Method and system for treating three-phase imbalance of power system

Abstract

The invention belongs to the related technical field of power system operation control, and provides a power system three-phase imbalance treatment method and system in order to solve the problems that an existing three-phase imbalance treatment method is difficult to adapt to real-time changes, neglects key constraint conditions and the like. Comparing the three-phase unbalance degree with a three-phase unbalance degree dynamic threshold value to judge whether the adjusting equipment needs to be optimized and adjusted or not; the three-phase unbalance degree dynamic threshold value is determined by the real-time working condition of the power system; minimizing the unbalance degree of the negative sequence voltage of the power system as a target, combining with a multi-constraint condition, and adopting a particle swarm optimization algorithm to optimize and adjust the active power output and the reactive power output of the equipment; according to the method, the three-phase imbalance treatment accuracy, adaptability and engineering practicability are effectively improved, and safe, high-quality and economical operation of a power grid is guaranteed.

Description

Technical Field

[0001] This invention belongs to the technical field of power system operation and control, and particularly relates to a method and system for managing three-phase imbalance in power systems. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the large-scale integration of distributed power sources and single-phase electrical loads, the problem of three-phase current and voltage imbalance in the power system has become increasingly prominent. Three-phase imbalance not only leads to increased copper losses in distribution transformers and increased line losses, but may also cause problems such as malfunctions of relay protection devices and abnormal operation of sensitive electrical equipment, seriously threatening the safe and economical operation of the power system and the reliability of power supply.

[0004] Current three-phase imbalance mitigation technologies still have many shortcomings that urgently need to be addressed. Traditional mitigation methods often focus solely on minimizing the imbalance, neglecting key constraints such as the upper limit of regulating equipment capacity, node voltage exceeding limits, and branch power overload. This can easily lead to overload damage to regulating equipment or a significant increase in system operational risks. Furthermore, most technologies rely on manual inspections to determine the imbalance state or use fixed-cycle calculations and adjustments, failing to adapt to load fluctuations and changes in distributed power output in real time. This results in delayed mitigation responses and difficulty in meeting the real-time operational needs of the system. In addition, existing technologies often focus on local optimization of single algorithms without deeply integrating core calculation formulas with technical steps, making the algorithms prone to logical inconsistencies during practical implementation and significantly reducing their practicality. Finally, the industry commonly uses fixed imbalance thresholds, such as the 2% benchmark value specified in GB / T 15543-2019, without dynamic adjustments based on real-time system operating conditions, which can easily lead to under-regulation or over-regulation.

[0005] In view of the shortcomings of the existing technologies, there is an urgent need for a three-phase imbalance control method that is tailored to the specific circumstances, so as to achieve accurate, safe and efficient control of three-phase imbalance. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, this invention provides a method and system for managing three-phase imbalance in power systems, which effectively improves the accuracy, adaptability, and engineering applicability of three-phase imbalance management, and ensures the safe, high-quality, and economical operation of the power grid.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for mitigating three-phase imbalance in a power system, comprising: The negative sequence voltage imbalance is calculated based on the basic parameters of the current power system, and the negative sequence voltage imbalance is compared with the three-phase imbalance dynamic threshold to determine whether the regulating equipment needs to be optimized; wherein, the three-phase imbalance dynamic threshold is determined by the real-time operating conditions of the power system. If the regulating equipment needs to be optimized, the goal is to minimize the negative sequence voltage imbalance of the power system. Combined with multiple constraints, the particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating equipment. Based on the optimized active and reactive power output of the regulating equipment, the optimized node voltage and negative sequence voltage imbalance are recalculated to verify whether the dynamic threshold and multiple constraints of the three-phase imbalance are simultaneously satisfied. If the optimization results are satisfied simultaneously, the three-phase imbalance management process of the power system is completed.

[0008] Secondly, the present invention provides a three-phase imbalance mitigation system for power systems, comprising: The comparison module is configured to: calculate the negative sequence voltage imbalance based on the basic parameters of the current power system, and compare the negative sequence voltage imbalance with the three-phase imbalance dynamic threshold to determine whether the regulating equipment needs to be optimized; wherein, the three-phase imbalance dynamic threshold is determined by the real-time operating conditions of the power system; The optimization module is configured to: if the regulating equipment needs to be optimized, then with the goal of minimizing the negative sequence voltage imbalance of the power system, and in combination with multiple constraints, use the particle swarm optimization algorithm to optimize the active and reactive power output of the regulating equipment; The governance module is configured to: recalculate the optimized node voltage and negative sequence voltage imbalance based on the optimized active and reactive power output of the regulating equipment, verify whether the dynamic threshold of three-phase imbalance and multiple constraints are met simultaneously, and complete the three-phase imbalance governance process of the power system if the optimization results are met simultaneously.

[0009] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0010] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0011] The above one or more technical solutions have the following beneficial effects: In this invention, the negative sequence voltage imbalance is calculated to determine whether the regulating equipment needs to be optimized. Then, with the goal of minimizing the negative sequence voltage imbalance, a particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating equipment. The optimization results are then checked in a closed loop to ensure that they simultaneously meet the dynamic threshold of three-phase imbalance and all safety constraints. This invention effectively improves the accuracy, adaptability, and engineering practicality of three-phase imbalance management, ensuring the safe, high-quality, and economical operation of the power grid.

[0012] In this invention, three types of constraints—equipment capacity, node voltage, and branch power—are treated as multiple constraints. Combined with a penalty function, these constraints ensure that the output of the regulating equipment, node voltage, and branch power are all within safe boundaries. This effectively avoids problems such as equipment overload damage and branch power overload caused by neglecting constraints in traditional technologies. It significantly reduces the risk of power system failures, extends the service life of the regulating equipment, and improves the overall reliability of power supply.

[0013] This invention introduces a dynamic threshold calculation mechanism for three-phase imbalance based on load factor and distributed generation output fluctuations. Compared to a fixed threshold, this reduces the number of invalid equipment actions, thereby lowering equipment maintenance costs and energy consumption. Furthermore, through parameter configuration, it can quickly adapt to different scenarios such as distribution networks, microgrids, and hybrid grids containing distributed generation, significantly improving the technology's versatility and practicality, reducing the cost of adapting to different scenarios, and providing a flexible and economical solution for managing three-phase imbalance in power systems.

[0014] In this invention, an improved particle swarm optimization algorithm is introduced, which incorporates a two-factor adaptive adjustment of inertial weights and a constraint penalty function that integrates the iterative process and particle fitness. Compared with the traditional particle swarm optimization algorithm, the convergence speed is improved by 30%, and the optimal adjustment parameters can be found quickly, so that the system imbalance can be reduced to below the dynamic threshold of the three-phase imbalance. The treatment response delay is controlled within 50ms, which effectively solves the problem of insufficient treatment accuracy of traditional technology.

[0015] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0017] Figure 1 This is a flowchart of the three-phase imbalance mitigation method for power systems in Embodiment 1 of the present invention; Figure 2This is a diagram showing the effect of the three-phase imbalance problem mitigation method based on this disclosure on a testing system in Embodiment 1 of the present invention. Detailed Implementation

[0018] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0020] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0021] Example 1 This embodiment discloses a method for managing three-phase imbalance in a power system. First, the basic parameters of the current power system are read, and multiple constraints are defined. Second, based on the basic parameters, power flow calculation is performed using the forward-backward substitution method to obtain the three-phase voltage phasors of each node. Then, the three-sequence components of the node three-phase voltage phasors are separated using the symmetrical component method, and the three-phase imbalance degree of the current power system is calculated. The negative-sequence voltage imbalance degree is used as an evaluation index, and the dynamic threshold of the three-phase imbalance degree is calculated based on the real-time operating conditions of the power system. Finally, with the goal of minimizing the negative-sequence voltage imbalance degree of the power system, combined with multiple constraints, an improved particle swarm optimization algorithm is used to optimize the active and reactive power outputs of the regulating equipment. The forward-backward substitution method is used to calculate the optimized node voltages and negative-sequence voltage imbalance degree of the power system, and it is verified whether the imbalance degree conditions and multiple constraints are met. If both conditions are met, the imbalance management ends; otherwise, the parameters of the particle swarm optimization algorithm are adjusted, and the iteration continues.

[0022] Combination Figure 1 This embodiment provides a detailed description of a three-phase imbalance mitigation method for power systems, specifically including: S100: Reads the basic parameters and constraint boundaries of the power system, specifically including: voltage level U N Node type, branch parameters, impedance Z line =R+jX, where R represents the resistance of the branch and X represents the reactance of the branch, in ohms (Ω); maximum current carrying capacity. I k,max Adjusting the active power adjustment range of equipment parameters [ P i,min , P i,max Reactive power adjustment rangeQ i,min , Q i,max This provides a data foundation for subsequent calculations.

[0023] S101: Define three types of core constraints as multiple constraints for subsequent optimization: Equipment capacity constraint: Ensure that the output of the regulating equipment does not exceed the rated range.

[0024]

[0025] in, P i and Q i The first i The active and reactive power outputs of the regulating equipment; P i,min For the first i Minimum active power output of the regulating equipment. P i,max For the first i The maximum active power output of the regulating equipment; Q i,min For the first i Minimum reactive power output of the regulating equipment. Q i,max For the first i The maximum reactive power output of the regulating equipment.

[0026] Node voltage constraints: Ensure that the voltage of each node is within the allowable deviation range.

[0027]

[0028] in, U j For the first j The voltage amplitude at each node, U j,min and U j,max For nodes j The upper and lower limits of the voltage.

[0029] Branch power constraint: Ensure that the apparent power of the branch does not exceed the rated power corresponding to the maximum current carrying capacity.

[0030]

[0031] in, Let the apparent power of the k-th branch be... The rated voltage of branch k; This represents the active power of the k-th branch. This represents the reactive power of the k-th branch; This represents the maximum current carrying capacity of the k-th branch. Let be the maximum apparent power of the k-th branch.

[0032] S200: Using the forward-backward substitution method, based on the current power system node power and line parameters, the power flow of the power grid is solved through forward-backward substitution calculation.

[0033] The following is an explanation of the principle of the forward substitution method: Given the voltage at the first node = U s ∠α, U s The current node voltage amplitude is given by α, which is the corresponding voltage phase angle; the load node power is given by α. S L = P L + jQ L , P L and Q L This represents the active and reactive power of the current node.

[0034] First, calculate the branch current. Then, derive the total power of the branch:

[0035] Where * denotes the complex conjugate operation; To calculate the total apparent power of a branch, the calculation needs to be performed segment by segment from the load end to the power supply end until all branches are covered.

[0036] Based on branch impedance Z line = R + jX Branch current obtained by forward deduction Correct the terminal node voltage segment by segment from the power supply end to the load end:

[0037] in, For the terminal node voltage phasor.

[0038] Repeat the forward and backward iteration process until the node voltage difference between two adjacent iterations is less than the convergence threshold, which is set here as follows: ; U j ( t +1) indicates the first t The node voltage obtained by +1 iteration.

[0039] S300: Based on the three-phase voltage phasors of each node obtained from S200, the positive-sequence, negative-sequence, and zero-sequence components are separated using the symmetrical component method. Then, the negative-sequence voltage imbalance of the power system is calculated to determine whether regulating equipment needs to address it. The following is an explanation of the principle of the symmetrical component method: Transform the matrix by symmetric components T The three-phase voltage phasors at the nodes are decomposed into positive-sequence, negative-sequence, and zero-sequence components:

[0040] in, Rotation factor, positive order component Reflects the symmetrical operating state of the power system, negative sequence component It is the core factor causing three-phase imbalance in electricity; , , Represents the voltage at the three-phase nodes a, b, and c; Represents the zero-order component.

[0041] S301: Employs negative sequence voltage imbalance The formula for the evaluation indicator is as follows:

[0042] in, , express The values ​​of the positive and negative order components; those marked with a dot refer to phasors.

[0043] Meanwhile, based on real-time operating conditions of the power system, such as load factor l load Distributed power generation output fluctuation coefficient d gen Calculate the dynamic threshold of three-phase imbalance to avoid the rigidity problem of fixed thresholds.

[0044] Three-phase imbalance dynamic threshold The calculation is as follows:

[0045] in, l load ( t )= P load ( t ) / P load,max , d gen ( t )=|Δ P gen ( t )| / Pgen,max , k 1 = 0.2, k 2 = 0.3 is the adjustment coefficient. If inbalance > threshold ( t If the result is positive, proceed to the next optimization step; otherwise, no adjustment is needed. P load ( t () represents the real-time total active load of the power system at time t; P load,max Indicates the maximum active load involved in the power system; Δ P gen ( t ) represents the real-time output fluctuation of the distributed power source at time t, that is, the absolute value of the difference between the actual output at this time and the output at the previous time. P gen,max hreshold indicates the rated maximum output of the distributed power source. base This refers to the three-phase unbalance threshold in the GB / T 15543-2019 standard, which is 2% in the standard and is used here as the benchmark for the dynamic threshold.

[0046] This embodiment changes the evaluation threshold from a fixed value to a variable that is dynamically related to the real-time operating conditions of the system. This overcomes the shortcomings of traditional fixed thresholds that cannot adapt to dynamic changes in the system, and provides more accurate and reasonable triggering and termination criteria for subsequent optimization control.

[0047] S400: With the goal of minimizing the negative sequence voltage imbalance of the power system, and combined with the multiple constraints of S101, an improved particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating equipment.

[0048] S401: Construct the objective function, with the core objective being to reduce the negative sequence voltage imbalance.

[0049]

[0050] in, U 1( PQ )and U 2( PQ () represents the positive sequence voltage amplitude and negative sequence voltage amplitude of the optimized power system.

[0051] S402: Implements an improved particle swarm optimization algorithm, introducing a two-factor adaptive adjustment of inertial weights that integrates iterative process and particle fitness, along with a constraint penalty function, to ensure that the optimization process satisfies multiple constraints and converges quickly.

[0052] The improved particle swarm optimization algorithm still relies on velocity and position updates as its core iterative logic, but now incorporates inertia weights. oh The calculation method has been improved, and the formulas for updating velocity and position are as follows: Particle velocity update formula:

[0053] Particle position update formula:

[0054] in, c 1= c 2=2 is the learning factor. r 1, r 2∈[0,1] is a uniformly distributed random number. pbest i For particles i The best historical position gbest The globally optimal position; For the i-th particle Particle velocity at the next iteration; For the first i The particle in the first d Dimensional space Particle velocity at the next iteration; The i-th particle is in the... d Dimensional space Position at the next iteration For the first t Inertia weights in the next iteration.

[0055] Unlike traditional linearly decreasing inertia weights, this embodiment adjusts the weights by fusing the iterative process and particle fitness. oh The specific formula and logic are as follows: Inertia weight base value :

[0056] in, oh max =0.9, representing the initial iteration weight; oh min =0.4, representing the weight for the maximum number of iterations. t max =100 indicates the maximum number of iterations. When the number of nodes exceeds 50, it can be set to 150.

[0057] Inertia weight correction: Introducing fitness bias coefficient The basic value of the inertial weight is corrected to achieve particle differentiation adjustment.

[0058]

[0059] in, Represents particles i No.t The final inertia weight for each iteration is calculated individually for each particle. Represents particles i No. t The fitness of the next iteration, i.e., the value of the objective function; The particle swarm is represented by the first t The global optimal fitness in the next iteration; k =0.8, representing the sensitivity coefficient, used to control the degree of influence of bias on the weights; exp(·) is used to ensure ∈(0,1], to avoid weights going out of range.

[0060] Core adjustment logic for inertia weight: When the particle fitness is close to the global optimum ≈1, ≈ Maintain the current step size; when the particle fitness deviates significantly from the global optimum, , Decrease, reduce the step size and converge towards the optimal direction; early stage of iteration Large (close to 0.9), ensuring global exploration; later stages Small (close to 0.4), focusing on local search.

[0061] For particles that exceed the constraint range, a penalty term is introduced to modify the objective function, forcing the particles to converge towards the constrained region:

[0062] in, k penalty =100 is the penalty coefficient, ensuring that the objective function value increases significantly when the constraint is exceeded, thus avoiding invalid optimization results. n represents the number of particles that exceed the constraint range; P i and Q i The first i The active and reactive power outputs of the regulating equipment; P i,min For the first i Minimum active power output of the regulating equipment. P i,max For the first i The maximum active power output of the regulating equipment; Q i,min For the first i Minimum reactive power output of the regulating equipment. Q i,max For the first i The maximum reactive power output of the regulating equipment.

[0063] Iterate until the objective function converges, then take... The optimized active and reactive power outputs of the regulating equipment are output.

[0064] This embodiment introduces inertial weights synchronously into the standard PSO (Particle Swarm Optimization) algorithm. oh The adaptive adjustment and penalty function for solution space out-of-bounds behavior achieve a dynamic balance between emphasizing global exploration in the early stages of optimization and focusing on fine-grained local search in the later stages. Simultaneously, the penalty function is integrated into the objective function, effectively guiding the particle swarm to search within the feasible region that satisfies multiple constraints such as device capacity, node voltage, and branch power, significantly improving the algorithm's convergence reliability and the practical feasibility of the optimization results.

[0065] S403: Combine the optimal active and reactive power outputs obtained in S402 with the node active and reactive power from the previous iteration, substitute them into the forward-backward power flow calculation, recalculate the optimized node voltage, branch power, and negative sequence voltage imbalance, and verify whether the imbalance condition and multiple constraint conditions are met. If the above conditions are met simultaneously, the governance ends; if not, continue the iteration.

[0066] The first node voltage calculation does not require regulating equipment; it calculates the node voltage of the power system that is not currently connected to regulating equipment. In each subsequent iteration, the output of the optimal regulating equipment is obtained and combined with the node power obtained in the first step of the current iteration to obtain the new node power. Then, the node voltage and unbalance of the next iteration are obtained through power flow calculation.

[0067] Figure 2 This diagram illustrates the effect of three-phase imbalance problem mitigation based on this disclosure in a test system, as provided in this embodiment. As can be seen from the data in the diagram, this embodiment achieves precise control of system imbalance through accurate data support from forward-backward power flow calculations, optimal output optimization using an improved particle swarm optimization algorithm, and adaptive judgment of dynamic thresholds.

[0068] This embodiment combines safety, efficiency, and adaptability. In terms of safety, multi-constraint collaborative optimization and constraint penalty mechanisms avoid risks such as equipment overload and voltage exceeding limits, ensuring the stable operation of the power system and equipment. In terms of efficiency, relying on the accurate data support of forward-backward power flow calculation and the rapid convergence characteristics of the improved particle swarm optimization algorithm, it achieves accurate assessment and rapid management of imbalances, with timely and high-precision management response. In terms of adaptability and economy, the dynamic threshold mechanism for three-phase imbalance reduces ineffective equipment actions and lowers operation and maintenance costs. Flexible parameter configuration can adapt to various power grid scenarios, significantly improving the practical and economic value of the technology, effectively compensating for the shortcomings of existing three-phase imbalance management technologies, and providing strong support for the safe and economical operation of the power system.

[0069] Example 2 The purpose of this embodiment is to provide a three-phase imbalance mitigation system for power systems, including: The comparison module is configured to: calculate the negative sequence voltage imbalance based on the basic parameters of the current power system, and compare the negative sequence voltage imbalance with the three-phase imbalance dynamic threshold to determine whether the regulating equipment needs to be optimized; wherein, the three-phase imbalance dynamic threshold is determined by the real-time operating conditions of the power system; The optimization module is configured to: if the regulating equipment needs to be optimized, then with the goal of minimizing the negative sequence voltage imbalance of the power system, and in combination with multiple constraints, use the particle swarm optimization algorithm to optimize the active and reactive power output of the regulating equipment; The governance module is configured to: recalculate the optimized node voltage and negative sequence voltage imbalance based on the optimized active and reactive power output of the regulating equipment, verify whether the dynamic threshold of three-phase imbalance and multiple constraints are met simultaneously, and complete the three-phase imbalance governance process of the power system if the optimization results are met simultaneously.

[0070] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0071] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0072] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0073] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0074] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0075] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0076] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for mitigating three-phase imbalance in a power system, characterized in that, include: The negative sequence voltage imbalance is calculated based on the basic parameters of the current power system, and the negative sequence voltage imbalance is compared with the three-phase imbalance dynamic threshold to determine whether the regulating equipment needs to be optimized; wherein, the three-phase imbalance dynamic threshold is determined by the real-time operating conditions of the power system. If the regulating equipment needs to be optimized, the goal is to minimize the negative sequence voltage imbalance of the power system. Combined with multiple constraints, the particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating equipment. Based on the optimized active and reactive power output of the regulating equipment, the optimized node voltage and negative sequence voltage imbalance are recalculated to verify whether the dynamic threshold and multiple constraints of the three-phase imbalance are simultaneously satisfied. If the optimization results are satisfied simultaneously, the three-phase imbalance management process of the power system is completed.

2. The method for mitigating three-phase imbalance in a power system as described in claim 1, characterized in that, The negative sequence voltage imbalance is calculated based on the current power system's fundamental parameters, specifically as follows: Based on the fundamental parameters of the current power system, the forward-backward substitution method is used to calculate the power flow and obtain the three-phase voltage phasors of each node. Based on the three-phase voltage phasors of each node, the three-sequence components are separated using the symmetrical component method, and the negative-sequence voltage unbalance is calculated.

3. The method for mitigating three-phase imbalance in a power system as described in claim 2, characterized in that, Based on the three-phase voltage phasors at each node, the three-sequence components are separated using the symmetrical component method, and the negative-sequence voltage unbalance is calculated as follows: The three-phase voltage at each node is decomposed into positive-sequence, negative-sequence, and zero-sequence components. The negative sequence voltage imbalance is calculated based on the positive and negative sequence components.

4. The method for mitigating three-phase imbalance in a power system as described in claim 1, characterized in that, An improved particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating equipment. The improved particle swarm optimization algorithm is as follows: during the particle velocity update process, a fitness deviation coefficient is introduced to correct the inertia weight, so as to realize the differentiated adjustment of particles; a penalty function is introduced for particles that exceed the constraint range to correct the objective function, so that the particles converge to the constrained region.

5. The method for mitigating three-phase imbalance in a power system as described in claim 4, characterized in that, A fitness bias coefficient is introduced to correct the inertia weight, specifically as follows: ； ； in, Represents particles i No. t The final inertia weight of the next iteration; Represents particles i No. t Fitness of the next iteration The particle swarm is represented by the first t The global optimal fitness in the next iteration; k The sensitivity coefficient is exp(·); exp(·) is used to ensure ∈(0,1], For the first t The base value of the inertia weight for the next iteration.

6. The method for mitigating three-phase imbalance in a power system as described in claim 1, characterized in that, The multiple constraints include: equipment capacity constraints, node voltage constraints, and branch power constraints.

7. The method for mitigating three-phase imbalance in a power system as described in claim 1, characterized in that, The calculation of the dynamic threshold of the three-phase unbalance is as follows: ； λ load ( t )= P load ( t ) / P load,max ； δ gen ( t )=|D P gen ( t )| / P gen,max ; in, k 1. k 2 represents the adjustment coefficient, P load ( t () represents the real-time total active load of the power system at time t; P load,max Indicates the maximum active load of the power system; Δ P gen ( t () represents the real-time power output fluctuation of the distributed power source at time t; P gen,max This indicates the rated maximum output of the distributed power source; threshold base As the baseline threshold, This represents the dynamic threshold of the three-phase imbalance at time t.

8. A three-phase imbalance mitigation system for power systems, characterized in that, include: The comparison module is configured to: calculate the negative sequence voltage imbalance based on the basic parameters of the current power system, and compare the negative sequence voltage imbalance with the three-phase imbalance dynamic threshold to determine whether the regulating equipment needs to be optimized; wherein, the three-phase imbalance dynamic threshold is determined by the real-time operating conditions of the power system; The optimization module is configured to: if the regulating equipment needs to be optimized, then with the goal of minimizing the negative sequence voltage imbalance of the power system, and in combination with multiple constraints, use the particle swarm optimization algorithm to optimize the active and reactive power output of the regulating equipment; The governance module is configured to: recalculate the optimized node voltage and negative sequence voltage imbalance based on the optimized active and reactive power output of the regulating equipment, verify whether the dynamic threshold of three-phase imbalance and multiple constraints are met simultaneously, and complete the three-phase imbalance governance process of the power system if the optimization results are met simultaneously.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-7.

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