[0037] It should be noted that the low-temperature environment of the line is conducive to the formation and accumulation of ice layers, increasing the difficulty of de-icing and resulting in a higher de-icing index; the high-humidity environment provides a sufficient water vapor source for icing, further aggravating the icing degree and affecting the de-icing index; a larger wind speed will accelerate the heat dissipation on the surface of the wire, making the water vapor easily freeze on the wire, and at the same time, it will also make the line icing uneven, increasing the de-icing difficulty. In addition, a thicker ice layer is also an important factor leading to a higher de-icing index.

[0038] Table 1 Association table of the line number of line A and the corresponding real-time environmental data and line icing thickness data Line number Real-time temperature data Real-time humidity data Real-time wind speed data Line ice thickness data Line A -4℃ 85% 10m / s 21mm Furthermore, according to the actual situation of each line, the line de-icing index can be determined based on the de-icing index calculation model. Then, the de-icing sequence and resource allocation can be reasonably arranged according to the line de-icing index. Specifically, according to the size of the de-icing index, the lines with greater de-icing difficulty and higher urgency can be de-iced preferentially, so as to avoid waste of resources, improve the de-icing efficiency, reduce the de-icing cost, and enhance the overall operation efficiency of the power system under icing conditions.

[0039] Obtain the first ice melting index threshold and the second ice melting index threshold corresponding to the ice melting line, and determine the real-time icing state according to the line ice melting index: In the formula, FBZT represents the real-time icing state; RBZS represents the line ice melting index; represents the first ice melting index threshold; represents the second ice melting index threshold.

[0040] In practical applications, the first ice melting index threshold and the second ice melting index threshold can be set, and the mild, moderate, and severe icing states can be clearly divided according to the line ice melting index. This quantitative judgment method is more accurate than subjective evaluation, and it can intuitively and accurately reflect the current icing severity of the ice melting line.

[0041] Furthermore, the accurate icing state can enable the operation and maintenance personnel to timely grasp the line risks. When it is judged as a severe icing state, the operation and maintenance personnel can quickly start the emergency plan, give priority to arranging ice melting operations, prevent serious faults such as line breakage and tower collapse due to excessive icing, ensure the stability of power transmission to the greatest extent, and reduce the impact of power outages on production and life.

[0042] In addition, the ice melting resources can be reasonably allocated according to different icing states. Specifically, for the mild icing state, the resource input can be appropriately reduced; while for the moderate and severe icing states, the resource input intensity needs to be increased to ensure the efficient progress of ice melting work, avoid resource waste, and improve the overall resource utilization efficiency of ice melting operations.

[0043] Obtain the moving distance and cost of the ice melting equipment corresponding to the ice melting line, combine the line ice melting index, calculate the comprehensive evaluation index, and determine the line ice melting sequence, specifically including: The expression of the comprehensive evaluation index is as follows: In the formula, represents the comprehensive evaluation index of the i-th ice melting line; respectively represent the weights corresponding to the line ice melting index, the moving distance of the ice melting equipment, and the moving cost of the ice melting equipment; respectively represent the line ice melting index, the moving distance of the ice melting equipment, and the moving cost of the ice melting equipment of the i-th ice melting line; respectively represent the maximum moving distance and the maximum moving cost of the ice melting equipment for all ice melting lines; Arrange the comprehensive evaluation indexes from large to small, and the obtained arrangement order is the line ice melting sequence.

[0044] In practical applications, the line de-icing index reflects the urgency of line de-icing, and the moving distance and cost of de-icing equipment reflect the feasibility and economy of de-icing operations. Through weight allocation, the influence of each factor on the comprehensive evaluation index can be flexibly adjusted according to actual needs, making the de-icing sequence planning more in line with the actual situation, and thus improving the overall efficiency of de-icing operations.

[0045] Subsequently, the de-icing sequence can be determined based on the comprehensive evaluation index. Lines with high comprehensive evaluation indexes are given priority for de-icing, avoiding the phenomenon of long-distance movement of de-icing equipment or high-cost operations caused by blindly selecting lines, reducing unnecessary resource consumption, achieving reasonable allocation of de-icing resources, and reducing the total de-icing cost.

[0046] In addition, a reasonable de-icing sequence can ensure that lines with severe icing and easy operation are processed first, timely eliminating high-risk hidden dangers, minimizing the time that the line is in a dangerous icing state, effectively reducing the probability of power grid failures, and maintaining the continuity and stability of power supply.

[0047] The construction of the energy consumption allocation optimization function, obtaining the real-time electrical parameters of the de-icing line, and combining the line icing thickness data to determine the line de-icing energy consumption of each de-icing line specifically includes: Obtaining the real-time electrical parameters of the de-icing line, namely the line resistance and the line length; Obtaining the current de-icing coefficient, and combining the line icing thickness data to calculate the minimum line de-icing power as follows: In the formula, represents the minimum line de-icing power of the i-th de-icing line; represents the line length of the i-th de-icing line; represents the line icing thickness data of the i-th de-icing line; represents the current de-icing coefficient; The expression of the energy consumption allocation optimization function is as follows: In the formula, min represents the operation of taking the minimum value; n represents the number of de-icing lines; represents the de-icing current of the i-th de-icing line; represents the line resistance of the i-th de-icing line; respectively represent the minimum allowable current and the maximum allowable current of the i-th de-icing line; T represents the de-icing time; Solving the energy consumption allocation optimization function based on the particle swarm optimization algorithm to determine the line de-icing energy consumption of each de-icing line. The corresponding solution process is as follows: Set an n-dimensional search space, that is, the combination of de-icing currents for n de-icing lines. Then the i-th dimension corresponds to the i-th de-icing line, and there are M particles in the n-dimensional search space. The m-th particle corresponds to the m-th de-icing current distribution scheme. The velocity update formula of the m-th particle at the k-th iteration is as follows:

[0048] In the formula, represents the velocity component of the m-th particle on the i-th dimension after the k-th iteration; u represents the search balance coefficient, which is used to balance the global search degree and local search degree of the particle. represents the learning factor; represents a random number; represents the velocity component of the m-th particle on the i-th de-icing line before the k-th iteration; represents the component of the individual historical optimal position of the m-th particle on the i-th dimension at the k-th iteration; represents the position component of the m-th particle on the i-th dimension at the k-th iteration; represents the component of the global historical optimal position of the entire particle swarm on the i-th dimension at the k-th iteration; The position update formula of the m-th particle at the k-th iteration is as follows: In the formula, represents the position component of the m-th particle on the i-th dimension after the k-th iteration; represents the position component of the m-th particle on the i-th dimension before the k-th iteration; represents the velocity component of the m-th particle on the i-th dimension after the k-th iteration; The position component of the m-th particle on the i-th dimension after the k-th iteration is the de-icing current value of the i-th line in the m-th de-icing current distribution scheme. The position component of the m-th particle on the i-th dimension after the k-th iteration has the following boundary values: In the formula, represents the position component of the m-th particle on the i-th dimension after the k-th iteration; respectively represent the minimum allowable current and maximum allowable current of the i-th de-icing line; Repeat the above update steps until the maximum number of iterations is reached, and complete the solution process of the energy consumption distribution optimization function.

[0049] Through the above method, the particle swarm algorithm can be used to solve the current distribution optimization problem, determine the most suitable line de-icing energy consumption for each de-icing line, and minimize the total energy consumption of all de-icing lines.

[0050] It is understandable that the energy consumption allocation optimization function aims to minimize the total energy consumption and finds the optimal solution through the particle swarm optimization algorithm. Under the conditions of meeting the line ice melting power demand and the upper and lower limit constraints of the current, the algorithm can optimize the ice melting current, effectively reducing the energy consumption in the overall ice melting process, thereby avoiding the problem of resource waste caused by unreasonable current setting and improving energy utilization efficiency.

[0051] Specifically, it can ensure that each line can obtain sufficient power to meet the line de-icing power requirements and achieve effective de-icing. At the same time, by setting the upper and lower limits of the current, it can prevent the de-icing current from being too large to damage the line and equipment, or the de-icing current from being too small to meet the de-icing requirements, thereby providing a guarantee for the smooth progress of the de-icing operation.

[0052] It should be noted that accurate energy consumption calculation and allocation can help to reasonably plan the cost of ice-melting operations and optimize resource allocation. By scientifically determining the ice-melting energy consumption of each line, it is possible to better arrange ice-melting equipment and improve the overall efficiency of ice-melting operations, thereby improving the power system's ability to cope with icing disasters, reducing power outage losses caused by line icing, and improving the economic benefits of the power system.

[0053] A multi-line coordinated DC de-icing method further includes obtaining future environmental data of the de-icing line based on a meteorological data interface; Constructing an ice thickness prediction model, and predicting future ice thickness data of the ice melting line according to the future environmental data; If the difference between the future ice thickness data and the line ice thickness data exceeds the maximum thickness growth threshold, an emergency ice melting alarm mechanism is triggered.

[0054] Among them, the future environmental data can be obtained through the meteorological data interface, and the ice thickness prediction model can be used to estimate the future ice thickness of the ice-melting line, so as to obtain the development trend of line ice in advance. If the difference between the predicted future ice thickness and the current ice thickness exceeds the maximum thickness growth threshold, the alarm mechanism will be triggered immediately and the emergency procedure will be quickly started.

[0055] Then, the operation and maintenance personnel can rationally allocate ice-melting equipment and manpower, and concentrate resources on lines that may be severely covered with ice to avoid wasting resources, thereby making the ice-melting work more targeted and scientific, reducing the probability of failures, reducing the risk of power outages, and improving the emergency response efficiency of the power system in the face of sudden icing disasters, ensuring the stable operation of the power system under severe weather conditions.

[0056] like Figure 3 FIG. 1 is a system block diagram of a multi-line coordinated DC ice melting system provided by an embodiment of the present invention. The ice melting system includes: A data acquisition module, configured to acquire real-time environmental data and line ice thickness data corresponding to a de-icing line based on a line sensor network; An index calculation module, configured to build a de-icing index calculation model, calculate a line de-icing index according to the real-time environmental data and the line ice thickness data, and determine a real-time icing state; An order determination module, configured to acquire the moving distance and cost of a de-icing device corresponding to the de-icing line, combine the line de-icing index, calculate a comprehensive evaluation index, and determine a line de-icing order; An energy consumption determination module, configured to build an energy consumption allocation optimization function, acquire real-time electrical parameters of the de-icing line, and combine the line ice thickness data to determine the line de-icing energy consumption of each de-icing line; A strategy generation module, configured to determine a multi-line collaborative de-icing strategy based on the line de-icing order and the line de-icing energy consumption of each de-icing line.

[0057] Figure 3 The device of the illustrated embodiment can correspondingly be used to execute Figure 1 the steps in the method embodiment shown, and its implementation principle and technical effects are similar, and will not be elaborated here.

[0058] A multi-line collaborative DC de-icing device includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a multi-line collaborative DC de-icing method as described in any one of the above.

[0059] As Figure 4 shown, it is a schematic hardware structure diagram of a multi-line collaborative DC de-icing device provided by an embodiment of the present invention. A multi-line collaborative DC de-icing device 40 includes: a processor 41, a memory 42, and a computer program; wherein The memory 42 is configured to store the computer program, and this memory can also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0060] The processor 41 is configured to execute the computer program stored in the memory to implement each step executed by the device in the above method. Specifically, reference can be made to the relevant descriptions in the foregoing method embodiments.

[0061] Optionally, the memory 42 can be either independent or integrated with the processor 41.

[0062] When the memory 42 is a device independent of the processor 41, the device may further include: A bus 43, configured to connect the memory 42 and the processor 41.

[0063] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the above various embodiments.

[0064] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0065] The present invention also provides a program product, which includes execution instructions stored in a readable storage medium. At least one processor of the device can read the execution instructions from the readable storage medium, and the execution of the execution instructions by at least one processor causes the device to implement the methods provided by the above various embodiments.

[0066] In the above embodiments of the device, it should be understood that the processor can be a central processing unit (CPU for short), and can also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the present invention can be directly embodied as being completed by the execution of a hardware processor, or by a combination of hardware and software modules in the processor.

[0067] Through the introduction of the above embodiments, the multi-line collaborative DC de-icing method, system and equipment of the present invention can obtain the real-time environmental data and line ice coating thickness data corresponding to the de-icing line based on the line sensor network; construct a de-icing index calculation model, calculate the line de-icing index according to the real-time environmental data and line ice coating thickness data, and judge the real-time ice coating state; obtain the moving distance and cost of the de-icing equipment corresponding to the de-icing line, combine with the line de-icing index, calculate the comprehensive evaluation index and determine the line de-icing sequence; construct an energy consumption allocation optimization function, obtain the real-time electrical parameters of the de-icing line, combine with the line ice coating thickness data, and determine the line de-icing energy consumption of each de-icing line; based on the line de-icing sequence and the line de-icing energy consumption of each de-icing line, determine the multi-line collaborative de-icing strategy, so as to improve the overall de-icing efficiency, reduce the total de-icing time and energy consumption, enhance the system's ability to cope with extreme weather conditions, improve the energy utilization efficiency, and solve the problem of the lack of an effective coordination mechanism in the existing multi-line de-icing process.

[0068] By adopting the multi-line collaborative de-icing strategy, the present invention can optimize the de-icing order and power configuration, significantly shorten the overall de-icing cycle, reduce the total de-icing energy consumption, and achieve more efficient resource allocation. By introducing the energy consumption allocation optimization function, the present invention can minimize the total de-icing energy consumption while maintaining efficient de-icing work, achieving the effect of energy conservation and emission reduction. Through the ice coating thickness prediction model, the present invention can predict the future ice coating thickness of the line, sense potential de-icing crises in advance and make corresponding adjustments, greatly improving the emergency response speed and ensuring that the power grid can quickly resume normal operation under bad weather.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-line collaborative DC de-icing method, characterized in that, The ice melting method includes: Based on the line sensor network, obtaining the real-time environmental data and line ice coating thickness data corresponding to the ice melting line; Constructing an ice melting index calculation model, calculating the line ice melting index according to the real-time environmental data and the line ice coating thickness data, and judging the real-time ice coating state; Obtaining the moving distance and cost of the ice melting equipment corresponding to the ice melting line, combining with the line ice melting index, calculating a comprehensive evaluation index, and determining the line ice melting sequence; Constructing an energy consumption allocation optimization function, obtaining the real-time electrical parameters of the ice melting line, and combining with the line ice coating thickness data, determining the line ice melting energy consumption of each ice melting line; Based on the line ice melting sequence and the line ice melting energy consumption of each ice melting line, determining a multi-line collaborative ice melting strategy.

2. The multi-line collaborative DC de-icing method according to claim 1, characterized in that, The obtaining the real-time environmental data and line ice coating thickness data corresponding to the ice melting line based on the line sensor network specifically includes: The line sensor network includes a temperature sensor, a humidity sensor, a wind speed sensor, and an ice coating thickness measurement sensor; The temperature sensor, humidity sensor, and wind speed sensor are respectively used to measure the real-time temperature data, real-time humidity data, and real-time wind speed data of the environment where the ice melting line is located, and summarize the real-time temperature data, real-time humidity data, and real-time wind speed data to obtain the real-time environmental data; The ice coating thickness measurement sensor is used to measure the line ice coating thickness data of the ice melting line.

3. The multi-line collaborative DC de-icing method according to claim 2, wherein Obtaining an ice coating thickness threshold, a preset judgment time, and a growth rate threshold; If the line ice coating thickness data of multiple ice melting lines exceeds the ice coating thickness threshold, or within the preset judgment time, the thickness growth rate of all the line ice coating thickness data exceeds the growth rate threshold, then a multi-line ice melting operation is required, and the calculation process of the line ice melting index is automatically started.

4. A multi-line collaborative DC de-icing method according to claim 3, characterized in that Associating the line number of the ice melting line with the corresponding real-time environmental data and line ice coating thickness data; Obtaining the historical temperature data, historical humidity data, historical wind speed data, a first ice coating thickness threshold, and a second ice coating thickness threshold corresponding to the ice melting line, combining with the real-time environmental data and the line ice coating thickness data, and calculating the line ice melting index based on the ice melting index calculation model as follows: In the formula, RBZS represents the line de-icing index; respectively represent the temperature weight, humidity weight, and wind speed weight; respectively represent the temperature influence factor, humidity influence factor, and wind speed influence factor; represents the icing thickness correction coefficient; respectively represent the real-time temperature data, real-time humidity data, and real-time wind speed data; respectively represent the average values of historical temperature data, historical humidity data, and historical wind speed data; respectively represent the maximum values of historical temperature data, historical humidity data, and historical wind speed data; respectively represent the minimum values of historical temperature data, historical humidity data, and historical wind speed data; represents the line icing thickness data; respectively represent the first correction coefficient, second correction coefficient, and third correction coefficient; respectively represent the first icing thickness threshold and the second icing thickness threshold.

5. A multi-line collaborative DC de-icing method according to claim 4, characterized in that, Obtaining a first ice melting index threshold and a second ice melting index threshold corresponding to the ice melting line, and judging the real-time ice coating state according to the line ice melting index: In the formula, FBZT represents the real-time icing state; RBZS represents the line de-icing index; represents the first de-icing index threshold; represents the second de-icing index threshold.

6. The multi-line collaborative DC de-icing method according to claim 1, characterized in that The obtaining the moving distance and cost of the ice melting equipment corresponding to the ice melting line, combining with the line ice melting index, calculating a comprehensive evaluation index, and determining the line ice melting sequence specifically includes: The expression of the comprehensive evaluation index is as follows: In the formula, represents the comprehensive evaluation index of the i-th ice melting line; respectively represent the weights corresponding to the line ice melting index, the moving distance of the ice melting equipment, and the moving cost of the ice melting equipment; respectively represent the line ice melting index, the moving distance of the ice melting equipment, and the moving cost of the ice melting equipment of the i-th ice melting line; respectively represent the maximum moving distance and the maximum moving cost of the ice melting equipment for all ice melting lines; Arranging the comprehensive evaluation index from large to small, and the obtained arrangement order is the line ice melting sequence.

7. A multi-line collaborative DC de-icing method according to claim 1, characterized in that, The constructing an energy consumption allocation optimization function, obtaining the real-time electrical parameters of the ice melting line, and combining with the line ice coating thickness data, determining the line ice melting energy consumption of each ice melting line specifically includes: Obtaining the real-time electrical parameters of the ice melting line, that is, the line resistance and the line length; Obtain the current ice melting coefficient, and combine the line icing thickness data to calculate the minimum line ice melting power as follows: In the formula, represents the minimum line de-icing power of the i-th de-icing line; represents the line length of the i-th de-icing line; represents the line ice coating thickness data of the i-th de-icing line; represents the current de-icing coefficient; The expression of the energy consumption allocation optimization function is as follows: In the formula, min represents the operation of taking the minimum value; n represents the number of ice melting lines; represents the ice melting current of the i-th ice melting line; represents the line resistance of the i-th ice melting line; respectively represent the minimum allowable current and the maximum allowable current of the i-th ice melting line; T represents the ice melting time; Solve the energy consumption allocation optimization function based on the particle swarm optimization algorithm to determine the line ice melting energy consumption of each ice melting line.

8. A multi-line collaborative DC de-icing method according to claim 1, characterized in that, It further includes obtaining the future environmental data of the ice melting line based on the meteorological data interface; Construct an icing thickness prediction model and predict the future icing thickness data of the ice melting line according to the future environmental data; If the difference between the future icing thickness data and the line icing thickness data exceeds the maximum thickness growth threshold, trigger the emergency ice melting alarm mechanism.

9. A multi-line collaborative DC de-icing system, which is applied to a multi-line collaborative DC de-icing method according to any one of claims 1-8, characterized in that, The ice melting system includes: A data acquisition module for obtaining the real-time environmental data and line icing thickness data corresponding to the ice melting line based on the line sensor network; An index calculation module for constructing an ice melting index calculation model, calculating the line ice melting index according to the real-time environmental data and the line icing thickness data, and judging the real-time icing state; A sequence determination module for obtaining the moving distance and cost of the ice melting equipment corresponding to the ice melting line, combining the line ice melting index, calculating the comprehensive evaluation index, and determining the line ice melting sequence; An energy consumption determination module for constructing an energy consumption allocation optimization function, obtaining the real-time electrical parameters of the ice melting line, and combining the line icing thickness data to determine the line ice melting energy consumption of each ice melting line; A strategy generation module for determining a multi-line collaborative ice melting strategy based on the line ice melting sequence and the line ice melting energy consumption of each ice melting line.

10. A multi-line collaborative DC de-icing device, characterized in that, It includes a memory and a processor. A computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a multi-line collaborative DC ice melting method according to any one of claims 1-8.

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