Energy storage converter regulation and control method and system

Abstract

According to the energy storage converter regulation and control method and system, voltage, current and power data of a grid-connected point of a power grid are collected, feature extraction and fluctuation feature analysis are combined, the problems of voltage fluctuation and current distortion existing in the power grid can be accurately recognized, and then the control strategy of the energy storage converter is adjusted to balance the voltage of the power grid. Through application of a power fluctuation suppression model and a current peak value optimization model, decoupling calculation of active power and reactive power and balance control of three-phase current are realized, and through accurate power compensation and current amplitude limiting control, power grid fluctuation can be effectively reduced, equipment can be protected, and the service life of the power grid is prolonged. And the operation stability and safety of the power grid are improved.

Description

Technical Field

[0001] This invention relates to the field of power system stability control technology, and in particular to a control method and system for an energy storage converter. Background Technology

[0002] With the integration of new energy power systems into the grid, the grid is prone to voltage instability. Energy storage converters can effectively regulate grid voltage fluctuations and have been widely used in the grid.

[0003] Existing energy storage converter control methods are often unable to fully collect detailed voltage, current and power data at the grid connection point, resulting in an inability to accurately identify voltage fluctuations and current distortions in the grid. Furthermore, existing methods often rely on simple monitoring or overly coarse control strategies, failing to comprehensively capture unstable factors in the grid. Summary of the Invention

[0004] This invention provides a method and system for regulating an energy storage converter, which addresses the technical problem that existing energy storage converter regulation methods often fail to adequately collect detailed voltage, current, and power data at the grid connection point, resulting in an inability to accurately identify voltage fluctuations and current distortions in the grid. Furthermore, existing methods often rely on simple monitoring or overly coarse regulation strategies, failing to comprehensively capture unstable factors in the grid.

[0005] In view of this, the first aspect of the present invention provides a method for regulating an energy storage converter, comprising:

[0006] Collect the three-phase voltage, three-phase current and power output of the energy storage converter at the grid connection point within a preset monitoring period to construct a grid operation status characteristic matrix;

[0007] Based on the power grid operation status feature matrix, voltage fluctuation feature vector and current distortion feature vector are extracted;

[0008] Based on the power fluctuation suppression model, the active power and reactive power of the voltage fluctuation feature vector are decoupled and calculated to obtain the power compensation control parameters. Based on the current peak optimization model, the current distortion feature vector is subjected to three-term current equalization control to obtain the current limiting control parameters.

[0009] By inputting the power compensation control parameters and current limiting control parameters into a multi-objective optimization model and combining them with the real-time operating conditions of the energy storage converter, a parameter coupling analysis is performed to obtain the energy storage converter control command set.

[0010] The energy storage converter is regulated based on the energy storage converter control instruction set.

[0011] Optionally, the energy storage converter is regulated based on the energy storage converter control command set, and the following steps are also included:

[0012] Acquire initial operating data of the power grid connection point within a preset monitoring period;

[0013] Based on the ratio of negative-sequence voltage components to positive-sequence voltage components in the initial operational big data, the voltage imbalance at the grid connection point is determined. Based on the difference between the voltage imbalance at the grid connection point and the preset imbalance threshold, dynamic weighting coefficients are generated.

[0014] Based on dynamic weighting coefficients, the active power fluctuation value, reactive power fluctuation value and three-phase current peak value in the initial operating data are weighted and fused to generate a comprehensive control reference value.

[0015] Based on the matching degree between the comprehensive control reference value and the initial operating data, control commands for the energy storage converter are generated to control the energy storage converter.

[0016] Optionally, the three-phase voltage, three-phase current, and power output of the energy storage converter at the grid connection point are collected within a preset monitoring period to construct a grid operation status characteristic matrix, including:

[0017] The three-phase voltage and three-phase current at the grid connection point are sampled at equal intervals to obtain the time-domain waveform data of the three-phase voltage and three-phase current;

[0018] Fourier transform is performed on the time-domain waveform data of three-phase voltage and three-phase current to extract the fundamental and harmonic components and obtain the frequency domain feature matrix.

[0019] Based on the frequency domain feature matrix, the three-phase voltage imbalance and total harmonic distortion of current are calculated to obtain the power quality assessment vector.

[0020] Collect the active power, reactive power, and power factor at the output of the energy storage converter to construct a power output characteristic matrix;

[0021] The power quality assessment vector and the power output feature matrix are fused to obtain the power grid operation status feature matrix.

[0022] Optionally, based on the power grid operating state feature matrix, voltage fluctuation feature vectors and current distortion feature vectors are extracted, including:

[0023] The power grid operation state characteristic matrix is ​​transformed by the symmetric component method to separate the positive sequence component, negative sequence component and zero sequence component, and obtain the sequence component characteristic matrix.

[0024] The negative sequence component suppression index is determined based on the sequence component feature matrix, and the negative sequence component amplitude change rate corresponding to the negative sequence component suppression index is determined based on the sliding window method, thus obtaining the voltage fluctuation feature vector.

[0025] Harmonic group analysis is performed on the current components in the sequence component characteristic matrix to extract the characteristic subharmonic amplitude and phase offset, thus obtaining the current distortion characteristic vector.

[0026] The voltage fluctuation feature vector and current distortion feature vector are nonlinearly reduced based on an autoencoder network to obtain a low-dimensional fluctuation feature code.

[0027] Optionally, based on the power fluctuation suppression model, the active and reactive power of the voltage fluctuation characteristic vector are decoupled and calculated to obtain power compensation control parameters, including:

[0028] The low-dimensional fluctuation feature code corresponding to the voltage fluctuation feature vector is input into the droop control model. The deviation between the active power reference value and the actual value is determined according to the droop control model, and the active power deviation vector is obtained.

[0029] The reactive power compensation function is determined based on the active power deviation vector, and the reactive power compensation amount is calculated by the fuzzy logic controller to obtain the reactive power adjustment command.

[0030] The active power deviation vector and reactive power regulation command are decoupled by parameters to obtain power compensation control parameters, which include amplitude regulation coefficient and phase regulation coefficient.

[0031] Optionally, based on the current peak optimization model, a three-term current equalization control is performed on the current distortion feature vector to obtain the current limiting control parameters, including:

[0032] The low-dimensional fluctuation feature encoding of the current distortion feature vector is input into the instantaneous power theory model. The effective value and peak value of the three-phase current are determined according to the instantaneous power theory model to obtain the current over-limit evaluation vector.

[0033] The current balance index is determined based on the current over-limit evaluation vector. The current limiting threshold of each phase current is calculated by the particle swarm optimization algorithm based on the current balance index, and the initial limiting parameter set is obtained.

[0034] The initial limiting parameter set is corrected based on the hysteresis comparator to obtain the current limiting control parameters, which include dead time and hysteresis width.

[0035] Optionally, the power compensation control parameters and current limiting control parameters are input into a multi-objective optimization model, and parameter coupling analysis is performed in conjunction with the real-time operating conditions of the energy storage converter to obtain a set of control commands for the energy storage converter, including:

[0036] Construct a multi-objective optimization function that includes power regulation rate, current harmonic content, and converter loss;

[0037] The power compensation control parameters and current limiting control parameters are weighted and summed, and constraints are established based on the battery SOC state, temperature parameters and switching frequency of the energy storage converter.

[0038] The multi-objective optimization function is solved using a genetic algorithm to obtain the Pareto optimal solution set. The target control parameter combination is then selected from the Pareto optimal solution set to obtain the energy storage converter control instruction set. The energy storage converter control instruction set includes the converter control instruction set of PWM modulation wave parameters and drive circuit timing.

[0039] Optionally, based on dynamic weighting coefficients, the active power fluctuation value, reactive power fluctuation value, and three-phase current peak value in the initial operating data are weighted and fused to generate a comprehensive control reference value, including:

[0040] The active power fluctuation value is weighted based on the dynamic weighting coefficient to generate the first weighting parameter;

[0041] The reactive power fluctuation value is weighted based on the dynamic weighting coefficient to generate a second weighting parameter;

[0042] The peak value of the three-phase current is weighted based on the dynamic weighting coefficient to generate a third weighting parameter;

[0043] The first weighted parameter, the second weighted parameter, and the third weighted parameter are time-series aligned to generate a time-series parameter set;

[0044] Based on the fluctuation frequency of each parameter in the time-series parameter set, corresponding time-domain weights are assigned, and the time-series parameter set is weighted and summed based on the time-domain weights to generate a comprehensive control reference value.

[0045] Optionally, based on the matching degree between the comprehensive control reference value and the initial operating data, control commands for the energy storage converter are generated to control the energy storage converter, including:

[0046] Vector synthesis is performed on the positive-sequence voltage component and the negative-sequence voltage component in the initial running data to generate synthesized voltage waveform data;

[0047] The first time-domain waveform diagram is plotted based on the synthesized voltage waveform data;

[0048] The comprehensive control reference value is phase-corrected to generate a corrected reference value.

[0049] The second time-domain waveform is plotted based on the corrected reference value;

[0050] Calculate the waveform similarity between the first time-domain waveform and the second time-domain waveform;

[0051] Based on the joint evaluation results of matching degree and waveform similarity, control commands for the energy storage converter are generated.

[0052] A second aspect of the present invention provides an energy storage converter control system, comprising:

[0053] The data acquisition module is used to collect the three-phase voltage, three-phase current and power output of the energy storage converter at the grid connection point within a preset monitoring period, and to construct a characteristic matrix of the grid operation status.

[0054] The feature extraction module is used to extract voltage fluctuation feature vectors and current distortion feature vectors based on the power grid operating state feature matrix.

[0055] The decoupling module is used to perform active and reactive power decoupling calculations on the voltage fluctuation feature vector based on the power fluctuation suppression model to obtain power compensation control parameters. Based on the current peak optimization model, it performs three-term current balance control on the current distortion feature vector to obtain current limiting control parameters.

[0056] The coupling module is used to input power compensation control parameters and current limiting control parameters into a multi-objective optimization model, and perform parameter coupling analysis in combination with the real-time operating conditions of the energy storage converter to obtain the energy storage converter control instruction set.

[0057] The operation module is used to regulate the energy storage converter based on the energy storage converter control instruction set.

[0058] As can be seen from the above technical solutions, the energy storage converter control method provided by the present invention has the following advantages:

[0059] The energy storage converter control method provided by this invention collects voltage, current, and power data at the grid connection point, and combines feature extraction and fluctuation feature analysis to help accurately identify voltage fluctuations and current distortions in the grid. This allows for adjustment of the energy storage converter's control strategy to balance the grid voltage, reduce voltage imbalance and fluctuations, and achieve decoupled calculations of active and reactive power and balanced control of three-phase currents through the application of power fluctuation suppression and current peak optimization models. This precise power compensation and current limiting control can effectively reduce grid fluctuations, protect equipment, and improve the grid's operational stability and security. It solves the technical problems of existing energy storage converter control methods, which often fail to collect detailed voltage, current, and power data at the grid connection point, leading to inaccurate identification of voltage fluctuations and current distortions in the grid. Furthermore, existing methods often rely on simple monitoring or overly coarse control strategies, failing to comprehensively capture unstable factors in the grid.

[0060] The energy storage converter control method provided by this invention uses a multi-objective optimization model to couple and analyze power compensation control parameters and current limiting control parameters, comprehensively considers the operating state of the energy storage converter, and optimizes the control command set, thereby improving the voltage stability of the power grid in multiple dimensions. Furthermore, based on the difference between the voltage imbalance degree and the preset threshold in the initial operating data of the power grid, dynamic weight coefficients are generated to automatically adjust the control strategy. This adaptive adjustment capability enables the method to respond to the dynamic changes of the power grid in real time, improving the flexibility and responsiveness of the control.

[0061] The energy storage converter control method provided by this invention generates a comprehensive control reference value by weighted fusion of active power, reactive power fluctuation values ​​and current peak values, further optimizing control commands, enabling the energy storage converter to make more precise adjustments under different operating conditions, and further improving the voltage stability and overall operating efficiency of the power grid.

[0062] The energy storage converter control method provided by this invention reduces the impact of unstable factors in the power grid, avoiding damage to equipment caused by excessively high or low voltage, and reducing the maintenance costs of the power system and the energy storage converter. Long-term stable operation of power grid equipment also helps extend its service life and reduce the frequency of system failures. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating a method for controlling an energy storage converter provided in an embodiment of the present invention.

[0065] Figure 2 This is another flowchart illustrating a method for controlling an energy storage converter provided in an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of the structure of an energy storage converter control system provided in an embodiment of the present invention. Detailed Implementation

[0067] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] For easier understanding, please refer to Figure 1 This invention provides an embodiment of a method for controlling an energy storage converter, comprising:

[0069] Step 101: Collect the three-phase voltage, three-phase current and power output of the energy storage converter at the grid connection point within the preset monitoring period, and construct the grid operation status characteristic matrix.

[0070] It should be noted that in AC power grids, voltage and current are typically three-phase. Three-phase voltage and three-phase current refer to a system consisting of three voltage and current signals with a phase difference of 120° between them. An energy storage converter is a power electronic device that converts DC power stored in a battery into AC power (or vice versa) to inject electricity into the grid or provide power when grid load demand is excessive; energy storage converters play a crucial role in improving grid voltage stability. The power grid operating state characteristic matrix is ​​a data set generated by monitoring the state of various grid parameters (such as voltage and current) over a certain period. It describes the operating characteristics and status of the grid, providing fundamental data for subsequent analysis.

[0071] In one embodiment, the specific process of constructing the power grid operation state characteristic matrix is ​​as follows:

[0072] Step A1: Perform equal-interval sampling of the three-phase voltage and three-phase current at the grid connection point to obtain time-domain waveform data of the three-phase voltage and current. The time-domain waveform data includes positive-sequence and negative-sequence components. Equal-interval sampling refers to collecting the three-phase voltage and current at fixed intervals (e.g., once per second). This method obtains the time-domain waveform data of the voltage and current of each phase of the grid. Time-domain waveform data refers to the changes in voltage and current over time. For each phase voltage and current signal, its positive-sequence and negative-sequence components are extracted. The positive-sequence component represents the portion of the three-phase voltage (or current) with equal amplitude and a 120° phase difference in an ideal balanced system. The negative-sequence component represents the asymmetrical portion of the voltage or current, usually caused by unbalanced loads. This imbalance may affect the stability of the grid and the normal operation of equipment.

[0073] Step A2: Perform a Fourier transform on the time-domain waveform data of the three-phase voltage and three-phase current to extract the fundamental and harmonic components, obtaining the frequency domain feature matrix. The Fourier transform is a mathematical transformation used to convert a signal from the time domain to the frequency domain and analyze its frequency components. By performing a Fourier transform on the three-phase voltage and current signals in the power grid, the frequency domain characteristics of the signal can be obtained. The fundamental component represents the main frequency component of the signal (usually 50Hz or 60Hz in power systems), which is the normal frequency for stable operation in the power system. Harmonic components refer to frequency components in the signal that are multiples of the fundamental frequency (e.g., components at 2x, 3x, etc.). Harmonics are usually caused by nonlinear loads (such as frequency converters, rectifiers, etc.) and may pollute the power grid, affecting the normal operation of power equipment. The obtained frequency domain feature matrix contains the amplitude and phase information of each frequency component.

[0074] Step A3: Based on the frequency domain feature matrix, calculate the three-phase voltage imbalance and total harmonic distortion (THD) of the current to obtain the power quality assessment vector. Three-phase voltage imbalance is an indicator of whether the three-phase voltages are symmetrical. Voltage imbalance can lead to decreased equipment operating efficiency, increased losses, and even equipment damage. It is usually represented by a negative-sequence component; the larger the negative-sequence component, the higher the voltage imbalance. The THD of the current is an indicator of the intensity of harmonic components in the current signal. A higher THD indicates more severe harmonic components in the current, which may affect the stability of the power grid and power quality. By calculating these indicators, the resulting power quality assessment vector provides assessment information about the power grid's power quality, helping to analyze whether the power grid meets standards and whether power quality problems exist.

[0075] Step A4: Collect the active power, reactive power, and power factor at the output of the energy storage converter to construct a power output characteristic matrix. Active power is the power that actually does work in the power system, used to drive electrical equipment. Reactive power is the power that supports the electromagnetic field; it cannot directly do work but is crucial for the stability of the power system. The power factor is the ratio of active power to total power (including active and reactive power), commonly used to measure power system efficiency. A low power factor can lead to power system instability and even equipment damage. The power output characteristic matrix contains this power data, which can help monitor the performance of the energy storage converter and its contribution to the power grid.

[0076] Step A5: Fuse the power quality assessment vector and the power output feature matrix to obtain the power grid operating state feature matrix, which also includes time-frequency domain information. The power quality assessment vector and the power output feature matrix contain key information about the power grid's power quality and the output of the energy storage converter. By fusing these two, the operating state of the power grid and the role of the energy storage converter can be comprehensively evaluated. The fused power grid operating state feature matrix provides a complete picture of the power grid, including not only time-domain and frequency-domain information but also reflecting power fluctuations, power quality problems, and the regulation effect of the energy storage system. This information is of great significance for power grid dispatch and optimization management. Time-frequency domain information refers to feature data involved in both the time and frequency domains; it can provide more comprehensive signal characteristics, helping to more accurately identify potential fluctuations and instabilities in the power grid.

[0077] Step 102: Based on the power grid operation status feature matrix, extract the voltage fluctuation feature vector and the current distortion feature vector.

[0078] It should be noted that the current distortion eigenvector is a quantitative representation of current distortion, reflecting the degree to which the current waveform deviates from an ideal sine wave, typically caused by nonlinear loads. By analyzing the amplitude and phase of harmonic groups and subharmonics, a eigenvector representing the distortion characteristics of the current can be generated. This vector effectively describes the current distortion caused by harmonics in the power grid.

[0079] In one embodiment, the specific execution process of this step is as follows:

[0080] Step B1: Perform a symmetrical component method transformation on the power grid operating state characteristic matrix to separate the positive-sequence, negative-sequence, and zero-sequence components, obtaining the sequence component characteristic matrix. The symmetrical component method is a method used to analyze three-phase unbalanced systems. It decomposes the three-phase voltage or current signal into three different components: the positive-sequence component represents the balanced portion of the three-phase voltage or current, where the amplitudes of the three phases are equal and the phase difference is 120 degrees; the negative-sequence component represents the unbalanced portion of the unbalanced system, with its amplitude and phase angle opposite to the positive-sequence component; the zero-sequence component represents the portion where the voltage or current of all three phases is equal and in the same direction, usually caused by neutral point imbalance or faults in the system. Through the symmetrical component method transformation, the three-phase voltage and current signals can be decomposed into these components, thereby helping to analyze the balance and potential problems of the power grid. The negative-sequence component suppression index is used to measure the system's ability to suppress negative-sequence components. Negative-sequence components usually reflect asymmetry problems in the power grid, such as load imbalance or fault conditions. An efficient power grid should be able to suppress negative-sequence components to maintain grid stability.

[0081] Step B2: Determine the negative-sequence component suppression index based on the sequence component characteristic matrix, and determine the negative-sequence component amplitude change rate corresponding to the negative-sequence component suppression index using the sliding window method, thus obtaining the voltage fluctuation feature vector. The sliding window method is a time series analysis method that uses a fixed-size window to gradually slide along the time axis and analyze the signal to calculate the negative-sequence component amplitude change rate. This allows for a more accurate assessment of instantaneous changes and instability factors in the power grid state. The negative-sequence component amplitude change rate represents the change in the negative-sequence component amplitude over time, reflecting the changing trend of the imbalance degree in the power grid. By calculating the amplitude change rate, it is possible to more intuitively detect whether there are problems such as voltage fluctuations in the power grid.

[0082] Step B3: Perform harmonic group analysis on the current components in the sequence component feature matrix to extract the amplitude and phase offset of characteristic subharmonics, obtaining the current distortion feature vector. Harmonic group analysis is used to analyze different frequency components in current signals, especially subharmonics. Harmonics are frequency components caused by nonlinear loads (such as frequency converters, motors, etc.), which may affect power grid quality. Harmonic amplitude refers to the amplitude of frequency components below the fundamental frequency (usually 50Hz or 60Hz). Subharmonics can adversely affect power equipment and systems, especially in motors and converters, potentially causing overheating and performance degradation. Phase offset represents the phase shift of the harmonic component in time, typically used to describe the relative timing relationship between current and voltage, helping to understand the relative position of the harmonic signal and the fundamental signal. The current distortion feature vector, generated by analyzing the harmonic group and subharmonic amplitude and phase, represents the distortion characteristics in the current. This vector effectively describes the current distortion caused by harmonics in the power grid.

[0083] Step B4: Using an autoencoder network, nonlinear dimensionality reduction is performed on the voltage fluctuation feature vector and current distortion feature vector to obtain a low-dimensional fluctuation feature encoding. An autoencoder is an unsupervised learning method that compresses input data into a low-dimensional representation through encoding and decoding processes. In power system analysis, autoencoders are used for dimensionality reduction, compressing high-dimensional feature vectors (such as voltage fluctuation features and current distortion features) into a low-dimensional representation for easier subsequent analysis and processing. Nonlinear dimensionality reduction: Unlike traditional linear dimensionality reduction methods (such as Principal Component Analysis (PCA), autoencoders perform dimensionality reduction by learning complex nonlinear mappings. This better preserves important features in the input data while reducing redundant information. The low-dimensional fluctuation feature encoding, obtained by compressing voltage fluctuation features and current distortion features into low-dimensional vectors using an autoencoder, effectively represents the fluctuation characteristics of the power grid, which is helpful for subsequent analysis, model training, and prediction.

[0084] Step 103: Based on the power fluctuation suppression model, decouple the active power and reactive power of the voltage fluctuation feature vector to obtain the power compensation control parameters. Based on the current peak optimization model, perform three-term current balance control on the current distortion feature vector to obtain the current limiting control parameters.

[0085] It should be noted that by adjusting the active and reactive power outputs of the energy storage system, power instability caused by load fluctuations can be suppressed. Active power is the power actually used to perform work, while reactive power is the power provided to support the electromagnetic field in the power system. In this invention, these two types of power are controlled separately through decoupling calculations to precisely regulate the grid voltage. A peak current optimization model is used to control the magnitude of the peak current, preventing the current from exceeding the equipment's safety limits.

[0086] In one embodiment, the specific execution process of decoupling the active and reactive power calculations of the voltage fluctuation feature vector based on the power fluctuation suppression model to obtain the power compensation control parameters is as follows:

[0087] Step C1: Input the low-dimensional fluctuation feature encoding corresponding to the voltage fluctuation feature vector into the droop control model. Determine the deviation between the active power reference value and the actual value based on the droop control model, obtaining the active power deviation vector. The droop control model is a control strategy commonly used in power systems, especially in distributed generation and microgrids. It maintains grid stability by dynamically adjusting the generator output power according to load changes. In this model, there is a "droop" relationship between voltage and frequency: when the load increases, both frequency and voltage decrease accordingly, similar to the droop characteristic in mechanical systems. Through this characteristic, the various generating units in the grid can work in coordination, thereby ensuring system stability. Active power refers to the power used to perform useful work (such as mechanical motion, heat energy, etc.). In power systems, active power is usually used to measure the system's load demand. The deviation between the reference value and the actual value refers to the difference between the active power reference value output by the droop control model and the actual power. This deviation reflects the fluctuation of the grid load and is used to adjust the system's generating capacity. The active power deviation vector is a vector containing these deviations, used to quantify and track power deviations in the system, helping to optimize grid regulation. Reactive power refers to the power used to maintain an electromagnetic field. It does not perform actual work during power transmission, but it is crucial for the stability and voltage level of the power system.

[0088] Step C2: Determine the reactive power compensation function based on the active power deviation vector, calculate the reactive power compensation amount using a fuzzy logic controller, and obtain the reactive power regulation command. The reactive power compensation function is an algorithm or model that calculates the amount of reactive power to be compensated based on the active power deviation. Compensating for reactive power helps regulate voltage and improve the stability of the power system. The fuzzy logic controller is a controller based on fuzzy set theory; it does not require a precise mathematical model. The controller's input and output are fuzzy, and reasoning and decision-making are then performed using a rule base. Fuzzy logic controllers are widely used in complex systems, especially under conditions of parameter uncertainty or system nonlinearity. In power systems, fuzzy logic controllers can be used to calculate the reactive power compensation amount based on active power deviation and other variables, thereby optimizing voltage and frequency regulation. The reactive power regulation command is a command issued by the control system to adjust the reactive power output in the system. This command aims to optimize the voltage level of the power grid and ensure the stable operation of the power system.

[0089] Step C3: Decouple the active power deviation vector and reactive power regulation command to obtain power compensation control parameters, including amplitude adjustment coefficients and phase adjustment coefficients. Parameter decoupling refers to processing multiple interrelated parameters in a complex system separately, allowing them to be optimized independently in the control system. Decoupling the active power deviation vector and reactive power regulation command means handling the control of active and reactive power separately without allowing them to interfere with each other, thus making the control more precise. Power compensation control parameters are key parameters in the power compensation process and typically include: the amplitude adjustment coefficient, which determines how to adjust the amplitude of active or reactive power. It is used to adjust the power magnitude so that the system output power matches the reference value. The phase adjustment coefficient determines the phase adjustment of power, controlling the timing of power output. Phase adjustment is crucial for optimizing grid stability and reducing phase differences.

[0090] In one embodiment, the specific execution process of obtaining the current limiting control parameters by performing three-term current equalization control on the current distortion feature vector based on the current peak optimization model is as follows:

[0091] Step D1: Input the low-dimensional fluctuation characteristic encoding of the current distortion feature vector into the instantaneous power theory model. Based on the instantaneous power theory model, determine the effective and peak values ​​of the three-phase currents to obtain the current over-limit assessment vector. Current distortion refers to the degree of deviation of the current waveform from an ideal sine wave, usually caused by factors such as harmonics and instantaneous fluctuations. The current distortion feature vector is a mathematical representation used to quantify the degree of current distortion, containing key characteristics of the current waveform, such as frequency, amplitude, and phase. This vector is used in subsequent model analysis to help assess current quality. Instantaneous power theory is a mathematical model used to analyze instantaneous power in power systems. It typically uses the Park transform or Clarke transform to convert three-phase AC current and voltage into a balanced two-phase coordinate system, and then calculates the instantaneous power. This theoretical model helps assess changes in instantaneous power and is commonly used in power electronics, filtering, reactive power compensation, and other fields.

[0092] Step D2: Determine the current balance index based on the current over-limit assessment vector. Calculate the current limiting threshold for each phase using the particle swarm optimization algorithm based on the current balance index to obtain the initial limiting parameter set. The current over-limit assessment vector refers to the assessment of whether the current exceeds the set maximum safety limit based on the effective and peak values ​​of the three-phase current. It is calculated by analyzing the current characteristics to obtain a vector reflecting the degree of current over-limit. This is typically used to protect circuits and power systems, ensuring that the current does not exceed the safe operating range. The current balance index measures whether the three-phase current is balanced. If the three-phase current is unbalanced, it may cause power system faults or equipment damage. This index quantifies the degree of deviation between the three-phase currents, usually obtained by calculating the difference in current between each phase. The particle swarm optimization algorithm is an optimization algorithm that simulates the foraging behavior of a flock of birds; in this algorithm, each particle represents a possible solution, and the global optimal solution is found through cooperation and competition with other particles. Particle swarm optimization is often used to solve complex nonlinear optimization problems, such as the current balance optimization problem in power systems. The current limiting threshold refers to the maximum allowable current amplitude set when implementing current limiting control; any current exceeding this threshold will be reduced to that threshold. The calculation of the current limiting threshold needs to consider current balance and the effective and peak values ​​of the current to ensure the stability of the power system and the safety of protection equipment. The initial current limiting parameter set is a set of parameters derived based on the current balance index and particle swarm optimization algorithm, including parameters used for current limiting control, such as the maximum allowable current amplitude; these initial parameters guide the current limiting control strategy.

[0093] Step D3: Based on the hysteresis comparator, the initial limiting parameter set is corrected to obtain the current limiting control parameters, which include dead time and hysteresis width. A hysteresis comparator is a control element commonly used for comparing and adjusting signals, exhibiting hysteresis characteristics. Hysteresis refers to the delay or lag between the system's response and changes in the input signal. In current control, hysteresis comparators are used to avoid frequent system switching and ensure the stability of control decisions when the current approaches a threshold. The current limiting control parameters include several key control variables used to adjust the current limiting behavior. Common control parameters include: Dead time, which is the time interval that must be waited before the system executes a control action. In limiting control, dead time can prevent over-response and reduce erroneous actions. Hysteresis width is a characteristic of the hysteresis comparator, representing the tolerance range of the control action when the signal reaches a certain threshold. The larger the hysteresis width, the greater the fluctuation range of the system response, thereby improving system stability.

[0094] Step 104: Input the power compensation control parameters and current limiting control parameters into the multi-objective optimization model, and perform parameter coupling analysis in conjunction with the real-time operating conditions of the energy storage converter to obtain the energy storage converter control command set.

[0095] It should be noted that the multi-objective optimization model combines the control objectives of power compensation and current limiting to ultimately generate suitable control parameters for the energy storage converter. The energy storage converter control instruction set is a set of control instructions obtained through optimization analysis, used to guide the operation of the energy storage converter to achieve the desired voltage and current regulation effects.

[0096] In one embodiment, the specific execution process of this step is as follows:

[0097] Step E1: Construct a multi-objective optimization function that includes power regulation rate, current harmonic content, and converter losses. Power regulation rate refers to the rate at which the energy storage converter regulates its output power. A faster regulation rate helps in quickly responding to load changes but may result in higher losses. Current harmonic content refers to the potential presence of harmonics in the output current of the energy storage converter. Harmonics can pollute the power system and increase equipment losses. The optimization objective is to minimize current harmonic content. Converter losses refer to the energy losses that occur during converter operation. The optimization objective is to reduce these losses and improve the overall system efficiency. These three objectives may conflict with each other, therefore a multi-objective optimization function is needed to balance them. A weighted sum approach is typically used to transform multiple objectives into a single comprehensive objective for optimization.

[0098] Step E2: Weighted summation of the power compensation control parameters and current limiting control parameters is performed, establishing constraints based on the battery SOC state, temperature parameters, and switching frequency of the energy storage converter. The weighted summation of the power compensation control parameters and current limiting control parameters means combining these two control parameters according to certain weighting coefficients to form a comprehensive control parameter. This balances different control requirements, ensuring that power compensation is satisfied while limiting peak current during optimization. Constraints include: The battery's state of charge determines the converter's operating range; the SOC (State of Charge, a core parameter characterizing the remaining usable battery capacity, expressed as a percentage of remaining capacity to total capacity) must be within a reasonable range to avoid over-discharging or over-charging. The temperature of the energy storage converter directly affects its efficiency and safety; it must operate within the permissible temperature range. The converter's switching frequency affects its efficiency and output current quality. Too high a frequency may lead to excessive switching losses, while too low a frequency may affect the power regulation rate. These constraints ensure that the optimization process does not violate the system's physical or safety limitations, guaranteeing stable system operation.

[0099] Step E3: Solve the objective optimization function based on the genetic algorithm to obtain the Pareto optimal solution set. Select the combination of control parameters that meets the real-time conditions from the Pareto optimal solution set to obtain the energy storage converter control instruction set. The energy storage converter control instruction set includes the converter control instruction set of PWM modulation wave parameters and drive circuit timing.

[0100] Genetic Algorithm (GA) is an optimization algorithm that simulates natural selection and genetic processes. It approximates the optimal solution step by step through population search, selection, crossover, and mutation operations. In multi-objective optimization, Pareto optimal solution refers to the set of solutions where one objective cannot be improved without harming another. Genetic algorithm optimization yields a set of Pareto optimal solutions, representing the best trade-offs between different objectives. Based on the system's real-time performance requirements, control parameters that can complete the control task within a specified time are selected from the Pareto optimal solution set. This step ensures that the selected solution is not only theoretically optimal but also meets the real-time requirements of practical operation. Pulse Width Modulation (PWM) is a method of controlling converter output by adjusting the switching time and period of switches. PWM modulation wave parameters include key parameters such as modulation frequency and duty cycle, which directly affect the quality and efficiency of output power. The timing of the drive circuit determines the switching sequence and time of the switches inside the converter. Correct timing ensures that the converter operates as expected and optimizes its efficiency.

[0101] Step 105: Adjust the energy storage converter based on the energy storage converter control instruction set.

[0102] It should be noted that the energy storage converter control instruction set is a set of control instructions obtained through optimization analysis, which is used to guide the operation of the energy storage converter in order to achieve the desired voltage and current regulation effect.

[0103] The energy storage converter control method provided by this invention collects voltage, current, and power data at the grid connection point, and combines feature extraction and fluctuation feature analysis to help accurately identify voltage fluctuations and current distortions in the grid. This allows for adjustment of the energy storage converter's control strategy to balance the grid voltage, reduce voltage imbalance and fluctuations, and achieve decoupled calculations of active and reactive power and balanced control of three-phase currents through the application of power fluctuation suppression and current peak optimization models. This precise power compensation and current limiting control can effectively reduce grid fluctuations, protect equipment, and improve the grid's operational stability and security. It solves the technical problems of existing energy storage converter control methods, which often fail to collect detailed voltage, current, and power data at the grid connection point, leading to inaccurate identification of voltage fluctuations and current distortions in the grid. Furthermore, existing methods often rely on simple monitoring or overly coarse control strategies, failing to comprehensively capture unstable factors in the grid.

[0104] Please see Figure 2 In one embodiment, after step 105, the method further includes:

[0105] Step 106: Obtain the initial operating data of the grid connection point within the preset monitoring period.

[0106] Step 107: Based on the ratio of the negative-sequence voltage component to the positive-sequence voltage component in the initial running big data, determine the voltage imbalance degree of the grid connection point, and generate dynamic weighting coefficients based on the difference between the voltage imbalance degree of the grid connection point and the preset imbalance threshold.

[0107] It should be noted that the dynamic weighting coefficient is dynamically generated based on changes in the power grid's operating status. It is used to weight the impact of different control parameters, which helps to adjust the control strategy according to the actual power grid conditions, thereby achieving more efficient regulation.

[0108] Step 108: Based on the dynamic weighting coefficient, the active power fluctuation value, reactive power fluctuation value and three-phase current peak value in the initial operating data are weighted and fused to generate a comprehensive control reference value.

[0109] It should be noted that active power fluctuation and reactive power fluctuation represent the range of change in active and reactive power in the power grid over a certain period of time. Excessive fluctuations can lead to grid instability, therefore, devices such as energy storage converters are needed to control and suppress these fluctuations. Three-phase current peak values ​​refer to the maximum value of the current in each phase of the power grid. Excessively high current peak values ​​can cause equipment damage or system instability, thus requiring optimization and control. The comprehensive control reference value is a reference value that comprehensively considers various fluctuations and control parameters, used as the basis for energy storage converter regulation; by weighted fusion of different control parameters, an overall control standard is derived. Control commands are the actual operating commands generated by the energy storage converter based on the comprehensive control reference value, used to adjust the operating state of the energy storage converter, thereby maintaining grid stability.

[0110] In one embodiment, the specific execution process of this step is as follows:

[0111] Step F1: The active power fluctuation value is weighted and calculated based on dynamic weighting coefficients to generate the first weighting parameter. Active power is the portion of power actually transmitted, and the fluctuation value represents the magnitude of power output change. Excessive fluctuation may affect system stability. Dynamic weighting coefficients mean that the weights are adjusted according to changes in the system state. The weight values ​​can be dynamically adjusted based on the real-time power fluctuation level or other environmental factors, thus assigning different importance to power fluctuations at different time points. The first weighting parameter obtained through weighted calculation reflects the degree of influence of active power fluctuations on system regulation, providing a reference for subsequent optimization.

[0112] Step F2: The reactive power fluctuation value is weighted based on dynamic weighting coefficients to generate a second weighting parameter. Reactive power is mainly used to maintain voltage stability; fluctuations can affect the voltage quality of the system, thus impacting the stability of the power system. Similarly, reactive power fluctuations are weighted using dynamic weighting coefficients, taking into account the impact of reactive power changes on system stability, to obtain the second weighting parameter. This parameter will help balance the requirements of power fluctuations and voltage quality during the optimization process.

[0113] Step F3: Calculate the three-phase current peak values ​​based on dynamic weighting coefficients to generate a third weighting parameter. The three-phase current peak value refers to the maximum instantaneous current value reached in the three phases. Excessively high current peak values ​​may cause overload or equipment damage, therefore they need to be controlled within a reasonable range. The dynamic weighting coefficients are used here to dynamically adjust the weighting based on changes in the current peak value, generating the third weighting parameter. This parameter reflects the potential impact of current changes on the system, especially when peak currents may cause system overload or instability.

[0114] Step F4: Perform time-series alignment on the first, second, and third weighted parameters to generate a time-series parameter set. Different weighted parameters (active power, reactive power, peak current) may have different time scales or fluctuation frequencies, and direct combination may lead to imbalances in the comparison between different parameters. Time-series alignment aligns these three weighted parameters in time, ensuring their time steps match the data points, facilitating further analysis and processing. The purpose of this step is to ensure data synchronization, which is beneficial for subsequent weighting and optimization.

[0115] Step F5: Based on the fluctuation frequency of each parameter in the time-series parameter set, assign corresponding time-domain weights. Then, perform a weighted summation of the time-series parameter set based on these time-domain weights to generate a comprehensive control reference value. Fluctuation frequency refers to the degree and frequency of fluctuation of each parameter. Higher-frequency fluctuations typically require faster responses and adjustments, while lower-frequency fluctuations may have less impact on system stability. Different time-domain weights are assigned according to the fluctuation frequency of each parameter. Higher-frequency fluctuations are assigned higher time-domain weights to ensure the system can respond quickly to these fluctuations. Lower-frequency fluctuations are assigned lower time-domain weights. After redistributing the time-domain weights, all time-series parameters are combined using a weighted summation method to obtain the final comprehensive control reference value. This value integrates the influence of all important parameters and provides a holistic control reference for the system, ensuring that the system can respond efficiently and stably to various changes.

[0116] Step 109: Based on the matching degree between the comprehensive control reference value and the initial operating data, generate control commands for the energy storage converter and control the energy storage converter.

[0117] It should be noted that the specific execution process of this step is as follows:

[0118] Step G1: Perform vector synthesis on the positive-sequence and negative-sequence voltage components in the initial operating data to generate a composite voltage waveform. The positive-sequence voltage component represents the voltage waveform during normal operation of the system (i.e., the voltage waveform under ideal conditions), which usually corresponds to the positive rotation direction of the system. The negative-sequence voltage component represents the unbalanced part of the power grid, which usually occurs under fault or asymmetrical load conditions. Its frequency is the same as the positive-sequence component, but its rotation direction is opposite, which may affect power quality. By performing vector synthesis on the positive-sequence and negative-sequence components, a composite voltage waveform can be generated. This waveform reflects the overall voltage state in the power grid, taking into account both normal and abnormal (such as asymmetrical) conditions.

[0119] Step G2: Plot the first time-domain waveform based on the synthesized voltage waveform data. The first time-domain waveform is plotted using the synthesized voltage waveform data. This waveform shows how the voltage changes over time, visually displaying voltage fluctuations, frequency characteristics, and any asymmetries or faults in the power grid.

[0120] Step G3: Perform phase correction processing on the comprehensive control reference value to generate a corrected reference value. Since the voltage waveform in the power grid may have phase differences due to noise, equipment failure, or timing asynchrony, phase correction is required. The purpose of correction is to make the reference value more consistent with the waveform of the actual system, ensuring more precise control. Through phase correction, the obtained corrected reference value will provide control parameters that are more in line with the actual power grid conditions, providing an accurate reference for subsequent control.

[0121] Step G4: Plot a second time-domain waveform based on the corrected reference values. Using the phase-corrected reference values, plot the second time-domain waveform. This waveform should have a higher similarity to the first time-domain waveform because the corrected reference values ​​should be more consistent with the actual grid voltage waveform.

[0122] Step G5: Calculate the waveform similarity between the first and second time-domain waveforms. The similarity between the first and second time-domain waveforms is calculated by comparing them. This can be achieved in various ways, such as using root mean square error (RMSE) or correlation coefficients to quantify the waveform similarity. Similarity analysis: Higher similarity indicates better phase correction, a more consistent corrected reference value with the actual voltage waveform, and potentially better control.

[0123] Step G6: Based on the joint evaluation results of matching degree and waveform similarity, generate control commands for the energy storage converter. The accuracy of the control reference value is evaluated by combining matching degree and waveform similarity; matching degree reflects the difference between the reference value and the actual waveform, while waveform similarity reflects the synchronization between the two; the joint evaluation provides a comprehensive assessment of control accuracy. Based on the joint evaluation results, the final control commands for the energy storage converter are generated. These commands adjust the operation of the energy storage converter according to the actual grid conditions and the optimized control reference value, thereby optimizing the energy conversion and storage process to ensure system stability and efficiency.

[0124] In one embodiment, based on the joint evaluation results of matching degree and waveform similarity, control commands for the energy storage converter are generated, including:

[0125] If the matching degree is lower than the first preset threshold and / or the waveform similarity is lower than the second preset threshold, then control commands for the energy storage converter, including active power compensation commands and reactive power compensation commands, are generated.

[0126] If the matching degree remains below the third preset threshold for a preset time and the waveform similarity shows a decreasing trend, then a control command for the energy storage converter, including a three-phase current peak limiting command, is generated, wherein the third preset threshold is less than the first preset threshold.

[0127] It should be noted that a matching degree below the first preset threshold means that if the matching degree between the system output waveform and the expected reference waveform is lower than a certain set value, it indicates a significant deviation between the system's operating state and the ideal state. This deviation may be caused by unbalanced loads in the power grid, sudden faults, or untimely response of system equipment. A waveform similarity below the second preset threshold means that a low waveform similarity indicates a large difference between the actual voltage waveform and the ideal waveform, which may lead to system instability or decreased efficiency. This may be due to voltage fluctuations, frequency offsets, etc. Active power refers to the power actually converted into mechanical work or electrical energy. If the matching degree or waveform similarity is low, it may be because the system needs more active power to return to a normal state. For example, when the grid load increases, the energy storage system may need to release active power to balance the load and ensure system stability. Reactive power is the "ineffective" power in the power system. It is not converted into useful work, but it is crucial for maintaining voltage stability and power quality. If the waveform similarity is low, it may indicate a voltage imbalance, and the energy storage converter needs to adjust the reactive power. The power supply is used to stabilize voltage and prevent excessive fluctuations in the power grid. When the matching degree or waveform similarity is lower than a preset threshold, the energy storage converter adjusts the power distribution of the power grid by issuing active and reactive power compensation commands to ensure the stability of the power system. If the matching degree is consistently lower than the third preset threshold, it means that the waveform matching degree of the system is not in line with expectations, indicating that there may be long-term and serious problems in the power grid or energy storage system. For example, long-term grid imbalance or equipment failure may lead to this situation. If the waveform similarity shows a continuous downward trend, it may indicate that the waveform difference is getting bigger and bigger during the dynamic operation of the system, the instability of the power grid is increasing, and it may even lead to large voltage fluctuations or instability in the system. The peak current limit is used to prevent the current from fluctuating too much at one moment. Such fluctuations may cause equipment overload, damage or reduced grid stability. When the matching degree is consistently low and the waveform similarity is decreasing, the system may be in an unstable state and the peak current may exceed the safety limit. Therefore, issuing the peak current limit command is to avoid instantaneous current overload and protect the safety of the power grid equipment and energy storage system.

[0128] The energy storage converter control method provided by this invention uses a multi-objective optimization model to couple and analyze power compensation control parameters and current limiting control parameters, comprehensively considers the operating state of the energy storage converter, and optimizes the control command set, thereby improving the voltage stability of the power grid in multiple dimensions. Furthermore, based on the difference between the voltage imbalance degree and the preset threshold in the initial operating data of the power grid, dynamic weight coefficients are generated to automatically adjust the control strategy. This adaptive adjustment capability enables the method to respond to the dynamic changes of the power grid in real time, improving the flexibility and responsiveness of the control.

[0129] The energy storage converter control method provided by this invention generates a comprehensive control reference value by weighted fusion of active power, reactive power fluctuation values ​​and current peak values, further optimizing control commands, enabling the energy storage converter to make more precise adjustments under different operating conditions, and further improving the voltage stability and overall operating efficiency of the power grid.

[0130] The energy storage converter control method provided by this invention reduces the impact of unstable factors in the power grid, avoiding damage to equipment caused by excessively high or low voltage, and reducing the maintenance costs of the power system and the energy storage converter. Long-term stable operation of power grid equipment also helps extend its service life and reduce the frequency of system failures.

[0131] For easier understanding, please refer to Figure 3 This invention provides an embodiment of an energy storage converter control system, comprising:

[0132] The data acquisition module is used to collect the three-phase voltage, three-phase current and power output of the energy storage converter at the grid connection point within a preset monitoring period, and to construct a characteristic matrix of the grid operation status.

[0133] The feature extraction module is used to extract voltage fluctuation feature vectors and current distortion feature vectors based on the power grid operating state feature matrix.

[0134] The decoupling module is used to perform active and reactive power decoupling calculations on the voltage fluctuation feature vector based on the power fluctuation suppression model to obtain power compensation control parameters. Based on the current peak optimization model, it performs three-term current balance control on the current distortion feature vector to obtain current limiting control parameters.

[0135] The coupling module is used to input power compensation control parameters and current limiting control parameters into a multi-objective optimization model, and perform parameter coupling analysis in combination with the real-time operating conditions of the energy storage converter to obtain the energy storage converter control instruction set.

[0136] The operation module is used to regulate the energy storage converter based on the energy storage converter control instruction set.

[0137] In one embodiment, it also includes:

[0138] The operation data acquisition module is used to acquire the initial operation data of the power grid connection point within a preset monitoring period;

[0139] The weight acquisition module is used to determine the voltage imbalance at the grid connection point based on the ratio of the negative-sequence voltage component to the positive-sequence voltage component in the initial running big data, and to generate dynamic weight coefficients based on the difference between the voltage imbalance at the grid connection point and the preset imbalance threshold.

[0140] The weighting module is used to weight and fuse the active power fluctuation value, reactive power fluctuation value and three-phase current peak value in the initial operating data based on dynamic weighting coefficients to generate a comprehensive control reference value.

[0141] The command control module is used to generate control commands for the energy storage converter based on the matching degree between the comprehensive control reference value and the initial operating data, and to control the energy storage converter.

[0142] In one embodiment, the three-phase voltage, three-phase current, and power output of the energy storage converter at the grid connection point are collected within a preset monitoring period to construct a grid operating state characteristic matrix, including:

[0143] The three-phase voltage and three-phase current at the grid connection point are sampled at equal intervals to obtain the time-domain waveform data of the three-phase voltage and three-phase current;

[0144] Fourier transform is performed on the time-domain waveform data of three-phase voltage and three-phase current to extract the fundamental and harmonic components and obtain the frequency domain feature matrix.

[0145] Based on the frequency domain feature matrix, the three-phase voltage imbalance and total harmonic distortion of current are calculated to obtain the power quality assessment vector.

[0146] Collect the active power, reactive power, and power factor at the output of the energy storage converter to construct a power output characteristic matrix;

[0147] The power quality assessment vector and the power output feature matrix are fused to obtain the power grid operation status feature matrix.

[0148] In one embodiment, based on the power grid operating state feature matrix, voltage fluctuation feature vectors and current distortion feature vectors are extracted, including:

[0149] The power grid operation state characteristic matrix is ​​transformed by the symmetric component method to separate the positive sequence component, negative sequence component and zero sequence component, and obtain the sequence component characteristic matrix.

[0150] The negative sequence component suppression index is determined based on the sequence component feature matrix, and the negative sequence component amplitude change rate corresponding to the negative sequence component suppression index is determined based on the sliding window method, thus obtaining the voltage fluctuation feature vector.

[0151] Harmonic group analysis is performed on the current components in the sequence component characteristic matrix to extract the characteristic subharmonic amplitude and phase offset, thus obtaining the current distortion characteristic vector.

[0152] The voltage fluctuation feature vector and current distortion feature vector are nonlinearly reduced based on an autoencoder network to obtain a low-dimensional fluctuation feature code.

[0153] In one embodiment, based on the power fluctuation suppression model, active power and reactive power are decoupled and calculated from the voltage fluctuation characteristic vector to obtain power compensation control parameters, including:

[0154] The low-dimensional fluctuation feature code corresponding to the voltage fluctuation feature vector is input into the droop control model. The deviation between the active power reference value and the actual value is determined according to the droop control model, and the active power deviation vector is obtained.

[0155] The reactive power compensation function is determined based on the active power deviation vector, and the reactive power compensation amount is calculated by the fuzzy logic controller to obtain the reactive power adjustment command.

[0156] The active power deviation vector and reactive power regulation command are decoupled by parameters to obtain power compensation control parameters, which include amplitude regulation coefficient and phase regulation coefficient.

[0157] In one embodiment, a three-term current equalization control is performed on the current distortion feature vector based on a current peak optimization model to obtain current limiting control parameters, including:

[0158] The low-dimensional fluctuation feature encoding of the current distortion feature vector is input into the instantaneous power theory model. The effective value and peak value of the three-phase current are determined according to the instantaneous power theory model to obtain the current over-limit evaluation vector.

[0159] The current balance index is determined based on the current over-limit evaluation vector. The current limiting threshold of each phase current is calculated by the particle swarm optimization algorithm based on the current balance index, and the initial limiting parameter set is obtained.

[0160] The initial limiting parameter set is corrected based on the hysteresis comparator to obtain the current limiting control parameters, which include dead time and hysteresis width.

[0161] In one embodiment, power compensation control parameters and current limiting control parameters are input into a multi-objective optimization model, and parameter coupling analysis is performed in conjunction with the real-time operating conditions of the energy storage converter to obtain a set of control instructions for the energy storage converter, including:

[0162] Construct a multi-objective optimization function that includes power regulation rate, current harmonic content, and converter loss;

[0163] The power compensation control parameters and current limiting control parameters are weighted and summed, and constraints are established based on the battery SOC state, temperature parameters and switching frequency of the energy storage converter.

[0164] The multi-objective optimization function is solved using a genetic algorithm to obtain the Pareto optimal solution set. The target control parameter combination is then selected from the Pareto optimal solution set to obtain the energy storage converter control instruction set. The energy storage converter control instruction set includes the converter control instruction set of PWM modulation wave parameters and drive circuit timing.

[0165] In one embodiment, the active power fluctuation value, reactive power fluctuation value, and three-phase current peak value in the initial operating data are weighted and fused based on dynamic weighting coefficients to generate a comprehensive control reference value, including:

[0166] The active power fluctuation value is weighted based on the dynamic weighting coefficient to generate the first weighting parameter;

[0167] The reactive power fluctuation value is weighted based on the dynamic weighting coefficient to generate a second weighting parameter;

[0168] The peak value of the three-phase current is weighted based on the dynamic weighting coefficient to generate a third weighting parameter;

[0169] The first weighted parameter, the second weighted parameter, and the third weighted parameter are time-series aligned to generate a time-series parameter set;

[0170] Based on the fluctuation frequency of each parameter in the time-series parameter set, corresponding time-domain weights are assigned, and the time-series parameter set is weighted and summed based on the time-domain weights to generate a comprehensive control reference value.

[0171] In one embodiment, based on the matching degree between the comprehensive control reference value and the initial operating data, control commands for the energy storage converter are generated to control the energy storage converter, including:

[0172] Vector synthesis is performed on the positive-sequence voltage component and the negative-sequence voltage component in the initial running data to generate synthesized voltage waveform data;

[0173] The first time-domain waveform diagram is plotted based on the synthesized voltage waveform data;

[0174] The comprehensive control reference value is phase-corrected to generate a corrected reference value.

[0175] The second time-domain waveform is plotted based on the corrected reference value;

[0176] Calculate the waveform similarity between the first time-domain waveform and the second time-domain waveform;

[0177] Based on the joint evaluation results of matching degree and waveform similarity, control commands for the energy storage converter are generated.

[0178] The energy storage converter control system provided in this invention is used to execute the energy storage converter control method provided in this invention. Its principle and the technical effects achieved are the same as those of the energy storage converter control method provided in this invention, and will not be repeated here.

[0179] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0180] The terms "first," "second," "third," etc., used in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0181] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0182] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0183] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0184] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0185] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling an energy storage converter, characterized in that, include: Collect the three-phase voltage, three-phase current and power output of the energy storage converter at the grid connection point within a preset monitoring period to construct a grid operation status characteristic matrix; Based on the power grid operation status feature matrix, voltage fluctuation feature vector and current distortion feature vector are extracted; Based on the power fluctuation suppression model, the active power and reactive power of the voltage fluctuation feature vector are decoupled and calculated to obtain the power compensation control parameters. Based on the current peak optimization model, the current distortion feature vector is subjected to three-term current equalization control to obtain the current limiting control parameters. By inputting the power compensation control parameters and current limiting control parameters into a multi-objective optimization model and combining them with the real-time operating conditions of the energy storage converter, a parameter coupling analysis is performed to obtain the energy storage converter control command set. The energy storage converter is regulated based on the energy storage converter control instruction set.

2. The energy storage converter control method according to claim 1, characterized in that, The energy storage converter is regulated based on the energy storage converter control instruction set, and the following is also included: Acquire initial operating data of the power grid connection point within a preset monitoring period; Based on the ratio of negative-sequence voltage components to positive-sequence voltage components in the initial operational big data, the voltage imbalance at the grid connection point is determined. Based on the difference between the voltage imbalance at the grid connection point and the preset imbalance threshold, dynamic weighting coefficients are generated. Based on dynamic weighting coefficients, the active power fluctuation value, reactive power fluctuation value and three-phase current peak value in the initial operating data are weighted and fused to generate a comprehensive control reference value. Based on the matching degree between the comprehensive control reference value and the initial operating data, control commands for the energy storage converter are generated to control the energy storage converter.

3. The energy storage converter control method according to claim 1, characterized in that, Collect the three-phase voltage, three-phase current, and power output of the energy storage converter at the grid connection point within a preset monitoring period to construct a grid operation status characteristic matrix, including: The three-phase voltage and three-phase current at the grid connection point are sampled at equal intervals to obtain the time-domain waveform data of the three-phase voltage and three-phase current; Fourier transform is performed on the time-domain waveform data of three-phase voltage and three-phase current to extract the fundamental and harmonic components and obtain the frequency domain feature matrix. Based on the frequency domain feature matrix, the three-phase voltage imbalance and total harmonic distortion of current are calculated to obtain the power quality assessment vector. Collect the active power, reactive power, and power factor at the output of the energy storage converter to construct a power output characteristic matrix; The power quality assessment vector and the power output feature matrix are fused to obtain the power grid operation status feature matrix.

4. The energy storage converter control method according to claim 1, characterized in that, Based on the power grid operation state feature matrix, voltage fluctuation feature vectors and current distortion feature vectors are extracted, including: The power grid operation state characteristic matrix is ​​transformed by the symmetric component method to separate the positive sequence component, negative sequence component and zero sequence component, and obtain the sequence component characteristic matrix. The negative sequence component suppression index is determined based on the sequence component feature matrix, and the negative sequence component amplitude change rate corresponding to the negative sequence component suppression index is determined based on the sliding window method, thus obtaining the voltage fluctuation feature vector. Harmonic group analysis is performed on the current components in the sequence component characteristic matrix to extract the characteristic subharmonic amplitude and phase offset, thus obtaining the current distortion characteristic vector. The voltage fluctuation feature vector and current distortion feature vector are nonlinearly reduced based on an autoencoder network to obtain a low-dimensional fluctuation feature code.

5. The energy storage converter control method according to claim 4, characterized in that, Based on the power fluctuation suppression model, the active and reactive power of the voltage fluctuation characteristic vector are decoupled and calculated to obtain the power compensation control parameters, including: The low-dimensional fluctuation feature code corresponding to the voltage fluctuation feature vector is input into the droop control model. The deviation between the active power reference value and the actual value is determined according to the droop control model, and the active power deviation vector is obtained. The reactive power compensation function is determined based on the active power deviation vector, and the reactive power compensation amount is calculated by the fuzzy logic controller to obtain the reactive power adjustment command. The active power deviation vector and reactive power regulation command are decoupled by parameters to obtain power compensation control parameters, which include amplitude regulation coefficient and phase regulation coefficient.

6. The energy storage converter control method according to claim 5, characterized in that, Based on the current peak optimization model, a three-term current equalization control is performed on the current distortion feature vector to obtain the current limiting control parameters, including: The low-dimensional fluctuation feature encoding of the current distortion feature vector is input into the instantaneous power theory model. The effective value and peak value of the three-phase current are determined according to the instantaneous power theory model to obtain the current over-limit evaluation vector. The current balance index is determined based on the current over-limit evaluation vector. The current limiting threshold of each phase current is calculated by the particle swarm optimization algorithm based on the current balance index, and the initial limiting parameter set is obtained. The initial limiting parameter set is corrected based on the hysteresis comparator to obtain the current limiting control parameters, which include dead time and hysteresis width.

7. The energy storage converter control method according to claim 6, characterized in that, The power compensation control parameters and current limiting control parameters are input into a multi-objective optimization model. Combined with the real-time operating conditions of the energy storage converter, parameter coupling analysis is performed to obtain the energy storage converter control command set, including: Construct a multi-objective optimization function that includes power regulation rate, current harmonic content, and converter loss; The power compensation control parameters and current limiting control parameters are weighted and summed. Based on the battery SOC state, temperature parameters and switching frequency of the energy storage converter, the constraints of the multi-objective optimization function are established. The multi-objective optimization function is solved using a genetic algorithm to obtain the Pareto optimal solution set. The target control parameter combination is then selected from the Pareto optimal solution set to obtain the energy storage converter control instruction set. The energy storage converter control instruction set includes the converter control instruction set of PWM modulation wave parameters and drive circuit timing.

8. The energy storage converter control method according to claim 7, characterized in that, Based on dynamic weighting coefficients, the active power fluctuations, reactive power fluctuations, and three-phase current peak values ​​in the initial operating data are weighted and fused to generate comprehensive control reference values, including: The active power fluctuation value is weighted based on the dynamic weighting coefficient to generate the first weighting parameter; The reactive power fluctuation value is weighted based on the dynamic weighting coefficient to generate a second weighting parameter; The peak value of the three-phase current is weighted based on the dynamic weighting coefficient to generate a third weighting parameter; The first weighted parameter, the second weighted parameter, and the third weighted parameter are time-series aligned to generate a time-series parameter set; Based on the fluctuation frequency of each parameter in the time-series parameter set, corresponding time-domain weights are assigned, and the time-series parameter set is weighted and summed based on the time-domain weights to generate a comprehensive control reference value.

9. The energy storage converter control method according to claim 8, characterized in that, Based on the matching degree between the comprehensive control reference value and the initial operating data, control commands are generated for the energy storage converter to control the energy storage converter, including: Vector synthesis is performed on the positive-sequence voltage component and the negative-sequence voltage component in the initial running data to generate synthesized voltage waveform data; The first time-domain waveform diagram is plotted based on the synthesized voltage waveform data; The comprehensive control reference value is phase-corrected to generate a corrected reference value. The second time-domain waveform is plotted based on the corrected reference value; Calculate the waveform similarity between the first time-domain waveform and the second time-domain waveform; Based on the joint evaluation results of matching degree and waveform similarity, control commands for the energy storage converter are generated.

10. A control system for an energy storage converter, characterized in that, include: The data acquisition module is used to collect the three-phase voltage, three-phase current and power output of the energy storage converter at the grid connection point within a preset monitoring period, and to construct a characteristic matrix of the grid operation status. The feature extraction module is used to extract voltage fluctuation feature vectors and current distortion feature vectors based on the power grid operating state feature matrix. The decoupling module is used to perform active and reactive power decoupling calculations on the voltage fluctuation feature vector based on the power fluctuation suppression model to obtain power compensation control parameters. Based on the current peak optimization model, it performs three-term current balance control on the current distortion feature vector to obtain current limiting control parameters. The coupling module is used to input power compensation control parameters and current limiting control parameters into a multi-objective optimization model, and perform parameter coupling analysis in combination with the real-time operating conditions of the energy storage converter to obtain the energy storage converter control instruction set. The operation module is used to regulate the energy storage converter based on the energy storage converter control instruction set.

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