A global optimization configuration method and system for a neutral point capacitor direct current blocking device in a transformer

Abstract

The application discloses a transformer neutral point capacitor direct-current isolation device global optimization configuration method and system, the method comprises the following steps: building an equivalent field-circuit coupling model, and initializing original magnetic bias current distribution of a power grid based on the equivalent field-circuit coupling model; introducing a whole-network current imbalance degree as an evaluation index of whole-network current balance based on equivalent electrical distance of equivalent impedance between nodes of the power grid, establishing a capacitor direct-current isolation device optimization configuration balance game model with the power grid direct-current isolation device configuration and whole-network current distribution balance as game participants, and solving and optimizing the optimization configuration balance game model by using a multi-objective discrete particle swarm optimization algorithm to obtain an optimal configuration scheme of a capacitor direct-current isolation device in a ground electrode near zone of the power grid. The application considers power grid planning information and performs optimization configuration of the capacitor direct-current isolation device from a global perspective.

Description

Technical Field

[0001] This invention relates to the field of high voltage direct current transmission technology, specifically to a global optimization configuration method and system for a transformer neutral point capacitor DC blocking device. Background Technology

[0002] As high-voltage direct current (HVDC) transmission technology matures and its applications become more widespread, the impact of DC bias current problems is becoming increasingly apparent. When a HVDC transmission system operates in a single-polarity loop mode, the DC line and the ground form a loop, and the DC current flowing into the ground equals the system operating current on the DC line. This ground current will cause a significant imbalance in the ground potential over a wide area. When an excessively large DC bias current enters the transformer windings through the neutral point, it will cause a series of adverse consequences that damage the transformer, such as increased transformer vibration, harmonics, increased noise, overheating of oil, and increased measurement errors. This also has a very negative impact on the safe and stable operation of the power grid and even the surrounding environment.

[0003] Currently, DC suppression methods for individual transformers are relatively mature, and can be broadly categorized into series small resistor method, reverse current compensation method, and neutral point series capacitor method. Among these, the neutral point series capacitor method has the best suppression effect and is therefore the most widely used. Nowadays, for problems caused by DC bias, the method of addressing the issue locally only when the DC bias current exceeds the limit is no longer applicable, because adhering to the principle of "installing only when the limit is exceeded" will lead to other transformers experiencing excessive neutral point DC current. At present, the optimization configuration methods for transformer DC blocking devices can be roughly divided into two categories. The first category mainly uses intelligent optimization algorithms to optimize the DC blocking configuration of capacitors connected to the grid, or to optimize the configuration of a hybrid configuration introducing new DC equalization devices and DC blocking devices. This type of method usually does not consider the planning information of the grid topology and may result in inaccurate calculation of the transformer neutral point DC current and the algorithm being prone to local convergence. The second category mainly utilizes the connectivity of the grid to identify key sites for bias current across the entire network, but this type of method is difficult to optimize the global configuration of the grid's DC blocking devices. Summary of the Invention

[0004] To address the shortcomings of the existing technologies, this invention provides a method and system for globally optimizing the configuration of transformer neutral point capacitor DC blocking devices. This invention takes into account power grid planning information and optimizes the configuration of capacitor DC blocking devices from a global perspective.

[0005] This invention is achieved through the following technical solution:

[0006] A method for globally optimizing the configuration of a transformer neutral point capacitor DC blocking device includes:

[0007] An equivalent field-circuit coupling model is constructed, and the original bias current distribution of the power grid is initialized based on the equivalent field-circuit coupling model.

[0008] Based on the equivalent electrical distance of the equivalent impedance between power grid nodes, the current imbalance of the whole network is introduced as an evaluation index of the current balance of the whole network. A game model for the optimal configuration of capacitor DC blocking device is established with the configuration of DC blocking device and the balance of current distribution of the whole network as the game participants.

[0009] The optimal configuration equilibrium game model is solved and optimized using a multi-objective discrete particle swarm optimization algorithm to obtain the optimal configuration scheme of the DC blocking device of the near-grounding pole power grid capacitor.

[0010] In a preferred embodiment, the evaluation index ε for the network-wide current balance of the present invention is expressed as:

[0011]

[0012] L i =(1-tanθ) i ) 2 z pp +tanθ i 2 z qq -2(tanθ i 2 -tanθ i )z pq +z ii -2(1-tanθ i )z ip -2tanθ i z iq

[0013] In the formula, i represents the station number, N represents the number of stations, and I i L represents the current flowing through the neutral point of the transformer at station i. i X represents the equivalent electrical distance between the i-th site and the grounding electrode, used to measure the degree of coupling between the nodes and the changes in the bias current amplitude of the grounding electrode within the system. i Z represents the number of transformers at the i-th site. pp and Z qq Z represents the self-impedance of the two grounding electrodes. pq Z represents the mutual impedance between two grounded electrodes. ii Z represents the self-impedance of the i-th station. ip and Z iq Let tanθ represent the mutual impedance between the i-th station and the two grounding electrodes, respectively. i This represents the tangent of the angle formed between the i-th station and the two grounding electrodes.

[0014] As a preferred embodiment, the present invention takes the overall grid current balance and the minimum number of capacitor DC blocking devices as optimization objectives. Based on the team maximum equilibrium Position-Nash equilibrium, a capacitor DC blocking device optimization configuration equilibrium game model is established with grid DC blocking device configuration and overall grid current distribution balance as game participants.

[0015] As a preferred embodiment, the present invention employs a multi-objective discrete particle swarm optimization algorithm to solve and optimize the optimized configuration equilibrium game model, specifically including:

[0016] The neutral point DC current matrix under each strategy is calculated, and the number of DC blocking devices and the current balance coefficient under each strategy are obtained. Different strategies are selected, their evaluation indicators are calculated, and the magnitudes of the indicators under different states are compared.

[0017] A dynamic inertia weight adjustment strategy based on position similarity is introduced. In each iteration, the inertia weight of the particle after the iteration is dynamically calculated based on the position similarity between the current particle vector and the optimal particle vector.

[0018] Record the inertia weight and evaluation index value for each iteration;

[0019] A position similarity particle position update strategy based on random correction coefficients is introduced to make particles move closer to the position of the optimal particle. This is used for secondary updates to correct particles that do not meet the constraints, while particles that meet the constraints after the initial update are saved. If the evaluation index value calculated in the (T+1)th time is better than the result calculated in the Tth time, the result calculated in the (T+1)th time is recorded.

[0020] As a preferred embodiment, the dynamic inertia weight adjustment strategy based on position similarity of the present invention is specifically as follows:

[0021]

[0022]

[0023] In the formula, cos i Indicates similarity, x i (k) represents the particle vector in the current k-th iteration, g ＊ For the optimal particle vector, w i (k) represents the inertial weight of particle i in the k-th iteration, w min and w max These represent the upper and lower limits of the inertia weight, respectively; when cos i A higher similarity value indicates a higher similarity between particles. When the similarity is greater than the preset value, it means that the particle is close to the optimal particle in the population, and the inertia weight should be reduced. When the similarity is less than the preset value, it means that the particle is far away from the optimal particle in the population, and the inertia weight should be increased.

[0024] As a preferred embodiment, the position update strategy for position similarity particles based on random correction coefficients of the present invention is specifically as follows:

[0025]

[0026] D = (1-cos i )×rand(-0.5, 0.5)

[0027] In the formula, x i (k) represents the position of particle i after the k-th iteration, D represents the random position correction coefficient, and k max This indicates the maximum number of iterations.

[0028] In a preferred embodiment, the present invention constructs an equivalent field-circuit coupling model and initializes the original bias current distribution of the power grid based on the equivalent field-circuit coupling model, specifically as follows:

[0029] An equivalent field-circuit coupling model is established based on the power grid line topology, grounding electrode characteristics, physical parameters, and local soil characteristics. Based on the equivalent field-circuit coupling model, the initial value of the current distribution of the entire network is calculated after adding a capacitor DC blocking device at the neutral point of the over-limit transformer.

[0030] Secondly, this invention proposes a global optimization configuration system for a transformer neutral point capacitor DC blocking device, comprising:

[0031] The initialization module is used to build an equivalent field-circuit coupling model and initialize the original bias current distribution of the power grid based on the equivalent field-circuit coupling model.

[0032] The game model construction module introduces the current imbalance of the whole network as an evaluation index of the current balance of the whole network based on the equivalent electrical distance of the equivalent impedance between nodes. It establishes a game model for the optimal configuration balance of capacitor DC blocking device with the configuration of DC blocking device and the balance of current distribution of the whole network as game participants.

[0033] The optimization module uses a multi-objective discrete particle swarm optimization algorithm to solve and optimize the equilibrium game model of the optimization configuration, and obtains the optimal configuration scheme of the DC blocking device of the near-grounding electrode power grid capacitor.

[0034] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the present invention.

[0035] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the present invention.

[0036] The present invention has the following advantages and beneficial effects:

[0037] 1. This invention establishes an optimization model for DC blocking device configuration based on game theory that considers the equilibrium of the bias current distribution across the entire network. The model uses the configuration of DC blocking devices in the power grid and the equilibrium of the current distribution across the entire network as the game participants. The two sides in the game change their game strategies to make the power grid reach the optimal state, which effectively ensures the economy and reliability of the optimized DC blocking device configuration.

[0038] 2. This invention comprehensively considers power grid topology planning information and introduces the overall network imbalance as an evaluation index for overall network current balance. Compared with general DC blocking configuration optimization methods, it has higher effectiveness and is more suitable for analyzing and solving practical problems.

[0039] 3. This invention adopts an improved particle swarm optimization algorithm by using a dynamic adjustment strategy of inertia weight based on position similarity. The magnitude of the inertia weight is dynamically adjusted according to the similarity between particles in each iteration, which enhances the convergence speed while keeping the particle close to the global optimum. A particle update strategy is introduced into the particle swarm algorithm to enhance the diversity of the population, avoid the problem of local optima, and improve the convergence speed of the algorithm. Attached Figure Description

[0040] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of the computer device structure according to an embodiment of the present invention.

[0043] Figure 3 This is a system principle block diagram according to an embodiment of the present invention.

[0044] Figure 4 This is a schematic diagram of the power grid structure near a DC grounding electrode.

[0045] Figure 5 These are the DC current values ​​at the neutral point of the transformers in each power plant under the original conditions.

[0046] Figure 6 This is a schematic diagram of the potential distribution of various power plants and substations in the power grid.

[0047] Figure 7 The DC current values ​​at the neutral point of transformers in each power plant are optimized for the DC blocking device configuration.

[0048] Figure 8 The graph shows how the current imbalance across the entire network changes with the number of iterations in the game. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0050] Example 1

[0051] Existing transformer DC blocking device optimization techniques fail to consider grid topology planning information and may suffer from inaccurate calculation of transformer neutral point DC current, algorithm susceptibility to local convergence, or difficulty in globally optimizing the configuration of grid DC blocking devices. This invention proposes a global optimization configuration method for transformer neutral point capacitor DC blocking devices. This invention employs a field-circuit coupling approach to calculate the transformer neutral point DC current using finite element analysis, comprehensively considering grid topology planning information. Based on equivalent electrical distance, it introduces the concept of grid-wide bias current distribution balance. Using Position-Nash equilibrium based on team maximum equilibrium, it treats grid DC blocking device configuration and grid-wide current distribution balance as game participants. The game strategy consists of the DC blocking strategy sets of each player. Players change their strategies to achieve the optimal grid state. Simultaneously, a multi-objective discrete particle swarm optimization algorithm is employed to dynamically improve inertia weights and use random correction coefficients to update particles that do not meet the constraints, ensuring all particles satisfy the constraints and avoiding local convergence. This improves global convergence and convergence speed, ultimately achieving the optimal configuration scheme for multi-grounded near-field grid capacitor DC blocking devices.

[0052] Specifically, such as Figure 1 As shown, the global optimization configuration method proposed in this embodiment of the invention specifically includes the following steps:

[0053] Step 1: Build an equivalent field-circuit coupling model and initialize the original bias current distribution of the power grid based on the equivalent field-circuit coupling model.

[0054] An equivalent field-circuit coupling model is established based on the power grid line topology, grounding electrode characteristics, physical parameters, and local soil characteristics. Based on the equivalent field-circuit coupling model, the initial value of the current distribution of the entire network is calculated using the finite element method.

[0055] Step 2: Based on the equivalent electrical distance of the equivalent impedance between nodes, the current imbalance of the whole network is introduced as an evaluation index of the current balance of the whole network. A game model for the optimal configuration balance of capacitor DC blocking device is established with the configuration of DC blocking device and the balance of current distribution of the whole network as the game participants.

[0056] Step 3: The multi-objective discrete particle swarm optimization algorithm is used to solve and optimize the equilibrium game model of the optimization configuration to obtain the optimal configuration scheme of the DC blocking device of the near-field power grid capacitor.

[0057] As an optional implementation, step 2 introduces the concept of network current imbalance based on the equivalent electrical distance of the equivalent impedance between nodes to define the impact of network topology planning on the current power grid. With the optimization objectives of network current balance and minimizing the number of capacitor DC blocking devices, a capacitor DC blocking device optimization configuration equilibrium game model is established based on the team maximum equilibrium Position-Nash equilibrium, with the power grid DC blocking device configuration and network current distribution balance as game participants.

[0058] The evaluation index ε for network-wide current balance is expressed as:

[0059]

[0060] L i =(1-tanθ) i ) 2 z pp +tanθ i 2 z qq -2(tanθ i 2 -tanθ i )z pq +z ii -2(1-tanθ i )z ip -2tanθ i z iq (2)

[0061] In the formula, i represents the station number, N represents the number of stations, and I i L represents the current flowing through the neutral point of the transformer at station i. i X represents the equivalent electrical distance between the i-th site and the grounding electrode, used to measure the degree of coupling between the nodes and the changes in the bias current amplitude of the grounding electrode within the system. i Z represents the number of transformers at the i-th site. pp and Z qq Z represents the self-impedance of the two grounding electrodes. pq Z represents the mutual impedance between two grounded electrodes. ii Z represents the self-impedance of the i-th station. ip and Z iq Let tanθ represent the mutual impedance between the i-th station and the two grounding electrodes, respectively. i This represents the tangent of the angle formed between the i-th station and the two grounding electrodes. Because the more uniform the current flowing through the neutral point of the transformer in the power grid, the smaller the probability that the transformer center point current will change significantly after the power grid topology changes, a threshold is defined. When the absolute value of the neutral point current of the transformers that have not exceeded the limit in the entire network is distributed around the threshold, it indicates that the bias current distribution of the entire network is balanced.

[0062] The objective function F can be expressed as:

[0063] F = min[(W×N), ε] (3)

[0064] In the formula, W represents the cost of a single capacitor DC blocking device, and N represents the number of capacitor DC blocking devices configured.

[0065] As an optional implementation, step 3 uses a multi-objective discrete particle swarm optimization algorithm to solve the optimization process, specifically including: calculating the neutral point DC current matrix under each strategy, obtaining the number of DC blocking devices and the current balancing coefficient under each strategy; selecting different strategies, calculating their evaluation indices, and comparing the magnitude of the indices under different states; introducing a dynamic inertia weight adjustment strategy based on position similarity, dynamically calculating the inertia weight of the particle after each iteration based on the position similarity between the current particle vector and the optimal particle vector; recording the inertia weight and evaluation index value of each iteration; introducing a position similarity particle position update strategy based on random correction coefficients, so that the particles move closer to the position of the optimal particle, used for secondary update correction of particles that do not meet the constraints, while particles that meet the constraints after the initial update are saved; if the evaluation index value calculation result of the (T+1)th iteration is better than the calculation result of the Tth iteration, then the calculation result of the (T+1)th iteration is recorded.

[0066] Specifically, the dynamic inertia weight adjustment strategy based on position similarity involves: in each iteration, dynamically adjusting the magnitude of the inertia weight according to the positional relationship between the current particle vector and the optimal particle vector; defining the cosine similarity between the particle vector and the optimal particle vector as similarity, and introducing an adaptive positional cosine similarity factor cos... i The similarity is expressed as:

[0067]

[0068]

[0069] In the formula, x i (k) represents the particle vector in the current k-th iteration, g ＊ For the optimal particle vector, w i (k) represents the inertial weight of particle i in the k-th iteration, w min and w max These represent the upper and lower limits of the inertia weight, respectively; when cos i A higher value indicates a higher similarity between particles. When the similarity is large, it means that the particle is close to the optimal particle in the population, and the inertia weight should be reduced in a timely manner. When cos iWhen the value is small, it means that the particle is far away from the best particle in the population. At this time, the value of the inertia weight should be increased in a timely manner to improve the global search capability, which is conducive to the best particle vector guiding the global search and gives the poor particle vector more opportunities for local guidance.

[0070] The specific strategy for updating particle positions based on position similarity with random correction coefficients is as follows:

[0071]

[0072] D = (1-cos i )×rand(-0.5,0.5) (7)

[0073] In the formula, x i (k) represents the position of particle i after the k-th iteration, D represents the random position correction coefficient, and k max This indicates the maximum number of iterations. After the position update, particles that do not meet the constraints not only meet the requirements but are also moved closer to the optimal particle, thus improving the convergence speed of the algorithm.

[0074] This embodiment also proposes a computer device for performing the methods described above in this embodiment.

[0075] Specifically, such as Figure 2 As shown, a computer device includes a processor, internal memory, and a system bus; various device components, including the internal memory and processor, are connected to the system bus. The processor is hardware used to execute computer program instructions through basic arithmetic and logical operations within the computer system. Internal memory is a physical device used for temporary or permanent storage of computational programs or data (e.g., program state information). The system bus can be any of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, and a local bus. The processor and internal memory can communicate via the system bus. Internal memory includes read-only memory (ROM) or flash memory (not shown in the figure), and random access memory (RAM), which typically refers to the main memory loaded with the operating system and computer programs.

[0076] Computer devices typically include an external storage device. The external storage device can be selected from a variety of computer-readable media, which are any usable media accessible by a computer device, including both removable and fixed media. Examples of computer-readable media include, but are not limited to, flash memory (microSD cards), CD-ROMs, digital versatile optical discs (DVDs) or other optical disc storage, magnetic tape cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other media that can be used to store desired information and is accessible by a computer device.

[0077] Computer devices can logically connect to one or more network terminals in a network environment. Network terminals can be personal computers, servers, routers, smartphones, tablets, or other public network nodes. Computer devices connect to network terminals through network interfaces (LAN interfaces). A Local Area Network (LAN) is a computer network interconnected within a limited area, such as a home, school, computer lab, or office building using network media. WiFi and twisted-pair Ethernet are the two most commonly used technologies for building LANs.

[0078] It should be noted that other computer systems, including more or fewer subsystems than computer equipment, are also applicable to the invention.

[0079] As described in detail above, the computer device applicable to this embodiment can perform specified operations of a global optimization configuration method for a transformer neutral point capacitor DC blocking device. The computer device executes these operations through software instructions run by a processor on a computer-readable medium. These software instructions can be read into memory from a storage device or from another device via a local area network interface. The software instructions stored in memory cause the processor to execute the aforementioned group membership information processing method. Furthermore, the present invention can also be implemented through hardware circuitry or hardware circuitry combined with software instructions. Therefore, implementing this embodiment is not limited to any specific combination of hardware circuitry and software.

[0080] Example 2

[0081] This embodiment proposes a global optimization configuration system for a transformer neutral point capacitor DC blocking device, such as... Figure 3 As shown, the system includes:

[0082] The initialization module is used to build an equivalent field-circuit coupling model and initialize the original bias current distribution of the power grid based on the equivalent field-circuit coupling model.

[0083] The game model construction module introduces the current imbalance of the whole network as an evaluation index of the current balance of the whole network based on the equivalent electrical distance of the equivalent impedance between nodes. Based on the position-Nash equilibrium of the team maximum equilibrium, a game model for optimizing the configuration of DC blocking devices is established, with the configuration of DC blocking devices in the power grid and the balance of current distribution of the whole network as the game participants.

[0084] The optimization module uses a multi-objective discrete particle swarm optimization algorithm to solve and optimize the equilibrium game model of the optimization configuration, and obtains the optimal configuration scheme of the DC blocking device of the near-grounding electrode power grid capacitor.

[0085] Example 3

[0086] This embodiment uses a multi-DC grounding electrode as an example to explain in detail the optimization configuration method proposed in Embodiment 1 above. Specifically, it includes: building an equivalent field-circuit coupling model; using the finite element method to calculate the DC current value at the neutral point of each transformer in the power grid; using a DC blocking device strategy based on power grid bias control and a DC blocking device strategy based on balanced current distribution across the entire power grid as game participants; and optimizing the configuration of the capacitor DC blocking device through the game between the two, using a multi-objective discrete particle swarm optimization algorithm to suppress the DC bias phenomenon of the transformer from a global perspective. The specific process is as follows:

[0087] The first step is to construct a model of the near-field power grid with dual DC grounding.

[0088] This dual-DC grounded near-area power grid includes 24 substations, of which 6 are 500kV substations and 18 are 220kV substations, numbered 1-24. A schematic diagram of its DC grounded near-area power grid structure is shown below. Figure 4 As shown.

[0089] If we establish a coordinate system with grounding electrode 1 as the origin, the positive X-axis direction from west to east, the positive Y-axis direction from south to north, and the positive Z-axis direction perpendicular to the Earth's surface towards the Earth's center, the geographical coordinates of the grounding electrode of the high-voltage direct current transmission system and the various substations of the nearby AC power grid are shown in Table 1.

[0090] Table 1 Geographical coordinates of each plant / site

[0091]

[0092] To accurately calculate the earth potential distribution, the soil should be divided into multiple layers, and the resistivity of each layer should be determined with reference to a geological model of the Earth's soil to simulate actual soil conditions. Simultaneously, an equivalent field-circuit coupling model should be established based on the power grid topology, grounding electrode characteristics, and physical parameters. Based on this model, the initial values ​​of the entire network current distribution are calculated using the finite element method.

[0093] When the grounding electrode current to ground is 5000A, the neutral point DC current values ​​of the transformers in the 24 substations near the multi-grounding electrode area are as follows: Figure 5 As shown, the ground potential distribution of the neutral points of the transformers in the 24 substations near the multi-grounded electrode area is as follows: Figure 6 As shown.

[0094] The second step is to build a game theory model.

[0095] The configuration of DC blocking devices in the power grid and the equilibrium of the current distribution across the entire grid are considered as game participants. The game strategy consists of the DC blocking strategy sets of each player. The two players change their game strategies to achieve the optimal state of the power grid. The strategy set I1 generated under the guidance of installing DC blocking devices in the power grid is obtained by constraining the neutral point current of the transformer.

[0096]

[0097] In the formula, This represents the DC current matrix under the i-th DC blocking strategy, considering the condition that DC bias does not exceed the limit.

[0098] Considering that future changes in the power grid topology and grid planning will cause uneven fluctuations in the neutral point current of the original network transformers, and that the more uniform the DC current flowing through the stations in the original grid structure is, the smaller the probability that the neutral point current of the original network transformers will change significantly after the network topology changes.

[0099] The necessary and sufficient condition for achieving current balance:

[0100] m1i1=m2i2 ＝ …=m N i N (9)

[0101] In the formula, i i Let m represent the neutral point current of the transformer at station i. i The coefficient representing the inverse current at station i.

[0102] Therefore, in order to evaluate the balance of the power grid current, this method introduces the concept of the current imbalance of the entire grid. By using the relationship between impedance and the distance between the station and the grounding electrode, the inverse current coefficient of each station is solved, and an evaluation index for the current imbalance of the power grid distribution is proposed, which is represented by ε.

[0103] Therefore, the set of DC blocking strategies I2 generated under the guidance of balanced current distribution across the entire network is:

[0104]

[0105] In the formula, This is the DC current matrix under the i-th DC blocking strategy, considering the overall network current balance.

[0106] The third step is to solve and optimize the configuration.

[0107] The multi-objective discrete particle swarm optimization algorithm is used to optimize the configuration of the capacitor DC blocking device in the near-field power grid of the grounding electrode. The optimization objectives are to minimize the number of capacitor DC blocking devices and achieve current balance across the entire network.

[0108] In the multi-objective discrete particle swarm optimization algorithm, a dynamic adjustment strategy of inertial weight based on position similarity is adopted, and a particle update strategy is introduced to balance the global search capability and local exploitation capability of particles, thereby enhancing the convergence speed and computational accuracy of the algorithm.

[0109] The decision-making strategy for obtaining the optimal solution is to minimize the number of capacitor DC blocking devices installed, reduce investment costs, and minimize the current imbalance of the entire network, while ensuring that the DC current value of the neutral point of the main transformer in each power plant does not exceed the limit. This will reduce the probability of a significant change in the neutral point current of the transformer in the original network after the topology of the power grid changes in the future.

[0110] The neutral point DC current limit for the transformer near the grounding electrode is 5A, and the fluctuation of the neutral point current between the transformers at each site is set between -5A and 5A to achieve a balanced current state across the entire network. An optimization algorithm is used to solve the game theory model to obtain the magnitude of the neutral point DC current of the main transformers at each substation after the optimized configuration of the capacitor DC blocking device, as shown below. Figure 7 As shown, the optimization algorithm outputs the network imbalance degree in each iteration, as follows: Figure 8 As shown, when the DC current limit at the transformer neutral point is 5A, the optimal installation sites for the capacitor DC blocking device are sites 1, 6, 9, and 21. Figure 8 The optimization results show that the calculation results can converge quickly and reliably during the process of optimizing the configuration of the capacitor DC blocking device using the multi-objective discrete particle swarm optimization algorithm.

[0111] The embodiments show that the method proposed in this invention, from a global perspective, takes into account the impact of the overall network current balance on power grid planning and transformer neutral point current, and obtains an optimized strategy for DC blocking devices that simultaneously ensures the economy of DC blocking management and the reliability of power system grid planning through the game between the two, thereby improving the convergence accuracy and speed of the algorithm.

[0112] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for globally optimizing the configuration of a transformer neutral point capacitor DC blocking device, characterized in that, include: An equivalent field-circuit coupling model is constructed, and the original bias current distribution of the power grid is initialized based on the equivalent field-circuit coupling model. Based on the equivalent electrical distance of the equivalent impedance between power grid nodes, the current imbalance of the whole network is introduced as an evaluation index of the current balance of the whole network. A game model for the optimal configuration of capacitor DC blocking device is established with the configuration of DC blocking device and the balance of current distribution of the whole network as the game participants. The optimal configuration equilibrium game model is solved and optimized using a multi-objective discrete particle swarm optimization algorithm to obtain the optimal configuration scheme of the DC blocking device of the near-grounding electrode power grid capacitor; the evaluation index of the whole network current balance is also used. Represented as: ； ； In the formula, Indicates the station number. Indicates the number of sites. Indicates the first The current flowing through the neutral point of the station transformer This represents the equivalent electrical distance between the i-th site and the grounding electrode, used to measure the degree of coupling between the nodes and the changes in the bias current amplitude of the grounding electrode within the system. This indicates the number of transformers at the i-th site. and These represent the self-impedances of the two grounding electrodes, This represents the mutual impedance between the two grounding electrodes. This represents the self-impedance of the i-th station. and Let represent the mutual impedances between the i-th station and the two grounding electrodes, respectively. This represents the tangent of the angle formed between the i-th station and the two grounding electrodes.

2. The global optimization configuration method for a transformer neutral point capacitor DC blocking device according to claim 1, characterized in that, With the optimization objectives of achieving grid-wide current balance and minimizing the number of capacitor DC blocking devices, a game model for optimizing the configuration of capacitor DC blocking devices is established based on the team-maximized equilibrium Position-Nash equilibrium. The model uses grid DC blocking device configuration and grid-wide current distribution balance as game participants.

3. The global optimization configuration method for a transformer neutral point capacitor DC blocking device according to claim 1, characterized in that, The optimization configuration equilibrium game model is solved and optimized using a multi-objective discrete particle swarm optimization algorithm, specifically including: The neutral point DC current matrix under each strategy is calculated, and the number of DC blocking devices and the current balance coefficient under each strategy are obtained. Different strategies are selected, their evaluation indicators are calculated, and the magnitudes of the indicators under different states are compared. A dynamic inertia weight adjustment strategy based on position similarity is introduced. In each iteration, the inertia weight of the particle after the iteration is dynamically calculated based on the position similarity between the current particle vector and the optimal particle vector. Record the inertia weight and evaluation index value for each iteration; A position similarity particle position update strategy based on random correction coefficients is introduced to make particles move closer to the position of the optimal particle. This is used for secondary updates to correct particles that do not meet the constraints, while particles that meet the constraints after the initial update are saved. If the evaluation index value calculated in the (T+1)th time is better than the result calculated in the Tth time, the result calculated in the (T+1)th time is recorded.

4. The global optimization configuration method for a transformer neutral point capacitor DC blocking device according to claim 3, characterized in that, The dynamic inertia weight adjustment strategy based on position similarity is as follows: ； ； In the formula, Indicates similarity. This represents the particle vector in the current k-th iteration. The optimal particle vector is... This represents the inertial weight of particle i in the k-th iteration. and These represent the upper and lower limits of the inertia weight, respectively; when A higher similarity value indicates a higher similarity between particles. When the similarity is greater than the preset value, it means that the particle is close to the optimal particle in the population, and the inertia weight should be reduced. When the similarity is less than the preset value, it means that the particle is far away from the optimal particle in the population, and the inertia weight should be increased.

5. The global optimization configuration method for a transformer neutral point capacitor DC blocking device according to claim 4, characterized in that, The position update strategy for position similarity particles based on random correction coefficients is as follows: ； ； In the formula, Let D represent the position of particle i after the k-th iteration, and let D represent the random position correction coefficient. This indicates the maximum number of iterations.

6. A global optimization configuration method for a transformer neutral point capacitor DC blocking device according to any one of claims 1-5, characterized in that, An equivalent field-circuit coupling model is constructed, and the original bias current distribution of the power grid is initialized based on the equivalent field-circuit coupling model, specifically as follows: An equivalent field-circuit coupling model is established based on the power grid line topology, grounding electrode characteristics, physical parameters, and local soil characteristics. Based on the equivalent field-circuit coupling model, the initial value of the current distribution of the entire network is calculated after adding a capacitor DC blocking device at the neutral point of the over-limit transformer.

7. A global optimization configuration system for a transformer neutral point capacitor DC blocking device, characterized in that, include: The initialization module is used to build an equivalent field-circuit coupling model and initialize the original bias current distribution of the power grid based on the equivalent field-circuit coupling model. The game model construction module introduces the current imbalance of the whole network as an evaluation index of the current balance of the whole network based on the equivalent electrical distance of the equivalent impedance between nodes. It establishes a game model for the optimal configuration balance of capacitor DC blocking device with the configuration of DC blocking device and the balance of current distribution of the whole network as game participants. The optimization module uses a multi-objective discrete particle swarm optimization algorithm to solve and optimize the equilibrium game model of the optimization configuration, and obtains the optimal configuration scheme of the DC blocking device of the near-grounding pole power grid capacitor. The evaluation index of the whole network current balance Represented as: ； ； In the formula, Indicates the station number. Indicates the number of sites. Indicates the first The current flowing through the neutral point of the station transformer This represents the equivalent electrical distance between the i-th site and the grounding electrode, used to measure the degree of coupling between the nodes and the changes in the bias current amplitude of the grounding electrode within the system. This indicates the number of transformers at the i-th site. and These represent the self-impedances of the two grounding electrodes, This represents the mutual impedance between the two grounding electrodes. This represents the self-impedance of the i-th station. and Let represent the mutual impedances between the i-th station and the two grounding electrodes, respectively. This represents the tangent of the angle formed between the i-th station and the two grounding electrodes.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-6.

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