Hybrid inverter collaborative energy management system and method suitable for optical storage system

Abstract

The invention relates to the technical field of power electronics and new energy control, in particular to a hybrid inverter collaborative energy management system and method suitable for an optical storage system. According to the system, by constructing an alternating current and direct current side impedance collaborative matching framework, the power grid impedance and the direct current link state are sensed in real time, direct current equivalent impedance and alternating current reactive output are optimized in a collaborative mode, and the frequency domain impedance characteristic of the system is actively reconstructed to suppress oscillation. The invention provides a hybrid inverter collaborative energy management system and method suitable for an optical storage system, which solve or at least alleviate the problem of AC-DC coupling oscillation of the optical storage system under weak power grid and power fluctuation, improve the dynamic robustness, operation efficiency and safety reliability of the system, realize millisecond-level oscillation detection and suppression, and improve the reliability of the system. The energy storage life is prolonged; and the overall energy efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of power electronics and new energy control technology, and in particular to a hybrid inverter collaborative energy management system and method suitable for photovoltaic-storage systems. Background Technology

[0002] With the rapid increase in renewable energy penetration and the gradual popularization of distributed energy architecture, hybrid photovoltaic-storage systems, as carriers for achieving local energy balance, improving system power supply resilience, and supporting grid stability, face significant dynamic operational challenges in their core power conversion equipment—the hybrid inverter. On the one hand, it still needs to efficiently complete the MPPT (maximum power point tracking) and DC-AC (direct current to alternating current) conversion of photovoltaic energy; on the other hand, it needs to achieve bidirectional energy flow and dynamic balance among the source, storage, and grid with the deep participation of energy storage units. Correspondingly, the control architecture and energy management strategies of hybrid inverters have gradually evolved from simple power regulation functions to the nerve center that determines the stability, economy, and security of the entire system. Especially under complex conditions such as weak grid access, high proportion of distributed power generation grid connection, and severe fluctuations in internal power sources, the shortcomings of traditional design paradigms based on fixed parameters and independent control loops are gradually being exposed, urgently requiring disruptive innovation from the perspective of system dynamics.

[0003] The control architecture of mainstream photovoltaic-storage hybrid inverters mostly adopts a separated and decoupled approach: the DC side maintains bus voltage stability through a fixed-parameter lc filter network or passive damping elements; the AC side uses voltage source control based on phase-locked loops and current inner loops for grid-connected power regulation and reactive power support. This architecture can still guarantee basic functions under steady-state or slowly changing conditions, and its initial purpose was to simplify control logic and reduce implementation difficulty, demonstrating good engineering applicability in early grid environments where power changes were relatively stable and internal power variations were gradual. However, as application scenarios place higher demands on system dynamic performance, disturbance rejection capability, and multi-terminal coordination, the inherent static impedance matching and control domain isolation characteristics of the above scheme reveal deep-seated mismatches when facing single composite transient impacts. Moreover, the root cause is not the performance defects of a single control loop, but the frequency domain and impedance domain isolation and decoupling changes in the dynamic behavior on both AC and DC sides, inducing the risk of system-level coupled oscillations.

[0004] Furthermore, when the external power grid exhibits high impedance (such as a weak grid in a remote area or the end of a long feeder) or suffers transient interference such as voltage drops or frequency shifts, the equivalent output impedance of the AC-side inverter will be forced into a non-ideal region in order to maintain power output or support the AC-side voltage. Alternatively, when the internal photovoltaic array experiences a sudden power drop due to cloud cover or other reasons, or when the energy storage battery responds to dispatch commands by performing large-current step charging and discharging, the DC bus will be subjected to severe power imbalance impacts, resulting in a significant increase in voltage ripple and dynamic hysteresis. Under these dual influences, the inherent resonant frequency and damping characteristics of the DC link, which should act as a static buffer, will drift due to large fluctuations in power flow. Meanwhile, the rapid adjustment performed by the AC-side control loop to track the grid status will inject high-frequency dynamic components back into the DC side through the power coupling channel. This cross-domain energy and information interaction can easily generate positive feedback loops at specific frequency points, meaning that the AC grid impedance and the DC link dynamic impedance exhibit negative damping characteristics within a certain frequency band, thereby inducing AC-DC coupled oscillations that are difficult to attenuate. This oscillation causes significant fluctuations in DC bus voltage, accelerates the aging of energy storage batteries, and reduces overall system efficiency. More seriously, it can trigger malfunctions in protection devices, leading to unplanned inverter disconnection from the grid, and even damage to power electronic devices due to overvoltage or overcurrent, threatening system safety. Current solutions, such as increasing DC bus capacitance to increase inertia or introducing virtual impedance on the AC side to enhance grid adaptability, are all local, passive, and static compensation measures. They do not address the root cause of impedance interaction between the AC and DC sides, nor do they establish real-time sensing, coordinated matching, and active suppression of dynamic parameters on both sides, making it difficult to resolve this deep-seated stability contradiction.

[0005] Therefore, it is necessary to provide an energy management architecture that can deeply integrate the operational status sensing capabilities of both AC and DC sides, dynamically decouple their impedance interaction paths, and achieve bidirectional collaborative optimization, thereby realizing the active suppression of cross-domain coupled oscillations and comprehensively (AC and DC) improving the dynamic robustness and operational reliability of photovoltaic-storage hybrid inverters under extreme conditions. This is a technical problem faced by those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a hybrid inverter collaborative energy management system and method suitable for photovoltaic-storage systems. This system can solve or at least alleviate the stability defects caused by static impedance design and control domain isolation in existing hybrid inverters. By sensing the impedance characteristics of the external AC grid and the dynamic state of the internal DC link in real time, a cross-domain impedance interaction model is established. Then, the equivalent impedance parameters of the DC link and the reactive power output strategy on the AC side are coordinated and adjusted to achieve active reconstruction of the overall impedance frequency domain characteristics of the system, thereby suppressing AC-DC coupling oscillations at the source.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a hybrid inverter collaborative energy management system suitable for photovoltaic-storage systems, the system comprising:

[0008] The AC / DC side operation status integrated sensing module is used to acquire the impedance characteristics and power quality information of the external AC power grid in real time, as well as the dynamic status and power flow information of the internal DC link.

[0009] The coupled oscillation risk assessment module is used to construct a small-signal model of the system and analyze the frequency domain stability based on the sensed information, so as to identify the dominant oscillation frequency and the corresponding risk level.

[0010] The DC link dynamic impedance optimization control module is used to calculate the optimal DC link equivalent impedance target value according to the risk level and system constraints, and dynamically adjust the impedance characteristics of the DC link through the controllable impedance adjustment unit.

[0011] An AC-side adaptive reactive power and voltage support control module is used to collaboratively generate reactive power output commands and voltage support commands based on the aforementioned risk level and grid impedance characteristics, in order to adjust the inverter's AC output impedance characteristics; and

[0012] The AC / DC coupled oscillation active suppression closed-loop feedback module is used to detect oscillation characteristics in real time. When oscillation is detected to have started, it generates AC / DC coordinated adjustment commands and sends them in parallel to the DC link dynamic impedance optimization control module and the AC side adaptive reactive power and voltage support control module to achieve rapid coordinated suppression of oscillation.

[0013] To further realize the present invention, the following technical solutions may be preferred:

[0014] Preferably, the AC / DC side operation status integrated sensing module includes an external AC grid impedance and power quality dynamic sensing submodule and an internal DC link dynamic status and power flow monitoring submodule;

[0015] The external AC grid impedance and power quality dynamic sensing submodule is equipped with a disturbance signal injection unit, which is used to inject disturbance signals of preset type, amplitude and frequency range, and to calculate the AC equivalent impedance matrix using an online identification algorithm.

[0016] The internal DC link dynamic status and power flow monitoring submodule is used to collect DC bus voltage and current, and calculate voltage ripple index and dynamic response parameters to evaluate DC link stability.

[0017] Preferably, the coupled oscillation risk assessment module constructs a multi-order state-space small-signal impedance model and performs Nyquist stability criterion analysis and eigenvalue mode decomposition to identify the dominant oscillation frequency, damping ratio and corresponding risk level. The analysis covers a preset wide frequency range.

[0018] Preferably, the controllable impedance adjustment unit in the DC link dynamic impedance optimization control module includes a variable inductor array and a switchable capacitor bank. The DC link dynamic impedance optimization control module calculates the optimal DC link equivalent impedance target value through a multi-objective optimization algorithm. The objective function comprehensively considers voltage ripple suppression, DC link anti-interference capability, and oscillation frequency avoidance. Based on the target value, the controllable impedance adjustment unit is driven to adjust physical parameters. The adjustment process adopts a progressive step strategy.

[0019] Preferably, the AC-side adaptive reactive power and voltage support control module uses a model predictive control algorithm to generate the reactive power output trajectory and introduces a virtual impedance compensation term in the current control loop. The gain parameter of the compensation term is related to the grid impedance characteristics and the dominant oscillation frequency, and the gain coefficient is dynamically adjusted according to the risk level to improve the stability of the inverter's interaction with the external grid.

[0020] Preferably, the AC / DC coupling oscillation active suppression closed-loop feedback module uses a narrowband energy detection algorithm to detect oscillation characteristics in real time. When oscillation is detected to have started, it sends a DC-side impedance compensation command with anti-phase characteristics to the DC link dynamic impedance optimization control module and an AC-side reactive power disturbance command with in-phase characteristics to the AC-side adaptive reactive power and voltage support control module to achieve millisecond-level coordinated suppression.

[0021] Preferably, the main control unit uses a high-performance multi-core processor running a real-time operating system, the high-speed data bus uses a high-bandwidth serial interface, the power electronic switching device uses a wide bandgap semiconductor, the sensor signal conditioning circuit includes preamplifier and anti-aliasing filter, and the communication interface module supports multiple industrial standard protocols to ensure high system reliability, fast response capability and seamless integration with photovoltaic storage components.

[0022] A hybrid inverter collaborative energy management method applicable to photovoltaic-storage systems includes the following steps:

[0023] S1: Periodically perform AC / DC side operation status sensing to obtain the impedance characteristics and power quality information of the external AC power grid in real time, as well as the dynamic status and power flow information of the internal DC link.

[0024] S2: Based on the perceived information, construct a small-signal model of the system and analyze its frequency domain stability to identify the dominant oscillation frequency and the corresponding risk level;

[0025] S3: Based on the risk level and system constraints, calculate the target value of the optimal DC link equivalent impedance, and dynamically adjust the impedance characteristics of the DC link through the controllable impedance adjustment unit;

[0026] S4: Based on the aforementioned risk level and grid impedance characteristics, collaboratively generate reactive power output commands and voltage support commands to adjust the inverter's AC output impedance characteristics; and

[0027] S5: Real-time detection of oscillation characteristics. When oscillation is detected to have started, an AC / DC coordinated adjustment command is generated and sent in parallel to the DC link dynamic impedance optimization control module and the AC side adaptive reactive power and voltage support control module to achieve rapid coordinated suppression of oscillation.

[0028] Preferably, in step S1, the impedance characteristics of the external AC power grid are obtained by injecting pseudo-random or sinusoidal disturbance signals and combining them with an online identification algorithm; in step S2, the small-signal model of the system is constructed using a modular method, and the impedance ratio trajectory in the dq coordinate system is analyzed based on the generalized Nyquist criterion to evaluate stability.

[0029] Preferably, in step S3, the target value of the optimal DC link equivalent impedance is calculated using a weighted objective function. The weights are dynamically adjusted based on the grid strength and power change rate. Priority is given to adjusting the capacitor in the controllable impedance adjustment unit to change the resonant frequency, followed by adjusting the inductor to improve damping characteristics. In step S4, the AC side reactive power output trajectory is optimized using a model predictive control algorithm, and a virtual impedance compensation term is introduced into the current control loop. The gain parameter of this compensation term is adaptively adjusted according to the oscillation risk level. In step S5, oscillation detection uses a narrowband energy detection algorithm. The DC side impedance compensation has an anti-phase characteristic, while the AC side reactive power disturbance has an in-phase characteristic. The total delay from detection to execution meets the millisecond requirement.

[0030] The beneficial effects of this invention are:

[0031] This invention utilizes the aforementioned system architecture and method to achieve dynamic sensing, coordinated matching, and proactive optimization of the AC and DC side impedances of a photovoltaic-storage hybrid inverter. It reshapes the impedance frequency domain characteristics at the system level, eliminates cross-domain coupling oscillations, and overcomes the system stability defects caused by the isolation between static impedance and the control domain in existing technologies.

[0032] Under weak grid connection conditions, the above-mentioned system has good grid connection point voltage regulation capability, effectively suppressing grid connection point voltage fluctuations and keeping the grid connection point voltage within a certain range, maintaining a high power factor, and ensuring that reactive power control accuracy and power quality meet strict grid connection requirements. For dynamic operating conditions such as rapid step changes in photovoltaic power generation, the above-mentioned system has good DC bus voltage regulation performance, with low overshoot and fast recovery characteristics, enabling it to quickly complete dynamic transition and re-enter steady-state operation, achieving rapid and stable energy transition of various parts of the system.

[0033] When sudden changes in grid impedance trigger complex instability risks such as multi-band coupled oscillations, the above-mentioned system integrates a real-time monitoring module, which can quickly identify oscillation characteristics and trigger active oscillation suppression in a short time, block the propagation path of unstable oscillation modes, realize rapid oscillation decay and system stability reconstruction, and has good robustness and self-healing properties.

[0034] Compared with traditional control architectures and operating strategies, this invention significantly improves dynamic response speed and enhances the system's safety in dealing with internal and external disturbances. It also offers greater control bandwidth and damping injection capability in oscillation suppression, avoiding the diffusion and chain reactions of broadband oscillations. Furthermore, it establishes a more rational charging and discharging management strategy for the energy storage system, reducing stress losses under frequent fluctuations and extending its service life. The overall energy efficiency is also further improved, with lower energy losses and higher conversion efficiency across different operating ranges. In summary, this invention significantly enhances the operational safety, stability, and economy of photovoltaic-storage hybrid systems under complex and uncertain grid conditions. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the overall structure of the system of the present invention.

[0036] Figure 2 This is a schematic diagram of the internal sub-module connections and data flow of the AC / DC side operation status integrated sensing module of the present invention.

[0037] Figure 3 This is a flowchart illustrating the system impedance modeling and frequency domain analysis process of the coupled oscillation risk assessment module of the present invention.

[0038] Figure 4 This is a schematic diagram of the physical adjustment mechanism and closed-loop control circuit of the DC link dynamic impedance optimization control module of the present invention.

[0039] Figure 5 This is a diagram illustrating the instruction generation and virtual impedance compensation structure of the AC-side adaptive reactive power and voltage support control module of the present invention.

[0040] Figure 6 The timing diagram shows the real-time detection and coordinated response of the AC / DC coupled oscillation active suppression closed-loop feedback module.

[0041] Figure 7 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1

[0045] This embodiment discloses a hybrid inverter collaborative energy management system suitable for photovoltaic-storage systems, such as... Figure 1 - Figure 6 As shown, it includes an AC / DC side operation status integrated sensing module, a coupling oscillation risk assessment module, a DC link dynamic impedance optimization control module, an AC side adaptive reactive power and voltage support control module, and an AC / DC coupling oscillation active suppression closed-loop feedback module. These modules are physically integrated into the main control unit of the hybrid inverter.

[0046] The AC / DC side operation state comprehensive perception module consists of an external AC grid impedance and power quality dynamic perception sub-module and an internal DC link dynamic state and power flow monitoring sub-module. The high-frequency voltage and current sampling circuit of the external AC grid impedance and power quality dynamic perception sub-module is deployed at the AC output port of the inverter. A 16-bit synchronous sampling ADC chip is used, with a sampling rate of 125 kHz, corresponding to the actual grid voltage from 0 to 480 V (line voltage) and the actual current from 0 to 1000 A. A instrumentation amplifier with a high common-mode rejection ratio is configured at the front end of the sampling circuit, and a second-order Butterworth low-pass filter is connected at the rear stage to ensure distortion-free acquisition of high-frequency disturbance signals from 50 Hz fundamental frequency to 2 kHz. The disturbance signal injection unit is implemented by a Xilinx Artix-7 FPGA to generate digital signal generation functions, generating a pseudo-random binary sequence (PRBS) with an amplitude of 5% of the rated output current (i.e., 50 A) and a frequency range from 10 Hz to 2 kHz. After being driven by a power operational amplifier, it is injected into the inverter output circuit by means of current command superposition. The injection process lasts for 200 ms, and a complete frequency sweep is performed every 5 minutes to ensure real-time update of the grid impedance identification results. The impedance calculation coprocessor runs the recursive least squares (RLS) online identification algorithm, and the output impedance matrix has a matrix dimension of 3×3 complex matrix, corresponding to the equivalent impedance values of the three-phase grid at the fundamental frequency, 3rd, 5th, 7th, 11th, 13th, 17th, and 19th harmonic frequency points. The power quality analysis unit synchronously performs FFT operations, calculates the total harmonic distortion rate (THD), the harmonic content rate of each order (HR), the voltage deviation rate (ΔU / U0), and the frequency offset (Δf), classifies the grid strength, and outputs the grid stiffness index GSI (Grid Stiffness Index), which is defined as the reciprocal of the ratio of the fundamental frequency impedance modulus to the rated load impedance. GSI>1.0 represents a strong grid, 0.3<GSI≤1.0 represents a medium grid, and GSI≤0.3 represents a weak grid.

[0047] The DC bus voltage and current sensors of the internal DC link dynamic status and power flow monitoring submodule are converted by an ADC and sent to the main control unit. The power module status acquisition interface communicates with the photovoltaic MPPT controller, the battery bidirectional DC / DC converter, and the DC input controller of the main inverter, polling the output power value, power change rate dP / dt, and operating mode identifier (MPPT / constant voltage / constant current / standby) of each module every 10ms. After receiving the above data, the dynamic stability evaluation unit first performs a moving average filter on the original DC bus voltage sampling sequence with a window length of 10ms (500 sampling points) to eliminate 100kHz switching ripple interference and extract the effective voltage fluctuation component. The peak-to-peak voltage ripple Vpp is calculated as the difference between the maximum and minimum values ​​within the current window, and the root mean square value Vrms is calculated as the square root of the sum of squares of the voltage deviations within the window. The ripple suppression ratio is Vpp / Vdc_nominal, where Vdc_nominal is the rated DC bus voltage of 800V. The dynamic response bandwidth is modeled using a first-order differential equation: C·dVdc / dt=Pin-Pout, where C is the total capacitance of the DC bus support capacitor (4000μF), Pin is the total input power of the photovoltaic and battery, and Pout is the output power of the main inverter. The relationship between dVdc / dt and (Pin-Pout) is fitted using the least squares method to calculate the equivalent time constant τ=C·ΔVdc / ΔP, and the dynamic response bandwidth fBW=1 / (2πτ). The disturbance rejection rating is defined as S_dcs=1-(Vpp / 50V)-(τ / 10ms), with a rating range of 0 to 1. A higher rating indicates stronger DC-side buffering capability.

[0048] The coupled oscillation risk assessment module consists of a system impedance modeling unit, a frequency domain stability analysis unit, and an oscillation mode identification unit. The system impedance modeling unit receives the external grid impedance matrix Zg(f) and internal DC link parameters (C=4000μF, τ=8ms, S_dcs=0.85), combined with the inherent parameters of the hybrid inverter: LCL filter inductance L1=300μH, L2=150μH, capacitor Cf=30μF; PLL bandwidth f_pll=20Hz, proportional gain Kp_pll=5.0, integration time constant Ti_pll=0.1s; current inner loop proportional gain Kp_i=0.8, integration time constant Ti_i=0.5ms; switching device parasitic resistance Rsw=10mΩ, parasitic inductance Lsw=20nH. A modular modeling approach is adopted, decomposing the system into seven sub-modules: the DC bus dynamic module transfer function G_dc(s) = 1 / (C·s); the photovoltaic MPPT module is equivalent to a constant power source with an output impedance Z_pv(s) = ∞; the battery DC / DC module is equivalent to a constant current source in charging mode and a voltage source in discharging mode, with the output impedance Z_bat(s) switching according to the mode; the phase-locked loop module transfer function G_pll(s) = Kp_pll·(1+1 / (Ti_pll·s)); the current inner loop module transfer function G_i(s) = Kp_i·(1+1 / (Ti_i·s)); the LCL filter module state space model is dx / dt = A·x + B·u, y = C·x, where x = [iL1, vCf, iL2]^T, u = [Vdc, Vg]^T, y = [iL2]^T; the grid impedance module directly inputs Zg(f). The various modules are interconnected via feedback to form a closed loop for the entire control system. The final state-space equation has a 7th-order dimension, and the state variables include ΔVdc, ΔiL1, ΔvCf, ΔiL2, Δθ_pll, Δxi_pll, and Δxi_i, where Δxi_pll and Δxi_i are the state variables of the phase-locked loop and the current inner-loop integrator, respectively. The frequency domain stability analysis unit performs eigenvalue decomposition on the above state-space model in the Matlab / Simulink environment, with a frequency scan range of 0.1Hz to 5kHz and a step size of 1Hz, to calculate the Nyquist curve of the open-loop transfer function Zout(f) / Zin(f), where Zout(f) is the output impedance of the inverter, and Zin(f) is the input impedance of the grid. The system stability criteria are that the Nyquist curve does not enclose the point (-1,j0), and the phase margin is greater than 30° and the gain margin is greater than 6dB. The oscillation mode recognition unit performs modal analysis on the eigenvalue λ=σ±jω, calculates the damping ratio ζ=-σ / √(σ²+ω²), and the participation factor PF_k=|v_k·w_k|, where v_k is the k-th component of the right eigenvector and w_k is the k-th component of the left eigenvector.The high-risk oscillation criterion is ζ < 0.05 and 100 Hz < f < 2000 Hz, and the output risk level R_osc is: R_osc = 0 (no risk), R_osc = 1 (low risk, ζ ≥ 0.05 or f ≤ 100 Hz or f ≥ 2000 Hz), R_osc = 2 (medium risk, 0.02 ≤ ζ < 0.05), R_osc = 3 (high risk, ζ < 0.02).

[0049] The DC link dynamic impedance optimization control module consists of an impedance target value calculation unit, a controllable impedance adjustment unit, and an impedance adjustment actuator. The impedance target value calculation unit receives four types of input parameters: R_osc, dominant oscillation frequency f_dominant, S_dcs, and GSI, and executes a sequential quadratic programming (SQP) optimization algorithm. The objective function is defined as J = w1·(Vpp / 50V)² + w2·(1-S_dcs)² + w3·exp(-|f_dominant-f_res| / 100Hz), where f_res is the DC link LC resonant frequency, and w1, w2, and w3 are dynamic weighting coefficients. The weighting rules are as follows: when GSI ≤ 0.3, w1 = 0.6, w2 = 0.2, w3 = 0.2; when dP_pv / dt > 10kW / s, w1 = 0.2, w2 = 0.7, w3 = 0.1; for other operating conditions, w1 = 0.4, w2 = 0.3, w3 = 0.3. Constraints include: DC bus voltage fluctuation range 750V ≤ Vdc ≤ 850V; maximum charge / discharge current of the energy storage battery ±300A; variable inductor adjustment range 50μH ≤ L_adj ≤ 500μH; switchable capacitor adjustment range 0μF ≤ C_adj ≤ 1200μF. The SQP algorithm is implemented on an ADSP-SC589DSP, with a maximum of 20 iterations and a convergence accuracy of 1e-4. The output target impedance matrix Z_target(f) is in complex form, containing impedance values ​​at four key frequency points: 100Hz, 500Hz, 1kHz, and 2kHz. The controllable impedance adjustment unit consists of a variable inductor array, a switchable capacitor bank, and a high-speed solid-state relay matrix. The variable inductor array contains three independent windings, each with an inductance of L0 = 200μH. A stepper motor drives the core displacement, with a displacement of 0 to 10mm corresponding to an inductance of 50μH to 500μH. The switchable capacitor bank consists of 12 film capacitors connected in parallel, each with a capacitance of 100μF, a withstand voltage of 1200V, and an ESR < 5mΩ. Switching is achieved through a solid-state relay matrix, with a relay on-time < 1ms, an off-time < 0.5ms, and a maximum switching current of 50A. After receiving Z_target(f), the impedance adjustment actuator first calculates the required total inductance L_total and total capacitance C_total, using the formula Z_target(jω) = jωL_total + 1 / (jωC_total). L_total and C_total are then solved by fitting data from four frequency points using the least squares method. The adjustment task is then assigned: first, the capacitor bank is adjusted so that f_res = 1 / (2π√(L0·C_total)) avoids the f_dominant ±50Hz range; second, the inductance value is adjusted so that the damping ratio ζ = R_total / (2√(L_total / C_total)) > 0.1, where R_total is the equivalent series resistance, with a value of 10mΩ.The adjustment process adopts a gradual adjustment strategy, with each adjustment not exceeding 20% ​​of the total amount, to ensure no voltage or current surges.

[0050] The AC-side adaptive reactive power and voltage support control module consists of a reactive power target value collaborative calculation unit, a voltage support command generation unit, and an impedance characteristic adjustment unit. The reactive power target value collaborative calculation unit receives four types of inputs: R_osc, Zg(f), Z_dc(f), and Q_ref_grid, and executes a model predictive control (MPC) algorithm. The prediction time domain Np = 50 steps (50ms), the control time domain Nc = 10 steps (10ms), and the sampling period Ts = 1ms. The state variable x(k) = [Vpcc(k), Q(k), Vdc(k)]^T, and the control variable u(k) = [ΔVd(k), ΔVq(k)]^T. The system's discrete state equation is x(k+1) = A·x(k) + B·u(k), where matrices A and B are obtained by discretizing the small-signal model. The objective function is J_mpc = Σ_{i=1}^{Np}[w_v·(Vpcc(k+i)-Vref)²+w_q·(Q(k+i)-Q_ref)²+w_ζ·exp(-ζ(k+i) / 0.05)], with constraints including: |S|≤S_rated=500kVA; |ΔVd|≤50V,|ΔVq|≤50V; dQ / dt≤50kVar / ms. The weighting coefficients w_v, w_q, and w_ζ are dynamically adjusted according to R_osc: when R_osc=0, w_v=0.5, w_q=0.5, w_ζ=0; when R_osc=3, w_v=0.3, w_q=0.2, w_ζ=0.5. The optimal reactive power instruction Q_opt(k) is output through the MPC algorithm. The voltage support command generation unit calculates the dq-axis voltage command based on Q_opt(k) and the current active power P(k): Vd_ref=Vdc / √2·cosδ, Vq_ref=Vdc / √2·sinδ, where δ=arctan(Q_opt / P). Switching signals are generated using Space Vector Pulse Width Modulation (SVPWM) with a carrier frequency of 100kHz and a dead time of 200ns. The impedance characteristic adjustment unit introduces virtual impedance compensation in the inner current loop PI controller: Vd_comp=Kp·Δid+Kd·d(Δid) / dt, Vq_comp=Kp·Δiq+Kd·d(Δiq) / dt, where Δid and Δiq are the d-axis and q-axis current errors, respectively. The proportional gain Kp=Rg·α, where Rg is the real part of the grid impedance, and α is an adaptive coefficient; α=0.5 when R_osc=0 and α=2.0 when R_osc=3. The differential gain Kd = 2πf0·Lg·β, where f0 is the f_dominant, Lg is the imaginary part of the grid impedance, and β is 0.8 when R_osc = 0 and 1.5 when R_osc = 3. The compensation term is added to the PI output, so that the inverter output impedance has a positive damping characteristic near the f_dominant.

[0051] The AC / DC coupled oscillation active suppression closed-loop feedback module consists of an oscillation characteristic real-time detection unit, a collaborative adjustment command generation unit, and a fast response execution unit. The oscillation characteristic real-time detection unit applies an improved Goertzel algorithm to the AC / DC voltage and current sampling data, performing narrowband detection within the f_dominant ± 50Hz frequency band. Algorithm parameters: sampling rate fs = 50kHz, detection frequency f0 = f_dominant, window length N = 1000 points (20ms), window overlap rate 50%. The Goertzel recursive formula is: y(n) = x(n) + 2cos(2πf0 / fs)·y(n-1) - y(n-2), and the energy calculation is E = y(N)² + y(N-1)² - 2cos(2πf0 / fs)·y(N)·y(N-1). When E increases by more than 10% for three consecutive windows and f0 ∈ [100Hz, 2000Hz], an oscillation trigger signal is output, with a detection delay < 5ms. Upon receiving the oscillation trigger signal, the coordinated adjustment command generation unit immediately interrupts the regular control program and initiates the emergency suppression program. It outputs an impedance compensation signal to the DC side: ΔZ_dc_comp = -0.3·|Z_dc(f0)|·exp(j(φ_i+π)), where φ_i is the detected oscillating current phase, and the compensation amplitude does not exceed 30% of the current impedance amplitude. It also outputs a reactive power disturbance signal to the AC side: ΔQ_comp = 0.2·Q_rated·exp(jφ_v), where φ_v is the oscillating voltage phase, and the disturbance amplitude does not exceed 20% of the rated reactive power. The fast response execution unit uses hardware interrupts to send the above signals to the actuator within 1ms: the DC side updates the inductor and capacitor adjustment target values ​​via direct memory access; the AC side updates the SVPWM modulation ratio and phase angle via a high-priority task queue. The physical parameter changes are completed within 5ms, forming a negative feedback suppression closed loop.

[0052] In the system hardware, the main power electronic circuit uses a silicon carbide MOSFET, model Wolfspeed C3M0065170K, and the drive circuit uses a UCC21520 isolated driver. The DC bus support capacitor uses a Vishay 340632 film capacitor. The LCL filter inductor uses an iron-silicon-aluminum magnetic core, L1=300μH, DC resistance R1=5mΩ; L2=150μH, R2=3mΩ; the filter capacitor Cf=30μF, ESR=10mΩ. An operational amplifier is placed at the front end of the sensor signal conditioning circuit, and an ADC is connected to the subsequent stage.

[0053] Example 2

[0054] This invention provides a hybrid inverter collaborative energy management method suitable for photovoltaic-storage systems. By sensing the impedance characteristics of the external AC grid and the dynamic response characteristics of the internal DC link, a collaborative matching mechanism for AC and DC dynamic impedance is established. This enables coordinated optimization of the overall stability of the photovoltaic-storage system and suppression of internal and external coupled oscillations. The method breaks away from the traditional control architecture where the AC and DC sides operate independently with fixed parameters. It couples the reactive power regulation capability of the AC side with the equivalent impedance reconstruction capability of the DC side. Under the conditions of instantaneous disturbances in the external grid or severe power fluctuations in the internal grid, the global impedance distribution of the system is adaptively adjusted. On the one hand, this maintains the stability of the DC bus voltage, and on the other hand, it improves the voltage support capability of the AC grid connection point, fundamentally weakening the physical basis for AC-DC coupled oscillations.

[0055] In this embodiment, such as Figure 7 As shown, the method includes the following steps: Step 1, initiate comprehensive sensing of AC / DC side operating status, periodically perform external AC grid impedance identification and power quality assessment, and constantly monitor the voltage, current, and power flow status of the internal DC link; Step 2, construct a small-signal impedance model of the system based on the sensing data, perform frequency domain stability analysis, identify potential coupled oscillation frequency points, and determine the risk level; Step 3, based on the risk assessment results and system operating constraints, calculate the optimal DC link equivalent impedance target value, enabling the controllable impedance adjustment unit to adjust its physical parameters; Step 4, collaboratively calculate the optimal AC side reactive power output and voltage support command, and adjust the inverter output impedance characteristics; Step 5, monitor the system oscillation characteristics in real time, and once oscillation is detected, immediately issue a coordinated high-frequency adjustment command for AC / DC side to implement active suppression; Step 6, continuously evaluate the suppression effect, adjust the adjustment parameters until the oscillation is completely eliminated and the system returns to normal and stable operation.

[0056] In step one, the external AC power grid impedance identification employs a dual-frequency sinusoidal disturbance injection method. The disturbance frequencies are set to 1.1 and 1.2 times the fundamental frequency, respectively, with a disturbance amplitude of 3% of the rated current and an injection duration of 100 milliseconds. The phase difference and amplitude ratio of the voltage and current responses are measured to calculate the impedance value. For internal DC link monitoring, a moving average filter is used to preprocess the raw voltage and current sampling data. The window length is 10 milliseconds to filter out high-frequency switching noise and obtain effective dynamic characteristics.

[0057] In step two, the system's small-signal impedance model is constructed using a modular approach. The hybrid inverter is decomposed into a DC bus dynamic module, a photovoltaic MPPT control module, a battery charge / discharge control module, a phase-locked loop module, a current inner loop control module, an LCL filter module, and a grid impedance module. Each module establishes a transfer function based on actual control and circuit parameters. Finally, the modules are integrated into a complete system model through state-space serial connection and feedback connections. Frequency domain stability analysis is performed on the system using the generalized Nyquist criterion and the impedance ratio trajectory in the dq coordinate system to determine the stability boundary.

[0058] In step three, the target value of the optimal DC link equivalent impedance is calculated using a weighted objective function, with the weighting coefficients dynamically adjusted according to the current operating conditions: when the external grid rigidity index is less than 0.3, the voltage ripple suppression weight is 0.6, and the oscillation frequency avoidance weight is 0.4; when the external grid photovoltaic power change rate is greater than 10kW / s, the internal buffering capacity weight is 0.7, and the remaining weights are 0.3. When the controllable impedance adjustment unit is adjusted, the capacitor bank is first adjusted to change the resonant frequency, and then the inductance value is adjusted to change the damping characteristics, achieving smooth adjustment without impact.

[0059] In step four, the AC-side reactive power collaborative calculation is performed using rolling time-domain optimization. The prediction time-domain length is 50 milliseconds, the control time-domain length is 10 milliseconds, the state variables are the grid connection point voltage amplitude, reactive power output value, and DC bus voltage value, and the control variable is the dq-axis voltage command increment. The proportional gain Kp in the virtual impedance compensation term is calculated as Kp = Rg × α, where Rg is the real part of the grid impedance, α is an adaptive coefficient ranging from 0.5 to 2.0, dynamically adjusted according to the oscillation risk level; the differential gain Kd is calculated as Kd = 2πf0 × Lg × β, where f0 is the dominant oscillation frequency, Lg is the imaginary part of the grid impedance, and β is an adaptive coefficient, β ∈ [0.8 to 1.5], set according to the oscillation risk level.

[0060] In step five, the real-time oscillation characteristic detection employs an improved Goertzel algorithm to perform narrowband energy detection at the dominant oscillation frequency point. This method has a lower computational complexity than full-band FFT and a detection delay of less than 5 milliseconds. In the coordinated high-frequency regulation command, the DC-side impedance compensation phase is set to be 180 degrees out of phase with the detected oscillation current, while the AC-side reactive power disturbance phase is set to be the same as the oscillation voltage phase, forming precise negative feedback cancellation.

[0061] In step six, the suppression effect is evaluated using the oscillation amplitude decay rate index, defined as the logarithm of the ratio of the amplitudes of the oscillation components within two consecutive 10-millisecond windows. When the decay rate is less than -0.1, the suppression is deemed effective, and the current adjustment is maintained. When the decay rate is greater than 0, the adjustment amplitude is increased until the decay rate is less than -0.1. When the oscillation amplitude is lower than the threshold of 0.5% of the rated value for 100 consecutive milliseconds, the oscillation is deemed eliminated, the emergency suppression mode is exited, and normal optimized control is restored.

[0062] Through the above specific embodiments, this invention fully realizes the collaborative sensing, modeling, optimization and active suppression of dynamic impedance on both AC and DC sides, and constructs a fully closed-loop control system from millisecond-level state perception to microsecond-level execution response.

[0063] Example 3

[0064] Although the systems in the above embodiments can detect, accurately assess, and actively suppress coupled oscillations in a timely manner, the current suppression methods are all in-process suppression, that is, adjustments are made only after the oscillations occur. Such measures are somewhat passive and have certain problems of response lag and insufficient prevention capabilities.

[0065] Therefore, in this embodiment, by deeply integrating multi-source historical operating data (grid impedance, photovoltaic forecast, load trend) and real-time system status, and employing advanced time-series prediction models and anomaly detection methods, based on multi-source and multi-dimensional historical operating data and real-time system status, the system provides early warning of coupled oscillations before they actually occur. Based on the warning information, preventive adjustment commands are issued in advance (such as fine-tuning direct-chain damping, pre-adjusting virtual impedance on the cross-chain side, and dynamically shrinking control bandwidth), thereby avoiding or mitigating coupled oscillations. This upgrades in-process suppression to pre-process prevention and in-process mitigation, thereby improving the robustness and reliability of the system.

[0066] The coupled oscillation risk assessment module further includes a measurement and analysis unit (e.g., located in the DSP or AI unit of the main control unit). This unit is used to issue a predictive alarm and a preventative adjustment command before coupled oscillations occur in the AC / DC system. Based on historical external grid impedance, photovoltaic power generation forecast information, load change trend information, and internal DC link parameters, and using a time-series prediction model or anomaly detection algorithm, a predictive alarm is issued before the AC / DC coupled oscillations occur in the AC / DC system. The alarm is then sent to the AC / DC coupled oscillation active suppression closed-loop feedback module to execute the preventative adjustment command in advance. The time-series prediction model uses an LSTM network, with inputs including historical Zg(f), P_pv, and Vdc sequences (1 minute in length), and outputs including future stability margins. The anomaly detection algorithm uses an isolated forest, with features including GSI gradient, dP_pv / dt, and Vpp variance.

[0067] When the predicted stability margin output by the time-series prediction model is less than a first threshold (e.g., the Nyquist phase margin is less than 45° within the next 15 minutes), and / or the anomaly score output by the anomaly detection algorithm is greater than a second threshold (e.g., the anomaly score is greater than 0.7), the predictive analysis unit issues a predictive coupled oscillation alarm. The potential coupled oscillation warning signal output by the predictive analysis unit is sent to the AC / DC coupled oscillation active suppression closed-loop feedback module or the DC link dynamic impedance optimization control module. The AC / DC coupled oscillation active suppression closed-loop feedback module or the DC link dynamic impedance optimization control module executes one or more preventive adjustment commands in advance, the preventive adjustment commands including instructions;

[0068] 1. Enhanced Dynamic Damping of DC Link: The DC link dynamic impedance optimization control module is instructed to fine-tune the controllable impedance adjustment unit to slightly increase the equivalent resistance (e.g., increase the current value by 5% to 10%), thereby improving the damping characteristics of the DC link.

[0069] 2. AC-side virtual impedance pre-adjustment: Instruct the AC-side adaptive reactive power and voltage support control module to slightly increase the inductive component of the inverter's AC output impedance in its current controller's virtual impedance compensation term (e.g., slightly increase the virtual inductance by 0.5mH) to increase the system's reserve under weak grid conditions.

[0070] 3. Dynamic bandwidth contraction: The bandwidth of the main inverter current or voltage control loop is slightly contracted (for example, the current value is contracted by 10% to 20%) in exchange for a certain stability margin.

[0071] 4. Battery micro-precharge and discharge buffer: Instructs the battery bidirectional DC / DC converter to perform micro-precharge and pre-discharge (e.g., discharging and charging at a rate of 0.05C) to buffer against upcoming shocks.

[0072] The adjustment amount of the preventive adjustment command is a certain percentage (e.g., 20% to 50%) of the adjustment amount preset in the emergency suppression command when the oscillation occurs, and it is issued in the form of a smooth ramp to avoid affecting the normal operation of the system. After the preventive adjustment command is executed, operation continues. If the risk decreases, the preventive adjustment is gradually withdrawn; if the risk persists or intensifies, the preventive strategy is further adjusted until the risk is eliminated or the system enters active suppression mode.

[0073] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A hybrid inverter-coordinated energy management system suitable for photovoltaic-storage systems, characterized in that, The system includes: The AC / DC side operation status integrated sensing module is used to acquire the impedance characteristics and power quality information of the external AC power grid in real time, as well as the dynamic status and power flow information of the internal DC link. The coupled oscillation risk assessment module is used to construct a small-signal model of the system and analyze the frequency domain stability based on the perceived information, so as to identify the dominant oscillation frequency and the corresponding risk level. The DC link dynamic impedance optimization control module is used to calculate the optimal DC link equivalent impedance target value according to the risk level and system constraints, and dynamically adjust the impedance characteristics of the DC link through the controllable impedance adjustment unit. An AC-side adaptive reactive power and voltage support control module is used to collaboratively generate reactive power output commands and voltage support commands based on the aforementioned risk level and grid impedance characteristics, in order to adjust the inverter's AC output impedance characteristics; and The AC / DC coupled oscillation active suppression closed-loop feedback module is used to detect oscillation characteristics in real time. When oscillation is detected to have started, it generates AC / DC coordinated adjustment commands and sends them in parallel to the DC link dynamic impedance optimization control module and the AC side adaptive reactive power and voltage support control module to achieve rapid coordinated suppression of oscillation.

2. The system according to claim 1, characterized in that, The AC / DC side operation status integrated sensing module includes an external AC grid impedance and power quality dynamic sensing submodule and an internal DC link dynamic status and power flow monitoring submodule. The external AC grid impedance and power quality dynamic sensing submodule is equipped with a disturbance signal injection unit, which is used to inject disturbance signals of preset type, amplitude and frequency range, and to calculate the AC equivalent impedance matrix using an online identification algorithm. The internal DC link dynamic status and power flow monitoring submodule is used to collect DC bus voltage and current, and calculate voltage ripple index and dynamic response parameters to evaluate DC link stability.

3. The system according to claim 1, characterized in that, The coupled oscillation risk assessment module constructs a multi-order state-space small-signal impedance model and performs Nyquist stability criterion analysis and eigenvalue mode decomposition to identify the dominant oscillation frequency, damping ratio and corresponding risk level. The analysis covers a preset wide frequency range.

4. The system according to claim 1, characterized in that, The controllable impedance adjustment unit in the DC link dynamic impedance optimization control module includes a variable inductor array and a switchable capacitor bank. The DC link dynamic impedance optimization control module calculates the optimal DC link equivalent impedance target value by combining the system small-signal model and risk assessment results through a multi-objective optimization algorithm. The objective function comprehensively considers voltage ripple suppression, DC link anti-interference capability and oscillation frequency avoidance, and drives the controllable impedance adjustment unit to adjust physical parameters according to the target value. The adjustment process adopts a gradual step strategy.

5. The system according to claim 1, characterized in that, The AC-side adaptive reactive power and voltage support control module uses a model predictive control algorithm to generate the reactive power output trajectory and introduces a virtual impedance compensation term in the current control loop. The gain parameter of the compensation term is related to the grid impedance characteristics and the dominant oscillation frequency, and the gain coefficient is dynamically adjusted according to the risk level to improve the stability of the inverter's interaction with the external grid.

6. The system according to claim 1, characterized in that, The AC / DC coupling oscillation active suppression closed-loop feedback module uses a narrowband energy detection algorithm to detect oscillation characteristics in real time. When oscillation is detected to have started, it sends a DC-side impedance compensation command with anti-phase characteristics to the DC link dynamic impedance optimization control module and an AC-side reactive power disturbance command with in-phase characteristics to the AC-side adaptive reactive power and voltage support control module to achieve millisecond-level coordinated suppression.

7. The system according to claim 1, characterized in that, The main control unit uses a high-performance multi-core processor running a real-time operating system, the high-speed data bus uses a high-bandwidth serial interface, the power electronic switching devices use wide-bandgap semiconductors, the sensor signal conditioning circuit includes preamplifier and anti-aliasing filter, and the communication interface module supports multiple industrial standard protocols to ensure high system reliability, fast response capability and seamless integration with photovoltaic and energy storage components.

8. A hybrid inverter collaborative energy management method suitable for photovoltaic-storage systems, characterized in that, Includes the following steps: S1: Periodically perform AC / DC side operation status sensing to obtain the impedance characteristics and power quality information of the external AC power grid in real time, as well as the dynamic status and power flow information of the internal DC link. S2: Based on the perceived information, construct a small-signal model of the system and analyze its frequency domain stability to identify the dominant oscillation frequency and the corresponding risk level; S3: Based on the risk level and system constraints, calculate the target value of the optimal DC link equivalent impedance, and dynamically adjust the impedance characteristics of the DC link through the controllable impedance adjustment unit; S4: Based on the risk level and grid impedance characteristics, generate reactive power output command and voltage support command in a coordinated manner to adjust the AC output impedance characteristics of the inverter. as well as S5: Real-time detection of oscillation characteristics. When oscillation is detected to have started, an AC / DC coordinated adjustment command is generated and sent in parallel to the DC link dynamic impedance optimization control module and the AC side adaptive reactive power and voltage support control module to achieve rapid coordinated suppression of oscillation.

9. The method according to claim 2, characterized in that, In step S1, the impedance characteristics of the external AC power grid are obtained by injecting pseudo-random or sinusoidal disturbance signals and combining them with an online identification algorithm; in step S2, the small-signal model of the system is constructed using a modular method, and the impedance ratio trajectory in the dq coordinate system is analyzed based on the generalized Nyquist criterion to evaluate stability.

10. The method according to claim 2, characterized in that, In step S3, the target value of the optimal DC link equivalent impedance is calculated through a weighted objective function. The weights are dynamically adjusted according to the grid strength and power change rate. The capacitor in the controllable impedance adjustment unit is adjusted first to change the resonant frequency, and the inductor is adjusted second to improve the damping characteristics. In step S4, the reactive power output trajectory on the AC side is optimized through a model predictive control algorithm, and a virtual impedance compensation term is introduced into the current control loop. The gain parameter of this compensation term is adaptively adjusted according to the oscillation risk level. In step S5, the oscillation detection adopts a narrowband energy detection algorithm. The DC side impedance compensation has an anti-phase characteristic, the AC side reactive power disturbance has an in-phase characteristic, and the total delay from detection to execution meets the millisecond requirement.

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