A method and system for three-phase imbalance treatment of a power system

Abstract

The present application belongs to the technical field of power system operation control, in order to solve the problems of existing three-phase imbalance treatment method, such as difficult to adapt to real-time changes, ignoring key constraint conditions, etc., a power system three-phase imbalance treatment method and system are proposed, the negative sequence voltage imbalance degree is calculated, and compared with the three-phase imbalance dynamic threshold to determine whether the regulating equipment needs to be optimized and adjusted; the three-phase imbalance dynamic threshold is determined by the real-time working condition of the power system; taking minimizing the power system negative sequence voltage imbalance degree as the target, combining with multiple constraint conditions, the particle swarm optimization algorithm is used to optimize the active power output and reactive power output of the regulating equipment; and the optimization result is closed loop checked to ensure that it meets the imbalance condition and multiple constraint conditions at the same time, the method effectively improves the accuracy, adaptability and engineering practicability of three-phase imbalance treatment, and guarantees the safe, high-quality and economic operation of the power grid.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system operation control, and particularly relates to a power system three-phase imbalance treatment method and system. BACKGROUND

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

[0003] With the large-scale access of distributed power sources and single-phase power consumption loads, the three-phase current and voltage imbalance of the power system is increasingly significant. The three-phase imbalance not only causes the increase of copper loss of distribution transformers and the rise of line loss, but also may cause the misoperation of relay protection devices and the abnormal operation of sensitive power consumption equipment, which seriously threatens the safe and economic operation of the power system and the power supply reliability.

[0004] The current existing three-phase imbalance treatment technology still has many defects to be solved. The traditional treatment method mostly only focuses on the minimization of the imbalance degree as a single target, often ignores the upper limit of the capacity of the regulating device, the over-limit of the node voltage, the overload of the branch power, and other key constraint conditions, and is easy to cause the damage of the regulating device due to overloading or the significant increase of the system operation risk; at the same time, most of the technologies rely on manual inspection to judge the imbalance state, or use fixed cycles for calculation and adjustment, which cannot adapt to the load fluctuation and the change of the distributed power output in real time, resulting in lagging response of the treatment and difficulty in meeting the real-time operation demand of the system; in addition, the existing technologies mostly focus on the local optimization of a single algorithm, and do not deeply combine the core calculation formula and the technical steps, so that the algorithm is easy to have logical discontinuity when actually landing, and the practicality is greatly reduced; finally, the industry generally uses a fixed imbalance degree threshold, such as the 2% reference value specified in GB / T 15543-2019, which is not dynamically adjusted combined with the real-time operation condition of the system, and is easy to cause under-regulation or over-regulation.

[0005] In view of the above deficiencies of the existing technologies, it is urgent to provide a three-phase imbalance treatment method combined with the actual situation to realize the accurate, safe and efficient treatment of the three-phase imbalance. SUMMARY

[0006] In order to overcome the deficiencies of the above-mentioned existing technologies, the present application provides a power system three-phase imbalance treatment method and system, which effectively improves the accuracy, adaptability and engineering practicability of the three-phase imbalance treatment, and guarantees the safe, high-quality and economic operation of the power grid.

[0007] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] In a first aspect, the present application provides a power system three-phase imbalance treatment method, comprising:

[0009] The negative sequence voltage unbalance degree is calculated based on basic parameters of the current power system, and the negative sequence voltage unbalance degree is compared with a three-phase unbalance degree dynamic threshold to determine whether the regulating device needs to be optimized and regulated; wherein the three-phase unbalance degree dynamic threshold is determined by real-time working conditions of the power system.

[0010] If the regulating device needs to be optimized and regulated, the active power output and the reactive power output of the regulating device are optimized by using a particle swarm optimization algorithm in combination with multiple constraint conditions, with the objective of minimizing the negative sequence voltage unbalance degree of the power system.

[0011] Based on the active power output and the reactive power output of the regulating device after optimization, the optimized node voltage and the negative sequence voltage unbalance degree are recalculated to verify whether the three-phase unbalance degree dynamic threshold and the multiple constraint conditions are simultaneously satisfied, and if the optimization results are simultaneously satisfied, the three-phase unbalance treatment process of the power system is completed.

[0012] In the second aspect, the present application provides a three-phase unbalance treatment system of a power system, comprising:

[0013] The comparison module is configured to calculate a negative sequence voltage unbalance degree based on basic parameters of the current power system, and compare the negative sequence voltage unbalance degree with a three-phase unbalance degree dynamic threshold to determine whether the regulating device needs to be optimized and regulated; wherein the three-phase unbalance degree dynamic threshold is determined by real-time working conditions of the power system.

[0014] The optimization module is configured to, if the regulating device needs to be optimized and regulated, optimize the active power output and the reactive power output of the regulating device by using a particle swarm optimization algorithm in combination with multiple constraint conditions, with the objective of minimizing the negative sequence voltage unbalance degree of the power system.

[0015] The treatment module is configured to, based on the active power output and the reactive power output of the regulating device after optimization, recalculate the optimized node voltage and the negative sequence voltage unbalance degree to verify whether the three-phase unbalance degree dynamic threshold and the multiple constraint conditions are simultaneously satisfied, and if the optimization results are simultaneously satisfied, complete the three-phase unbalance treatment process of the power system.

[0016] In the third aspect, the present application provides an electronic device, comprising a memory and a processor, and computer instructions stored in the memory and running on the processor, when the computer instructions are run by the processor, the method of the first aspect is completed.

[0017] In the fourth aspect, the present application provides a computer readable storage medium for storing computer instructions, when the computer instructions are executed by the processor, the method of the first aspect is completed.

[0018] The above one or more technical solutions have the following beneficial effects:

[0019] In the application, the negative sequence voltage unbalance degree is calculated to determine whether the regulating device needs to be optimized, and then the particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating device with the target of minimizing the negative sequence voltage unbalance degree, and the closed-loop verification is performed on the optimization result to ensure that it meets the dynamic threshold of three-phase unbalance degree and all safety constraints at the same time, the method effectively improves the accuracy, adaptability and engineering practicability of three-phase imbalance treatment, and guarantees the safe, high-quality and economic operation of the power grid.

[0020] In the application, the device capacity, node voltage and branch power are used as multi-constraint conditions, and the penalty function is combined to ensure that the regulating device output, node voltage and branch power are within the safety boundary, effectively avoiding the problems of device overload damage and branch power overload caused by ignoring constraints in traditional technologies, significantly reducing the power system fault risk, prolonging the service life of the regulating device and improving the overall power supply reliability.

[0021] In the application, the three-phase unbalance degree dynamic threshold calculation mechanism based on load rate and distributed power output fluctuation is introduced, which reduces the number of invalid actions of the device compared with the fixed threshold, and reduces the operation and maintenance cost and energy consumption of the device. At the same time, through parameter configuration, different scenes such as distribution network, microgrid and hybrid grid containing distributed power can be quickly adapted, which greatly improves the universality and practicability of the technology, reduces the technical adaptation cost in different scenes, and provides a flexible and economical solution for three-phase imbalance treatment of the power system.

[0022] In the application, through the improved particle swarm optimization algorithm, the double-factor inertia weight self-adaptive adjustment and constraint penalty function are introduced, which improves the convergence speed by 30% compared with the traditional particle swarm optimization algorithm, and quickly finds the optimal adjustment parameters, so that the system unbalance degree is quickly reduced to below the three-phase unbalance degree dynamic threshold, the response time delay of the treatment is controlled within 50ms, and the problem of insufficient treatment accuracy of the traditional technology is effectively solved.

[0023] The advantages of the additional aspects of the application will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0024] The drawings accompanying the specification of the application form part of the application and serve to provide further understanding of the application, the illustrative embodiments of the application and their description serve to explain the application without constituting an inappropriate limitation thereof.

[0025] Figure 1 The flow chart of the three-phase imbalance treatment method of the power system in the embodiment one of the application;

[0026] Figure 2The figure of the effect of the three-phase imbalance problem treatment based on the disclosure on the test system in embodiment one of the present application. DETAILED DESCRIPTION

[0027] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. Unless otherwise defined, 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 application belongs.

[0028] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application.

[0029] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0030] Embodiment one

[0031] The present embodiment discloses a three-phase imbalance treatment method for a power system. First, the basic parameters of the current power system are read, and multiple constraint conditions are defined. Second, based on the basic parameters of the current power system, the power flow calculation is performed by the forward-backward sweep method to obtain the three-phase voltage phasor of each node. Then, the three-sequence components are separated from the node three-phase voltage phasor using the symmetrical component method, 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 three-phase imbalance degree dynamic threshold is calculated based on the real-time working condition of the power system. Finally, taking the minimization of the power system negative sequence voltage imbalance degree as the target, combining the multiple constraint conditions, the improved particle swarm optimization algorithm is used to optimize the active and reactive power output of the regulating device, the optimized power system node voltage and negative sequence voltage imbalance degree are calculated using the forward-backward sweep method, and it is verified whether the imbalance degree condition and the multiple constraint conditions are satisfied. If the above conditions are satisfied at the same time, the imbalance treatment is ended, if not, the particle swarm optimization algorithm parameters are adjusted, and the iteration is continued.

[0032] In combination Figure 1 A three-phase imbalance treatment method for a power system is described in detail in the present embodiment, which specifically includes:

[0033] S100: reading the basic parameters of the power system and the constraint boundary, specifically including:

[0034] Voltage level U N , node type, branch parameter impedance Z line =R+jX, R represents the resistance of the branch, X represents the reactance of the branch, and the unit is ohm Ω; maximum carrying capacity I k,max , regulating device parameter active regulation range P i,min ,P i,max Reactive power adjustment range Q i,min , Q i,max This provides a data foundation for subsequent calculations.

[0035] S101: Define three types of core constraints as multiple constraints for subsequent optimization:

[0036] Equipment capacity constraint: Ensure that the output of the regulating equipment does not exceed the rated range.

[0037]

[0038] 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.

[0039] Node voltage constraints: Ensure that the voltage of each node is within the allowable deviation range.

[0040]

[0041] 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.

[0042] Branch power constraint: Ensure that the apparent power of the branch does not exceed the rated power corresponding to the maximum current carrying capacity.

[0043]

[0044] in, Let the apparent power of the k-th branch be... The rated voltage of branch k; Pk represents the active power of the kth branch, Qk represents the reactive power of the kth branch; Pkmax represents the maximum current-carrying capacity of the kth branch; Skmax represents the maximum apparent power of the kth branch.

[0045] S200: Using the forward-backward substitution method, based on the current power system node power and line parameters, the power flow is solved by forward-backward substitution calculation.

[0046] The following is a description of the principle of the forward-backward substitution method:

[0047] The voltage of the first end node is known = U s ∠α, U s is the current node voltage amplitude, and α is the corresponding voltage phase angle; the load node power S L = P L + jQ L , P L and Q L represent the current node active power and reactive power.

[0048] First, the branch current is calculated, and then the total power of the branch is derived:

[0049]

[0050] where * represents complex conjugate operation; is the total apparent power of the branch, which needs to be calculated by forward calculation from the load end to the power supply end until all branches are covered.

[0051] Based on the branch impedance Z line = R + jX and the branch current obtained by forward calculation , the end node voltage is corrected from the power supply end to the load end:

[0052]

[0053] wherein is the end node voltage phasor.

[0054] Repeat the forward-backward substitution process until the node voltage difference between adjacent two iterations is less than the convergence threshold value, which is set to here; U j ( t+1) represents the first t +1 iteration of the node voltage.

[0055] S300: Based on the node three-phase voltage phasor obtained in S200, the positive sequence, negative sequence and zero sequence components are separated by using the symmetrical component method, and then the power system negative sequence voltage unbalance degree is calculated, and it is judged whether the regulating device needs to be treated. The following is the principle of the symmetrical component method:

[0056] Through the symmetrical component transformation matrix T , the node three-phase voltage phasor is decomposed into positive sequence, negative sequence and zero sequence components:

[0057]

[0058] Among them, is the rotation factor, the positive sequence component reflects the symmetrical operation state of the power system, and the negative sequence component is the core factor of the power system leading to three-phase imbalance; , , represent the abc three-phase node voltage; represents the zero sequence component.

[0059] S301: The negative sequence voltage unbalance degree is used as an evaluation index, and the formula is as follows:

[0060]

[0061] Among them, , represent the values of the positive sequence component and the negative sequence component; the superscript indicates the phasor.

[0062] At the same time, based on the real-time working conditions of the power system such as the load rate λ load and the fluctuation coefficient of the distributed power output δ gen , the dynamic threshold of three-phase unbalance degree is calculated to avoid the rigid problem of fixed threshold.

[0063] The calculation of the dynamic threshold of three-phase unbalance degree is as follows:

[0064]

[0065] Among them, λ load ( t )= P load ( t ) / Pload,max , δ gen ( t )=|Δ P gen ( t )| / P gen,max , k 1=0.2, k 2=0.3 are adjustment coefficients. If inbalance>threshold( t ), the next step of optimization is entered; 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 represents the maximum active load involved in the power system; Δ P gen ( t ) represents the real-time fluctuation of the distributed power at time t, i.e., the absolute value of the difference between the actual output at this time and the output at the previous time; P gen,max represents the rated maximum output of the distributed power. threshold base refers to the three-phase unbalance degree threshold in GB / T 15543-2019 standard, which is 2% in the standard, and is used as the benchmark of the dynamic threshold here.

[0066] In this embodiment, the evaluation threshold is changed from a fixed value to a variable dynamically associated with the real-time operating condition of the system, overcoming the shortcomings of the traditional fixed threshold that cannot adapt to dynamic changes of the system, and providing more accurate and reasonable triggering and termination criteria for subsequent optimization control.

[0067] S400: With the objective of minimizing the power system negative sequence voltage unbalance degree, the active and reactive outputs of the regulating equipment are optimized using the improved particle swarm optimization algorithm in combination with the multi-constraint conditions of S101.

[0068] S401: The objective function is constructed, and the core objective is to reduce the negative sequence voltage unbalance degree.

[0069]

[0070] wherein, U 1( PQ ) and U 2( PQ ) are the positive sequence voltage amplitude and negative sequence voltage amplitude of the optimized power system.

[0071] 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.

[0072] The improved particle swarm optimization algorithm still relies on velocity and position updates as its core iterative logic, but now incorporates inertia weights. ω The calculation method has been improved, and the formulas for updating velocity and position are as follows:

[0073] Particle velocity update formula:

[0074]

[0075] Particle position update formula:

[0076]

[0077] 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.

[0078] Unlike traditional linearly decreasing inertia weights, this embodiment adjusts the weights by fusing the iterative process and particle fitness. ω The specific formula and logic are as follows:

[0079] Inertia weight base value :

[0080]

[0081] in, ω max =0.9, representing the initial iteration weight; ω min =0.4, representing the weight for the maximum number of iterations. tmax =100, represents the maximum number of iterations, which can be set to 150 when the number of nodes exceeds 50.

[0082] Inertia weight correction:

[0083] Introducing fitness bias coefficient , correcting the inertia weight base value, realizing the differentiated adjustment of particles.

[0084]

[0085] where, represents the particle i The final inertia weight of the t th iteration, calculated separately for each particle; represents the particle i The fitness of the t th iteration, that is, the value of the objective function; represents the global optimal fitness of the particle swarm t th iteration; k =0.8, represents the sensitivity coefficient, used to control the degree of influence of bias on weight; exp(·) is used to ensure ∈(0,1], to avoid weight out of range.

[0086] Inertia weight core adjustment logic:

[0087] When the particle fitness is close to the global optimum, ≈1, ≈ , keep the current step size; when the particle fitness deviates greatly from the global optimum, , Decrease, reduce the step size and converge to the optimal direction; in the early stage of iteration Large (close to 0.9), ensure global exploration; in the later stage Small (close to 0.4), focus on local search.

[0088] For particles that exceed the constraint range, introduce a penalty term to correct the objective function, forcing the particle to converge to the constraint region:

[0089]

[0090] where, k penalty =100 is the penalty coefficient, which ensures that the objective function value increases significantly when the constraint is exceeded, avoiding invalid optimization results. n represents the particle that exceeds the constraint range; P i and Q i are the active power and reactive power of the i th regulating device, respectively;P i,min the first i active power minimum value of the regulating device, P i,max the first i active power maximum value of the regulating device; Q i,min the first i reactive power minimum value of the regulating device, Q i,max the first i reactive power maximum value of the regulating device.

[0091] Iterate to the convergence of the objective function, here take , and output the optimal active power and reactive power of the optimized regulating device.

[0092] In this embodiment, the adaptive adjustment of the inertia weight ω and the penalty function for the out-of-bound behavior of the solution space are introduced into the standard PSO (Particle Swarm Optimization) algorithm simultaneously, so that the dynamic balance of emphasizing global exploration in the early stage of optimization and focusing on local fine search in the later stage is realized. Meanwhile, the penalty function is integrated into the objective function, which effectively guides the particle swarm to search in the feasible region that meets the device capacity, node voltage, branch power and other multiple constraints, and significantly improves the convergence reliability of the algorithm and the actual feasibility of the optimization result.

[0093] S403: Combine the optimal active power and reactive power obtained in S402 with the node active power and reactive power of the last iteration, and substitute them into the forward-backward sweep power flow calculation to recalculate the optimized node voltage, branch power and negative sequence voltage unbalance degree, and verify whether the unbalance degree condition and multiple constraint conditions are met. If both conditions are met, the treatment is completed; if not, the iteration is continued.

[0094] Among them, the first node voltage calculation does not need to adjust the device, which is to calculate the node voltage of the current power system without connecting the regulating device. After that, the optimal output of the regulating device obtained in each iteration will be combined with the node power obtained in the first step of this iteration to obtain new node power, and then the node voltage and unbalance degree of the next iteration are obtained through the power flow calculation.

[0095] Figure 2 The figure provided by this embodiment shows the effect of the three-phase unbalance problem treatment based on the present disclosure in the test system. As can be seen from the data in the figure, this embodiment realizes the accurate control of the system unbalance degree through the accurate data support of the forward-backward sweep power flow calculation, the optimization of the optimal output of the improved particle swarm algorithm, and the adaptability judgment of the dynamic threshold.

[0096] The embodiment scheme has safety, high efficiency and adaptability. In terms of safety, through multi-constraint collaborative optimization and constraint penalty mechanism, the risks of device overload and voltage out-of-limit are avoided, and the stable operation of the power system and the device is ensured. In terms of efficiency, relying on the accurate data support of the forward-backward substitution power flow calculation and the fast convergence characteristics of the improved particle swarm optimization algorithm, accurate evaluation and rapid treatment of the unbalance degree are realized, and the treatment response is timely and accurate. In terms of adaptability and economy, the three-phase unbalance degree dynamic threshold mechanism reduces the invalid action of the device and reduces the operation and maintenance cost, and the flexible parameter configuration can adapt to various power grid scenes, significantly improving the practical value and economic value of the technology, and effectively making up for the defects of the existing three-phase imbalance treatment technology, and providing strong support for the safe and economic operation of the power system.

[0097] Embodiment two

[0098] The purpose of the embodiment is to provide a three-phase imbalance treatment system for a power system, comprising:

[0099] The comparison module is configured to calculate the negative sequence voltage unbalance degree based on the basic parameters of the current power system, and compare the negative sequence voltage unbalance degree with the three-phase unbalance degree dynamic threshold to determine whether the regulating device needs to be optimized and adjusted; wherein the three-phase unbalance degree dynamic threshold is determined by the real-time working condition of the power system;

[0100] The optimization module is configured to, if the regulating device needs to be optimized and adjusted, minimize the negative sequence voltage unbalance degree of the power system as the target, combine the multi-constraint condition, and use the particle swarm optimization algorithm to optimize the active power output and the reactive power output of the regulating device;

[0101] The treatment module is configured to, based on the active power output and the reactive power output of the optimized regulating device, recalculate the optimized node voltage and the negative sequence voltage unbalance degree, verify whether the three-phase unbalance degree dynamic threshold and the multi-constraint condition are satisfied at the same time, and if the optimization result satisfies at the same time, complete the three-phase imbalance treatment process of the power system.

[0102] In more embodiments, there are also provided:

[0103] An electronic device comprising a memory and a processor, and computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, the method described in embodiment one is completed. For the sake of brevity, it will not be repeated here.

[0104] It should be understood that the processor in the embodiments can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0105] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0106] A computer readable storage medium is used to store computer instructions, and the computer instructions are executed by a processor to complete the method described in Embodiment I.

[0107] The method in Embodiment I can be directly embodied by a hardware processor to complete, or be completed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads information in the memory to complete the steps of the above method in combination with the hardware. To avoid repetition, it will not be described in detail here.

[0108] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed 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 the present application.

[0109] Although the specific embodiments of the application are described above with reference to the accompanying drawings, the description is not a limitation on the scope of protection of the application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the application without creative labor are still within the scope of protection of the application.

Claims

1. A method for three-phase imbalance mitigation in a power system, characterized by, The method comprises the following steps: calculating the negative sequence voltage unbalance degree based on the basic parameters of the current power system, and comparing the negative sequence voltage unbalance degree with a dynamic threshold of three-phase unbalance degree to determine whether the regulating device needs to be optimized; wherein the dynamic threshold of three-phase unbalance degree is determined by the real-time working condition of the power system; if the regulating device needs to be optimized, the active power output and the reactive power output of the regulating device are optimized by using a particle swarm optimization algorithm in combination with multiple constraint conditions, with the objective of minimizing the negative sequence voltage unbalance degree of the power system; based on the active power output and the reactive power output of the optimized regulating device, the node voltage and the negative sequence voltage unbalance degree after optimization are recalculated to verify whether the dynamic threshold of three-phase unbalance degree and the multiple constraint conditions are simultaneously satisfied, and if the optimization result satisfies both, the three-phase unbalance treatment process of the power system is completed; the active power output and the reactive power output of the regulating device are optimized by using an improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm is that, in the process of updating the particle speed, a fitness deviation coefficient is introduced to correct the inertia weight, so as to realize the differential adjustment of the particles; a penalty function is introduced to correct the objective function for the particles exceeding the constraint range, so that the particles converge to the area within the constraint; the fitness deviation coefficient is introduced to correct the inertia weight, and the specific process is as follows: ； ； wherein, representing a particle i the t final inertia weight of the representing a particle i the t fitness of the representing a particle swarm t global best fitness of the k is a sensitivity coefficient; exp(·) is used to ensure ∈(0, 1], is the inertia weight base value of the t iteration; the dynamic threshold of three-phase unbalance degree is calculated as follows: ； λ load ( t )= P load ( t ) / P load,max ； δ gen ( t )=|Δ 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.

2. A method for three-phase imbalance mitigation in a power system as claimed in claim 1, wherein, the negative sequence voltage unbalance degree is calculated based on the basic parameters of the current power system, and the specific process is as follows: the power flow of the power grid is calculated by using the forward-backward substitution method based on the basic parameters of the current power system, to obtain the three-phase voltage phasor of each node; the three-sequence components are separated by using the symmetrical component method based on the three-phase voltage phasor of each node, to calculate the negative sequence voltage unbalance degree.

3. A method for three-phase imbalance mitigation in a power system as claimed in claim 2, wherein, the three-sequence components are separated by using the symmetrical component method based on the three-phase voltage phasor of each node, to calculate the negative sequence voltage unbalance degree, and the specific process is as follows: the three-phase voltage of each node is decomposed into positive sequence components, negative sequence components and zero sequence components; the negative sequence voltage unbalance degree is calculated according to the positive sequence components and the negative sequence components.

4. A method for three-phase imbalance mitigation in a power system as claimed in claim 1, wherein, The multiple constraint conditions include: device capacity constraint, node voltage constraint and branch power constraint.

5. A power system three-phase imbalance mitigation system, characterized by, The method comprises the following steps: a comparison module is configured to calculate the negative sequence voltage unbalance degree based on the basic parameters of the current power system, and compare the negative sequence voltage unbalance degree with a dynamic threshold of three-phase unbalance degree to determine whether the regulating device needs to be optimized; wherein the dynamic threshold of three-phase unbalance degree is determined by the real-time working condition of the power system; the dynamic threshold of three-phase unbalance degree is calculated as follows: ； λ load ( t )= P load ( t ) / P load,max ； δ gen ( t )=|Δ P gen ( t )| / P gen,max ; wherein, k 1、 k 2are adjustment coefficients, P load t represents the real-time total active load of the power system at time t; P load,max represents the maximum active load of the power system; Δ P gen t represents the real-time output fluctuation of the distributed power supply at time t; P gen,max represents the rated maximum output of the distributed power supply; threshold base is a reference threshold, represents the dynamic threshold of three-phase imbalance at time t;​​ an optimization module is configured to, if the regulating device needs to be optimized, optimize the active power output and the reactive power output of the regulating device by using a particle swarm optimization algorithm in combination with multiple constraint conditions, with the objective of minimizing the negative sequence voltage unbalance degree of the power system; the active power output and the reactive power output of the regulating device are optimized by using an improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm is that, in the process of updating the particle speed, a fitness deviation coefficient is introduced to correct the inertia weight, so as to realize the differential adjustment of the particles; a penalty function is introduced to correct the objective function for the particles exceeding the constraint range, so that the particles converge to the area within the constraint; the fitness deviation coefficient is introduced to correct the inertia weight, and the specific process is as follows: ； ； wherein, representing a particle i the first t iteration of the final inertia weight; representing a particle i the first t iteration of the fitness, representing a global optimum fitness of the swarm of particles at the first t iteration; k is a sensitivity coefficient; exp(·) is used to ensure ∈(0, 1], is a base value of the inertia weight at the first t iteration; The treatment module is configured to: based on the active power and the reactive power of the adjusted device, recalculate the optimized node voltage and the negative sequence voltage unbalance degree, verify whether the three-phase unbalance degree dynamic threshold and the multi-constraint condition are simultaneously satisfied, and if the optimization result is simultaneously satisfied, complete the three-phase unbalance treatment process of the power system.

6. An electronic device, comprising: A computer program product comprising a memory and a processor, and computer instructions stored on the memory and run on the processor, when the computer instructions are run by the processor, the method in any one of claims 1-4 is completed.

7. A computer readable storage medium characterized in that, A computer program product for storing computer instructions, when the computer instructions are executed by a processor, the method in any one of claims 1-4 is completed.

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