Harbor shore power system oscillation damping suppression method based on heuristic probability optimization

Abstract

The application discloses a port shore power system oscillation damping suppression method based on heuristic probability optimization. The method carries out sliding window pretreatment on the time domain oscillation signal of the current on the AC bus side and implements modal decomposition based on the Prony algorithm, calculates modal energy to identify the oscillation mode; the identified oscillation mode is aggregated and mapped into equivalent circuit elements, a corresponding third-order circuit state matrix is constructed, and the damping ratio of each mode is determined from the characteristic value; a multi-objective optimization function is constructed with the damping ratio as an evaluation index and an influence factor is introduced, a parallel adjustable impedance is parameterized and limited in a range; a heuristic probability optimization strategy is used to search for control parameters, and the final damping control parameters are obtained and transmitted to the parallel adjustable impedance device to realize online dynamic adjustment. The method can realize multi-modal identification, physical quantity level mapping and online adaptive optimization, and improve the suppression ability of the shore power system to sub- / ultra-synchronous oscillation and the operation stability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of new power system stability control technology, and particularly relates to a port shore power system oscillation damping suppression method based on heuristic probability optimization. BACKGROUND

[0002] With the accelerated promotion of global green port construction, the shore power system, as a key facility for replacing fuel power generation during ship berthing and reducing pollution emissions, its reliability and dynamic stability are directly related to the realization of port energy utilization efficiency and environmental protection goals. During the large-scale popularization and application of the shore power system, a series of technical bottlenecks related to power quality and dynamic stability are exposed, which become one of the restricting factors of the port shore power layout scheme.

[0003] On the one hand, there is a significant impedance difference between the ship power grid and the shore power system in the port scene and the diversity of parallel access, and the fixed parameter filter and the traditional parallel device are difficult to adapt to the access requirements of ships with different tonnages and different electrical characteristics. For example, there have been accidents of low-frequency (about 10 Hz) voltage flicker caused by LCL filter resonance in actual operation. Such impedance mismatch and resonance problems can cause significant voltage and current oscillation, and even in extreme cases, the instantaneous power swing far exceeds the rated value, which seriously threatens the continuity of power supply and equipment safety.

[0004] On the other hand, the high volatility of renewable energy grid-connected and port loads aggravates the dynamic complexity of the system. The integration of distributed new energy such as wind power and photovoltaic power changes the equivalent impedance characteristics of the system. The doubly-fed wind power machine may induce subsynchronous or subsynchronous oscillation under certain conditions (such as low wind speed), forming a low-frequency unstable mode that interacts with the shore power system. In addition, the mutation of high-power mobile loads (such as bridge cranes) in the port area and the ship start-stop and charge-discharge behavior make it difficult for traditional steady-state and linear control strategies to maintain robustness under multiple working conditions.

[0005] In the prior art, Chinese patent CN119742822A discloses a new oscillation damping optimization control device and method based on a quantum annealing algorithm. The device includes: an analog quantity acquisition module connected with a bus and an analog-digital conversion module, which completes the acquisition of voltage analog quantity of the AC bus and completes analog-digital conversion. A band-stop filter, a band-pass filter, a proportional phase-shifting module, and an amplitude limiting module constitute a multi-channel damping controller. A quantum annealing intelligent optimization module completes the optimization of proportional and compensation parameters to compensate the current of the additional parallel converter. Finally, the output suppression current of the additional parallel converter is injected into the bus to complete the new oscillation suppression.

[0006] However, the method has the following limitations: 1. The method focuses on extracting oscillation modes through transfer functions and characteristic equations, but lacks time-domain and energy-level discrimination strategies for short-time, noise-polluted multi-modal signal capture and aggregation of multiple modes under the same oscillation mode, which may lead to misjudgment or delay in responding to transient dominant modes. 2. The method uses the optimization results to adjust the controller parameters and inject current through the parallel converter, but does not provide a closed-form relationship for mapping the optimization parameters to equivalent elements (resistance / inductance / capacitance) of the parallel impedance, resulting in uncertainty in the physical level conversion of optimization parameters to actuator electrical quantities in engineering. 3. Quantum annealing optimization helps global search, but relies on special intelligent optimization hardware or platform, which may increase system deployment complexity and integration cost; in addition, the disclosed scheme does not fully demonstrate the end-to-end real-time closed-loop implementation details from short-time signal recognition to online parameter adaptive update. SUMMARY

[0007] The purpose of the present application is to overcome the defects of the prior art and provide a port shore power system oscillation damping suppression method based on heuristic probability optimization.

[0008] The purpose of the present application can be achieved by the following technical solutions:

[0009] The present application provides a port shore power system oscillation damping suppression method based on heuristic probability optimization, comprising the following steps:

[0010] Collecting the current time-domain oscillation signal on the AC bus side and pre-processing it according to the sliding window;

[0011] Based on the Prony algorithm, the modal parameters of each mode are obtained by modal decomposition of the pre-processed oscillation signal;

[0012] Based on the modal parameters of each mode, the energy of each mode is calculated and each oscillation mode is distinguished;

[0013] Mapping each oscillation mode identified to the equivalent circuit element parameters;

[0014] Based on the equivalent circuit element parameters, a three-order circuit state matrix of each oscillation mode is constructed;

[0015] Based on the three-order circuit state matrix of each oscillation mode, the damping ratio of each oscillation mode is determined;

[0016] Construct a multi-objective optimization objective function with the damping ratio as the evaluation index, set the influence factor for each oscillation mode, define the parameterized representation of the parallel adjustable impedance and determine the adjustable parameter range of the damping controller;

[0017] The adjustable parameters of the damping controller are searched based on a multi-objective optimization objective function using a heuristic probabilistic optimization strategy to determine the final set of damping control parameters;

[0018] The final set of damping control parameters is transmitted to the parallel adjustable impedance device and adjusted online.

[0019] Furthermore, the preprocessing specifically includes:

[0020] The current time-domain oscillation signal on the AC bus side is processed by an anti-aliasing low-pass filter and then sampled according to a preset sampling period to obtain a discrete sequence.

[0021] Wherein, the discrete sequence represents the signal sample obtained at each sampling time; the current time-domain oscillation signal is a continuous function of the instantaneous voltage or current on the AC bus side changing with time; the sampling period is the time interval between two adjacent samples, the reciprocal of which is the sampling frequency, and the sampling period is determined according to the range of the oscillation frequency to be detected, the Nyquist sampling theorem, and the real-time requirements;

[0022] The discrete sequence is divided into frames using a sliding window to obtain a series of overlapping or non-overlapping frame signals, and a window function is applied to each frame.

[0023] Remove DC or trend components from the windowed frame signal;

[0024] The amplitude of the detrended frame signal is normalized to obtain the preprocessed oscillation signal.

[0025] Furthermore, the modal parameters include amplitude, attenuation factor, frequency, and initial phase.

[0026] Furthermore, the modal decomposition of the preprocessed oscillation signal based on the Prony algorithm to obtain the modal parameters of each mode specifically includes:

[0027] The preprocessed oscillation signal is denoted as The preprocessed oscillation signal was fitted using an exponential function superposition model to obtain the fitted signal:

[0028]

[0029] in, This is the pre-processed oscillation signal, reflecting the AC bus voltage or current at time [time value missing]. t The possible values ​​of ; Indicates time t The fitted signal; The model order is represented by the number of fitted exponential functions. For the first i The amplitude of each mode, is the damping factor of the i th mode, is the frequency of the i th mode, is the initial phase of the i th mode;

[0030] The coefficient matrix constructed according to the Prony algorithm is used to solve the frequency domain component information of the fitted signal to obtain the modal parameters:

[0031]

[0032] wherein, is the sampling period, is the pole of the i th mode, is the complex residual coefficient of the i th mode; represents a real part operation, represents an imaginary part operation, represents an amplitude angle operation.

[0033] Further, the modal parameters of each mode are used to calculate the energy of each mode and identify each oscillation mode, and specifically comprising:

[0034] According to the obtained modal parameters of each mode, the energy of each mode is defined as:

[0035]

[0036] wherein, represents the energy of the i th mode, n represents the number of sampling points, is the amplitude of the i th mode, represents the power expansion result of the pole of the i th mode at the j th sampling point, represents a real part operation;

[0037] The dominant oscillation mode is determined by comparing the size of the energy of each mode, and the oscillation mode is identified according to the energy distribution characteristics of the dominant oscillation mode: when the modal energy is mainly concentrated below the fundamental frequency of the power grid, it is determined to be a subsynchronous oscillation mode; when the modal energy is mainly concentrated above the fundamental frequency of the power grid, it is determined to be a supersynchronous oscillation mode.

[0038] Further, the identified each oscillation mode is mapped to the equivalent circuit element parameters, and specifically comprising:

[0039] The modal parameters of the multiple modes determined as the same oscillation mode are aggregated to obtain the modal parameters of each oscillation mode;

[0040] According to each oscillation mode and the corresponding modal parameters, an equivalent circuit representation of each mode is constructed; wherein the frequency and damping factor of the oscillation mode correspond to the inductance, capacitance and resistance parameters in the equivalent circuit, and the modal amplitude corresponds to the voltage or current source parameter in the equivalent circuit;

[0041] The sub-synchronous oscillation mode is mapped to an equivalent circuit dominated by an inductive element, the super-synchronous oscillation mode is mapped to an equivalent circuit dominated by a capacitive element, and the modal damping effect is characterized by combining a resistive element, to obtain a set of equivalent circuit element parameters corresponding to each oscillation mode.

[0042] Further, the equivalent circuit element parameters include a wind turbine end equivalent resistance, a grid side equivalent resistance, a grid side equivalent inductance, a wind turbine end equivalent capacitance, and equivalent resistance and inductance parameters of a parallel adjustable impedance;

[0043] The third-order circuit state matrix of each oscillation mode is constructed based on the equivalent circuit element parameters, and specifically includes:

[0044] For the determined first i oscillation mode, according to the equivalent circuit element parameters 、 、 、 、 、 of each oscillation mode, a corresponding third-order state matrix is constructed:

[0045]

[0046] wherein, represents the third-order circuit state matrix of the first i oscillation mode, is the wind turbine end equivalent resistance corresponding to the first i oscillation mode, is the grid side equivalent resistance of the first i oscillation mode, is the grid side equivalent inductance of the first i oscillation mode, is the wind turbine end equivalent capacitance of the first i oscillation mode, and are the equivalent resistance and inductance parameters of the parallel adjustable impedance of the first i oscillation mode, and are represented as:

[0047]

[0048] wherein, is a damping controller gain coefficient for the i th oscillation mode, is a damping controller time constant for the i th oscillation mode, is a target oscillation component angular frequency for the i th oscillation mode, i.e. a frequency of the i th oscillation mode, obtained from a frequency aggregation of a plurality of modes of the oscillation mode.

[0049] Further, the damping ratio of each oscillation mode is determined based on the third-order circuit state matrix of each oscillation mode, specifically comprising:

[0050] calculating the eigenvalue of the third-order circuit state matrix of each oscillation mode, and taking the eigenvalue of the third-order circuit state matrix of each oscillation mode as the damping ratio of each oscillation mode .

[0051] Further, the multi-objective optimization objective function with the damping ratio as the evaluation index is constructed, and an influence factor is set for each oscillation mode, a parameterized representation of the parallel adjustable impedance is defined and the adjustable parameter range of the damping controller is determined, specifically comprising:

[0052] for each oscillation mode, the damping ratio of the oscillation mode is taken as the evaluation index, and an influence factor is introduced to reflect the weight of different oscillation modes on the overall stability of the system, forming a multi-objective optimization objective function:

[0053]

[0054] wherein, is a multi-objective optimization function for measuring the comprehensive damping performance of each oscillation mode, is a damping ratio of the i th oscillation mode, is a parameter related to , is an influence factor of the i th oscillation mode, is a damping controller gain coefficient for the i th oscillation mode, is a damping controller time constant for the i th oscillation mode; m is the total number of oscillation modes;

[0055] a parameterized representation of the parallel adjustable impedance device is defined, and the damping controller adjustable parameter set is denoted as ;

[0056] determining the adjustable parameter range of the damping controller:

[0057]

[0058] wherein, is the upper limit of the gain coefficient of the damping controller of the i th oscillation mode, is the upper limit of the time constant of the damping controller of the i th oscillation mode.

[0059] Further, the heuristic probability optimization strategy searches for the final damping control parameter set based on a multi-objective optimization objective function, and specifically includes:

[0060] setting an initial control parameter vector and an initial temperature wherein, m is the total number of oscillation modes, is the gain coefficient of the damping controller of the i th oscillation mode in the 0th iteration, is the time constant of the damping controller of the i th oscillation mode in the 0th iteration;

[0061] in the t th iteration, generating a neighborhood candidate control parameter vector from the current control parameter vector i in the adjustable parameter range of each th oscillation mode, wherein the generation of each control parameter is represented as:

[0062]

[0063] wherein, and are random perturbation amounts within a preset neighborhood scale, represents a clipping operation to ensure that the parameters fall within the interval ; is the upper limit of the gain coefficient of the damping controller of the i th oscillation mode, is the upper limit of the time constant of the damping controller of the i th oscillation mode; , respectively represent the damping controller gain and the damping controller time constant of the i th oscillation mode under the neighborhood candidate control parameter vector ;

[0064] according to the neighborhood candidate control parameter vector Obtain the set of candidate damping ratios corresponding to the neighborhood candidate control parameter vectors. With candidate impact factor set ;

[0065] Based on the candidate damping ratio set With candidate impact factor set By optimizing the objective function through multiple objectives, candidate objective function values ​​are calculated:

[0066]

[0067] in, The candidate objective function value;

[0068] Calculate the current control parameter vector objective function value Based on the Metropolis criterion, using probability P Decide whether to accept the neighborhood candidate control parameter vector :

[0069]

[0070] in, Indicates the first t The temperature of the next iteration;

[0071] Update the control parameter vector for the next iteration based on the accepted results. The temperature is updated according to the predetermined cooling strategy.

[0072]

[0073] in, This is the temperature update coefficient;

[0074] The iteration terminates and the final set of damping control parameters is output when any of the following termination conditions are met:

[0075] The iteration terminates when the temperature drops to a preset temperature threshold during the current iteration.

[0076] The iteration terminates when the current iteration count reaches the preset iteration count threshold.

[0077] The iteration terminates when the damping ratio corresponding to the current set of damping control parameters meets the preset minimum damping ratio requirement.

[0078] Compared with the prior art, the present invention has the following advantages:

[0079] (1) In the port shore power scene, the oscillation signal is multi-modal, short-time changing and greatly affected by noise. The traditional method based on steady-state frequency domain or coarse-grained recognition is difficult to accurately identify the instantaneous dominant mode in a short window, resulting in poor control alignment and response lag. The present application directly obtains the amplitude, attenuation factor, frequency and phase of each mode by using sliding window time domain preprocessing on the preprocessed oscillation signal and modal decomposition based on Prony algorithm. By using short-time Prony fitting, the present application can analyze the decay / growth rate and frequency information in a short time window, improve the time resolution and noise immunity of short-time modal recognition, and thus make the subsequent discrimination and control parameter calculation more accurate, reducing the risk of control mis-triggering or under-compensation caused by modal recognition error.

[0080] (2) The modal estimation at a single time may be poor due to transient, measurement point difference or noise, and directly using a single pole to drive control may easily cause mis-compensation or over-sensitivity to disturbance. The present application aggregates multiple modal parameters determined as the same oscillation mode, and obtains representative modal parameters by frequency, attenuation and amplitude (or energy) weighting. The aggregation processing reduces the influence of transient and noise, so that each "oscillation mode" is represented by multiple observations or multiple modes, improving the robustness and stability of the mode parameters, and avoiding parameter jitter and frequent switching of control parameters caused by single-point estimation.

[0081] (3) The existing technology only uses transfer function or characteristic equation to indirectly evaluate the oscillation suppression effect, lacks clear mapping between physical circuit elements, and is not conducive to converting the optimization results into executable parallel impedance / converter instructions. The present application maps the identified oscillation mode to equivalent circuit element parameters, and constructs a three-order circuit state matrix with subscript for each oscillation mode. The equivalent RLC mapping physicalizes the modal parameters, and the three-order state matrix links the physical quantities with system dynamics, so that the optimized control parameters can be directly mapped to resistance / inductance values and executed, reducing the intermediate uncertainty between parameters and execution, and improving the implementation accuracy and verifiability.

[0082] (4) The existing technology lacks a direct and engineering usable stability quantitative index (damping ratio) from the state matrix, or uses characteristic equation processing but the relationship with parallel impedance parameters is not clear, resulting in disconnection between optimization target and control parameter. The present application calculates the eigenvalue of the three-order state matrix of each oscillation mode, and calculates the damping ratio of the oscillation mode with the dominant pole. The damping ratio is calculated by matrix eigenvalue, which directly reflects the dynamic stability; the damping ratio can be used as a measurable and comparable evaluation quantity, which facilitates coupling the stability target and control parameter into the same optimization framework, and realizes target-driven parameter adjustment.

[0083] (5) The short-time online optimization of the prior art does not consider parameter boundaries, safety and stability checking, and may produce an unsafe solution, leading to protection misoperation or system instability. The present application guarantees that the solution falls within the preset safety interval by clipping during neighborhood generation, and checks the dominant pole through eigenvalue checking of the state matrix before each candidate solution evaluation, and sets clear termination conditions (temperature threshold, iteration upper limit or performance achievement). Parameter clipping and eigenvalue checking ensure that the candidate parameters are implementable in terms of numerical value and stability, and the termination criteria guarantee that the computing resources are controlled and can be exited in advance when the performance target is achieved, reducing the control risk brought by online optimization and improving engineering safety.

[0084] (6) In the target function establishment process, in order to achieve rapid damping of different oscillation modes, different influence factor weights can be given to different oscillation modes to improve the response speed of the damping controller under low oscillation mode conditions, so as to realize the coordinated optimization of different oscillation modes and multi-target damping control parameters.

[0085] (7) The heuristic probabilistic optimization algorithm for controller parameter optimization can quickly respond to the frequency oscillation of the system in a short time, effectively suppress the frequency oscillation, and promote the system to quickly recover to a stable state. The heuristic probabilistic optimization algorithm dynamically balances the damping strength and energy loss, further reduces the system harmonic distortion rate under the premise of ensuring oscillation suppression effect. BRIEF DESCRIPTION OF DRAWINGS

[0086] Figure 1 The flowchart of the damping optimization control method of the embodiment of the present application;

[0087] Figure 2 The multi-channel damping parameter heuristic probabilistic optimization suppression block diagram of the port shore power system of the embodiment of the present application;

[0088] Figure 3 The multi-channel damper structure diagram of the embodiment of the present application;

[0089] Figure 4 The convergence analysis result schematic diagram of the target function of the embodiment of the present application;

[0090] Figure 5 The flowchart of the heuristic probabilistic optimization method of the embodiment of the present application;

[0091] Figure 6 The wind farm output current schematic diagram of the non-damping controller of the embodiment of the present application;

[0092] Figure 7 The wind farm output current schematic diagram of the non-optimized damping controller installed in the embodiment of the present application; ​

[0093] Figure 8 Fig. 1 is a schematic diagram of wind farm output current for installing heuristic probability optimization damping controller according to an embodiment of the present application. DETAILED DESCRIPTION

[0094] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present application.

[0095] Embodiment 1

[0096] The present embodiment provides a port shore power system oscillation damping suppression method based on heuristic probability optimization, as shown in Figure 1 The method comprises the following steps:

[0097] Step S1: collecting current time domain oscillation signals on the AC bus side and pre-processing according to a sliding window;

[0098] The pre-processing specifically comprises:

[0099] After the current time domain oscillation signals on the AC bus side are processed by an anti-aliasing low-pass filter, sampling is performed according to a preset sampling period to obtain a discrete sequence;

[0100] The discrete sequence is represented as signal samples obtained at each sampling time; the current time domain oscillation signal is a continuous function of the instantaneous voltage or current on the AC bus side varying with time; the sampling period is the time interval between adjacent two samplings, and the reciprocal of the sampling period is the sampling frequency; the sampling period is determined according to the to-be-detected oscillation frequency range, the Nyquist sampling theorem and real-time requirements;

[0101] The discrete sequence is processed by frame according to a sliding window to obtain a series of overlapping or non-overlapping frame signals, and a window function is applied to each frame;

[0102] The DC component or trend component is removed from the windowed frame signal;

[0103] The frame signal after the trend removal is subjected to amplitude normalization processing to obtain the pre-processed oscillation signal.

[0104] Step S2: modal decomposition is performed on the pre-processed oscillation signal based on the Prony algorithm to obtain modal parameters of each mode; the modal parameters include amplitude, attenuation factor, frequency and initial phase.

[0105] The pre-processed oscillation signal is subjected to modal decomposition based on the Prony algorithm to obtain modal parameters of each mode, specifically including:

[0106] The preprocessed oscillation signal is denoted as The preprocessed oscillation signal was fitted using an exponential function superposition model to obtain the fitted signal:

[0107]

[0108] in, This is the pre-processed oscillation signal, reflecting the AC bus voltage or current at time [time value missing]. t The value of ; Indicates time t The fitted signal; The model order is represented by the number of fitted exponential functions. For the first i The amplitude of each mode, For the first i The attenuation factor of each mode, For the first i The frequency of each mode, For the first i The initial phase of each mode;

[0109] The frequency domain components of the fitted signal are obtained by solving the coefficient matrix constructed using the Prony algorithm, thus yielding the modal parameters:

[0110]

[0111] in, The sampling period is For the first i The poles of each mode, For the first i Complex residual coefficients for each mode; Represents the operation of the real part. Represents the imaginary part operation. This indicates argument operation.

[0112] Step S3: Calculate the modal energy of each mode based on the modal parameters of each mode and identify each oscillation mode, specifically including:

[0113] Based on the obtained modal parameters of each mode, the energy of each mode is defined as:

[0114]

[0115] in, Indicates the first i Energy of a mode n Indicates the number of sampling points. For the first i The amplitude of each mode, Indicates the firsti pole of the i-th mode at the j-th sampling point, j represents the real part operation; represents the real part operation;

[0116] The dominant oscillation mode is determined by comparing the sizes of the energy of each mode, and the oscillation mode is determined according to the energy distribution characteristics of the dominant oscillation mode: when the mode energy is mainly concentrated below the fundamental frequency of the power grid, it is determined as a subsynchronous oscillation mode; when the mode energy is mainly concentrated above the fundamental frequency of the power grid, it is determined as a supersynchronous oscillation mode.

[0117] Step S4: mapping each oscillation mode determined to an equivalent circuit element parameter, specifically including:

[0118] The mode parameters of the multiple modes determined as the same oscillation mode are aggregated to obtain the mode parameters of each oscillation mode;

[0119] According to each oscillation mode and the corresponding mode parameters, an equivalent circuit representation of each mode is constructed; wherein the frequency and attenuation factor of the oscillation mode correspond to the inductance, capacitance and resistance parameters in the equivalent circuit, and the mode amplitude corresponds to the voltage or current source parameter in the equivalent circuit;

[0120] The subsynchronous oscillation mode is mapped to an equivalent circuit dominated by an inductance element, the supersynchronous oscillation mode is mapped to an equivalent circuit dominated by a capacitance element, and the damping effect of the mode is represented by a resistance element, to obtain a set of equivalent circuit element parameters corresponding to each oscillation mode.

[0121] Step S5: constructing a third-order circuit state matrix of each oscillation mode based on the equivalent circuit element parameters;

[0122] As shown in Figure 2 is a diagram of a port shore power system oscillation multi-channel damping parameter heuristic probability optimization suppression, the port shore power system includes a new energy power generation system, a voltage and bridge crane, a ship and a shore power load. When the bus of the shore power system fluctuates, the multi-channel damper collects the time domain oscillation signal of the current of the AC bus, and after power and current control, the output voltage and current through the inverter, which plays a role in oscillation suppression.

[0123] As shown in Figure 3 is a multi-channel damper structure diagram, the multi-channel damper is composed of three filters, a proportional and gain module.

[0124] According to the law distribution of the damping ratio on the oscillation decay speed, further add an influence factor to different oscillation modes in the objective function to improve the response speed of the damping controller, so as to realize the coordinated optimization of different oscillation modes and multi-objective damping control parameters.

[0125] The equivalent circuit element parameters include an equivalent resistance at a wind turbine end, an equivalent resistance at a grid side, an equivalent inductance at the grid side, an equivalent capacitance at the wind turbine end, and equivalent resistance and inductance parameters of the parallel adjustable impedance.

[0126] The third-order circuit state matrix of each oscillation mode is constructed based on the equivalent circuit element parameters, and specifically includes:

[0127] For the determined first i oscillation mode, the corresponding third-order state matrix is constructed according to the equivalent circuit element parameters , , , , of each oscillation mode. :

[0128]

[0129] wherein, represents the third-order circuit state matrix of the first i oscillation mode, represents the equivalent resistance at the wind turbine end of the first i oscillation mode, represents the equivalent resistance at the grid side of the first i oscillation mode, represents the equivalent inductance at the grid side of the first i oscillation mode, represents the equivalent capacitance at the wind turbine end of the first i oscillation mode, and respectively represent the equivalent resistance and inductance parameters of the parallel adjustable impedance of the first i oscillation mode, and are represented as:

[0130]

[0131] wherein, represents a damping controller gain coefficient of the first i oscillation mode, represents a damping controller time constant of the first i oscillation mode, represents a target oscillation component angular frequency of the first i oscillation mode, that is, a frequency of the first i oscillation mode, and is obtained according to frequency aggregation of multiple modes of the oscillation mode.

[0132] Step S6: determining a damping ratio of each oscillation mode based on the third-order circuit state matrix of each oscillation mode, and specifically including:

[0133] Calculate the eigenvalues ​​of the third-order circuit state matrix for each oscillation mode, and use the eigenvalues ​​of the third-order circuit state matrix for each oscillation mode as the damping ratio of each oscillation mode. .

[0134] Step S7: Construct a multi-objective optimization objective function with damping ratio as the evaluation index, set influence factors for each oscillation mode, define the parameterized representation of the parallel adjustable impedance, and determine the adjustable parameter range of the damping controller, specifically including:

[0135] For each oscillation mode, its damping ratio As an evaluation indicator, an impact factor is introduced. To reflect the weights of different oscillation modes on the overall stability of the system, a multi-objective optimization objective function is formed:

[0136]

[0137] in, This is a multi-objective optimization function used to measure the overall damping performance of various oscillation modes. For the first i Damping ratio of each oscillation mode It is about The parameters, For the first i Influence factors of each oscillation mode For the first i Each oscillation mode damping controller gain coefficient For the first i The time constant of the damping controller for each oscillation mode; m This represents the total number of oscillation modes.

[0138] The convergence of the designed objective function was verified, and the results are as follows: Figure 4 As shown, the designed objective function converges to the set threshold, demonstrating the effectiveness of the objective function design.

[0139] Define a parameterized representation of a parallel adjustable impedance device, and denote the set of adjustable parameters of the damping controller as . ;

[0140] Determine the adjustable parameter range of the damping controller:

[0141]

[0142] in, For the first i Upper limit of the gain coefficient of the damping controller for each oscillation mode For the first i Upper limit of the time constant of the oscillation mode damping controller.

[0143] Step S8: Using a heuristic probabilistic optimization strategy, search for the adjustable parameters of the damping controller based on a multi-objective optimization objective function to determine the final set of damping control parameters, such as... Figure 5 As shown, it specifically includes:

[0144] Set the initial control parameter vector and initial temperature ,in, m The total number of oscillation modes. For the 0th iteration i Each oscillation mode damping controller gain coefficient For the 0th iteration i The time constant of the damping controller for each oscillation mode;

[0145] In the t In this iteration, the current control parameter vector In each of the i Generate neighborhood candidate control parameter vectors within the adjustable parameter range of each oscillation mode. The generation of each control parameter is represented as follows:

[0146]

[0147] in, and This refers to the random perturbation within a preset neighborhood scale. This represents a clipping operation, ensuring that the parameters fall within the specified interval. Inside; For the first i Upper limit of the gain coefficient of the damping controller for each oscillation mode For the first i Upper limit of the time constant of the damping controller for each oscillation mode; , They represent the first i Each oscillation mode in the neighborhood candidate control parameter vector The damping controller gain and damping controller time constant are shown below.

[0148] Based on the neighborhood candidate control parameter vector Obtain the set of candidate damping ratios corresponding to the neighborhood candidate control parameter vectors. With candidate impact factor set ;

[0149] Based on the candidate damping ratio set With candidate impact factor set By optimizing the objective function through multiple objectives, candidate objective function values ​​are calculated:

[0150]

[0151] in, The candidate objective function value;

[0152] Calculate the current control parameter vector objective function value Based on the Metropolis criterion, using probability P Decide whether to accept the neighborhood candidate control parameter vector :

[0153]

[0154] in, Indicates the first t Temperature of the next iteration;

[0155] Update the control parameter vector for the next iteration based on the accepted results. The temperature is updated according to the predetermined cooling strategy.

[0156]

[0157] in, This is the temperature update coefficient;

[0158] The iteration terminates and the final set of damping control parameters is output when any of the following termination conditions are met:

[0159] The iteration terminates when the temperature drops to a preset temperature threshold during the current iteration.

[0160] The iteration terminates when the current iteration count reaches the preset iteration count threshold.

[0161] The iteration terminates when the damping ratio corresponding to the current set of damping control parameters meets the preset minimum damping ratio requirement.

[0162] Step S9: Download the final set of damping control parameters to the parallel adjustable impedance device and perform online adjustment.

[0163] Example 2:

[0164] This embodiment employs three methods: undamped control, unoptimized damped controller, and heuristic probabilistic optimization. The output active power performance indicators of the new energy power generation system under different algorithms are shown in Table 1. It is evident that the controller using the heuristic probabilistic optimization algorithm is more effective in suppressing system oscillation modes. Its eigenvalues ​​are located in the left half of the complex plane, and the damping ratio is increased, indicating that it can provide more positive damping to the system, better ensuring the suppression of multimodal frequency oscillations and enabling the system to quickly recover to a stable state.

[0165] Table 1 Performance index of active power output of new energy power generation system under different algorithms

[0166]

[0167] The output current of the new energy power generation system under the action of different algorithms is observed, and the results are as shown in Figure 6 、 Figure 7 、 Figure 8 It can be seen that the current harmonics of the three are greatly improved, and the THD value of the heuristic probability optimization damping controller is smaller than that under the action of the other two, indicating that the suppression effect is better.

[0168] In summary, in combination with the eigenvalue change of the oscillation mode and the change of the output active power and output current, it can be seen that the heuristic probability optimization algorithm for controller parameter optimization can make the damping controller quickly respond to the frequency oscillation of the system in a short time, effectively suppress the frequency oscillation, and promote the system to quickly recover to a stable state.

[0169] Table 2 Comparison of THD of output current in stable stage

[0170]

[0171] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0172] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for suppressing oscillation damping in port shore power systems based on heuristic probabilistic optimization, characterized in that, Includes the following steps: Acquire the time-domain oscillation signal of the current on the AC bus side and preprocess it using a sliding window; The preprocessed oscillation signal is decomposed using the Prony algorithm to obtain the modal parameters of each mode. Calculate the energy of each mode based on the modal parameters of each mode and identify each oscillation mode; The identified oscillation modes are mapped to equivalent circuit element parameters; Construct the third-order circuit state matrix for each oscillation mode based on the equivalent circuit element parameters; The damping ratio of each oscillation mode is determined based on the third-order circuit state matrix of each oscillation mode. A multi-objective optimization objective function with the damping ratio as the evaluation index is constructed, and an influence factor is set for each oscillation mode. The parameterized representation of the parallel adjustable impedance is defined, and the adjustable parameter range of the damping controller is determined. The adjustable parameters of the damping controller are searched based on a multi-objective optimization objective function using a heuristic probabilistic optimization strategy to determine the final set of damping control parameters; The final set of damping control parameters is transmitted to the parallel adjustable impedance device and adjusted online. The equivalent circuit element parameters include the equivalent resistance at the wind turbine end, the equivalent resistance at the grid side, the equivalent inductance at the grid side, the equivalent capacitance at the wind turbine end, and the equivalent resistance and inductance parameters of the parallel adjustable impedance. The construction of the third-order circuit state matrix for each oscillation mode based on equivalent circuit element parameters specifically includes: For the judgment of the first i Each oscillation mode is determined based on its equivalent circuit element parameters. , , , , , Construct the corresponding third-order state matrix : in, Indicates the first i The third-order circuit state matrix of each oscillation mode. For the first i Each oscillation mode corresponds to the equivalent resistance at the wind turbine terminal. For the first i The equivalent resistance of the power grid side for each oscillation mode For the first i The equivalent inductance on the grid side for each oscillation mode For the first i The equivalent capacitance at the wind turbine end of each oscillation mode. and The first i The equivalent resistance and inductance parameters of the parallel adjustable impedance for each oscillation mode are expressed as follows: in, For the first i Each oscillation mode damping controller gain coefficient For the first i The time constant of the damping controller for each oscillation mode For the first i The target oscillation component angular frequency of the oscillation mode, i.e., the angular frequency of the oscillation mode. i The frequency of each oscillation mode is obtained by aggregating the frequencies of multiple modes of the oscillation mode; The construction of a multi-objective optimization objective function with the damping ratio as the evaluation index, setting influence factors for each oscillation mode, defining the parameterized representation of the parallel adjustable impedance, and determining the adjustable parameter range of the damping controller specifically includes: For each oscillation mode, its damping ratio As an evaluation indicator, an impact factor is introduced. To reflect the weights of different oscillation modes on the overall stability of the system, a multi-objective optimization objective function is formed: in, This is a multi-objective optimization function used to measure the overall damping performance of various oscillation modes. For the first i Damping ratio of each oscillation mode It is about The parameters, For the first i Influence factors of each oscillation mode For the first i Each oscillation mode damping controller gain coefficient For the first i The time constant of the damping controller for each oscillation mode; m This represents the total number of oscillation modes. Define a parameterized representation of a parallel adjustable impedance device, and denote the set of adjustable parameters of the damping controller as . ; Determine the adjustable parameter range of the damping controller: in, For the first i Upper limit of the gain coefficient of the damping controller for each oscillation mode For the first i Upper limit of the time constant of the oscillation mode damping controller.

2. The method for suppressing oscillation damping in a port shore power system based on heuristic probabilistic optimization as described in claim 1, characterized in that, The preprocessing specifically includes: The current time-domain oscillation signal on the AC bus side is processed by an anti-aliasing low-pass filter and then sampled according to a preset sampling period to obtain a discrete sequence. Wherein, the discrete sequence represents the signal sample obtained at each sampling time; the current time-domain oscillation signal is a continuous function of the instantaneous voltage or current on the AC bus side changing with time; the sampling period is the time interval between two adjacent samples, the reciprocal of which is the sampling frequency, and the sampling period is determined according to the range of the oscillation frequency to be detected, the Nyquist sampling theorem, and the real-time requirements; The discrete sequence is divided into frames using a sliding window to obtain a series of overlapping or non-overlapping frame signals, and a window function is applied to each frame. Remove DC or trend components from the windowed frame signal; The amplitude of the detrended frame signal is normalized to obtain the preprocessed oscillation signal.

3. The method for suppressing oscillations in a port shore power system based on heuristic probabilistic optimization as described in claim 1, characterized in that, The modal parameters include amplitude, attenuation factor, frequency, and initial phase.

4. The method for suppressing oscillation damping in a port shore power system based on heuristic probabilistic optimization according to claim 1, characterized in that, The modal decomposition of the preprocessed oscillation signal based on the Prony algorithm to obtain the modal parameters of each mode specifically includes: The preprocessed oscillation signal is denoted as The preprocessed oscillation signal was fitted using an exponential function superposition model to obtain the fitted signal: in, This is the pre-processed oscillation signal, reflecting the AC bus voltage or current at time [time value missing]. t The value of ; Indicates time t The fitted signal; The model order is represented by the number of fitted exponential functions. For the first i The amplitude of each mode, For the first i The attenuation factor of each mode, For the first i The frequency of each mode, For the first i The initial phase of each mode; The frequency domain components of the fitted signal are obtained by solving the coefficient matrix constructed using the Prony algorithm, thus yielding the modal parameters: in, The sampling period is For the first i The poles of each mode, For the first i Complex residual coefficients for each mode; Represents the operation of the real part. Represents the imaginary part operation. This indicates argument operation.

5. The method for suppressing oscillation damping in a port shore power system based on heuristic probabilistic optimization according to claim 1, characterized in that, The calculation of modal energy and identification of oscillation modes based on modal parameters of each mode specifically includes: Based on the obtained modal parameters of each mode, the energy of each mode is defined as: in, Indicates the first i Energy of each mode n Indicates the number of sampling points. For the first i The amplitude of each mode, Indicates the first i The poles of the mode are at the th mode in the th mode. j The power expansion result at each sampling point Represents the operation of the real part; The dominant oscillation mode is determined by comparing the magnitudes of the energy of each mode, and the oscillation mode is identified based on the energy distribution characteristics of the dominant oscillation mode: when the mode energy is mainly concentrated below the fundamental frequency of the power grid, it is determined to be a subsynchronous oscillation mode; when the mode energy is mainly concentrated above the fundamental frequency of the power grid, it is determined to be a supersynchronous oscillation mode.

6. A method for suppressing oscillations in a port shore power system based on heuristic probabilistic optimization as described in claim 1 or 5, characterized in that, The process of mapping each identified oscillation mode to equivalent circuit element parameters specifically includes: The modal parameters of multiple modes that are determined to be the same oscillation mode are aggregated to obtain the modal parameters of each oscillation mode; Based on each oscillation mode and its corresponding mode parameters, an equivalent circuit representation of each mode is constructed; where the frequency and attenuation factor of the oscillation mode correspond to the inductance, capacitance and resistance parameters in the equivalent circuit, and the mode amplitude corresponds to the voltage or current source parameters in the equivalent circuit. The subsynchronous oscillation mode is mapped to an equivalent circuit dominated by inductors, and the supersynchronous oscillation mode is mapped to an equivalent circuit dominated by capacitors. By combining resistors to characterize the modal damping effect, the set of equivalent circuit element parameters corresponding to each oscillation mode is obtained.

7. The method for suppressing oscillations in a port shore power system based on heuristic probabilistic optimization according to claim 1, characterized in that, The determination of the damping ratio for each oscillation mode based on the third-order circuit state matrix of each oscillation mode specifically includes: Calculate the eigenvalues ​​of the third-order circuit state matrix for each oscillation mode, and use the eigenvalues ​​of the third-order circuit state matrix for each oscillation mode as the damping ratio of each oscillation mode. .

8. The method for suppressing oscillation damping in a port shore power system based on heuristic probabilistic optimization according to claim 1, characterized in that, The step of using a heuristic probabilistic optimization strategy to search for the adjustable parameters of the damping controller based on a multi-objective optimization objective function to determine the final set of damping control parameters specifically includes: Set the initial control parameter vector and initial temperature ,in, m The total number of oscillation modes. For the 0th iteration i Each oscillation mode damping controller gain coefficient For the 0th iteration i The time constant of the damping controller for each oscillation mode; In the t In this iteration, the current control parameter vector In each of the i Generate neighborhood candidate control parameter vectors within the adjustable parameter range of each oscillation mode. The generation of each control parameter is represented as follows: in, and This refers to the random perturbation within a preset neighborhood scale. This represents a clipping operation, ensuring that the parameters fall within the specified interval. Inside; For the first i Upper limit of the gain coefficient of the damping controller for each oscillation mode For the first i Upper limit of the time constant of the damping controller for each oscillation mode; , They represent the first i Each oscillation mode in the neighborhood candidate control parameter vector The damping controller gain and damping controller time constant are shown below. Based on the neighborhood candidate control parameter vector Obtain the set of candidate damping ratios corresponding to the neighborhood candidate control parameter vectors. With candidate impact factor set ; Based on the candidate damping ratio set With candidate impact factor set By optimizing the objective function through multiple objectives, candidate objective function values ​​are calculated: in, The candidate objective function value; Calculate the current control parameter vector objective function value Based on the Metropolis criterion, using probability P Decide whether to accept the neighborhood candidate control parameter vector : in, Indicates the first t Temperature of the next iteration; Update the control parameter vector for the next iteration based on the accepted results. The temperature is updated according to the predetermined cooling strategy. in, This is the temperature update coefficient; The iteration terminates and the final set of damping control parameters is output when any of the following termination conditions are met: The iteration terminates when the temperature drops to a preset temperature threshold during the current iteration. The iteration terminates when the current iteration count reaches the preset iteration count threshold. The iteration terminates when the damping ratio corresponding to the current set of damping control parameters meets the preset minimum damping ratio requirement.

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