Networking inverter for photovoltaic energy storage system and virtual synchronous machine control method

Abstract

The invention discloses a network construction type inverter for a photovoltaic energy storage system and a virtual synchronous machine control method. The network construction type inverter comprises an inverter power module and a central controller. The central controller is used for collecting power grid operation data in real time and generating a power grid impedance state vector through online impedance identification; and dynamically adjusting virtual inertia and damping parameters of the inverter power module according to the impedance state vector by using an adaptive mapping function so as to realize cooperative control and circulating current suppression between the modules. Performing stability boundary evaluation based on the adjusted parameters, then performing intelligent early warning of the photovoltaic energy storage system, and smoothly switching the operation mode of the inverter power module; according to the invention, through deep integration of a modular hardware architecture and intelligent adaptive control, the inverter has strong autonomous network construction capability, the problems of weak power grid adaptability and transient stability are effectively solved, and the dynamic response and reliability of a photovoltaic energy storage system are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of photovoltaic energy storage grid-connected control, and particularly relates to a grid-constructing inverter for a photovoltaic energy storage system and a virtual synchronous machine control method. BACKGROUND

[0002] With the deepening of the double carbon strategy, the penetration rate of renewable energy represented by photovoltaic and wind power in photovoltaic energy storage systems continues to rise. As a key equipment for stabilizing fluctuations and realizing efficient energy utilization, the control technology of the core component grid-connected inverter of the photovoltaic energy storage system directly determines the grid-connected characteristics and support capability of the photovoltaic energy storage system to the power grid.

[0003] Traditional grid-connected inverters generally adopt a grid-following control strategy, that is, the output voltage and frequency strictly follow the grid, which is characterized by a controlled current source. This control method works well when the grid is strong, but when the grid is weak or the photovoltaic penetration rate is too high, the inherent defects of the grid-connected inverter become increasingly prominent. First, the grid-connected inverter cannot independently provide voltage and frequency support, and is prone to disconnection from the grid during power grid failure, exacerbating the instability of the photovoltaic energy storage system. Second, it does not have inertia response capability and cannot provide the necessary damping for the grid like a synchronous generator, which worsens the frequency stability of the high-proportion new energy power grid.

[0004] In order to simulate the operation mechanism of a synchronous generator, virtual synchronous machine technology has emerged. This technology enables the inverter to have inertia and damping characteristics through a control algorithm, that is, grid-constructing control, which can autonomously establish the voltage and frequency of the grid, thereby enhancing the stability of the grid. However, existing virtual synchronous machine control methods still face a series of challenges. First, the contradiction between dynamic response and stability. The control loop introduced to simulate rotor inertia may cause power oscillation and slow dynamic response when the grid frequency changes rapidly. Second, the stability problem of multiple machines operating in parallel. When multiple grid-constructing inverters operate in parallel, their internal virtual impedance and power distribution mechanism are difficult to accurately coordinate, which may cause circulating current and oscillation instability. Third, insufficient adaptability to complex conditions. In complex transient processes such as power grid failure, load mutation and operation mode switching, how to achieve smooth and rapid switching and coordination between different control objectives is still a technical difficulty.

[0005] In summary, the existing grid-connected technology cannot meet the demand of high-proportion new energy power grid for stability, and the early virtual synchronous machine technology still has deficiencies in dynamic performance, multi-machine parallel stability and adaptability to complex working conditions. Therefore, there is an urgent need for a new type of grid-connected inverter and virtual synchronous machine control method, which can not only simulate the external characteristics of synchronous generators, but also optimize the dynamic performance, realize the coordinated stability of multi-machine parallel, and have intelligent adaptive ability to cope with complex power grid working conditions, thereby providing core technical support for building a new type of high-elasticity and high-reliability photovoltaic energy storage system. SUMMARY

[0006] The purpose of the present application is to overcome the deficiencies of the prior art and provide a grid-connected inverter and virtual synchronous machine control method for a photovoltaic energy storage system, which can realize dynamic response, fast and smooth, multi-machine parallel stable operation and self-adaptation to complex power grid working conditions.

[0007] The purpose of the present application can be achieved by the following technical solutions:

[0008] The present application provides a grid-connected inverter for a photovoltaic energy storage system, comprising an inverter power module and a central controller; the central controller is connected with the inverter power module through a communication network, used to obtain a power grid impedance state vector through online impedance identification, and dynamically adjust control parameters of a virtual synchronous machine based on the impedance state vector; the central controller evaluates the stability boundary of the system based on the adjusted control parameters and the impedance state vector, and generates corresponding system control instructions; the inverter power module is used to receive the system control instructions, perform smooth switching of the working state of the power conversion circuit through a virtual synchronous machine control algorithm, and provide voltage and frequency support for the power grid. Further, the central controller comprises an impedance identification unit, a parameter mapping unit, a stability evaluation unit and an instruction generation unit; the impedance identification unit collects operation data of the photovoltaic energy storage system in real time, processes the operation data using an online impedance identification algorithm, and generates a power grid impedance state vector; the parameter mapping unit analyzes the correlation between the impedance state and the stability of the photovoltaic energy storage system through an adaptive mapping function, fuses the power grid impedance state vector into the virtual synchronous machine control, and dynamically adjusts the virtual inertia and damping parameters; the stability evaluation unit simulates different disturbance scenarios of the power grid through the adjusted virtual inertia and damping parameters, inputs the power grid impedance state vector into the virtual synchronous machine controller, calculates the stability boundary of the photovoltaic energy storage system and performs dynamic evaluation; the instruction generation unit generates operation mode switching instructions and parameter adjustment instructions based on the evaluation results of the stability boundary, and delivers them to the inverter power module.

[0009] Further, the operation data includes three-phase voltage and three-phase current of the grid public connection point; based on the three-phase voltage and three-phase current in the operation data, the current harmonic background of the grid is determined through a spectrum analysis algorithm, and a specific frequency point that is staggered with the harmonic frequency of the current harmonic background is selected;

[0010] Further, the instruction generation unit sets a plurality of intelligent early warning thresholds including a first early warning threshold, a second early warning threshold and a mode switching threshold; the quantitative evaluation result of the stability margin obtained by the stability boundary evaluation unit is compared with the plurality of early warning thresholds, and corresponding operation mode switching instructions and virtual synchronous machine control parameter adjustment instructions are generated based on the comparison result;

[0011] The operation mode includes a grid-connected operation mode, an off-grid operation mode and a limited power operation mode.

[0012] Further, the inverter power module includes a local controller and a power conversion circuit.

[0013] The local controller is connected to the central controller through a communication network, and is used to receive the system control instructions of the instruction generation unit, and to generate corresponding pulse width modulation driving signals by smoothly adjusting the voltage, frequency and phase reference values of the virtual synchronous machine control algorithm.

[0014] The power conversion circuit is connected to the local controller, and is used to control the working state of the power conversion circuit according to the pulse width modulation driving signal; the amplitude of the output voltage is adjusted through the timing and duty cycle of the working state, the frequency is adjusted through the period of the output waveform, and the phase is adjusted through the initial phase angle of the output waveform.

[0015] As a preferred, a virtual synchronous machine control method for a photovoltaic energy storage system, applied to the grid-connected inverter, characterized in that it comprises the following steps:

[0016] S1, real-time acquisition of operation data of a grid public connection point in a photovoltaic energy storage system, including three-phase voltage and three-phase current, and processing the operation data using an online impedance identification algorithm to generate a grid impedance state vector; S2, analyzing the correlation between the grid impedance state vector and the stability of the photovoltaic energy storage system through an adaptive mapping function, and dynamically adjusting the virtual inertia parameters and damping parameters of each inverter power module; S3, based on the adjusted virtual inertia parameters and damping parameters, calculating the stability boundary of the photovoltaic energy storage system and dynamically evaluating it to output a quantitative evaluation result of the stability margin; S4, intelligently warning according to the quantitative evaluation result of the stability margin, and generating a running mode switching instruction and a parameter adjustment instruction to be transmitted to the inverter power module to switch the running mode and realize self-adaptive adjustment.

[0017] Advantages:

[0018] The application solves the technical problems of slow dynamic response, multi-machine parallel instability and poor working condition adaptability in the traditional virtual synchronous machine technology by the network construction control architecture of multi-controller cooperation and adaptive adjustment; the intelligent control photovoltaic energy storage system composed of the improved power synchronization ring, adaptive virtual impedance ring and multi-mode smooth switching logic realizes seamless support of the inverter from grid-connected to islanded in all working conditions;

[0019] Specifically, by improving the adaptive damping algorithm of the power synchronization ring, a balance mechanism of inertia support and dynamic stability is established, providing the photovoltaic energy storage system with a fast and smooth frequency response capability; by real-time current monitoring and impedance adjustment of the adaptive virtual impedance ring, active suppression of the circulating current component in the multi-machine parallel photovoltaic energy storage system and accurate division of reactive power are realized; by grid state sensing and feedforward compensation control of the multi-mode smooth switching logic, seamless transition and stable operation between different operating conditions are completed;

[0020] The network construction type inverter and the virtual synchronous machine control method effectively solve the contradiction between dynamic performance and steady-state accuracy in the traditional technology, forming a coordinated optimization of inertia support, power distribution and fault ride-through; compared with the prior art, the application significantly improves the transient stability, parallel operation reliability and working condition adaptability of the photovoltaic energy storage system, and realizes the comprehensive effects of fast dynamic response, high control accuracy and strong robustness, fully meeting the stringent requirements of new photovoltaic energy storage systems on the network construction capability of new energy power generation equipment. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to better understand and implement, the technical solutions of the application are described in detail below with reference to the accompanying drawings.

[0022] Fig. 1 A flowchart of a network construction type inverter for a photovoltaic energy storage system provided by the application;

[0023] Fig. 2 A flowchart of a network construction type inverter and a virtual synchronous machine control method for a photovoltaic energy storage system provided by the application; DETAILED DESCRIPTION

[0024] To further clarify the technical hand and the effect of the present application for the predetermined application purpose, hereinafter the exemplary embodiments will be described in detail, which are shown in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings represent the same, similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0025] The terminology used in this application is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting of the present application. As used in this application, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising", when used in this application, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0026] The specific implementations, features and effects of the present application according to the embodiments thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0027] Embodiment 1:

[0028] Please refer to Figs. 1-2 The embodiment provides a grid-connected inverter for a photovoltaic energy storage system, the grid-connected inverter comprises a central controller and an inverter power module; the central controller comprises an impedance identification unit, a parameter mapping unit, a stability evaluation unit and an instruction generation unit; the inverter power module comprises a local controller and a power conversion circuit; the implementation process is as follows:

[0029] The central controller is connected with the inverter power module through a communication network, the impedance identification unit obtains a grid impedance state vector through online impedance identification, and the control parameters of a virtual synchronous machine are dynamically adjusted based on the impedance state vector;

[0030] The central controller evaluates the stability boundary of the system based on the adjusted control parameters and the impedance state vector, and generates corresponding system control instructions; the inverter power module is used for receiving the system control instructions, performing smooth switching of the working state of the power conversion circuit through a virtual synchronous machine control algorithm, and providing voltage and frequency support for the grid;

[0031] Specifically, the embodiment introduces a modular hardware architecture (central controller and inverter power module) and endows it with the ability of collaborative autonomy, and builds a closed-loop intelligent control process of perception, decision-making, evaluation and execution, solves the poor adaptability of the traditional network type inverter to the weak power grid caused by the fixed control parameters, the transient stability problem caused by the contradiction between dynamic response and stability, and the circulating current and instability risk of multi-machine parallel caused by the lack of collaborative mechanism, achieves the comprehensive effect of making the photovoltaic energy storage system have real-time perception of the power grid state, prospective adjustment of the control strategy, and smooth response to various disturbances and working condition switching, and finally significantly improves the dynamic response speed, operation reliability and overall stability of the system in the complex power grid environment;

[0032] Further, the impedance identification unit of the central controller collects the operation data of the photovoltaic energy storage system in real time, and the operation data includes the three-phase voltage and three-phase current of the power grid public connection point; the operation data is processed by using an online impedance identification algorithm to generate a power grid impedance state vector;

[0033] Among them, the operation data includes the three-phase voltage and three-phase current of the power grid public connection point, based on these operation data, the current harmonic background of the power grid is determined through a spectrum analysis algorithm, and a specific frequency point that is staggered with the harmonic frequency of the current harmonic background is selected, specifically: the voltage signal u(t) of the collected power grid public connection point is subjected to fast Fourier transform (FFT) to obtain its frequency domain representation U(f), and the amplitude at the fundamental frequency f1 is set as Then all frequency components satisfying the following conditions are determined as significant background harmonics that need to be avoided, which is specifically represented as: Wherein, η is a set harmonic significance judgment threshold, the value range is 1% to 5%, that is, η∈[0.01,0.05]η∈[0.01,0.05];

[0034] Let the set of all significant background harmonic frequencies be Then, a specific frequency point of the injected signal is selected, which needs to ensure that it satisfies the following constraint condition:

[0035] ,

[0036] Wherein, is a set frequency safety margin; this condition ensures that the injected disturbance signal maintains a safe distance from all significant background harmonics in the frequency domain, thereby effectively avoiding spectral overlap and mutual interference, and ensuring the accuracy of the subsequent impedance identification result;

[0037] Further, the operating data is processed by an online impedance identification algorithm to generate a power grid impedance state vector, specifically including: injecting a harmonic disturbance signal of a specific frequency point into a power grid point of common coupling through a signal injection algorithm;

[0038] The signal injection is implemented in the control loop of the inverter, and the specific steps are as follows:

[0039] A sinusoidal disturbance signal of the specific frequency point is superimposed on the current inner loop reference value of the virtual synchronous machine controller , and the expression is:

[0040]

[0041] Among them, is the amplitude reference value of the disturbance current, which is calculated by the front-stage power to ensure that the harmonic voltage disturbance amplitude generated at the point of common coupling of the power grid is controlled within 1%-3% of the rated voltage;

[0042] After the superimposed current reference value is adjusted by the current loop controller, a voltage modulation signal is generated through Park inverse transformation; the voltage modulation signal is finally driven by space vector pulse width modulation (SVPWM) to drive the DC / AC inverter bridge, so as to inject the harmonic disturbance signal into the power grid.

[0043] At the same time of injecting the disturbance signal, the three-phase voltage and the three-phase current under the disturbance of the harmonic disturbance signal are synchronously collected, and the sampling frequency satisfies the Nyquist sampling theorem to avoid spectrum aliasing, and is specifically expressed as:

[0044] Among them is the highest frequency of the signal to be analyzed, which should be at least higher than the injected frequency , and is usually selected to ensure accuracy;

[0045] The collected three-phase voltage and current response data are subjected to sequence component decomposition and fast Fourier transform (FFT) to obtain the positive sequence voltage amplitude, positive sequence current amplitude and phase difference therebetween at the specific frequency point; the specific processing process is as follows:

[0046] The positive sequence component is separated from the three-phase response signal by sequence component decomposition, and the collected three-phase voltage and three-phase current data are subjected to coordinate transformation;

[0047] Among them, the sequence component decomposition adopts coordinate transformation, and the three-phase voltage and three-phase current are specifically represented as:​

[0048]

[0049]

[0050] wherein represents two orthogonal components of voltage in two-phase stationary coordinate system represents two orthogonal components of current in two-phase stationary coordinate system

[0051] After obtaining the orthogonal components in two-phase stationary coordinate system (α, β), the positive sequence component is calculated by constructing a complex signal, and the positive sequence component calculation formula is:

[0052]

[0053] wherein X is a general symbol, representing voltage U and current I respectively in this step; j is an imaginary unit, satisfying ; is the calculated positive sequence component complex signal, the length of which represents the amplitude of the positive sequence component, and the argument represents the phase of the positive sequence component;

[0054] According to the sequence component calculation formula, the positive sequence voltage amplitude is obtained

[0055] Positive sequence voltage amplitude:

[0056] Positive sequence current amplitude:

[0057] Based on the positive sequence voltage amplitude, the positive sequence current amplitude, and the phase difference between them, the impedance amplitude and impedance angle of the power grid at the specific frequency point are calculated by an impedance calculation algorithm:

[0058] wherein represents the impedance amplitude of the power grid at frequency ;

[0059] The power grid impedance state vector is formed: wherein, is a two-dimensional state vector representing the impedance characteristics of the power grid at a specific frequency point;

[0060] Specifically, by intelligently selecting a specific injection frequency point that is offset from the background harmonic frequency based on FFT spectrum analysis, the problem of measurement inaccuracy caused by the interference of the power grid background harmonic in the traditional impedance measurement method is solved, ensuring the spectral isolation of the injected signal and the power grid environment; by injecting a specific frequency harmonic disturbance signal with an amplitude of only 1%-3% of the rated voltage in the inverter control loop, the problem of power grid power quality impact caused by high-power disturbance is solved, achieving safe and non-inductive measurement of the power grid; by performing sequence component decomposition and FFT analysis on the collected three-phase voltage and current response data to extract pure positive sequence components, the problem of response signal extraction pollution caused by power grid voltage imbalance and background harmonic is solved, achieving the purpose of accurately obtaining the positive sequence voltage amplitude, positive sequence current amplitude and phase difference at a specific frequency point; finally, by calculating the power grid impedance amplitude and impedance angle based on the physical definition using the positive sequence voltage and current amplitude and phase difference, the power grid impedance state vector is formed, solving the pain points of traditional methods for power grid impedance characteristic perception lag and inaccuracy, achieving the goal of real-time and accurate generation of a two-dimensional state vector representing the power grid strength and characteristics, providing the most basic and key data input for the environment perception and forward-looking decision-making of the entire control system;

[0061] Further, the parameter mapping unit of the central controller analyzes the correlation between the impedance state and the stability of the photovoltaic energy storage system through an adaptive mapping function, and fuses the power grid impedance state vector into the virtual synchronous machine control to dynamically adjust the virtual inertia and damping parameters;

[0062] Further, the correlation between the impedance state and the stability of the photovoltaic energy storage system is analyzed through an adaptive mapping function, specifically including: establishing a parameter mapping relationship with the power grid impedance state vector as input and the virtual synchronous machine control parameters as output, specifically represented as:

[0063] Where F represents the adaptive mapping function, which defines the conversion relationship from the impedance state to the control parameters;

[0064] represents the power grid impedance state vector,

[0065] represents the amplitude of the power grid impedance, reflecting the strength of the power grid; represents the phase angle of the power grid impedance, reflecting the impedance characteristics of the power grid; represents the virtual inertia parameter of the virtual synchronous machine, simulating the rotor inertia of the synchronous generator; represents the damping parameter of the virtual synchronous machine, providing damping for the oscillation of the photovoltaic energy storage system

[0066] The mapping relationship is realized by using a lookup table method, impedance amplitudes are divided into a plurality of continuous intervals, and each interval corresponds to a set of optimized virtual inertia parameters and damping parameters;

[0067] Interval division criteria:

[0068] Interval I (strong grid): when the identified grid impedance amplitude is less than 10% of the reference impedance of the photovoltaic energy storage system, it is determined to be a strong grid operating state;

[0069] Interval II (medium-intensity grid): when the identified grid impedance amplitude is between 10% and 30% of the reference impedance of the photovoltaic energy storage system, it is determined to be a medium-intensity grid operating state;

[0070] Interval III (weak grid): when the identified grid impedance amplitude reaches or exceeds 30% of the reference impedance of the photovoltaic energy storage system, it is determined to be a weak grid operating state;

[0071] According to the interval in which the real-time identified impedance amplitude is located, the corresponding control parameters are selected as the output;

[0072] When the real-time identified impedance amplitude is in the transition area of different intervals, the final control parameter value is determined by a smooth transition method, and the virtual inertia parameter and the damping parameter are respectively expressed by linear interpolation as follows: ,

[0073] wherein, : the final value of the virtual inertia parameter calculated by linear interpolation; : the final value of the damping parameter calculated by linear interpolation; : the reference value of the virtual inertia parameter corresponding to the current impedance interval; : the reference value of the virtual inertia parameter corresponding to the next impedance interval; : the reference value of the damping parameter corresponding to the current impedance interval; : the real-time identified grid impedance amplitude; : the reference value of the damping parameter corresponding to the next impedance interval; : the lower threshold of the current impedance interval; : the lower threshold of the next impedance interval

[0074] According to the adjustment amount output by the mapping relationship, the control parameters of the virtual synchronous machine are adjusted in real time: when the grid impedance amplitude increases, the virtual inertia parameter and the damping parameter are correspondingly increased; when the grid impedance amplitude decreases, the virtual inertia parameter and the damping parameter are correspondingly decreased;

[0075] Specifically, by establishing a parameter mapping relationship with the grid impedance state vector as the input and the virtual synchronous machine control parameters as the output, the core problem of the fixed control parameters of the traditional virtual synchronous machine that cannot adapt to the changes in the grid state is solved, and the dynamic correlation between the control strategy and the grid impedance state is realized. The impedance partition-based table lookup method combined with the smooth transition mechanism of the boundary linear interpolation is adopted to solve the mutation and oscillation problems that may be caused in the parameter switching process, and the fast, continuous and disturbance-free matching of the control parameters and the grid strength (impedance amplitude) is achieved. Finally, according to the mapping relationship, the virtual inertia (J) and damping (D) parameters are adjusted in real time to solve the inherent contradiction between the slow dynamic response under strong grid and the insufficient stability under weak grid in the traditional control, achieve the optimal balance between stability and dynamic performance of the photovoltaic energy storage system under all-scenario grid conditions, and significantly improve the adaptive operation ability and stability of the system in the real complex grid environment.

[0076] The stability evaluation unit of the central controller simulates different disturbance scenarios of the grid by adjusting the virtual inertia and damping parameters, inputs the grid impedance state vector into the virtual synchronous machine controller, calculates the stability boundary of the photovoltaic energy storage system and performs dynamic evaluation, specifically including: based on the grid impedance state vector, using the impedance ratio criterion to quantify the boundary of the photovoltaic energy storage system in terms of amplitude stability and phase stability;

[0077] A closed-loop photovoltaic energy storage system model containing the virtual synchronous machine controller and the grid impedance is established to calculate the loop gain of the photovoltaic energy storage system:

[0078] wherein : represents the loop gain of the photovoltaic energy storage system, which is a key frequency domain index for evaluating stability; : represents the open-loop transfer function of the virtual synchronous machine controller;

[0079] : represents the equivalent admittance of the grid side,

[0080] : represents the grid impedance frequency characteristic based on the impedance state vector

[0081] : represents the angular frequency, ω = 2πf

[0082] : represents the imaginary unit, satisfying

[0083] Based on the calculated loop gain , the stability boundary of the photovoltaic energy storage system is quantified from the amplitude and phase dimensions respectively:

[0084] Amplitude stability boundary quantification:

[0085]

[0086] in, Amplitude stability margin reflects the stability reserve of a photovoltaic energy storage system in terms of amplitude.

[0087] Gain crossover frequency, satisfying

[0088] The amplitude stability margin characterizes the stability boundary of the photovoltaic energy storage system in terms of gain. The larger the GM value, the more sufficient the safety reserve of the photovoltaic energy storage system in terms of amplitude stability.

[0089] Phase-stable boundary quantization:

[0090] in, Phase stability margin reflects the phase stability reserve of a photovoltaic energy storage system.

[0091] Phase crossover frequency, satisfying

[0092] The phase stability margin characterizes the stability boundary of the photovoltaic energy storage system in terms of phase. The higher the value, the more sufficient the safety reserve of the photovoltaic energy storage system in terms of phase stability;

[0093] To verify and supplement the frequency domain pre-evaluation results, the virtual synchronous machine controller was set to operate under the adjusted virtual inertia parameters and damping parameters. By simulating power disturbances and voltage drops of different intensities in the power grid, the transient response curves of the photovoltaic energy storage system in terms of frequency and voltage were obtained.

[0094] Based on grid impedance state vector and S2 optimized control parameters From the transient response curves of frequency f(t) and voltage U(t), the maximum frequency deviation and settling time are extracted and specifically expressed as follows:

[0095] Maximum frequency deviation:

[0096] in The maximum frequency deviation reflects the system's ability to resist disturbances.

[0097] Real-time frequency measurement value

[0098] System rated frequency (50Hz)

[0099] The frequency maximum deviation directly verifies the actual ability of the system to resist disturbance impact and prevent frequency collapse within the stable boundary determined in S301

[0100] Adjustment time:

[0101] Wherein : adjustment time, representing the speed of the system to recover to steady state

[0102] : allowable frequency deviation threshold, typically ±0.2 Hz

[0103] The condition time supplements the dynamic recovery speed information that the frequency domain analysis cannot provide, reflecting the speed of the system to recover to a stable state from a disturbance

[0104] According to the frequency maximum deviation and the adjustment time based on the amplitude stability boundary, the phase stability boundary and the transient response curve, the stability boundary of the photovoltaic energy storage system under current and expected disturbances is comprehensively evaluated, and a quantitative evaluation result of the stability margin is output:

[0105] Wherein : comprehensive stability margin evaluation result

[0106] : maximum allowable adjustment time, typically 2s

[0107] α,β,γ: weight coefficients

[0108] Satisfy α+β+γ=1, typically α=0.5,β=0.3,γ=0.2

[0109] Specifically, the frequency domain stability boundary quantification based on the impedance state vector of the power grid using the impedance ratio criterion solves the problem that the traditional single criterion cannot comprehensively evaluate the stability of the system, and realizes accurate boundary description of the photovoltaic energy storage system in two dimensions of amplitude stability margin (GM) and phase stability margin (PM); Combined with the adjusted control parameters, the power grid disturbance is simulated and the transient response curve is obtained, which solves the limitation that the frequency domain analysis cannot truly reflect the dynamic response characteristics of the system, and achieves the quantification and verification of key time domain indicators such as frequency maximum deviation (Δf_max) and adjustment time (t_s); Finally, through S303, an integrated evaluation model combining the frequency domain boundary (GM, PM) and the time domain response (Δf_max, t_s) is constructed, which solves the technical problems of one-sidedness and lack of unified quantitative standard in stability evaluation, and achieves the goal of comprehensive, accurate and quantitative evaluation of the stability margin (SM) of the photovoltaic energy storage system, providing a reliable data basis and decision basis for subsequent intelligent early warning and mode smooth switching;

[0110] Further, the instruction generating unit sets a multi-level intelligent early warning including a first early warning threshold, a second early warning threshold, and a mode switching threshold; the multi-level early warning threshold is compared with the quantitative evaluation result of the stability margin obtained by the stability boundary evaluation unit, and corresponding operation mode switching instructions and virtual synchronous machine control parameter adjustment instructions are generated based on the comparison result;

[0111] The operation mode includes a grid-connected operation mode, an off-grid operation mode, and a limited power operation mode.

[0112] Further, the inverter power module includes a local controller and a power conversion circuit.

[0113] The local controller is connected to the central controller through a communication network, and is used to receive the system control instructions of the instruction generating unit, and to generate corresponding pulse width modulation driving signals by smoothly adjusting the voltage, frequency, and phase reference values of the virtual synchronous machine control algorithm.

[0114] The power conversion circuit is connected to the local controller, and is used to control the working state of the power conversion circuit according to the pulse width modulation driving signal; the amplitude of the output voltage is adjusted by the timing and duty cycle of the working state, the frequency is adjusted by controlling the period of the output waveform, and the phase is adjusted by changing the initial phase angle of the output waveform for intelligent early warning and smooth switching of the operation mode, specifically including: setting a multi-level intelligent early warning mechanism including a first early warning threshold SM1=80%, a second early warning threshold SM2=60%, and a mode switching threshold SM3=40% according to the quantitative evaluation result of the comprehensive stability margin SM output by the stability evaluation unit.

[0115] When the stability margin SM is lower than the first early warning threshold SM1 but higher than SM2, a primary early warning is triggered, the stability is prompted to decrease on the monitoring interface, and the current operation mode is maintained; when the stability margin SM further decreases to be lower than SM2 but higher than the mode switching threshold SM3, a secondary early warning is triggered, and the operation mode is actively switched from the grid-connected operation mode to the limited power operation mode to improve the stability margin; when the stability margin SM continuously deteriorates to be lower than the mode switching threshold SM3, a tertiary early warning is triggered, and a switching instruction is generated to switch the photovoltaic energy storage system from the current mode to the off-grid operation mode, ensuring the safety of the system itself.

[0116] The smooth switching of the operation mode is realized by a parameter gradual change mode, specifically including: a parameter smooth transition is realized by using a ramp function control algorithm, and the parameter change is calculated by the following formula:

[0117] Wherein P(t) represents the parameter value at time t, including the virtual inertia parameter J(t), the damping parameter D(t), and the power reference value P_{ref}(t)

[0118] : represents the initial value of the parameter before switching

[0119] : represents the parameter value corresponding to the target operating mode

[0120] T represents a preset transition period, t ∈ [0, T] During the operating mode switching process, the virtual inertia parameter and the damping parameter are simultaneously linearly changed according to the slope function control algorithm, and the transition time is T. Through the parameter gradual change mode, the power output of the photovoltaic energy storage system during the operating mode switching process is smoothly transitioned, the impact current and voltage oscillation are effectively suppressed, and seamless switching between different operating modes of the system is ensured.

[0121] This parameter gradual change mechanism closely cooperates with the aforementioned intelligent early warning system. When the mode switching instruction triggered by S403 or S404 is issued, the system does not immediately jump to the target mode, but smoothly transitions the control parameter from the current value to the target value through the slope function control algorithm, thereby avoiding power impact and system oscillation caused by parameter mutation, and improving the stability and reliability of the mode switching process.

[0122] Specifically, multi-level early warning thresholds are set based on the quantitative evaluation results of the comprehensive stability margin (SM), solving the misoperation or refusal problem of the traditional protection system due to lack of fine classification, and establishing a gradient defense system from early warning to limited power to off-grid. According to the different threshold intervals of the stability margin, differential control strategies are executed, solving the technical problem of single control response and lack of transition in the system stability deterioration process, and realizing intelligent matching and gradual adjustment of the operating mode and the grid state. Finally, by using the parameter gradual change algorithm based on the slope function, the virtual inertia J(t), the damping D(t) and the power reference value P_ref(t) are cooperatively and smoothly adjusted, solving the secondary stability problems such as power impact and current oscillation caused by control parameter mutation during mode switching, enabling the photovoltaic energy storage system to realize seamless, smooth and reliable transition between different operating modes, and finally building a complete control closed loop from stability evaluation to intelligent early warning to smooth switching, significantly improving the survival ability and operation reliability of the system under complex grid conditions.

[0123] Example 2:

[0124] Please refer to Figs. 1-2 The embodiment provides a virtual synchronous machine control method for a photovoltaic energy storage system, which is applied to a grid-connected inverter, and has the following steps:

[0125] S1, real-time acquisition of DC side operation data and generation of system admittance state vector: real-time acquisition of voltage U_dc and current I_dc operation data of the DC bus of the photovoltaic energy storage system; injecting a current disturbance signal of a specific frequency into the DC bus through a small signal disturbance injection algorithm, and synchronously acquiring voltage and current response data under disturbance; performing fast Fourier transform analysis on the response data to obtain the amplitude and phase difference of the voltage and current at the specific frequency point; based on the amplitude and phase difference, calculating the admittance amplitude and admittance angle of the system at the specific frequency point through an admittance calculation algorithm to form a system admittance state vector ;

[0126] Specifically, Y_vec represents the system admittance state vector,

[0127] |Y(f_i)| represents the admittance amplitude at a specific frequency point , and the calculation formula is:

[0128] θ_Y(f_i) represents the admittance angle at a specific frequency point , and the calculation formula is:

[0129] wherein, and represent the amplitudes of the voltage response signal and the current response signal collected at the frequency , respectively;

[0130] and represent the phase angles of the corresponding voltage and current signals; the two-dimensional state vector completely characterizes the admittance characteristics of the DC side system at the specific frequency point, and provides accurate system strength perception information for subsequent adaptive control;

[0131] S2, dynamically adjusting the virtual capacitance and virtual resistance parameters through an adaptive mapping function: establishing a parameter mapping relationship taking the system admittance state vector as input and taking the virtual capacitance C_v and the virtual resistance R_v as output; using a lookup table method to realize the mapping relationship, dividing the admittance amplitude into multiple continuous intervals, and each interval corresponds to a set of optimized C_v and R_v parameters; according to the interval where the real-time identified admittance amplitude is located, selecting the corresponding control parameters as the output; when the admittance amplitude is in the transition region of the interval, realizing parameter smooth transition through linear interpolation; adjusting the control parameters of the network-forming type DC converter in real time according to the adjustment amount output by the mapping relationship;

[0132] S3, calculating the stability boundary of the DC bus based on the adjusted parameters and performing dynamic evaluation: based on the system admittance state vector, the admittance ratio criterion is used to quantify the boundary of the system in terms of amplitude stability and phase stability; the controller is set to run under the adjusted C_v and R_v parameters, and through the simulation of load mutation and power fluctuation scenarios, the transient response curve data of the DC bus voltage is obtained; based on the amplitude stability boundary, the phase stability boundary, and the maximum deviation and regulation time in the transient response curve, the stability boundary of the system is comprehensively evaluated, and the quantitative evaluation result of the DC stability margin DSM is output;

[0133] S4, hierarchical voltage stabilization control and mode switching according to the stability margin evaluation result: based on the quantitative evaluation result of the DC stability margin DSM, set multiple levels of intelligent early warning thresholds; when the DSM is lower than the first early warning threshold, trigger the early warning and keep the current running mode; when the DSM is lower than the second early warning threshold, switch to the limited power voltage stabilization mode; when the DSM is lower than the mode switching threshold, switch to the forced voltage stabilization mode; all mode switching is realized through parameter gradual change, ensuring smooth transition of power and voltage, effectively inhibiting DC bus voltage impact and oscillation.

[0134] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes and replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A grid-type inverter for a photovoltaic energy storage system, characterized in that, Including the central controller and inverter power module; The central controller is connected to the inverter power module via a communication network. It is used to obtain the grid impedance state vector through online impedance identification and dynamically adjust the control parameters of the virtual synchronous machine based on the impedance state vector. Based on the adjusted control parameters and the impedance state vector, it evaluates the stability boundary of the system and generates corresponding system control commands. The inverter power module is used to receive the system control commands and smoothly switch the working state of the power conversion circuit through the virtual synchronous machine control algorithm to provide voltage and frequency support to the grid.

2. The grid-type inverter for a photovoltaic energy storage system according to claim 1, characterized in that, The central controller includes an impedance identification unit, a parameter mapping unit, a stability evaluation unit, and an instruction generation unit. The impedance identification unit collects operational data from the photovoltaic energy storage system, processes the data using an online impedance identification algorithm, and generates a grid impedance state vector. The parameter mapping unit dynamically optimizes the virtual inertia and damping parameters of the inverter power module based on the grid impedance state vector using an adaptive mapping function. The stability evaluation unit simulates different grid disturbance scenarios based on the adjusted virtual inertia and damping parameters, inputs the grid impedance state vector into a virtual synchronous machine, calculates the stability boundary of the photovoltaic energy storage system, and performs dynamic evaluation. The instruction generation unit generates operation mode switching instructions and parameter adjustment instructions based on the evaluation results of the stability boundary and sends them to the inverter power module.

3. A grid-type inverter for a photovoltaic energy storage system according to claim 2, characterized in that, The operating data of the photovoltaic energy storage system includes: three-phase voltage and three-phase current at the grid's point of common coupling; based on the three-phase voltage and three-phase current in the operating data, the current harmonic background of the power grid is determined by a spectrum analysis algorithm, and a specific frequency point that is offset from the harmonic frequency of the current harmonic background is selected.

4. A grid-type inverter for a photovoltaic energy storage system according to claim 2, characterized in that, The process of using an online impedance identification algorithm to process the operational data and generate a power grid impedance state vector specifically includes: The impedance identification unit injects harmonic disturbance signals at a specific frequency point into the power grid common coupling point through a signal injection algorithm; and simultaneously collects the three-phase voltage and three-phase current response data under the disturbance of the harmonic disturbance signals. The collected three-phase voltage and current response data are subjected to sequence component decomposition and fast Fourier transform algorithm to obtain the positive sequence voltage amplitude, positive sequence current amplitude and phase difference between them at the specific frequency point; based on the positive sequence voltage amplitude, positive sequence current amplitude and phase difference between them, the impedance amplitude and impedance angle of the power grid at the specific frequency point are calculated by impedance calculation algorithm to form the power grid impedance state vector.

5. A grid-type inverter for a photovoltaic energy storage system according to claim 2, characterized in that: The method of dynamically optimizing the virtual inertia and damping parameters of the inverter power module through an adaptive mapping function specifically includes: the parameter mapping unit establishing a parameter mapping relationship with the grid impedance state vector as input and the virtual synchronous machine control parameters as output; The mapping relationship includes pre-storing the adjustment amounts of control parameters corresponding to different impedance ranges through a lookup table method, and querying the adjustment amounts of virtual inertia parameters and damping parameters corresponding to the impedance amplitude and impedance angle in the power grid impedance state vector. Based on the adjustment amount output by the mapping relationship, the control parameters of the virtual synchronous machine are adjusted in real time. Specifically, when the grid impedance amplitude increases, the virtual inertia parameter and damping parameter are increased accordingly; when the grid impedance amplitude decreases, the virtual inertia parameter and damping parameter are decreased accordingly.

6. A grid-type inverter for a photovoltaic energy storage system according to claim 5, characterized in that, The method of pre-storing control parameter adjustment amounts corresponding to different impedance ranges using a lookup table includes: dividing the impedance amplitude into multiple continuous intervals, each interval corresponding to a set of optimized virtual inertia parameters and damping parameters; selecting the corresponding control parameters as outputs based on the interval of the real-time identified impedance amplitude; and determining the final control parameter values ​​through a smooth transition method when the real-time identified impedance amplitude is in the transition region between different intervals.

7. A grid-type inverter for a photovoltaic energy storage system according to claim 2, characterized in that: The calculation and dynamic evaluation of the stability boundary of the photovoltaic energy storage system specifically includes: the stability evaluation unit quantifies the boundary of the photovoltaic energy storage system in terms of amplitude stability and phase stability based on the grid impedance state vector and using the impedance ratio criterion; the virtual synchronous machine is set to operate under the adjusted virtual inertia parameters and damping parameters, and the transient response curve data of the photovoltaic energy storage system in terms of frequency and voltage are obtained by simulating power disturbances and voltage drops of different intensities in the grid. Based on the amplitude stability boundary, phase stability boundary, and transient response curve data, the stability boundary of the photovoltaic energy storage system under current and expected disturbances is comprehensively evaluated, and a quantitative evaluation result of the stability margin is output.

8. A grid-type inverter for a photovoltaic energy storage system according to claim 1, characterized in that, The instruction generation unit sets up multi-level intelligent early warning, including a first early warning threshold, a second early warning threshold, and a mode switching threshold; by comparing the stability margin quantitative evaluation result obtained by the stability boundary evaluation unit with the multi-level early warning threshold, the unit generates corresponding operating mode switching instructions and virtual synchronizer control parameter adjustment instructions based on the comparison results. The operating modes include grid-connected operating mode, off-grid operating mode, and power-limited operating mode.

9. A grid-type inverter for a photovoltaic energy storage system according to claim 1, characterized in that, The inverter power module includes a local controller and a power conversion circuit. The local controller is connected to the central controller via a communication network to receive system control commands from the command generation unit and to generate corresponding pulse width modulation drive signals by smoothly adjusting the voltage, frequency, and phase reference values ​​of the virtual synchronous machine control algorithm. The power conversion circuit is connected to the local controller and is used to control the operating state of the power conversion circuit according to the pulse width modulation drive signal; adjust the amplitude of the output voltage by the timing and duty cycle of the operating state, adjust the frequency by controlling the period of the output waveform, and adjust the phase by changing the initial phase angle of the output waveform.

10. A virtual synchronous machine control method for a photovoltaic energy storage system, applied to a grid-type inverter for a photovoltaic energy storage system as described in any one of claims 1-9, characterized in that, Includes the following steps: S1. Real-time acquisition of operating data from the grid common connection point in the photovoltaic energy storage system, including three-phase voltage and three-phase current, and processing of the operating data using an online impedance identification algorithm to generate a grid impedance state vector; S2. Analysis of the correlation between the grid impedance state vector and the stability of the photovoltaic energy storage system using an adaptive mapping function, and dynamic adjustment of the virtual inertia parameters and damping parameters of each inverter power module; S3. Calculation of the stability boundary of the photovoltaic energy storage system based on the adjusted virtual inertia parameters and damping parameters, and dynamic evaluation, outputting a quantitative evaluation result of the stability margin; S4. Intelligent early warning based on the quantitative evaluation result of the stability margin, and generation of operating mode switching instructions and parameter adjustment instructions, which are transmitted to the inverter power modules to switch operating modes and achieve adaptive adjustment.

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