Collaborative optimization method and device for control parameters of network construction type equipment

Abstract

The invention provides a collaborative optimization method and device for control parameters of network construction type equipment, and the method comprises the steps: S1, determining an identification sequence of to-be-identified control parameters according to a dual-loop control strategy of the network construction type equipment; s2, according to the identification sequence, the control parameters are identified in a field test and laboratory simulation cooperation mode; s3, on the basis of the identified control parameters, adjusting the identified control parameters by performing step response tests for multiple times until the response characteristics of the equipment meet preset requirements; s4, the optimized control parameters are synchronized to a laboratory simulation platform, multiple preset working conditions are simulated in the laboratory simulation platform, and the adaptability of the optimized control parameters under different preset working conditions is verified; and S5, if the verification result of the adaptability shows that the preset requirements are not met under the partial preset working conditions, repeatedly executing the step S3 and the step S4 until the adaptability verification is passed under all the preset working conditions.

Description

Technical Field

[0001] This application relates to the field of new energy power generation technology, and in particular to a method and apparatus for the collaborative optimization of control parameters of grid-connected equipment. Background Technology

[0002] Currently, power systems dominated by new energy sources are increasingly exhibiting characteristics of low inertia and weak damping. To address these challenges, grid-connected equipment, due to its excellent ability to actively support grid voltage and frequency, has become a key technology for improving the stability of weak power grids and has been applied in many places.

[0003] Before grid-connected equipment is put into operation, its control parameters must be accurately identified and tuned to ensure that the simulation model matches the behavior of the equipment in the field and that the equipment can operate safely and stably under various operating conditions. Accurate parameters are the fundamental basis for subsequent grid mode calculations, operational limit analysis, and the formulation of control strategies.

[0004] However, existing methods for identifying and optimizing network-type equipment parameters have the following significant drawbacks:

[0005] Traditional parameter identification methods, such as those based on frequency domain analysis, require the injection of complex disturbance signals into the power grid in the field. This is difficult and risky in practice, making it hard to implement. Other identification methods based on optimal algorithms (such as particle swarm optimization) require a large amount of field step test data for training, which is also difficult to obtain in time-constrained and demanding field performance tests. Therefore, there is a lack of an effective means to efficiently, accurately, and safely identify all control parameters under field conditions.

[0006] After initial parameter tuning of grid-connected equipment in the field, the ability of its control parameters to adapt to the impact of the power grid under different operating modes (such as variations in system strength) and various unknown faults (such as short circuits of different types and locations) is crucial to ensuring the long-term safe operation of the equipment. However, due to limited field testing conditions, it is neither possible nor permissible to simulate various extreme or fault conditions in the field to verify the adaptability of the parameters one by one. Current technologies lack clear provisions and effective solutions regarding how to conduct comprehensive and systematic parameter adaptability verification after field parameter tuning and ensure its performance under various complex operating conditions.

[0007] In summary, existing technologies have problems such as difficulties in on-site implementation and insufficient accuracy in identifying parameters of grid-type equipment. In particular, the lack of a systematic adaptive verification process after parameter optimization may result in the set parameters failing to cope with the complex and ever-changing operating conditions of the power grid, leaving hidden dangers for the stable operation of the equipment and even the power grid. Summary of the Invention

[0008] In view of this, this application provides a method and apparatus for collaborative optimization of control parameters of network-type equipment to solve at least one of the aforementioned problems.

[0009] To achieve the above objectives, this application adopts the following approach:

[0010] According to a first aspect of this application, a method for collaborative optimization of control parameters of network-type devices is provided, the method comprising:

[0011] Step S1: Based on the dual-loop control strategy of the network-type device, determine the identification order of the control parameters to be identified, wherein the order is to identify the amplitude limiting parameters first, and then identify the proportional-integral parameters.

[0012] Step S2: Identify the control parameters by combining on-site testing and laboratory simulation according to the identification sequence;

[0013] Step S3: Based on the identified control parameters, adjust the identified control parameters on the field equipment by conducting multiple step response tests according to the actual strength of the power grid and the requirements of relevant technical standards, until the response characteristics of the equipment meet the preset requirements, thereby obtaining a set of optimized control parameters;

[0014] Step S4: Synchronize the optimized control parameters to the laboratory simulation platform, and simulate various preset working conditions in the laboratory simulation platform to fully verify the adaptability of the optimized control parameters under different preset working conditions.

[0015] Step S5: If the adaptive verification results show that the preset requirements are not met under some preset operating conditions, then repeat steps S3 and S4 until the adaptive verification is passed under all preset operating conditions, and the corresponding optimal control parameters are obtained.

[0016] As an embodiment of this application, the identification order of the limiting parameter in the above method is from the outer loop to the inner loop; the identification order of the proportional-integral parameter is to first identify the active power control loop and then identify the reactive power control loop, and both are performed from the inner loop to the outer loop.

[0017] As an embodiment of this application, the method described above, which identifies the control parameters through a combination of field testing and laboratory simulation based on the identification sequence, includes:

[0018] On the field equipment, the first type of control parameters are directly identified by applying preset operations. The first type of control parameters includes the limiting parameter, the inertial time constant, the damping coefficient, and the proportional coefficient.

[0019] The system strength information and the identified first type of control parameters are synchronized to the laboratory simulation platform. A step test is performed on the simulation platform, and the simulation response curve is compared with the field measured response curve. The second type of control parameter is determined by the parameter value with the smallest error between the two curves. The second type of control parameter is the integral coefficient.

[0020] As an embodiment of this application, the above method for directly identifying the limiting parameter by applying a preset operation includes: gradually increasing the input change of the corresponding control loop until the output of the control loop no longer changes, and using the no-change output as the corresponding limiting value.

[0021] As an embodiment of this application, the above method for directly identifying the inertial time constant by applying a preset operation includes: superimposing a linearly changing angular frequency on the active power control output, and calculating the inertial time constant based on the quotient of active power and the rate of change of angular frequency.

[0022] As an embodiment of this application, the method described above for directly identifying the damping coefficient by applying a preset operation includes: superimposing an active power step on an active power reference value and calculating the damping coefficient based on the quotient of the change in active power and the change in angular frequency.

[0023] As an embodiment of this application, the method described above for directly identifying the proportional coefficient by applying a preset operation includes: applying a voltage step change and calculating the proportional coefficient based on the instantaneous change in the internal potential of the statically adjusted output, the instantaneous change in the reference AC voltage of the statically adjusted output, and the instantaneous change in the output of the statically adjusted AC voltage difference through the control loop.

[0024] As an embodiment of this application, the above method for performing multiple step response tests specifically includes: performing reactive power step response tests, AC voltage step response tests, and active power step response tests.

[0025] As an embodiment of this application, the preset operating conditions in the above method include: short-circuit fault operating conditions of different locations and types, system frequency offset operating conditions, and operating conditions under different system strengths.

[0026] According to a second aspect of this application, a collaborative optimization device for control parameters of network-type equipment is provided, the device comprising:

[0027] The identification sequence determination unit is used to determine the identification sequence of the control parameters to be identified according to the dual-loop control strategy of the network-type device, wherein the sequence is to identify the amplitude limiting parameter first and then the proportional-integral parameter.

[0028] A collaborative identification unit is used to identify the control parameters according to the identification sequence through a combination of field experiments and laboratory simulations.

[0029] The parameter adjustment unit is used to adjust the identified control parameters on the field equipment according to the actual strength of the power grid and the requirements of relevant technical standards by conducting multiple step response tests until the response characteristics of the equipment meet the preset requirements, thereby obtaining a set of optimized control parameters.

[0030] The simulation verification unit is used to synchronize the optimized control parameters to the laboratory simulation platform, and simulate various preset working conditions in the laboratory simulation platform to fully verify the adaptability of the optimized control parameters under different preset working conditions. If the verification results of the adaptability show that the preset requirements are not met under some preset working conditions, the parameter adjustment unit and the simulation verification unit will run repeatedly in sequence until the adaptability verification is passed under all preset working conditions, and the corresponding optimal control parameters are obtained.

[0031] As an embodiment of this application, the identification order of the above-mentioned limiting parameters is from the outer loop to the inner loop; the identification order of the proportional-integral parameters is to first identify the active power control loop and then identify the reactive power control loop, and both are performed from the inner loop to the outer loop.

[0032] As one embodiment of this application, the above-mentioned collaborative identification unit includes:

[0033] The on-site identification module is used to directly identify the first type of control parameters on the on-site equipment by applying preset operations. The first type of control parameters includes the amplitude limiting parameter, the inertial time constant, the damping coefficient, and the proportional coefficient.

[0034] The synchronous simulation module is used to synchronize the system strength information and the identified first type of control parameters from the field to the laboratory simulation platform. By performing a step test on the simulation platform and comparing the simulation response curve with the field measured response curve, the second type of control parameters are determined based on the parameter value with the smallest error between the two curves. The second type of control parameters are integral coefficients.

[0035] As an embodiment of this application, the above-mentioned field identification module directly identifies the limiting parameter by applying a preset operation, including: gradually increasing the input change of the corresponding control loop until the output of the control loop no longer changes, and using the no-change output as the corresponding limiting value.

[0036] As an embodiment of this application, the above-mentioned field identification module directly identifies the inertial time constant by applying a preset operation, including: superimposing a linearly changing angular frequency on the active power control output, and calculating the inertial time constant based on the quotient of active power and the rate of change of angular frequency.

[0037] As an embodiment of this application, the above-mentioned field identification module directly identifies the damping coefficient by applying a preset operation, including: superimposing an active power step on the active power reference value, and calculating the damping coefficient based on the quotient of the active power change and the angular frequency change.

[0038] As an embodiment of this application, the above-mentioned field identification module directly identifies the proportional coefficient by applying a preset operation, including: applying a voltage step change and calculating the proportional coefficient based on the instantaneous change of the internal potential of the static adjustment output, the instantaneous change of the static adjustment AC voltage reference, and the instantaneous change of the static adjustment AC voltage difference output through the control loop.

[0039] As an embodiment of this application, the parameter adjustment unit performs multiple step response tests, specifically including: performing reactive power step response tests, AC voltage step response tests, and active power step response tests.

[0040] As an embodiment of this application, the above-mentioned preset operating conditions include: short-circuit fault operating conditions of different locations and types, system frequency offset operating conditions, and operating conditions under different system strengths.

[0041] According to a third aspect of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0042] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0043] According to a fifth aspect of this application, a computer program product is provided, comprising a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0044] The collaborative optimization method and apparatus for control parameters of grid-connected equipment proposed in this application divides parameters into two categories: one category consists of parameters that can be directly identified on-site through simple and safe operation; the other category consists of integral coefficients that are difficult to accurately identify on-site, which are identified by synchronizing on-site information to a laboratory simulation platform for comparison. This approach effectively avoids the risks of complex and dangerous on-site operations while ensuring the accuracy of all parameter identifications. Furthermore, by synchronizing the on-site optimized parameters back to the laboratory simulation platform, this application conducts comprehensive scanning tests simulating various grid strengths, different fault types, and operating conditions. This overcomes the limitation of on-site testing in simulating extreme or fault conditions, enabling systematic verification and assurance of the equipment's stability and adaptability under various foreseeable scenarios before commissioning. Finally, the iterative approach of this application ensures that the final control parameters not only meet basic performance indicators but also possess robustness to various complex operating conditions, thereby significantly improving the safety and reliability of grid-connected equipment operating in actual power grids. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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 based on these drawings without creative effort. In the drawings:

[0046] Figure 1 This is a flowchart illustrating a collaborative optimization method for control parameters of a network-type device provided in an embodiment of this application;

[0047] Figure 2 This is a block diagram of a typical dual-loop control strategy for a network-type device provided in the embodiments of this application;

[0048] Figure 3 This is a schematic diagram of the process for identifying control parameters provided in an embodiment of this application;

[0049] Figure 4 This is a graph showing the consistency comparison between field and laboratory simulation curves provided in the embodiments of this application;

[0050] Figure 5 This application provides on-site simulation of frequency and active power output curves when the frequency changes by 0.4%.

[0051] Figure 6 This is a graph showing the changes in active power and angular frequency when the static adjustment is stepped by 10% of the rated active power, as provided in the embodiments of this application.

[0052] Figure 7This is a response characteristic diagram of a static adjustment when subjected to a 0.037 pu voltage step change, as provided in an embodiment of this application.

[0053] Figure 8 This is a schematic diagram of the structure of a collaborative optimization device for control parameters of a network-type device provided in an embodiment of this application;

[0054] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of this application are used to explain this application, but are not intended to limit this application.

[0056] like Figure 1 The diagram shown is a flowchart illustrating a collaborative optimization method for control parameters of network-type devices according to an embodiment of this application. The method includes the following steps:

[0057] Step S1: Based on the dual-loop control strategy of the network-type device, determine the identification order of the control parameters to be identified. The order is to identify the amplitude limiting parameters first, and then identify the proportional-integral parameters.

[0058] The purpose of this step is to develop a clear and orderly plan for subsequent parameter identification. Based on the classic dual-loop control strategy (i.e., active power loop and reactive power loop) of network-type equipment, all control parameters to be identified are divided into two categories: limiting parameters and proportional-integral (PI) parameters.

[0059] In the control system of networked equipment, the limiting element directly determines the maximum and minimum values ​​of the control loop output. If the system response is flattened because it reaches the limiting value when identifying the proportional-integral (PI) parameters, the resulting response curve will be distorted and cannot accurately reflect the effect of the PI parameters. This will lead to inaccurate identification of the PI parameters. By first determining the limiting parameters, a suitable, small step signal can be applied during subsequent PI parameter identification to ensure that the entire dynamic response process occurs within the limiting range (i.e., the linear region). In this way, the system output is entirely determined by the PI controller, thus allowing for accurate identification of the proportional and integral coefficients.

[0060] Furthermore, from a control logic perspective, amplitude limiting is a form of upper-level protection or constraint, while the PI parameters determine the dynamic response process. First, determine the constraint boundary (amplitude limiting), and then fine-tune the dynamic process (PI parameters) within this boundary. This is a decoupling process that proceeds from the outside in and from coarse to fine.

[0061] Therefore, the overall identification order determined in this step is: first identify the amplitude limiting parameter, then identify the proportional-integral parameter.

[0062] Step S2: According to the identification sequence, the control parameters are identified through a combination of field tests and laboratory simulations.

[0063] The collaboration between field testing and laboratory simulation here refers to the fact that some parameters can be directly identified in the field, while others require collaborative identification between the field and the laboratory.

[0064] Step S3: Based on the identified control parameters, adjust the identified control parameters on the field equipment by conducting multiple step response tests according to the actual strength of the power grid and the requirements of relevant technical standards, until the response characteristics of the equipment meet the preset requirements, thereby obtaining a set of optimized control parameters.

[0065] After the initial identification of all control parameters, the purpose of this step is to fine-tune these parameters on-site to better adapt them to the actual conditions of the current power grid. Specifically, this involves conducting multiple step response tests on the on-site equipment based on the actual strength of the power grid and the requirements of relevant technical specifications (such as national and industry standards) for performance indicators such as response time and overshoot. During the testing process, the identified control parameters are continuously adjusted, and changes in the equipment's response characteristics are observed until all performance indicators (such as response speed and stability) meet the preset requirements, resulting in a set of optimized control parameters for the current operating conditions.

[0066] Step S4: Synchronize the optimized control parameters to the laboratory simulation platform, and simulate various preset working conditions in the laboratory simulation platform to fully verify the adaptability of the optimized control parameters under different preset working conditions.

[0067] This step is crucial for ensuring the universality and robustness of the optimized parameters. Since field tests cannot simulate all possible power grid operating conditions, especially fault and extreme conditions, comprehensive verification using a laboratory simulation platform is necessary. The optimized control parameters from step S3, along with the system strength information from the field, are synchronized back to the laboratory simulation platform. In the simulation platform, various preset normal and fault conditions of the power grid are simulated for a scanning test. These conditions include, but are not limited to: operating states at different initial operating points, short-circuit faults at different locations and of different types (such as near-end / far-end three-phase, two-phase, and single-phase short circuits), response characteristics when the system frequency shifts, and various operating conditions after changing the system strength (e.g., ±50% of the original strength).

[0068] The response characteristics of network-type equipment under these preset operating conditions are fully recorded and analyzed to verify whether it can always maintain normal operation and provide effective voltage and inertia support for the system.

[0069] Step S5: If the adaptive verification results show that the preset requirements are not met under some preset operating conditions, then repeat steps S3 and S4 until the adaptive verification is passed under all preset operating conditions, and the corresponding optimal control parameters are obtained.

[0070] This step is a closed-loop feedback optimization process, aiming to obtain the optimal control parameters that perform best under all preset operating conditions through iterative verification. If, during the comprehensive verification in step S4, the optimized parameters are found to cause the equipment response to fail to meet requirements under certain preset operating conditions (e.g., under a specific remote single-phase ground fault), then step S3 needs to be returned to and repeated, i.e., the parameters need to be fine-tuned again in the field. After the readjustment, step S4 needs to be repeated again to conduct a new round of comprehensive adaptive verification on the simulation platform. This "field adjustment-lab verification" cycle will continue until a set of control parameters can pass the adaptive verification under all preset operating conditions simulated in step S4. At this point, the obtained set of parameters is considered to be the optimal control parameters suitable for that field.

[0071] As described above, the collaborative optimization method for control parameters of grid-connected equipment proposed in this application categorizes parameters into two types: one type consists of parameters that can be directly identified on-site through simple and safe operation; the other type consists of integral coefficients that are difficult to accurately identify on-site, which are identified by synchronizing on-site information to a laboratory simulation platform for comparison. This approach effectively avoids the risks of complex and dangerous on-site operations while ensuring the accuracy of all parameter identifications. Furthermore, by synchronizing the optimized parameters from the field back to the laboratory simulation platform, this application conducts comprehensive scanning tests simulating various grid strengths, different fault types, and operating conditions. This overcomes the limitation of on-site testing in simulating extreme or fault conditions, enabling systematic verification and assurance of the equipment's stability and adaptability under various foreseeable scenarios before commissioning. Finally, the iterative approach of this application ensures that the final control parameters not only meet basic performance indicators but also possess robustness to various complex operating conditions, thereby significantly improving the safety and reliability of grid-connected equipment operating in actual power grids.

[0072] In one embodiment of this application, the identification order of the limiting parameter in step S1 is from the outer loop to the inner loop; the identification order of the proportional-integral parameter is to first identify the active power control loop and then identify the reactive power control loop, and both are performed from the inner loop to the outer loop.

[0073] like Figure 2The diagram shown illustrates a typical dual-loop control strategy for network-type equipment. This control strategy mainly consists of two core parts: the active power control loop (…). Figure 1 (upper part) and reactive power control loop ( Figure 2 (Lower half)

[0074] The active power control loop simulates the inertia and damping characteristics of a synchronous generator, comparing the input active power reference value with the actual active power P. After a series of controls, it ultimately generates the phase angle of the internal electromotive force of the grid-type equipment. The reactive power control loop is responsible for controlling the voltage of the equipment. It compares the externally given AC voltage reference value with the actual measured voltage, and adjusts it through internal and external dual-loop PI controllers (proportional-integral controllers) to ultimately generate the amplitude of the internal electromotive force.

[0075] In this embodiment, the identification order of the limiting parameter is from the outer ring to the inner ring; specifically, it is identified first. Figure 2 The outer ring limiting LMT1 is identified, and then the inner ring limiting LMT2 is identified.

[0076] After all the limiting values ​​are determined, the proportional-integral (PI) parameters are identified. The identification order of the PPI parameters is to identify the active control loop first, and then the reactive control loop, and to identify the reactive control loop from the inner loop to the outer loop.

[0077] In the control strategy of network-type equipment, the active power control loop and reactive power control loop are relatively independent, controlling the phase angle and amplitude of the internal electromotive force (EMF) respectively. Typically, the dynamic response speed of the active power loop differs from that of the reactive power loop. Fixing one loop first and then identifying the other is a decoupling method. After the active power loop parameters (such as the inertia time constant T and damping coefficient D) are determined and stabilized, identifying the reactive power loop provides a stable frequency and power basis for reactive power loop testing.

[0078] In multi-loop control systems, the dynamic response speed of the inner loop is usually faster than that of the outer loop. The performance of the inner loop directly determines the control quality of the outer loop and the stability of the entire system. Only by first tuning the inner loop parameters to ensure they have fast and stable response characteristics can the outer loop controller be effectively adjusted based on them. If the outer loop is identified first, the dynamic process of the inner loop will be superimposed, making the observed system response a result of the combined effects of the inner and outer loops. It will be difficult to separate the characteristics belonging solely to the outer loop, leading to inaccurate identified outer loop parameters. Conversely, by identifying and fixing the inner loop parameters first, the already stable inner loop can be treated as a whole when identifying the outer loop, thus simplifying the analysis and identification of the outer loop.

[0079] Therefore, this embodiment adopts a "positive first, negative later, inside to outside" approach, which is a systematic and hierarchical identification method. By peeling away and fixing layers one by one, it decomposes a complex multivariate coupled system into multiple relatively independent single-variable systems for analysis, greatly reducing the difficulty of identification and improving the accuracy of parameters.

[0080] In another embodiment of this application, such as Figure 3 As shown, step S2 above, which identifies the control parameters according to the identification sequence through a combination of field testing and laboratory simulation, may further include:

[0081] Step S21: On the field equipment, the first type of control parameters are directly identified by applying a preset operation. The first type of control parameters includes the limiting parameter, the inertial time constant, the damping coefficient, and the proportional coefficient.

[0082] In this step, operation can be performed directly on the networked equipment in the field. By applying a preset disturbance, the equipment's response data is recorded, and then the parameter values ​​are calculated directly using physical formulas. These parameters are characterized by their clear physical meaning and direct mathematical relationship with the response under a specific disturbance.

[0083] Step S22: Synchronize the system strength information and the identified first type of control parameters on site to the laboratory simulation platform. Perform a step test on the simulation platform and compare the simulation response curve with the actual measured response curve on site. Determine the second type of control parameters based on the parameter value with the smallest error between the two curves. The second type of control parameters are integral coefficients.

[0084] First, in this embodiment, the power grid system strength information (such as short-circuit ratio) measured on-site and all the first-type control parameters identified in step S21 can be input and configured into the laboratory simulation platform. This step ensures that the simulation model reproduces the equipment characteristics and power grid environment on-site to the greatest extent possible.

[0085] Then, in the simulation platform, simulate the exact same operating conditions as the field test, for example, apply a voltage step of the same magnitude as in the field test. Run the simulation to obtain a simulated response curve (such as a voltage or reactive power response curve). Compare this simulated curve with the response curve measured from the field equipment. By continuously fine-tuning the second type of control parameters (integral coefficients) in the simulation model and repeating the simulation, the error between the simulated curve and the field measured curve is minimized.

[0086] At this point, the integral coefficient values ​​used in the simulation platform are determined to be the actual integral coefficient values ​​of the field equipment. For example... Figure 4 The image shown is a comparison of the consistency between the field and laboratory simulation curves. Figure 4The two curves are highly overlapping, indicating that the integral coefficients have been accurately identified at this point.

[0087] By combining the realism of on-site testing with the flexibility of laboratory simulation, the accurate identification of all key control parameters of the network-type equipment is achieved.

[0088] In another embodiment of this application, the step S21 above, which directly identifies the limiting parameter by applying a preset operation, includes: gradually increasing the input change of the corresponding control loop until the output of the control loop no longer changes, and using the no-change output as the corresponding limiting value.

[0089] In step S21, the limiting parameter is classified as a first-class control parameter, which is a parameter that can be identified by direct operation on the field equipment. The core identification method in this embodiment is: by gradually increasing the input of a certain control loop and continuously observing its output, until the output value reaches a saturation state and no longer increases, this saturated output value is the limiting value of that loop.

[0090] The following is combined with Figure 2 Let's take the two clipping stages, LMT1 and LMT2, as an example to explain in detail:

[0091] 1. Identify the limiting parameter LMT1 of the AC voltage loop.

[0092] LMT1 is located in the outer loop of voltage control, after the proportional stage Kv. During operation, the AC voltage reference value Vref is continuously and gradually increased. As Vref increases, the difference between Vref and the grid voltage Vg increases, causing the signal after passing through the Kv stage to also increase. The output value of the stage containing LMT1 can be monitored in real time. When the increment of Vref reaches a certain level, the output value of LMT1 will no longer change, but will remain at a fixed maximum value. This saturated output value is the limiting value of LMT1.

[0093] 2. Identify the limiting parameter LMT2 of the reactive voltage control inner loop.

[0094] LMT2 is located in the inner loop of reactive power and voltage control, following the inner loop PI controller, and is the output limiter of the inner loop controller. Identifying LMT2 is similar to identifying LMT1; both involve changing the input to bring it to saturation. For example, this can be done by changing the input Qref of the reactive power control outer loop, or by changing the input Uref of the voltage control outer loop. Both methods ultimately affect the input signal of the inner loop PI controller. The output value of the stage containing LMT2 is monitored in real time, which is the instantaneous output of the inner loop PI controller. When a change in the input signal causes the output of the inner loop PI controller to saturate and no longer change, this saturation value is the limit value of LMT2.

[0095] By using this on-site operation method of "gradually increasing the input until the output is saturated", the actual boundary values ​​of each limiting element in the network-type equipment can be determined intuitively and accurately, laying the foundation for ensuring that the controller operates within the linear region when identifying other parameters such as proportional and integral parameters in the future.

[0096] In another embodiment of this application, the step S21 above, which directly identifies the inertial time constant by applying a preset operation, includes: superimposing a linearly changing angular frequency on the active power control output, and calculating the inertial time constant based on the quotient of active power and the rate of change of angular frequency.

[0097] Identifying the inertial time constant Previously, the DC voltage control loop of the network-type equipment needed to be deactivated. This step was to isolate the influence of other control loops on active power and frequency (or angular frequency), ensuring that the changes in subsequent measurements were mainly determined by the characteristics of the inertial loop, thereby simplifying the calculation model and improving the accuracy of identification. On the field equipment, a linearly changing angular frequency disturbance with a known slope was superimposed on the output of the active control loop. Then the changes in active power and frequency were recorded, and the result could be obtained by calculating the ratio of active power to the rate of change of angular frequency, i.e., according to the following formula (1). :

[0098] (1)

[0099] Among them, P out P represents the output active power of a network-type device. N The rated active power of the grid-connected equipment is represented by f, and the voltage frequency at the grid connection point of the grid-connected equipment is represented by f. N This indicates the rated voltage and frequency at the grid connection point of grid-connected equipment.

[0100] Figure 5 To simulate the frequency and active power output curves with a 0.4% frequency change on-site, the following was obtained: , and the set value Basically the same.

[0101] In another embodiment of this application, the step S21 above, in which the damping coefficient is directly identified by applying a preset operation, includes: superimposing an active power step on an active power reference value and calculating the damping coefficient based on the quotient of the change in active power and the change in angular frequency.

[0102] In this embodiment, the core idea for identifying the damping coefficient is to utilize its physical definition, namely, the change in active power caused by a unit frequency change. This coefficient is calculated by actively applying a power step disturbance on-site to simulate the frequency change.

[0103] The specific operation and calculation steps are as follows: First, exit the DC voltage loop. Similar to identifying the inertial time constant, before identifying the damping coefficient D, the DC voltage control loop of the network-type equipment also needs to be exited. This is to eliminate the coupling effect of other control links and ensure that the measured active power and frequency response are mainly determined by the damping characteristics. Then, on the field equipment, a step disturbance of appropriate size is superimposed on the reference value of active power. This step disturbance will break the original power balance of the system and force the frequency of the equipment to change. Record the instantaneous changes in active power and frequency, and obtain the damping coefficient D according to the following formula (2):

[0104] (2)

[0105] Among them, P ref P represents the target active power value for network-type equipment. out Δω represents the output active power of the network-type equipment, and Δω represents the change in angular frequency caused by the change in active power of the network-type equipment.

[0106] Figure 6 When the static adjustment is performed with a step change of 10% of the rated active power, the active power and Changes, calculation , and the set value Basically the same.

[0107] In another embodiment of this application, the step S21 above, which directly identifies the proportional coefficient by applying a preset operation, includes: applying a voltage step change and calculating the proportional coefficient based on the instantaneous change of the internal potential of the static adjustment output, the instantaneous change of the static adjustment AC voltage reference, and the instantaneous change of the static adjustment AC voltage difference output through the control loop.

[0108] Specifically, regarding the proportionality coefficient and In this case, the parameters are identified by the step change and the output of the control loop at the instant of the step change. First, the parameters are calculated using equation (3). Then calculate using equation (4) Thus, respectively obtain and . Figure 7 The response characteristics under static adjustment when subjected to a voltage step change of 0.037 pu. , According to equation (3), the following can be calculated: , and the actual settings Basically the same.

[0109] (3)

[0110] (4)

[0111] In the formula, This represents the instantaneous change in internal potential at static output. This represents the instantaneous change in AC voltage reference at rest. This represents the instantaneous change in the static AC voltage difference output through the control loop.

[0112] In another embodiment of this application, the step S3 described above, which involves multiple step response tests, specifically includes performing reactive power step response tests, AC voltage step response tests, and active power step response tests.

[0113] The goal of this step is to ensure that the equipment exhibits optimal dynamic response characteristics at the actual grid connection point, meeting the technical requirements of the power grid. In this embodiment, the main method to achieve this goal is to conduct multiple step response tests and repeatedly adjust (tune) the control parameters by observing the response curves. Specifically, this includes the following three tests:

[0114] Reactive power step response test: Apply a step change to the equipment's reactive power reference value, such as a sudden increase of 10% or 20% of the rated reactive power from the current value. Record the response curves of the actual reactive power output and the terminal voltage. The main focus is on the speed, stability, and accuracy of the response. This test is primarily used to optimize reactive power control loops (such as...). Figure 2 The parameters of the reactive power control outer loop and reactive power voltage control inner loop (such as K) q K iu K pu For example, if the response is too slow, the proportional or integral gain can be increased; if oscillations occur, the parameters of the inner and outer loops can be adjusted.

[0115] AC Voltage Step Response Test: Apply a step change to the AC voltage reference value of the equipment, for example, suddenly increase the voltage reference value by 0.02 pu. Record the response curves of the equipment terminal voltage and the reactive power output. The focus is on evaluating the voltage build-up speed and stability. This test is mainly used to optimize the voltage control loop (e.g., ...). Figure 2 The parameters of the voltage control outer loop and reactive voltage control inner loop (such as K) v K pu K iu The purpose is to enable the equipment to provide voltage support quickly and stably when the grid voltage fluctuates.

[0116] Active power step response test: A step change is applied to the active power reference value of the equipment, and the response curves of the actual active power and frequency emitted by the equipment are recorded. This test is mainly used to optimize the active power control loop (such as...). Figure 2 The parameters of the inertial and damping elements (such as the inertial time constant T) in the system.j And the damping coefficient D. The purpose is to enable the equipment to respond to the frequency changes of the system as required, providing appropriate inertia and damping support.

[0117] This step involves repeatedly performing one or more of the above-mentioned step tests and adjusting the control parameters based on whether the response characteristics meet the requirements of technical specifications or relevant standards (such as response time) until the dynamic performance of the equipment meets the requirements of the field system, ultimately obtaining a set of optimized control parameters.

[0118] In another embodiment of this application, the aforementioned preset operating conditions include: short-circuit fault operating conditions of different locations and types, system frequency offset operating conditions, and operating conditions under different system strengths.

[0119] To fully verify the adaptability and robustness of this parameter set under various power grid disturbances, in step S4, a series of stringent preset operating conditions are simulated in a laboratory simulation platform using a field-laboratory collaborative approach. These operating conditions can be exemplified in Table 1 below. Under these fault conditions, the response characteristics of the equipment are recorded to ensure that the equipment can maintain normal operation and provide voltage support to the system within its capabilities.

[0120] Table 1

[0121]

[0122] By conducting a comprehensive scan test under all the aforementioned preset operating conditions, if the network-type equipment can maintain normal operation and provide effective voltage and inertia support, the current parameter scheme is verified. If the characteristics do not meet the requirements under certain operating conditions, it is necessary to return to step three to readjust the parameters and perform the comprehensive verification in this step again. This process should be iterated until the optimal control parameters are found.

[0123] like Figure 8 The diagram shown is a structural schematic of a collaborative optimization device for control parameters of a network-type device according to an embodiment of this application. The device includes:

[0124] The identification sequence determination unit 810 is used to determine the identification sequence of the control parameters to be identified according to the dual-loop control strategy of the network-type device. The sequence is to identify the amplitude limiting parameters first, and then identify the proportional-integral parameters.

[0125] The collaborative identification unit 820 is used to identify the control parameters in a collaborative manner through field tests and laboratory simulations, according to the identification sequence.

[0126] The parameter adjustment unit 830 is used to adjust the identified control parameters on the field equipment by performing multiple step response tests according to the actual strength of the power grid and the requirements of relevant technical standards, based on the identified control parameters, until the response characteristics of the equipment meet the preset requirements, thereby obtaining a set of optimized control parameters.

[0127] The simulation verification unit 840 is used to synchronize the optimized control parameters to the laboratory simulation platform, and simulate various preset working conditions in the laboratory simulation platform to fully verify the adaptability of the optimized control parameters under different preset working conditions. If the verification results of the adaptability show that the preset requirements are not met under some preset working conditions, the parameter adjustment unit 830 and the simulation verification unit 840 are run repeatedly in sequence until the adaptability verification is passed under all preset working conditions, and the corresponding optimal control parameters are obtained.

[0128] In one embodiment of this application, the identification order of the above-mentioned limiting parameters is from the outer loop to the inner loop; the identification order of the proportional-integral parameters is to first identify the active power control loop and then identify the reactive power control loop, and both are performed from the inner loop to the outer loop.

[0129] In one embodiment of this application, the cooperative identification unit 820 includes:

[0130] The on-site identification module is used to directly identify the first type of control parameters on the on-site equipment by applying preset operations. The first type of control parameters includes the amplitude limiting parameter, the inertial time constant, the damping coefficient, and the proportional coefficient.

[0131] The synchronous simulation module is used to synchronize the system strength information and the identified first type of control parameters from the field to the laboratory simulation platform. By performing a step test on the simulation platform and comparing the simulation response curve with the field measured response curve, the second type of control parameters are determined based on the parameter value with the smallest error between the two curves. The second type of control parameters are integral coefficients.

[0132] In one embodiment of this application, the above-mentioned field identification module directly identifies the limiting parameter by applying a preset operation, including: gradually increasing the input change of the corresponding control loop until the output of the control loop no longer changes, and using the no-change output as the corresponding limiting value.

[0133] In one embodiment of this application, the above-mentioned field identification module directly identifies the inertial time constant by applying a preset operation, including: superimposing a linearly changing angular frequency on the active power control output, and calculating the inertial time constant based on the quotient of active power and the rate of change of angular frequency.

[0134] In one embodiment of this application, the above-mentioned field identification module directly identifies the damping coefficient by applying a preset operation, including: superimposing an active power step on an active power reference value and calculating the damping coefficient based on the quotient of the change in active power and the change in angular frequency.

[0135] In one embodiment of this application, the above-mentioned field identification module directly identifies the proportional coefficient by applying a preset operation, including: applying a voltage step change and calculating the proportional coefficient based on the instantaneous change of the internal potential of the static adjustment output, the instantaneous change of the static adjustment AC voltage reference, and the instantaneous change of the static adjustment AC voltage difference output through the control loop.

[0136] In one embodiment of this application, the parameter adjustment unit 830 performs multiple step response tests, specifically including: performing reactive power step response tests, AC voltage step response tests, and active power step response tests.

[0137] In one embodiment of this application, the aforementioned preset operating conditions include: short-circuit fault operating conditions of different locations and types, system frequency offset operating conditions, and operating conditions under different system strengths.

[0138] As described above, the collaborative optimization device for control parameters of grid-connected equipment proposed in this application divides parameters into two categories: one category consists of parameters that can be directly identified on-site through simple and safe operation; the other category consists of integral coefficients that are difficult to accurately identify on-site, which are identified by synchronizing on-site information to a laboratory simulation platform for comparison. This approach effectively avoids the risks of complex and dangerous on-site operations while ensuring the accuracy of all parameter identifications. Furthermore, this application synchronizes the on-site optimized parameters back to the laboratory simulation platform to conduct comprehensive scanning tests simulating various grid strengths, different fault types, and operating conditions. This compensates for the shortcomings of on-site testing in simulating extreme or fault conditions, enabling systematic verification and assurance of the equipment's stability and adaptability under various foreseeable scenarios before commissioning. Finally, the iterative approach of this application ensures that the final control parameters not only meet basic performance indicators but also possess robustness to various complex operating conditions, thereby significantly improving the safety and reliability of grid-connected equipment operating in actual power grids.

[0139] Figure 9 This is a schematic diagram of the electronic device provided in the embodiments of this application. Figure 9The illustrated electronic device is a general-purpose data processing apparatus, comprising a general-purpose computer hardware structure, including at least a processor 801 and a memory 802. The processor 801 and memory 802 are connected via a bus 803. The memory 802 is adapted to store one or more instructions or programs executable by the processor 801. These instructions or programs are executed by the processor 801 to implement the steps in the aforementioned method for the coordinated optimization of control parameters for networked devices.

[0140] The processor 801 described above can be a standalone microprocessor or a collection of one or more microprocessors. Thus, the processor 801 executes commands stored in the memory 802, thereby performing the method flow described in the embodiments of this application to process data and control other devices. The bus 803 connects the aforementioned components together, and also connects these components to the display controller 804, the display device, and the input / output (I / O) device 805. The input / output (I / O) device 805 can be a mouse, keyboard, modem, network interface, touch input device, motion-sensing input device, printer, and other devices known in the art. Typically, the input / output (I / O) device 805 is connected to the system via an input / output (I / O) controller 806.

[0141] The memory 802 can store software components, such as an operating system, a communication module, an interaction module, and application programs. Each of the modules and application programs described above corresponds to a set of executable program instructions that perform one or more functions and the methods described in the embodiments of the invention.

[0142] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for the coordinated optimization of control parameters of networked devices.

[0143] The collaborative optimization method and apparatus for control parameters of grid-connected equipment proposed in this application divides parameters into two categories: one category consists of parameters that can be directly identified on-site through simple and safe operation; the other category consists of integral coefficients that are difficult to accurately identify on-site, which are identified by synchronizing on-site information to a laboratory simulation platform for comparison. This approach effectively avoids the risks of complex and dangerous on-site operations while ensuring the accuracy of all parameter identifications. Furthermore, by synchronizing the on-site optimized parameters back to the laboratory simulation platform, this application conducts comprehensive scanning tests simulating various grid strengths, different fault types, and operating conditions. This overcomes the limitation of on-site testing in simulating extreme or fault conditions, enabling systematic verification and assurance of the equipment's stability and adaptability under various foreseeable scenarios before commissioning. Finally, the iterative approach of this application ensures that the final control parameters not only meet basic performance indicators but also possess robustness to various complex operating conditions, thereby significantly improving the safety and reliability of grid-connected equipment operating in actual power grids.

[0144] Preferred embodiments of this application have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are apparent from this detailed description, and therefore the claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. Furthermore, since many modifications and alterations will readily occur to those skilled in the art, the embodiments of this application are not intended to be limited to the precise structures and operations illustrated and described, but rather to encompass all suitable modifications and equivalents falling within their scope.

[0145] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0147] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0149] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for collaborative optimization of control parameters of network-type equipment, characterized in that, The method includes: Step S1: Based on the dual-loop control strategy of the network-type device, determine the identification order of the control parameters to be identified, wherein the order is to identify the amplitude limiting parameters first, and then identify the proportional-integral parameters. Step S2: Identify the control parameters by combining on-site testing and laboratory simulation according to the identification sequence; Step S3: Based on the identified control parameters, adjust the identified control parameters on the field equipment by conducting multiple step response tests according to the actual strength of the power grid and the requirements of relevant technical standards, until the response characteristics of the equipment meet the preset requirements, thereby obtaining a set of optimized control parameters; Step S4: Synchronize the optimized control parameters to the laboratory simulation platform, simulate various preset working conditions in the laboratory simulation platform, and verify the adaptability of the optimized control parameters under different preset working conditions. Step S5: If the adaptive verification results show that the preset requirements are not met under some preset operating conditions, then repeat steps S3 and S4 until the adaptive verification is passed under all preset operating conditions, and the corresponding optimal control parameters are obtained.

2. The collaborative optimization method for control parameters of network-type equipment as described in claim 1, characterized in that, The identification order of the limiting parameter is from the outer loop to the inner loop; the identification order of the proportional-integral parameter is to first identify the active power control loop and then identify the reactive power control loop, and both are performed from the inner loop to the outer loop.

3. The collaborative optimization method for control parameters of network-type equipment as described in claim 1, characterized in that, The step of identifying the control parameters according to the identification sequence, through a combination of field testing and laboratory simulation, includes: On the field equipment, the first type of control parameters are directly identified by applying preset operations. The first type of control parameters includes the amplitude limiting parameter, the inertial time constant, the damping coefficient, and the proportional coefficient. The system strength information and the identified first type of control parameters are synchronized to the laboratory simulation platform. A step test is performed on the simulation platform, and the simulation response curve is compared with the field measured response curve. The second type of control parameter is determined by the parameter value with the smallest error between the two curves. The second type of control parameter is the integral coefficient.

4. The collaborative optimization method for control parameters of network-type equipment as described in claim 3, characterized in that, The limiting parameter can be directly identified by applying a preset operation, including gradually increasing the input change of the corresponding control loop until the output of the control loop no longer changes, and using the no-change output as the corresponding limiting value.

5. The collaborative optimization method for control parameters of network-type equipment as described in claim 3, characterized in that, The inertial time constant can be directly identified by applying a preset operation, including superimposing a linearly changing angular frequency on the active power control output and calculating the inertial time constant based on the quotient of active power and the rate of change of angular frequency.

6. The collaborative optimization method for control parameters of network-type equipment as described in claim 3, characterized in that, The damping coefficient can be directly identified by applying a preset operation, including superimposing an active power step on the active power reference value and calculating the damping coefficient based on the quotient of the change in active power and the change in angular frequency.

7. The collaborative optimization method for control parameters of network-type equipment as described in claim 3, characterized in that, The proportional coefficient can be directly identified by applying a preset operation, including applying a voltage step change and calculating the proportional coefficient based on the instantaneous change of the internal potential of the static adjustment output, the instantaneous change of the static adjustment AC voltage reference, and the instantaneous change of the static adjustment AC voltage difference output through the control loop.

8. The collaborative optimization method for control parameters of network-type equipment as described in claim 1, characterized in that, The aforementioned multiple step response tests specifically include: reactive power step response test, AC voltage step response test, and active power step response test.

9. The collaborative optimization method for control parameters of network-type equipment as described in claim 1, characterized in that, The preset operating conditions include: short-circuit fault conditions of different locations and types, system frequency offset conditions, and operating conditions under different system strengths.

10. A collaborative optimization device for control parameters of network-type equipment, characterized in that, The device includes: The identification sequence determination unit is used to determine the identification sequence of the control parameters to be identified according to the dual-loop control strategy of the network-type device, wherein the sequence is to identify the amplitude limiting parameter first and then the proportional-integral parameter. A collaborative identification unit is used to identify the control parameters according to the identification sequence through a combination of field experiments and laboratory simulations. The parameter adjustment unit is used to adjust the identified control parameters on the field equipment according to the actual strength of the power grid and the requirements of relevant technical standards by conducting multiple step response tests until the response characteristics of the equipment meet the preset requirements, thereby obtaining a set of optimized control parameters. The simulation verification unit is used to synchronize the optimized control parameters to the laboratory simulation platform, and simulate various preset working conditions in the laboratory simulation platform to fully verify the adaptability of the optimized control parameters under different preset working conditions. If the verification results of the adaptability show that the preset requirements are not met under some preset working conditions, the parameter adjustment unit and the simulation verification unit will run repeatedly in sequence until the adaptability verification is passed under all preset working conditions, and the corresponding optimal control parameters are obtained.

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