Transformer DC magnetic bias resistance-capacitance hybrid optimization treatment method and system

Abstract

The invention relates to the technical field of transformer direct-current magnetic bias treatment, in particular to a transformer direct-current magnetic bias resistance-capacitance hybrid optimization treatment method and system, main factors causing transformer direct-current magnetic bias are analyzed, main reasons of direct-current magnetic bias can be treated, the target is clearer, and the effect is more remarkable. Meanwhile, a transformer resistance-capacitance hybrid optimization strategy is constructed in combination with the comprehensive cost generated by actual optimization treatment and the DC magnetic bias risk after optimization treatment, and the optimal strategy of the transformer resistance-capacitance hybrid optimization is solved, so that the cost of the DC magnetic bias resistance-capacitance hybrid optimization treatment of the transformer is lower; the method can meet the requirements of direct-current magnetic bias suppression of different transformer substations, is higher in applicability, is simple in operation, and improves the efficiency of direct-current magnetic bias resistance-capacitance hybrid optimization treatment of the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer DC bias control, and in particular to a transformer DC bias resistor-capacitor hybrid optimization control method and system. Background Art

[0002] The problem of DC bias of transformer is a series of problems caused by the rapid development of HV / UHV DC transmission in recent years.

[0003] The risk of DC bias magnetism mainly manifests itself in the core saturation phenomenon caused by the invasion of DC current in the ground by the power transformer operating in the grounded power system, as well as a series of abnormal working conditions such as overexcitation, vibration, heat and noise, which cause serious damage to the life of the transformer and even endanger the safe and stable operation of the power system.

[0004] In order to control the problem of DC bias in transformers, the DC bias current at the neutral point of the transformer is usually limited. For example, the Chinese invention patent with application number 201410674457.0 provides a multi-stage progressive comprehensive control evaluation method for DC bias, which records that the devices for suppressing DC bias are mainly divided into two types: resistive current limiting devices and capacitive DC isolation devices; however, if all transformers are improved in the above manner to suppress DC bias, additional installation costs and maintenance costs will be added; although the above application screened out the best control method by testing the two devices for suppressing DC bias.

[0005] However, the above scheme cannot optimize the cost of DC bias magnetic treatment on the one hand, and on the other hand, multiple tests and screenings are required for different sites to obtain a treatment method that meets the DC current test standards. The operation is cumbersome and inefficient, and cannot meet actual operational needs. Summary of the invention

[0006] The purpose of the present invention is to provide a transformer DC bias magnetic resistance and capacitance hybrid optimization management method and system in combination with the site environment and field-circuit coupling, so as to solve the technical problem of high cost and low efficiency of transformer DC bias magnetic management.

[0007] The present invention solves the above-mentioned technical problems by: A transformer DC bias magnetic resistance and capacitance hybrid optimization management method, comprising the following steps: S1. Determine the main factors that cause DC bias of the transformer; S2. Determine the DC bias risk of the transformer according to the main factors causing the DC bias of the transformer. If the transformer has the risk of DC bias, execute S3; if not, end; S3. Establish a transformer resistance-capacitance hybrid optimization strategy based on the operation and maintenance cost of the transformer installed with a DC bias suppression device and the treatment effect after the transformer is installed with a DC bias suppression device; S4. Solve the established transformer resistance-capacitance hybrid optimization strategy to determine the best management method for transformer DC bias resistance-capacitance hybrid optimization.

[0008] It is further defined that the main factor causing the DC bias of the transformer is the single pole operation of the transformer grounding pole.

[0009] It is further defined that step S2 comprises: S2.1. Acquire AC power grid information, the AC power grid information includes: Soil model near the grounding electrode site, substation line data, AC power grid connection data, substation grounding resistance data, substation geographical location data and DC grounding electrode geographical location data; S2.2, modeling the AC power grid according to the AC power grid information, and establishing a distribution model of the DC current of the AC power grid; S2.3. Calculate the DC current I injected into the substation grounding grid based on the DC current distribution model of the AC grid. A ; S2.4. Determine the DC current injected into the substation grounding grid Is it less than the limit value of the DC bias current? If so, end; if not, execute step S3.

[0010] It is further defined that step S2.2 comprises the following steps: S2.2.1. Establish a ground model of the substation based on the AC power grid information; S2.2.2. Establishing a substation underground model based on the AC power grid information, wherein the substation underground model includes the substation grounding resistance and the substation surface potential; The substation grounding resistance is obtained by measuring the power frequency grounding resistance of the substation; The substation induced potential ; in, is the mutual resistance matrix between the DC grounding electrode and the substation, is the DC grounding electrode current entering the ground, is the mutual resistance matrix between substations, It is the DC current injected into the substation grounding grid.

[0011] It is further defined that step S2.3 is specifically as follows: S2.3.1. Based on the ground model and underground model of the substation, the field-circuit coupling model of the AC power grid is obtained:

[0012]

[0013] in, is the grid node conductance matrix, is the substation node voltage column vector, Inject current column vector into the grid node, is the association matrix between the substation node and all nodes, for The transpose of is the substation grounding conductivity matrix, is the conductivity matrix of the ground network nodes of the AC power grid; S2.3.2, the field-circuit coupling model obtains the DC current injected into the substation grounding grid through the Thevenin equivalent , ; in, is the node voltage of the substation, through Get the DC current injected into the substation grounding grid , , It is the total resistance of the substation underground line.

[0014] Further defined, the step S3 is specifically: Establish a transformer resistance and capacitance hybrid optimization strategy:

[0015] in, is the number of transformers, To invest in operation and maintenance costs, For the The sum of the equipment cost and maintenance cost required to install a capacitor DC isolation device on each transformer is For the Is the transformer equipped with a capacitor to isolate the DC power? If so, , if not, ; For the The sum of the equipment cost and maintenance cost required to install a resistor current limiting device on each transformer. For the Is the transformer equipped with a capacitor to isolate the DC power? If so, , if not, ; For the The geographical cost around each transformer, For the Is the transformer equipped with a DC bias suppression device? If so, , if not ; Optimize the sum of treatment costs for all transformers; For the DC bias magnetic treatment effect, For the After optimizing the management of the transformer, the DC current injected into the substation grounding grid, The sum of the DC currents injected into the substation grounding grid after optimal treatment of all transformers.

[0016] Further defined, the and Determined by entropy weight method-analytic hierarchy process.

[0017] Further defined, the step S4 is specifically as follows: The chaotic grey wolf algorithm is used to solve the transformer resistance-capacitance hybrid optimization strategy, and the optimal strategy for transformer resistance-capacitance hybrid optimization is obtained.

[0018] It is further defined that step S4 comprises: S4.1. Input initial value; S4.2. Set relevant parameters of the Chaos Grey Wolf Algorithm; S4.3, using Tent chaotic mapping to initialize the gray wolf population; S4.4, determine whether the number of iterations has been reached, if so, execute S4.9; if not, calculate the fitness of the initialized population, sort them according to their fitness, record the Pareto optimal solution, and assign the best three individuals to the wolf in turn. ,Wolf and wolf ; S4.5, update the weight coefficient parameters of the current population; S4.6, determine whether the population size has been reached, if so, use the gray wolf algorithm to find the candidate wolves; if not, modulate S4.4; S4.7, calculate the neighborhood of the candidate wolf, and perform mixed crossover on the individuals in the neighborhood to screen out new candidate wolves; S4.8, compare the candidate wolves obtained in S4.6 with the candidate wolves obtained in S4.7, select the candidate wolves with high fitness to update the population and replace the external solution set; S4.9. The algorithm ends and the optimal solution set is obtained.

[0019] A transformer DC bias magnetic resistance and capacitance hybrid optimization management system is applied to the above transformer DC bias magnetic resistance and capacitance hybrid optimization management method, and the transformer DC bias magnetic resistance and capacitance hybrid optimization management system comprises: DC bias factor analysis module, used to determine the main factors causing DC bias of transformer; The DC bias risk judgment module is used to determine the DC bias risk of the transformer according to the main factors causing the DC bias of the transformer. When the transformer has the risk of DC bias, the transformer resistance-capacitance hybrid optimization strategy module is started, otherwise, it ends; The transformer resistance-capacitance hybrid optimization strategy module is used to establish a transformer resistance-capacitance hybrid optimization strategy based on the cost of installing a DC bias suppression device on the transformer and the DC bias risk after installing the DC bias suppression device; The transformer resistance-capacitance hybrid optimization strategy solving module is used to solve the established transformer resistance-capacitance hybrid optimization strategy and determine the transformer DC bias resistance-capacitance hybrid optimal strategy.

[0020] The beneficial effects of the present invention are: The present invention analyzes the main factors causing the DC bias of the transformer and can treat the main causes of the DC bias, with clearer goals and more significant effects. At the same time, a transformer resistor-capacitor hybrid optimization strategy is constructed in combination with the comprehensive cost generated by the actual optimization treatment and the DC bias risk after the optimization treatment. By solving the optimal strategy for the transformer resistor-capacitor hybrid optimization, not only the cost of the transformer DC bias resistor-capacitor hybrid optimization treatment is reduced, but also the DC bias suppression requirements of different substations can be met, the applicability is stronger, the operation is simple, and the efficiency of the transformer DC bias resistor-capacitor hybrid optimization treatment is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the steps of the transformer DC bias magnetic resistance and capacitance hybrid optimization treatment method of the present invention; Figure 2 The distribution model diagram of the DC current of the 500kV AC power grid with the medium voltage side busbar of the double autotransformer in the present invention in segmented operation; Figure 3 It is an equivalent diagram of the distribution model of the DC current of the 500kV AC power grid with segmented operation of the busbar on the medium voltage side of the autotransformer of the present invention; Figure 4 This is a solution flow chart of the chaotic grey wolf algorithm of the present invention. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The factors that cause DC bias of transformers usually include geomagnetic storms, urban rail transit and single-pole DC grounding loop operation. Among them, geomagnetic storms are caused by the interaction between solar flares and the geomagnetic field to produce auroral electrojet, which causes transient fluctuations in the geomagnetic field. The transient fluctuations in the geomagnetic field place part of the earth in this time-varying magnetic field, causing induced surface potential ESP (Earth Surface Potentail). In areas with high soil resistivity, when geomagnetic storms are severe, the value can reach 1.2-6V / km; but since geomagnetic storms occur less frequently and mainly affect high-latitude areas, they are not the main factors causing DC bias of transformers.

[0024] Even if DC bias occurs in transformers near urban rail transit, the current flowing through the neutral point usually does not exceed the specified limit, so it is not the main factor causing DC bias of the transformer.

[0025] When a DC system operates using an earth return method, the injected current is the DC transmission current, and the higher the grounding electrode resistance and soil resistivity, the higher the potential and the wider the impact range.

[0026] The probability of single-pole operation of the grounding electrode is relatively high. Refer to Table 1, which shows the number of single-pole operations of the grounding electrode in Hubei Province. The ground current generated is coupled to the neutral point of the transformer through the earth, causing a risk of DC magnetic bias to the transformer, which is suitable as a typical scenario.

[0027] Table 1 Single-pole operation times of grounding electrodes in Hubei region

[0028] Therefore, the present invention is explained by taking the single pole operation of the transformer grounding pole as the main factor causing the DC bias magnetization of the transformer.

[0029] Example 1 refer to Figure 1 This embodiment provides a transformer DC bias magnetic resistance and capacitance hybrid optimization management method, including the following steps: S1. Determine the main factors that cause DC bias of the transformer; S2. Determine the DC bias risk of the transformer according to the main factors causing the DC bias of the transformer. If the transformer has the risk of DC bias, execute S3; if not, end; S3. Establish a transformer resistance-capacitance hybrid optimization strategy based on the operation and maintenance cost of the transformer installed with a DC bias suppression device and the treatment effect after the transformer is installed with a DC bias suppression device; S4. Solve the established transformer resistance-capacitance hybrid optimization strategy to determine the best management method for transformer DC bias resistance-capacitance hybrid optimization.

[0030] Further explanation, step S2 includes the following steps: S2.1. Obtain AC power grid information, which includes: Substation line information, grid connection information, substation grounding resistance, substation geographical location and DC grounding electrode geographical location.

[0031] Specifically, if the AC power grid has been put into use, relevant information of the current AC power grid is obtained; if the AC power grid is in the design and planning stage, information of the pre-constructed AC power grid is obtained.

[0032] Among them, the AC power grid information includes the soil model near the grounding electrode site, substation line data, AC power grid connection data, substation grounding resistance data, substation geographical location data and DC grounding electrode geographical location data.

[0033] The acquisition of the soil model near the grounding electrode includes the following steps: Use the four-pole method to test the soil resistivity near the grounding electrode:

[0034] in, is the inter-electrode spacing (m), is the burial depth of each level (m), is the measured resistance (Ω).

[0035] because Usually no more than 0.1 , so it can be assumed , so that the soil resistivity near the grounding electrode can be obtained .

[0036] Move each electrode and readjust the inter-electrode distance to obtain a new soil resistivity; repeat this process multiple times to obtain the soil resistivity corresponding to different depths, as shown in Table 2.

[0037] Table 2 Soil resistivity test results

[0038] By using the CDEGS grounding calculation software and inputting the test results of soil resistivity, the inverted soil model near the grounding electrode site can be obtained.

[0039] S2.2. Model the AC power grid according to the AC power grid information and establish a distribution model of the DC current of the AC power grid.

[0040] By establishing a distribution model of DC current in the AC power grid, it is possible to avoid modeling and calculating each transformer individually. On the one hand, this can reduce the difficulty of data collection and improve work efficiency. On the other hand, it can also make the DC bias suppression of the transformer more adaptable to its operating environment, thereby meeting actual optimization management needs.

[0041] Wherein, step S2.2 comprises the following steps: S2.2.1. Establish a ground model of the substation based on the AC power grid information; Specifically, the distribution model of DC current in the AC grid is mainly composed of the substation ground model and the substation underground model; Figure 2 , is the distribution model diagram of the DC current of the 500kV AC power grid with the medium-voltage side busbar of the double autotransformer in segmented operation, where the part above the surface is the ground model of the substation, and the part below the surface is the underground model of the substation; the equivalent diagram of the distribution model of the DC current of the 500kV AC power grid with the medium-voltage side busbar of the double autotransformer in segmented operation, as shown Figure 3 shown.

[0042] Among them, the substation ground model is an AC transmission network model, which belongs to a circuit model; the substation ground model includes a substation model and a substation line model.

[0043] Substation model, including substation node, transformer model and neutral point series equipment model; the transformer model includes transformer winding type, winding DC resistance, transformer bus, such as Figure 3 shown.

[0044] The substation line model is the three resistance branches connecting the three-phase busbars of the substation. The substation line model can be expressed as ;in, is the resistivity coefficient of the line material (Ω·m); is the length of one-phase line (m); is the number of substation lines, is the cross-sectional area of ​​one-phase line (m 2 ); Usually the substation line consists of 3 items, namely =3.

[0045] S2.2.2. Establish a substation underground model based on the AC power grid information. The substation underground model includes the substation grounding resistance and the substation surface potential; The substation grounding resistance is obtained by measuring the substation power frequency grounding resistance; Substation ground potential ; in, is the mutual resistance matrix between the DC grounding electrode and the substation, is the DC grounding electrode current entering the ground, is the mutual resistance matrix between substations, It is the DC current injected into the substation grounding grid.

[0046] Specifically, the substation grounding resistance is the equivalent resistance between the neutral point of the substation and the infinite zero potential point; usually, since the substation grounding resistance is not easy to measure and it is only related to the soil model near the grounding pole, it can be obtained by measuring the substation power frequency grounding resistance.

[0047] The surface potential is the surface potential formed by the ground current flowing in the earth. According to the grounding theory, the induced potential of the substation can be obtained. .

[0048] Among them, the mutual resistance matrix between substations is and mutual resistance matrix between DC grounding electrode and substation All of them can be obtained through the classical grounding calculation formula.

[0049] S2.3. Calculate the DC current injected into the substation grounding grid based on the DC current distribution model of the AC grid ; Step S2.3 comprises the following steps: S2.3.1. Based on the ground model and underground model of the substation, the field-circuit coupling model of the AC power grid is obtained:

[0050]

[0051] in, is the grid node conductance matrix (i.e., substation node conductance matrix), is the substation node voltage, Inject current column vector into the grid node, is the association matrix between the substation node and all nodes (i.e., substation nodes, busbar nodes, and neutral point nodes), for The transpose of is the substation grounding conductivity matrix, is the conductivity matrix of the ground network nodes of the AC power grid.

[0052] Specifically, for an AC power grid, if the number of substations is k, the number of independent neutral points is b, and there are h lines in total, that is, there are k substation nodes and (k+h+b) branches. Based on the distribution model of the DC current in the AC power grid, a field-circuit coupling model can be constructed:

[0053]

[0054] =

[0055] in, is the substation node voltage column vector (V), is the independent neutral point voltage column vector (V), is the bus node voltage column vector (V); Inject current column vector (A) into the substation node, is the independent neutral point injection current column vector (A), Inject current column vector (A) into the bus node; is the induced potential column vector of the substation (V).

[0056] S2.3.2, the field-circuit coupling model obtains the DC current injected into the substation grounding grid through the Thevenin equivalent , ; in, , then the DC current injected into the substation grounding grid is obtained , It is the total resistance of the substation underground line.

[0057] Specifically, the definition of substation grounding resistance and induced potential can be called "Thevenin equivalent" at the neutral point. At this time, the DC current injected into the substation grounding grid is:

[0058]

[0059] pass Get the DC current injected into the substation grounding grid , ,matrix is the field-circuit coupling quantity.

[0060] Therefore, the DC current of the current substation grounding grid is obtained through the above steps:

[0061] S2.4. Determine the DC current injected into the substation grounding grid Is it less than the limit value of the DC bias current? If so, end; if not, execute step S3.

[0062] Generally, for transformers with different parameters, the limit values ​​of the neutral point DC bias current are different, that is, the risk values ​​are different; for example, for a 1000kV UHV transformer, the DC bias current limit is 2A; for a 500kV UHV transformer, the DC bias current limit is 5A; for a 220kV UHV transformer, the DC bias current limit is 7A; for a 110kV UHV transformer, no requirements are temporarily required.

[0063] Therefore, after obtaining the DC current currently injected into the substation grounding grid using step S2.3, it is compared with the DC bias current limit of the corresponding parameter transformer. When the DC current currently injected into the substation grounding grid is less than its limit, no optimization management is required; otherwise, the corresponding substation needs to be optimized to avoid serious losses.

[0064] To further illustrate, step S3 is specifically as follows: Establish a transformer resistance and capacitance hybrid optimization strategy:

[0065] Where m=k, is the number of transformers, To invest in operation and maintenance costs, For the The sum of the equipment cost and maintenance cost required to install a capacitor DC isolation device on each transformer is For the Is the transformer equipped with a capacitor to isolate the DC power? If so, , if not, ; For the The sum of the equipment cost and maintenance cost required to install a resistor current limiting device on each transformer. For the Is the transformer equipped with a capacitor to isolate the DC power? If so, , if not, ; For the The geographical cost around each transformer, For the Is the transformer equipped with a DC bias suppression device? If so, , if not ; Optimize the sum of treatment costs for all transformers; For the DC bias magnetic treatment effect, For the After optimizing the management of the transformer, the DC current injected into the substation grounding grid, The sum of the DC currents injected into the substation grounding grid after optimal treatment of all transformers.

[0066] Specifically, in order to achieve effective suppression of DC bias at low cost, it is necessary to consider the cost of installing a DC bias suppression device on the transformer and the treatment effect of installing a DC bias suppression device. Therefore, a cost analysis of investment and operation costs is constructed. And DC bias magnetic treatment effect The objective function of .

[0067] Among them, the investment and operation and maintenance costs include the cost of installing the DC bias suppression device equipment and the maintenance cost, and it is also necessary to consider the substation land cost and the environmental protection costs caused by the construction.

[0068] Therefore, based on the total investment and operation cost of the regional power grid substation and the minimum sum of the transformer neutral point current, a transformer resistance and capacitance hybrid optimization strategy is established. , thereby converting the multi-objective optimization problem into a single-objective optimization solution in a weighted manner; the specific weight determination method can be determined by the entropy weight method, the analytic hierarchy process (AHP) or the entropy weight method-analytic hierarchy process, preferably the entropy weight method-analytic hierarchy process, which can find a balance between the subjectivity of AHP and the objectivity of the entropy weight method, which can not only maintain the judgment of expert experience, but also reduce the errors caused by human subjective factors, thereby improving the reliability and scientificity of decision-making and ensuring that the strategy is more reliable.

[0069] Transformer resistance and capacitance hybrid optimization strategy The optimization solution can be found by particle swarm algorithm, genetic algorithm or chaos grey wolf algorithm, preferably by chaos grey wolf algorithm.

[0070] refer to Figure 4 , taking the chaotic grey wolf algorithm to solve the transformer resistance-capacitance hybrid optimization strategy and obtain the transformer resistance-capacitance hybrid optimization optimal strategy as an example for explanation, at this time, step S4 includes the following steps: S4.1. Input initial value; S4.2. Set the relevant parameters of the Chaos Grey Wolf Algorithm (IGWO), such as the population size N and the maximum number of iterations T max ; S4.3, using Tent chaotic mapping to initialize the gray wolf population; S4.4, determine whether the number of iterations has been reached. If so, execute S4.9; if not, calculate the fitness of the initialized population, sort them according to their fitness, record the Pareto optimal solution, and assign the best three individuals to the wolf in turn. ,Wolf and wolf ; S4.5, update the weight coefficient parameters of the current population; S4.6, determine whether the population size has been reached, if so, use the gray wolf algorithm to find the candidate wolves; if not, modulate S4.4; S4.7, calculate the neighborhood of the candidate wolf, and perform mixed crossover on the individuals in the neighborhood to screen out new candidate wolves; S4.8, compare the candidate wolves obtained in S4.6 with the candidate wolves obtained in S4.7, select the candidate wolves with high fitness to update the population and replace the external solution set; S4.9. The algorithm ends and the optimal solution set is obtained.

[0071] Specifically, since the initial population of the traditional grey wolf algorithm is randomly generated, this leads to slow convergence of the algorithm and the phenomenon of being easily trapped in local optimality; among them, chaos mapping, as a special and random nonlinear phenomenon, has the characteristics of randomness, ergodicity and regularity, and can effectively enhance the global search capability, so the combination of the two avoids the shortcomings of the traditional grey wolf algorithm; at the same time, the typical Logistic-Sine-Cosine chaos mapping is selected to optimize the grey wolf initial population, which can be more evenly distributed in space and has better ergodicity and uniformity, thereby improving the efficiency and quality of solving the transformer resistance-capacitance hybrid optimization strategy and improving reliability.

[0072] Example 2 Based on Example 1, this embodiment provides a transformer DC bias magnetic resistance and capacitance hybrid optimization management system, including: DC bias factor analysis module, used to determine the main factors causing DC bias of transformer; The DC bias risk judgment module is used to determine the DC bias risk of the transformer according to the main factors causing the DC bias of the transformer. When the transformer has the risk of DC bias, the transformer resistance-capacitance hybrid optimization strategy module is started, otherwise, it ends; The transformer resistance-capacitance hybrid optimization strategy module is used to establish a transformer resistance-capacitance hybrid optimization strategy based on the cost of installing a DC bias suppression device on the transformer and the DC bias risk after installing the DC bias suppression device; The transformer resistance-capacitance hybrid optimization strategy solving module is used to solve the established transformer resistance-capacitance hybrid optimization strategy and determine the transformer DC bias resistance-capacitance hybrid optimal strategy.

[0073] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the present invention.

Claims

1. A transformer DC bias magnetic resistance and capacitance hybrid optimization management method, characterized in that: The following steps are involved: S1. Determine the main factors that cause DC bias of the transformer; S2. Determine the DC bias risk of the transformer according to the main factors causing the DC bias of the transformer. If the transformer has the risk of DC bias, execute S3; if not, end; S3. Establish a transformer resistance-capacitance hybrid optimization strategy based on the operation and maintenance cost of the transformer installed with a DC bias suppression device and the treatment effect after the transformer is installed with a DC bias suppression device; S4. Solve the established transformer resistance-capacitance hybrid optimization strategy to determine the best management method for transformer DC bias resistance-capacitance hybrid optimization.

2. The transformer DC bias magnetic resistance and capacitance hybrid optimization management method according to claim 1 is characterized in that: The main factor causing the DC bias of the transformer is the single pole operation of the transformer grounding pole.

3. The transformer DC bias magnetic resistance and capacitance hybrid optimization management method according to claim 2 is characterized in that: The step S2 comprises: S2.

1. Acquire AC power grid information, the AC power grid information includes: Soil model near the grounding electrode site, substation line data, AC power grid connection data, substation grounding resistance data, substation geographical location data and DC grounding electrode geographical location data; S2.2, modeling the AC power grid according to the AC power grid information, and establishing a distribution model of the DC current of the AC power grid; S2.

3. Calculate the DC current injected into the substation grounding grid based on the DC current distribution model of the AC grid ; S2.

4. Determine the DC current injected into the substation grounding grid Is it less than the limit value of the DC bias current? If so, end; if not, execute step S3.

4. The transformer DC bias resistance and capacitance hybrid optimization management method according to claim 3 is characterized in that: The step S2.2 comprises the following steps: S2.2.

1. Establish a ground model of the substation based on the AC power grid information; S2.2.

2. Establishing a substation underground model based on the AC power grid information, wherein the substation underground model includes the substation grounding resistance and the substation surface potential; The substation grounding resistance is obtained by measuring the power frequency grounding resistance of the substation; The substation induced potential ; in, is the mutual resistance matrix between the DC grounding electrode and the substation, is the DC grounding electrode current entering the ground, is the mutual resistance matrix between substations, It is the DC current injected into the substation grounding grid.

5. The transformer DC bias magnetic resistance and capacitance hybrid optimization management method according to claim 4 is characterized in that: The step S2.3 is specifically as follows: S2.3.

1. Based on the ground model and underground model of the substation, the field-circuit coupling model of the AC power grid is obtained: in, is the grid node conductance matrix, is the substation node voltage column vector, Inject current column vector into the grid node, is the association matrix between the substation node and all nodes, for The transpose of is the substation grounding conductivity matrix, is the conductivity matrix of the ground network nodes of the AC power grid; S2.3.2, the field-circuit coupling model obtains the DC current injected into the substation grounding grid through the Thevenin equivalent , ; in, is the node voltage of the substation, through Get the DC current injected into the substation grounding grid , , It is the total resistance of the substation underground line.

6. The transformer DC bias resistance and capacitance hybrid optimization management method according to claim 5 is characterized in that: The step S3 is specifically as follows: Establish a transformer resistance and capacitance hybrid optimization strategy: in, is the number of transformers, To invest in operation and maintenance costs, For the The sum of the equipment cost and maintenance cost required to install a capacitor DC isolation device on each transformer is For the Is the transformer equipped with a capacitor to isolate the DC power? If so, , if not, ; For the The sum of the equipment cost and maintenance cost required to install a resistor current limiting device on each transformer. For the Is the transformer equipped with a capacitor to isolate the DC power? If so, , if not, ; For the The geographical cost around each transformer, For the Is the transformer equipped with a DC bias suppression device? If so, , if not ; Optimize the sum of treatment costs for all transformers; For the DC bias magnetic treatment effect, For the After optimizing the management of the transformer, the DC current injected into the substation grounding grid, The sum of the DC currents injected into the substation grounding grid after optimal treatment of all transformers.

7. The transformer DC bias magnetic resistance and capacitance hybrid optimization management method according to claim 6 is characterized in that: Said and Determined by entropy weight method-analytic hierarchy process.

8. The transformer DC bias magnetic resistance and capacitance hybrid optimization management method according to claim 6 is characterized in that: The step S4 is specifically as follows: The chaotic grey wolf algorithm is used to solve the transformer resistance-capacitance hybrid optimization strategy, and the optimal strategy for transformer resistance-capacitance hybrid optimization is obtained.

9. The transformer DC bias magnetic resistance and capacitance hybrid optimization treatment method according to claim 8 is characterized in that: The step S4 comprises: S4.

1. Input initial value; S4.

2. Set relevant parameters of the Chaos Grey Wolf Algorithm; S4.3, using Tent chaotic mapping to initialize the gray wolf population; S4.4, determine whether the number of iterations has been reached, if so, execute S4.9; if not, calculate the fitness of the initialized population, sort them according to their fitness, record the Pareto optimal solution, and assign the best three individuals to the wolf in turn. ,Wolf and wolf ; S4.5, update the weight coefficient parameters of the current population; S4.6, determine whether the population size has been reached, if so, use the gray wolf algorithm to find the candidate wolves; if not, modulate S4.4; S4.7, calculate the neighborhood of the candidate wolf, and perform mixed crossover on the individuals in the neighborhood to screen out new candidate wolves; S4.8, compare the candidate wolves obtained in S4.6 with the candidate wolves obtained in S4.7, select the candidate wolves with high fitness to update the population and replace the external solution set; S4.

9. The algorithm ends and the optimal solution set is obtained.

10. A transformer DC bias magnetic resistance and capacitance hybrid optimization management system, characterized in that: The transformer DC bias magnetic resistance and capacitance hybrid optimization management system is applied to the transformer DC bias magnetic resistance and capacitance hybrid optimization management method as claimed in any one of claims 1 to 9, and the transformer DC bias magnetic resistance and capacitance hybrid optimization management system comprises: DC bias factor analysis module, used to determine the main factors causing DC bias of transformer; The DC bias risk judgment module is used to determine the DC bias risk of the transformer according to the main factors causing the DC bias of the transformer. When the transformer has the risk of DC bias, the transformer resistance-capacitance hybrid optimization strategy module is started, otherwise, it ends; The transformer resistance-capacitance hybrid optimization strategy module is used to establish a transformer resistance-capacitance hybrid optimization strategy based on the cost of installing a DC bias suppression device on the transformer and the DC bias risk after installing the DC bias suppression device; The transformer resistance-capacitance hybrid optimization strategy solving module is used to solve the established transformer resistance-capacitance hybrid optimization strategy and determine the transformer DC bias resistance-capacitance hybrid optimal strategy.

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