A method and related equipment for identifying black-start loads in energy storage based on dynamic response.

Abstract

This invention discloses a dynamic response-based load identification method and related equipment for energy storage black start, overcoming the high risk of secondary system collapse caused by blindly closing the circuit to unknown loads during the energy storage black start process. This method generates customized safety test pulses based on the power line physical parameters using a pulse parameter model before formally closing the circuit to power, and collects and analyzes the dynamic response signals of the load to achieve proactive identification of the load's health status. This invention transforms load recovery during energy storage black start from blind closing to a controllable process of diagnosis before power supply, fundamentally reducing the risk of secondary system collapse due to faulty load access.

Description

Technical Field

[0001] This invention relates to the field of energy storage systems, specifically to a method and related equipment for identifying black-start loads in energy storage based on dynamic response. Background Technology

[0002] Black start refers to the process of gradually restoring power supply to an energy storage system without relying on the external power grid after a failure, through units within the energy storage system that have self-starting capabilities. It serves as the last line of defense for power system security. In recent years, energy storage systems, especially electrochemical energy storage power stations, have increasingly become the preferred starting power source for black start operations due to their advantages of rapid response, independent networking, and precise power control. During the energy storage system recovery process, once the energy storage power source successfully establishes voltage on the unloaded line, it faces the initial stage of supplying power to the unknown load clusters at the end of the line. The current standard operating procedure is direct energization, based on the assumption that the vast majority of loads are in a healthy state. This allows for the application of the full power frequency voltage directly to all loads through a single circuit breaker closure, aiming to achieve rapid batch recovery of the load clusters.

[0003] However, the load is in an unknown state during a complete blackout, and various hidden faults may exist, such as inter-turn short circuits in motor windings, insulation breakdown of power cable joints, or damage to compensation capacitors. Applying full voltage directly under these conditions would immediately trigger an inrush current far exceeding the rated value in the line, leading to instantaneous tripping of upstream overcurrent or instantaneous overcurrent protection devices. The direct consequence is that the voltage of the newly established, unstable local power grid will collapse again, interrupting the entire black-start process.

[0004] Current technical solutions fail to fully utilize the inherent capabilities of energy storage systems. Energy storage converters, as the core of energy storage systems, possess the potential for precise and flexible power conversion and programmable control; however, in existing direct closing procedures, they are merely used as simple power sources. Furthermore, existing solutions treat the load side to be restored as a passive black box; the closing operation itself only yields a binary result of on or off, failing to proactively acquire and analyze the dynamic electromagnetic characteristics of the load under excitation before irreversible system collapse, thus missing the opportunity to identify faults in advance.

[0005] Therefore, existing technologies rely on probabilistic assumptions rather than reliable scientific diagnosis to determine the success or failure of black starts, leading to a higher risk of secondary collapse in the power system during the recovery process. Summary of the Invention

[0006] The purpose of this invention is to provide a dynamic response-based method and related equipment for identifying loads during the black start of energy storage, so as to overcome the technical problem that the existing technology causes a high risk of secondary system collapse due to blindly closing the circuit to supply power to unknown loads during the black start process of energy storage.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for identifying black-start loads in energy storage based on dynamic response, comprising:

[0009] During the black start phase of energy storage, the physical parameters of the power line where the load to be identified is located are obtained;

[0010] The physical parameters are input into the pulse parameter model to obtain the test pulse parameters. The pulse parameter model is based on multiple physical constraints related to the time-domain characteristics of the test pulse parameters, including a first constraint that associates the pulse width of the test pulse with the physical length of the power line, a second constraint that associates the voltage amplitude of the test pulse with the pulse width, and a third constraint that makes the waveform of the test pulse satisfy the preset attenuation characteristics.

[0011] The test pulse is input to the load to be identified based on the test pulse parameters, and the dynamic response signal is collected. The dynamic response signal is judged to complete the identification of the load to be identified.

[0012] The judgment of the dynamic response signal includes:

[0013] Calculate at least one characteristic index based on the dynamic response signal;

[0014] Compare the feature indicators with the corresponding preset thresholds;

[0015] Based on the comparison results, the health status of the load to be identified is determined as the identification result.

[0016] The characteristic indicators include the current ratio and the attenuation consistency index. If the current ratio exceeds the first threshold, it is determined to be a short circuit fault. The formula for obtaining the current ratio is as follows:

[0017]

[0018] In the formula, It is the current ratio. To test the pulse voltage amplitude, For the expected impedance, This is the peak current;

[0019] If the attenuation consistency index is lower than the second threshold, it is determined that there is an insulation fault or a nonlinear component fault. The formula for obtaining the attenuation consistency index is as follows:

[0020]

[0021] In the formula, For decaying consistency index, This is the average value of the actual current. The total number of sampling points. In order to be in The actual current collected at all times In order to be in The output current value of the damped sinusoidal model at time t.

[0022] The input of test pulses to the load to be identified based on test pulse parameters is achieved through an energy storage converter.

[0023] The acquired dynamic response signals include:

[0024] The instantaneous waveforms of voltage and current at the beginning of the power line where the load is located are collected at a sampling rate higher than the power frequency.

[0025] The physical parameters of the power line where the load to be identified is located include the length and type of the power line.

[0026] Secondly, the present invention provides a dynamic response-based energy storage black-start load identification system, comprising:

[0027] The parameter acquisition module is used to acquire the physical parameters of the power line where the load to be identified is located during the black start phase of energy storage.

[0028] The pulse parameter module is used to input physical parameters into the pulse parameter model to obtain test pulse parameters; the pulse parameter model is based on multiple physical constraints related to time-domain characteristics in the test pulse parameters.

[0029] The load identification module is used to input test pulses to the load to be identified based on test pulse parameters, collect dynamic response signals, judge the dynamic response signals, and complete the identification of the load to be identified.

[0030] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the dynamic response-based energy storage black start load identification method as described above.

[0031] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the dynamic response-based energy storage black-start load identification method as described above.

[0032] Compared with the prior art, the present invention has the following beneficial technical effects:

[0033] Firstly, this invention provides a dynamic response-based method for identifying black-start loads in energy storage systems. Before formally closing the circuit and energizing the system, a controlled testing-based identification step is added. This method generates test pulse parameters adapted to the line characteristics based on known line physical parameters in the power grid, using a pulse parameter model containing multiple physical constraints. According to these parameters, a low-voltage, short-duration test pulse is applied to the load to be identified, and the load's current and voltage dynamic response signals are simultaneously acquired. These signals are then analyzed to determine the load status. This process changes the direct, one-time closing operation. By actively probing the load in a controlled manner before applying full voltage, loads with faults such as short circuits or insulation damage can be identified, thus avoiding direct connection to the system. This eliminates inrush currents and relay protection maloperation caused by such faulty loads, reducing the risk of secondary system failure during recovery.

[0034] Secondly, this invention provides a dynamic response-based energy storage black-start load identification system, comprising a parameter acquisition module, a pulse parameter module, and a load identification module. The parameter acquisition module reads the physical parameters of the line during the energy storage black-start phase; the pulse parameter module calculates safe test pulse parameters based on these parameters using a built-in constraint model; and the load identification module is responsible for controlling the application of test pulses according to these parameters, completing high-speed acquisition of dynamic response signals, and performing signal analysis and status judgment. This system constitutes a complete online diagnostic unit, making the safe testing of the load before closing the circuit a standard, automatically executed procedure, technically eliminating the possibility of blindly closing the circuit and ensuring the smooth progress of the black-start process.

[0035] Thirdly, the present invention provides a computer device that, through a processor executing a specific computer program, can efficiently implement the steps of the method of the present invention. When performing data processing tasks, the computer device can accurately perform numerical calculations and logical judgments, avoiding errors caused by human factors. At the same time, since the computer program has high stability and reliability, it can ensure the accuracy and consistency of the data processing results.

[0036] Fourthly, the present invention provides a computer-readable storage medium. By programming the steps of the method of the present invention into a computer program and storing it on the computer-readable storage medium, users can easily load these programs onto any compatible computer device and execute them without rewriting or converting the code, which greatly improves the convenience and flexibility of program execution. Attached Figure Description

[0037] Figure 1 This is a flowchart of a black-start load identification method for energy storage based on dynamic response, as described in an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of a dynamic response-based energy storage black start load identification system in an embodiment of the present invention. Detailed Implementation

[0039] During the black start process of grid energy storage, the existing standard operation is to directly close the circuit breaker to supply power to the unknown load. This operation is based on the statistical assumption of load health. However, in practice, the load to be restored may harbor hidden faults such as short circuits in motor windings or cable insulation breakdown. Directly applying full voltage will immediately trigger an inrush current tens of times the rated value, causing the upstream protection device to trip instantaneously, resulting in the collapse of the fragile grid newly established by the energy storage system, and forcing the entire black start process to be interrupted.

[0040] This invention proposes a dynamic response-based method and related equipment for identifying black-start loads in energy storage systems. An active diagnostic step is added before formal circuit breaker closing. During the black-start phase of energy storage, the physical parameters of the line are first acquired, and then these parameters are converted into specific test pulse commands using a pulse parameter model. Based on these commands, a controlled test pulse can be applied to the load, while simultaneously acquiring and judging the load's response signal. In this way, the load status can be assessed before applying full voltage, thereby reducing the possibility of powering faulty loads.

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Reference Figure 1 The image shows a specific implementation of the dynamic response-based black-start load identification method for energy storage provided by the present invention, comprising:

[0043] During the black start phase of energy storage, the physical parameters of the power line where the load to be identified is located are obtained;

[0044] The physical parameters are input into the pulse parameter model to obtain the test pulse parameters. The pulse parameter model is based on multiple physical constraints related to the time-domain characteristics of the test pulse parameters, including a first constraint that associates the pulse width of the test pulse with the physical length of the power line, a second constraint that associates the voltage amplitude of the test pulse with the pulse width, and a third constraint that makes the waveform of the test pulse satisfy the preset attenuation characteristics.

[0045] The test pulse is input to the load to be identified based on the test pulse parameters, and the dynamic response signal is collected. The dynamic response signal is judged to complete the identification of the load to be identified.

[0046] In this specific embodiment, the step of obtaining the physical parameters of the power line where the load to be identified is located provides a definite and quantifiable input basis for the entire identification process. This step of obtaining the physical parameters of the power line is key information characterizing the inherent electrical characteristics of the power line where the load to be identified is located, enabling subsequent test pulse generation to be adapted to the specific power grid structure, thereby avoiding the predicament of blindly assuming unknown load parameters.

[0047] The step of inputting physical parameters into the pulse parameter model and obtaining test pulse parameters serves to convert static power grid information into dynamic test commands. The pulse parameter model, through its embedded multiple physical constraints, collaboratively optimizes and solves the time-domain characteristic parameters of the test pulse. This ensures that the final generated test pulse parameters simultaneously meet the dual requirements of safely injecting energy into the load to be identified and effectively covering the key spectrum, providing precise digital commands for generating a test pulse that is both safe and capable of evoking an effective response.

[0048] The steps of inputting test pulses to the load to be identified based on test pulse parameters and acquiring dynamic response signals are processes that utilize the aforementioned digital instructions to complete physical interaction and data acquisition. Based on the test pulse parameters, a specific low-voltage test pulse waveform can be output through a power supply or other equipment. This controlled excitation replaces the original direct closing operation, actively acting on the load to be identified with extremely low energy levels. Simultaneous signal acquisition fully records the instantaneous changes in voltage and current of the load to be identified under this specific excitation, thereby capturing the true dynamic response data reflecting the internal state of the load.

[0049] The step of judging the dynamic response signal and identifying the load to be identified involves extracting and quantifying the features of the dynamic response signal and comparing it with preset criteria. This transforms the complex electromagnetic transient response of the load to be identified into a clear health status classification, such as being identified as normal, having a short-circuit fault, or having an insulation fault. The output of this step provides a direct basis for black-start control decisions, thereby realizing load access management based on real-time diagnosis at the system level.

[0050] The above steps are executed sequentially and are interdependent, forming a complete technical chain from acquiring and applying controlled stimuli, dynamic data acquisition to decision-making. The parameter acquisition step provides objective input, the pulse parameter modeling step performs core calculations and optimizations, the application and acquisition step achieves safe physical interaction and data acquisition, and the analysis and judgment step completes the transformation of information into decision. The synergy of these links enables the technical effect of online, safe, and effective diagnosis of unknown loads before formal power transmission.

[0051] In another specific embodiment of the present invention, a dynamic response-based black-start load identification method for energy storage provides a refined design to address the high-risk issue of first-time energization of unknown loads during the black-start process. This addresses the shortcomings of existing technologies where direct energization can easily lead to black-start failure due to hidden load faults. During the black-start process, the first energization of an unknown load after restoring an unloaded line is one of the most dangerous and prone-to-failure stages. Existing practices typically employ a direct energization mode. If the load has hidden faults such as short circuits in motor windings, inter-turn short circuits in transformers, or breakdown of compensation capacitors, it can generate a huge inrush current, triggering protection tripping and causing the newly established fragile power grid to collapse again. Furthermore, existing technologies often focus on large power supply startup and voltage / frequency stability control, neglecting this crucial detail. The present invention fills this technological gap by applying controlled soft pulses for pre-testing before formal energization and combining this with dynamic response analysis to determine the load's health status.

[0052] The test pulse parameters include voltage amplitude, pulse width, and waveform. These parameters are all precisely calculated using corresponding formulas to achieve coordinated control of energy, spectrum, and temporal morphology, ensuring a safe testing process and effectively stimulating load response. The voltage amplitude is calculated using the following formula:

[0053]

[0054] In the formula, The pulse voltage amplitude is calculated using this formula to test the pulse voltage amplitude; The safety factor is dimensionless and ranges from 0.1 to 0.3. It is set based on engineering experience to balance test sensitivity and safety, with a typical value of 0.2. The rated voltage of the system, in volts (V), is obtained directly from the grid parameters, such as 10kV or 35kV. This is the pulse width, measured in seconds (s), which is derived from the subsequent pulse width calculation formula. The critical time constant, measured in seconds, characterizes the thermal time constant or fault development time of the load. It is set to 0.1 s based on the typical time constant of a motor or transformer, referencing the IEEE Std 115-1995 standard for motor test time constants. This formula is derived by... and Correlation, introducing the square root of the time ratio to adjust the energy, so that Follow Increase and reasonable change, while being subject to Limitations enable precise energy management.

[0055] The pulse width is calculated using the following formula:

[0056]

[0057] In the formula, The pulse width is expressed in seconds (s). This is the bandwidth coverage factor, which is dimensionless and has an empirical value range of 3 to 10. In engineering, it is usually taken as 5. It can be adjusted through simulation optimization to ensure that there is sufficient energy in the target frequency band. The target frequency span is expressed in Hz. The highest frequency to be detected, in Hz, is determined by the physical length of the line to be restored and the wave propagation characteristics. In engineering practice, it can be set to the frequency corresponding to a quarter wavelength of the line or an estimated value of the line's first series resonant frequency. Specifically, it is determined through... Calculation, where For wave speed, This refers to the line length; The system power frequency, measured in Hz, is obtained from the grid's rated parameters and is typically 50Hz. This formula is based on electromagnetic wave propagation and spectrum analysis concepts, referencing transmission line theory and swept-frequency signal analysis, and has been simplified and innovated through engineering. Its core idea is to optimize the spectrum by utilizing the inverse relationship between pulse duration and the covered frequency range. The pulse width is dynamically adjusted according to the physical length of the line to ensure that the pulse can excite a wideband response from the power frequency to the line's critical high frequencies, providing rich spectral information for subsequent analysis and improving fault detection sensitivity.

[0058] The waveform of the test pulse is generated using the following formula:

[0059]

[0060] In the formula, This is the instantaneous test voltage, measured in V, which varies over time. To test the pulse voltage amplitude, the unit is V, which is obtained from the voltage amplitude calculation formula above; This is the attenuation coefficient, measured in units of 1 / s, and is set according to the load characteristics. , The load time constant is taken as 0.1s, therefore Typical value is 10 s -1 f represents the frequency in Hz, which is the system's rated frequency (50Hz or 60Hz) obtained from the power grid parameters. The time is expressed in seconds, starting from the pulse. This formula originates from the engineering linear attenuation model, based on the concept of a damped sine wave and referencing the natural response of an RLC circuit for waveform modulation innovation. The generated waveform possesses specific attenuation characteristics, which on the one hand ensures rapid dissipation of injected energy, avoiding energy accumulation that could affect the load; on the other hand, it establishes a clear theoretical reference model for the subsequent calculation of the Attenuation Consistency Index (ACI), providing a benchmark for healthy load response.

[0061] The multiple physical constraints form a parameter collaborative control mechanism to ensure the rationality and adaptability of the test pulse. The first constraint correlates the pulse width with the physical length of the power line, as shown in the pulse width calculation formula above. The calculation relationship with the physical length of the line ensures that the pulse width can adapt to the wave propagation characteristics of lines of different lengths, thereby guaranteeing that the frequency coverage of the test pulse matches the characteristics of the line and load, avoiding missed fault detection or misjudgment due to inappropriate frequency range. The second constraint correlates the voltage amplitude with the pulse width, through the voltage amplitude calculation formula mentioned above. and The coupling relationship is achieved by dynamically adjusting the voltage amplitude according to the pulse width variation, ensuring that the injected energy of the test pulse is always controlled within the preset maximum allowable test energy, regardless of how the pulse width is adapted to the circuit. It balances the load response excitation effect and test safety, avoiding both insufficient energy to detect faults and excessive energy to trigger protection actions.

[0062] The input of test pulses to the load to be identified is achieved through an energy storage converter. The energy storage converter plays a core role as a controlled voltage source throughout the testing process, outputting soft pulses that fully meet the requirements of the voltage amplitude, pulse width, and waveform calculation formulas through programmed control. The energy storage converter can accurately respond to the test pulse parameters output by the pulse parameter model, replacing traditional direct closing operations. It stably applies low-voltage, short-duration test pulses with specific attenuation waveforms to the line to be restored and the unknown load, achieving controlled active excitation. This avoids the huge inrush current that may be generated by direct closing and provides standardized excitation signals for subsequent response acquisition. It is a key execution component connecting the pulse parameter model and the actual load test.

[0063] The acquisition of dynamic response signals requires a high-speed, high-precision data acquisition system. This system synchronously and continuously acquires the instantaneous waveforms of voltage and current at the beginning of the power line where the load is located, using a sampling rate higher than the power frequency. The acquisition process spans the entire duration of the applied test pulse. The high sampling rate design can completely capture the transient and steady-state electrical behavior of the load under controlled excitation, accurately recording peak current. Actual current values ​​at each time point The system acquires core raw data such as voltage waveforms, providing accurate data support for subsequent characteristic index calculations. The synchronous acquisition mode ensures the time consistency of voltage and current data, avoiding the impact of timing deviations on the accuracy of characteristic index calculations. The performance of the data acquisition system directly determines the accuracy of subsequent analysis and judgment. It works closely with the energy storage converter to build a complete link between test pulse application and dynamic response acquisition.

[0064] The physical parameters of the power line containing the load to be identified include the length and type of the power line. These parameters are the fundamental inputs for the accurate calculation of the pulse parameter model. The length of the power line is directly used in the pulse width calculation formula mentioned above. The calculation determines the pulse width value; the type of power line affects the line wave velocity. and equivalent impedance characteristics, indirectly affecting Calculation accuracy and expected impedance The estimation ensures that the pulse parameters are adapted to the actual power grid structure. Accurate acquisition of these parameters eliminates the need for blind assumptions about unknown load parameters in the test pulse, providing an objective basis for adaptive adjustment of the pulse parameters. This, combined with the pulse parameter model and energy storage converter, ensures the relevance and feasibility of the test plan.

[0065] The process of judging dynamic response signals is the core step in accurately identifying the health status of loads through quantitative calculation and threshold comparison. It covers three levels: feature index calculation, threshold comparison, and status determination, and can also achieve specific identification of capacitive loads. The feature indicators calculated based on dynamic response signals include current ratio and attenuation consistency index. These two indicators construct a fault judgment system from different dimensions, forming cross-validation and improving the accuracy of identification.

[0066] The current ratio is calculated using the following formula:

[0067]

[0068] In the formula, This is the current ratio, dimensionless, used for short-circuit fault detection; The measured peak current, in amperes (A), is obtained from a high-speed sampling device. The pulse voltage amplitude is measured in volts (V) and is derived from the voltage amplitude calculation formula described above. The expected impedance, in Ω, is calculated based on the system's rated parameters using the following formula:

[0069]

[0070] In the formula, This is the system rated current, in amperes (A), obtained from grid parameters. The system rated voltage, in volts (V), is obtained directly from the power grid parameters. This formula, based on Ohm's law and incorporating the concept of dynamic current ratio, is suitable for soft-pulse testing scenarios. Short-circuit faults cause a sharp drop in loop impedance, increasing the peak current. An abnormal increase occurs when the current ratio exceeds a preset first threshold, i.e., the short-circuit fault threshold. When a short-circuit fault is detected in the load to be identified, the short-circuit fault threshold is determined. Based on the IEEE Std C37.104 standard for overcurrent protection settings, the value is typically 2 to 5, with 5 being the most common. Upon determining that a short-circuit fault exists in the load to be identified, the system immediately blocks the formal power-on command and issues an alarm, proactively avoiding the risk of inrush current that might be caused by direct closing, and exposing short-circuit hazards at low energy levels.

[0071] The attenuation consistency index is calculated using the following formula:

[0072]

[0073] In the formula, The decay consistency index is dimensionless and ranges from (-∞, 1]. The closer it is to 1, the better the measured response fits the theoretical model and the healthier the load. This represents the total number of sampling points used for analysis; In order to be in The actual current collected at all times, in amperes (A); In order to be in The damped sinusoidal model outputs the current value at time t, in amperes (A). This model is constructed based on the voltage pulse waveform corresponding to the waveform calculation formula mentioned above, and its expression is:

[0074]

[0075] In the formula, For amplitude, The attenuation coefficient is... The phase is obtained through fitting and optimization using a nonlinear least squares method (such as the Levenberg-Marquardt algorithm); This is the average value of the actual current, expressed in amperes (A), used to calculate the total sum of squares. This formula, based on system identification and model fit goodness of fit concepts, is applied to transient power waveform analysis. When the load has insulation faults or nonlinear component faults, nonlinear conductivity characteristics are introduced, leading to severe distortion of the current waveform, the presence of numerous harmonics, or drastic changes. This makes it impossible to fit well with a linear damped sinusoidal model, thus... The value decreases significantly. If the decay consistency index is lower than the preset second threshold, i.e., the fault determination threshold... If the load to be identified is found to have an insulation fault or a nonlinear component fault, the system will block the power supply command to proactively identify potential hazards. The fault determination threshold is specified in the figure. Based on statistical calibration using a large amount of health load test data, the typical value is 0.90.

[0076] During the characteristic index judgment process, capacitive loads can also be identified and evaluated. When the current leads the voltage in the collected dynamic response signal, the load to be identified is determined to be a capacitive load (such as a compensation capacitor bank). At the same time, based on the collected voltage and current data, it is evaluated whether the capacitive load may cause resonance, providing additional reference for subsequent load access management after black start, further improving the load identification system, and covering the classification and judgment needs of fault loads and normal loads.

[0077] In this specific implementation, the various technical components form a tightly coordinated technical closed loop, enabling safe, accurate, and online diagnosis of the health status of unknown loads. The physical parameters of the power line provide fundamental data support for the pulse parameter model. This model, through two physical constraints and the aforementioned calculation formulas for voltage amplitude, pulse width, and waveform, achieves adaptive optimization of the test pulse parameters, ensuring that the pulse energy, spectrum, and waveform are precisely adapted to the characteristics of the power grid and load. The energy storage converter accurately responds to and executes pulse output commands, operating synchronously with the high-speed data acquisition system to achieve seamless connection between excitation application and response capture. The acquired dynamic response signal, through the aforementioned calculation formulas for current ratio and attenuation consistency index, completes the quantitative calculation of dual-dimensional characteristic indicators. Combined with preset thresholds and capacitive load identification rules, it achieves accurate judgment of multiple types of faults and load states. The various technical components rely on each other and work in synergy, effectively solving the high-risk defects of existing direct closing methods and improving the accuracy of load state identification through parametric design and quantitative analysis. This provides a feasible technical path for predicting and detecting the load state before formal power transmission during black start-up.

[0078] This implementation method offers multiple advantages, effectively improving the reliability and engineering practicality of black-start load access for energy storage. By pre-applying low-energy controlled soft pulses, hidden dangers such as short circuits and insulation faults can be detected in advance, avoiding inrush currents and grid collapse caused by direct closing, thus overcoming a key bottleneck in the load power supply process during black start. The combination of frequency-optimized pulse excitation, high-precision synchronous acquisition technology, and a dual-dimensional characteristic index cross-verification mechanism not only considers the comprehensive analysis of current amplitude and waveform characteristics but also enables capacitive load identification and resonance risk assessment, significantly improving the accuracy of load status identification, reducing missed fault detections and misjudgments, and perfecting the overall identification function. Relying on the synergistic effect of voltage amplitude, pulse width, waveform calculation formulas, and physical constraints, precise control of test energy can be achieved, avoiding damage to healthy loads. At the same time, the application of energy storage converters can adapt to different grid structures without requiring significant modifications to existing equipment, ensuring both operational safety and scenario compatibility. All calculation formulas are innovatively derived based on existing physical principles and engineering standards. Parameter values ​​rely on mature engineering experience and industry norms, ensuring uniform dimensions and ease of engineering implementation. This makes it widely adaptable to various energy storage black-start scenarios and highly practical. Through comprehensive synergistic optimization of components, parameters, and algorithms, this technical solution significantly improves the stability and success rate of the black-start process, demonstrating significant engineering application value.

[0079] In a specific embodiment of the present invention, a black-start load identification system for energy storage based on dynamic response is also provided, referring to... Figure 2 As shown, it includes:

[0080] The parameter acquisition module is used to acquire the physical parameters of the power line where the load to be identified is located during the black start phase of energy storage.

[0081] The pulse parameter module is used to input physical parameters into the pulse parameter model to obtain test pulse parameters; the pulse parameter model is based on multiple physical constraints related to time-domain characteristics in the test pulse parameters.

[0082] The load identification module is used to input test pulses to the load to be identified based on test pulse parameters, collect dynamic response signals, judge the dynamic response signals, and complete the identification of the load to be identified.

[0083] In this specific embodiment, the parameter acquisition module operates during the energy storage black start phase. It reads the physical parameters of the power lines connected to the load to be identified from the power grid dispatch system or asset database, such as line length and cable type.

[0084] The pulse parameter module receives the line physical parameters provided by the parameter acquisition module. This module has a built-in or invoked pulse parameter model, which calculates the time-domain characteristic parameters of the test pulse based on multiple physical constraints. The model processes these constraints according to the input line physical parameters and outputs a set of defined test pulse parameters, such as voltage amplitude, pulse width, and specific waveform definitions.

[0085] The load identification module operates based on the test pulse parameters output by the pulse parameter module. This module applies test pulses matching the parameters to the load to be identified; this process is typically achieved by controlling the energy storage converter. Simultaneously with the application of the test pulses, the load identification module synchronously acquires the load's voltage and current dynamic response signals through connected sensors and acquisition equipment. Subsequently, the module processes and analyzes the acquired dynamic response signals, and determines the load's health status according to preset rules.

[0086] The parameter acquisition module, pulse parameter module, and load identification module are connected and work sequentially through the system's internal data path. The parameter acquisition module provides basic input, the pulse parameter module performs calculations and generates parameters, and the load identification module is responsible for performing tests, acquiring signals, and deriving judgment results. These three modules work in sequence to jointly achieve online load identification during the energy storage black start process.

[0087] In a specific embodiment of the present invention, a computer device is also provided. Specifically, the computer device includes a processor and a memory. The memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, 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, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to realize the corresponding method flow or corresponding function. The processor described in this embodiment of the invention can be used to obtain the physical parameters of the power line where the load to be identified is located during the black start phase of energy storage; input the physical parameters into the pulse parameter model to obtain test pulse parameters; the pulse parameter model is based on multiple physical constraints related to time-domain characteristics in the test pulse parameters; input test pulses to the load to be identified based on the test pulse parameters, collect dynamic response signals, judge the dynamic response signals, and complete the identification of the load to be identified.

[0088] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the methods in the above embodiments; the one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: During the energy storage black start phase, the physical parameters of the power line where the load to be identified is located are obtained;

[0089] Physical parameters are input into the pulse parameter model to obtain test pulse parameters; the pulse parameter model is based on multiple physical constraints related to time-domain characteristics in the test pulse parameters.

[0090] The test pulse is input to the load to be identified based on the test pulse parameters, and the dynamic response signal is collected. The dynamic response signal is judged to complete the identification of the load to be identified.

[0091] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), 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.

[0093] 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 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] 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.

[0095] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0096] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. 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 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 dynamic response-based energy storage black start load identification method, characterized in that, include: During the black start phase of energy storage, the physical parameters of the power line where the load to be identified is located are obtained; The physical parameters are input into the pulse parameter model to obtain the test pulse parameters; The pulse parameter model is based on multiple physical constraints related to time-domain characteristics in the test pulse parameters, including a first constraint that associates the pulse width of the test pulse with the physical length of the power line, a second constraint that associates the voltage amplitude of the test pulse with the pulse width, and a third constraint that makes the waveform of the test pulse satisfy a preset attenuation characteristic. Based on test pulse parameters, a test pulse is input to the load to be identified through an energy storage converter. Dynamic response signals are collected, and characteristic indicators are calculated to judge the dynamic response signals, thus completing the identification of the load to be identified. These characteristic indicators include the current ratio and the attenuation consistency index. If the current ratio exceeds a first threshold, it is determined to be a short-circuit fault. The formula for obtaining the current ratio is as follows: In the formula, It is the current ratio. To test the pulse voltage amplitude, For the expected impedance, This is the peak current; If the attenuation consistency index is lower than the second threshold, it is determined that there is an insulation fault or a nonlinear component fault. The formula for obtaining the attenuation consistency index is as follows: In the formula, For decaying consistency index, This is the average value of the actual current. The total number of sampling points. In order to be in The actual current collected at all times In order to be in The output current value of the damped sinusoidal model at time t.

2. The method for identifying black-start loads of energy storage based on dynamic response according to claim 1, characterized in that, The calculated characteristic indexes are used to judge the dynamic response signal, including: Calculate at least one characteristic index based on the dynamic response signal; Compare the feature indicators with the corresponding preset thresholds; Based on the comparison results, the health status of the load to be identified is determined as the identification result.

3. The method for identifying black-start loads of energy storage based on dynamic response according to claim 1, characterized in that, The acquired dynamic response signals include: The instantaneous waveforms of voltage and current at the beginning of the power line where the load is located are collected at a sampling rate higher than the power frequency.

4. The method for identifying black-start loads of energy storage based on dynamic response according to claim 1, characterized in that, The physical parameters of the power line where the load to be identified is located include the length and type of the power line.

5. A black-start load identification system for energy storage based on dynamic response, characterized in that, The method for identifying black-start loads of energy storage based on dynamic response, as described in any one of claims 1 to 4, includes: The parameter acquisition module is used to acquire the physical parameters of the power line where the load to be identified is located during the black start phase of energy storage. The pulse parameter module is used to input physical parameters into the pulse parameter model to obtain test pulse parameters; the pulse parameter model is based on multiple physical constraints related to time-domain characteristics in the test pulse parameters. The load identification module is used to input test pulses to the load to be identified based on test pulse parameters, collect dynamic response signals, judge the dynamic response signals, and complete the identification of the load to be identified.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic response-based energy storage black start load identification method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic response-based energy storage black start load identification method as described in any one of claims 1 to 4.

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