UPFC configuration optimization method and system

Abstract

The invention discloses a UPFC configuration optimization method and system, and the method comprises the steps: carrying out the analysis of the obtained topological structure information and operation parameters of a target power system, so as to construct a key channel line set; simulating power grid power flow distribution after the UPFC to be configured is connected to different key channel lines, and calculating load rates of the key channel lines based on simulation results of the power grid power flow distribution; calculating weighted power flow entropy according to the load rate, and taking the key channel line with the minimum weighted power flow entropy as the optimal access line of the UPFC to be configured; performing load flow calculation on the power grid operation state of the target power system in the preset fault scene, and calculating the power transfer support strength of the key channel line according to a load flow calculation result; and establishing a UPFC grid-connected capacity optimization model, and calculating the UPFC grid-connected capacity optimization model to obtain the optimal capacity of the UPFC to be configured. The method provided by the invention can be widely used for optimizing the power flow distribution of the regional power grid.

Description

Technical Field

[0001] The present application relates to the field of power transmission technology, and in particular to a UPFC configuration optimization method and system. Background Art

[0002] With the increasing development of power system load and grid, the unbalanced power supply of transmission lines can easily cause transmission congestion in some lines, while the transmission capacity of other lines cannot be fully utilized, resulting in a waste of investment. As a representative of the advanced third-generation Flexible AC Transmission System (FACTS) device, the Unified Power Flow Controller (UFPC) plays an important role in system power flow control and voltage stability by controlling the bus voltage, line impedance, and active and reactive power in the system.

[0003] However, when it comes to the site selection and sizing of UPFC, existing methods often lack systematicity and fail to comprehensively consider the overall structure and operating characteristics of the power system, which may lead to unreasonable configuration locations and capacity mismatches of UPFC, thus failing to fully exert its control effect. Summary of the invention

[0004] The present application provides a UPFC configuration optimization method and system to solve the technical problem of how to optimize the regional power grid power flow distribution.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a UPFC configuration optimization method, including:

[0006] Analyze the acquired topological structure information and operating parameters of the target power system, and build a critical channel line set based on the analysis results;

[0007] Initializing the capacity of the UPFC to be configured, simulating the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculating the load rate of the key channel line based on the simulation result of the power flow distribution;

[0008] Calculate the weighted power flow entropy according to the load rate, and use the key channel line with the smallest weighted power flow entropy as the optimal access line of the UPFC to be configured;

[0009] Performing power flow calculation on the power grid operation state of the target power system under a preset fault scenario, and calculating the power transfer support strength of the key channel line according to the result of the power flow calculation;

[0010] Based on the optimal access line and the power transfer support strength, a UPFC grid-connected capacity optimization model is established, and the UPFC grid-connected capacity optimization model is calculated according to an improved particle swarm optimization algorithm to obtain the optimal capacity of the UPFC to be configured, so as to optimize the configuration of the UPFC to be configured.

[0011] As one of the preferred solutions, the capacity of the UPFC to be configured is used as the compensation value, the power flow distribution of the power grid after the UPFC is connected to different key channel lines in the key channel line set is simulated, and the load rate of the key channel line is calculated based on the simulation result of the power flow distribution, including:

[0012] Establishing a power grid model of the target power system;

[0013] Taking the capacity of the UPFC to be configured as the compensation value, determining the compensation strategy of the UPFC to be configured in different critical channel lines;

[0014] Connecting the UPFC to be configured to the power grid model according to the compensation strategy, and simulating the power flow distribution of the power grid model after the UPFC to be configured is connected to the power grid model based on the power flow calculation method;

[0015] The load rate of the key channel line is calculated according to the power flow distribution.

[0016] As one preferred solution, the step of calculating the weighted power flow entropy according to the load rate includes:

[0017] The load rate is differentiated according to S = [0, u, 2u, ..., nu, (n + 1)u, ..., 100%], each interval length is u, and the load rate is expressed as:

[0018]

[0019] Among them, μ l is the load factor, is the maximum transmission capacity of line l, is the power flow during actual operation, M is the total number of transmission lines in the power system;

[0020] The proportion p(n) of line load rate in the interval [nu,(n+1)u] is defined as:

[0021]

[0022] Among them, L n is the total number of lines in the interval [nu, (n+1)u];

[0023] The weighted power flow entropy is calculated as:

[0024]

[0025] Among them, H Pw is the weighted power flow entropy, H Pw is the mean line load rate in the interval [nu,(n+1)u].

[0026] As one of the preferred solutions, the power transfer support strength is expressed as:

[0027]

[0028] Among them, P l_fault is the transmission power of the critical channel after the failure, P l0 is the initial power of the key channel; P l_lim Provide emergency power support capabilities for regional power grids.

[0029] As one of the preferred solutions, the UPFC grid-connected capacity optimization model is calculated according to the improved particle swarm optimization algorithm to obtain the optimal configuration capacity of the UPFC to be configured, including:

[0030] Randomly initializing the capacity value of the UPFC to be configured as a particle, wherein the particle represents a UPFC configuration scheme;

[0031] Calculating the fitness value of each particle, and iteratively updating the particle according to the fitness value and a nonlinear decreasing inertia weight method;

[0032] When the result of the iterative update satisfies the preset convergence condition, the current result is output as the optimal configuration capacity of the UPFC to be configured.

[0033] Another embodiment of the present application provides a park energy consumption decision optimization system, including:

[0034] An analysis module is used to analyze the acquired topological structure information and operating parameters of the target power system and construct a key channel line set according to the analysis results;

[0035] A simulation module, used for initializing the capacity of the UPFC to be configured, simulating the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculating the load rate of the key channel line based on the simulation result of the power flow distribution;

[0036] A configuration module, used for calculating the weighted power flow entropy according to the load rate, and taking the key channel line with the smallest weighted power flow entropy as the optimal access line of the UPFC to be configured;

[0037] A calculation module, used to perform power flow calculation on the power grid operation state of the target power system under a preset fault scenario, and calculate the power transfer support strength of the key channel line according to the result of the power flow calculation;

[0038] The optimization module is used to establish a UPFC grid-connected capacity optimization model based on the optimal access line and the power transfer support strength, calculate the UPFC grid-connected capacity optimization model according to the improved particle swarm optimization algorithm, obtain the optimal capacity of the UPFC to be configured, and optimize the configuration of the UPFC to be configured.

[0039] As one preferred solution, the simulation module is specifically used for:

[0040] Establishing a power grid model of the target power system;

[0041] Taking the capacity of the UPFC to be configured as the compensation value, determining the compensation strategy of the UPFC to be configured in different critical channel lines;

[0042] Connecting the UPFC to be configured to the power grid model according to the compensation strategy, and simulating the power flow distribution of the power grid model after the UPFC to be configured is connected to the power grid model based on the power flow calculation method;

[0043] The load rate of the key channel line is calculated according to the power flow distribution.

[0044] As one preferred solution, the configuration module is further used for:

[0045] The load rate is differentiated according to S = [0, u, 2u, ..., nu, (n + 1)u, ..., 100%], each interval length is u, and the load rate is expressed as:

[0046]

[0047] Among them, μ l is the load factor, P l max is the maximum transmission capacity of line l, P l 0 is the power flow during actual operation, M is the total number of transmission lines in the power system;

[0048] The proportion p(n) of line load rate in the interval [nu,(n+1)u] is defined as:

[0049]

[0050] Among them, L n is the total number of lines in the interval [nu, (n+1)u];

[0051] The weighted power flow entropy is calculated as:

[0052]

[0053] Among them, H Pw is the weighted power flow entropy, H Pw is the mean line load rate in the interval [nu,(n+1)u].

[0054] As one of the preferred solutions, the power transfer support strength is expressed as:

[0055]

[0056] Among them, P l_fault is the transmission power of the critical channel after the failure, P l0 is the initial power of the key channel; P l_lim Provide emergency power support capabilities for regional power grids.

[0057] As one preferred solution, the optimization module is specifically used for:

[0058] Randomly initializing the capacity value of the UPFC to be configured as a particle, wherein the particle represents a UPFC configuration scheme;

[0059] Calculating the fitness value of each particle, and iteratively updating the particle according to the fitness value and a nonlinear decreasing inertia weight method;

[0060] When the result of the iterative update satisfies the preset convergence condition, the current result is output as the optimal configuration capacity of the UPFC to be configured.

[0061] Another embodiment of the present application provides a UPFC configuration optimization device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the UPFC configuration optimization method as described above when executing the computer program.

[0062] Yet another embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the UPFC configuration optimization method as described above is implemented.

[0063] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0064] (1) This application constructs a critical channel line set by conducting an in-depth analysis of the topological structure information and operating parameters of the target power system, and can accurately locate the lines that have the greatest impact on the system power flow distribution. This not only helps to understand the operating characteristics of the system, but also provides a scientific basis for subsequent UPFC configuration, thereby improving the operating efficiency of the entire power system.

[0065] (2) This application can scientifically determine the optimal access line of UPFC by simulating the power flow distribution after the UPFC to be configured is connected to different key channel lines, calculating the load rate, and then calculating the weighted power flow entropy. This method avoids blind configuration of resources, ensures that UPFC can achieve maximum benefits, and optimizes resource allocation.

[0066] (3) This application can evaluate the stability of the system under fault conditions by calculating the power flow of the grid operation status under preset fault scenarios and calculating the power transfer support strength of key channel lines. This provides an important reference for the capacity configuration of UPFC and helps to enhance the stability of the system under various conditions.

[0067] (4) Based on the optimal access line and power transfer support strength, this application establishes a UPFC grid-connected capacity optimization model, and uses an improved particle swarm optimization algorithm for calculation, which can obtain the optimal capacity of the UPFC to be configured. This method not only improves the accuracy of configuration optimization, but also improves the calculation efficiency through algorithm optimization, making the configuration optimization process faster and more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a simplified schematic diagram of the UPFC principle in one embodiment of the present application;

[0069] Figure 2 is a flow chart of a UPFC configuration optimization method in one embodiment of the present application;

[0070] Figure 3 This is a typical power flow diagram of IEEE 3 machines and 9 nodes in one embodiment of the present application;

[0071] Figure 4 is a schematic diagram of a UPFC configuration optimization system in one embodiment of the present application;

[0072] Figure 5 It is a structural block diagram of a UPFC configuration optimization device in one embodiment of the present application. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0074] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0075] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used in this article are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0076] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0077] It should be noted that the UPFC structure can be divided into a series part, a DC part, and a parallel part. The series part is equivalent to a static synchronous series compensator (SSSC), and the parallel part can be regarded as a static synchronous compensator (STATCOM, SVG). The two are connected through the DC side, and the active power can flow freely in either direction at the AC end of the series and parallel parts, and reactive power can be independently emitted or absorbed at its AC output end.

[0078] Figure 1 Several main parameters of the UPFC device are shown, such as Figure 1 As shown, it is a simplified schematic diagram of a UPFC provided in an embodiment of the present application, wherein V s ∠θ s 、V j ∠θ j is the voltage at the beginning and end of the transmission line; V se ∠θ se The voltage injected into the AC system on the UPFC series side; S se and S sh are the power injected (absorbed) into the system by the series side and parallel side of UPFC respectively; I line is the transmission line current value; V dc is the DC bus voltage value. After adding UPFC, the power at both ends becomes:

[0079]

[0080] If UPFC is not added, the initial power at the beginning and end of the line are The above formula can be expressed as:

[0081]

[0082] Therefore, it can be considered that after adding UPFC, the power of the sending end and the receiving end is based on the power without adding UPFC plus the power due to the introduction of V se For UPFC, what needs to be controlled is the receiving end power and the voltage caused by the introduction of V se After the reactive power changes.

[0083] UPFC mainly regulates the active power and reactive power of the line through the series converter. The active power required by the series converter is provided by the parallel converter, and the transmission of active power is realized through a common DC link. Both the series side and the parallel side can output reactive power within their capacity, and the control link can ensure the exchange of active power on the DC bus. The operation mode of UPFC can be determined by the operation mode of its series side and parallel side respectively.

[0084] Parallel side: The working principle of the parallel converter is to exchange power with the transmission line through the parallel transformer, so as to achieve active transmission and reactive compensation. The current can be considered in two parts, namely the active component and the reactive component, where the active component is used to provide the active power required by the series converter. According to the purpose of the reactive component, it is divided into three categories: reactive current injection control, reactive power control, and voltage control.

[0085] Reactive current injection control can be set with the sending end voltage Orthogonal reactive current reference vector The actual reactive current vector Simply following the reference vector is very useful in the commissioning process of the UPFC device or in the operation with certain special requirements. To achieve this control method, the parallel converter is usually required to have good dynamic quality and following accuracy, that is, the control should be closed-loop.

[0086] In the reactive power control mode, the parallel converter injects reactive current orthogonal to the voltage into the system sending end through closed-loop or open-loop control, thereby achieving the purpose of injecting the required reactive power. In the reactive power control mode, it is necessary to convert the reactive power demand into the demand for reactive current. The parallel side can send or absorb a certain amount of active power to the system to maintain the stability of the DC voltage and the active power loss inside the system.

[0087] Voltage control is similar to reactive power control mode. The parallel side injects reactive current orthogonal to the voltage at the access point into the system sending end through closed-loop control to adjust the voltage at the injection point. Its reference input is the voltage at the access point of the parallel side, and the feedback measurement is the actual voltage value at that point. The error signal after comparison between the two generates a correction value for the injected reactive current through appropriate closed-loop control.

[0088] Series side: The series converter directly injects a voltage vector with a certain amplitude and phase into the system. To change the distribution of line power flow, there are usually four control modes: direct voltage injection control, phase angle control, line impedance control, and power flow control.

[0089] In direct voltage injection control mode, UPFC generates a given voltage vector, and the magnitude and phase angle can be arbitrarily given. The actual voltage is compared with the given voltage, and the error is processed as the input of the series converter. A special mode of direct voltage injection is that the injected voltage vector is orthogonal to the line current vector, so that UPFC only provides series reactive power compensation.

[0090] The phase angle control mode controls the injected voltage vector so that the output voltage is offset by a given angle, but the amplitude remains unchanged. The actual voltage is compared with the given voltage, and the error is processed as the input of the series converter.

[0091] Line impedance control injects a voltage proportional to the line current into the system through its series side, making the series transformer appear as an impedance when viewed from the line side. The required impedance value is usually given as a reference value and usually consists of two parts: resistance and reactance. In this control mode, special care should be taken to avoid negative impedance or capacitive reactance to reduce the risk of oscillation and instability.

[0092] Power flow control is the main control mode of UPFC. In this control mode, the control quantity is the active power and reactive power transmitted by the line. The control target is to maintain the control quantity at a given value P ref , Q ref The system measures the actual active and reactive power transmitted by the line and compares them with the given values, and generates a voltage injection vector through appropriate closed-loop control.

[0093] The two converters of UPFC can also be operated separately and independently, the parallel converter operates as an independent SVG, and the series converter operates as an independent SSSC. When working in these two states, neither converter can generate or absorb active power, that is, they can only be used as reactive power compensators. The power of the line can still be controlled, but the active power and reactive power cannot be controlled separately.

[0094] From the control characteristics of UPFC, it can be seen that the rapid conditions of active power and reactive power can be achieved, which lays the foundation for taking into account the emergency power mutual assistance and power flow optimization between regional power grids. During steady-state operation, the line load is balanced by optimizing the line flow and node voltage. When a regional power grid fails, emergency power support for the regional power grid can be achieved by controlling the rapid transfer of line flow and dynamic reactive power support, thereby improving the frequency and voltage stability characteristics of the regional power grid.

[0095] Based on the above description, an embodiment of the present application provides a UPFC configuration optimization method. For details, see Figure 2 , Figure 2The flowchart of the UPFC configuration optimization method in one embodiment of the present application is shown, which specifically includes steps S1-S5:

[0096] S1: Analyze the acquired topological structure information and operating parameters of the target power system, and build a critical channel line set based on the analysis results;

[0097] Analyzing the acquired topological information and operating parameters of the target power system to construct a critical channel line set is a complex but crucial process. The following is a detailed description of this process:

[0098] Obtain the topological structure information of the power system from the design documents, operation records or real-time monitoring system of the power system. This information usually includes the connection relationship of each node in the power grid (such as substations, power plants, etc.), the transmission capacity of the line, the switch status, etc. Collect the real-time operating parameters of the power system, such as the voltage, current, power factor, etc. of each node, as well as the power flow distribution and loss of the line. These parameters can be obtained through the real-time monitoring system of the power system or the Supervisory Control and Data Acquisition System (SCADA).

[0099] Graph theory and network analysis methods are used to conduct in-depth analysis of the topology of the power system. This includes identifying key nodes and lines in the power grid, analyzing the connectivity, redundancy and vulnerability of the power grid, etc. According to the real-time operating parameters, the load conditions, power flow distribution and stability of each line in the power grid are evaluated. Special attention is paid to those lines with heavy loads, large power flow changes or poor stability. Combining the results of topological structure analysis and operating parameter evaluation, the key channel lines in the power grid are identified. These lines are usually located at the hub of the power grid, undertake important power flow transmission tasks, and may have a great impact on the power grid once a failure occurs. The identified key channel lines are integrated into a set, namely the key channel line set. This set will serve as the basis for the subsequent UPFC (unified power flow controller) configuration optimization.

[0100] S2: Initializing the capacity of the UPFC to be configured, simulating the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculating the load rate of the key channel line based on the simulation result of the power flow distribution;

[0101] Preferably, in one embodiment of the present application, the capacity of the UPFC to be configured is used as the compensation value, the power grid flow distribution after the UPFC is connected to different key channel lines in the key channel line set is simulated, and the load rate of the key channel line is calculated based on the simulation result of the power grid flow distribution, including:

[0102] Establishing a power grid model of the target power system;

[0103] Taking the capacity of the UPFC to be configured as the compensation value, determining the compensation strategy of the UPFC to be configured in different critical channel lines;

[0104] Connecting the UPFC to be configured to the power grid model according to the compensation strategy, and simulating the power flow distribution of the power grid model after the UPFC to be configured is connected to the power grid model based on the power flow calculation method;

[0105] The load rate of the key channel line is calculated according to the power flow distribution.

[0106] The goal of power flow optimization is to make the power flow distribution of the line more balanced. Usually, the power flow balance of the line can be calculated by analyzing the line load rate. In order to evaluate the effect of installing UPFC devices on reducing the power flow differences between lines, weighted power flow entropy can be used for quantitative evaluation.

[0107] Preferably, in an embodiment of the present application, the calculating the weighted power flow entropy according to the load rate includes:

[0108] The load rate is differentiated according to S = [0, u, 2u, ..., nu, (n + 1)u, ..., 100%], each interval length is u, and the load rate is expressed as:

[0109]

[0110] Among them, μ l is the load factor, is the maximum transmission capacity of line l, is the power flow during actual operation, M is the total number of transmission lines in the power system;

[0111] The proportion p(n) of line load rate in the interval [nu,(n+1)u] is defined as:

[0112]

[0113] Among them, L n is the total number of lines in the interval [nu, (n+1)u]; when the line is overloaded, the load rate is distinguished in the interval [100%, +∞];

[0114] The weighted power flow entropy is calculated as:

[0115]

[0116] Among them, H Pw is the weighted power flow entropy, H Pw is the mean line load rate in the interval [nu,(n+1)u].

[0117] S3: Calculate the weighted power flow entropy according to the load rate, and use the key channel line with the smallest weighted power flow entropy as the optimal access line of the UPFC to be configured;

[0118] From S2, we can know that the weighted power flow entropy H Pw It is negatively correlated with the uniformity of tidal distribution. Pw The smaller the value of is, the more balanced the power flow distribution of the power system is. Considering the optimal situation, that is, when the load rates of all lines fall into the same equal division interval, H Pw The value of is 0.

[0119] When determining the site selection of the UFPC device, the location that has the best impact on the balance of the system's power distribution after the UPFC is connected is considered, that is, the location where the weighted power entropy of the system is minimized.

[0120] S4: performing power flow calculation on the grid operation state of the target power system under a preset fault scenario, and calculating the power transfer support strength of the key channel line according to the result of the power flow calculation;

[0121] Emergency power mutual assistance is mainly used when a power grid in a certain area has a shortage of active power due to power grid failure or maintenance. It can control the power grid in the adjacent area to provide minute-level, large-scale and rapid power support to the power grid in the faulty area according to the power supply margin of the adjacent area power grid, thus realizing power transfer and fault support in a large time and space. Evaluating the emergency power mutual assistance capability between regional power grids can analyze the power supply margin of regional power grids and the power transmission capacity of key channels.

[0122] The power supply margin of a regional power grid is the maximum power that can be absorbed or emitted without affecting the power supply of the regional power grid. The power supply margin is not only related to the regional load and total power generation, but also closely related to the power supply composition. The power sources considered for emergency power mutual assistance mainly include thermal power units, hydropower adjustable units, pumped storage units and other power sources that can flexibly adjust power according to demand. Nuclear power units, new energy units and other units are not considered to participate in inter-regional emergency power mutual assistance. The maximum minute-level upward power margin P that the regional power grid can provide margin_high It can be expressed as follows:

[0123]

[0124] Among them, m, n, and l are the number of thermal power units, hydropower adjustable units, and pumped storage units in the regional power grid respectively; P t0 is the initial output of the thermal power unit, P tmax P is the maximum ramp power of the thermal power unit at the minute level; h0 is the initial output of the hydropower unit, P hmax The power upper limit corresponding to the hydropower storage capacity; P p0 is the initial output of the pumped storage unit, P pmaxIt is the upper power limit corresponding to the storage capacity of the pumped storage power station.

[0125] The maximum minute-level power reduction margin P that the regional power grid can provide margin_low It can be expressed as follows:

[0126]

[0127] Among them, P tmin The minimum technical output of thermal power units; P tmin is the lower power limit corresponding to the hydropower reservoir capacity; P pmin It is the upper power limit corresponding to the lower storage capacity of the pumped storage power station.

[0128] The power transmission capacity of the key channel is determined by the thermal stability limit of the line and the safe and stable operation boundary. Considering different operation modes, the power transmission capacity of the key channel P tr_lim Take the line thermal stability limit P lhot_lim and the smaller value of the safe and stable operation limit. Assuming that the line flow outflow from the regional power grid is positive, the safe and stable operation range of the key channel is [-P m_max ,P p_max ], the maximum outbound delivery capacity of the key channel P trp_lim It can be expressed as:

[0129] P trp_lim =min[P lhot_lim , P p_max ]

[0130] Maximum input capacity of key channel P trm_lim It can be expressed as:

[0131] P trm_lim =min[P lhot_lim , P m_max ]

[0132] Taking into account the adjustable power margin of the regional power grid and the power transmission capacity of key channels, after the adjacent regional power grid fails, the emergency power supply capacity of the regional power grid P lp_lim It can be expressed as:

[0133] P lp_lim =min[P margin_high , P trp_lim ]

[0134] Regional power grid emergency power absorption capacity P lm_lim It can be expressed as:

[0135] P lm_lim =min[P margin_low , P trm_lim ]

[0136] The power transfer support strength is the ratio of the power transferred to the critical channel after a fault to the power limit of the channel.

[0137] Preferably, in one embodiment of the present application, the power transfer support strength is expressed as:

[0138]

[0139] Among them, P l_fault is the transmission power of the critical channel after the failure, P l0 is the initial power of the key channel; P l_lim Provide emergency power support capabilities for regional power grids.

[0140] When power is supplied, P lp_lim , when absorbing power, it is P lm_lim . K trans When it is in the interval [0,1], the adjacent regional power grid can normally provide emergency power support for the faulty power grid. The larger the value, the higher the power transfer support intensity of the key channel after the fault.

[0141] From the above analysis, it can be seen that in order to improve the support strength of power transfer, on the one hand, the initial power of key channels can be controlled to leave sufficient margin for the channels; on the other hand, the power supply margin of the regional power grid can be improved by adjusting the operation mode of the regional power grid.

[0142] S5: Based on the optimal access line and the power transfer support strength, a UPFC grid-connected capacity optimization model is established, and the UPFC grid-connected capacity optimization model is calculated according to an improved particle swarm optimization algorithm to obtain the optimal capacity of the UPFC to be configured, so as to optimize the configuration of the UPFC to be configured.

[0143] The optimization goal of UPFC configuration takes into account the uniform distribution of power flow in steady state and the stability of grid frequency through emergency power mutual assistance between regions after a fault. To achieve the above goals, on the one hand, it is necessary to optimize the grid power flow during steady-state operation to avoid overload of individual line power flow and make the power flow distribution more balanced as much as possible; on the other hand, the initial power flow of key channels between regional power grids should be pre-controlled to leave enough margin for power mutual assistance between regional power grids in the event of a fault. The UPFC site selection is determined by steady-state power flow optimization, and the UPFC capacity is determined by further considering the emergency power mutual assistance capability in the event of a fault, thereby completing the optimal configuration of UPFC.

[0144] Construct the weighted power flow entropy H Pw The site selection model that characterizes the balance of power flow distribution is as follows:

[0145] minH Pw (D)

[0146] Where D is the power flow distribution of the system after different lines are connected to UPFC unit capacity.

[0147] By solving the UPFC site selection model, the line that has the best impact on the balance of power flow distribution after UPFC access can be determined.

[0148] Building strength K supported by power transfer trans The objective function of emergency power mutual assistance capability between regional power grids under fault conditions is characterized as follows:

[0149] maxK trans (S F )

[0150] Among them, S F is the installed capacity of UPFC.

[0151] The constraints mainly include: inequality constraints of unit output, node voltage, line load rate, and power transfer support strength; equality constraints of active power and reactive power of system flow after installing UPFC; the installation capacity of UPFC should consider the maximum rated capacity limit of UPFC device under the current voltage level:

[0152]

[0153] Among them, P Gi , Q Gi are the active and reactive output of the unit respectively; U i is the node voltage; μ l is the line load rate; P Li , Q Li are the active and reactive output of the load respectively; ΔP F , ΔQ F are the changes in active power and reactive power of the line before and after installing UPFC; S equip It is the maximum rated capacity of the UPFC device at this voltage level.

[0154] The UPFC grid-connected capacity optimization model is constructed based on the site selection model, the objective function of the emergency power mutual assistance capability between regional power grids under fault conditions, and the constraints.

[0155] Specifically, the present application adopts a particle swarm optimization algorithm to solve nonlinear optimization problems. The particle swarm optimization algorithm (PSO) performs well in nonlinear optimization due to its simple structure, fewer parameters and good convergence. PSO can quickly converge to a better solution by simulating the intelligent behavior of the group, and is suitable for dealing with various complex nonlinear problems. Although PSO is prone to the risk of falling into local optimality, this problem can be alleviated to a certain extent through appropriate improvements and parameter adjustments. Based on these characteristics, the particle swarm optimization algorithm is selected to solve the nonlinear optimization problem in the UPFC configuration model.

[0156] Preferably, in one embodiment of the present application, the UPFC grid-connected capacity optimization model is calculated according to the improved particle swarm optimization algorithm to obtain the optimal configuration capacity of the UPFC to be configured, including:

[0157] Randomly initializing the capacity value of the UPFC to be configured as a particle, wherein the particle represents a UPFC configuration scheme;

[0158] Calculating the fitness value of each particle, and iteratively updating the particle according to the fitness value and a nonlinear decreasing inertia weight method;

[0159] When the result of the iterative update satisfies the preset convergence condition, the current result is output as the optimal configuration capacity of the UPFC to be configured.

[0160] Specifically, the particle swarm optimization algorithm for solving nonlinear optimization problems includes the following steps:

[0161] Step 1: Initialize the particle swarm parameters: determine the particle swarm size N, particle dimension D, maximum number of iterations T, inertia weight ω, and learning factor c;

[0162] Step 2: Randomly initialize the position and velocity of each particle: the individual historical optimal position x i_best 、The historical optimal position of the group x gbest , individual historical optimal fitness value p i_best 、The historical optimal fitness value p of the group gbest ;

[0163] Step 3: Determine whether the end condition is met. If so, output the optimal solution; if not, update the speed and position of each particle;

[0164] v i (t+1)=ωv i (t)+c1r1[x i_best (t)-x i (t)]+c2r2[x gbest (t)-x i (t)]

[0165] x i (t+1)=x i (t)+v i (t+1)

[0166] In the formula, t is the number of iterations, v is i is the velocity of the ith particle, x i is the position of the ith particle, c1 and c2 are learning factors, and r1 and r2 are random numbers in the interval [0,1].

[0167] Step 4: Calculate the fitness value p of each particle i ;

[0168] Step 5: Update the individual historical optimal fitness value and position of each particle;

[0169] Step 6: Update the historical optimal fitness value and position of the group;

[0170] Step7: Update other parameters, such as inertia weight, number of iterations, etc.; return to Step3.

[0171] Based on the particle swarm optimization algorithm, a nonlinear decreasing inertia weight method is used to dynamically adjust the search behavior of particles. Inertia weight plays a role in balancing global search and local search in the particle swarm algorithm. Through nonlinear decreasing inertia weight, the algorithm can optimize the search strategy at different stages of the search process: in the early stage of the search, a larger inertia weight prompts particles to conduct extensive global searches, enhancing the exploration ability of the algorithm; in the later stage of the search, the inertia weight gradually decreases, allowing particles to focus on local searches, thereby improving the accuracy and speed of local convergence. This strategy effectively avoids the risk of the particle swarm falling into the local optimum too early, while ensuring the diversity of the search process and the ability to approximate the optimal solution, thereby improving the overall convergence performance. The specific formula is shown below:

[0172]

[0173] Among them, ω max With ω min are the upper and lower limits of the inertia weight respectively.

[0174] In addition, a perturbation mechanism is introduced, that is, the speed and position of the particles are regularly perturbed with small random amplitudes during the search process. This mechanism ensures that even if the particle swarm is close to the local optimal position, the particles still have the opportunity to jump out of the local area and continue to explore a better solution. By introducing randomness at the right time, the state of the particle swarm being trapped in the local optimal state is effectively broken, thereby enhancing the global search ability of the algorithm and reducing the risk of being trapped in the local optimal state. The specific formula is shown as follows:

[0175]

[0176] Among them, α is the random perturbation weight, α0 is the initial value of the random perturbation weight, and r is a random number in the interval [0,1].

[0177] By dynamically adjusting the inertia weight and introducing a particle perturbation mechanism, the risk of falling into local optimality during the solution process is further reduced, and the global search capability and convergence performance of the algorithm are improved.

[0178] An embodiment is specifically listed below to illustrate the technical solution of the present application.

[0179] Take the IEEE39 node system as an example. Figure 3 As shown, Figure 3 This is a typical power flow diagram of IEEE 3 machines and 9 nodes. The IEEE 39-node system contains two voltage levels and two main power supply areas. BUS25 to BUS30 constitute power supply area 1, and the remaining nodes constitute power supply area 2. During normal operation, power supply area 1 mainly obtains power from the BUS28 high-voltage node, and the power flow of the BUS24 to BUS25 line (the connecting line between the two power supply areas) is basically zero.

[0180] Step 1: BUS6-BUS28, BUS8-BUS28, and BUS24-BUS25 constitute the key channels.

[0181] Step 2: Configure UPFCs of the same capacity on the above lines and calculate the weighted power flow entropy. The results are shown in Table 1. Table 1 shows the weighted power flow entropy of the system after UPFC configuration.

[0182] Table 1

[0183] Line front end Line back end BUS6-BUS28 1.0193 1.0197 BUS8-BUS28 0.9430 0.9429 BUS24-BUS25 0.9443 0.9446

[0184] From the above results, it can be seen that configuring UPFC at BUS28 of the BUS8-BUS28 line can make the system power flow distribution more uniform.

[0185] Step 3: Analyze that power supply partition 1 has only loads, and power supply partition 2 BUS1, BUS2, BUS5, BUS8, and BUS11 are generator nodes, of which BUS1 is a turbine. According to the current active output of the generator, the minimum technical output, the maximum ramp power of the thermal power unit at the minute level, and the power upper limit corresponding to the hydropower reservoir capacity, calculate the maximum power margin P that power supply partition 2 can provide for power supply partition 1. margin_high The maximum power margin is 861.2MW. margin_low The result is shown in Table 2, which shows the adjustable power margin of power supply partition 1 (unit: MW).

[0186] Table 2

[0187]

[0188] The rated transmission powers of BUS6-BUS28, BUS8-BUS28, and BUS24-BUS25 are 32MVA, 32MVA, and 16MVA respectively, without considering the overload capacity of the line. Combined with the maximum adjustable power of power supply partition 1 and the critical channel capacity between power supply partitions, in the event of a fault in power supply partition 2, the emergency power support capacity that power supply partition 1 can provide is constrained by the rated transmission power of the critical channel.

[0189] Taking the load change of 5MW in power supply zone 2 (total load is 16.5MW) as the check fault, calculate the power transfer support strength K of the key channel trans , the optimal configuration capacity of UPFC is 1.2MVA. At this time, the enhanced power flow entropy is 0.9260, and the key channel power transfer support strength is 7.5%.

[0190] Another embodiment of the present application provides a UPFC configuration optimization method. For details, see Figure 4 , Figure 4 The flowchart of the UPFC configuration optimization method in one embodiment of the present application is shown, which specifically includes: an analysis module 11, a simulation module 12, a configuration module 13, a calculation module 14, and an optimization module 15, wherein:

[0191] The analysis module 11 is used to analyze the acquired topological structure information and operating parameters of the target power system and construct a key channel line set according to the analysis results;

[0192] A simulation module 12 is used to initialize the capacity of the UPFC to be configured, simulate the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculate the load rate of the key channel line based on the simulation result of the power flow distribution;

[0193] A configuration module 13, configured to calculate the weighted power flow entropy according to the load rate, and use the key channel line with the smallest weighted power flow entropy as the optimal access line of the UPFC to be configured;

[0194] A calculation module 14 is used to perform power flow calculation on the power grid operation state of the target power system under a preset fault scenario, and calculate the power transfer support strength of the key channel line according to the result of the power flow calculation;

[0195] The optimization module 15 is used to establish a UPFC grid-connected capacity optimization model based on the optimal access line and the power transfer support strength, calculate the UPFC grid-connected capacity optimization model according to the improved particle swarm optimization algorithm, and obtain the optimal capacity of the UPFC to be configured, so as to optimize the configuration of the UPFC to be configured.

[0196] Preferably, in one embodiment of the present application, the simulation module is specifically used for:

[0197] Establishing a power grid model of the target power system;

[0198] Taking the capacity of the UPFC to be configured as the compensation value, determining the compensation strategy of the UPFC to be configured in different critical channel lines;

[0199] Connecting the UPFC to be configured to the power grid model according to the compensation strategy, and simulating the power flow distribution of the power grid model after the UPFC to be configured is connected to the power grid model based on the power flow calculation method;

[0200] The load rate of the key channel line is calculated according to the power flow distribution.

[0201] Preferably, in one embodiment of the present application, the configuration module is further used to:

[0202] The load rate is differentiated according to S = [0, u, 2u, ..., nu, (n + 1)u, ..., 100%], each interval length is u, and the load rate is expressed as:

[0203]

[0204] Among them, μ l is the load factor, is the maximum transmission capacity of line l, is the power flow during actual operation, M is the total number of transmission lines in the power system;

[0205] The proportion p(n) of line load rate in the interval [nu,(n+1)u] is defined as:

[0206]

[0207] Among them, L n is the total number of lines in the interval [nu, (n+1)u];

[0208] The weighted power flow entropy is calculated as:

[0209]

[0210] Among them, H Pw is the weighted power flow entropy, H Pw is the mean line load rate in the interval [nu,(n+1)u].

[0211] Preferably, in one embodiment of the present application, the power transfer support strength is expressed as:

[0212]

[0213] Among them, P l_fault is the transmission power of the critical channel after the failure, P l0 is the initial power of the key channel; P l_lim Provide emergency power support capabilities for regional power grids.

[0214] Preferably, in one embodiment of the present application, the optimization module is specifically used to:

[0215] Randomly initializing the capacity value of the UPFC to be configured as a particle, wherein the particle represents a UPFC configuration scheme;

[0216] Calculating the fitness value of each particle, and iteratively updating the particle according to the fitness value and a nonlinear decreasing inertia weight method;

[0217] When the result of the iterative update satisfies the preset convergence condition, the current result is output as the optimal configuration capacity of the UPFC to be configured.

[0218] Another embodiment of the present application provides a UPFC configuration optimization device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the UPFC configuration optimization method as described above when executing the computer program.

[0219] See also Figure 5 , which is a structural block diagram of a UPFC configuration optimization device provided in an embodiment of the present application. The UPFC configuration optimization device provided in an embodiment of the present application comprises a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the above-mentioned UPFC configuration optimization method embodiment are implemented, for example Figure 1 or, when the processor 21 executes the computer program, the functions of each module in the above-mentioned device embodiments are implemented, such as the analysis module 11.

[0220] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the UPFC configuration optimization device.

[0221] The UPFC configuration optimization device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of a UPFC configuration optimization device and does not constitute a limitation on the UPFC configuration optimization device. The UPFC configuration optimization device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the UPFC configuration optimization device may also include input and output devices, network access devices, buses, and the like.

[0222] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the UPFC configuration optimization device, and uses various interfaces and lines to connect various parts of the entire UPFC configuration optimization device.

[0223] The memory 22 can be used to store the computer program and / or module, and the processor 21 realizes various functions of the UPFC configuration optimization device by running or executing the computer program and / or module stored in the memory 22, and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0224] Wherein, if the module integrated in the UPFC configuration optimization device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0225] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0226] Accordingly, another embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the UPFC configuration optimization method as described above is implemented.

[0227] The computer-readable storage medium is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a UPFC configuration optimization method in the above embodiment.

[0228] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0229] Compared with the prior art, the beneficial effects of the embodiments of the present application are at least one of the following:

[0230] (1) This application constructs a critical channel line set by conducting an in-depth analysis of the topological structure information and operating parameters of the target power system, and can accurately locate the lines that have the greatest impact on the system power flow distribution. This not only helps to understand the operating characteristics of the system, but also provides a scientific basis for subsequent UPFC configuration, thereby improving the operating efficiency of the entire power system.

[0231] (2) This application can scientifically determine the optimal access line of UPFC by simulating the power flow distribution after the UPFC to be configured is connected to different key channel lines, calculating the load rate, and then calculating the weighted power flow entropy. This method avoids blind configuration of resources, ensures that UPFC can achieve maximum benefits, and optimizes resource allocation.

[0232] (3) This application can evaluate the stability of the system under fault conditions by calculating the power flow of the grid operation status under preset fault scenarios and calculating the power transfer support strength of key channel lines. This provides an important reference for the capacity configuration of UPFC and helps to enhance the stability of the system under various conditions.

[0233] (4) Based on the optimal access line and power transfer support strength, this application establishes a UPFC grid-connected capacity optimization model, and uses an improved particle swarm optimization algorithm for calculation, which can obtain the optimal capacity of the UPFC to be configured. This method not only improves the accuracy of configuration optimization, but also improves the calculation efficiency through algorithm optimization, making the configuration optimization process faster and more efficient.

[0234] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A UPFC configuration optimization method, characterized in that: include: Analyze the acquired topological structure information and operating parameters of the target power system, and build a critical channel line set based on the analysis results; Initializing the capacity of the UPFC to be configured, simulating the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculating the load rate of the key channel line based on the simulation result of the power flow distribution; Calculate the weighted power flow entropy according to the load rate, and use the key channel line with the smallest weighted power flow entropy as the optimal access line of the UPFC to be configured; Performing power flow calculation on the power grid operation state of the target power system under a preset fault scenario, and calculating the power transfer support strength of the key channel line according to the result of the power flow calculation; Based on the optimal access line and the power transfer support strength, a UPFC grid-connected capacity optimization model is established, and the UPFC grid-connected capacity optimization model is calculated according to an improved particle swarm optimization algorithm to obtain the optimal capacity of the UPFC to be configured, so as to optimize the configuration of the UPFC to be configured.

2. The UPFC configuration optimization method according to claim 1, characterized in that: Initializing the capacity of the UPFC to be configured, simulating the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculating the load rate of the key channel line based on the simulation result of the power flow distribution, including: Establishing a power grid model of the target power system; Taking the capacity of the UPFC to be configured as the compensation value, determining the compensation strategy of the UPFC to be configured in different critical channel lines; Connecting the UPFC to be configured to the power grid model according to the compensation strategy, and simulating the power flow distribution of the power grid model after the UPFC to be configured is connected to the power grid model based on the power flow calculation method; The load rate of the key channel line is calculated according to the power flow distribution.

3. The UPFC configuration optimization method according to claim 1, characterized in that: The calculating the weighted power flow entropy according to the load rate includes: The load rate is differentiated according to S = [0, u, 2u, ..., nu, (n + 1)u, ..., 100%], each interval length is u, and the load rate is expressed as: Among them, μ l is the load factor, is the maximum transmission capacity of line l, is the power flow during actual operation, M is the total number of transmission lines in the power system; The proportion p(n) of line load rate in the interval [nu,(n+1)u] is defined as: Among them, L n is the total number of lines in the interval [nu, (n+1)u]; The weighted power flow entropy is calculated as: in, is the weighted power flow entropy, is the mean line load rate in the interval [nu,(n+1)u].

4. The UPFC configuration optimization method according to claim 1, characterized in that: The power transfer support strength is expressed as: Among them, P l_fault is the transmission power of the critical channel after the failure, P l0 is the initial power of the key channel; P l_lim Provide emergency power support capabilities for regional power grids.

5. The UPFC configuration optimization method according to claim 1, characterized in that: The step of calculating the UPFC grid-connected capacity optimization model according to the improved particle swarm optimization algorithm to obtain the optimal configuration capacity of the UPFC to be configured includes: Randomly initializing the capacity value of the UPFC to be configured as a particle, wherein the particle represents a UPFC configuration scheme; Calculating the fitness value of each particle, and iteratively updating the particle according to the fitness value and a nonlinear decreasing inertia weight method; When the result of the iterative update satisfies the preset convergence condition, the current result is output as the optimal configuration capacity of the UPFC to be configured.

6. A UPFC configuration optimization system, characterized in that: include: An analysis module is used to analyze the acquired topological structure information and operating parameters of the target power system and construct a key channel line set according to the analysis results; A simulation module, used for initializing the capacity of the UPFC to be configured, simulating the power flow distribution of the power grid after the UPFC to be configured is connected to different key channel lines in the key channel line set, and calculating the load rate of the key channel line based on the simulation result of the power flow distribution; A configuration module, used for calculating the weighted power flow entropy according to the load rate, and taking the key channel line with the smallest weighted power flow entropy as the optimal access line of the UPFC to be configured; A calculation module, used to perform power flow calculation on the power grid operation state of the target power system under a preset fault scenario, and calculate the power transfer support strength of the key channel line according to the result of the power flow calculation; The optimization module is used to establish a UPFC grid-connected capacity optimization model based on the optimal access line and the power transfer support strength, calculate the UPFC grid-connected capacity optimization model according to the improved particle swarm optimization algorithm, obtain the optimal capacity of the UPFC to be configured, and optimize the configuration of the UPFC to be configured.

7. The UPFC configuration optimization system according to claim 6, characterized in that: The simulation module is specifically used for: Establishing a power grid model of the target power system; Taking the capacity of the UPFC to be configured as the compensation value, determining the compensation strategy of the UPFC to be configured in different critical channel lines; Connecting the UPFC to be configured to the power grid model according to the compensation strategy, and simulating the power flow distribution of the power grid model after the UPFC to be configured is connected to the power grid model based on the power flow calculation method; The load rate of the key channel line is calculated according to the power flow distribution.

8. The UPFC configuration optimization system according to claim 6, characterized in that: The configuration module is also used for: The load rate is differentiated according to S = [0, u, 2u, ..., nu, (n + 1)u, ..., 100%], each interval length is u, and the load rate is expressed as: Among them, μ l is the load factor, is the maximum transmission capacity of line l, is the power flow during actual operation, M is the total number of transmission lines in the power system; The proportion p(n) of line load rate in the interval [nu,(n+1)u] is defined as: m=100 / u, Among them, L n is the total number of lines in the interval [nu, (n+1)u]; The weighted power flow entropy is calculated as: in, is the weighted power flow entropy, is the mean line load rate in the interval [nu,(n+1)u].

9. The UPFC configuration optimization system according to claim 6, characterized in that: The power transfer support strength is expressed as: Among them, P l_fault is the transmission power of the critical channel after the failure, P l0 is the initial power of the key channel; P l_lim Provide emergency power support capabilities for regional power grids.

10. The UPFC configuration optimization system according to claim 6, characterized in that: The optimization module is specifically used for: Randomly initializing the capacity value of the UPFC to be configured as a particle, wherein the particle represents a UPFC configuration scheme; Calculating the fitness value of each particle, and iteratively updating the particle according to the fitness value and a nonlinear decreasing inertia weight method; When the result of the iterative update satisfies the preset convergence condition, the current result is output as the optimal configuration capacity of the UPFC to be configured.

11. A UPFC configuration optimization device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the UPFC configuration optimization method according to any one of claims 1 to 5 when executing the computer program.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the UPFC configuration optimization method according to any one of claims 1 to 5 is implemented.

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