Energy storage black start control method based on load identification and related equipment

Abstract

The application discloses a kind of energy storage black start control method and related equipment based on load identification, belong to electric energy storage technical field.The method includes: obtaining the identification result of load to be started;Identification result is input to control decision model, and output access control parameter;The control decision model includes the adaptive decision formula for determining voltage adaptive coefficient and frequency adjustment coefficient, the voltage dynamic compensation formula for determining voltage reference value, and the adaptive pulse width formula for determining frequency reference value;Based on access control parameter, control the power output unit of energy storage system to execute the access operation to the load to be started.The application inputs load identification result to control decision model, so that access parameter can be dynamically adjusted according to load identification result, realizes the accurate mapping from identification result to access control parameter, overcomes the technical defects that access parameter is fixed in prior art and cannot be adaptively adjusted.

Description

Technical Field

[0001] This invention relates to the field of energy storage system technology, specifically to an energy storage black-start control method and related equipment based on load identification. Background Technology

[0002] Black start refers to the process of gradually restoring power supply through self-starting units in the power system after a complete power outage due to a fault, without relying on the external power grid. Energy storage systems, with their advantages of rapid response, independent networking, and precise power control, are increasingly becoming the preferred starting power source for black start operations.

[0003] During a black start, the energy storage system first needs to establish the voltage and frequency of the unloaded lines to provide a stable grid foundation for subsequent load restoration. Once the voltage and frequency of the unloaded lines are successfully established, the critical step for the system is to initially supply power to the unknown load cluster at the end of the line, a high-risk step. Existing technologies typically use direct energization, which implicitly assumes the load is in a healthy state. However, in real-world applications, loads may have various hidden faults, such as inter-turn short circuits in motor windings, insulation breakdown at cable joints, or damage to compensation capacitors. Applying full voltage directly under such fault conditions will immediately generate a huge inrush current, triggering upstream protection devices and causing the newly established fragile grid to collapse again, resulting in a failed black start.

[0004] To address the aforementioned issues, existing technologies have proposed solutions for identifying the load before closing the circuit breaker. This involves applying test pulses and analyzing dynamic response signals to determine the load's health status and type. However, effective technical means to apply the load identification results to black-start control decisions remain lacking. Existing control methods often employ fixed parameters or simple lookup tables, failing to dynamically adjust connection parameters based on the real-time load status. Connecting large-capacity inductive loads may still result in significant voltage drops; connecting capacitive loads may induce resonance; and when a load fault exists, the connection risks cannot be proactively avoided. Therefore, existing technologies lack a solution capable of adaptively adjusting connection control parameters based on load identification results. Summary of the Invention

[0005] The purpose of this invention is to provide a black start control method and related equipment for energy storage based on load identification, so as to overcome the technical problem that the prior art lacks an effective means to adaptively adjust the black start access parameters of energy storage according to the load identification results.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a black-start control method for energy storage based on load identification, comprising: Obtain the identification results of the load to be started; The identification results are input into the control decision model, and the access control parameters of the load to be started are output. The control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and the frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters. Based on the access control parameters, the power output unit of the energy storage system is controlled to perform the access operation of the load to be started.

[0007] In one embodiment of the present invention, the step of inputting the identification result into the control decision model and outputting the access control parameters of the load to be started includes: Calculate the voltage adaptive coefficient and frequency adjustment coefficient based on the adaptive decision formula; Calculate the voltage reference value based on the voltage adaptive coefficient and the voltage dynamic compensation formula; Calculate the frequency reference value based on the frequency adjustment coefficient and the adaptive pulse width formula; Voltage reference values ​​and frequency reference values ​​are used as the access control parameters for the load to be started.

[0008] In one embodiment of the present invention, the adaptive decision formula is:

[0009] In the formula, For voltage adaptive coefficients, This is the frequency adjustment factor. For load type decision vectors, The system stability coefficient, This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system.

[0010] In one embodiment of the present invention, the system stability coefficient The calculation formula is:

[0011] In the formula, The system stability coefficient, The frequency change rate weighting coefficient, The voltage change rate weighting coefficient. The absolute value of the rate of change of frequency. This is the absolute value of the rate of change of voltage.

[0012] In one embodiment of the present invention, the identification result of the load to be started includes load type information, which includes: large induction motor, unloaded transformer, nonlinear load, and resistive load; The load type decision vector Based on the load type information, it is defined as follows:

[0013] In the formula, the first element of each vector corresponds to the voltage adaptive coefficient. The empirical benchmark value, the second element corresponds to the frequency adjustment coefficient. The empirical benchmark value.

[0014] In one embodiment of the present invention, the voltage dynamic compensation formula is:

[0015] In the formula, This is the voltage reference value. The rated voltage of the energy storage system. For voltage adaptive coefficients, This is the power factor estimate. This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system.

[0016] In one embodiment of the present invention, the adaptive pulse width formula is:

[0017] In the formula, This is a frequency reference value. The rated frequency of the energy storage system. This is the frequency adjustment factor. This is the estimated apparent power of the load. For the rated capacity of the energy storage system, For time variables, is the time constant.

[0018] Secondly, the present invention provides an energy storage black-start control system based on load identification, comprising: The parameter acquisition module is used to obtain the identification results of the load to be started; The decision generation module is used to input the identification results into the control decision model and output the access control parameters of the load to be started; the control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters. The execution control module is used to control the power output unit of the energy storage system to perform the connection operation of the load to be started, based on the access control parameters.

[0019] 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 load identification-based energy storage black start control method as described above.

[0020] 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 load identification-based energy storage black-start control method as described above.

[0021] Compared with the prior art, the present invention has the following beneficial technical effects: Firstly, this invention provides a black-start control method for energy storage based on load identification. By acquiring the identification result of the load to be started and inputting it into a control decision model containing a voltage dynamic compensation formula, an adaptive pulse width formula, and an adaptive decision formula, the method outputs access control parameters for the load to be started, ultimately controlling the power output unit to perform the access operation. This method directly correlates the load identification result with the access control decision. Utilizing the synergistic effect of the three formulas, the access control parameters can be dynamically adjusted according to the load identification result: the voltage dynamic compensation formula determines the voltage reference value at the time of access, the adaptive pulse width formula determines the frequency reference value at the time of access, and the adaptive decision formula determines the voltage adaptive coefficient and the frequency adjustment coefficient. The three formulas together constitute a complete control decision model, achieving a precise mapping from the load identification result to the access control parameters. This allows the access parameters to adapt to the characteristics and requirements of different loads, overcoming the technical defects of existing technologies where access parameters are fixed and cannot be dynamically adjusted according to the load identification result. This effectively reduces the risk of electrical surges during load access and improves the safety and reliability of the black-start process.

[0022] Secondly, this invention provides a load identification-based energy storage black-start control system, including a parameter acquisition module, a decision generation module, and an execution control module. The parameter acquisition module acquires the identification results of the load to be started; the decision generation module inputs the identification results into a control decision model that incorporates a voltage dynamic compensation formula, an adaptive pulse width formula, and an adaptive decision formula, and outputs access control parameters for the load to be started; the execution control module drives the power output unit of the energy storage system to complete the access operation based on these parameters. This system integrates load identification, parameter decision-making, and execution control into a complete automated link, ensuring that the load identification results can be seamlessly converted into precise control commands. Through the collaborative calculation of the three formulas in the decision module, the system can dynamically generate optimal voltage and frequency reference values ​​for different load identification results without the need for preset fixed parameters. It exhibits high adaptability and reliability in dealing with variable load scenarios, providing a practical technical guarantee for the access of energy storage black-start loads.

[0023] 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.

[0024] 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

[0025] Figure 1 This is a flowchart of the energy storage black start control method based on load identification in an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of a black-start control system for energy storage based on load identification in an embodiment of the present invention. Detailed Implementation

[0027] During the black start process of energy storage, directly connecting and supplying power to unknown loads can easily cause inrush currents, voltage drops, or even secondary system collapse due to hidden faults or differences in load characteristics. Although existing technologies have proposed load identification methods, they lack adaptive adjustment means to effectively apply the identification results to access control. They still rely on fixed parameters or simple table lookup methods, which are difficult to meet the differentiated access requirements of different loads.

[0028] Based on the above background, this invention proposes a black-start control method and related equipment for energy storage based on load identification. The method inputs the identification result of the load to be started into a control decision model, which includes an adaptive decision formula, a voltage dynamic compensation formula, and an adaptive pulse width formula. Specifically, the adaptive decision formula determines the voltage adaptive coefficient and frequency adjustment coefficient, the voltage dynamic compensation formula determines the voltage reference value, and the adaptive pulse width formula determines the frequency reference value. The control decision model outputs access control parameters for the load to be started, achieving a precise mapping from the identification result to the access control parameters. This allows the access parameters to be dynamically adjusted according to the load characteristics, overcoming the technical defects of fixed parameters and inability to adaptively adjust in existing technologies, and effectively reducing the risk of electrical surges during load connection.

[0029] 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.

[0030] Example 1 A specific embodiment of the present invention provides a black-start control method for energy storage based on load identification, comprising: Obtain the identification results of the load to be started; The identification results are input into the control decision model, and the access control parameters of the load to be started are output. The control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and the frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters. Based on the access control parameters, the power output unit of the energy storage system is controlled to perform the access operation of the load to be started.

[0031] In this specific embodiment, the first step is to obtain the identification result of the load to be started. The load identification result is information obtained through pre-diagnosis of the load. The specific method of obtaining the result is not the core of this invention; any existing technology capable of obtaining load identification results can be used in conjunction with this solution. The load identification result should at least include information that characterizes the load characteristics, such as load type information, health status information, or electrical parameter information. This information will serve as the input basis for subsequent control decisions.

[0032] After obtaining the identification results of the loads to be started, they are input into the control decision model. The control decision model is the core of this invention, integrating three collaborative formulas: a voltage dynamic compensation formula, an adaptive pulse width formula, and an adaptive decision formula. The voltage dynamic compensation formula determines the voltage reference value in the access control parameters, ensuring that the voltage output by the energy storage system matches the voltage demand of the load. The adaptive pulse width formula determines the frequency reference value in the access control parameters, ensuring that the frequency output by the energy storage system adapts to the dynamic process of load access. The adaptive decision formula determines the voltage adaptation coefficient and frequency adjustment coefficient, enabling the control decision model to output appropriate access parameters based on the identification results of different loads to be started. These three formulas do not work independently but form an organic whole: the adaptive decision formula calculates the voltage adaptation coefficient and frequency adjustment coefficient applicable to the current load based on the identification results of the loads to be started; the voltage dynamic compensation formula calculates the final voltage reference value based on the voltage adaptation coefficient; and the adaptive pulse width formula calculates the final frequency reference value based on the frequency adjustment coefficient. Through this collaborative mechanism, the control decision model can output differentiated access control parameters for different identification results of loads to be started.

[0033] Based on the access control parameters output by the control decision model, the power output unit of the energy storage system performs the access operation for the load to be started. The power output unit is the component in the energy storage system responsible for outputting electrical energy to the outside, and typically includes an energy storage converter. The access control parameters include the voltage and frequency reference values ​​required for this access operation. The power output unit adjusts its output according to these parameters, so that the output electrical energy is injected into the load in a manner adapted to the load characteristics, thus completing the safe access of the load.

[0034] Through the above steps, this embodiment achieves a direct correlation between the identification result of the load to be started and the access control decision. The three formulas in the control decision model work together to enable the access parameters to be dynamically adjusted according to the identification results of different loads to be started. This avoids the electrical shocks that may occur when using fixed parameters to access different types of loads, overcomes the technical defects of the prior art that lacks adaptive adjustment of access parameters based on load identification results, and provides a reliable technical solution for the load access link in the black start process of energy storage.

[0035] Example 2 Based on Example 1, this embodiment further refines the specific process of inputting the identification results of the load to be started into the control decision model and outputting access control parameters.

[0036] The black start control method for energy storage based on load identification provided in this embodiment includes the following steps: inputting the identification result of the load to be started into the control decision model and outputting access control parameters.

[0037] Calculate the voltage adaptive coefficient based on the adaptive decision formula. and frequency adjustment coefficient The adaptive decision formula receives the identification result of the load to be started as input. The identification result includes the type information, status information, and electrical parameter information of the load to be started. Based on the load type and the real-time operating status of the system, the adaptive decision formula dynamically calculates the voltage adaptive coefficient. and frequency adjustment coefficient Voltage adaptive coefficient Frequency adjustment coefficient is used to characterize the degree of voltage support required when a load is connected. This is used to characterize the impact of load connection on frequency stability. The voltage adaptive coefficient is calculated through the adaptive decision formula. and frequency adjustment coefficient It can adaptively adjust to changes in load characteristics and system state, providing an accurate coefficient basis for subsequent calculations of voltage and frequency reference values.

[0038] Based on the voltage adaptive coefficient The voltage reference value is calculated using the aforementioned voltage dynamic compensation formula. The voltage dynamic compensation formula uses a voltage adaptive coefficient. Estimated load power factor estimated apparent power of load and the rated capacity of the energy storage system As input parameters. Load power factor estimate. The estimated apparent power of the load reflects the reactive power demand characteristics of the load. This reflects the load capacity. The voltage dynamic compensation formula introduces a voltage adaptive coefficient. By weighting the reactive power demand and capacity of the load, a voltage reference value that can suppress the voltage drop at the moment of load connection is calculated. This voltage reference value reflects the voltage pre-compensation during the load connection process, enabling the power output unit of the energy storage system to output an appropriate voltage level when a load is connected, effectively maintaining the stability of the bus voltage.

[0039] Based on the frequency adjustment coefficient The frequency reference value is calculated using the adaptive pulse width formula. The adaptive pulse width formula uses a frequency adjustment coefficient. estimated apparent power of load Rated capacity of energy storage system and time variables As input parameters, the adaptive pulse width formula introduces a frequency adjustment coefficient. The load capacity is weighted and combined with a time decay factor. Calculate the frequency reference value that can buffer the instantaneous frequency fluctuations when the load is connected. This frequency reference value allows for a brief, controllable shift in frequency at the moment of load connection to absorb the power surge brought by the load connection. Subsequently, it gradually recovers to the rated frequency over time, providing effective support for the transient stability of the system.

[0040] Voltage adaptive coefficient and frequency adjustment coefficient The coefficients are calculated using the same adaptive decision formula based on the identification results of the same load to be started, ensuring consistency between the two coefficients. The calculated... Substitute into the voltage dynamic compensation formula, and After substituting into the adaptive pulse width formula, the final voltage reference value is obtained. and frequency reference value Together, these constitute the access control parameters adapted to the load characteristics. Through the coordinated efforts of the above steps, a precise mapping from load identification results to access control parameters is achieved, enabling the access parameters to be dynamically adjusted according to load characteristics, thus providing an accurate control basis for subsequent load access operations.

[0041] Example 3 This embodiment, based on Embodiment 1, further refines the specific implementation of the adaptive decision formula in the control decision model.

[0042] In the energy storage black-start control method based on load identification provided in this embodiment, the adaptive decision formula is specifically expressed as follows:

[0043] In the formula, For voltage adaptive coefficients, This is the frequency adjustment factor. For load type decision vectors, The system stability coefficient, This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system.

[0044] The physical meaning of the adaptive decision formula is that it multiplies the load type information, the real-time stable state of the system, and the load capacity ratio to jointly determine the voltage adaptive coefficient. and frequency adjustment coefficient The value of . Load type decision vector This reflects the baseline differences in voltage and frequency regulation requirements among different types of loads; system stability coefficient This reflects the real-time stability state of the isolated power grid established by the energy storage system and the correction requirements for the adjustment coefficient; load relative capacity ratio This reflects the weighted impact of load size on the system's impact level. The product of these three factors serves as the final voltage adaptive coefficient. and frequency adjustment coefficient This ensures the voltage adaptive coefficient. and frequency adjustment coefficient It can comprehensively reflect the load characteristics of the load to be started, the state of the islanded power grid, and the combined impact of capacity factors between the energy storage system and the load to be started.

[0045] The load type decision vector It is an empirical coefficient vector preset according to the load type, and its definition is:

[0046] In the formula, the first element of each vector corresponds to the voltage adaptive coefficient. The empirical benchmark value, the second element corresponds to the frequency adjustment coefficient. The empirical benchmark value. For large induction motors, significant voltage compensation and frequency buffering are required during startup, therefore the voltage adaptation coefficient... The empirical benchmark value is taken as 0.12, and the frequency adjustment coefficient is... The empirical benchmark value is taken as 0.15; for unloaded transformers, inrush current will be generated at the moment of closing, requiring appropriate voltage compensation and small frequency adjustment, therefore the voltage adaptive coefficient is... The empirical benchmark value is taken as 0.10, and the frequency adjustment coefficient is... The empirical benchmark value is taken as 0.08; for nonlinear loads, their harmonic characteristics affect both voltage and frequency, therefore the voltage adaptive coefficient is... The empirical benchmark value is taken as 0.1, and the frequency adjustment coefficient is... The empirical benchmark value is taken as 0.05; for resistive loads, their electrical characteristics are simple and no special adjustments are required during connection, therefore the voltage adaptation coefficient is... empirical benchmark and frequency adjustment factor The empirical baseline value is 0.

[0047] The system stability coefficient The formula used to reflect the current real-time stable state of the power grid is as follows:

[0048] In the formula, The system stability coefficient, The frequency change rate weighting coefficient, The voltage change rate weighting coefficient. The absolute value of the rate of change of frequency. This is the absolute value of the rate of change of voltage.

[0049] The system stability coefficient The calculation formula is based on the following principle: when the power grid is in a steady state, the rate of frequency change... and voltage change rate All are close to zero, at this time A value close to 1 indicates that there is no need to reduce the adjustment factor due to system stability issues; when the power grid is subjected to disturbances, rapid changes in frequency or voltage... or The increase causes the denominator to be greater than 1. The corresponding reduction, thus affecting the voltage adaptive coefficient and frequency adjustment coefficient This is done to mitigate the impact of excessive adjustments applied when the system is unstable, preventing further deterioration. Frequency change rate weighting coefficient. and voltage change rate weighting coefficient The degree to which the rate of change of frequency and the rate of change of voltage affect system stability can be determined through system stability analysis.

[0050] The estimated apparent power of the load This reflects the capacity of the load to be connected, which can be obtained from the identification results of the load to be started; the rated capacity of the energy storage system. These are parameters from the equipment nameplate, and are known, fixed values. The ratio of the two... The load-to-capacity ratio is used to quantify the severity of the impact of load access on the system: the larger the ratio, the larger the load capacity relative to the energy storage system, the more severe the impact on the system when accessing the system, and the greater the adjustment required.

[0051] By integrating the load type empirical benchmark, the real-time stable state of the system, and the relative load capacity ratio using the above adaptive decision formula, a voltage adaptive coefficient that adapts to the current load characteristics and system state is dynamically calculated. and frequency adjustment coefficient These two coefficients will serve as key inputs to the subsequent voltage dynamic compensation formula and adaptive pulse width formula, ensuring that the final access control parameters can fully reflect the combined effects of load characteristics, system state, and capacity factors.

[0052] Example 4 Based on Example 1, this embodiment further refines the specific implementation methods of the voltage dynamic compensation formula and the adaptive pulse width formula in the control decision model.

[0053] In the load identification-based energy storage black-start control method provided in this embodiment, the voltage dynamic compensation formula is specifically expressed as follows:

[0054] In the formula, This is a voltage reference value, in V (volts), calculated using the voltage dynamic compensation formula; The rated voltage of the energy storage system is expressed in volts (V), and the design parameters of the energy storage system are also specified. is the voltage adaptive coefficient, which is dimensionless and is calculated from the adaptive decision formula; The power factor estimate is dimensionless and is obtained from the identification results of the load to be started, with a value range of [0,1]. This is the estimated apparent power of the load, in VA (volt-ampere), obtained from the identification results of the load to be started; The rated capacity of the energy storage system is expressed in VA (volt-ampere), and is a parameter on the equipment nameplate.

[0055] The voltage dynamic compensation formula is designed based on the voltage regulation principle in power systems and has been improved to address load access requirements under black start scenarios. In the formula... This item reflects the degree of reactive power demand of the load: when the load power factor is close to 1, the load is mainly an active load with low reactive power demand, this item is close to 0, and the voltage compensation is small; when the load power factor is low, the load has high reactive power demand, this item is large, and a larger voltage compensation is required. Load-to-capacity ratio: The larger the load capacity is relative to the energy storage system capacity, the more severe the voltage surge to the system upon connection, requiring greater voltage compensation. The voltage adaptive coefficient is dynamically adjusted by the adaptive decision formula based on load type and system state, and the product of the first two terms is weighted. The product of the three factors yields the comprehensive voltage compensation, which is then added to the rated voltage to obtain the final voltage reference value.

[0056] The voltage dynamic compensation formula allows the voltage reference value to be dynamically adjusted based on the load's power factor, relative capacity, and load type. For large-capacity inductive loads with low power factors, the formula outputs a higher voltage reference value, pre-raising the voltage level before load connection to compensate for the expected voltage drop at the moment of connection. For resistive loads with a power factor close to 1, the voltage reference value output by the formula is close to the rated voltage, requiring no special compensation. This dynamic compensation mechanism actively suppresses voltage drops during load connection, preventing system instability or protection activation caused by sudden voltage drops.

[0057] The adaptive pulse width formula is specifically expressed as follows:

[0058] In the formula, This is a frequency reference value, in Hz (Hertz), calculated using the adaptive pulse width formula; The rated frequency of the energy storage system is measured in Hz (Hertz), while the design parameter of the energy storage system is typically 50Hz or 60Hz. is the frequency adjustment coefficient, which is dimensionless and calculated by the adaptive decision formula; This is the estimated apparent power of the load, in VA (volt-ampere), obtained from the identification results of the load to be started; The rated capacity of the energy storage system is expressed in VA (volt-ampere), and the parameters are listed on the nameplate of the energy storage system equipment. It is a time variable, with the unit being seconds (s), and the timing starts from the moment the load is connected; is the time constant, measured in seconds (s), and is a response characteristic parameter of the energy storage system. It is determined through stability analysis of the energy storage system, and its typical value range is 0.5 to 2.0 seconds.

[0059] The adaptive pulse width formula is designed based on the physical concepts of the synchronous generator rotor motion equations and improved to address the fast response characteristics of energy storage systems. The adaptive pulse width formula contains... The initial magnitude of the frequency offset is determined by the load-to-capacity ratio and the frequency adjustment coefficient. The larger the load-to-capacity ratio and the larger the frequency adjustment coefficient, the larger the initial frequency offset. The time decay factor causes the frequency offset to decay exponentially with time: at the instant the load is connected. When the attenuation factor is 1, the frequency deviation reaches its maximum; as time progresses, the attenuation factor gradually decreases, and the frequency reference value gradually recovers to the rated frequency. (Time constant) The speed of frequency recovery is determined. The smaller the value, the faster the frequency recovers; The larger the value, the longer the frequency buffer time.

[0060] The adaptive pulse width formula ensures a brief, controllable shift in the frequency reference value at the moment of load connection, followed by a gradual return to the rated frequency. This frequency buffering mechanism allows the energy storage system's frequency to deviate slightly from the rated value for a short period when a large load is connected, absorbing the power surge and mitigating drastic frequency fluctuations. Once the load connection transient process ends, the frequency smoothly returns to the rated value. This short-term shift and gradual recovery frequency control strategy effectively suppresses frequency surges at the moment of load connection and ensures long-term system frequency stability.

[0061] The voltage dynamic compensation formula and the adaptive pulse width formula together constitute the core of the load connection control parameter calculation. The voltage dynamic compensation formula focuses on suppressing voltage dips, while the adaptive pulse width formula focuses on buffering frequency fluctuations. Both provide refined control of the load connection process from different dimensions. Key coefficients in the two formulas... and Both voltage and frequency control are calculated using the same adaptive decision formula based on the identification results of the same load to be started, ensuring the coordination and consistency of voltage and frequency control. Through the synergistic effect of the two formulas, the energy storage system can output appropriate voltage and frequency reference values ​​for different types and capacities of loads, providing an accurate control basis for the safe and stable connection of loads.

[0062] Example 5 To more clearly illustrate the practical application process of the technical solution of the present invention, the complete process of the energy storage black start control method based on load identification is illustrated below with a specific black start scenario.

[0063] Suppose an industrial park is equipped with an energy storage system, and the rated capacity of the energy storage system is... The rated voltage of the energy storage system is 1MVA. 380V, rated frequency of energy storage system The frequency is 50Hz. Various electrical loads are distributed within the park, including large induction motors, lighting fixtures, and compensation capacitor banks. After a grid failure caused a complete power outage, the energy storage system successfully established an isolated grid. Now, it is necessary to connect a large induction motor to the system to restore power.

[0064] First, obtain the identification results of the load to be started. The load type is a large induction motor, the load status is healthy, and the power factor estimate is... The estimated apparent power is 0.3. 400kVA (i.e., relative capacity) (0.4).

[0065] The identification results of the load to be started are input into the control decision model to calculate the access control parameters. The control decision model includes adaptive decision formulas, voltage dynamic compensation formulas, and adaptive pulse width formulas, which work together to complete the parameter calculation.

[0066] Calculate the voltage adaptive coefficient based on the adaptive decision formula. and frequency adjustment coefficient :

[0067] For large induction motors, load type decision vector The value is [0.12; 0.15]. Assuming the current islanded power grid established by the energy storage system is in a steady state, the frequency change rate... and voltage change rate All are close to zero, and the system stability coefficient is... Approaching 1. Substitute The calculation yields:

[0068] The calculated voltage adaptive coefficient Substitute into the voltage dynamic compensation formula:

[0069] Substitution , , , The calculation yields:

[0070] This voltage reference value is slightly higher than the rated voltage of the energy storage system, reflecting pre-compensation for voltage dips during motor connection. In practical engineering, a reduced-voltage starting strategy can also be used for large induction motors. In this case, the voltage can be adjusted in the load type decision vector. The empirical benchmark value or the introduction of a pressure drop factor, so that The voltage is lower than the rated voltage of the energy storage system.

[0071] The calculated frequency adjustment coefficient Substitute into the adaptive pulse width formula:

[0072] Substitution , , time constant Take 1.0 second. At the moment of load connection. hour, The calculation yields:

[0073] That is, at the moment of load connection, the frequency reference value is briefly lowered to 49.976Hz; as time goes by, the frequency reference value will gradually recover to 50Hz according to an exponential law.

[0074] At this point, the control decision model outputs the access control parameters for this large induction motor: voltage reference value. 385.1V, frequency reference value The frequency was 49.976Hz at the moment of connection and gradually recovered over time. The closing sequence was immediate connection.

[0075] Based on the aforementioned access control parameters, the power output unit of the energy storage system is controlled to perform load access operations. Before closing, a bumpless switching process is performed: within 5 milliseconds before closing, the output voltage and frequency of the power output unit are smoothly transitioned from their current values ​​to the target reference values. A first-order inertial element is used to achieve a smooth transition, ensuring that the parameter change process does not generate additional system disturbances. When the voltage deviation is less than 1% of the rated voltage of the energy storage system (i.e., 3.8V) and the frequency deviation is less than 0.02Hz, the switching is considered complete, and a closing command is immediately issued.

[0076] At the moment of closing, the power output unit supplies power to the motor at 385.1V and 49.976Hz. Since the voltage reference value has been pre-compensated for the motor characteristics and the frequency reference value provides a brief buffer, the motor starting current is effectively suppressed, the bus voltage drop is controlled within the allowable range, and the system frequency fluctuation is smooth.

[0077] After the load is connected, the system enters the closed-loop fine-tuning stage. The effective value of the bus voltage is continuously monitored. actual system frequency Total harmonic distortion of voltage Calculate the deviation from the target value based on the operating parameters. Assume the voltage deviation threshold is set to 2% of the energy storage system's rated voltage (7.6V), and the frequency deviation threshold is set to 0.1Hz. If detected... |or If the deviation is not specified, the PID correction algorithm is activated. The PID controller calculates the voltage correction based on the deviation. and frequency correction amount These parameters are superimposed on the voltage and frequency reference values ​​respectively to form the corrected control parameters. The correction process continues until the system reaches a stable state.

[0078] When the following conditions are met simultaneously and last for 2 seconds: voltage deviation Less than 0.5% of the rated voltage (1.9V) and frequency deviation of the energy storage system. Less than 0.01Hz, total harmonic distortion of voltage If the value is below 3%, the system is determined to have entered a stable operating state, exits the fine-tuning control mode, and maintains the current control parameters.

[0079] Through the complete process described above, the large induction motor achieved safe and stable connection. Throughout the process, the load identification results provided the input basis for control decisions. The three formulas in the control decision model collaboratively calculated the connection parameters adapted to the motor characteristics. Seamless switching ensured the smoothness of parameter transition, and closed-loop fine-tuning guaranteed rapid stabilization after connection. This application example fully demonstrates the implementation process of the technical solution of this invention in a real black-start scenario, verifying its feasibility and effectiveness.

[0080] Example 6 In a specific embodiment of the present invention, an energy storage black-start control system based on load identification is also provided, referring to... Figure 2 As shown, it includes: The parameter acquisition module is used to obtain the identification results of the load to be started; The decision generation module is used to input the identification results into the control decision model and output the access control parameters of the load to be started; the control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters. The execution control module is used to control the power output unit of the energy storage system to perform the connection operation of the load to be started, based on the access control parameters.

[0081] In this specific embodiment, the parameter acquisition module is responsible for acquiring the identification results of the load to be started. The identification results should at least include data characterizing the load's properties, such as load type information, health status information, or electrical parameter information. The parameter acquisition module can be communicatively connected to a load diagnostic device, or receive identification result data input manually or preset by the system. The function of this module is to provide an input basis for the entire control system, ensuring that subsequent decision-making processes are based on sound information.

[0082] The decision generation module is communicatively connected to the parameter acquisition module and receives the identification results transmitted by the parameter acquisition module. The decision generation module integrates a control decision model, which includes three cooperating formulas: an adaptive decision formula for determining the voltage adaptive coefficient and frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value based on the voltage adaptive coefficient, and an adaptive pulse width formula for determining the frequency reference value based on the frequency adjustment coefficient. The decision generation module takes the received identification results as input and feeds them into the control decision model for processing. During the calculation, the adaptive decision formula calculates the voltage adaptive coefficient and frequency adjustment coefficient based on the identification results; the voltage dynamic compensation formula outputs the voltage reference value based on the calculated voltage adaptive coefficient and other parameters in the identification results; and the adaptive pulse width formula outputs the frequency reference value based on the calculated frequency adjustment coefficient and other parameters in the identification results. The three formulas operate collaboratively within the decision generation module, ultimately outputting a complete set of access control parameters adapted to the load characteristics. These access control parameters include at least the voltage reference value, frequency reference value, and closing timing information. The function of the decision generation module is to transform the abstract identification results into specific, executable control commands.

[0083] The execution control module is communicatively connected to the decision generation module and receives the access control parameters output by the decision generation module. The execution control module is also communicatively connected to the power output unit of the energy storage system, and is used to send control commands to the power output unit. The power output unit is the component in the energy storage system responsible for outputting electrical energy to the outside, and typically includes an energy storage converter. Based on the received access control parameters, the execution control module generates corresponding drive signals and controls the power output unit to perform the access operation on the load to be started according to the set voltage reference value, frequency reference value, and closing sequence. The function of the execution control module is to translate the decision result into physical actions, ensuring the accurate execution of the load access process.

[0084] The parameter acquisition module, the decision generation module, and the execution control module are sequentially connected and cooperate to form a complete load access control link. The parameter acquisition module provides the identification result as input; the decision generation module, based on this input, performs collaborative calculations using three internally integrated formulas to output access control parameters; and the execution control module drives the power output unit to complete the access operation according to these parameters. The three modules each perform their respective functions while working closely together to achieve a complete process of applying the identification results of the load to be started to the energy storage black-start access control, transforming the load access process from blind closing to precise control based on load characteristics.

[0085] Example 7 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, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or functions. The processor described in this embodiment can be used to obtain the identification results of the load to be started; input the identification results to the control decision model, and output the access control parameters of the load to be started; the control decision model includes adaptive decision formulas for determining voltage adaptive coefficients and frequency adjustment coefficients, voltage dynamic compensation formulas for determining voltage reference values ​​in the access control parameters, and adaptive pulse width formulas for determining frequency reference values ​​in the access control parameters; based on the access control parameters, the power output unit of the energy storage system is controlled to perform the access operation of the load to be started.

[0086] Example 8 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 that stores the terminal's operating system. Furthermore, this 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 high-speed RAM or non-volatile memory, such as at least one disk storage device. 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 methods in the above embodiments; one or more instructions in the computer-readable storage medium are loaded by the processor and executed as follows: obtaining the identification result of the load to be started; inputting the identification result into the control decision model and outputting the access control parameters of the load to be started; the control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and the frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters; based on the access control parameters, controlling the power output unit of the energy storage system to perform the access operation of the load to be started.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 not limited by the foregoing description. Thus, all changes falling within the meaning and scope of equivalents are intended to be included within the scope of the invention. No reference numerals in the drawings should be considered limiting.

[0092] 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 used to limit the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention fall within the scope of protection of this invention.

Claims

1. A black-start control method for energy storage based on load identification, characterized in that, include: Obtain the identification results of the load to be started; The identification results are input into the control decision model, and the access control parameters of the load to be started are output. The control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and the frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters; wherein, the adaptive decision formula is: In the formula, For voltage adaptive coefficients, This is the frequency adjustment factor. For load type decision vectors, The system stability coefficient, This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system. The voltage dynamic compensation formula is as follows: In the formula, This is the voltage reference value. The rated voltage of the energy storage system. For voltage adaptive coefficients, This is the power factor estimate. This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system. The adaptive pulse width formula is: In the formula, This is a frequency reference value. The rated frequency of the energy storage system. This is the frequency adjustment factor. This is the estimated apparent power of the load. For the rated capacity of the energy storage system, For time variables, It is a time constant; Based on the access control parameters, the power output unit of the energy storage system is controlled to perform the access operation of the load to be started.

2. The energy storage black-start control method based on load identification according to claim 1, characterized in that, The process of inputting the identification results into the control decision model and outputting the access control parameters for the load to be started includes: Calculate the voltage adaptive coefficient and frequency adjustment coefficient based on the adaptive decision formula; Calculate the voltage reference value based on the voltage adaptive coefficient and the voltage dynamic compensation formula; The frequency reference value is calculated based on the frequency adjustment coefficient and the adaptive pulse width formula. Voltage reference values ​​and frequency reference values ​​are used as the access control parameters for the load to be started.

3. The energy storage black-start control method based on load identification according to claim 1, characterized in that, The system stability coefficient The calculation formula is: In the formula, The system stability coefficient, The frequency change rate weighting coefficient, The voltage change rate weighting coefficient. The absolute value of the rate of change of frequency. This is the absolute value of the rate of change of voltage.

4. The energy storage black-start control method based on load identification according to claim 1, characterized in that, The identification result of the load to be started includes load type information, which includes: large induction motor, no-load transformer, nonlinear load, and resistive load; The load type decision vector Based on the load type information, it is defined as follows: In the formula, the first element of each vector corresponds to the voltage adaptive coefficient. The empirical benchmark value, the second element corresponds to the frequency adjustment coefficient. The empirical benchmark value.

5. A black-start control system for energy storage based on load identification, characterized in that, include: The parameter acquisition module is used to obtain the identification results of the load to be started; The decision generation module is used to input the identification results into the control decision model and output the access control parameters of the load to be started. The control decision model includes an adaptive decision formula for determining the voltage adaptive coefficient and the frequency adjustment coefficient, a voltage dynamic compensation formula for determining the voltage reference value in the access control parameters, and an adaptive pulse width formula for determining the frequency reference value in the access control parameters; wherein, the adaptive decision formula is: In the formula, For voltage adaptive coefficients, This is the frequency adjustment factor. For load type decision vectors, The system stability coefficient, This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system. The voltage dynamic compensation formula is as follows: In the formula, This is the voltage reference value. The rated voltage of the energy storage system. For voltage adaptive coefficients, This is the power factor estimate. This is the estimated apparent power of the load. This refers to the rated capacity of the energy storage system. The adaptive pulse width formula is: In the formula, This is a frequency reference value. The rated frequency of the energy storage system. This is the frequency adjustment factor. This is the estimated apparent power of the load. For the rated capacity of the energy storage system, For time variables, It is a time constant; The execution control module is used to control the power output unit of the energy storage system to perform the connection operation of the load to be started, based on the access control parameters.

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 energy storage black start control method based on load identification 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 energy storage black start control method based on load identification as described in any one of claims 1 to 4.

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