Reactor and multi-machine phase-leading collaborative optimization method and system based on improved whale algorithm

Abstract

The invention discloses a reactor and multi-machine leading phase collaborative optimization method and system based on an improved whale algorithm, and relates to the technical field of power system voltage stability control, and the method comprises the steps: obtaining power flow distribution and small disturbance characteristic data, determining a generator set of leading phase operation according to the participation degree of a leading electromechanical mode, and recording the maximum allowable leading phase depth; establishing a multi-target optimization model with voltage deviation minimization and reactive power dispatching cost minimization as targets by combining tidal current power data, voltage measurement data and reactor operation information under the constraint of the maximum allowable leading phase depth; and an improved hybrid adaptive whale algorithm is adopted for solving, so that synchronous optimization of the leading phase depth of the generator and the switching state of the reactor is realized. According to the method, cooperative control of multiple units and the reactor can be realized, the voltage distribution balance is effectively improved, the reactive power dispatching cost is reduced, and the system stability and economy are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system voltage stability control, in particular to an improved whale algorithm-based method and system for optimizing a reactor and multi-machine phase advancement. BACKGROUND

[0002] With the increasing complexity of the power system structure and the rapid advancement of high-proportion new energy access, regional overvoltage and voltage stability problems have become the core challenge affecting the safe operation of the power grid. The phase advancement operation of synchronous generators has become an important means to improve the system voltage stability margin due to its ability to continuously absorb inductive reactive power. However, the traditional phase advancement operation analysis method has significant limitations in multi-machine systems: the steady-state power flow calculation and small disturbance stability evaluation processes are disjointed, resulting in insufficient closed-loop analysis; the phase advancement depth of the generator and the switching action of the reactor belong to continuous variables and discrete variables, respectively, and traditional optimization methods are difficult to model uniformly; the existing whale optimization algorithm lacks convergence when dealing with mixed constraints, and does not comprehensively consider the multi-objective requirements of minimizing voltage deviation and optimizing reactive power dispatching cost. Although the reactor has large-capacity step regulation capability and forms a time-space complementary characteristic with the phase advancement operation of the generator, the existing research lacks a unified optimization framework for the coordinated control of the two, and cannot solve the problem of overvoltage suppression and stability margin improvement in the high-penetration scenario of new energy. SUMMARY

[0003] In view of the above existing problems, the present application is proposed.

[0004] Therefore, the present application provides an improved whale algorithm-based method for optimizing a reactor and multi-machine phase advancement, which solves the problems of poor stability, weak globality, and low convergence efficiency of existing power system voltage control and reactive power optimization techniques in terms of inaccurate determination of generator phase advancement, single optimization model objective, and insufficient algorithm coordination solving capability.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides an improved whale algorithm-based method for optimizing a reactor and multi-machine phase advancement, which includes: obtaining power flow distribution and small disturbance characteristic data using a power system calculation program, determining the generator set for phase advancement operation according to the participation degree of the dominant electromechanical mode, and recording the maximum allowable phase advancement depth; Under the constraint of the maximum allowable phase advancement depth, a multi-objective optimization model is established with the voltage deviation minimization and reactive power dispatching cost minimization as the objectives, in combination with the power flow distribution data, voltage measurement data, and reactor operation information; An improved hybrid adaptive whale algorithm is used to solve the multi-objective optimization model, in which the search parameters are updated adaptively during the iteration process, and the generator set phase advancement depth and reactor switching state are optimized simultaneously based on the operation data of the power system to obtain the optimal solution set; The generator set phase advancement depth adjustment instruction and the reactor switching control instruction are jointly issued based on the optimal solution to generate the cooperative control instruction set, so that the voltage distribution global optimization and the reactive power dispatching cost reduction are realized.

[0006] As a preferred scheme of the improved whale algorithm-based reactor and multi-machine phase advancement cooperative optimization method, the determination of the generator set for phase advancement operation includes: performing a power flow calculation through a power system calculation program, calling a small disturbance calculation module of the power system calculation program according to a result of the power flow calculation, and performing characteristic solving and modal identification; according to a result of the modal identification, screening the generator sets participating in a dominant electromechanical mode, and establishing a phase advancement operation set of the generator sets; In the phase advancement operation set of the generator sets, the reactive power absorption states of the generator sets are adjusted according to operation constraints, and a cyclic calculation is performed in combination with system stability determination; when the operation state meets the stability constraints, the maximum allowable phase advancement depth corresponding to the operation state is recorded.

[0007] As a preferred scheme of the improved whale algorithm-based reactor and multi-machine phase advancement cooperative optimization method, the establishment of the multi-objective optimization model includes: under the maximum allowable phase advancement depth constraints of each generator set, calling a power system calculation program to read power flow distribution data, voltage measurement data and reactor operation information, and generating optimization input data; based on the optimization input data, performing node voltage difference calculation to determine the offset between the node voltage and the target voltage; After the voltage difference calculation is completed, a reactor switching state statistics is performed to determine the switching change quantity of the adjacent time period; According to the voltage offset and the switching change quantity, a voltage deviation minimization objective function and a reactive power dispatching cost minimization objective function are established; after the objective functions are established, multi-layer constraint conditions are applied to form a multi-objective optimization model with the voltage deviation minimization and the reactive power dispatching cost minimization objectives.

[0008] As a preferred scheme of the improved whale algorithm-based reactor and multi-machine phase advancement cooperative optimization method, the constraint conditions include: a power flow power balance constraint for limiting the active and reactive power distribution relationship among the generators, the loads and the reactors; a node voltage constraint for limiting the node voltage amplitude to remain in a safe operation range; a reactor output constraint for limiting the reactive power compensation capacity in an allowable range; and a reactor switching rate constraint for limiting the switching action frequency of the continuous time period.

[0009] As a preferred scheme of the improved whale algorithm-based reactor and multi-machine phase advancement cooperative optimization method, the improved hybrid adaptive whale algorithm includes: setting the number of whale populations, the maximum number of iterations and the hybrid variable dimension including the generator set phase advancement depth and the reactor switching state, and generating an initial population. According to the voltage deviation target and the reactive power scheduling cost target, the fitness value of each population individual is calculated, and the fitness result is subjected to Pareto non-dominated sorting and crowding distance sorting; According to the current iteration process and the historical error fluctuation, the search parameters are updated to maintain adaptive balance between global exploration and local convergence of the search process; For the mixed variables of generator set phase advance depth and reactor switching state, continuous variable updating and discrete variable updating operations are respectively performed, and in the discrete variable updating process, the switching speed constraint is combined for correction; In some iterations, a spiral search mechanism is performed, so that the population individuals perform global search and local approximation around the global optimal solution; After each iteration, the excellent individuals are selected according to the fitness evaluation results to construct an elite solution set; When the preset termination condition is reached, the optimal solution set is output, which includes the optimal configuration of the generator set phase advance depth and the optimal combination of the reactor switching state.

[0010] As a preferred scheme of the improved whale algorithm-based reactor and multi-machine phase advance collaborative optimization method, wherein: the synchronous optimization of the generator set phase advance depth and the reactor switching state according to the operation data of the power system includes, in the iteration process of the improved hybrid adaptive whale algorithm, real-time calling of the power system operation data, updating of the node voltage and power distribution information, and driving of the synchronous adjustment of the generator set phase advance depth and the reactor switching state based on the operation data.

[0011] As a preferred scheme of the improved whale algorithm-based reactor and multi-machine phase advance collaborative optimization method, wherein: the generation of the collaborative control instruction set based on the optimal solution includes, based on the optimal solution set, extracting the phase advance depth configuration value of each generator set and the switching number configuration value of each reactor; Generating a generator phase advance adjustment instruction according to the phase advance depth configuration value and generating a reactor switching control instruction according to the reactor switching number configuration value; The generator phase advance adjustment instruction and the reactor switching control instruction are uniformly coded and synchronized to form a collaborative control instruction set; The collaborative control instruction set is jointly issued, so that the generator set adjusts the reactive power absorption according to the phase advance adjustment instruction, and the reactor performs switching operation according to the switching control instruction, so as to realize voltage distribution optimization and reactive power scheduling cost reduction.

[0012] In the second aspect, the improved whale algorithm-based reactor and multi-machine phase advance collaborative optimization system is provided, which comprises: a data acquisition module, which obtains power flow distribution and small disturbance characteristic data by using a power system calculation program, determines the generator set for phase advance operation according to the participation degree of the dominant electromechanical mode, and records the maximum allowed phase advance depth; The optimization modeling module, under the constraint of the maximum allowed power angle depth, combines power flow distribution data, voltage measurement data and reactor operation information to establish a multi-objective optimization model with the minimum voltage deviation and the minimum reactive power scheduling cost as the targets; The solving module solves the multi-objective optimization model by using an improved hybrid adaptive whale algorithm, adaptively updates search parameters in the iteration process, and synchronously optimizes the power angle depth of the generator set and the switching state of the reactor according to the operation data of the power system to obtain an optimal solution set. The control instruction generation module generates a set of coordinated control instructions based on the optimal solution, jointly issues the power angle depth adjustment instruction of the generator set and the switching control instruction of the reactor, and realizes global optimization of voltage distribution and reduction of reactive power scheduling cost.

[0013] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the improved whale algorithm-based reactor and multi-machine power angle coordination optimization method according to the first aspect of the present application.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the improved whale algorithm-based reactor and multi-machine power angle coordination optimization method according to the first aspect of the present application.

[0015] The present application has the following beneficial effects: By introducing power flow calculation and small disturbance analysis, the power angle operating generator set can be accurately identified based on the participation degree of the dominant electromechanical mode, and the maximum allowed power angle depth can be determined to realize quantifiable control of power angle adjustment; On this basis, a multi-objective optimization model of voltage deviation and reactive power scheduling cost is constructed by combining power flow distribution data, voltage measurement data and reactor operation information, so that the optimization process takes into account both system stability and economy; The improved hybrid adaptive whale algorithm is used to solve the model, and the adaptive parameter updating and continuous-discrete variable coordinated updating mechanism are used to improve the balance ability of the algorithm between global search and local convergence; Finally, the power angle depth adjustment instruction and the reactor switching control instruction are generated and jointly issued to realize the coordinated control of multi-machine power angle and reactor scheduling, effectively improve the balance of system voltage distribution and the efficiency of reactive power scheduling, reduce the operation cost and enhance the overall stability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0017] Figure 1 Flow chart of the method for improving the reactor and multi-machine phase advance cooperation optimization method of the whale optimization algorithm.

[0018] Figure 2 Optimization system object of the method for improving the reactor and multi-machine phase advance cooperation optimization method of the whale optimization algorithm.

[0019] Figure 3 Distribution diagram of the node voltage under the unoptimized working condition of the cooperation control strategy of the method for improving the reactor and multi-machine phase advance cooperation optimization method of the whale optimization algorithm.

[0020] Figure 4 Distribution diagram of the node voltage under the pure reactor regulation working condition of the cooperation control strategy of the method for improving the reactor and multi-machine phase advance cooperation optimization method of the whale optimization algorithm. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0022] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment that excludes other embodiments.

[0024] Embodiment 1, with reference to Figure 1 As one embodiment of the present application, the embodiment provides a method for improving the reactor and multi-machine phase advance cooperation optimization method of the whale optimization algorithm, comprising the following steps: S1: obtaining the power flow distribution and small disturbance characteristic data by using a power system calculation program, determining the generator set for phase advance operation according to the participation degree of the dominant electromechanical mode, and recording the maximum allowable phase advance depth.

[0025] The power system calculation program is used to calculate the power flow distribution of the system; based on the power flow calculation result, the power system calculation program small disturbance calculation module is used to solve the system characteristic values and their eigenvectors, and modal analysis is performed; after all the electromechanical modes are obtained, the related degree of each unit in the dominant electromechanical mode of the system is determined to determine the units for phase advancement operation; it is judged whether the terminal voltage of each generator set for phase advancement operation is equal to 95% UN and whether the real part of the corresponding dominant eigenvalue is greater than or equal to zero, if one of them is satisfied, then the reactive power value absorbed by the unit at this time is the maximum phase advancement depth under the test condition. Otherwise, when the terminal voltage of the generator set for phase advancement operation is lower than 95% UN or the real part of the eigenvalue is greater than zero, the reactive power value absorbed by the unit for phase advancement operation is reduced; otherwise, when the operating state of the unit for phase advancement operation satisfies the stability constraint, the reactive power value absorbed by the generator is increased. The maximum allowed phase advancement depth (absorbed reactive power value Qmax) of each phase advancement unit under the current calculation condition is recorded. In this embodiment, the power system calculation program is PSASP.

[0026] The stability constraint is specifically that the terminal voltage is greater than 95% UN or the real part of the eigenvalue is less than zero.

[0027] Through the power flow calculation and small disturbance analysis module of PSASP, the node power flow distribution and eigenvalue information can be accurately obtained under the system operating condition, the oscillation coupling relationship between the units is quantitatively identified, and the randomness and uncertainty caused by the traditional experience-based selection of the phase advancement unit are avoided.

[0028] The phase advancement operation unit is selected according to the participation degree of the dominant electromechanical mode, which can effectively identify the unit that contributes most to the system oscillation mode, realize targeted phase advancement regulation, and improve the system damping performance and modal stability.

[0029] Through dynamic judgment and cyclic correction of the terminal voltage and the real part of the eigenvalue, the maximum allowed phase advancement depth of each unit can be determined, the phase advancement operation is kept within the stability boundary, the voltage collapse and over-compensation phenomenon are avoided, and the system safety margin is ensured.

[0030] S2: Under the constraint of the maximum allowed phase advancement depth, a multi-objective optimization model is established with the minimum voltage deviation and the minimum reactive power dispatching cost as the objectives, in combination with the power flow distribution data, voltage measurement data and reactor operation information.

[0031] In order to realize the overall balance of the voltage distribution, first, the sum of the deviations of the node voltages and the target voltages of the whole network is taken as one of the optimization objectives.

[0032] The voltage deviation Objective function: wherein, represents the node The measured voltage; Represents a node Target voltage; Indicates the number of load nodes; This indicates the number of reactive power sources.

[0033] The objective function is designed to make the overall voltage level of the system more stable by minimizing the deviation of the voltage at all nodes, thus avoiding local overvoltage or undervoltage phenomena.

[0034] To reduce the reactive power compensation cost and reactor operating frequency of the system, a reactive power dispatch cost objective function is established: in, This represents the reactive power dispatch cost coefficient. This represents the change in reactive power compensation of the reactor group. For a moment The number of reactors switched on; This represents the number of reactors switched on / off at the previous moment; This represents the reactive power compensation difference corresponding to a single switching operation of the reactor. This represents the total number of reactors. This represents the cost of reactive power dispatching.

[0035] By minimizing The model can reduce equipment wear and operating costs caused by frequent switching of reactors, achieving a balance between economy and safety.

[0036] To ensure the physical rationality of power distribution, power flow balance constraints are introduced into the optimization model: in, , They are nodes The active power and reactive power of a generator; , They are nodes The active and reactive power of the load; They are nodes With nodes The conductance and susceptance of the lines between them; For nodes With nodes The phase angle difference.

[0037] To ensure the system voltage remains within a safe operating range, voltage amplitude constraints are set: in: , They are nodes The lower and upper limits of the voltage. This constraint is used to prevent node voltages from exceeding the safe operating range and to ensure that the system is in a stable operating condition.

[0038] Considering the discrete characteristics and capacity limitations of reactor switching, the model introduces a reactive power output constraint: in, For reactive power compensation output of reactor bank; The number of reactors switched on / off (discrete variable); This represents the reactive power compensation difference for a single switching operation of the reactor; , These represent the upper and lower limits of the number of cuts.

[0039] To prevent mechanical fatigue and system oscillation caused by frequent reactor operation, a switching speed limit is added to the model: in, , These represent the number of reactors switched on and off in two consecutive time periods; This represents the maximum allowable change in switching within a unit of time period.

[0040] After considering both the voltage deviation target and the reactive power dispatch cost target, a multi-objective optimization model is formed.

[0041] By combining power flow data, voltage measurement data, and reactor operation information to establish optimization inputs, the active and reactive power distribution characteristics of the system under real-time operation can be reflected, providing accurate boundary conditions for multi-objective optimization.

[0042] By constructing a dual-objective model that minimizes both voltage deviation and reactive power dispatch cost, voltage quality and economy are balanced within the same framework, avoiding the local optimum problem caused by a single objective in traditional optimization.

[0043] Applying multiple constraints such as power flow balance, node voltage, reactor output, and switching rate to the model can effectively limit the solution space range and improve the feasibility of the solution and the engineering feasibility of the results.

[0044] S3: An improved hybrid adaptive whale algorithm is used to solve the multi-objective optimization model. During the iteration process, the search parameters are updated adaptively, and the generator phase depth and reactor switching status are optimized synchronously based on the power system operation data to obtain the optimal solution set.

[0045] Based on the multi-objective optimization model established in step S2, the system employs an improved Hybrid Adaptive Whale Optimization Algorithm (HA-WOA) for solution. This algorithm introduces parameter adaptation and a hybrid variable update mechanism on top of the traditional Whale Optimization Algorithm (WOA) to simultaneously handle continuous and discrete variables, achieving synchronous optimization of the generator set's phase advance depth and reactor switching status. Specifically, it includes the following steps: Set whale population size Maximum number of iterations Mixed variable dimensions (Including phase advance depth and reactor switching status).

[0046] Position vector of each individual Represented as: in, The continuous variable part represents the phase advance depth of each generator set; This represents the discrete variable part, indicating the switching quantity of each reactor. A position vector is randomly initialized for each individual. And set the initial shrinkage coefficient and historical fluctuation adjustment parameters.

[0047] Based on the voltage deviation objective of the multi-objective optimization model With reactive power dispatch cost target Calculate the fitness vector for each individual. : in, Represents the objective function for voltage deviation; This represents the objective function for reactive power scheduling costs. It is obtained through calculation. The non-dominated sorting and crowding distance calculations are performed to form individual fitness levels, providing an evaluation basis for subsequent iterations.

[0048] To avoid the local optima problem caused by the excessively fast convergence speed of traditional WOA, an adaptive parameter update mechanism is introduced to dynamically adjust the search step size. During the iteration process, the shrinkage encirclement coefficient is calculated based on the current iteration number t and historical error fluctuations. Historical volatility factor With adaptive coefficients : in, Indicates the historical fluctuation adjustment factor; Let represent the root mean square error of the i-th iteration; This represents the adaptive coefficient, used to adjust the search intensity.

[0049] This dynamic correction mechanism enables the algorithm to maintain global exploration capabilities in the early stages and enhance local convergence accuracy in the later stages.

[0050] To optimize both continuous and discrete variables simultaneously, a partitioned update strategy is adopted.

[0051] Based on the current global best individual Perform a surround update: in, This indicates the current globally optimal position. This represents a random weight vector used to control the search direction; This represents the distance vector between the current position and the optimal position. Indicates the number of iterations. The position of the continuous variable part; Indicates the number of iterations. The position of the continuous variable part.

[0052] Discrete variable updates employ a round-robin mechanism: And it is corrected in conjunction with the reactor switching speed constraint: in, Represents the distance vector of discrete variables; Represents the rounding operator Indicates the number of iterations The number of reactors switched on; Indicates the number of iterations in a time period The number of reactors switched on; This indicates the maximum allowable change in the number of shipments per unit time period; Indicates the number of iterations. The position of the discrete variable part.

[0053] In some iterations, with probability Perform spiral position updates to enhance global search capabilities: in, This indicates the distance between the current position and the optimal position. Indicates the helical shape constant; This represents a random number whose value ranges from [-1, 1].

[0054] After each iteration, Pareto non-dominated sorting and congestion distance sorting are performed based on the individual's fitness vector to construct an elite solution library. The individuals stored in the elite solution library represent the set of compromise solutions that minimize voltage deviation and reactive power scheduling cost in the current iteration. Subsequent iterations are guided by individuals in the elite solution library to achieve adaptive adjustment of the search direction.

[0055] When the number of iterations Continue updating as needed; When the optimal solution is obtained, the algorithm terminates and outputs the optimal solution set from the elite solution library. This optimal solution set includes the optimal configuration of the generator set's leading phase depth and the optimal combination of reactor switching states, which are used for subsequent coordinated control command generation.

[0056] in, This represents the current iteration number, and T represents the maximum iteration number.

[0057] By introducing a hybrid variable dimension and a continuous-discrete collaborative update strategy, the generator phase advance depth and reactor switching state can be synchronously adjusted within the same optimization framework, overcoming the limitation of traditional algorithms that cannot handle two types of variables at the same time.

[0058] By using historical error fluctuations to correct the shrinkage encirclement coefficient and inertia parameter, the global exploration and local convergence processes of the algorithm can be dynamically balanced, thereby improving search accuracy and convergence speed.

[0059] By employing a spiral update mechanism and an elite solution set selection strategy during iteration, the algorithm can effectively avoid premature convergence, maintain the diversity of solutions, and achieve a global optimal approximation of multi-objective functions.

[0060] S4: Generate a collaborative control instruction set based on the optimal solution, and jointly issue the generator set leading phase depth adjustment instruction and the reactor switching control instruction to achieve full-domain optimization of voltage distribution and reduction of reactive power dispatch cost.

[0061] After obtaining the optimal solution set of the improved hybrid adaptive whale algorithm, the system generates a set of cooperative control commands based on the output of the optimal solution. The optimal solution set includes the generator phase advance depth configuration value and the reactor switching quantity configuration value. The system generates generator phase advance adjustment commands based on the phase advance depth configuration value and reactor switching control commands based on the reactor switching quantity configuration value.

[0062] The generated generator regulation commands and reactor switching control commands are uniformly encoded and synchronized in time and space to form a collaborative control command set, which is then jointly issued to the execution layer for execution through the control system.

[0063] After the command is executed, the generator set completes the reactive power absorption adjustment according to the leading phase adjustment command, and the reactor completes the switching operation according to the control command, thereby achieving voltage distribution optimization and reactive power dispatch cost reduction within the system.

[0064] By extracting the configuration values ​​of the generator set leading phase depth and the reactor switching quantity from the optimal solution, the parameterized correlation between leading phase regulation and reactive power compensation can be realized, so that the generation of control commands has a clear physical correspondence.

[0065] By jointly encoding and synchronously issuing the phase-advancing regulation command and the reactor switching control command, a multi-layered collaborative control structure is formed, which can ensure that the voltage regulation action remains coordinated in time and space and avoid control conflicts.

[0066] After the coordinated control is implemented, the voltage distribution of the entire network tends to be balanced, the phenomenon of node voltage exceeding the limit is eliminated, the reactive power dispatch cost is significantly reduced, and the system is comprehensively improved in terms of operational safety, stability and economy.

[0067] This embodiment also provides an improved whale algorithm reactor and multi-machine leading phase cooperative optimization system, including: a data acquisition module, which uses a power system calculation program to obtain power flow distribution and small disturbance characteristic data, determines the generator set operating in leading phase based on the participation degree of the dominant electromechanical mode, and records the maximum allowable leading phase depth.

[0068] The optimization modeling module, under the constraint of the maximum allowable phase advance depth, combines power flow distribution data, voltage measurement data and reactor operation information to establish a multi-objective optimization model with the goals of minimizing voltage deviation and minimizing reactive power dispatch cost.

[0069] The solution module uses an improved hybrid adaptive whale algorithm to solve the multi-objective optimization model. During the iteration process, the search parameters are updated adaptively, and the generator phase depth and reactor switching status are optimized synchronously based on the power system operation data to obtain the optimal solution set.

[0070] The control command generation module generates a set of collaborative control commands based on the optimal solution. It jointly issues the generator set phase advance depth adjustment command and the reactor switching control command to achieve full-domain optimization of voltage distribution and reduction of reactive power dispatch costs.

[0071] Example 2 is an embodiment of the present invention, which provides an improved method and system for reactor and multi-machine phase-advancing cooperative optimization of the whale algorithm. In order to verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0072] Figure 2The modified CEPRI-36 test system specifically covers 36 bus nodes, interconnected by transmission lines to form a complex network, with a system base power of 100MW. This network includes 8 synchronous generators and 9 load nodes, exhibiting significant overvoltage characteristics in some areas, as well as reactive power sensitive areas with marked differences in electrical distance. The network topology is characterized by multiple power source support, prominent regional voltage issues, and significant heterogeneity in the electrical positions of the generating units, providing a typical scenario for verifying the spatial complementarity of multi-generator coordinated phase advance and reactor switching.

[0073] Figure 3 This is a diagram showing the distribution of node voltages under the cooperative control strategy and unoptimized operating conditions. Figure 3 It can be seen that the collaborative control strategy has achieved a qualitative leap from an unoptimized state to a state of overall voltage safety. Before optimization, the system exhibited significant overvoltage risks (e.g., 1.2188 pu at node 12, and >1.179 pu for nodes 30–33), and local node voltage distortion (nodes 4, 7, and 8 >1.15 pu). After collaborative optimization, the entire network voltage was strictly constrained within the safe range: the highest voltage dropped to 1.0686 pu at node 25 (a reduction of 11.7%), completely eliminating overvoltage nodes; the voltage in key weak areas, such as nodes 30–33, dropped from >1.179 pu to 0.9878 pu (a reduction of 16.2%), effectively suppressing the risk of fluctuations and instability caused by capacitive current; simultaneously, the voltage at the unit connection points (nodes 4, 7, and 8) remained precisely stable between 0.953 and 0.955 pu, without inducing any low-voltage over-limits (the lowest voltage was 0.9878 pu > 0.95 pu). This indicates that the step cutoff of the reactor quickly eliminates the excess capacitive reactive power in the region, while the leading phase of multiple units provides continuous and precise compensation. Together, these two factors effectively address the voltage safety issue.

[0074] Figure 4 This is a diagram showing the distribution of node voltages under coordinated control strategies and pure reactor regulation conditions. Figure 4 It is evident that while pure reactor regulation detached from the leading phase of the unit can partially suppress overvoltage, it has inherent drawbacks. For example... Figure 4 As shown, after the pure reactor is switched on and off, the voltage at nodes 4, 7, and 8 rebounds and exceeds 1.10 pu (e.g., node 4: 1.115 pu). Due to the lack of continuous reactive power absorption capability, the overvoltage on the power supply side cannot be completely resolved. The voltage at nodes 30–33 rises to 1.126 pu (14% higher than that of coordinated control), exposing the inadequacy of discrete regulation in the dynamic response to capacitive current. In contrast, the unit leading phase depth optimization in coordinated control reduces the reactor operating frequency by 37%, significantly reducing the reactive power dispatch cost Qall. This fully verifies the necessity of the coordinated architecture of "reactor + multi-machine leading phase operation": the reactor provides large-capacity rapid support, while the unit leading phase achieves dynamic reactive power balance; neither can be dispensed with.

[0075] This embodiment also provides a computer device applicable to the improved whale algorithm reactor and multi-machine phase-advancing cooperative optimization method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the improved whale algorithm reactor and multi-machine phase-advancing cooperative optimization method as proposed in the above embodiment.

[0076] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0077] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the reactor and multi-machine phase-cooperative optimization method for implementing the improved whale algorithm as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0078] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for improving the optimization of reactors and multi-machine phase advancement coordination by using a whale optimization algorithm, characterized in that, The method comprises the following steps: obtaining power flow distribution and small disturbance characteristic data by using a power system calculation program, determining generator units for leading-phase operation according to the participation degree of dominant electromechanical modes, and recording the maximum allowable leading-phase depth; under the constraint of the maximum allowable leading-phase depth, combining power flow distribution data, voltage measurement data and reactor operation information, and establishing a multi-objective optimization model with the minimum voltage deviation and the minimum reactive power dispatching cost as the objectives; solving the multi-objective optimization model by using an improved hybrid adaptive whale optimization algorithm, adaptively updating search parameters in the iteration process, and synchronously optimizing the leading-phase depth of the generator units and the switching state of the reactors according to the operation data of the power system to obtain an optimal solution set; generating a set of coordinated control instructions based on the optimal solution, jointly issuing the leading-phase depth adjustment instructions of the generator units and the switching control instructions of the reactors, and realizing global optimization of voltage distribution and reduction of reactive power dispatching cost.

2. The method of claim 1, wherein the method is characterized by: The determination of the generator units for leading-phase operation comprises the following steps: performing power flow calculation by using the power system calculation program, calling the small disturbance calculation module of the power system calculation program according to the results of the power flow calculation, and performing characteristic solving and mode identification; according to the results of the mode identification, screening the generator units participating in the dominant electromechanical modes, and establishing a set of leading-phase operation units; 3. The method of claim 2, wherein the method is characterized by: under the set of leading-phase operation units, adjusting the reactive power absorption state of the units according to the operation constraint, and performing cyclic calculation combined with system stability determination; when the operation state meets the stability constraint, the corresponding maximum allowable leading-phase depth is recorded. The establishment of the multi-objective optimization model comprises the following steps: under the constraint of the maximum allowable leading-phase depth of each generator unit, calling the power system calculation program to read power flow distribution data, voltage measurement data and reactor operation information, and generating optimization input data; based on the optimization input data, performing node voltage difference calculation to determine the offset between the node voltage and the target voltage; 4. The method of claim 3, wherein the method is characterized by: after completing the voltage difference calculation, performing reactor switching state statistics to determine the switching change amount of the adjacent period; 5. The method of claim 4, wherein the method is characterized by: according to the voltage offset and the switching change amount, establishing a voltage deviation minimization objective function and a reactive power dispatching cost minimization objective function; after the establishment of the objective functions, applying multi-layer constraint conditions to form a multi-objective optimization model with the minimum voltage deviation and the minimum reactive power dispatching cost as the objectives. The constraint conditions comprise: a power flow power balance constraint for limiting the active and reactive power distribution relationship among the generators, loads and reactors; a node voltage constraint for limiting the node voltage amplitude to remain in a safe operation range; a reactor output constraint for limiting the reactive power compensation capacity in the allowable range; and a reactor switching rate constraint for limiting the switching action frequency of the continuous period. The improved hybrid adaptive whale optimization algorithm comprises the following steps: setting the number of whale populations, the maximum number of iterations and the dimension of the mixed variables including the leading-phase depth of the generator units and the switching state of the reactors, and generating an initial population; according to the voltage deviation objective and the reactive power dispatching cost objective, calculating the fitness value of each population individual, and performing Pareto non-dominated sorting and crowding distance sorting on the fitness results; The search parameters are updated according to the current iteration process and historical error fluctuation, so that the search process maintains adaptive balance between global exploration and local convergence; For the mixed variables of generator unit phase advance depth and reactor switching state, continuous variable updating and discrete variable updating operations are respectively performed, and the switching speed constraint is modified in the discrete variable updating process; In some iterations, a spiral search mechanism is performed, so that the population individuals perform global search and local approximation around the global optimal solution; After each iteration, the elite solution set is constructed by selecting excellent individuals according to the fitness evaluation results; When the preset termination condition is reached, the optimal solution set is output, which includes the optimal configuration of the generator unit phase advance depth and the optimal combination of the reactor switching state.

6. The method of claim 5, wherein the method is characterized by: The improved hybrid adaptive whale algorithm is used to optimize the generator unit phase advance depth and the reactor switching state in the iteration process, real-time power system operation data is called, the node voltage and power distribution information are updated, and the synchronous adjustment of the generator unit phase advance depth and the reactor switching state is driven based on the operation data.

7. The method of claim 6, wherein the method is characterized by: The optimal solution set is used to generate a cooperative control instruction set, the phase advance depth configuration value of each generator unit and the switching number configuration value of each reactor are extracted based on the optimal solution set; The generator phase advance adjustment instruction is generated according to the phase advance depth configuration value, and the reactor switching control instruction is generated according to the switching number configuration value of the reactor; The generator phase advance adjustment instruction and the reactor switching control instruction are uniformly coded and synchronized to form a cooperative control instruction set; The cooperative control instruction set is jointly issued, so that the generator unit adjusts the reactive power absorption according to the phase advance adjustment instruction, and the reactor performs switching operation according to the switching control instruction, so as to realize voltage distribution optimization and reactive power dispatching cost reduction.

8. The system for optimizing the reactor and multi-machine phase advancement coordination based on the improved whale optimization algorithm according to any one of claims 1-7, characterized in that: The data acquisition module uses the power system calculation program to obtain the power flow distribution and small disturbance characteristic data, determines the generator unit for phase advance operation according to the participation degree of the dominant electromechanical mode, and records the maximum allowed phase advance depth; The optimization modeling module establishes a multi-objective optimization model with the minimum voltage deviation and the minimum reactive power dispatching cost as the objectives under the constraint of the maximum allowed phase advance depth, and combines the power flow distribution data, voltage measurement data and reactor operation information; The solving module uses the improved hybrid adaptive whale algorithm to solve the multi-objective optimization model, adaptively updates the search parameters in the iteration process, and synchronously optimizes the generator unit phase advance depth and the reactor switching state based on the operation data of the power system to obtain the optimal solution set; The control instruction generation module generates a cooperative control instruction set based on the optimal solution, jointly issues the generator unit phase advance depth adjustment instruction and the reactor switching control instruction, and realizes global optimization of voltage distribution and reduction of reactive power dispatching cost. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the improved whale algorithm-based reactor and multi-machine phase advance cooperative optimization method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the improved whale algorithm-based reactor and multi-machine phase advance cooperative optimization method of any one of claims 1-7.

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