Micro-grid-oriented optical storage equipment comprehensive operation control method and system

Abstract

The invention relates to the technical field of energy control, in particular to a micro-grid-oriented optical storage equipment comprehensive operation control method and system. The method comprises the following steps: acquiring current and voltage waveform data at two ends of a filter inductor of the optical storage equipment; performing frequency domain frequency sweeping on the current and voltage waveform data to generate filter inductance complex impedance data; mapping based on the complex impedance data of the filter inductor to obtain a local saturation starting point and a nonlinear section of the magnetic core, and generating saturation characteristic data; and connecting a bidirectional direct current bias winding beside the filter inductor in parallel, and performing bias current adjustment on the saturation characteristic data to generate magnetic core working point adjustment data. According to the invention, the problems of unstable filtering performance, fixed modulation parameters and insufficient system collaboration of the traditional optical storage equipment are solved by accurately identifying the nonlinear characteristic of the filtering inductor, dynamically adjusting the working point of the magnetic core, adaptively optimizing the PWM modulation parameters and cooperatively controlling the micro-grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy control, and in particular to a micro-grid-oriented photovoltaic storage device comprehensive operation control method and system. BACKGROUND

[0002] The traditional power grid has limited access and regulation capacity for distributed energy, resulting in low operating efficiency of photovoltaic storage devices and difficulty in meeting the dual demands of stability and economy of microgrids. Early photovoltaic storage device operation control methods rely on single devices or centralized control, lack adaptability to system diversity and dynamic changes, and are prone to cause power fluctuations and waste of energy storage resources. With the rise of smart grids and Internet of Things technologies, distributed control strategies based on real-time data collection and edge computing have been gradually proposed, effectively improving the collaborative scheduling capability and response speed of photovoltaic storage devices. At the same time, optimization algorithms based on big data and artificial intelligence are beginning to be applied to the operation and management of photovoltaic storage systems, promoting the development of control methods towards intelligence and precision. However, the filter inductance of traditional photovoltaic storage devices often has local saturation of the magnetic core and nonlinearity problems, resulting in unstable filtering effect and affecting the harmonic suppression capability of the system. At the same time, the PWM carrier slope and dead zone design are mostly fixed values, which are difficult to adapt to the nonlinearity of the filter and the dynamic changes of the system, resulting in increased switching loss and insufficient harmonic suppression. SUMMARY

[0003] Therefore, it is necessary to provide a micro-grid-oriented photovoltaic storage device comprehensive operation control method and system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a micro-grid-oriented photovoltaic storage device comprehensive operation control method, the method comprising the following steps: Step S1: Obtain the current and voltage waveform data of the filter inductance of the photovoltaic storage device; perform frequency domain sweep on the current and voltage waveform data to generate filter inductance complex impedance data; map the local saturation starting point and nonlinearity section of the magnetic core based on the filter inductance complex impedance data to generate saturation characteristic data; Step S2: Parallel a bidirectional DC bias winding to the filter inductance, and adjust the bias current of the saturation characteristic data to generate magnetic core operating point adjustment data; split the filter into main and auxiliary groups and dynamically parallel / series switch them under different working conditions by solid-state relays, and configure the filter impedance of the saturation characteristic data to generate filter impedance configuration data; Step S3: Adjust the PWM carrier slope and dead zone of the photovoltaic storage device through the magnetic core operating point adjustment data and the filter impedance configuration data to obtain PWM modulation parameter optimization data; superimpose virtual inductance / resistance compensation on the PWM modulation parameter optimization data to generate adaptive compensation control data; Step S4: control instruction generation is performed on the adaptive compensation control data, and the generated control instruction is input into the preset micro-grid for instruction cooperative control, so as to execute the comprehensive operation control optimization operation of the light storage equipment facing the micro-grid.

[0005] The application obtains the current and voltage waveform data across the filter inductor of the light storage equipment, and performs frequency domain sweep analysis to accurately obtain the complex impedance characteristics of the filter inductor, and further map the local saturation starting point and nonlinear section of the magnetic core, thereby realizing accurate modeling of the saturation characteristics of the magnetic core. Further, a bidirectional DC bias winding is connected in parallel to the filter inductor, and the saturation characteristics of the magnetic core are dynamically adjusted to realize controllable adjustment of the working point of the magnetic core. The filter inductor is divided into two groups, and intelligent switching in series / parallel under different operating conditions is realized by means of solid-state relays, so that the equivalent impedance of the filter can be flexibly configured under complex conditions, and the electromagnetic compatibility and stability of the system are enhanced. On this basis, the PWM carrier slope and dead zone are adaptively adjusted in combination with the magnetic core working point adjustment data and the filter impedance configuration data, which not only optimizes the PWM modulation strategy, but also realizes effective suppression of voltage and current distortion through virtual inductance and virtual resistance compensation superposition. The finally generated adaptive compensation control instruction can work cooperatively with other control units of the micro-grid, and significantly improves the operation stability, response flexibility and overall energy efficiency of the light storage equipment in the micro-grid. Therefore, by accurately identifying the nonlinear characteristics of the filter inductor, dynamically adjusting the working point of the magnetic core, adaptively optimizing the PWM modulation parameters and cooperative control of the micro-grid, the application solves the problems of unstable filter performance, fixed modulation parameters and insufficient system cooperation of traditional light storage equipment.

[0006] Preferably, step S1 comprises the following steps: Step S11: the transient waveforms at the input side and the output side of the filter inductor are synchronously collected by using a current probe and a differential voltage probe to obtain the current and voltage waveform data across the filter inductor of the light storage equipment; Step S12: the current and voltage waveform data are subjected to fast Fourier transform to generate frequency domain sweep response spectrum data; the amplitude and phase ratio are calculated according to the frequency domain sweep response spectrum data to generate filter inductor complex impedance data; Step S13: the filter inductor complex impedance data are subjected to admittance phasor inverse transform analysis to extract the equivalent magnetic guide nonlinear inflection point, and generate the magnetic core local saturation starting point data; the filter inductor complex impedance data are subjected to section trend slope segmentation based on the magnetic core local saturation starting point data to identify the nonlinear gain interval, and generate the magnetic core nonlinear section data; Step S14: the saturation characteristics data are generated by fusing the magnetic core local saturation starting point data and the magnetic core nonlinear section data.

[0007] The application effectively captures the transient response characteristics in the operation process of the light storage device by high-precision synchronous sampling of the filter inductor through the current probe and the differential voltage probe, and improves the integrity and accuracy of the waveform data; with the help of fast Fourier transform (FFT) technology, the collected current and voltage waveforms are analyzed in the frequency domain, and the frequency domain sweep response spectrum is constructed, so that the fine modeling of the impedance behavior of the filter inductor at different frequencies is realized; the complex impedance data calculated based on the frequency domain amplitude and phase ratio can truly reflect the electromagnetic characteristics of the magnetic core under different working conditions; the complex impedance data is further analyzed by the admittance phasor inverse transform, and the nonlinear inflection point of the equivalent magnetic conductance can be accurately extracted, so that the starting point of the magnetic core entering the local saturation state is identified; then the trend of the complex impedance section is classified by the slope division method, the distribution boundary of the nonlinear gain region is determined, and the ambiguity and error accumulation in the modeling of the nonlinear section by the traditional method are avoided; finally, the local saturation starting point and the nonlinear section information are fused to form the high-resolution magnetic core saturation characteristic data, which provides scientific and quantifiable basis support for subsequent magnetic core working point adjustment and filter dynamic configuration.

[0008] Preferably, the admittance phasor inverse transform analysis on the filter inductor complex impedance data in step S13 includes: Conjugate normalization is performed on the filter inductor complex impedance data to generate unit amplitude admittance spectrum data; According to the unit amplitude admittance spectrum data, a virtual part admittance curve is extracted to generate an inductive admittance change trajectory; The inductive admittance change trajectory is analyzed by the local extreme point slope change rate to generate a first-order slope derivative curve; The slope mutation point is identified in the first-order slope derivative curve, the magnetic conductance sudden drop turning frequency point is extracted, and the magnetic conductance change mutation point data is generated; Combined with the complex impedance phase angle change rate corresponding to the mutation point frequency, the phase lag characteristic is verified, the key frequency point meeting the magnetic core saturation response is output, and the magnetic core local saturation starting point data is generated.

[0009] The application effectively eliminates the interference of dimension and amplitude difference on nonlinear feature recognition by conjugate normalization processing of the filter inductance complex impedance data, and provides a unified dimension basis for subsequent feature analysis; the imaginary part of the admittance spectrum is extracted to form the inductive admittance change trajectory, which can truly reflect the inductive response behavior of the magnetic core at different frequencies; then, the first-order slope derivative curve is constructed by analyzing the slope change rate of the local extreme points of the admittance trajectory, so as to realize the quantitative description of the inductive admittance mutation trend; the slope mutation point is identified on the slope derivative curve, so as to effectively locate the turning frequency of the magnetic permeance sudden drop of the magnetic core, thereby extracting the key magnetic permeance change mutation point data; in combination with the phase angle change rate at the mutation point, it is further determined whether the significant phase lag feature is possessed, so as to prove from the frequency response angle that the local saturation state of the magnetic core is entered; finally, the output magnetic core local saturation starting point data not only has high resolution and high robustness, but also is significantly superior to the traditional saturation identification method based on steady state or empirical model, so as to ensure the accuracy and dynamic adaptability of the physical basis of subsequent magnetic core working point adjustment and nonlinear compensation.

[0010] Preferably, the step S2 comprises the following steps: Step S21: a bidirectional DC bias winding is connected in parallel to the filter inductor, and an adjustable constant current source initial bias value is set based on the magnetic core local saturation starting point data, to generate an initial excitation parameter of the bias winding; Step S22: bias current adjustment response curve analysis is performed on the saturation characteristic data, to generate a gain coefficient curve of the bias current to the magnetic permeance change; the bias winding control instruction is dynamically set according to the gain coefficient curve of the bias current to the magnetic permeance change, to generate the magnetic core working point adjustment data; Step S23: the filter inductor is split into a main filter branch and an auxiliary filter branch, and independent sampling channels are configured to measure the voltage and current of the two branches respectively, to generate filter branch working state monitoring data; Step S24: working condition load data of the main filter branch and the auxiliary filter branch are acquired; load disturbance mode identification is performed on the filter branch working state monitoring data based on the working condition load data, to generate dynamic switching threshold condition data; Step S25: a solid state relay control logic is configured, the main and auxiliary filter branches are executed in series-parallel dynamic switching according to the dynamic switching threshold condition data and based on the solid state relay control logic, to generate filter topology switching state data; the filter impedance configuration data under the current topology is generated by performing filter impedance mapping analysis on the topology switching state data in combination with the saturation characteristic data.

[0011] The application realizes precise injection control of bias current by introducing a bidirectional DC bias winding on the filter inductor and combining the adjustable constant current source configured with the local saturation starting point data of the magnetic core, effectively expanding the working interval of the magnetic core and enhancing its linear control capability; on this basis, by performing bias current adjustment response curve analysis on the saturation characteristic data, the gain coefficient curve of the bias current-magnetic permeability change is constructed, realizing quantitative modeling of the nonlinear characteristics of the magnetic core and providing data support for dynamic adjustable optimization of the working point of the magnetic core; further, the filter inductor structure is split into main and auxiliary branches, and independent sampling channels are configured for real-time monitoring, effectively improving the observability and management granularity of the operating state of the filter branch; combined with the working condition load data, the disturbance mode of the filter branch monitoring data is identified, which can dynamically generate switching threshold condition data to support the solid-state relay to configure the topology switching control logic; this mechanism can intelligently select the main and auxiliary branch combination mode according to the load fluctuation, thereby realizing the topology adaptive adjustment of the filter in complex operating environments; finally, combined with the topology switching state and the saturation characteristic data, the filter impedance mapping analysis is performed to generate the optimal filter impedance configuration data under the current topology, which not only improves the consistency and stability of the filtering performance, but also provides dynamic matching physical support for subsequent PWM modulation parameter optimization, significantly enhancing the power quality control capability of the optical storage equipment.

[0012] Preferably, the load disturbance mode identification of the filter branch working state monitoring data based on the working condition load data in step S24 comprises: periodic stability discrimination is performed on the working condition load data to generate load period drift characteristic data; the power fluctuation threshold of the filter branch working state monitoring data is extracted to obtain power fluctuation mutation index data; joint mode classification is performed on the load period drift characteristic data and the power fluctuation mutation index data to generate disturbance mode classification label data, wherein the joint mode classification includes high-frequency impact type, intermittent oscillation type and continuous jitter type; mapping rules are set for the disturbance mode classification label data to generate disturbance response priority data; event trigger thresholds are configured based on the disturbance response priority data to generate dynamic switching threshold condition data.

[0013] The application can effectively capture the cycle instability phenomenon caused by load side fluctuation by performing periodic stability discriminant analysis on working condition load data, constructing load cycle drift characteristic data, providing pre-criterion for filter response adjustment; at the same time, power fluctuation threshold is extracted from filter branch working state monitoring data, power fluctuation mutation index data is generated, and fine perception of power disturbance under high dynamic working condition is realized; further, by combining the periodic drift characteristics and the power fluctuation mutation index for joint pattern classification, the dynamic characteristic category of load disturbance is determined, and the typical working conditions such as high-frequency impact type, intermittent oscillation type and continuous jitter type are divided, which effectively improves the decision pertinence of filter topology switching; on the basis of the classification result, the disturbance response priority rule mapping mechanism is introduced, and the priority processing level is set for different types of disturbance, so as to avoid the blindness of switching logic and resource waste; finally, through the disturbance response priority configuration event trigger threshold, the dynamic switching threshold condition data which can be adaptively adjusted is generated, which greatly improves the response timeliness, selectivity and stability of filter topology adjustment under multi-working condition, and enhances the anti-interference ability and dynamic adaptability of the system to power disturbance.

[0014] Preferably, the joint pattern classification of the load cycle drift characteristic data and the power fluctuation mutation index data comprises: performing sliding cross-spectrum focusing analysis on the load cycle drift characteristic data to generate frequency disturbance coupling index data; extracting disturbance edge acceleration characteristics of the power fluctuation mutation index data to obtain mutation response strength data; constructing a double-factor disturbance characteristic matrix according to the frequency disturbance coupling index data and the mutation response strength data, performing non-convex fuzzy clustering processing, and generating a disturbance distribution label graph; calculate the area density gradient projection of the disturbance distribution label graph, and perform projection shape discrimination on the area density gradient projection to obtain disturbance pattern classification label data, wherein the projection shape discrimination is specifically: when the projection shape is core-nuclear-oblique-impact type, it is marked as high-frequency impact type; when the projection shape is scattered-loop type, it is marked as intermittent oscillation type; when the projection shape is expansion-co-seismic type, it is marked as continuous jitter type.

[0015] The application generates frequency disturbance coupling index data reflecting the coupling degree of disturbance frequency by implementing sliding cross spectrum focus analysis on the load cycle drift characteristic data, effectively improving the recognition resolution of cycle instability characteristics and frequency disturbance coupling behavior; meanwhile, the disturbance edge acceleration characteristics are extracted from the power fluctuation mutation index to construct mutation response strength data for accurately describing the transient impact amplitude of load disturbance; based on the two key disturbance factors, a double-factor disturbance characteristic matrix is constructed, and a non-convex fuzzy clustering algorithm is introduced for adaptive classification, so that the disturbance pattern recognition can take into account the boundary ambiguity and morphological complexity, breaking through the adaptability bottleneck of traditional clustering algorithms in dealing with nonlinear disturbances; the clustering results are further compressed in dimension and the spatial distribution characteristics of the disturbance pattern are strengthened through the area density gradient projection calculation of the disturbance distribution label graph; by structurally discriminating the projected shape, three typical disturbance patterns, namely, the aggregation-nose type (high-frequency impact type), the scattering-cyclic type (intermittent oscillation type) and the expansion-co-seismic type (continuous shaking type), can be effectively divided, realizing high-accuracy labeling and semantic association of disturbance types; the method has strong generalization ability and real-time adaptability under high-complexity load conditions, provides accurate and controllable disturbance recognition results for subsequent filter topology switching, and significantly improves the intelligent identification and response efficiency of the optical storage system to external disturbances.

[0016] Preferably, the step S3 of adjusting the PWM carrier slope and dead zone of the optical storage device through the magnetic core operating point adjustment data and the filter impedance configuration data comprises: identifying a nonlinear saturation interval of the magnetic core operating point adjustment data; performing frequency response matching analysis on the filter impedance configuration data to generate filter dynamic impedance characteristic data; generating a PWM carrier slope adaptive adjustment rule based on the nonlinear saturation interval and the filter dynamic impedance characteristic data to obtain carrier slope adjustment strategy data; correcting the dead zone based on the carrier slope adjustment strategy data to generate dead zone time correction parameter data; jointly mapping the carrier slope adjustment strategy data and the dead zone time correction parameter data to generate PWM modulation parameter optimization data.

[0017] The application realizes active perception of the local nonlinear response characteristics of the magnetic core by identifying the nonlinear saturation interval in the magnetic core operating point adjustment data, avoiding the risk of power quality deterioration caused by the traditional fixed modulation strategy when the magnetic core enters the saturation state; combined with the filter impedance configuration data, frequency response matching analysis is performed to construct filter dynamic impedance characteristic data reflecting the filter dynamic behavior under actual operating conditions, effectively revealing the influence channel of carrier modulation on the energy response of the filter; on this basis, the PWM carrier slope adaptive adjustment rule is constructed for the nonlinear interval characteristics, and personalized carrier slope adjustment strategy data is generated, which can accurately optimize the modulation slope according to the nonlinear degree of the magnetic core and the filter response; further, according to the carrier strategy data, the dead zone time correction of the optical storage equipment is carried out, and the dead zone correction parameter is generated, which effectively avoids the system instability factors caused by the hysteresis and cross conduction of the switching device; finally, through the joint mapping of the carrier slope and the dead zone correction parameter, the PWM modulation parameter optimization data with nonlinear adaptive ability is generated, which not only significantly improves the real-time and matching of the modulation strategy, but also improves the switching accuracy, filtering efficiency and overall stability of the optical storage equipment under complex working conditions, supporting the efficient, low-loss and high-compatibility operation of the optical storage system in the microgrid.

[0018] Preferably, the dead zone correction of the optical storage equipment based on the carrier slope adjustment strategy data comprises: Modeling the switching delay caused by the carrier slope adjustment strategy data to generate switching delay error modeling data; Calculating the dead zone time correction amount based on the switching delay error model data to generate dead zone correction amount data; Correcting the dead zone time width of the optical storage equipment using the dead zone correction amount data, and feedback adjusting the corrected dead zone time width to generate dead zone time correction parameter data.

[0019] The application can accurately reflect the switching delay error model of the switching dynamic response offset in the PWM modulation process by modeling the dead zone caused by the switching delay of the carrier slope adjustment strategy data, effectively making up for the defects of the traditional dead zone setting which relies on empirical parameters and rough adjustment. Based on the model, the dead zone correction amount calculation can dynamically generate dead zone correction amount data for the device conduction lag characteristics under different carrier slope conditions, and realize more refined switching dead zone control. Further, the correction amount is used to adjust the dead zone time width, and a feedback regulation mechanism is introduced, which can track the dead zone adjustment effect in real time and dynamically converge to the optimal control interval, thereby generating robust dead zone time correction parameter data. This mechanism not only improves the symmetry and effectiveness of the PWM modulation, significantly reduces the voltage distortion, bridge arm oscillation and switching loss caused by unreasonable dead zone setting, but also enhances the robustness and dynamic adaptability of the modulation strategy to high-frequency disturbance, laying a key foundation for realizing high-precision and high-reliability optical storage equipment modulation control.

[0020] Preferably, step S4 comprises the following steps: Step S41: dynamically instructing coding of the adaptive compensation control data to generate multi-dimensional control instruction set data; Step S42: identifying instruction redundancy conflicts of the multi-dimensional control instruction set data, eliminating redundant and mutually exclusive instructions, thereby generating control instruction deduplication optimization data; Step S43: inputting the control instruction deduplication optimization data into the micro-grid power routing module to perform regional-level load response mapping, thereby generating micro-grid instruction matching mapping data; Step S44: calculating the control timing consistency of the micro-grid instruction matching mapping data to obtain collaborative control scheduling sequence data; inputting the collaborative control scheduling sequence data into the preset micro-grid control interface for instruction distribution and execution, thereby generating operation feedback response data to perform optical storage equipment comprehensive operation control optimization operation for micro-grid.

[0021] The application realizes multi-level expression of the light storage equipment control strategy in time sequence, space and function dimension by dynamically instructing coding of adaptive compensation control data, constructing multi-dimensional control instruction set data facing multi-dimensional operation targets; further identifies and eliminates redundant and mutually exclusive control instructions in the instruction set, generates optimized deduplicated control instruction data, significantly reduces instruction conflict and resource waste risk, and improves the execution efficiency and consistency of the control system; inputs the optimized instruction data into the power routing module of the microgrid for regional level load response mapping, generates microgrid instruction matching mapping data, realizes accurate docking of the control strategy and the actual operation state of the microgrid; further performs control time sequence consistency calculation on the matching mapping data, generates a control scheduling sequence with global collaboration, thereby ensuring the collaborative operation of multiple devices and multiple nodes without delay and conflict; finally, through inputting the scheduling sequence into the microgrid control interface, real-time instruction distribution and execution are completed, and operation feedback response data is generated, thereby constructing a closed-loop linkage mechanism between the light storage equipment and the microgrid. Overall, this step effectively improves the dynamic optimization capability of the control instruction, the microgrid adaptability and the collaborative control capability, realizes efficient operation, flexible scheduling and intelligent response of the light storage system in the microgrid, and enhances the system-level operation stability and optimization of the light storage system in complex load scenarios.

[0022] In the present specification, a light storage equipment comprehensive operation control system for microgrid is provided for executing the light storage equipment comprehensive operation control method for microgrid described above, and the light storage equipment comprehensive operation control system for microgrid comprises: A data acquisition module is configured to acquire current and voltage waveform data across the filter inductor of the light storage equipment; perform frequency domain sweep on the current and voltage waveform data to generate filter inductor complex impedance data; map the filter inductor complex impedance data to obtain a local saturation starting point and a nonlinear section of the magnetic core, and generate saturation characteristic data; A data intervention configuration module is configured to parallelly connect a bidirectional DC bias winding to the filter inductor, and adjust the bias current of the saturation characteristic data to generate magnetic core working point adjustment data; split the filter into a main filter and an auxiliary filter, and dynamically switch the main filter and the auxiliary filter in parallel / series under different working conditions through solid-state relays, and configure the filter impedance of the saturation characteristic data to generate filter impedance configuration data; A control optimization module is configured to perform PWM carrier slope and dead zone adaptive adjustment on the light storage equipment through the magnetic core working point adjustment data and the filter impedance configuration data to obtain PWM modulation parameter optimization data; and perform virtual inductance / resistance compensation superposition on the PWM modulation parameter optimization data to generate adaptive compensation control data; The instruction distribution module is used to generate control instructions from adaptive compensation control data and input the generated control instructions into a preset microgrid for instruction-coordinated control, so as to perform integrated operation control optimization of photovoltaic and energy storage equipment for microgrid.

[0023] The beneficial effects of this invention lie in the fact that by setting up a data acquisition module, high-precision acquisition of the voltage and current waveforms across the filter inductor of the photovoltaic energy storage device is achieved. Furthermore, by using frequency domain sweep technology to extract the complex impedance characteristics of the filter inductor, the local saturation start point and nonlinear segment of the magnetic core are identified, effectively constructing a high-resolution saturation characteristic model reflecting the dynamic saturation behavior of the magnetic core, providing precise physical support for subsequent control strategies. The data intervention configuration module, by introducing an adjustable DC bias winding and a magnetic core operating point adjustment mechanism, enhances the controllability and linear range of the magnetic core. It also supports dynamic reconfiguration and series-parallel switching of the main and auxiliary branches of the filter inductor under multiple operating conditions. Combined with saturation characteristic data, intelligent configuration of the filter impedance is performed, significantly improving the filtering performance. The invention enhances the equivalent impedance adaptation capability and response sensitivity of the device under complex operating conditions. A control optimization module integrates core operating point adjustment data and filter impedance configuration data to achieve dynamic adaptive adjustment of the PWM carrier slope and dead zone. Furthermore, virtual inductance / resistance compensation is superimposed, enabling fine-grained tuning of control parameters. This effectively reduces switching losses, harmonic distortion, and system response lag, improving the real-time performance and stability of the modulation strategy. The instruction distribution module supports intelligent encoding, conflict avoidance, and microgrid scenario matching of adaptive compensation control data, thereby achieving coordinated distribution and closed-loop feedback of control instructions. This significantly improves the control coordination, operating efficiency, and intelligent response level of the photovoltaic-storage equipment in the microgrid. Therefore, this invention solves the problems of unstable filtering performance, fixed modulation parameters, and insufficient system coordination in traditional photovoltaic-storage equipment by accurately identifying the nonlinear characteristics of the filter inductor, dynamically adjusting the core operating point, adaptively optimizing PWM modulation parameters, and implementing microgrid coordinated control. Attached Figure Description

[0024] Figure 1 A flowchart illustrating the steps of a comprehensive operation and control method for photovoltaic and energy storage devices in a microgrid. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S1. Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S4. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The technical method of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0026] In addition, the drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference signs in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element, and the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0028] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 A micro-grid-oriented optical storage device comprehensive operation control method, the method comprises the following steps: Step S1: Obtain the current and voltage waveform data across the filter inductance of the optical storage device; perform frequency domain sweep on the current and voltage waveform data to generate filter inductance complex impedance data; map the filter inductance complex impedance data to obtain the local saturation starting point of the magnetic core and the nonlinear section, and generate saturation characteristic data; Step S2: Parallel a bidirectional DC bias winding to the filter inductance, and adjust the bias current of the saturation characteristic data to generate magnetic core operating point adjustment data; split the filter into two groups of main and auxiliary filters, and switch them in series / parallel under different working conditions by solid-state relays, and configure the filter impedance of the saturation characteristic data to generate filter impedance configuration data; Step S3: Adjust the PWM carrier slope and dead zone of the optical storage device through the magnetic core operating point adjustment data and the filter impedance configuration data to obtain PWM modulation parameter optimization data; superimpose virtual inductance / resistance compensation on the PWM modulation parameter optimization data to generate adaptive compensation control data; Step S4: control instruction generation is performed on the adaptive compensation control data, and the generated control instruction is input into the preset micro-grid for instruction cooperative control, so as to execute the comprehensive operation control optimization operation of the light storage equipment facing the micro-grid.

[0029] The application obtains the current and voltage waveform data across the filter inductor of the light storage equipment, and performs frequency domain sweep analysis to accurately obtain the complex impedance characteristics of the filter inductor, and then maps the local saturation starting point and nonlinear section of the magnetic core to realize accurate modeling of the saturation characteristics of the magnetic core. Further, the bidirectional DC bias winding is connected in parallel to the filter inductor, and the saturation characteristics of the magnetic core are dynamically adjusted to realize controllable adjustment of the working point of the magnetic core. The filter inductor is divided into main and auxiliary groups, and the intelligent parallel / series switching under different operating conditions is realized by means of solid-state relays, so that the equivalent impedance of the filter can be flexibly configured under complex conditions, and the electromagnetic compatibility and stability of the system are enhanced. On this basis, the PWM carrier slope and dead zone are adaptively adjusted in combination with the magnetic core working point adjustment data and the filter impedance configuration data, which not only optimizes the PWM modulation strategy, but also realizes effective suppression of voltage and current distortion through virtual inductance and virtual resistance compensation superposition. The finally generated adaptive compensation control instruction can work cooperatively with other control units of the micro-grid, and significantly improves the operation stability, response flexibility and overall energy efficiency of the light storage equipment in the micro-grid. Therefore, the application accurately identifies the nonlinear characteristics of the filter inductor, dynamically adjusts the working point of the magnetic core, adaptively optimizes the PWM modulation parameters, and cooperates with the micro-grid to solve the problems of unstable filter performance, fixed modulation parameters and insufficient system cooperation of traditional light storage equipment.

[0030] In the embodiment of the application, as shown in the reference Figure 1 The method comprises the following steps: Step S1: obtaining current and voltage waveform data across the filter inductor of the light storage equipment; performing frequency domain sweep on the current and voltage waveform data to generate filter inductor complex impedance data; and mapping the local saturation starting point and nonlinear section of the magnetic core based on the filter inductor complex impedance data to generate saturation characteristic data; Step S2: connecting a bidirectional DC bias winding in parallel to the filter inductor, and adjusting the bias current of the saturation characteristic data to generate magnetic core working point adjustment data; splitting the filter into main and auxiliary groups, and dynamically switching the groups in parallel / series under different operating conditions by means of solid-state relays, and configuring the filter impedance based on the saturation characteristic data to generate filter impedance configuration data; Step S3: Adjusting the PWM carrier slope and dead zone of the optical storage device through the magnetic core operating point adjustment data and the filter impedance configuration data to obtain the PWM modulation parameter optimization data; superimposing the virtual inductance / resistance compensation on the PWM modulation parameter optimization data to generate adaptive compensation control data;

[0031] Step S4: Generating control instructions for the adaptive compensation control data, and inputting the generated control instructions into the preset micro-grid for instruction cooperative control to execute the comprehensive operation control optimization of the optical storage device for the micro-grid.

[0032] In the embodiment of the present application, the instantaneous voltage waveform and current waveform data of the filter inductor are collected by high-precision data acquisition devices (such as isolated voltage probes and shunt current detectors). The data sampling frequency is not less than 500 kHz to ensure that high-frequency harmonic components are not lost. The obtained data is sent to an embedded digital signal processing unit, and short-time Fourier transform (STFT) is used for frequency domain sweep processing to calculate the impedance amplitude and phase at different frequency points and generate filter inductor complex impedance data. In combination with the B-H curve parameter table or experimental measurement curve provided by the magnetic core material, the phase delay mutation region and amplitude anomaly point in the complex impedance data are compared to mark the initial point of the magnetic core entering the saturation region, that is, the local saturation starting point, and in combination with the nonlinear response segment, the nonlinear segment range is demarcated. Finally, the saturation characteristic data containing the starting point, response curvature change and nonlinear segment threshold value are output. A bidirectional DC bias winding is configured in the parallel channel of the filter inductor, and a current source control mode is used to inject a controllable DC bias current into the bias winding. The DC current is adjusted in real time by the DSP control module according to the magnetic flux density control threshold value of the magnetic core before entering the saturation region in the saturation characteristic data, and is generally controlled between 60%-85% of the rated magnetic flux density of the magnetic core, to generate the magnetic core operating point adjustment data for magnetic core bias control. At the same time, the filter topology structure is split from the traditional fixed structure into two paths of main filter group and auxiliary filter group, and solid-state relays (SSRs) are connected in series / parallel between the paths. The control unit dynamically switches the main and auxiliary filter group combination mode according to the light storage equipment working condition (such as charge and discharge switching and current fluctuation frequency) to respond to the impedance demand under different working modes. Through analysis of the impedance change characteristics in the saturation characteristic data, adaptive impedance distribution configuration is performed to generate filter impedance configuration data. The magnetic core operating point adjustment data and the filter impedance configuration data are input into the PWM control module. The control module dynamically adjusts the PWM carrier slope (unit slope range is 100V / µs to 500V / µs) and dead time (adjustment range is 0.1µs to 1.5µs) according to the current waveform and the current waveform, to form adaptive PWM parameter combination for different magnetic core responses and filter path impedance states, and output is PWM modulation parameter optimization data. On this basis, a virtual inductance / resistance compensation algorithm is introduced by using digital signal simulation. The required additional inductance or resistance is calculated by the current sampling value, the inductive response and energy consumption characteristics of the simulator are simulated, the modulation waveform is adjusted in real time, the dynamic compensation of the system response is realized, and the adaptive compensation control data is output. The adaptive compensation control data is input into the control strategy generation unit, and the corresponding PWM control logic instructions and equipment operation mode switching instructions are generated in combination with the microgrid operation state (such as light storage conversion mode, grid-connected / off-grid state, voltage load demand, etc.), to build a complete control instruction set. The control instruction set is input into the microgrid centralized control platform through a communication interface (such as CAN bus or RS485).The platform synchronously distributes instructions to the photovoltaic side, the energy storage side and the inverter unit in real time according to the topology structure and the demand response strategy, realizes fine collaborative control inside the photovoltaic and energy storage system, and finally completes comprehensive operation control optimization of the photovoltaic and energy storage equipment for the operation demand of the microgrid, so that the equipment response is more intelligent, coordinated and efficient.

[0033] As an example of the present application, reference is made to Fig. 1, which shows a schematic diagram of a photovoltaic and energy storage system according to the present application. Figure 2 In this example, the step S1 includes: Step S11: synchronously collecting transient waveforms at the input side and the output side of the filter inductor by using a current probe and a differential voltage probe, so as to obtain current and voltage waveform data at both ends of the filter inductor of the photovoltaic and energy storage equipment; Step S12: performing fast Fourier transform on the current and voltage waveform data to generate frequency domain sweep response spectrum data; calculating the amplitude and phase ratio according to the frequency domain sweep response spectrum data to generate filter inductor complex impedance data; Step S13: performing admittance phasor inverse transform analysis on the filter inductor complex impedance data to extract equivalent magnetic guide nonlinear inflection points and generate magnetic core local saturation starting point data; based on the magnetic core local saturation starting point data, performing section trend slope segmentation on the filter inductor complex impedance data to identify nonlinear gain intervals and generate magnetic core nonlinear section data; Step S14: fusing the magnetic core local saturation starting point data and the magnetic core nonlinear section data to generate saturation characteristic data.

[0034] In the embodiment of the present application, under the operating state of the optical storage system, high-bandwidth current probes (such as Tektronix TCPA300+TCP312A, with a bandwidth of not less than 100 MHz) and differential voltage probes (such as P5205A, with a bandwidth of not less than 100 MHz) are synchronously connected to the input and output ends of the filter inductor to collect transient current and voltage waveforms. The probes need to be connected to a high-speed digital oscilloscope, and the sampling rate is set to be above 1 MS / s, and the sampling window length covers at least one complete PWM period (for example, for 10 kHz PWM, a window of 100 μs or above is set). The waveform data is aligned in the time domain through a system timestamp synchronization mechanism, and finally the current waveform data and voltage waveform data sequences are output. The collected current and voltage time domain data are sent to the main control platform or FPGA signal processing unit, and fast Fourier transform (FFT) algorithm is used for frequency domain conversion. The FFT operation should cover from the base frequency to at least 20th harmonic component (for example, when the base frequency is 10 kHz, the analysis bandwidth needs to be expanded to above 200 kHz), so as to comprehensively extract the impedance behavior under the influence of harmonics. Based on the transformation result, the voltage amplitude and phase and the current amplitude and phase data at each frequency point are extracted, and the complex voltage and current at the same frequency point are subjected to complex ratio operation, and the frequency domain complex impedance data of the filter inductor is calculated, which is in ohm and includes real part (equivalent resistance) and imaginary part (equivalent reactance). The generated complex impedance data is subjected to admittance phasor inverse transformation analysis. The specific operation is as follows: the impedance is taken as the inverse to obtain the admittance, then the admittance real part (conductance) and imaginary part (susceptance) are expanded along the frequency axis, the curve of the admittance phasor changing with the frequency is fitted, and the local slope change is analyzed. In the admittance phasor diagram, the first mutation point position where the magnetic core changes from linear state to nonlinear state is identified. The mutation point is defined as the starting frequency area where the magnetic core enters local saturation, the corresponding impedance characteristic change rate increases or an inflection point appears, the frequency and the corresponding voltage and current amplitude data of the point are extracted, and the local saturation starting point data of the magnetic core are generated. On this basis, the complex impedance curve is segmented and divided, and the nonlinear response segment is determined according to the range where the local first-order derivative slope significantly rises. The response segment shows abnormal growth of the impedance real part or imaginary part under the increase of frequency or waveform change, and the output is the nonlinear segment data of the magnetic core. The local saturation starting point data and the nonlinear segment data of the magnetic core are fused to construct the magnetic core saturation characteristic structure, and the data content includes: voltage amplitude corresponding to the starting frequency point (unit: volt), current amplitude corresponding to the starting frequency point (unit: ampere), nonlinear segment frequency range (unit: Hz), and nonlinear segment slope change range (unit: Ω / Hz). The structure is the saturation characteristic data, which is used as the parameter input basis for subsequent magnetic core working point adjustment and filter configuration module. The saturation characteristic data should be stored in the non-volatile memory (such as EEPROM) in the controller, so as to be conveniently called and associated with dynamic control logic.

[0035] Preferably, the admittance phasor inverse transformation analysis on the filtered inductance complex impedance data in step S13 includes: Conjugate normalization is performed on the filtered inductance complex impedance data to generate unit amplitude admittance spectrum data; According to the unit amplitude admittance spectrum data, a virtual part admittance curve is extracted to generate an inductive admittance change trajectory; Local extreme point slope change rate analysis is performed on the inductive admittance change trajectory to generate a first-order slope derivative curve; The slope mutation points in the first-order slope derivative curve are identified to extract the turning frequency points of the magnetic permeance sudden drop, and magnetic permeance change mutation point data are generated; The phase angle change rate of the mutation point frequency is combined to verify the phase lag characteristic, and the key frequency point meeting the magnetic core saturation response is output to generate the local saturation starting point data of the magnetic core.

[0036] In the embodiment of the application, conjugate normalization operation is performed on the filtered inductance complex impedance data (denoted as Z(f)) obtained in step S12. , wherein is the frequency. The purpose of this operation is to eliminate the influence of the energy amplitude difference of different frequency bands and focus the analysis on the relative admittance characteristic change trend. The normalization step is to calculate the admittance , wherein is the conductance, is the admittance (i.e. the inductive admittance component). The admittance modulus of each frequency point is normalized by the maximum value to obtain the unit amplitude admittance spectrum: , and the generated is the unit amplitude admittance spectrum data. Secondly, the virtual part of the unit amplitude admittance spectrum data is extracted, i.e. the inductive admittance change trajectory with frequency, to obtain the inductive admittance change trajectory. This trajectory reflects the inductive reactance change behavior of the magnetic core in response to high frequency, and is the basic data for identifying the saturation trend. Then, the first-order derivative of the virtual part trajectory is calculated using the central difference method (or higher-order numerical difference method) to construct the first-order slope derivative curve: , and the derivative curve is smoothed (a Savitzky-Golay filter can be used, and the window size is recommended to be 5-7 points) to suppress high-frequency noise interference. In the smoothed first-order derivative curve, local extreme points (i.e. derivative slope mutation positions) are identified. These extreme points usually reflect the sudden turning behavior of the response change of the magnetic core material. In combination with the original frequency coordinate, the frequency points corresponding to these positions are extracted as the magnetic permeance change mutation point data. Subsequently, the original complex impedance phase angle corresponding to the mutation point data is extracted, and the phase change rate If the phase angle hysteresis rate at the mutation point increases significantly (for example, more than 3 times the average value of the previous interval), it indicates that the frequency point corresponds to an intensified magnetic response delay, which represents the entry of the magnetic core into the nonlinear saturation region. Finally, the frequency point that meets the following two conditions at the same time is marked as the starting point of the local saturation of the magnetic core: the derivative curve of the inductive admittance slope appears a local extreme mutation; the phase angle hysteresis rate is higher than the set hysteresis threshold (the empirical threshold is recommended to be >0.5 rad / kHz). The additional data such as complex impedance amplitude, phase angle, admittance, etc. corresponding to the frequency point meeting the above conditions are packaged into a structured output and marked as the local saturation starting point data of the magnetic core, which is used for the subsequent control system to judge the critical range of the working point of the magnetic core, and supports the PWM parameter optimization and filter dynamic configuration tasks.

[0037] Preferably, step S2 comprises the following steps: Step S21: A bidirectional DC bias winding is connected in parallel to the filter inductor, and an initial bias value of an adjustable constant current source is set based on the local saturation starting point data of the magnetic core to generate an initial excitation parameter of the bias winding; Step S22: Bias current adjustment response curve analysis is performed on the saturation characteristic data to generate a gain coefficient curve of the bias current to the magnetic permeance change; and the bias winding control instruction is dynamically set according to the gain coefficient curve of the bias current to the magnetic permeance change to generate the magnetic core working point adjustment data; Step S23: The filter inductor is split into a main filter branch and an auxiliary filter branch, and independent sampling channels are configured to measure the voltages and currents of the two branches respectively to generate filter branch working state monitoring data; Step S24: The working condition load data of the main filter branch and the auxiliary filter branch are obtained; and the load disturbance mode recognition is performed on the filter branch working state monitoring data based on the working condition load data to generate dynamic switching threshold condition data; Step S25: The solid state relay control logic is configured, the series-parallel dynamic switching of the main and auxiliary filter branches is performed according to the dynamic switching threshold condition data and based on the solid state relay control logic to generate filter topology switching state data; and the filter impedance configuration data under the current topology is generated by performing filter impedance mapping analysis on the topology switching state data in combination with the saturation characteristic data.

[0038] In the embodiment of the application, a set of bidirectional DC bias windings is integrated and accessed beside the filter inductor winding structure, the winding wire diameter is selected as 0.5 mm, the number of turns of the coil is set as 100 turns, and the magnetic coupling structure ensures that the magnetic flux of the winding interacts with the main winding significantly. One end of the bias winding is connected to an adjustable constant current source module, the initial bias current setting range is ±500 mA, and the adjustment resolution is 10 mA. The constant current source module receives the bias control parameters through a digital signal control channel. The frequency of the local saturation starting point of the magnetic core output by step S13 is used to calculate the magnetic flux density value required at the saturation permeability critical point, which is converted into an initial excitation current value, to generate the initial excitation parameters of the bias winding, and to serve as the constant current output setting value under the system initialization control condition. The constant current source bias current is scanned step by step (each step increment is 50 mA), the complex impedance response of the filter inductor is collected under each bias current, and the frequency response data is recorded by a frequency domain sweep instrument. The change of the permeability curve under each bias current is fitted to construct a “bias current-equivalent permeability” corresponding relationship curve, that is, a gain coefficient curve. The section with a gain coefficient change slope greater than 0.2 μH / mA is identified as a sensitive area of permeability adjustment. In actual operation, according to the curve, the control bias current command value is updated in real time according to the feedback permeability change trend of the system, a dynamic control function is set and output to the constant current source, and the dynamic adaptive adjustment of the magnetic core operating point is realized. The final output bias control current command is the magnetic core operating point adjustment data. The original single filter inductor is split into a main filter branch and an auxiliary filter branch, each branch is connected to a set of independent current / voltage sampling modules, the sampling modules use 16-bit ADC channels, and the sampling frequency is 100 kHz. The AC instantaneous current and voltage waveforms of the two branches are recorded respectively, the equivalent impedance, phase angle, amplitude change and other operating state characteristics are calculated by the processing unit, and the working state monitoring data of the two branches are generated, including impedance modulus, apparent power, harmonic content and other multi-dimensional indicators. The power data of the current operating load, the output frequency range and the inverter switching frequency change trend are obtained from the system control unit as the working condition load data. These data are jointly analyzed with the main and auxiliary filter branch operating state data, and a disturbance identification method based on decision tree logic is used to classify and train the data characteristics (such as identifying output power sudden change, load step switching, harmonic mutation, etc.). After identifying the corresponding working condition characteristic changes, these key turning points are extracted as dynamic switching threshold condition data, including: current slope greater than 5 A / ms, voltage mutation greater than 10 V / ms, or apparent power fluctuation greater than 20%. According to the above threshold condition data, the control logic table of the solid state relay is set. For example, when the load mutation event is triggered (satisfying any of the above conditions), the system triggers the auxiliary filter branch to be switched from parallel to series. Bidirectional solid state relay modules are used, each branch is configured with an independent control line, and the response time is less than 1 ms. The relay is managed by an FPGA controller, and the state transition is executed through a pre-set state transition diagram. The relay state record generates filter topology switching state data.Meanwhile, the switching state data is input into a filter parameter mapping module, and the equivalent impedance of the filter under the current topology is calculated in combination with the current saturation characteristic data of the magnetic core, and filter impedance configuration data is output for subsequent reading and optimal adjustment by the PWM controller.

[0039] Preferably, the load disturbance mode recognition of the filter branch working state monitoring data based on the working condition load data in step S24 comprises: Periodic stability discrimination is performed on the working condition load data to generate load periodic drift characteristic data; A power fluctuation threshold of the filter branch working state monitoring data is extracted to obtain power fluctuation mutation index data; Joint mode classification is performed on the load periodic drift characteristic data and the power fluctuation mutation index data to generate disturbance mode classification label data, wherein the joint mode classification includes high-frequency impact type, intermittent oscillation type and continuous jitter type; Mapping rule setting is performed on the disturbance mode classification label data to generate disturbance response priority data; An event triggering threshold is configured based on the disturbance response priority data to generate dynamic switching threshold condition data.

[0040] In the embodiment of the present application, the load current data and output active power data in the actual operation of the light storage device are obtained through the periodic stability discrimination operation, and the analysis cycle window is set to 500 milliseconds. The power data is analyzed by using the fast Fourier transform (FFT) algorithm to identify the main frequency component and extract the periodic distribution characteristics. In three consecutive cycles, if the main frequency offset amplitude exceeds 0.8 Hz or the cycle fluctuation amplitude is greater than 10%, it is determined that the cycle drift phenomenon occurs. Based on the time window, the offset strength, frequency fluctuation interval and change trend are recorded to form the load cycle drift characteristic data. Next, the current amplitude, voltage amplitude and power instantaneous value in the filter branch working state monitoring data are extracted, the sliding mean value is processed, the active power change amplitude is calculated every 50 ms interval, and the mutation threshold is set to 15% of the rated output power. If the power fluctuation rises or falls by more than the threshold within 100 ms, the point is recorded as the "mutation time" and the mutation direction and strength are marked to form the power fluctuation mutation index data. If the mutation strength exceeds 25% of the rated power, a "high-intensity mutation" label is added. The above load cycle drift characteristic data and power fluctuation mutation index data are used as input features, and a joint classification model based on support vector machine (SVM) is used to train and identify the pattern type of the disturbance. The classification output label includes: high-frequency impact type (such as high-frequency burst load caused by frequent switching of the light storage system); intermittent oscillation type (such as intermittent load fluctuation causing frequent switching of the filter task); and continuous jitter type (such as long-term instability of the load side working condition causing continuous low-amplitude disturbance). The model output result generates corresponding disturbance mode classification label data. For different disturbance modes, the system defines a disturbance response priority mapping rule in advance: the high-frequency impact type disturbance has the highest priority, followed by the continuous jitter type, and finally the intermittent oscillation type. According to this mapping logic, a numerical priority identifier is assigned to each label (such as 1 for the highest and 3 for the lowest), and the output is the disturbance response priority data. Finally, according to the disturbance response priority data, the corresponding event trigger threshold condition is set. For example: when the high-frequency impact type disturbance occurs, the threshold is set to a current mutation rate of more than 6A / ms; the continuous jitter type disturbance corresponds to a voltage fluctuation amplitude of more than 12V and lasts for more than 1s; and the intermittent oscillation type disturbance is set to a cycle variation of more than 5% and a power mutation amplitude of between 10% and 15%. According to the above settings, the final dynamic switching threshold condition data is output and used as a reference for the dynamic topology switching trigger of the solid-state relay control logic. Through the above multi-level recognition and classification mechanism, the filter system can respond quickly, switch reasonably and operate stably when facing different disturbance scenarios.

[0041] Preferably, the joint mode classification of the load cycle drift characteristic data and the power fluctuation mutation index data includes: performing sliding cross-spectrum focus analysis on the load cycle drift characteristic data to generate frequency disturbance coupling index data; extracting disturbance edge acceleration features of the power fluctuation mutation index data to obtain mutation response intensity data; constructing a double-factor disturbance feature matrix according to the frequency disturbance coupling index data and the mutation response intensity data, performing non-convex fuzzy clustering processing to generate a disturbance distribution label map; calculating the regional density gradient projection of the disturbance distribution label map, and performing projection shape discrimination on the regional density gradient projection to obtain disturbance mode classification label data, wherein the projection shape discrimination is specifically: when the projection shape is a nuclear-inclined impact type, it is marked as a high-frequency impact type; when the projection shape is a scattered-loop type, it is marked as an intermittent oscillation type; and when the projection shape is an expansion-co-seismic type, it is marked as a sustained jitter type.

[0042] In the embodiment of the application, for the load cycle drift characteristic data, the frequency drift trajectory in each cycle is extracted and converted into a time-frequency two-dimensional matrix. A sliding cross-spectrum focus processing is performed on the two-dimensional frequency sequence with a time window of 250 ms and a step of 50 ms. This processing quantifies the coupling degree of multiple frequency components in the same load cycle data based on wavelet packet decomposition combined with cross power spectral density function, and generates frequency disturbance coupling index data. The index reflects the mutation degree of frequency coupling change in unit time, and the value range is between 0.1 and 2.5, and the higher the value represents the stronger the disturbance focus effect. Then, the fluctuation edge in the power fluctuation mutation index data is extracted, the power waveform within 50 ms before and after the mutation point is fitted by using cubic spline interpolation, the second derivative of the fitting curve at the mutation edge point is calculated, and the disturbance edge acceleration feature is obtained. The feature is used to measure the strength of the mutation response, and the numerical unit is watts per millisecond square (W / ms²). The extracted feature is normalized to form mutation response intensity data. Subsequently, a two-dimensional double-factor disturbance feature matrix is constructed, and each disturbance trajectory corresponds to a feature point composed of frequency disturbance coupling index and mutation response intensity. A non-convex fuzzy clustering algorithm based on membership decay model is used for processing on the feature matrix, the number of clustering clusters is set to 3, and the iteration convergence threshold is set to After the clustering processing, a perturbation distribution label image is output, wherein each perturbation sample is labeled as the same perturbation type according to the clustering result. The generated perturbation distribution label image is further subjected to regional density gradient projection analysis. The specific method is as follows: in the two-dimensional perturbation space, kernel density estimation is performed along the diagonal direction, and an equal-density gradient line is constructed; then, one-dimensional projection is performed on the equal-density line, and the density gradient is projected onto the principal component axis direction to form a regional density gradient projection image. Subsequently, morphological discrimination is performed on the projection image, and the specific logic is as follows: when a sharp peak value is observed in the density center area, the left and right sides show a rapid downward trend, and the overall morphology is a single core that tilts and extends in the oblique diagonal direction, it is judged to be a polykaryon-oblique shock type, and the corresponding perturbation type is a high-frequency impact type; when the projection morphology appears multiple discontinuous wave peak and trough structures, showing the characteristics of periodic dispersion and return aggregation, it is judged to be a dispersion-return ring type, and the corresponding perturbation type is an intermittent oscillation type; when the projection morphology shows a slow stretching, fuzzy boundary, and edge density oscillation increasing trend, it is judged to be an expansion-common shock type, and the corresponding perturbation type is a sustained jitter type. According to the above discrimination result, perturbation mode classification label data is output, wherein the perturbation mode type of each sample is marked, and the classification label is used as key reference information for subsequent dynamic topology switching logic and control priority setting.

[0043] Preferably, the step S3 of adjusting the PWM carrier slope and dead zone of the optical storage device through the magnetic core working point adjustment data and the filter impedance configuration data comprises: identifying a nonlinear saturation interval of the magnetic core working point adjustment data; performing frequency response matching analysis on the filter impedance configuration data to generate filter dynamic impedance characteristic data; generating a PWM carrier slope adaptive adjustment rule based on the nonlinear saturation interval and the filter dynamic impedance characteristic data to obtain carrier slope adjustment strategy data; correcting the dead zone based on the carrier slope adjustment strategy data to generate dead zone time correction parameter data; jointly mapping the carrier slope adjustment strategy data and the dead zone time correction parameter data to generate PWM modulation parameter optimization data.

[0044] In the embodiment of the application, the excitation current-magnetic flux density curve data in the magnetic core working point adjustment data is called, and a five-point sliding differential method is used to calculate the first derivative to obtain the magnetic permeability change trend. The saturation judgment threshold is set to be that the magnetic permeability decrease rate is greater than 0.1, and the magnetic core is in the nonlinear saturation state. interval, when more than 3 consecutive data points in the interval satisfy the threshold condition, it is marked as a nonlinear saturation interval. The output data format is the start and end time of the interval (unit: ms) and the corresponding current amplitude range (unit: A), which constitutes the nonlinear saturation response interval data. Obtain the filter inductance and capacitance configuration parameters under the current topology, and combine the actual PWM modulation frequency to analyze the filter impedance in the frequency domain, with a frequency range of 1 kHz-100 kHz and a step of 100 Hz. Calculate the equivalent impedance and record the impedance modulus and phase angle at each frequency point to form a frequency response data table. Extract the envelope characteristics of the response curve through Hilbert transform and overlap analysis with the working frequency of the nonlinear saturation interval to obtain the dynamic impedance characteristic data of the filter in the key frequency band. According to the intersection area of the saturation interval and the impedance frequency response, the carrier frequency is divided into low response area (<5 kHz), medium response area (5-20 kHz) and high response area (>20 kHz), and different carrier slope ranges are set for each area. The set slope adjustment rules are as follows: the slope in the low response area is set to 0.2V / μs; the slope in the medium response area is set to 0.5V / μs; and the slope in the high frequency response area is set to 1.0V / μs. According to the section where the magnetic core saturation starting frequency point is located, the corresponding slope gear is matched, and the carrier frequency modulation step is limited within ±0.5 kHz to generate carrier slope adjustment strategy data for subsequent PWM control module scheduling. On the basis of the slope adjustment strategy, the switching delay characteristics of the main power switching device (such as MOSFET rise / fall delay, generally set to 20ns-50ns) are obtained, and the dead time compensation demand is calculated combined with the current rise slope. The dead time Dt is set as follows: Dt=max{Ton_delay, Toff_delay}+Δt_margin, where Δt_margin is fixed at 10ns safety margin, and the finally generated dead time correction parameter range is controlled between 150ns-400ns, ensuring to avoid conduction overlap. A parameter optimization mapping table is established, and a binary table matching method is used to form a combination index of the slope level (such as low / medium / high) and the corresponding dead time. The output PWM modulation parameters include: modulation frequency (unit kHz), rise / fall slope (unit V / μs), dead time (unit ns) and corresponding enable state word. All parameters are packaged as structured configuration data, which are loaded dynamically by a digital signal processor (DSP) to form the final PWM modulation parameter optimization data, realizing high adaptability PWM modulation control of the light storage equipment under dynamic magnetic core state and filter conditions.

[0045] Preferably, the dead time correction of the light storage equipment based on the carrier slope adjustment strategy data includes: modeling the switching delay caused by the carrier slope adjustment strategy data to generate switching delay error modeling data; The dead time correction amount data is generated based on the switch delay error model data. The dead time width of the optical storage device is corrected by using the dead time correction amount data, and the corrected dead time width is feedback adjusted to generate dead time correction parameter data.

[0046] In the embodiment of the application, the turn-on delay (Ton_delay) and turn-off delay (Toff_delay) of the main power switch device (such as SiC MOSFET or IGBT) used by the optical storage device under different slope conditions are modeled by discrete points according to the generated carrier slope adjustment strategy data (where the slope is generally controlled at 0.2-1.0V / μs). A high-speed sampling oscilloscope (bandwidth not less than 100MHz, sampling rate greater than 500MSa / s) is used to collect the driving waveform during the test, and the actual time difference between the slope input signal and the device response is recorded. At least 50 measurements are made under each slope condition, the mean and standard deviation are calculated, and the turn-on and turn-off delay curves are fitted with the slope as the horizontal axis and the delay as the vertical axis, using a second-order polynomial fitting function: ; ; Wherein S is the carrier slope (unit: V / μs), 、 、 is the turn-on delay coefficient, 、 、 is the turn-off delay coefficient. The switch delay error modeling data is finally formed and stored in the form of a table or a function, which is used for subsequent dead time correction amount calculation. According to the carrier slope value currently selected in the control system (such as 0.6V / μs), the switch delay error model is called to calculate the corresponding . In the default configuration of the system, the initial dead time is set to 300ns. The Ton_delay and Toff_delay in simulation or actual measurement are compared with . If , it is determined that there is a risk of overlapping conduction, and the dead time correction amount needs to be calculated, and the calculation formula is as follows: ; Wherein is a fixed safety margin time, which is 10ns. If is positive, it means that the dead time needs to be expanded; if it is negative, the dead time can be shortened under the premise of ensuring safety to reduce power loss. The final output dead time correction amount data is in ns, and the correction accuracy is controlled within ±5ns. According to the minimum dead time step size supported by the current PWM controller (such as 10ns), the dead time is set to And write the value to the PWM dead zone configuration register inside the DSP or FPGA. In actual operation, the following two types of feedback signals are monitored: using an ADC with a sampling accuracy of no less than 200ns to sample the bridge arm output voltage, detecting the overlap or air window between the two switch switching, and evaluating the error amplitude. Record the bus current waveform at the switching moment, extract the peak strength through the Savitzky-Golay filter, and use it to identify whether a short circuit or overshoot is caused by insufficient dead zone setting. If the above feedback indicators deviate from the set threshold (for example, the voltage mismatch exceeds ±10ns, or the current peak exceeds 20% of the rated value), adjust the dead zone time ±10ns according to the feedback direction, and rewrite the PWM control register. After finally converging to the optimal dead zone configuration, output the dead zone time correction parameter data, including the final setting value (such as 350ns), the adjustment history record, the corresponding carrier slope parameter, etc., for subsequent strategy fusion and adaptive control optimization.

[0047] As an example of the present application, reference is made to Fig. 1, which shows a flowchart of the method according to the present application. In this example, the step S4 comprises: Figure 3 Step S41: dynamically instructing encoding of adaptive compensation control data to generate multi-dimensional control instruction set data; Step S42: identifying instruction redundancy conflicts of the multi-dimensional control instruction set data, and eliminating redundant and mutually exclusive instructions to generate control instruction deduplication optimization data; Step S43: inputting the control instruction deduplication optimization data into the microgrid power routing module to perform regional-level load response mapping, and generating microgrid instruction matching mapping data; Step S44: calculating the control timing consistency of the microgrid instruction matching mapping data to obtain collaborative control scheduling sequence data; inputting the collaborative control scheduling sequence data into the preset microgrid control interface for instruction distribution and execution to generate operation feedback response data, so as to perform the microgrid-oriented photovoltaic storage equipment comprehensive operation control optimization task.

[0048] ​In the embodiment of the application, by adopting an embedded coding and decoding framework, a command coding module is called in a local DSP or FPGA system of the controller to convert adaptive compensation control data (including PWM slope parameters, dead zone correction parameters, inductance virtual compensation values, resistance dynamic adjustment values, etc.) into standardized control fields. The specific field definitions are as follows: CMD_TYPE (1 byte): control command type, such as 01 representing modulation control and 02 representing current control; PARAM_ID (1 byte): parameter identification, for example, A1 representing a carrier slope; VALUE (4 bytes): actual parameter value, encoded using an IEEE754 floating point number; PRIORITY (1 byte): priority level marker, with a value range of 0-15; TIME_TAG (2 bytes): relative timing identification of command issuance, with a unit of ms; the above structure is combined into a command unit, organized into a command set according to a device structure topology (inverter module, battery module, filter module), and forms a multi-dimensional control command set data, wherein the dimensions include module dimension, function dimension, timing dimension and parameter dimension. All command data is checked by CRC16 and buffered in a high-priority interrupt queue. For the multi-dimensional control command set output in step S41, a preset conflict identification rule library is loaded, and the following two types of checks are performed: instructions with the same CMD_TYPE and PARAM_ID but different TIME_TAG are traversed, the latest one is retained, and the rest are removed; for parameter pairs that cannot be simultaneously effective (such as carrier rising slope adjustment and carrier switching frequency dynamic jitter), a mutual exclusion table is consulted, and the one with higher priority or more urgent response time requirement is retained. During processing, a command hash index mechanism is called to achieve O(1) level fast matching and comparison, and finally the control command is output after being arranged and optimized. Each record is attached with a redundancy processing marker and a conflict handling description for record tracing. Taking regional load response as the core, the mapping logic of control commands to micro-grid nodes is realized through a micro-grid power routing module. First, the node load distribution map and device scheduling capability table of the current region are imported, and the parameters involved in the control command (such as filter compensation resistance change value or battery charge and discharge limit value) are matched with the actual power distribution between nodes. A spatial distribution matrix LxM (where L is the number of nodes and M is the number of control types) is applied to each instruction to perform the following matching operations: according to CMD_TYPE, the power module mapping matrix is queried; according to the current load coefficient of the device, it is determined whether the control instruction can be received (such as inverter current margin > 10%); if the mapping is successful, a matching record is generated, including target node ID, scheduling weight, delay compensation item, etc.; finally, the micro-grid instruction matching mapping data is output, and the control path, power adjustment direction and node acceptance state are marked in matrix form. The execution sequence relationship among multiple control instructions is analyzed for timing consistency.According to the response delay (τ) of each node and the expected execution time (T_req) of the instruction, the following timing scheduling strategy is adopted: if |T_req-τ|>maximum allowed deviation ΔT (such as 50 ms), the scheduling time is corrected; a scheduling directed graph is constructed according to the dependency graph, and the instructions are topologically sorted; the sorting results are written into the scheduling sequence table to form the collaborative control scheduling sequence data; then the sequence data is sent to each control terminal through the micro-grid communication interface (such as Modbus TCP / IP or IEC 61850 MMS), and the actual instruction is issued and the device response is started. All terminal devices return a status frame after the instruction execution is completed, including an execution success flag, an execution time, feedback parameters and a state exception identification code, which are integrated into operation feedback response data for the dispatching center to review the control results and adjust the next round of scheduling.

[0049] In the present specification, a micro-grid-oriented optical storage device comprehensive operation control system is provided for executing the micro-grid-oriented optical storage device comprehensive operation control method described above, and the micro-grid-oriented optical storage device comprehensive operation control system comprises:

[0050] A data acquisition module is configured to acquire current and voltage waveform data across the filter inductor of the optical storage device; perform frequency domain sweep on the current and voltage waveform data to generate filter inductor complex impedance data; and map the filter inductor complex impedance data to obtain a local saturation starting point of the magnetic core and a nonlinear section, and generate saturation characteristic data.

[0051] A data intervention configuration module is configured to parallel-connect a bidirectional DC bias winding to the filter inductor, and adjust the bias current of the saturation characteristic data to generate magnetic core working point adjustment data; split the filter into a main filter and an auxiliary filter, and dynamically switch the main filter and the auxiliary filter in parallel / series under different working conditions through solid-state relays, and configure the filter impedance of the saturation characteristic data to generate filter impedance configuration data.

[0052] A control optimization module is configured to perform PWM carrier slope and dead zone adaptive adjustment on the optical storage device based on the magnetic core working point adjustment data and the filter impedance configuration data to obtain PWM modulation parameter optimization data; and perform virtual inductance / resistance compensation superposition on the PWM modulation parameter optimization data to generate adaptive compensation control data.

[0053] An instruction distribution module is configured to generate control instructions based on the adaptive compensation control data, and input the generated control instructions into a preset micro-grid for instruction collaborative control to perform micro-grid-oriented optical storage device comprehensive operation control optimization.

[0054] The beneficial effects of the present application are that by setting the data acquisition module, high-precision acquisition of the voltage and current waveforms at both ends of the filter inductor of the optical storage device is realized, and the complex impedance characteristics of the filter inductor are extracted through the frequency domain sweep technology, further identifying the local saturation starting point and nonlinear section of the magnetic core, effectively constructing a high-resolution saturation characteristic model reflecting the dynamic saturation behavior of the magnetic core, providing accurate physical support for subsequent control strategies; the data intervention configuration module set introduces an adjustable DC bias winding and a magnetic core working point adjustment mechanism, enhances the controllability and linear interval of the magnetic core, and supports the dynamic reconstruction and parallel-serial switching of the main and auxiliary branches of the filter inductor under multiple working conditions, combines the saturation characteristic data to intelligently configure the filter impedance, significantly improves the equivalent impedance adaptation ability and response sensitivity of the filter under complex operating conditions; the control optimization module set combines the magnetic core working point adjustment data and the filter impedance configuration data, realizes the dynamic adaptive adjustment of the PWM carrier slope and dead zone, and further superimposes the virtual inductance / resistance compensation, realizes the fine-grained optimization of the control parameters, effectively reduces the switching loss, harmonic distortion and system response lag, improves the real-time and stability of the modulation strategy; the instruction distribution module supports intelligent coding, conflict avoidance and micro-grid scene matching of adaptive compensation control data, and further realizes the cooperative distribution and execution of the control instruction closed-loop feedback, significantly improves the control coordination, operation efficiency and response intelligence level of the optical storage device in the micro-grid. Therefore, by accurately identifying the nonlinear characteristics of the filter inductor, dynamically adjusting the magnetic core working point, adaptively optimizing the PWM modulation parameters and micro-grid cooperative control, the problems of unstable filter performance, fixed modulation parameters and insufficient system cooperation of traditional optical storage devices are solved.

[0055] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims rather than the above description, and it is therefore intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file.

[0056] The above description is only a specific implementation of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for integrated operation and control of photovoltaic and energy storage devices for microgrids, characterized in that, Includes the following steps: Step S1: Obtain the current and voltage waveform data across the filter inductor of the photovoltaic energy storage device; perform frequency domain sweep on the current and voltage waveform data to generate complex impedance data of the filter inductor; based on the complex impedance data of the filter inductor, map the local saturation start point and nonlinear segment of the magnetic core to generate saturation characteristic data; Step S2: Connect a bidirectional DC bias winding in parallel next to the filter inductor, and adjust the bias current based on the saturation characteristic data to generate core operating point adjustment data; split the filter into two groups, main and auxiliary, and dynamically switch them in parallel / series under different operating conditions using solid-state relays, and configure the filter impedance based on the saturation characteristic data to generate filter impedance configuration data. Step S3: Adaptively adjust the PWM carrier slope and dead zone of the photovoltaic energy storage device using the magnetic core operating point adjustment data and filter impedance configuration data to obtain optimized PWM modulation parameter data; Virtual inductance / resistance compensation is superimposed on the PWM modulation parameter optimization data to generate adaptive compensation control data; Step S4: Generate control commands from the adaptive compensation control data, and input the generated control commands into the preset microgrid for command-coordinated control, so as to perform integrated operation control optimization of photovoltaic and energy storage equipment for microgrid.

2. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Use a current probe and a differential voltage probe to synchronously acquire transient waveforms on the input and output sides of the filter inductor to obtain current and voltage waveform data across the filter inductor of the photovoltaic energy storage device. Step S12: Perform a fast Fourier transform on the current and voltage waveform data to generate frequency domain swept frequency response spectrum data; calculate the amplitude-phase ratio based on the frequency domain swept frequency response spectrum data to generate the complex impedance data of the filter inductor; Step S13: Perform inverse admittance phasor transformation analysis on the complex impedance data of the filter inductor, extract the equivalent permeability nonlinear inflection point, and generate the starting point data of local saturation of the magnetic core; based on the starting point data of local saturation of the magnetic core, perform segment trend slope segmentation on the complex impedance data of the filter inductor, identify the nonlinear gain interval, and generate the nonlinear segment data of the magnetic core. Step S14: Merge the local saturation start point data of the magnetic core with the nonlinear section data of the magnetic core to generate saturation characteristic data.

3. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 2, characterized in that, In step S13, the admittance phasor inverse transformation analysis is performed on the complex impedance data of the filter inductor to extract the equivalent permeability nonlinear inflection points, including: The complex impedance data of the filter inductor is conjugate normalized to generate unit amplitude admittance spectrum data. The imaginary admittance curve is extracted from the unit amplitude admittance spectrum data to generate the inductive admittance change trajectory; The slope change rate at local extreme points of the inductive admittance change trajectory is analyzed to generate a first-order slope derivative curve. Identify abrupt slope changes in the first-order slope derivative curve, extract the inflection frequency points of magnetic permeability drops, and generate data on abrupt changes in magnetic permeability. By combining the rate of change of complex impedance phase angle corresponding to the abrupt change frequency, the phase lag characteristic is verified, the key frequency points that conform to the core saturation response are output, and the data of the local saturation start point of the core are generated.

4. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Connect a bidirectional DC bias winding next to the filter inductor, and set the initial bias value of the adjustable constant current source based on the local saturation start point data of the magnetic core to generate the initial excitation parameters of the bias winding. Step S22: Analyze the bias current regulation response curve of the saturation characteristic data to generate the gain coefficient curve of the bias current to the change of magnetic permeability; based on the gain coefficient curve of the bias current to the change of magnetic permeability, dynamically set the bias winding control command and generate core operating point adjustment data. Step S23: Split the filter inductor into a main filter branch and an auxiliary filter branch, and configure independent sampling channels to measure the voltage and current of the two branches respectively, generating filter branch operating status monitoring data; Step S24: Obtain the operating load data of the main filter branch and the auxiliary filter branch; based on the operating load data, identify the load disturbance mode of the filter branch operating status monitoring data and generate dynamic switching threshold condition data. Step S25: Configure solid-state relay control logic, perform series-parallel dynamic switching of main and auxiliary filter branches based on dynamic switching threshold condition data and solid-state relay control logic, and generate filter topology switching status data; perform filter impedance mapping analysis on topology switching status data and saturation characteristic data to generate filter impedance configuration data under the current topology.

5. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 4, characterized in that, Step S24, which involves identifying load disturbance patterns in the filter branch operating status monitoring data based on the load data, includes: Perform periodic stability assessment on the load data and generate load period drift characteristic data; Extract the power fluctuation threshold from the filter branch operating status monitoring data to obtain power fluctuation mutation index data; Joint pattern classification is performed on load cycle drift characteristic data and power fluctuation mutation index data to generate disturbance pattern classification label data, which includes high-frequency impact type, intermittent oscillation type and continuous jitter type. Mapping rules are set for the disturbance pattern classification label data to generate disturbance response priority data; Configure event trigger thresholds based on disturbance response priority data, and generate dynamic switching threshold condition data.

6. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 5, characterized in that, Joint pattern classification of load cycle drift characteristic data and power fluctuation abrupt change index data includes: Sliding cross-spectral focusing analysis is performed on load cycle drift characteristic data to generate frequency perturbation coupling index data; The perturbation edge acceleration features of the power fluctuation abrupt change index data are extracted to obtain the abrupt change response intensity data; A two-factor perturbation feature matrix is ​​constructed based on frequency perturbation coupling index data and mutation response intensity data. Non-convex fuzzy clustering is then performed to generate a perturbation distribution label map. The regional density gradient projection of the disturbance distribution label map is calculated, and the projection morphology is determined to obtain disturbance pattern classification label data. Specifically, the projection morphology is determined as follows: when the projection morphology is a cluster-oblique impact type, it is marked as a high-frequency impact type; when the projection morphology is a divergence-loop type, it is marked as an intermittent oscillation type; and when the projection morphology is an expansion-resonance type, it is marked as a continuous shaking type.

7. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 1, characterized in that, Step S3 involves adaptively adjusting the PWM carrier slope and dead zone of the photovoltaic energy storage device using core operating point adjustment data and filter impedance configuration data, including: Identify the nonlinear saturation range of the magnetic core operating point adjustment data; Perform frequency response matching analysis on the filter impedance configuration data to generate dynamic impedance characteristic data of the filter. Based on the nonlinear saturation range, the dynamic impedance characteristic data of the filter are used to generate PWM carrier slope adaptive adjustment rules, and the carrier slope adjustment strategy data is obtained. Based on carrier slope adjustment strategy data, dead zone correction is performed on the optical storage device to generate dead zone time correction parameter data. The carrier slope adjustment strategy data and dead time correction parameter data are jointly mapped to generate PWM modulation parameter optimization data.

8. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 7, characterized in that, Dead-zone correction for photovoltaic storage devices based on carrier slope adjustment strategy data includes: Model the switching delay caused by dead zone on the carrier slope adjustment strategy data to generate switching delay error modeling data; The dead time correction amount is calculated based on the switching delay error model data, and the dead time correction amount data is generated. The dead time width of the optical storage device is corrected using the dead time correction data, and the corrected dead time width is adjusted by feedback to generate dead time correction parameter data.

9. The integrated operation and control method for photovoltaic and energy storage devices for microgrids according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Perform dynamic instruction encoding on the adaptive compensation control data to generate multi-dimensional control instruction set data; Step S42: Identify instruction redundancy conflicts in the multidimensional control instruction set data, eliminate redundant and mutually exclusive instructions, and thus generate deduplication and optimization data for control instructions; Step S43: Input the deduplication and optimization data of the control commands into the microgrid power routing module to perform regional load response mapping and generate microgrid command matching mapping data; Step S44: Calculate the control timing consistency of the microgrid command matching mapping data to obtain the collaborative control scheduling sequence data; input the collaborative control scheduling sequence data to the preset microgrid control interface for command distribution and execution, generate operation feedback response data, and perform integrated operation control optimization operation for microgrid-oriented photovoltaic and energy storage equipment.

10. A comprehensive operation and control system for photovoltaic and energy storage devices in microgrids, characterized in that, For executing the integrated operation control method for photovoltaic and energy storage devices for microgrids as described in claim 1, the integrated operation control system for photovoltaic and energy storage devices for microgrids includes: The data acquisition module is used to acquire the current and voltage waveform data across the filter inductor of the photovoltaic energy storage device; to perform frequency domain sweep on the current and voltage waveform data to generate complex impedance data of the filter inductor; and to map the local saturation start point and nonlinear segment of the magnetic core based on the complex impedance data of the filter inductor to generate saturation characteristic data. The data intervention configuration module is used to connect a bidirectional DC bias winding in parallel next to the filter inductor, and to adjust the bias current based on the saturation characteristic data to generate core operating point adjustment data; it also splits the filter into two groups, main and auxiliary, which are dynamically switched in parallel / series by solid-state relays under different operating conditions, and configures the filter impedance based on the saturation characteristic data to generate filter impedance configuration data. The control optimization module is used to adaptively adjust the PWM carrier slope and dead zone of the photovoltaic energy storage device by using magnetic core operating point adjustment data and filter impedance configuration data to obtain PWM modulation parameter optimization data; and to perform virtual inductance / resistance compensation superposition on the PWM modulation parameter optimization data to generate adaptive compensation control data. The instruction distribution module is used to generate control instructions from adaptive compensation control data and input the generated control instructions into a preset microgrid for instruction-coordinated control, so as to perform integrated operation control optimization of photovoltaic and energy storage equipment for microgrid.

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