Three-phase reactive power optimization method and system and computer equipment

Abstract

The invention relates to the technical field of electrical equipment, and discloses a three-phase reactive power optimization method and system and computer device.The three-phase reactive power optimization method comprises the steps that a dynamic constraint equation of three-phase neutral point voltage excursion is constructed; a hybrid planning model is constructed with minimization of reactive compensation errors, capacitor bank action frequency and neutral point voltage offset as targets, and optimization variables of the hybrid planning model comprise capacitor bank switching variables and state variables; performing iterative solution on the optimization variable of the hybrid programming model, and stopping iteration until a preset iteration termination condition is met to obtain an optimal switching instruction; and controlling the capacitor bank according to the optimal switching instruction. The neutral point voltage offset is constrained by setting a dynamic constraint equation, and the neutral point voltage offset is controlled while the split-phase compensation precision is guaranteed by setting the minimum reactive compensation error and the neutral point voltage offset, so that the reactive power optimization of the three-phase power distribution network is realized.

Description

Technical Field

[0001] The present application relates to the technical field of electrical equipment, and in particular to a three-phase reactive power optimization method, system and computer equipment. Background Art

[0002] In the industrial sector, the stability and reliability of power systems are crucial for ensuring smooth production processes. However, the operating conditions of electrical equipment often exhibit significant sudden and unstable characteristics. For example, a welding machine experiences a significant surge in power consumption upon startup. This phenomenon, characterized by significant power consumption fluctuations within a short period of time, is defined as "single-phase surge load."

[0003] The presence of single-phase surge loads can cause three-phase imbalance in the power system, leading to voltage fluctuations that, in severe cases, can damage equipment. Voltage fluctuations not only disrupt the normal operation of other electrical equipment but can also cause equipment failures or unplanned downtime during production, negatively impacting production efficiency and product quality. More seriously, long-term three-phase imbalance and voltage fluctuations can accelerate the aging of power equipment, significantly increasing maintenance costs and posing potential safety risks. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a three-phase reactive power optimization method, system and computer equipment, which can effectively solve the problem in the prior art that the existence of single-phase impact load will cause three-phase imbalance in the power system, thereby leading to voltage fluctuations, and in severe cases may cause equipment damage.

[0005] In a first aspect, an embodiment of the present application provides a three-phase reactive power optimization method, the method comprising:

[0006] Construct the dynamic constraint equation of three-phase neutral point voltage offset;

[0007] A hybrid planning model is constructed with the goal of minimizing reactive compensation error, capacitor bank operation frequency, and the neutral point voltage offset, wherein the optimization variables of the hybrid planning model include capacitor bank switching variables and state variables;

[0008] Iteratively solving the optimization variables of the hybrid programming model until a preset iteration termination condition is met, and then the iteration is stopped to obtain the optimal switching instruction;

[0009] The capacitor bank is controlled according to the optimal switching instruction.

[0010] In some embodiments, before constructing the dynamic constraint equation for the three-phase neutral point voltage offset and / or after controlling the capacitor bank according to the optimal switching instruction, the optimization method further includes:

[0011] According to the frequency domain complex admittance tensor model, the coupling relationship matrix of three-phase voltage and three-phase current is constructed;

[0012] The reactive power of each phase is obtained according to the admittance parameters of each phase in the frequency domain complex admittance tensor model and the coupling relationship matrix.

[0013] In some embodiments, obtaining the reactive power of each phase according to the admittance parameters of each phase in the frequency domain complex admittance tensor model and the coupling relationship matrix includes:

[0014] Obtaining complex power according to each of the admittance parameters and the three-phase voltage;

[0015] The reactive power is obtained according to the complex power.

[0016] In some embodiments, constructing a dynamic constraint equation for the three-phase neutral point voltage offset includes:

[0017] According to the reactive power of each phase, the admittance parameters of each phase and the random disturbance data represented by Brownian motion, and taking the preset range of the neutral point voltage not greater than the rated voltage as a hard constraint condition, a dynamic constraint equation of the three-phase neutral point voltage offset is constructed.

[0018] In some embodiments, the iteratively solving the optimization variables of the hybrid programming model includes:

[0019] The solution of the hybrid programming model is decomposed using the alternating direction multiplier method:

[0020] Solving the capacitor bank switching variable using a branch and bound method;

[0021] The state variables are solved using the Lyapunov equation.

[0022] In some embodiments, the dynamic constraint equation for the three-phase neutral point voltage offset is:

[0023]

[0024] Where: n represents the neutral point, φ=1, 2, 3 represent the first phase, the second phase, and the third phase respectively, t represents the current time, τ represents the integral variable, U n (t) represents the neutral point voltage of the random process, dU n (t) represents the offset of the neutral point voltage, Y φn (τ) represents the impulse response of the neutral point admittance, ΔQ φ represents the random fluctuation of the reactive power, σ represents the noise intensity coefficient, and dW(t) represents the random disturbance data of Brownian motion.

[0025] In some embodiments, the hybrid programming model is:

[0026]

[0027] in, represents the switching variable of the capacitor bank, represents the state variable, Q φ target Indicates the target reactive power, C φ Represents the three-phase capacitor bank capacity matrix, Indicates the switching variable of the three-phase capacitor bank, W φ represents the weighting matrix, λ represents the weight coefficient of the action frequency, i represents the ordinal number of the capacitor, represents the switching variable of the capacitor bank of the previous three phases, μ represents the weight coefficient of the neutral point voltage offset, U n represents the neutral point voltage, It represents the mean square expectation operation of the neutral point voltage.

[0028] In some embodiments, the constraints of the hybrid planning model include power flow equations, capacitor action logic, and random stability constraints.

[0029] In a second aspect, an embodiment of the present application provides a three-phase reactive power optimization system, comprising:

[0030] Dynamic constraint equation construction module, which constructs the dynamic constraint equation of three-phase neutral point voltage offset;

[0031] A hybrid planning model construction module is configured to construct a hybrid planning model with the goal of minimizing reactive compensation error, capacitor bank operation frequency, and neutral point voltage offset. The optimization variables of the hybrid planning model include capacitor bank switching variables and state variables.

[0032] A variable solving module iteratively solves the optimization variables of the hybrid programming model until a preset iteration termination condition is met, and stops the iteration to obtain an optimal switching instruction;

[0033] A capacitor control module controls the capacitor bank according to the optimal switching instruction.

[0034] In a third aspect, an embodiment of the present application provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the above-mentioned three-phase reactive power optimization method.

[0035] The embodiments of the present application have the following beneficial effects: The three-phase reactive power optimization method of the embodiments of the present application includes: constructing a dynamic constraint equation for the three-phase neutral point voltage offset; constructing a hybrid planning model with the goal of minimizing the reactive compensation error, the capacitor bank operation frequency, and the neutral point voltage offset, wherein the optimization variables of the hybrid planning model include the capacitor bank switching variable and the state variable; iteratively solving the optimization variables of the hybrid planning model until the iteration stops when the preset iteration termination condition is met, and obtaining the optimal switching instruction; and controlling the capacitor bank according to the optimal switching instruction. By constructing a hybrid planning model with the goal of minimizing the reactive compensation error, the capacitor bank operation frequency, and the neutral point voltage offset, reactive power optimization of the three-phase distribution network is achieved. Specifically, the neutral point voltage offset is constrained and controlled by setting a dynamic constraint equation, the phase compensation accuracy is guaranteed by setting the reactive compensation error to be minimized, and the capacitor bank operation frequency is set to reduce the number of capacitor bank switching times and improve reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A first flow chart of the three-phase reactive power optimization method according to an embodiment of the present application is shown;

[0038] Figure 2 A circuit diagram of a three-phase circuit according to an embodiment of the present application is shown;

[0039] Figure 3 A second flow chart of the three-phase reactive power optimization method according to an embodiment of the present application is shown;

[0040] Figure 4 A schematic structural diagram of a three-phase reactive power optimization method according to an embodiment of the present application is shown;

[0041] Figure 5 A structural diagram of a three-phase reactive power optimization system according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0043] The components of the embodiments of the present application generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but rather merely represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0044] Hereinafter, the terms "including", "having" and their cognates used in various embodiments of the present application are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the aforementioned items, and should not be understood as excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the aforementioned items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the aforementioned items. In addition, the terms "first", "second", "third" and the like are only used to distinguish descriptions and should not be understood as indicating or implying relative importance.

[0045] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meaning as in the context of the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning unless clearly defined in the various embodiments of the present application.

[0046] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0047] Considering that the existence of single-phase impact load in the prior art will cause three-phase imbalance problems in the power system, which will lead to voltage fluctuations, and in severe cases may cause equipment damage, etc. The present application provides a three-phase reactive power optimization method, system and computer equipment, which constructs a hybrid planning model with the goal of minimizing reactive power compensation error, capacitor group action frequency and neutral point voltage offset, thereby achieving reactive power optimization of the three-phase distribution network. Specifically, the offset of the neutral point voltage is constrained by setting a dynamic constraint equation to control the offset of the neutral point voltage. By setting the minimum reactive power compensation error, the phase compensation accuracy is guaranteed. By setting the capacitor group action frequency, the number of capacitor group switching times is reduced to improve reliability.

[0048] The three-phase reactive power optimization method is described below with reference to some specific embodiments.

[0049] Figure 1A flow chart of a three-phase reactive power optimization method according to an embodiment of the present application is shown. Figure 2 A schematic diagram of a three-phase circuit of an embodiment of the present application is shown, and the three-phase reactive power optimization method is applied to the three-phase circuit, wherein capacitors C1 and C2 are provided in the first phase circuit, capacitors C3 and C4 are provided in the second phase circuit, and capacitors C5 and C6 are provided in the third phase circuit. The optimal switching instruction is obtained by the three-phase reactive power optimization method of the present application. According to the optimal switching instruction, the connection and disconnection of each capacitor are controlled, and the reactive power distribution of the system is adjusted, thereby optimizing the reactive power of the system and reducing losses.

[0050] Exemplarily, the optimization method includes steps S101-S104:

[0051] S101, construct a dynamic constraint equation for the three-phase neutral point voltage offset.

[0052] The neutral point voltage offset is affected by the three-phase reactive power fluctuation. Considering the random load fluctuation in actual operation, the reactive power fluctuation is used as the random input, and Brownian motion is introduced to describe the system noise interference.

[0053] Specifically, based on the reactive power of each phase, the admittance parameters of each phase, and random disturbance data represented by Brownian motion, and with the neutral point voltage being no greater than a preset range of the rated voltage as a hard constraint condition, a dynamic constraint equation for the three-phase neutral point voltage offset is constructed. Specifically, the dynamic constraint equation for the three-phase neutral point voltage offset is expressed as a stochastic differential equation, using a convolution integral to describe the cumulative effect of reactive power fluctuations at historical moments on the current neutral point voltage through admittance. Based on the circuit node voltage law and combined with random process theory, the cumulative effect is superimposed with the noise term to form a stochastic differential equation. For example, the dynamic constraint equation for the three-phase neutral point voltage offset is:

[0054]

[0055] Where: n represents the neutral point, φ = 1, 2, 3 represent the first phase, the second phase, and the third phase respectively, t represents the current time, τ represents the integral variable, U n (t) represents the neutral point voltage of the random process, dU n (t) represents the offset of the neutral point voltage, Y φn (τ) represents the impulse response of the neutral point admittance, ΔQ φ represents the random fluctuation of reactive power, σ represents the noise intensity coefficient, and dW(t) represents the random disturbance data of Brownian motion.

[0056] It can be understood that the impulse response of the neutral point admittance can be obtained by inverse conversion of the neutral point admittance, reflecting the time domain characteristics of the admittance between each phase and the neutral point; the random disturbance of reactive power is obtained by monitoring the change of load reactive power or based on load model statistics; the noise intensity coefficient is determined according to the measured noise data or system disturbance analysis, and the noise intensity coefficient represents the amplitude of the influence of Brownian motion on the neutral point voltage.

[0057] It is understandable that the hard constraint condition can be set according to actual application conditions. Exemplarily, the hard constraint condition is that the deviation of the neutral point voltage does not exceed 2% of the rated voltage.

[0058] The hard constraints of the neutral point voltage offset can be converted into the acceptable domain of the Ito process. Specifically, the acceptable domain is:

[0059]

[0060] Where P(·) represents the probability, Indicates the maximum value in the time interval [0, T], U rated Represents the rated voltage, T represents the upper limit of the time interval, representing a time span such as a dynamic response time window, and ε represents the set probability threshold.

[0061] The dynamic evolution of the neutral-point voltage directly affects the feasible domain of the hybrid programming model. The dynamic constraint equations for the three-phase neutral-point voltage excursion define the system's hard safety margins. The stochastic dynamics of the neutral-point voltage excursion are described using stochastic differential equations (SDEs), transforming the randomness of the physical system into mathematical constraints that ensure the neutral-point voltage excursion never exceeds two percent of the rated voltage.

[0062] S102 , with the goal of minimizing reactive compensation error, capacitor bank operation frequency, and neutral point voltage offset, a hybrid planning model is constructed. The optimization variables of the hybrid planning model include capacitor bank switching variables and state variables.

[0063] Specifically, the minimized reactive power error term can be expressed by the difference between the target reactive power and the total compensation capacity of the capacitor group. Furthermore, the key errors can be highlighted through the weighted matrix to ensure the phase compensation accuracy; the capacitor group action frequency is calculated by the state change of the capacitor group switching variable. Furthermore, the weight coefficient of the action frequency term can be set to balance the compensation accuracy and the action number priority. By setting the capacitor group action frequency, the capacitor group switching times can be reduced and the reliability can be improved.

[0064] Specifically, the target reactive power is the reactive power calculated based on the frequency domain admittance tensor model. When it is detected that the reactive power of a phase suddenly changes due to the start-up of the load of that phase, the target reactive power is calculated and the capacitor bank is controlled to perform switching operations so that the reactive power of that phase changes to the recalculated target reactive power.

[0065] The neutral point voltage offset is controlled by the mean squared expected value of the neutral point voltage. Furthermore, the weight coefficient of the neutral point voltage offset can be set to coordinate the relationship between voltage control and other objective functions. For example, the hybrid programming model is:

[0066]

[0067] in, Indicates the capacitor bank switching variable, represents the state variable, Q φ target Indicates the target reactive power, C φ Represents the three-phase capacitor bank capacity matrix, Indicates the switching variable of the three-phase capacitor bank, W φ represents the weighting matrix, λ represents the weight coefficient of the action frequency, i represents the ordinal number of the capacitor, represents the switching variable of the capacitor bank of the previous three phases, μ represents the weight coefficient of the neutral point voltage offset, U n Indicates the neutral point voltage, It represents the mean square expectation operation of the neutral point voltage.

[0068] It is understandable that the weighting matrix is constructed by the inverse matrix of the covariance matrix. The range of the capacitor bank switching variable can be set according to the actual application. For example, The constraints of the hybrid planning model can be set according to the actual application situation. For example, the constraints of the hybrid planning model include equation constraints such as the power flow equation, inequality constraints such as capacitor action logic, and random stability constraints. Specifically, the power flow equation is:

[0069] Y(ω)U+I c (x)=I load ;

[0070] Where Y(ω) represents the frequency domain complex admittance tensor model, U represents the node voltage phasor, and I c (x) represents the current injected by the compensation device, I load represents the current flowing through the load. By introducing the frequency-domain complex admittance tensor model, the error caused by phase coupling is corrected.

[0071] The capacitor action logic is:

[0072]

[0073] Among them, t k Indicates the time of the current control cycle, x φ，i (t k ) represents the time point t k The switching variable of the φ phase i capacitor at time t k-1 Indicates the time of the previous control cycle, x φ,i (t k-1 ) represents the time point t k-1 The switching variable of the φ phase i-th capacitor at time.

[0074] The stochastic stability constraint is:

[0075]

[0076] Where Tr(·) represents the trace of the matrix, that is, the sum of the diagonal elements of the matrix, and γ represents a pre-set positive constant.

[0077] S103, iteratively solving the optimization variables of the hybrid programming model until the preset iteration termination condition is met, and the iteration is stopped to obtain the optimal switching instruction.

[0078] The capacitor bank switching variables and state variables can be solved based on actual application scenarios. As an example, the alternating direction multiplier method is used to decompose the model solution into a main problem and subproblems. The main problem is to solve the integer variables in the hybrid programming model, and the subproblem is to solve the state variables of the hybrid programming model. Specifically, the branch and bound method is used to solve the capacitor bank switching variables, and the Lyapunov equation is used to solve the state variables. In state variable optimization, the Lyapunov equation is solved to ensure that continuous quantities such as the neutral point voltage meet the voltage offset hard constraint. Solving the integer variables as the main problem ensures that global constraints are met during the overall optimization process.

[0079] Specifically, using the branch and bound method to solve the capacitor bank switching variables includes: taking the set of capacitor bank switching state combinations as the root node, selecting the current node from the root node according to a preset rule, relaxing the capacitor bank switching variables in the current node, converting them into state variables, and solving the corresponding relaxation problem. If the relaxation problem has no feasible solution, pruning the current node, if the solution to the relaxation problem satisfies the integer constraint, updating the global upper bound, and recording the solution as a candidate optimal solution, reselecting nodes from the root node according to the preset rule until all nodes in the root node are traversed or meet the convergence condition. The preset rules can be set according to the actual application situation.

[0080] The branch-and-bound method is implemented using an FPGA. Integer variables are discrete decision variables, resulting in high computational complexity. FPGAs, with their highly parallelizable nature, are well-suited for executing the iterative subtasks of the branch-and-bound method, significantly reducing solution time. The Lyapunov equation is solved using an ARM core, which offers low power consumption and high performance, making it suitable for complex numerical computations. Solving the Lyapunov equation ensures system stability and provides theoretical support for the entire optimization process.

[0081] The alternating direction multiplier method decomposes complex global optimization problems into main problems and sub-problems, and assigns them to different hardware platforms for processing. It utilizes the advantages of different hardware platforms to achieve collaborative work of heterogeneous computing, and solves the main problem and sub-problems in parallel, significantly improving the overall solution speed and achieving sub-millisecond dynamic response.

[0082] Exemplarily, the preset iteration termination condition is:

[0083]

[0084] in, Indicates the current capacitor bank switching variable, Indicates the switching variable of the next capacitor bank, Represents the current state variable, Represents the next state variable.

[0085] Update variables by iteratively and When the variable changes of adjacent iterations meet the preset iteration termination conditions, the algorithm is considered to have converged, that is, the optimal switching instruction that meets the compensation accuracy, minimization of action frequency, and neutral point voltage constraints is found.

[0086] S104: Control the capacitor bank according to the optimal switching instruction.

[0087] The capacitors in the circuit are controlled according to the optimal switching instructions, so that each capacitor is connected or disconnected, thereby optimizing the reactive power of the system.

[0088] The dynamic constraint equation for the three-phase neutral point voltage offset serves as the safety boundary definer for the hybrid planning model. The hybrid planning model, as the executor of the dynamic constraint, searches for the optimal compensation strategy within the safety boundary through multi-objective optimization. The two interact in real time through the admittance tensor parameters, random perturbation intensity, and action frequency weights to jointly achieve the goal of safe and rapid global optimization.

[0089] This embodiment incorporates compensation accuracy, action frequency, and random stability into a unified optimization framework. Through mathematical modeling, multiple objectives such as compensation accuracy, action frequency minimization, and dynamic response are coordinated to form a solvable optimization model.

[0090] Further, Figure 3 Another flow chart of the three-phase reactive power optimization method according to the embodiment of the present application is shown. Figure 4 Shown Figure 3 Schematic diagram of the structure of the three-phase reactive power optimization method. Before constructing the mixed integer nonlinear programming model, the optimization method also includes: S201-S202:

[0091] S201: Construct a coupling relationship matrix of three-phase voltages and three-phase currents based on a frequency-domain complex admittance tensor model.

[0092] Specifically, a frequency domain complex admittance tensor model is constructed based on system parameters, where the system parameters include impedance, susceptance, and load data of each phase, which can be obtained through current sensors, voltage sensors, or other detection equipment. Exemplarily, the frequency complex admittance tensor model is:

[0093]

[0094] Among them, a, b, and c represent the 1st, 2nd, and 3rd phases respectively.

[0095] Each element in the admittance tensor can be decomposed into conductance and susceptance, clarifying the real and imaginary characteristics of the admittance between each phase and reducing the computational complexity.

[0096] The frequency domain complex admittance tensor model can be used to accurately construct the coupling relationship matrix between the three-phase voltage and current:

[0097]

[0098] in, Indicates three-phase voltage; Indicates three-phase current.

[0099] The coupling relationship matrix can be used to quantify the interphase coupling strength and phase characteristics at different frequencies, such as low frequency band, high frequency band, and full frequency band. This provides a theoretical basis and implementation path for dynamic compensation under impact load scenarios.

[0100] S202 , obtaining reactive power of each phase according to the admittance parameters of each phase and the coupling relationship matrix in the frequency domain complex admittance tensor model.

[0101] Specifically, the complex power can be obtained based on the admittance parameters of each phase and the coupling relationship matrix in the frequency domain complex admittance tensor model, and the reactive power can be calculated based on the complex power. For example, the complex power is:

[0102]

[0103] Among them, S φ represents the complex power of the φ phase, Indicates the effective value of the voltage of the φ phase, The conjugate complex number representing the effective value of the current in the φ phase. Specifically, the effective value of the current is represented by the complex admittances and the effective value of the voltage in the admittance tensor model:

[0104]

[0105] Here, ψ=1, 2, and 3 represent the first phase, the second phase, and the third phase, respectively. It can be understood that ψ represents a phase different from φ.

[0106] Considering the impact of interphase coupling on reactive power, the reactive power of each phase is affected not only by its own admittance but also by the interphase admittance. For example, the reactive power is the sum of the imaginary part of the complex power and the cross-term coupling contribution, where the cross-term coupling contribution includes the reactive contribution of self-admittance and mutual admittance susceptance, as well as the reactive coupling caused by mutual admittance conductance and phase difference.

[0107] For example, for phase 1, the reactive contributions of the self-admittance and mutual-admittance susceptance of the other two phases are:

[0108]

[0109] Among them, B ab (ω) represents the cross-phase susceptance between the first and second phases, B ac (ω) represents the cross-phase susceptance of the first and third phases.

[0110] For example, for phase 1, the reactive coupling generated by the mutual admittance conductance and phase difference of the other two phases is:

[0111]

[0112] Among them, G ab (ω) represents the cross-phase conductance between the first and second phases, G ac (ω) represents the cross-phase conductance of the first and third phases.

[0113] By decoupling the admittance tensor model, the effects of each phase's susceptance and interphase coupling on reactive power are captured, ultimately enabling precise control of each phase's reactive power. For example, based on frequency characteristics, a reverse-phase coupling current can be introduced into a phase's compensation device. Alternatively, high-frequency coupling admittance can be suppressed to reduce the impact of cross-terms on the neutral-point voltage. This allows for synergistic effects between independent phase compensation and coupling effects, avoiding adverse impacts on other phases.

[0114] It is understandable that after the capacitor bank is controlled according to the optimal switching instruction, the frequency domain complex admittance tensor model can be reconstructed according to the system parameters, thereby forming a closed loop and continuously optimizing the three-phase reactive power.

[0115] It is understandable that steps S203-S206 in this application are identical to steps S101-S104 and will not be repeated here.

[0116] The optimization method of this embodiment collects data based on the collected data, constructs a frequency-domain complex admittance tensor model, quantifies the phase-to-phase coupling relationship, sets reactive power based on the frequency-domain complex admittance tensor model, uses differential equations to describe the dynamic evolution of the neutral point voltage offset under random load disturbances, quantifies noise interference, and greatly improves compensation accuracy.

[0117] In addition, hard constraints are used to limit the offset of the neutral point voltage, and a hybrid planning model is constructed. Through the alternating direction multiplier method, FPGA and ARM are used for parallel solution, which significantly improves the overall solution speed and achieves sub-millisecond dynamic response.

[0118] In order to verify the effectiveness of the optimization method of this application, multiple devices were started simultaneously with a rated voltage of 400V. After testing, the three-phase imbalance before optimization was 18%, and the neutral point voltage offset was 20.1V; while the three-phase imbalance after optimization was ≤1%, and the neutral point voltage offset was only 6.4V. It can be seen that this optimization method greatly improves the compensation accuracy and dynamic response capability.

[0119] like Figure 5 As shown, based on the method of the above embodiment, this embodiment provides a three-phase reactive power optimization system. Exemplarily, the three-phase reactive power optimization system 100 includes:

[0120] A dynamic constraint equation building module 110 builds a dynamic constraint equation for three-phase neutral point voltage offset;

[0121] A hybrid planning model construction module 120 constructs a hybrid planning model with the goal of minimizing reactive compensation error, capacitor bank operation frequency, and neutral point voltage offset. The optimization variables of the hybrid planning model include capacitor bank switching variables and state variables.

[0122] The variable solving module 130 iteratively solves the optimization variables of the hybrid programming model until the preset iteration termination condition is met, and the optimal switching instruction is obtained;

[0123] The capacitor control module 140 controls the capacitor bank according to the optimal switching instruction.

[0124] It can be understood that the system of this embodiment corresponds to the control method of the above embodiment, and the optional items in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0125] The present application also provides a computer device. Exemplarily, the computer device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to enable the device to execute the functions of the above-mentioned three-phase reactive power optimization method or the various modules in the above-mentioned three-phase reactive power optimization system.

[0126] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0127] The memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), and electrically erasable programmable read-only memory (EEPROM). The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving an execution instruction.

[0128] The present application also provides a computer-readable storage medium for storing a computer program used in the terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or flowchart, and the combination of boxes in the structure diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0130] In addition, the functional modules or units in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0131] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a smart phone, personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application.

[0132] The above is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the protection scope of the present application.

Claims

1. A three-phase reactive power optimization method, characterized in that: The method comprises: Construct the dynamic constraint equation of three-phase neutral point voltage offset; A hybrid planning model is constructed with the goal of minimizing reactive compensation error, capacitor bank operation frequency, and the neutral point voltage offset, wherein the optimization variables of the hybrid planning model include capacitor bank switching variables and state variables; Iteratively solving the optimization variables of the hybrid programming model until a preset iteration termination condition is met, and then the iteration is stopped to obtain the optimal switching instruction; The capacitor bank is controlled according to the optimal switching instruction.

2. The three-phase reactive power optimization method according to claim 1, characterized in that: Before constructing the dynamic constraint equation of the three-phase neutral point voltage offset, and / or after controlling the capacitor bank according to the optimal switching instruction, the optimization method further includes: According to the frequency domain complex admittance tensor model, the coupling relationship matrix of three-phase voltage and three-phase current is constructed; The reactive power of each phase is obtained according to the admittance parameters of each phase in the frequency domain complex admittance tensor model and the coupling relationship matrix.

3. The three-phase reactive power optimization method according to claim 2, characterized in that: The acquiring of the reactive power of each phase according to the admittance parameters of each phase in the frequency domain complex admittance tensor model and the coupling relationship matrix comprises: Obtaining complex power according to each of the admittance parameters and the three-phase voltage; The reactive power is obtained according to the complex power.

4. The three-phase reactive power optimization method according to claim 2, characterized in that: The dynamic constraint equation for constructing the three-phase neutral point voltage offset includes: According to the reactive power of each phase, the admittance parameters of each phase and the random disturbance data represented by Brownian motion, and taking the preset range of the neutral point voltage not greater than the rated voltage as a hard constraint condition, a dynamic constraint equation of the three-phase neutral point voltage offset is constructed.

5. The three-phase reactive power optimization method according to claim 1, characterized in that: The iteratively solving the optimization variables of the hybrid programming model includes: The solution of the hybrid programming model is decomposed using the alternating direction multiplier method: Using branch and bound method to solve the capacitor bank switching variable; The state variables are solved using the Lyapunov equation.

6. The three-phase reactive power optimization method according to claim 4, characterized in that: The dynamic constraint equation of the three-phase neutral point voltage offset is: in: represents the neutral point, φ=1, 2, 3 represent the first phase, the second phase, and the third phase respectively, t represents the current time, τ represents the integral variable, U n (t) represents the neutral point voltage of the random process, dU n (t) represents the offset of the neutral point voltage, Y φn (τ) represents the impulse response of the neutral point admittance, ΔQ φ represents the random fluctuation of the reactive power, σ represents the noise intensity coefficient, and dW(t) represents the random disturbance data of Brownian motion.

7. The three-phase reactive power optimization method according to claim 1, characterized in that: The hybrid programming model is: in, represents the switching variable of the capacitor bank, represents the state variable, Q φ target Indicates the target reactive power, C φ Represents the three-phase capacitor bank capacity matrix, Indicates the switching variable of the three-phase capacitor bank, W φ represents the weighting matrix, λ represents the weight coefficient of the action frequency, i represents the ordinal number of the capacitor, represents the switching variable of the capacitor bank of the previous three phases, μ represents the weight coefficient of the neutral point voltage offset, represents the neutral point voltage, It represents the mean square expectation operation of the neutral point voltage.

8. The three-phase reactive power optimization method according to claim 1, characterized in that: The constraints of the hybrid planning model include power flow equations, capacitor action logic and random stability constraints.

9. A three-phase reactive power optimization system, characterized in that: include: Dynamic constraint equation construction module, which constructs the dynamic constraint equation of three-phase neutral point voltage offset; A hybrid planning model construction module is configured to construct a hybrid planning model with the goal of minimizing reactive compensation error, capacitor bank operation frequency, and neutral point voltage offset. The optimization variables of the hybrid planning model include capacitor bank switching variables and state variables. A variable solving module iteratively solves the optimization variables of the hybrid programming model until a preset iteration termination condition is met, and stops the iteration to obtain an optimal switching instruction; A capacitor control module controls the capacitor bank according to the optimal switching instruction.

10. A computer device, characterized in that: The computer device includes: a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the three-phase reactive power optimization method according to any one of claims 1 to 8.

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