Series capacitance compensator configuration optimization method and device, terminal equipment and storage medium

Abstract

The invention discloses a configuration optimization method and device for series capacitance compensators, terminal equipment and a storage medium, and the method comprises the steps: taking the equipment operation parameters of a power distribution network, the installation positions of the series capacitance compensators, and the reactance compensation coefficients of the series capacitance compensators for a line where the series capacitance compensators are located as control variables; randomly generating a plurality of groups of initial control variable sets, taking one group of initial control variable sets as an initial planet, and repeatedly performing iterative optimization operation on the plurality of initial planets under a preset constraint function for maintaining the power flow stability of the power distribution network until the fitness value of the generated target sun is greater than a preset threshold value, and stopping the iterative optimization operation, and generating a configuration optimization scheme of the series capacitance compensator. Therefore, through the advantages of the Kepler optimization algorithm in the complex nonlinear optimization problem, the Kepler optimization algorithm is applied to configuration optimization of the series capacitor compensator, and the problem that a TCSC configuration scheme output by an existing optimization configuration algorithm cannot effectively reduce the power transmission loss is solved.

Description

Technical Field

[0001] This invention relates to the field of power system optimization configuration technology, and in particular to a configuration optimization method, apparatus, terminal equipment and storage medium for series capacitor compensators. Background Technology

[0002] With the large-scale grid integration of renewable energy and the diversification of power loads, the complexity and uncertainty of distribution networks have increased significantly. The static operation mode of traditional distribution networks is ill-suited to adapting to time-varying loads and power fluctuations from distributed generation, leading to increased line losses and decreased voltage stability. To address these challenges, Flexible AC Transmission Systems (FACTS) technology is widely used in grid regulation, among which the Thyristor-controlled Series Capacitor Compensator (TCSC) has become a key component due to its rapid response and continuous regulation capabilities. TCSC effectively improves power distribution and reduces transmission losses by dynamically adjusting the equivalent reactance of the line.

[0003] However, the optimal configuration and parameter tuning of TCSC are highly dependent on the grid topology and operating conditions. Existing studies mostly use linear programming or traditional intelligent algorithms for solving the problem. However, with the widespread integration of distributed generation, the nonlinear characteristics of the optimization problem are further enhanced. Therefore, existing configuration algorithms are difficult to adapt to the operating conditions of new distribution networks, resulting in the output TCSC configuration scheme failing to effectively improve power distribution and reduce transmission losses. Summary of the Invention

[0004] This invention provides a configuration optimization method, apparatus, terminal device, and storage medium for a series capacitor compensator. The method can overcome the problem that the TCSC configuration scheme output by existing optimization configuration algorithms cannot effectively reduce power transmission loss.

[0005] An embodiment of the present invention provides a method for optimizing the configuration of a series capacitor compensator, comprising: Obtain the topology, power flow parameters, and equipment operating parameters of the distribution network; The operating parameters of the equipment, the installation location of the series capacitor compensator, and the reactance compensation coefficient of each series capacitor compensator to the line are used as control variables, and the power flow parameters are used as state variables. Based on the topology, the control variables, and the state variables, an objective function is constructed with the goal of minimizing the active power loss of the distribution network. Several sets of initial control variables are randomly generated. Each set of initial control variables is used as an initial planet. The initial fitness value of each initial planet is calculated according to the objective function. The initial planet corresponding to the largest initial fitness value is taken as the initial sun. Under the preset constraint function used to maintain the stability of the power flow of the distribution network, the iterative optimization operation is repeatedly performed on several of the initial planets until the fitness value of the generated target sun is greater than the preset threshold, at which point the iterative optimization operation stops. Based on the set of control variables corresponding to the target sun, a configuration optimization scheme for the series capacitor compensator is generated.

[0006] Furthermore, the random generation of several sets of initial control variables, using one set of initial control variables as an initial planet, includes: Obtain the upper and lower threshold values ​​for each type of control variable; The difference between the upper and lower threshold values ​​of each type of control variable is multiplied by any random number to generate several initial values ​​for each type of control variable; wherein the random number ranges from [0,1]. The sum of each initial value of the control variables and the corresponding lower limit threshold is used as the initial control variable; Based on the initial control variables of each type of control variable, construct several sets of initial control variables; A set of initial control variables is used as an initial planet. Any random number is used as the orbital eccentricity of the initial planet, and the absolute value of any normally distributed random number is used as the orbital time of the initial planet. The initial control variables in each set of initial control variables are used as the initial elements in the initial planet.

[0007] Furthermore, the step of using the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly performing iterative optimization operations on several of the initial planets under a preset constraint function for maintaining the stability of the power flow in the distribution network, until the fitness value of the generated target sun is greater than a preset threshold, and then stopping the iterative optimization operation, includes: Based on the initial sun and several initial planets, the iterative optimization operation is repeated until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops. The iterative optimization operation includes: Obtain the planet to be optimized and the sun to be optimized; wherein, initially, the planet to be optimized is the initial planet, and the sun to be optimized is the initial sun; Based on the orbital eccentricity of the planets to be optimized, the gravitational force of the Sun to be optimized on each planet to be optimized is calculated, and the velocity of each planet to be optimized is calculated based on the gravitational force. Under a preset constraint function, the positions of each planet to be optimized are updated according to the velocity to generate planets to be evaluated; Calculate the first fitness value for each planet to be evaluated based on the objective function; Determine if there exists a first fitness value greater than a preset threshold; If so, the planet to be evaluated corresponding to the first fitness value that is greater than the preset threshold will be taken as the target sun, and the iterative optimization operation will end. If not, then based on the first fitness value and the second fitness value of the planet to be optimized, the planets to be optimized and the planets to be evaluated are sorted in reverse order, and the planet ranked first is selected as the sun to be optimized for the next round of iterative optimization operation. In addition, a preset number threshold of planets to be optimized and planets to be evaluated are selected in sequence as the planets to be optimized for the next round of iterative optimization operation.

[0008] Furthermore, generating the configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun includes: Based on the installation location and reactance compensation coefficient in the set of control variables corresponding to the target sun, calculate the firing angle of the thyristor controlling each of the series capacitor compensators; Based on the installation location and corresponding trigger angle of the series capacitor compensator, an optimized configuration scheme for the series capacitor compensator is generated.

[0009] An embodiment of the present invention also provides a configuration optimization device for a series capacitor compensator, comprising: The parameter acquisition module is used to acquire the topology, power flow parameters, and equipment operating parameters of the distribution network. The variable construction module is used to take the equipment operating parameters, the installation position of the series capacitor compensator, and the reactance compensation coefficient of each series capacitor compensator to the line as control variables, and the power flow parameters as state variables. The function construction module is used to construct an objective function with the goal of minimizing the active power loss of the distribution network, based on the topology, the control variables, and the state variables. An initialization module is used to randomly generate several sets of initial control variables, take one set of initial control variables as an initial planet, and calculate the initial fitness value of each initial planet according to the objective function. The iterative optimization module is used to take the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly perform iterative optimization operations on several initial planets under the preset constraint function used to maintain the stability of the power flow of the distribution network until the fitness value of the generated target sun is greater than the preset threshold, at which point the iterative optimization operation stops. The scheme generation module is used to generate a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun.

[0010] Furthermore, the initialization module randomly generates several sets of initial control variables, and uses one set of initial control variables as an initial planet, including: Obtain the upper and lower threshold values ​​for each type of control variable; The difference between the upper and lower threshold values ​​of each type of control variable is multiplied by any random number to generate several initial values ​​for each type of control variable; wherein the random number ranges from [0,1]. The sum of each initial value of the control variables and the corresponding lower limit threshold is used as the initial control variable; Based on the initial control variables of each type of control variable, construct several sets of initial control variables; A set of initial control variables is used as an initial planet. Any random number is used as the orbital eccentricity of the initial planet, and the absolute value of any normally distributed random number is used as the orbital time of the initial planet. The initial control variables in each set of initial control variables are used as the initial elements in the initial planet.

[0011] Furthermore, the iterative optimization module takes the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly performs iterative optimization operations on several of the initial planets under a preset constraint function used to maintain the stability of the power flow in the distribution network, until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops, including: Based on the initial sun and several initial planets, the iterative optimization operation is repeated until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops. The iterative optimization operation includes: Obtain the planet to be optimized and the sun to be optimized; wherein, initially, the planet to be optimized is the initial planet, and the sun to be optimized is the initial sun; Based on the orbital eccentricity of the planets to be optimized, the gravitational force of the Sun to be optimized on each planet to be optimized is calculated, and the velocity of each planet to be optimized is calculated based on the gravitational force. Under a preset constraint function, the positions of each planet to be optimized are updated according to the velocity to generate planets to be evaluated; Calculate the first fitness value for each planet to be evaluated based on the objective function; Determine if there exists a first fitness value greater than a preset threshold; If so, the planet to be evaluated corresponding to the first fitness value that is greater than the preset threshold will be taken as the target sun, and the iterative optimization operation will end. If not, then based on the first fitness value and the second fitness value of the planet to be optimized, the planets to be optimized and the planets to be evaluated are sorted in reverse order, and the planet ranked first is selected as the sun to be optimized for the next round of iterative optimization operation. In addition, a preset number threshold of planets to be optimized and planets to be evaluated are selected in sequence as the planets to be optimized for the next round of iterative optimization operation.

[0012] Furthermore, the scheme generation module generates a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun, including: Based on the installation location and reactance compensation coefficient in the set of control variables corresponding to the target sun, calculate the firing angle of the thyristor controlling each of the series capacitor compensators; Based on the installation location and corresponding trigger angle of the series capacitor compensator, an optimized configuration scheme for the series capacitor compensator is generated.

[0013] This application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a configuration optimization method for a series capacitor compensator as described in the above embodiments of the invention.

[0014] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a configuration optimization method for a series capacitor compensator as described in the above embodiments.

[0015] The following benefits can be obtained by implementing the present invention: This invention provides a method, apparatus, terminal device, and storage medium for optimizing the configuration of series capacitor compensators. The method uses the operating parameters of the distribution network equipment, the installation location of the series capacitor compensators, and the reactance compensation coefficient of each series capacitor compensator to its respective line as control variables, and power flow parameters as state variables. Based on the topology, the control variables, and the state variables, an objective function is constructed to minimize the active power loss of the distribution network. Several sets of initial control variables are randomly generated, and each set is considered an initial planet. The initial fitness value of each initial planet is calculated according to the objective function. The initial planet corresponding to the largest initial fitness value is taken as the initial sun. Under a preset constraint function used to maintain the stability of the distribution network power flow, iterative optimization operations are repeatedly performed on several initial planets until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops. Based on the set of control variables corresponding to the target sun, a configuration optimization scheme for the series capacitor compensators is generated. Therefore, this invention uses the Kepler optimization algorithm to simulate the gravitational interaction and orbital dynamics characteristics in planetary motion, demonstrating significant advantages in complex nonlinear optimization problems. It can effectively balance the exploration and development process, avoid premature convergence, and overcome the problem that the TCSC configuration scheme output by existing optimization configuration algorithms cannot effectively reduce power transmission loss. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a configuration optimization method for a series capacitor compensator according to a certain embodiment of this application; Figure 2 This is a schematic diagram of the configuration optimization device for a series capacitor compensator provided in a certain embodiment of this application; Figure 3 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of this application; Figure 4 This is a schematic diagram illustrating the convergence characteristics of the algorithm in Case 1 provided in a certain embodiment of this application; Figure 5 This is a schematic diagram illustrating the convergence characteristics of the algorithm in Case 2 provided in a certain embodiment of this application; Figure 6 This is a schematic diagram illustrating the convergence characteristics of the algorithm in Case 3 provided in a certain embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0020] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0023] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0024] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0025] See Figure 1 To address the problems in the prior art, an embodiment of the present invention provides a method for optimizing the configuration of a series capacitor compensator, comprising: S1. Obtain the topology, power flow parameters, and equipment operating parameters of the distribution network; In a preferred embodiment of the present invention, the topology includes the topological connection of each node in the distribution network and the configuration of equipment such as generators and transformers at each node. The power flow parameters include the voltage amplitude of each load bus, the apparent power flow, the admittance of the branches between buses, the voltage phase angle of the node, etc. The equipment operating parameters include the voltage amplitude of the generator, the transformer tap setting, the reactive power injected by the reactive power compensator, the apparent power injected by the generator, the voltage amplitude of the node, etc.

[0026] S2. The operating parameters of the equipment, the installation position of the series capacitor compensator, and the reactance compensation coefficient of each series capacitor compensator to the line are used as control variables, and the power flow parameters are used as state variables. In a preferred embodiment of the present invention, the control variable is: ; Among them, V G Q represents the voltage amplitude of the i-th generator at the photovoltaic bus, T represents the transformer tap setting, and Q represents the voltage amplitude of the i-th generator at the photovoltaic bus. C r represents the reactive power injected by the i-th parallel reactive power compensator. TCSC L is the compensation factor for line reactance by TCSC. TCSC This is the location of TCSC. N GE N represents the number of generators. R N represents the number of regulating transformers. GA This indicates the number of reactive power compensators; TCSCs is the number of TCSCs.

[0027] The state variables are: Among them, V Li Q represents the voltage amplitude of the i-th load bus. Gi S represents the reactive power output of all generator sets.li This represents the apparent power flow of the i-th load bus.

[0028] S3. Based on the topology, the control variables, and the state variables, construct an objective function with the goal of minimizing the active power loss of the distribution network. In a preferred embodiment of the present invention, the objective function is: ; Among them, V i and V j G represents the voltages of buses i and j, respectively. ij Let θ be the admittance of the branch between buses i and j. ij Let be the admittance angle of the transmission line connecting buses i and j. x and u are the control variable and state variable, respectively.

[0029] S4. Randomly generate several sets of initial control variables, take one set of initial control variables as an initial planet, and calculate the initial fitness value of each initial planet according to the objective function. Preferably, the random generation of several sets of initial control variables, and the use of one set of initial control variables as an initial planet, includes: Obtain the upper and lower threshold values ​​for each type of control variable; multiply the difference between the upper and lower threshold values ​​of each type of control variable by any random number to generate several initial values ​​for each type of control variable; wherein the random number ranges from [0,1]; take the sum of each initial value of each type of control variable and its corresponding lower threshold value as an initial control variable; construct several sets of initial control variables based on the initial control variables of each type of control variable; take a set of initial control variables as an initial planet, take any random number as the orbital eccentricity of the initial planet, take the absolute value of any normally distributed random number as the orbital time of the initial planet, and take the initial control variables in each set of initial control variables as the initial elements in the initial planet.

[0030] In a preferred embodiment of the present invention, when optimization begins, KOA distributes N planets in the search space, where N represents the number of planets, each planet consists of d elements, and d is the number of control variables. The following are the mathematical equations describing this random planet distribution technique.

[0031] ; ; in, This represents the j-th element of the i-th planet. and represents the upper and lower threshold values ​​for the j-th element of the i-th planet, respectively. r is a value that varies randomly from [0,1]; N is the number of planets; d is the number of control variables.

[0032] In addition, KOA requires the initialization of additional control parameters, namely orbital eccentricity (e) and orbital time (T), where e and T are randomly initialized for each solution in the population according to the following equations.

[0033] ; ; Where r n They are normally distributed random numbers; The orbital eccentricity of the i-th planet; r is a random number in the interval [0,1]; T i It is the orbital time of the i-th planet.

[0034] S5. Take the initial planet corresponding to the largest initial fitness value as the initial sun, and under the preset constraint function used to maintain the stability of the power flow of the distribution network, repeatedly perform iterative optimization operations on several of the initial planets until the fitness value of the generated target sun is greater than the preset threshold, and then stop the iterative optimization operation. Preferably, the step of using the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly performing iterative optimization operations on several initial planets under a preset constraint function for maintaining the stability of the power distribution network, until the fitness value of the generated target sun is greater than a preset threshold, and then stopping the iterative optimization operation, includes: repeatedly performing iterative optimization operations based on the initial sun and several initial planets until the fitness value of the generated target sun is greater than a preset threshold, and then stopping the iterative optimization operation; wherein, the iterative optimization operation includes: obtaining the planet to be optimized and the sun to be optimized; wherein, initially, the planet to be optimized is the initial planet, and the sun to be optimized is the initial sun; calculating the gravitational force of the sun to be optimized on each of the planets to be optimized based on the orbital eccentricity of the planet to be optimized. The velocity of each planet to be optimized is calculated based on the gravity. Under a preset constraint function, the position of each planet to be optimized is updated according to the velocity to generate planets to be evaluated. The first fitness value of each planet to be evaluated is calculated according to the objective function. It is determined whether there is a first fitness value greater than a preset threshold. If so, the planet to be evaluated corresponding to the first fitness value greater than the preset threshold is taken as the target sun, and the iterative optimization operation ends. If not, the planets to be optimized and the planets to be evaluated are sorted in reverse order according to the first fitness value and the second fitness value of the planets to be optimized. The planet ranked first is taken as the target sun required for the next round of iterative optimization operation, and a preset number threshold of planets to be optimized and planets to be evaluated are selected in sequence as the planets to be optimized required for the next round of iterative optimization operation.

[0035] In a preferred embodiment of the present invention, the preset constraint function includes: equality constraint conditions and inequality constraint conditions; Equality constraints: ; Where SDi represents the apparent power requirement of the i-th node; Inject apparent power into the generator at the i-th node; is the voltage amplitude of the i-th node; NB is the total number of nodes in the system; Let be the electrical conductance of the line between node i and node j; Let be the susceptance of the line between node i and node j; Let be the voltage phase angle of the i-th node; Let be the voltage phase angle of the j-th node.

[0036] Inequality constraints: ; Furthermore, the gravitational pull of the Sun on each of the planets to be optimized is calculated according to the following formula: ; ; ; ; ; ; ; ; Among them, M s and m i Let represent the mass of the Sun to be optimized and the mass of the i-th planet to be optimized, respectively; This represents the gravitational force of the Sun on the i-th planet at the t-th iteration; This represents the orbital eccentricity of the i-th planet; Let represent the gravitational constant at the t-th iteration; represents the normalized distance between planet i and the Sun; ri represents a random number in the interval [0,1]; ξ represents an intermediate variable in the gravitational calculation; ε is the minimum value. r2 is a number that randomly takes a value between 1 and 0, γ is the decay coefficient (constant), μ0 is the initial gravitational constant, and t and Tmax are the current iteration number and the maximum iteration number, respectively; It is the optimal solution at the t-th iteration; It is the j-th dimension position of planet i at the t-th iteration; and Let represent the maximum and minimum distances between all planets and the Sun at the t-th iteration; This represents the optimal fitness (minimum active power loss) at the t-th iteration. This represents the worst fitness (maximum active power loss) at the t-th iteration. This represents the fitness of the k-th planet.

[0037] Furthermore, the planet's orbital speed is directly calculated based on its relative distance from the Sun. In addition, the closer a planet is to the Sun, the stronger the Sun's gravitational pull becomes. To avoid being captured by the Sun's gravity, the planet will actively accelerate; the mathematical model for this behavior is as follows: Among them, V i (t) represents the velocity of the i-th object in the t-th iteration; It is a scaling factor used to adjust the overall speed; r3, r4, and r5 are all randomly generated values ​​in the range of 0 to 1. and The positions of any two planets; and Vectors representing the "upper boundary reference position" and "lower boundary reference position" of the solution space; The position vector corresponding to the i-th planet itself; is a normalized distance index used to determine the "relative distance between the current candidate solution and the global optimum"; F is a control factor used to reflect the search direction, randomly selected as 1 or -1, which allows for the simulation of certain planets rotating clockwise relative to the sun. and It is a random vector in the range [0,1].

[0038] Furthermore, the position of the planet to be optimized is updated using the following formula: ; Where X = x + 1, x = t.

[0039] The distance between the planet to be evaluated and the Sun is calculated using the following formula: ; ; ; ; Where h is the scaling factor used to adjust the overall magnitude of the planetary velocity; η is the dynamic adjustment parameter; r and r4 are random values; TC is the cycle number; and t% is the remainder operator.

[0040] Furthermore, after the planet to be evaluated is generated, the state variables are obtained again through power flow calculation, constraints are checked, and fitness is re-evaluated; finally, the "elite retention strategy" is used to retain only the positions of planets with better fitness, and so on, gradually approaching the optimal solution.

[0041] It should be noted that the equality constraints in the preset constraint functions are forcibly satisfied through power flow calculation (if they are not satisfied, the power flow will not converge and the candidate solution will be invalid); the inequality constraints (such as voltage limits and reactive power range) directly restrict the control variables to the specified upper and lower limits when "initializing planets (generating control variables)", and in the case of "state variables going out of bounds", the penalty function is used to transform the degree of going out of bounds into "fitness increment" (making the calculated value of active power loss larger), so that the algorithm "rejects" non-compliant candidate solutions.

[0042] S6. Generate a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun.

[0043] Preferably, generating the configuration optimization scheme of the series capacitor compensator based on the set of control variables corresponding to the target sun includes: calculating the firing angle of the thyristor controlling each of the series capacitor compensators based on the installation position and reactance compensation coefficient in the set of control variables corresponding to the target sun; and generating the configuration optimization scheme of the series capacitor compensator based on the installation position and corresponding firing angle of the series capacitor compensator.

[0044] In a preferred embodiment of the present invention, the function of the series capacitor compensator is to install TCSC (thyristor-controlled series capacitor) between nodes i to j in the electrical network, thereby enabling adjustment of the transmission path; The reactance of the TCSC can be controlled by adjusting the firing angle (α) of the thyristor. Variations in the firing angle (α) allow the TCSC to operate in either the inductive or capacitive region, thus avoiding steady-state resonance.

[0045] The relationship between firing angle α and XTCSC: In the formula: because , The value of the new reactance becomes: In the formula, The capacitive reactance of the parallel capacitor in the TCSC; The equivalent reactance of the thyristor-controlled reactor (TCR) in the TCSC is a function of the firing angle α, and its value is determined by both the firing angle and the inherent characteristics of the reactor. The original reactance of the transmission line before TCSC is installed; α is the compensation coefficient of TCSC for line reactance; α is the firing angle of the thyristor.

[0046] Therefore, based on the above formula, the installation location in the control variable set, and the reactance compensation coefficient, the firing angle of the thyristor controlling each of the series capacitor compensators is calculated; based on the installation location of the series capacitor compensator and the corresponding firing angle, a configuration optimization scheme for the series capacitor compensator is generated.

[0047] Furthermore, this embodiment also aims to determine the optimal location and size of TCSC devices when using the KOA algorithm in a standard IEEE 30-node test system. These FACTS devices can minimize power losses within the system. To verify this, the study considers three different cases for further analysis: Case 01: ORPD not included in TCSC, Case 02: ORPD including one TCSC, and Case 03: ORPD combined with two TCSCs. The algorithm convergence for the three cases can be found in [link to documentation]. Figure 4-6 .

[0048] Appendix Table 1 lists the main characteristics of the IEEE 30-node test system and the limits of each variable in TCSC. The method proposed in this study was implemented using MATLAB software and simulated on a computer equipped with an Intel Core i5 processor (1.6 GHz) and 8 GB of memory, running on a Windows 10 64-bit operating system. The population size and number of iterations for all algorithms were 30 and 500, respectively. To verify the reliability of the results, 20 consecutive runs were performed. The simulation results of the proposed algorithm are shown in Appendix Table 2.

[0049] Appendix 1: Appendix 2 Based on the results shown in Appendix 2, this table summarizes the optimal solution sets for the control variables using the KOA (Kepler Optimization Algorithm), QIO (Quantum-Inspired Optimization Algorithm), and GRO (Golden Ratio Optimization) algorithms in three cases. Furthermore, the results in Appendix 2 are compared with other existing techniques mentioned in the literature, as detailed in Appendix 3.

[0050] Appendix 3 Clearly, the KOA algorithm achieves the lowest power loss in all three cases (1, 2, and 3), at 4.5268 MW, 4.5024 MW, and 4.4776 MW respectively, compared to QIO, GRO, and other existing algorithms. Furthermore, the KOA algorithm is also faster than other algorithms. Therefore, the KOA algorithm demonstrates stronger robustness in solving the ORPD problem compared to other algorithms studied.

[0051] See Figure 2This is a configuration optimization device for a series capacitor compensator provided in an embodiment of the present invention, comprising: The parameter acquisition module is used to acquire the topology, power flow parameters, and equipment operating parameters of the distribution network. The variable construction module is used to take the equipment operating parameters, the installation position of the series capacitor compensator, and the reactance compensation coefficient of each series capacitor compensator to the line as control variables, and the power flow parameters as state variables. The function construction module is used to construct an objective function with the goal of minimizing the active power loss of the distribution network, based on the topology, the control variables, and the state variables. An initialization module is used to randomly generate several sets of initial control variables, take one set of initial control variables as an initial planet, and calculate the initial fitness value of each initial planet according to the objective function. The iterative optimization module is used to take the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly perform iterative optimization operations on several initial planets under the preset constraint function used to maintain the stability of the power flow of the distribution network until the fitness value of the generated target sun is greater than the preset threshold, at which point the iterative optimization operation stops. The scheme generation module is used to generate a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun.

[0052] Furthermore, the initialization module randomly generates several sets of initial control variables, and uses one set of initial control variables as an initial planet, including: Obtain the upper and lower threshold values ​​for each type of control variable; The difference between the upper and lower threshold values ​​of each type of control variable is multiplied by any random number to generate several initial values ​​for each type of control variable; wherein the random number ranges from [0,1]. The sum of each initial value of the control variables and the corresponding lower limit threshold is used as the initial control variable; Based on the initial control variables of each type of control variable, construct several sets of initial control variables; A set of initial control variables is used as an initial planet. Any random number is used as the orbital eccentricity of the initial planet, and the absolute value of any normally distributed random number is used as the orbital time of the initial planet. The initial control variables in each set of initial control variables are used as the initial elements in the initial planet.

[0053] Furthermore, the iterative optimization module takes the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly performs iterative optimization operations on several of the initial planets under a preset constraint function used to maintain the stability of the power flow in the distribution network, until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops, including: Based on the initial sun and several initial planets, the iterative optimization operation is repeated until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops. The iterative optimization operation includes: Obtain the planet to be optimized and the sun to be optimized; wherein, initially, the planet to be optimized is the initial planet, and the sun to be optimized is the initial sun; Based on the orbital eccentricity of the planets to be optimized, the gravitational force of the Sun to be optimized on each planet to be optimized is calculated, and the velocity of each planet to be optimized is calculated based on the gravitational force. Under a preset constraint function, the positions of each planet to be optimized are updated according to the velocity to generate planets to be evaluated; Calculate the first fitness value for each planet to be evaluated based on the objective function; Determine if there exists a first fitness value greater than a preset threshold; If so, the planet to be evaluated corresponding to the first fitness value that is greater than the preset threshold will be taken as the target sun, and the iterative optimization operation will end. If not, then based on the first fitness value and the second fitness value of the planet to be optimized, the planets to be optimized and the planets to be evaluated are sorted in reverse order, and the planet ranked first is selected as the sun to be optimized for the next round of iterative optimization operation. In addition, a preset number threshold of planets to be optimized and planets to be evaluated are selected in sequence as the planets to be optimized for the next round of iterative optimization operation.

[0054] Furthermore, the scheme generation module generates a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun, including: Based on the installation location and reactance compensation coefficient in the set of control variables corresponding to the target sun, calculate the firing angle of the thyristor controlling each of the series capacitor compensators; Based on the installation location and corresponding trigger angle of the series capacitor compensator, an optimized configuration scheme for the series capacitor compensator is generated.

[0055] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the configuration optimization method of a series capacitor compensator provided by any of the above-described method embodiments of the present invention.

[0056] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0057] See Figure 3 One embodiment of this application also provides a terminal device, including: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a configuration optimization method for a series capacitor compensator as described above.

[0058] The processor controls the overall operation of the terminal device to complete all or part of the steps in the configuration optimization method for a series capacitor compensator described above. The memory stores various types of data to support the operation of the terminal device. This data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The memory can be implemented using 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 Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0059] In an exemplary embodiment, the terminal device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform a configuration optimization method for a series capacitor compensator as described in any of the foregoing embodiments, and to achieve the same technical effect as the method described above.

[0060] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided. When executed by a processor, the computer program implements the steps of a configuration optimization method for a series capacitor compensator as described in any of the foregoing embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program, which may be executed by a processor of a terminal device to complete the configuration optimization method for a series capacitor compensator as described in any of the foregoing embodiments and achieve the same technical effects as the aforementioned method.

[0061] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of a series capacitor compensator, characterized in that, include: Obtain the topology, power flow parameters, and equipment operating parameters of the distribution network; The operating parameters of the equipment, the installation location of the series capacitor compensator, and the reactance compensation coefficient of each series capacitor compensator to the line are used as control variables, and the power flow parameters are used as state variables. Based on the topology, the control variables, and the state variables, an objective function is constructed with the goal of minimizing the active power loss of the distribution network. Several sets of initial control variables are randomly generated. Each set of initial control variables is used as an initial planet. The initial fitness value of each initial planet is calculated according to the objective function. The initial planet corresponding to the largest initial fitness value is taken as the initial sun. Under the preset constraint function used to maintain the stability of the power flow of the distribution network, the iterative optimization operation is repeatedly performed on several of the initial planets until the fitness value of the generated target sun is greater than the preset threshold, at which point the iterative optimization operation stops. Based on the set of control variables corresponding to the target sun, a configuration optimization scheme for the series capacitor compensator is generated.

2. The configuration optimization method for a series capacitor compensator as described in claim 1, characterized in that, The random generation of several sets of initial control variables, with each set of initial control variables serving as an initial planet, includes: Obtain the upper and lower threshold values ​​for each type of control variable; The difference between the upper and lower threshold values ​​of each type of control variable is multiplied by any random number to generate several initial values ​​for each type of control variable; wherein the random number ranges from [0,1]. The sum of each initial value of the control variables and the corresponding lower limit threshold is used as the initial control variable; Based on the initial control variables of each type of control variable, construct several sets of initial control variables; A set of initial control variables is used as an initial planet. Any random number is used as the orbital eccentricity of the initial planet, and the absolute value of any normally distributed random number is used as the orbital time of the initial planet. The initial control variables in each set of initial control variables are used as the initial elements in the initial planet.

3. The configuration optimization method for a series capacitor compensator as described in claim 2, characterized in that, The step of taking the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly performing iterative optimization operations on several of the initial planets under a preset constraint function for maintaining the stability of the power flow in the distribution network, until the fitness value of the generated target sun is greater than a preset threshold, and then stopping the iterative optimization operation, includes: Based on the initial sun and several initial planets, the iterative optimization operation is repeated until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops. The iterative optimization operation includes: Obtain the planet to be optimized and the sun to be optimized; wherein, initially, the planet to be optimized is the initial planet, and the sun to be optimized is the initial sun; Based on the orbital eccentricity of the planets to be optimized, the gravitational force of the Sun to be optimized on each planet to be optimized is calculated, and the velocity of each planet to be optimized is calculated based on the gravitational force. Under a preset constraint function, the positions of each planet to be optimized are updated according to the velocity to generate planets to be evaluated; Calculate the first fitness value for each planet to be evaluated based on the objective function; Determine if there exists a first fitness value greater than a preset threshold; If so, the planet to be evaluated corresponding to the first fitness value that is greater than the preset threshold will be taken as the target sun, and the iterative optimization operation will end. If not, then based on the first fitness value and the second fitness value of the planet to be optimized, the planets to be optimized and the planets to be evaluated are sorted in reverse order, and the planet ranked first is selected as the sun to be optimized for the next round of iterative optimization operation. In addition, a preset number threshold of planets to be optimized and planets to be evaluated are selected in sequence as the planets to be optimized for the next round of iterative optimization operation.

4. The configuration optimization method for a series capacitor compensator as described in claim 3, characterized in that, The step of generating a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun includes: Based on the installation location and reactance compensation coefficient in the set of control variables corresponding to the target sun, calculate the firing angle of the thyristor controlling each of the series capacitor compensators; Based on the installation location and corresponding trigger angle of the series capacitor compensator, an optimized configuration scheme for the series capacitor compensator is generated.

5. A configuration optimization device for a series capacitor compensator, characterized in that, include: The parameter acquisition module is used to acquire the topology, power flow parameters, and equipment operating parameters of the distribution network. The variable construction module is used to take the equipment operating parameters, the installation position of the series capacitor compensator, and the reactance compensation coefficient of each series capacitor compensator to the line as control variables, and the power flow parameters as state variables. The function construction module is used to construct an objective function with the goal of minimizing the active power loss of the distribution network, based on the topology, the control variables, and the state variables. An initialization module is used to randomly generate several sets of initial control variables, take one set of initial control variables as an initial planet, and calculate the initial fitness value of each initial planet according to the objective function. The iterative optimization module is used to take the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly perform iterative optimization operations on several initial planets under the preset constraint function used to maintain the stability of the power flow of the distribution network until the fitness value of the generated target sun is greater than the preset threshold, at which point the iterative optimization operation stops. The scheme generation module is used to generate a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun.

6. The configuration optimization device for a series capacitor compensator as described in claim 5, characterized in that, The initialization module randomly generates several sets of initial control variables, and uses one set of initial control variables as an initial planet, including: Obtain the upper and lower threshold values ​​for each type of control variable; The difference between the upper and lower threshold values ​​of each type of control variable is multiplied by any random number to generate several initial values ​​for each type of control variable; wherein the random number ranges from [0,1]. The sum of each initial value of the control variables and the corresponding lower limit threshold is used as the initial control variable; Based on the initial control variables of each type of control variable, construct several sets of initial control variables; A set of initial control variables is used as an initial planet. Any random number is used as the orbital eccentricity of the initial planet, and the absolute value of any normally distributed random number is used as the orbital time of the initial planet. The initial control variables in each set of initial control variables are used as the initial elements in the initial planet.

7. The configuration optimization device for a series capacitor compensator as described in claim 6, characterized in that, The iterative optimization module takes the initial planet corresponding to the largest initial fitness value as the initial sun, and repeatedly performs iterative optimization operations on several of the initial planets under a preset constraint function used to maintain the stability of the power flow in the distribution network, until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops, including: Based on the initial sun and several initial planets, the iterative optimization operation is repeated until the fitness value of the generated target sun is greater than a preset threshold, at which point the iterative optimization operation stops. The iterative optimization operation includes: Obtain the planet to be optimized and the sun to be optimized; wherein, initially, the planet to be optimized is the initial planet, and the sun to be optimized is the initial sun; Based on the orbital eccentricity of the planets to be optimized, the gravitational force of the Sun to be optimized on each planet to be optimized is calculated, and the velocity of each planet to be optimized is calculated based on the gravitational force. Under a preset constraint function, the positions of each planet to be optimized are updated according to the velocity to generate planets to be evaluated; Calculate the first fitness value for each planet to be evaluated based on the objective function; Determine if there exists a first fitness value greater than a preset threshold; If so, the planet to be evaluated corresponding to the first fitness value that is greater than the preset threshold will be taken as the target sun, and the iterative optimization operation will end. If not, then based on the first fitness value and the second fitness value of the planet to be optimized, the planets to be optimized and the planets to be evaluated are sorted in reverse order, and the planet ranked first is selected as the sun to be optimized for the next round of iterative optimization operation. In addition, a preset number threshold of planets to be optimized and planets to be evaluated are selected in sequence as the planets to be optimized for the next round of iterative optimization operation.

8. The configuration optimization device for a series capacitor compensator as described in claim 7, characterized in that, The scheme generation module generates a configuration optimization scheme for the series capacitor compensator based on the set of control variables corresponding to the target sun, including: Based on the installation location and reactance compensation coefficient in the set of control variables corresponding to the target sun, calculate the firing angle of the thyristor controlling each of the series capacitor compensators; Based on the installation location and corresponding trigger angle of the series capacitor compensator, an optimized configuration scheme for the series capacitor compensator is generated.

9. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a configuration optimization method for a series capacitor compensator as described in any one of claims 1-4.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a configuration optimization method for a series capacitor compensator as described in any one of claims 1-4.

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