Broadband oscillation positioning method and system based on internal control state characteristics of converter

Abstract

The invention discloses a broadband oscillation positioning method and system based on internal control state characteristics of a converter, and belongs to the technical field of power system stability control and new energy power generation. Comprising the following steps: constructing a converter internal control state monitoring system, and synchronously collecting key intermediate variables; calculating control error energy, loop causal interaction and internal potential sensitivity impedance multi-dimensional indexes based on the acquired data; in combination with the station topology and the electrical distance, adaptively selecting an index combination and carrying out weighted fusion; and by comparing the relative strength of the indexes with the sequential relationship, the oscillation source unit and the instability control link are positioned. According to the method, the black box limitation of the converter is broken through, precise positioning is realized by utilizing the microscopic difference of internal control signals, the problem of positioning failure in a homogenized unit scene in the prior art is solved, large-area generator tripping is not needed, the new energy utilization rate and the oscillation treatment efficiency are improved, and a support is provided for stable operation of a novel power system.

Description

Technical Field

[0001] This invention belongs to the field of power system stability control and new energy power generation technology, specifically relating to a broadband oscillation positioning method and system based on the internal control state characteristics of a converter. Background Technology

[0002] Unlike traditional synchronous generator-dominated power systems, new energy power generation units, represented by wind power and photovoltaics, are mainly connected to the grid through power electronic converters. This fundamental change in energy structure has shifted the dynamic characteristics of the power system from being dominated by "electromechanical transients" to being dominated by "electromagnetic transients," thereby triggering broadband oscillation problems with an extremely wide frequency range (from subsynchronous oscillations of a few hertz to high-frequency oscillations of several kilohertz).

[0003] Current broadband oscillation monitoring and tracing technologies are mainly divided into three categories: 1. Impedance Analysis Method: This method obtains the impedance characteristics of the point of connection (PCC) through online measurement or offline modeling, and analyzes stability using the Nyquist criterion. While theoretically rigorous, this method heavily relies on accurate system-side and equipment-side impedance models. In practical engineering, grid operation modes are highly variable, and converter control parameters are often considered trade secrets (black boxes) by manufacturers, making it difficult to obtain accurate impedance models and reflect oscillation sources in real time.

[0004] 2. Energy function-based method: Calculate the branch potential energy or dissipated energy flow direction to determine the source of oscillation energy. This method performs well in forced oscillation source localization, but within new energy power plants, due to short collector lines and low impedance, oscillation energy is rapidly exchanged and dissipated between units, often resulting in unclear energy flow direction and even misjudgment.

[0005] 3. Signal processing based on port electrical quantities: This involves using FFT, Prony, or wavelet analysis on the voltage and current signals at the PCC point. However, some studies indicate that oscillating signals exhibit "ripple-like" propagation characteristics. When an oscillation occurs in a unit within the station (such as a feeder-end unit), the oscillation wave propagates rapidly along the collector line, exciting nearby units with similar control parameters (homogeneous) to resonate. At this time, the port voltage and current of all units in the entire station exhibit large oscillations at the same frequency. Based solely on port external characteristics, it is impossible to effectively distinguish between the oscillation source and passive response equipment.

[0006] To address the aforementioned pain points, it is essential to break free from the black-box nature of the converter and delve into the internal control system to trace the source of the oscillations. Extensive simulation analyses and real-world case studies demonstrate that the root cause of oscillations often lies in the instability of a specific control loop within the converter (such as an excessively large PLL bandwidth setting, or a mismatch between the current inner loop PI parameters and the weak grid impedance). In the initial stages of oscillation, although the external ports may exhibit similar behavior, the control error signal within the oscillation source unit and the passive adjustment signal within the affected unit differ fundamentally in energy amplitude, phase relationship, and causal direction. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a broadband oscillation location method and system based on the internal control state characteristics of the converter, which addresses the shortcomings of the prior art. It utilizes microscopic differences to achieve accurate location of the oscillation source and unstable link, and solves the technical problem that the existing broadband oscillation monitoring technology only relies on the electrical quantity at the grid connection point, which makes it difficult to accurately locate the oscillation source in scenarios where the new energy power station units are highly homogeneous and the electrical distances are similar.

[0008] The present invention adopts the following technical solution: A broadband oscillation localization method based on the internal control state characteristics of a converter includes the following steps: S1. Construct an internal control status monitoring system for the converter to synchronously collect at least one key intermediate variable in the internal control loop of the converter when wideband oscillation occurs. S2. Based on the at least one key intermediate variable, calculate the control unit oscillation characteristic index set, including the control error energy index, the loop causal interaction index, and the internal potential sensitivity impedance index. S3. Combining the topology and electrical distance of the new energy power station, and adaptively selecting the combination of indicators from the set of oscillation characteristic indicators of the control unit according to the requirements of computing resources and control accuracy, and weighting and fusing the selected indicators; S4. Based on the weighted fusion results, by comparing the relative strength and timing lead-lag relationship of the characteristic indicators of each unit control unit, the oscillation source unit that causes the oscillation and its instability control link are located.

[0009] Preferably, in step S1, the frequency of synchronous acquisition is greater than or equal to 10kHz, and DMA technology is used to transfer key intermediate variables in the control closed loop to a circular buffer. The key intermediate variables include the d-axis voltage component of the phase-locked loop domain. q-axis voltage component Phase-locked loop output phase ,frequency The d-axis current of the current loop is given. Feedback q-axis current given Feedback Current loop PI output and the DC bus voltage of the outer ring domain Active / reactive power deviation.

[0010] Preferably, in step S2, the calculation process of the control error energy index is as follows: define the control error of any control loop j. ,in, For a given value, This is the feedback value; Calculation of oscillation frequency based on Passevar's theorem Nearby band error energy ;in, for Fourier transform; Introducing a normalization factor Calculate the comprehensive index ,in, Weights set based on sensitivity analysis; JCEE of all organic groups in the site was normalized. ,in, For the unit number, This represents the total number of generating units.

[0011] Preferably, in step S2, the calculation process of the loop causal interaction index is as follows: constructing the time series of internal variables of the unit. and common bus voltage time series And perform state space reconstruction; The KSG estimation method is used to calculate the transfer entropy. The formula for the transfer entropy is: , It is the Digamma function; Calculate net information flow ,in, The transfer entropy from internal unit variables to PCC voltage. This represents the transfer entropy from the PCC voltage to the internal variables of the unit.

[0012] Preferably, the state space reconstruction process is as follows: The historical lengths are set as follows: and The prediction step size is 1. For each time t, future state , Historical vectors: and Historical vectors: And combine them into high-dimensional joint spatial data points .

[0013] Preferably, in step S2, the calculation process for the internal potential sensitivity impedance index is as follows: Equivalent grid impedance at the grid connection point of computer group k

[0014] in, For system impedance, For transformer impedance, Let be the frequency-dependent impedance of the m-th collector line segment; The converter output admittance is obtained based on real-time parameter identification. Extract the oscillation frequency Real part of lower admittance ;like If the value is significantly negative and has the largest negative amplitude, then the unit is marked as a candidate for an oscillation source.

[0015] Preferably, the frequency-dependent impedance of the m-th collector line segment Let R+jωL be the value of ω, where ω is calculated using the oscillation frequency ωosc of the wideband oscillation.

[0016] Preferably, in step S3, the strategy for adaptively selecting the combination of indicators includes a fast adaptive mode and a precise mode; The fast adaptive mode selects only the control error energy index when computing resources are limited or in the early stage of oscillation, and quickly disconnects the unit with the largest error energy. In the precise mode, when offline analysis is performed or when computing power is sufficient, the control error energy index, loop causal interaction index, and internal potential sensitivity impedance index are jointly selected and weighted fusion is performed in conjunction with the site topology information.

[0017] Preferably, in step S3, a topology correction factor is introduced during weighted fusion.

[0018] in, The cumulative line impedance from PCC to unit k. As the reference impedance, For correction coefficients; final discrimination index for:

[0019] in, Let {k} be the control error energy index value of the unit.

[0020] Secondly, embodiments of the present invention provide a wideband oscillation positioning system based on the internal control state characteristics of a converter, comprising: The monitoring module is used to build an internal control status monitoring system for the converter, and synchronously collect at least one key intermediate variable in the internal control loop of the converter when wideband oscillation occurs. The index module is used to calculate a set of control unit oscillation characteristic indices, including control error energy index, loop causal interaction index and internal potential sensitivity impedance index, based on the at least one key intermediate variable. The fusion module is used to combine the topology and electrical distance of the new energy power station, adaptively select the combination of indicators from the set of oscillation characteristic indicators of the control unit according to the requirements of computing resources and control accuracy, weight and fuse the selected indicators, and output decision information based on the fusion result. The positioning module is used to receive the judgment information, locate the oscillation source unit that causes the oscillation and its instability control link by comparing the relative strength of the characteristic indicators of each unit control unit and the timing lead-lag relationship, and output the corresponding control command or parameter correction suggestion.

[0021] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the aforementioned wideband oscillation positioning method based on the internal control state characteristics of a converter.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described wideband oscillation positioning method based on the internal control state characteristics of a converter.

[0023] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the aforementioned wideband oscillation positioning method based on the internal control state characteristics of a converter.

[0024] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described wideband oscillation positioning method based on the internal control state characteristics of a converter.

[0025] Compared with the prior art, the present invention has at least the following beneficial effects: A broadband oscillation localization method based on the internal control state characteristics of converters is proposed. This method constructs a converter internal control state monitoring system, combining control error energy, loop causal interaction, and internal potential sensitivity impedance multi-dimensional indicators, and integrates station topology information to achieve adaptive weighted decision-making. It accurately locates the oscillation source and instability control links in homogeneous unit groups, solving the problems of difficult broadband oscillation source tracing and high misjudgment rate. Without large-scale unit shutdown, oscillations can be quelled simply by precise parameter adjustment or control of a single unit, improving the utilization rate of renewable energy and significantly enhancing the accuracy and efficiency of broadband oscillation management, providing technical support for the stable operation of new power systems.

[0026] Furthermore, the selected key intermediate variables cover core control loops such as the phase-locked loop, current loop, and outer loop, comprehensively reflecting the internal control status of the converter. Compared to the traditional method of only collecting port voltage and current, these internal variables can directly reflect the operating status of the control loop, providing first-hand accurate data for subsequent indicator calculations.

[0027] Furthermore, by introducing normalization factors and weighting coefficients, the influence of differences in operating conditions on the indicators is eliminated, adapting to different operating scenarios. Through the normalization of all unit indicators, candidate oscillation sources with the largest error energy can be quickly screened out. The calculation logic is simple, the response speed is fast, and it is suitable for initial screening scenarios with high real-time requirements. Utilizing the essential characteristics of the oscillation source unit's internal control error, which oscillates continuously and significantly with energy significantly higher than that of the disturbed unit, the oscillation source is initially identified, providing a reliable basis for subsequent precise positioning and effectively distinguishing between actively disruptive and passively responding units.

[0028] Furthermore, the transfer entropy exhibits strict asymmetry, enabling precise capture of the causal flow between internal unit variables and PCC voltage, thus resolving the difficulty in discerning causality caused by waveform synchronization in homogeneous units. By calculating the net information flow, it clearly distinguishes between the oscillation source and the disturbed unit, tracing the oscillation propagation path from a mechanistic perspective. Compared to traditional correlation analysis, it can deeply reveal the dynamic driving relationships between variables, adapting to the nonlinear and non-stationary signal characteristics of power electronic systems, and significantly improving the accuracy and robustness of the positioning.

[0029] Furthermore, clearly defining the historical length and prediction step size ensures that the state space fully reflects the temporal characteristics of the variables, avoiding causal judgment biases caused by incomplete data representation. The construction of high-dimensional joint data points completely preserves the spatiotemporal correlation information between variables, enabling the KSG estimation method to accurately statistically analyze local neighbor distributions and improve the accuracy of propagation entropy calculation. A standardized data preprocessing procedure is provided for the reliable calculation of loop causal interaction indices, ensuring the consistency and accuracy of causal relationship analysis.

[0030] Furthermore, the calculation of the equivalent grid impedance considers the frequency correlation of the collector lines, adapting to the frequency characteristics of wideband oscillations and avoiding errors caused by neglecting frequency effects in traditional impedance calculations. By determining whether the real part of the admittance is significantly negative and has the maximum amplitude, the oscillation source unit with negative damping characteristics can be quickly identified, verifying the oscillation source from the physical mechanism of impedance matching. Combining the physical essence of the small-signal model with data-driven indicators complements each other, improving the reliability of location under extreme operating conditions and effectively solving the misjudgment problem that may occur if only data features are relied upon.

[0031] Furthermore, it adapts to the wide frequency coverage of broadband oscillations, accurately reflecting the differences in line impedance at different oscillation frequencies. Precise line impedance calculation provides a reliable foundation for calculating equivalent grid impedance and topology correction factors, ensuring that subsequent weighted fusion results conform to physical laws. This allows the positioning results to reflect both data characteristics and the stability effects of the actual topology, further improving positioning accuracy.

[0032] Furthermore, the precise model combines three types of indicators with topological information to achieve high-precision positioning. The core advantage of this technology lies in balancing real-time performance and accuracy, resolving the conflict between computational resources and control precision in engineering. The hierarchical strategy achieves optimal allocation of computational pressure and communication bandwidth, meeting the needs of scenarios with limited computing power, such as edge gateways, while also providing refined positioning results in offline analysis or when computing power is sufficient, enhancing the method's engineering practicality and adaptability.

[0033] Furthermore, by weighting the control error energy index with a correction factor, the positioning results are made more consistent with the physical laws of the power system, avoiding misjudgments caused by relying solely on data characteristics. This achieves a deep integration of data-driven approaches and physical mechanisms, improving positioning robustness under extreme operating conditions. Under the same control error level, priority is given to identifying weak links in the topology, making the positioning more targeted, further shortening the oscillation mitigation time, and reducing losses in renewable energy generation.

[0034] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0035] In summary, the method of this invention breaks through the limitations of traditional methods that rely solely on port electrical quantities. By mining the microscopic characteristics of the internal control signals of the converter in the early stage of oscillation, and combining a multi-dimensional index set with an adaptive algorithm, it achieves accurate identification of the oscillation source and its fault link in a homogeneous cluster of generators.

[0036] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 A schematic diagram of the internal control structure and monitoring points of the converter; Figure 3 Schematic diagram for calculating and analyzing transfer entropy; Figure 4 This is a schematic diagram illustrating the interaction analysis principle of homogeneous generating units based on transfer entropy. Figure 5 This is a comparison chart of simulation verification results; Figure 6 This is a diagram showing the relationship between the topological impedance distribution of the site and the risk weight. Figure 7 A comparison chart showing the verification of oscillation sources based on machine switching operations; Figure 8 A diagram showing the energy contribution of the internal control loop error of each unit; Figure 9 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 10 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0038] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0039] 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, not all, of the embodiments of the present invention. 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.

[0040] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0041] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0042] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0043] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0044] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0045] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0046] This invention provides a broadband oscillation localization method based on the internal control state characteristics of a converter. First, an intermediate control process quantity acquisition system is constructed, including a converter phase-locked loop (PLL), an inner current loop, an outer power loop, and a modulation element. Upon detecting the onset of oscillation, the key deviation signal within the control unit is synchronously triggered for acquisition. Second, a multi-dimensional set of control unit oscillation characteristic indicators is established, including control error energy indicators, loop coupling causality index, and internal potential sensitivity indicators. Indicator combinations are adaptively selected based on computational resources and control accuracy requirements. Third, considering the topology and electrical distance of the renewable energy power plant, graph theory and transfer entropy theory are used to quantify the transmission path of oscillation energy between control loops and between units. Finally, by comparing the relative strength and timing lead-lag relationships of the characteristic indicators of each unit's control unit, the specific unit causing the oscillation and its unstable control element (such as PLL lockout or inner loop parameter mismatch) are accurately located, and targeted control commands or parameter correction suggestions are output. This invention utilizes internal data to directly observe the operating state of the control system, avoiding the blind guessing based solely on external waveforms. By comparing causal relationships and error energy, the oscillation source and the disturbed unit were effectively distinguished, solving the positioning failure problem caused by simultaneous vibration across the entire field when homogeneous units experience group vibration. Directly linked to specific control parameters, this enabled a refined shift from large-area unit switching to precise control and detailed parameter tuning, improving the utilization of renewable energy. It also achieved a leap from monitoring external characteristics to tracing the internal control mechanism, significantly enhancing the accuracy and efficiency of broadband oscillation management.

[0047] Please see Figure 2 The prerequisite for implementing this invention is to build a hardware foundation with the ability to monitor the internal control process of the converter. The system architecture is divided into three layers: Equipment layer (new energy substation): A monitoring subroutine is embedded in the controller (usually a DSP+FPGA architecture) of the photovoltaic inverter or wind power converter. This program utilizes DMA (Direct Memory Access) technology to transfer intermediate variables in the control closed loop at a frequency of no less than 10kHz. (etc.) are transferred to the circular buffer.

[0048] Aggregation Layer (Regional Master Station): When an oscillation is detected at the PCC point (triggered by a threshold, such as total harmonic distortion of voltage THD>2%), the data in the device layer buffer is frozen and read via fiber optic Ethernet, and the topology status of the new energy power station at this time (which switches are closed, line length, etc.) is obtained.

[0049] Analysis layer (scheduling master station / cloud): Receives data from the aggregation layer and runs the core algorithm of this invention.

[0050] Please see Figure 1 The present invention provides a broadband oscillation localization method based on the internal control state characteristics of a converter, comprising the following steps: S1. Construct a panoramic, visualized white-box internal control status monitoring system. Breaking away from the limitations of traditional converters that only upload slow data such as output power, this approach performs high-frequency sampling (>10kHz) and buffering of key intermediate variables at the FPGA or DSP control level. These key variables include: Phase-locked loop (PLL) domain: d-axis voltage component q-axis voltage component (Reflecting the PLL's tracking of the grid voltage), PLL output phase ,frequency .

[0051] Current loop domain: d-axis current given Feedback q-axis current given Feedback Current loop PI output .

[0052] Outer loop domain: DC bus voltage Active / reactive power deviation.

[0053] This mechanism ensures that first-hand internal data is available when oscillations occur.

[0054] S2. Establish a multi-dimensional set of oscillation characteristic indicators for the control unit. To comprehensively characterize oscillation features, this invention designs three complementary indices: 1. Control Error Energy (CEE): Extensive theoretical research and experimental analysis have shown that instability or oscillation in any control system is essentially due to the non-convergence of the deviation between the control target and the actual value. For oscillation sources, their internal controllers (such as phase-locked loops and current loops) are often in a state of rapid and drastic adjustment of control commands, attempting to respond to and counteract disturbances by significantly adjusting the output. This results in the energy of their internal error signal being significantly higher than that of the disturbed unit. Therefore, an index reflecting control error energy has been proposed, and its calculation formula is as follows:

[0055] in, Represents the unit number. As weight, This is the start time of the oscillation recording. This is the end time of the oscillation recording. This indicator is simple to calculate and has a fast response time, making it suitable for initial screening with high real-time requirements.

[0056] A typical grid-connected converter control system includes an outer loop (DC voltage / power), an inner loop (current), and a synchronization link (PLL). In a synchronously rotating coordinate system, its state-space equations can be described as follows:

[0057] in, These are the state variable vectors for each stage.

[0058] Most existing technologies only focus on output volume However, intermediate variables were ignored. The dynamics of the oscillation source are revealed through theoretical and experimental analysis. The essential characteristic of the oscillation source is that the motion trend of its internal state variables deviates from its control target (Reference), or its control target itself is oscillating violently.

[0059] For any control loop (e.g., d-axis current loop), its control error Defined as a given value With feedback value Difference:

[0060] The physical meaning is that for a stable unit (a disturbed unit), its controller operates within its design bandwidth. Faced with external voltage disturbances, the controller attempts to maintain current tracking of the setpoint through feedback regulation. Because the control parameters are well-matched, the error... Although fluctuations occur, their amplitude is small, mainly manifesting as residuals in the suppression of external disturbances. For unstable units (oscillation sources), the controller parameters become coupled with the grid impedance (e.g., negative damping), causing the controller output to diverge. In this situation, the controller, while trying to "correct" the deviation, actually amplifies it, leading to... It exhibits sustained, large-amplitude oscillations. Its energy is significantly higher than that of the disturbed unit.

[0061] According to Passevar's theorem, time-domain energy equals frequency-domain energy. This is defined at the oscillation frequency. Nearby band error energy for:

[0062] in, for Fourier transform.

[0063] To eliminate the influence of operating conditions (such as power levels), a normalization factor is introduced. (e.g., rated current value). Comprehensive indicators Defined as the weighted sum of the energies of each loop:

[0064] Due to weight The selection is based on sensitivity analysis. For example, in a weak power grid (high impedance) environment, the PLL stage has the greatest impact on system stability. Set it to a relatively large value (e.g., 0.5); however, for high-frequency oscillations, the inner current loop plays a dominant role. Larger.

[0065] Through extensive theoretical and experimental analysis, it has been found that when the system is unstable, the internal components of the converter... Continuous oscillations will occur. Physically, the controller attempts to eliminate the error but fails, resulting in the error signal itself containing the largest amount of oscillation energy information.

[0066] Define converter The d-axis current inner loop error signal is:

[0067] The same applies to the q-axis. Define the PLL output angular frequency deviation as:

[0068] Construct the frequency domain weighted error energy function:

[0069] in, For the unit The spectral amplitude of the d-axis current error, This is the current loop weighting function. Based on numerous simulation examples, broadband oscillations often concentrate in the subsynchronous range (tens of Hz) or mid-to-high frequencies (hundreds of Hz). The weights are designed according to the current loop bandwidth (typically several hundred Hz) to maximize gain near the bandwidth frequency, as this is the region where the inner loop is most prone to instability. This is the weighting function for the phase-locked loop (PLL). PLL instability typically occurs at low frequencies (such as subsynchronous oscillations under weak power grids), therefore, it is given a higher weight in the low-frequency range.

[0070] Criterion logic: Calculate the organic groups in the entire field. And normalize it:

[0071] like Then the unit It serves as an oscillation source.

[0072] Please see Figure 8According to simulation case analysis, when abnormal parameters of the oscillation source unit (such as excessive PLL bandwidth) lead to local instability, the amplitude of its internal PLL error signal will diverge first. Although the oscillation propagation will cause the port voltage of the disturbed unit to oscillate as well, since the control parameters of the disturbed unit are normal, they are passively tracking the distorted voltage. Therefore, their internal control error is much smaller than that of the oscillation source unit that actively causes the disturbance, which theoretically ensures the effectiveness of the CEE index.

[0073] 2. Loop Interaction Causality Index (LICI): To analyze the direction of information flow between control variables, this paper proposes using transfer entropy or Granger causality for location analysis. If the internal state change of unit A statistically significantly precedes or causes the change in the voltage of unit B or the common bus, then unit A is more likely to be the source. Since the causal flow is unidirectional even if the waveforms are similar, this index can effectively solve the problem of homogeneous resonance.

[0074] In complex dynamic systems, identifying the interactions between subsystems is crucial for locating disturbance sources. Traditional monitoring methods (such as cross-correlation analysis and coherence analysis) primarily measure the linear similarity between signals. However, correlation does not imply causation. In highly coupled systems, due to the presence of feedback loops, the response waveform of the disturbed system is often highly synchronized with the source waveform, rendering traditional methods based on "similarity" ineffective.

[0075] Please see Figure 3 This invention introduces Transfer Entropy (TE) from information theory as a core analytical tool. TE is essentially a non-parametric index based on probability statistics, used to quantify the directed, dynamic flow of information between two stochastic processes. Its core physical principle is based on reducing uncertainty. In information theory, entropy represents the uncertainty of a system's state. Transfer entropy measures "the uncertainty of a system's state under known target variables..." Based on its own historical information, if source variables are introduced... Historical information can be used for prediction "How much uncertainty is reduced in the future state?" Unlike correlation, transitive entropy has strict asymmetry, that is... This asymmetry is key to identifying physical causal relationships—the state evolution of the driving source (cause) can predict the state of the responder (effect), but statistically, the state of the responder cannot predict the driving source in reverse.

[0076] Transfer entropy mathematical model The mathematical expression of transfer entropy is based on Shannon entropy and conditional mutual information. For a source signal time series... and target signal time series ,from arrive The transitive entropy is defined as follows:

[0077] in, Indicates from source variable To target variable The propagation entropy value. The larger this value, the more it indicates... right The stronger the predictive power or driving force of the future state, the better. Is it the cause The reasons for the change. Represents conditional Shannon entropy, used to measure the residual uncertainty of a random variable under given conditions. Represent the target variable At the present moment The state value. This is the object we need to predict. Represent the target variable The historical state sequence. Among them, The embedding delay or historical window length represents how much of its past data is used to predict the present.

[0078] First item The physical meaning is: using only Its own historical information predicts the current value At that time, the system still has some uncertainty. Represents source variable The historical state sequence. Second item. The physical meaning is that the random variable is known. In the case of random variables Uncertainty, while utilizing Its own history and Using historical information to predict At that time, the system retains uncertainty. The difference between the above two items represents the uncertainty due to the introduction of... Historical information, thus enabling The amount by which uncertainty is reduced, this reduction is Passed to The information content, in the context of broadband oscillation positioning in this invention, is mathematically based on the magnitude of a statistical quantity derived from a probability distribution. The specific calculation process is as follows: Step 1: Constructing the time series state space First, time series analysis needs to be performed on the collected intermediate control process variables (such as PLL phase angle, current error, etc.): This represents the target variable (e.g., PCC voltage) at future times. The state. Represent the target variable The historical state, among which, This represents the embedding dimension or the length of the history window. This represents the historical state of the source variable (e.g., the unit's internal control variable), where, This represents the length of its historical window.

[0079] Step 2: Calculate the joint probability and conditional probability By using statistical methods such as sliding window or histogram methods, the frequency of the above state variables in the time series can be calculated, thereby estimating the probability distribution: Joint probability: It is the probability that "future state, its own history, and the history of source variables" occur simultaneously.

[0080] Conditional probability: : The probability of a future state occurring when only its own history is known.

[0081] Given its own history and the history of the source variables, the probability of the future state occurring.

[0082] Step 3: Substitute into the logarithmic formula to sum The general formula for calculating conditional Shannon entropy is: In this invention, this process is integrated into the final formula for calculating the transfer entropy (TE). To calculate the transfer entropy, the difference between the two conditional entropies is actually calculated: First calculate (Not introduced) (remaining uncertainty at the time), quantification relies solely on historical data. predict The uncertainty of time. Then calculate. (Introduction) Residual uncertainty after quantification: quantification in the known and In the case of prediction The uncertainty of time.

[0083] The final formula for the transfer entropy is actually the process of subtracting the two conditional entropies mentioned above (the reduction in uncertainty):

[0084] During the calculation process, if the following is introduced Historical information (i.e.) (This allows conditional probability) If it becomes more "certain" (i.e., the probability distribution is more concentrated), then the conditional entropy... The value will decrease. This means Information helps reduce the impact on The uncertainty in predictions leads to a positive net information flow.

[0085] In practical engineering, when faced with continuously changing power waveform data (such as...) , (etc.), directly using the histogram method to estimate high-dimensional conditional probabilities. The "curse of dimensionality" can occur, leading to unreliable calculation results. This invention employs a robust Kraskov-Stögbauer-Grassberger (KSG) estimation method based on nearest neighbors to calculate the transfer entropy.

[0086] The KSG method avoids the challenge of directly estimating the high-dimensional probability density function. Instead, it directly estimates the entropy combination by statistically analyzing the local neighbor distances of data points in the high-dimensional phase space. The specific application calculation process in this patent is as follows: (1) State Space Reconstruction for synchronously acquired unit internal variable time series and common bus voltage time series First, a high-dimensional state space vector is constructed.

[0087] The historical lengths are set as follows: (for) )and (for) The prediction step size is 1. For each time step... Construct three vectors: Future state: (1-dimensional scalar) Historical vectors: ( (dimensional vector) Historical vectors: ( The three vectors (in dimensional vectors) are combined to form a data point in a high-dimensional joint space. .

[0088] (2) Nearest neighbor search and feature distance determination for each joint data point Searching for its first in higher-dimensional space The nearest neighbor ( Usually, small integers are used, such as 4). Calculation To its first The distance between each of the neighbors is denoted as the feature distance. .this Reflects the point Local data density in the vicinity.

[0089] (3) Subspace neighbor counting at a fixed distance radius Under the given conditions, count the number of points falling within the radius range in the low-dimensional subspace: : in only containing Correlation components ( and In the subspace of ), the distance is less than The number of points.

[0090] : in containing History and history( and In the subspace of ), the distance is less than The number of points.

[0091] (4) Substitute the above statistical results into the KSG formula to calculate the TE value:

[0092] in, It is the Digamma function.

[0093] By following the steps above, the propagation entropy can be robustly estimated using a limited number of engineering data points without explicitly calculating the probability distribution.

[0094] Step 4: Determine the output By calculating the entropy transferred through changes in internal state and PCC voltage, i.e. and Define net information flow as .like (Positive threshold) indicates that changes in the internal state of the unit have caused changes in the PCC voltage, thus identifying it as an oscillation source. If This indicates that the unit is affected by changes in the PCC voltage and is therefore identified as a source of disturbance.

[0095] This invention not only calculates the TE (Transmission Equipment) from the computer group to the bus, but also the TE between control loops, thereby achieving control loop-level positioning. For example, calculating the PLL output... To current loop input TE, and current loop output The input TE to the PLL. If This indicates that a problem with the synchronization mechanism is the primary cause of the current fluctuation. If The significant fluctuations indicate that the drastic fluctuations in the current loop are in turn interfering with the PLL's lockout (common in situations with high current and weak network). This granular analysis directly enables the tracing of the control unit's loop state.

[0096] Adaptability analysis of transfer entropy in the scenario of this invention Please see Figure 4 This invention utilizes high-frequency sampling data from inside the converter to introduce the transfer entropy theory, used in neuroscience and econometrics, into the analysis of micro-state quantities in power electronic control systems. Through entropy difference analysis, the causal relationships between various control quantities can be effectively distinguished. In the specific engineering challenge of broadband oscillation in new energy power plants, the introduction of the transfer entropy method has good adaptability and necessity, mainly reflected in the following three aspects: I. Solving the Synchronization Problem of Homogeneous Machine Clusters In renewable energy power plants, generating units are highly homogeneous (with identical control parameters and hardware topology) and are extremely close to each other electrically. Once oscillations occur, the energy rapidly spreads throughout the entire plant via strong electrical coupling in the collector lines, causing the port voltage and current waveforms of all units to be highly synchronized in the time domain, exhibiting extremely high correlation. Traditional methods cannot distinguish between active sources of disturbance and passively affected sources. Transfer entropy does not depend on waveform similarity but rather on the driving force of state evolution.

[0097] This invention calculates the net information flow. It can keenly grasp the direction of cause and effect. If This indicates that a sudden change in the internal state of the generating unit drove grid fluctuations, thus identifying it as an oscillation source. If This indicates that grid fluctuations forced the generating units to respond passively, and were identified as a source of disturbance.

[0098] II. In-depth tracing of the internal control loop status The root cause of wideband oscillations is often subtle, possibly stemming from parameter drift in a specific control loop within the converter (such as PLL lockout or current loop parameter mismatch). Simply observing port electrical quantities is insufficient to pinpoint the specific cause. This study delves into the application of transfer entropy at the microscopic "loop-to-loop" interaction level. By calculating the transfer entropy (TE) between control loops (e.g., ... This allows for the reconstruction of causal chains between control elements. If a significant unidirectional information flow is detected from the PLL output to the current inner loop setpoint, it can be determined from a mechanistic perspective that the oscillation originates from the synchronization element, thus achieving precise location of faulty elements within the control unit.

[0099] III. Adapting to Nonlinear and Non-stationary Signal Characteristics Power electronic control systems contain numerous nonlinear elements (such as amplitude limiting, dead zone, and coordinate transformation), and broadband oscillating signals often exhibit non-stationary and time-varying characteristics in the initial stage. Small-signal impedance analysis based on linearized models suffers from errors when dealing with large-amplitude oscillations or significant nonlinearities. Transfer entropy, as a statistic based on probability distribution, does not require linear assumptions about the system and can be directly calculated based on the probability density function of the time series, naturally handling nonlinear dynamic relationships. This makes it more robust and accurate than traditional methods based on linear models when analyzing complex converter control interactions.

[0100] 3. Internal Sensitivity Impedance Index (ISII): Based on the small-signal model, the converter can be equivalent to a controlled current source or voltage source in series impedance. Oscillating sources often exhibit negative resistance characteristics at a specific frequency, meaning the internal potential responds positively to voltage disturbances. Therefore, the converter output admittance can be determined based on real-time parameters. Calculate its actual part If the real part is significantly negative and has the largest negative value, then it is determined to be a source.

[0101] Because the units at the end of the feeder have the largest accumulated line impedance, they face the lowest short-circuit ratio (SCR) and are most prone to instability. Assume the unit... Located in the feeder Node. The equivalent grid impedance as seen from its grid connection point. for:

[0102] in, For the first Section collector line impedance ( The impedance is frequency-dependent (R + jωL), and in wideband oscillation scenarios, it must be explicitly calculated using the oscillation frequency ωosc. These impedance values ​​can be obtained through real-time electrical quantity identification. Converter output impedance. With grid impedance The interaction between them determines stability. According to the Nyquist penalty area criterion, when The Nyquist curve encloses Instability occurred at a certain point. Clearly, with... Increase (the closer to the end) As the value increases, the risk of instability increases nonlinearly.

[0103] This invention not only relies on purely data-driven metrics (CEE / TE), but also introduces a topology-based physical prior correction factor. :

[0104] Final discrimination index The above methods integrate physical mechanisms (topological impedance) with data characteristics (error energy), thereby improving the positioning robustness under extreme conditions.

[0105] Through broadband oscillation source tracing and propagation test analysis considering the topology of new energy power stations, the feeder terminal units have the lowest short-circuit ratio (SCR) and the weakest voltage support due to their large cumulative line impedance.

[0106] This invention introduces a topology correction factor. :

[0107] in, From PCC to generator set The cumulative line impedance.

[0108] Revised Oscillation Risk Index:

[0109] Under the same control error level, the unit located at the end of the feeder ( Larger structures (those with larger capacities) are more destructive to system stability, or in other words, more fragile. This weighting makes the positioning results more consistent with physical laws, prioritizing the investigation of the weakest link in the topology of new energy power stations.

[0110] Using offline simulation (such as PSCAD / EMTDC), simulate various typical faults: PLL instability mode: characterized by a frequency of 2-20Hz, concentrated PLL error signal energy, and high correlation with q-axis voltage.

[0111] Inner-loop instability mode: characterized by a frequency of several hundred Hz (close to 1 / 10 of the switching frequency), extremely large current error, and relatively stable DC bus voltage.

[0112] Weak network interaction mode: characterized by voltage active power sensitivity The higher the output, the greater the oscillation.

[0113] These features are stored in a database. Once the online monitoring calculates the indicators, a diagnostic result is directly output using a pattern matching algorithm (such as a decision tree or neural network): "Unit xx, PLL bandwidth is too high, it is recommended to reduce it."

[0114] Level 1 (Embedded): Only calculates the root mean square (RMS) error. If an inverter detects its... If the safety threshold is exceeded, the suspected source flag will be reported proactively.

[0115] Level 2 (substation): Collect all suspected sources, calculate FFT using a 100–200ms window or covering several oscillation cycles, and perform spectrum comparison.

[0116] Level 3 (Master Station): Retrieves waveform data, performs propagation entropy calculation and network-wide topology sensitivity analysis, and finally diagnoses the problem. This layered architecture achieves optimal allocation of computational load and communication bandwidth.

[0117] S3. Adaptive Indicator Selection and Comprehensive Judgment Considering the contradiction between computing resources (edge ​​gateway computing power) and control accuracy in actual engineering, this invention proposes an adaptive strategy, namely, a fast adaptive mode that considers computing resources. In the early stage of computing resource constraints or oscillation, only the CEE index is calculated to quickly disconnect the unit with the largest error energy.

[0118] Precise mode: When performing offline analysis or when computing power is sufficient, the LICI and ISII indices are calculated together, and the site topology information (such as the weighting of feeder end units) is combined to achieve precise positioning.

[0119] Please see Figure 6 The horizontal axis represents the distance of the generating unit from the PCC (grid connection point), and the vertical axis represents the oscillation risk weight of the unit. The figure clearly shows the positive correlation between the unit's oscillation risk and its distance from the PCC: as the unit moves further away from the PCC, the cumulative collector impedance gradually increases, the short-circuit ratio (SCR) decreases, the voltage support capability weakens, and the oscillation instability risk increases nonlinearly, with the corresponding risk weight increasing accordingly. For example, Unit N, located at the end of the feeder, has a significantly higher risk weight than Unit 1 and Unit 2, which are closer to the PCC. This figure intuitively reflects the impact of the station topology on unit stability, providing a visual basis for the introduction of the topology correction factor in the claims, demonstrating the necessity of considering topology distance and impedance distribution during the location process, and can help prioritize the investigation of units in high-risk areas, improving the targeting and efficiency of oscillation mitigation.

[0120] S4. Precision governance based on the characteristics of internal control processes To pinpoint the source of oscillation to a specific unit and detailed control loop, a detailed analysis of the state variables of each control loop is required. For example, the error energy in the PLL domain. If the error has the highest proportion and is dominated by phase jitter, it is determined to be a synchronization stability problem, and instructions to reduce PLL bandwidth or improve PLL performance are output. If the error energy in the current loop domain... If the highest percentage is accompanied by high-frequency noise, it is determined to be high-frequency resonance, and the output is instructed to increase active damping or adjust the switching frequency.

[0121] In another embodiment of the present invention, a broadband oscillation positioning system based on the internal control state characteristics of a converter is provided. This system can be used to implement the above-mentioned broadband oscillation positioning method based on the internal control state characteristics of a converter. Specifically, the broadband oscillation positioning system based on the internal control state characteristics of a converter includes a monitoring module, an index module, a fusion module, and a positioning module.

[0122] Among them, the monitoring module is used to build an internal control status monitoring system for the converter, and synchronously collect at least one key intermediate variable in the internal control loop of the converter when wideband oscillation occurs. The index module is used to calculate a set of control unit oscillation characteristic indices, including control error energy index, loop causal interaction index and internal potential sensitivity impedance index, based on the at least one key intermediate variable. The fusion module is used to combine the topology and electrical distance of the new energy power station, adaptively select the combination of indicators from the set of oscillation characteristic indicators of the control unit according to the requirements of computing resources and control accuracy, weight and fuse the selected indicators, and output decision information based on the fusion result. The positioning module is used to receive the judgment information, locate the oscillation source unit that causes the oscillation and its instability control link by comparing the relative strength of the characteristic indicators of each unit control unit and the timing lead-lag relationship, and output the corresponding control command or parameter correction suggestion.

[0123] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a wideband oscillation positioning method based on the internal control state characteristics of a converter, including: An internal control status monitoring system for the converter is constructed to synchronously collect at least one key intermediate variable in the converter's internal control loop when broadband oscillations occur. Based on the at least one key intermediate variable, a set of oscillation characteristic indicators for the control unit, including control error energy index, loop causal interaction index, and internal potential sensitivity impedance index, is calculated. Combining the topology and electrical distance of the new energy power station, a combination of indicators from the set of oscillation characteristic indicators for the control unit is adaptively selected according to computational resources and control accuracy requirements. The selected indicators are then weighted and fused. Based on the weighted and fused indicator results, the oscillation source unit and its instability control link are located by comparing the relative strength and timing lead-lag relationship of the characteristic indicators of each unit's control unit.

[0124] Please see Figure 9 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the broadband oscillation positioning method based on the internal control state characteristics of the converter in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the broadband oscillation positioning system based on the internal control state characteristics of the converter in this embodiment. To avoid repetition, these details are not elaborated here.

[0125] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 9 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0126] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0127] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0128] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0129] Please see Figure 10The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0130] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0131] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0132] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0133] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0134] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0135] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0136] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0137] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0138] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the wideband oscillation positioning method based on the internal control state characteristics of the converter in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: An internal control status monitoring system for the converter is constructed to synchronously collect at least one key intermediate variable in the converter's internal control loop when broadband oscillations occur. Based on the at least one key intermediate variable, a set of oscillation characteristic indicators for the control unit, including control error energy index, loop causal interaction index, and internal potential sensitivity impedance index, is calculated. Combining the topology and electrical distance of the new energy power station, a combination of indicators from the set of oscillation characteristic indicators for the control unit is adaptively selected according to computational resources and control accuracy requirements. The selected indicators are then weighted and fused. Based on the weighted and fused indicator results, the oscillation source unit and its instability control link are located by comparing the relative strength and timing lead-lag relationship of the characteristic indicators of each unit's control unit.

[0139] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0140] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0141] Simulation operating condition settings Please see Figure 5 Based on real parameters from broadband oscillation analysis and control at a certain new energy base, a site model containing 20 identical photovoltaic inverters was built in PSCAD.

[0142] Topology: Chain structure, unit numbers #61-#80, where #62 is located in the middle section of the feeder and #80 is located at the end of the feeder.

[0143] Abnormal settings: Adjust the PLL proportional coefficient of Unit #62. The simulation parameters were incorrectly set when the value was increased to five times the design value; the parameters of the other units were normal.

[0144] Oscillation phenomenon: When the simulation ran for 2.0s, a subsynchronous oscillation with a frequency of 23Hz appeared in the whole field, and the voltage distortion rate at the PCC point reached 5%.

[0145] Please see Figure 7 Discussion of oscillation characteristics and analysis of indicator changes 1. Analysis of internal control characteristics of different oscillation phenomena Traditional methods based on external port characteristics fail in monitoring internal control status. Observing the port current waveforms of #62 (oscillation source) and #65 (disturbed unit), both exhibit large oscillation components with nearly identical amplitudes (because they are connected in parallel on the same bus, have the same voltage, and the current response is affected by impedance). Based solely on external waveforms, it is impossible to determine which is the source, and it may even be mistakenly identified as the source because #80 is located at the end and experiences the largest voltage fluctuation. However, the characteristics of internal control quantity changes differ significantly under different oscillation conditions, as detailed below. Operating Condition 1: Phase-Locked Loop (PLL) Instability (Subsynchronous Oscillation in a Weak Power Grid) In weak power grids, the PCC voltage is greatly affected by the injected current. The PLL is oriented by the PCC voltage, and the injected current is determined by the PLL orientation, forming a coupled loop of "PCC voltage - PLL - current control - PCC voltage". Excessive bandwidth leads to positive feedback; therefore, its performance characteristics reflect the PLL output frequency. The amplitude of the fluctuations dominates the oscillation energy. shaft voltage (PLL input) and The correlation is extremely high. The oscillation source is directly located to the PLL stage. The auxiliary decision-making suggestion is to issue a command to reduce the output of the oscillation source or disconnect it, reduce the PLL bandwidth, or switch to Virtual Synchronizer (VSG) mode.

[0146] Operating Condition 2: Inner Current Loop Instability (High-Frequency Resonance) The high-frequency resonance is usually caused by insufficient phase margin of the current loop, resulting in LC resonance between the collector capacitance and the grid-side inductance. The resonant frequency falls near the bandwidth of the current loop (e.g., several hundred Hz to 1 kHz), thus forming a high-frequency resonance. Its characteristic feature is a high oscillation frequency (>100 Hz). Error energy The current loop is dominant, while the PLL frequency is relatively stable (the PLL has a low bandwidth, filtering out high-frequency components). The location results point to the inner current loop, and the auxiliary decision-making suggestion is to adjust the current loop proportional coefficient or increase active damping.

[0147] Operating Condition 3: Interaction between the power outer loop and the DC capacitor (low-frequency oscillation) When the constant power control loop in the outer power loop exhibits negative impedance characteristics, low-frequency oscillations typically occur due to coupling with the DC bus capacitance, power fluctuations, or upstream wind and solar power characteristics. The key characteristic is the DC bus voltage... The fluctuations are significant and precede the AC side current fluctuations. Outer loop error energy. Maximum. The auxiliary decision-making suggestion is to reduce the voltage loop bandwidth or introduce DC voltage feedforward control.

[0148] The specific steps of applying the method of this invention are as follows: 1. Trigger waveform recording The voltage harmonic content at the PCC point of the new energy power station exceeds the standard, triggering the recording of internal control state waveforms.

[0149] 2. Data Acquisition Get the internal structure of Array62,65,66 and .

[0150] 3. Indicator Calculation Because the Array62 controller rapidly adjusts to erroneous parameters, while the Array65 / 66 passively responds to external disturbances, its controller operates within its design bandwidth, resulting in smaller errors. The Array62's... energy Significantly higher than and (Approximately one order of magnitude higher). Calculating the transitive entropy reveals... It is much larger than the inverse value. The specific calculation process for this indicator is as follows: (1) Indicator 1: Control Error Energy (CEE) Unit #62 (oscillation source): Due to excessively large PLL parameters, its internally estimated grid phase angle... Severe shaking occurred, resulting in coordinate transformation Axis voltage components Significant fluctuations occurred. To maintain current tracking, the output of the current loop PI controller adjusted drastically. The calculated... The maximum value is 0.33.

[0151] Unit #65 (the affected unit): Its PLL parameters are normal, and it can track the fundamental phase of the PCC voltage well. Although the PCC voltage contains oscillating components, the current loop of #65 treats it as an external disturbance and suppresses it, and its internal error signal... The calculated energy is much smaller than #62. The value is extremely small, 0.026. By comparing the results, The oscillation source and the internal control characteristics of the disturbed unit are significantly different.

[0152] (2) Indicator 2: Loop Causal Interaction (LICI) Calculated using the aforementioned transfer entropy formula. For #62: Calculation revealed... Extremely high, and The value is significantly positive. This indicates that an anomaly in the internal PLL of #62 caused the current fluctuation, which in turn affected the PCC voltage. For #65: calculations revealed... The value is significantly higher than the reverse value. This indicates that #65 is passively affected by the PCC voltage. Result: The causal relationship clearly points to #62.

[0153] (3) Indicator 3: Topology correction Although #80 is at the end, its topological weight The largest, but due to its Very small (control parameters are normal), final comprehensive index Still much smaller This proves that our method avoids misjudgments caused by relying solely on topology.

[0154] 4. Output conclusions The system automatically determined that Array62 was the oscillation source and that the faulty link was the "current inner loop / PLL coupling point".

[0155] 5. Recommendations for Handling the Issue The system automatically issues commands to reduce or disconnect the output of Array62, and suggests modifying the current loop damping coefficient of Array62 to calm the oscillation.

[0156] As can be seen from the above applications, compared with traditional methods, which may require cutting off the entire feeder or even the entire power station, resulting in significant losses. The method of this invention uses internal control state characteristics to trace and locate the source of oscillation. When it is determined that a unit within the power station is the source of oscillation, only one unit is controlled, and the optimized parameters are directly provided to assist in decision-making, thereby achieving oscillation suppression at the lowest cost.

[0157] Current mainstream new energy converter controllers (such as TI C2000 series DSPs or Xilinx Zynq series FPGAs) have main frequencies reaching hundreds of MHz, possessing powerful data processing capabilities. Internal control variables are themselves digital quantities, residing in registers, eliminating the need for additional AD sampling circuits; they can be moved to a buffer using DMA technology. For storage, modern control boards are typically equipped with external SDRAM or Flash, sufficient to cache second-level high-frequency fault waveform data. For communication, data upload after oscillation triggering can be fully supported via Ethernet-based IEC 61850 GOOSE or MMS protocols, or proprietary UDP protocols (uploading only data during the oscillation period, with controllable bandwidth usage).

[0158] Traditional engineering thinking tends to monitor external characteristics of the converter (such as CT, PT, PMU), assuming the internal structure of the equipment is a black box and complex. This invention develops and enhances the self-diagnostic capabilities of the controller, delving into the internal workings of the converter control system. Furthermore, it introduces transfer entropy from information theory into power electronics stability analysis, solving the problem of highly similar waveforms in homogeneous units and the difficulty in distinguishing causality. Based on the combination of physics and data, a clear physical model of the control structure (inner loop, outer loop, PLL) is used to construct characteristic indicators, ensuring both physical interpretability and utilizing the statistical properties of the data.

[0159] In summary, this invention provides a broadband oscillation localization method and system based on the internal control state characteristics of converters. By constructing a panoramic and visualized internal control monitoring system and combining multi-dimensional indicators such as control error energy, causal cross-entropy, and topological impedance correction, it solves the industry challenges of difficult source tracing, unclear mechanisms, and extensive governance of broadband oscillations in high-proportion renewable energy power plants. It can not only accurately locate the oscillation source unit but also diagnose specific instability control links, providing strong technical support for the refined and intelligent operation and maintenance of new power systems.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0161] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0163] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0166] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0167] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A broadband oscillation localization method based on the internal control state characteristics of a converter, characterized in that, Includes the following steps: S1. Construct an internal control status monitoring system for the converter to synchronously collect at least one key intermediate variable in the internal control loop of the converter when wideband oscillation occurs. S2. Based on the at least one key intermediate variable, calculate the control unit oscillation characteristic index set, including the control error energy index, the loop causal interaction index, and the internal potential sensitivity impedance index. S3. Combining the topology and electrical distance of the new energy power station, and adaptively selecting the combination of indicators from the set of oscillation characteristic indicators of the control unit according to the requirements of computing resources and control accuracy, and weighting and fusing the selected indicators; S4. Based on the weighted fusion results, by comparing the relative strength and timing lead-lag relationship of the characteristic indicators of each unit control unit, the oscillation source unit that causes the oscillation and its instability control link are located.

2. The broadband oscillation localization method based on the internal control state characteristics of the converter according to claim 1, characterized in that, In step S1, the frequency of synchronous acquisition is greater than or equal to 10kHz, and DMA technology is used to transfer the key intermediate variables in the control closed loop to the ring buffer. The key intermediate variables include the d-axis voltage component of the phase-locked loop domain. q-axis voltage component Phase-locked loop output phase ,frequency The d-axis current of the current loop is given. Feedback q-axis current given Feedback Current loop PI output and the DC bus voltage of the outer ring domain Active / reactive power deviation.

3. The broadband oscillation localization method based on the internal control state characteristics of the converter according to claim 1, characterized in that, In step S2, the calculation process of the control error energy index is as follows: Define the control error of any control loop j. ,in, For a given value, This is the feedback value; Calculation of oscillation frequency based on Passevar's theorem Nearby band error energy ;in, for Fourier transform; Introducing a normalization factor Calculate the comprehensive index ,in, Weights set based on sensitivity analysis; JCEE of all organic groups in the site was normalized. ,in, For the unit number, This represents the total number of generating units.

4. The broadband oscillation localization method based on the internal control state characteristics of the converter according to claim 1, characterized in that, In step S2, the calculation process of the loop causal interaction index is as follows: constructing the time series of internal variables of the unit. and common bus voltage time series And perform state space reconstruction; The KSG estimation method is used to calculate the transfer entropy. The formula for the transfer entropy is: , For the Digamma function, In order to target the Given a set of high-dimensional joint data points, under a fixed feature distance, statistically, these points fall within a subspace containing both the historical vectors of the target variable and the historical vectors of the source variable, and are related to the first... The number of data points whose distance to each high-dimensional joint data point is less than the fixed feature distance. Within the subspace of the relevant components of the target variable Y, and related to the first The number of data points whose distance to each high-dimensional joint data point is less than the fixed feature distance. Within the subspace containing the history vector of the source variable X, and related to the first... The number of data points whose distance to each high-dimensional joint data point is less than the fixed feature distance; Calculate net information flow ,in, The transfer entropy from internal unit variables to PCC voltage. This represents the transfer entropy from the PCC voltage to the internal variables of the unit.

5. The broadband oscillation localization method based on the internal control state characteristics of the converter according to claim 4, characterized in that, The state space reconstruction process is as follows: The historical lengths are set as follows: and The prediction step size is 1. For each time t, future state , Historical vectors: and Historical vectors: And combine them into high-dimensional joint spatial data points .

6. The broadband oscillation localization method based on the internal control state characteristics of a converter according to claim 1, characterized in that, In step S2, the calculation process for the internal potential sensitivity impedance index is as follows: Equivalent grid impedance at the grid connection point of computer group k in, For system impedance, For transformer impedance, Let be the frequency-dependent impedance of the m-th collector line segment; The converter output admittance is obtained based on real-time parameter identification. Extract the oscillation frequency Real part of lower admittance ;like If the value is significantly negative and has the largest negative amplitude, then the unit is marked as a candidate for an oscillation source.

7. The broadband oscillation localization method based on the internal control state characteristics of the converter according to claim 6, characterized in that, The frequency-dependent impedance of the m-th segment of the collector line Let R+jωL be the value of ω, where ω is calculated using the oscillation frequency ωosc of the wideband oscillation.

8. The broadband oscillation localization method based on the internal control state characteristics of a converter according to claim 1, characterized in that, In step S3, the strategy for adaptively selecting the combination of indicators includes a fast adaptive mode and a precise mode. The fast adaptive mode selects only the control error energy index when computing resources are limited or in the early stage of oscillation, and quickly disconnects the unit with the largest error energy. In the precise mode, when offline analysis is performed or when computing power is sufficient, the control error energy index, loop causal interaction index, and internal potential sensitivity impedance index are jointly selected and weighted fusion is performed in conjunction with the site topology information.

9. The broadband oscillation localization method based on the internal control state characteristics of a converter according to claim 1, characterized in that, In step S3, a topology correction factor is introduced during weighted fusion. in, The cumulative line impedance from PCC to unit k. As the reference impedance, For correction coefficients; final discrimination index for: in, Let {k} be the control error energy index value of the unit.

10. A broadband oscillation positioning system based on the internal control state characteristics of a converter, characterized in that, include: The monitoring module is used to build an internal control status monitoring system for the converter, and synchronously collect at least one key intermediate variable in the internal control loop of the converter when wideband oscillation occurs. The index module is used to calculate a set of control unit oscillation characteristic indices, including control error energy index, loop causal interaction index and internal potential sensitivity impedance index, based on the at least one key intermediate variable. The fusion module is used to combine the topology and electrical distance of the new energy power station, adaptively select the combination of indicators from the set of oscillation characteristic indicators of the control unit according to the requirements of computing resources and control accuracy, weight and fuse the selected indicators, and output decision information based on the fusion result. The positioning module is used to receive the judgment information, locate the oscillation source unit that causes the oscillation and its instability control link by comparing the relative strength and timing lead-lag relationship of the characteristic indicators of each unit control unit, and output the corresponding control command or parameter correction suggestion.

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