Urban rail transit voltage-stabilizing power supply method based on multi-energy complementation

Abstract

The invention provides an urban rail transit voltage-stabilizing power supply method based on multi-energy complementation, and relates to the technical field of rail transit power supply, and the method comprises the steps: monitoring the voltage, current and frequency of a power grid, and the output states of solar energy, wind energy and an energy storage system in real time; predicting each energy power generation output based on historical and real-time data; calculating an energy complementation coefficient and a voltage stabilization index to quantitatively evaluate a system state; generating an energy distribution control signal according to the optimization parameter, and adjusting power supply output through an energy conversion device; the power supply voltage is monitored in real time and the control signal is dynamically adjusted through a feedback mechanism. Through multi-energy collaborative optimization and intelligent control, the problem of voltage instability caused by renewable energy grid-connected fluctuation and load impact is effectively solved, and the power supply quality, the system stability and the energy utilization efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of rail transit power supply technology, and in particular to a method for stabilizing power supply in urban rail transit based on multi-energy complementarity. Background Technology

[0002] Urban rail transit systems are crucial urban public transportation tools, and the quality of their power supply directly affects the safe, stable, and efficient operation of trains. Traditional rail transit power supply systems rely primarily on the urban power grid, lacking the ability to quickly and effectively regulate themselves when grid voltage fluctuates. Trains generate drastically fluctuating traction loads during startup and braking, easily causing voltage dips or spikes in the power grid, threatening the safe operation of other sensitive electrical equipment along the line, and also limiting the effective utilization of regenerative braking energy.

[0003] To improve power supply reliability and green, low-carbon levels, renewable energy sources such as solar and wind power, along with energy storage systems, have been introduced into rail transit systems in recent years, forming a multi-energy complementary power supply architecture. However, the inherent intermittency and randomness of renewable energy sources make it difficult to naturally match their output with the load variations of rail transit. Without effective coordination and control, simply superimposing multiple energy sources not only fails to mitigate system fluctuations but may also exacerbate grid instability risks due to the uncertainty of their output. Existing control strategies often focus on optimizing a single objective or rely on empirical rules, making it difficult to accurately quantify and assess the overall stability of the system and the complementary potential between energy sources, thus failing to achieve dynamic optimal allocation under multiple objectives.

[0004] Furthermore, traditional voltage regulation control often employs feedback control based on real-time voltage deviation. This control method is inherently lagging and struggles to cope with sudden and dramatic changes in train load. Although some studies have attempted to introduce predictive feedforward, the calculation and integration of feedforward quantities lack dynamic coupling with the real-time state of the system, and the control parameters are mostly fixed values, failing to adapt to complex changes in system operating conditions and the external environment, thus hindering further improvements in control performance. Summary of the Invention

[0005] To address the technical problems in existing technologies, such as unstable power supply voltage due to the intermittency of renewable energy and load fluctuations, as well as the lag in response of existing control strategies and the lack of multi-energy collaborative quantitative assessment and dynamic optimization capabilities, this invention provides a multi-energy complementary urban rail transit voltage stabilization power supply method.

[0006] The technical solution provided by this invention is as follows: This invention provides a method for stabilizing power supply in urban rail transit based on multi-energy complementarity, comprising: S1: Real-time monitoring of the power grid parameters of the urban rail transit power supply system, including voltage, current, frequency, and the output status of various energy sources, including solar energy, wind energy, and energy storage systems; S2: Based on historical data and real-time monitoring data, predict the power generation output of the various energy sources, including solar power generation output, wind power generation output, and available energy from energy storage systems; S3: Calculate the multi-energy complementarity optimization parameters, including the energy complementarity coefficient and the voltage regulation index, to evaluate the energy complementarity effect and voltage stability requirements. The energy complementarity coefficient reflects the degree of coordination of multi-energy output, and the voltage regulation index reflects the severity of voltage deviation. S4: Based on the multi-energy complementary optimization parameters, generate an energy distribution control signal and adjust the power supply output through the energy conversion device to achieve voltage stability; S5: Monitor the power supply voltage in real time through a feedback mechanism and dynamically adjust the energy distribution control signal to maintain voltage stability.

[0007] Furthermore, the prediction of power generation output from multiple energy sources in step S2 includes: S201: Collect data on solar irradiance, wind speed, and ambient temperature; S202: Predict solar power output using a time series analysis model based on historical irradiance data and weather forecast; S203: Predict wind power generation output using a wind energy prediction model, which is based on historical wind speed data and a turbulence model; S204: Based on the current state of charge, charge / discharge efficiency, and historical data of the energy storage system, predict the available energy of the energy storage system.

[0008] Furthermore, the method for calculating the energy complementarity coefficient in step S3 includes: S301: Obtaining solar power generation Wind power generation capacity and energy storage system output power These power values ​​are either real-time or predicted values; S302: Calculate the Energy Complementarity Factor (ECC). The calculation formula is as follows: in, The number of sampling points. For the first Solar power generation at each sampling point For the first Wind power generation at each sampling point For the first The output power of the energy storage system at each sampling point The standard deviation of solar power generation. The standard deviation of wind power generation capacity. The standard deviation of the output power of the energy storage system; S303: Evaluate the energy complementarity effect based on the ECC value. The higher the ECC value, the better the complementarity effect.

[0009] Furthermore, the method for calculating the voltage regulation index in step S3 includes: S311: Monitor actual voltage value and rated voltage value ; S312: Calculate voltage deviation ; S313: Calculate the voltage regulation index VSI using the following formula: in, For time windows, the unit is seconds; These are weighting coefficients, preset according to system characteristics, and are dimensionless. For time t Voltage deviation at that time, in volts; The rate of change of voltage deviation, in volts per second; S314: Determine voltage stability requirements based on VSI value. A higher VSI value indicates a more unstable voltage.

[0010] Further, generating the energy distribution control signal in step S4 includes: S401: Determine the type and proportion of energy to be used preferentially based on the energy complementarity coefficient and voltage regulation index; S402: Generates control signals to adjust the output power of solar inverters, wind power converters, and energy storage system converters; S403: Sends control signals to the energy conversion device via the communication module.

[0011] Further, adjusting the output power in step S402 includes: S4021: When the voltage regulation index exceeds the threshold, prioritize increasing the output of the energy storage system or reducing the output of high-fluctuation energy. S4022: When the energy complementarity coefficient is low, increase the charging and discharging of the energy storage system to balance the energy output.

[0012] Furthermore, the feedback mechanism in step S5 includes: S501: Real-time sampling of power supply voltage value and comparison with rated voltage value; S502: Dynamically adjusts the energy distribution control signal based on voltage deviation; S503: Regularly update multi-energy complementary optimization parameters to adapt to system changes.

[0013] Furthermore, the dynamic adjustment in step S502 includes: S5021: Uses a PID controller to generate an adjustment signal based on voltage deviation; S5022: Integrates the adjustment signal into the energy distribution control signal; S5023: Limit adjustment speed to prevent system oscillation.

[0014] Furthermore, the generation of the energy distribution control signal in step S4 also includes a feedforward control step based on predicted data: S411: Calculate the feedforward compensation amount based on the predicted solar power output, wind power output, and available energy of the energy storage system in step S2; S412: Superimpose the feedforward compensation onto the energy distribution control signal generated based on the multi-energy complementary optimization parameters; S413: Employs a fuzzy logic control algorithm to dynamically adjust the strength of feedforward compensation based on the real-time state of the system.

[0015] Furthermore, the feedback mechanism in step S5 also includes an adaptive adjustment step: S511: Real-time calculation of voltage fluctuation spectrum to identify dominant oscillation modes; S512: Adaptively adjust the calculation parameters of the voltage regulation index in step S3 according to the frequency and amplitude of the dominant oscillation mode; S513: Based on the adjusted voltage regulation index, correct the generation strategy of the energy distribution control signal in step S4 online.

[0016] The beneficial effects of the technical solution provided by this invention include at least the following: (1) In this invention, by innovatively proposing and calculating the energy complementarity coefficient and voltage stability index, the first accurate and real-time quantitative assessment of the multi-energy synergy effect and system voltage stability risk is realized, providing a clear and reliable basis for control decision-making, overcoming the ambiguity of traditional methods that rely on human experience judgment, and improving the scientificity and effectiveness of control from the source. (2) In this invention, by constructing a composite control architecture that integrates feedforward prediction and feedback regulation, and by using fuzzy logic to dynamically adjust the feedforward strength, the system can not only quickly compensate for the voltage deviation that has occurred, but also actively predict and offset potential power imbalances based on ultra-short-term new energy power generation predictions, which significantly improves the system's ability to suppress drastic load changes and energy fluctuations and its response speed. (3) In this invention, by introducing an adaptive adjustment mechanism based on real-time voltage spectrum analysis, the dominant oscillation mode of the system can be automatically identified and key control parameters can be corrected online, so that the entire control system has the ability to self-optimize and adjust, adapt to different operating conditions and changes in the external environment, always maintain the optimal control performance, and enhance the robustness and applicability of the system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for stabilizing power supply in urban rail transit based on multi-energy complementarity, provided as an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of predicting the power generation output of multiple energy sources in a multi-energy complementary urban rail transit voltage stabilization power supply method provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In embodiments of the present invention, sometimes the subscript is as follows: It may be mistakenly written as a non-subscript form such as W1. When the distinction is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Reference manual attached Figure 1 The diagram shows a flowchart of a multi-energy complementary urban rail transit voltage stabilization power supply method provided by an embodiment of the present invention.

[0025] This invention provides a method for stabilizing power supply in urban rail transit based on multi-energy complementarity. The process may include the following steps: S1: Real-time monitoring of the power grid parameters of the urban rail transit power supply system, including voltage, current, frequency, and the output status of various energy sources, including solar energy, wind energy, and energy storage systems.

[0026] The implementation of this step relies on a high-precision sensor network deployed at key nodes of the urban rail transit power supply system. Sensor types include voltage transformers, current transformers, frequency monitoring devices, and status monitoring units for various energy interfaces. The sensors continuously collect real-time operational data of the power grid at millisecond speeds. This data includes three-phase voltage and true RMS current values ​​on the power supply bus and branches, system frequency, and the instantaneous output power, start / stop status, and health status signals of each grid-connected energy unit. The collected multi-source heterogeneous data is transmitted at high speed to the data acquisition and monitoring control subsystem of the central energy management system via industrial Ethernet or power line carrier communication technology for centralized processing and data fusion. This provides a complete, real-time, and reliable system-wide status perception data foundation for subsequent prediction and decision-making.

[0027] S2: Based on historical data and real-time monitoring data, predict the power generation output of various energy sources, including solar power generation output, wind power generation output, and available energy from energy storage systems.

[0028] This step is performed by the forecasting module within the energy management system. The forecasting module operates on a data platform that integrates a historical operating database and external environmental data interfaces. It accesses the historical database to obtain historical power output data from photovoltaic and wind farms under similar date types and weather conditions. Simultaneously, it obtains high-precision ultra-short-term weather forecast data from meteorological departments, including minute-by-minute irradiance, cloud cover, wind speed, wind direction, and temperature for the next few hours. The module uses long short-term memory network models or time series analysis models from machine learning algorithms to perform rolling forecasts of future solar and wind power generation. For energy storage systems, the forecasting module simulates their maximum discharge or rechargeable capacity over a future time period based on their current state of charge, rated capacity, and historical charge / discharge efficiency curves, thereby predicting their available energy. All forecast results are quantified as time series data and fed into the optimization calculation engine.

[0029] S3: Calculate the multi-energy complementarity optimization parameters, including the energy complementarity coefficient and the voltage regulation index, to evaluate the energy complementarity effect and voltage stability requirements. The energy complementarity coefficient reflects the degree of coordination of multi-energy output, and the voltage regulation index reflects the severity of voltage deviation.

[0030] This step is the core computational component for the system's intelligent optimization. After receiving real-time monitoring and forecast data, the optimization calculation engine initiates the calculation program for multi-energy complementarity optimization parameters. The calculation process quantifies the system's state from two dimensions: the synergy between energy sources and voltage stability. For the energy complementarity coefficient, the engine comprehensively analyzes the output power curve characteristics of solar, wind, and energy storage over time series, evaluating their ability to compensate for fluctuations and smooth the overall output through specific mathematical operations, as well as their degree of coordination. For the voltage stability index, the engine focuses on analyzing voltage deviation data obtained from the monitoring system, considering not only the instantaneous magnitude of the deviation but also its duration and trend, thus forming a quantitative indicator that comprehensively reflects the degree of voltage instability and potential risks. The calculation results of these two parameters provide a precise and quantitative basis for subsequent control decisions.

[0031] S4: Based on the multi-energy complementary optimization parameters, generate energy distribution control signals and adjust the power supply output through the energy conversion device to achieve voltage stability.

[0032] This step is the system's control decision and output stage. The energy management system's control strategy module receives the energy complementarity coefficient and voltage regulation index calculated in the previous step. The control strategy module has multiple pre-set sets of control rules optimized based on expert experience and system simulation. Based on these rules and real-time parameters, the control strategy module generates specific, executable control command signals, which specify the target output power value or power adjustment amount for each energy converter. These digital command signals are then transmitted to the corresponding power electronic conversion device execution units via a reliable industrial communication protocol.

[0033] S5: Monitors the power supply voltage in real time through a feedback mechanism and dynamically adjusts the energy distribution control signal to maintain voltage stability.

[0034] This step constitutes the closed-loop feedback control circuit of the system, ensuring continuous voltage stability. After the control command is executed, the system continuously samples and monitors the voltage at key power supply points using high-speed voltage sensors. These new voltage measurements are fed back to the central controller in real time, compared with the set rated voltage value, and a new, real-time voltage deviation is calculated. This deviation value is input as a feedback signal into the controller's dynamic adjustment algorithm. The dynamic adjustment algorithm quickly calculates a small correction to the previously issued energy distribution control signal based on the current deviation and its cumulative changes, and immediately adds this correction to the original control signal. This achieves fine and dynamic readjustment of energy distribution, forming a complete negative feedback closed loop of monitoring, calculation, control, and re-monitoring, effectively suppressing system disturbances and keeping the voltage consistently within the allowable deviation range.

[0035] In one possible implementation, such as Figure 2 As shown, step S2, predicting the power generation output of multiple energy sources, includes: S201: Collect data on solar irradiance, wind speed, and ambient temperature; S202: Predict solar power output using a time series analysis model based on historical irradiance data and weather forecasts; S203: Use a wind energy prediction model to predict wind power output. The model is based on historical wind speed data and a turbulence model. S204: Based on the current state of charge, charge / discharge efficiency, and historical data of the energy storage system, predict the available energy of the energy storage system.

[0036] This step specifically enables accurate prediction of power generation output from various renewable energy sources. Solar power generation prediction first requires the collection of real-time irradiance data, acquired through irradiance sensors installed at the photovoltaic array site, combined with detailed weather forecasts for the next few hours provided by meteorological departments. The prediction model employs time series analysis, establishing an irradiance-power conversion model by analyzing the correlation between historical irradiance data and actual power generation data. Wind power generation prediction requires the collection of real-time wind speed and direction data, obtained from ultrasonic anemometers installed at the height of the wind turbine hub. The wind power prediction model is based on computational fluid dynamics principles, combining terrain data and turbulence models to predict future wind speeds and converting this into predicted power generation using the wind turbine's power curve. The energy storage system's available energy prediction is based on real-time state-of-charge data provided by the battery management system, comprehensively considering factors such as battery charge / discharge efficiency, aging level, and ambient temperature, and employing a Kalman filter algorithm to estimate future available energy.

[0037] In one possible implementation, the method for calculating the energy complementarity coefficient in step S3 includes: S301: Obtaining solar power generation Wind power generation capacity and energy storage system output power These power values ​​are either real-time or predicted values; S302: Calculate the Energy Complementarity Factor (ECC). The calculation formula is as follows: in, The number of sampling points. For the first Solar power generation at each sampling point For the first Wind power generation at each sampling point For the first The output power of the energy storage system at each sampling point The standard deviation of solar power generation. The standard deviation of wind power generation capacity. The standard deviation of the output power of the energy storage system; S303: Evaluate the energy complementarity effect based on the ECC value. The higher the ECC value, the better the complementarity effect.

[0038] This step defines in detail the calculation method for the energy complementarity coefficient. The system first acquires time-series power data from solar, wind, and energy storage systems from a data center, recorded at fixed sampling intervals. During the calculation, the standard deviation of each energy source's power data is first calculated using an unbiased estimation method to ensure the accuracy of the statistical results. Subsequently, the system calculates the power coordination degree between each pair of the three energy sources according to a specific mathematical formula, taking into account the relative magnitude and trend of power values.

[0039] Standard deviation of solar power generation Standard deviation of wind power generation and the standard deviation of the output power of the energy storage system The calculations are based on power time-series data from a complete past scheduling cycle, which can be 15 minutes. The Bessel formula from standard statistics is used for the calculations to ensure an unbiased estimate. Specifically, the standard deviation of solar power generation... The calculation formula is: in, The number of sampling points. For the first Solar power generation at each sampling point This represents the average solar power generation during that time period. and The calculation method follows the same logic.

[0040] The final calculation results are averaged and combined with the power extreme value ratio to form a comprehensive evaluation index. This index can effectively reflect the complementary characteristics of different energy sources over time, providing a quantitative basis for subsequent energy dispatch.

[0041] In one possible implementation, the method for calculating the voltage regulation index in step S3 includes: S311: Monitor actual voltage value and rated voltage value ; S312: Calculate voltage deviation ; S313: Calculate the voltage regulation index VSI using the following formula: in, For time windows, the unit is seconds; These are weighting coefficients, preset according to system characteristics, and are dimensionless. For time t Voltage deviation at that time, in volts; The rate of change of voltage deviation, in volts per second; S314: Determine voltage stability requirements based on VSI value. A higher VSI value indicates a more unstable voltage.

[0042] This step details the calculation process of the voltage stability index. The system continuously collects voltage data from each node in the power supply network using high-precision voltage sensors and records voltage deviation values ​​with microsecond-level time resolution. The calculation first integrates the voltage deviation over time; the integral term includes the instantaneous value of the deviation and its rate of change, reflecting the amplitude and speed of change of the voltage deviation, respectively. The calculation also considers the maximum voltage deviation within the monitoring time window, reflecting the most severe voltage deviation. The various calculated components are weighted and combined using pre-defined coefficients configured according to the characteristics and stability requirements of the power supply system. The resulting voltage stability index is a comprehensive quantitative indicator that fully reflects the stability of voltage quality.

[0043] Weighting coefficients The preset value is determined in the following way: First, during the system commissioning phase, a series of typical voltage disturbance events were artificially simulated, including train starting, braking, and sudden changes in renewable energy power, and the system voltage deviation was recorded. And its rate of change. Subsequently, system identification and controller parameter tuning methods, such as the Ziegler-Nichols tuning method, are used to analyze the dynamic response characteristics of the system in order to determine a set of coefficient values ​​that most effectively characterize the degree of voltage instability. A typical range of empirical values ​​is... Time window Based on the system inertia setting, the value is usually between 10 seconds and 60 seconds.

[0044] In one possible implementation, generating the energy distribution control signal in step S4 includes: S401: Determine the type and proportion of energy to be used preferentially based on the energy complementarity coefficient and voltage regulation index; S402: Generates control signals to adjust the output power of solar inverters, wind power converters, and energy storage system converters; S403: Sends control signals to the energy conversion device via the communication module.

[0045] This step details the generation mechanism of energy distribution control signals. The control strategy module determines the optimal energy allocation scheme based on the received energy complementarity coefficient and voltage stability index, using a built-in expert knowledge base. The decision-making process first assesses the stability requirements of the current system; when the voltage stability index is high, voltage stabilization measures are prioritized. The module then generates specific power regulation commands, including power setpoints for photovoltaic inverters, power limits for wind turbines, and charging / discharging power commands for energy storage systems. All control commands are encapsulated using standard communication protocols to ensure reliable and real-time data transmission. Command issuance employs a priority scheduling mechanism, with important control commands receiving higher transmission priority.

[0046] In one possible implementation, adjusting the output power in step S402 includes: S4021: When the voltage regulation index exceeds the threshold, prioritize increasing the output of the energy storage system or reducing the output of high-fluctuation energy. S4022: When the energy complementarity coefficient is low, increase the charging and discharging of the energy storage system to balance the energy output.

[0047] This step further refines the specific power regulation strategy. When the system detects that the voltage regulation index exceeds the safety threshold, the control module immediately activates the emergency regulation mechanism. The regulation strategy prioritizes the rapid response capability of the energy storage system, smoothing voltage fluctuations by increasing or decreasing the power output of the energy storage system. Simultaneously, the system automatically reduces the output ratio of renewable energy sources with higher volatility, and may activate the pitch angle adjustment of wind turbines or the power limiting function of photovoltaic inverters if necessary. When the energy complementarity coefficient indicates insufficient system coordination, the control module enhances the regulation role of the energy storage system, compensating for the intermittency and uncertainty of renewable energy output by rationally arranging charging and discharging plans.

[0048] In one possible implementation, the feedback mechanism in step S5 includes: S501: Real-time sampling of power supply voltage value and comparison with rated voltage value; S502: Dynamically adjusts the energy distribution control signal based on voltage deviation; S503: Regularly update multi-energy complementary optimization parameters to adapt to system changes.

[0049] This step details the implementation of the feedback control mechanism. The system is configured with multiple voltage monitoring points, evenly distributed across key locations in the power supply network. The data acquisition system continuously collects voltage data at a fixed sampling frequency. The collected data is digitally filtered and then compared with the rated voltage value. The control system dynamically adjusts the energy distribution strategy based on the magnitude and direction of the voltage deviation, employing an incremental adjustment method to avoid over-adjustment. Simultaneously, the system periodically recalculates the multi-energy complementary optimization parameters. The parameter update frequency is dynamically adjusted based on the system's operating status, increasing when system fluctuations are significant and decreasing when the system is stable to conserve computing resources.

[0050] In one possible implementation, the dynamic adjustment in step S502 includes: S5021: Uses a PID controller to generate an adjustment signal based on voltage deviation; S5022: Integrates the adjustment signal into the energy distribution control signal; S5023: Limit adjustment speed to prevent system oscillation.

[0051] This step details the technical implementation of dynamic adjustment. The system employs a digital PID controller as the core regulator, with its parameters carefully tuned to adapt to the dynamic characteristics of the power supply system. The controller calculates the adjustment amount based on the real-time voltage deviation, comprehensively considering the contributions of the proportional, integral, and derivative terms during the calculation. The resulting adjustment signal is then amplitude-limited and superimposed on the original control signal to form the final control command. To prevent system oscillations during adjustment, the controller is equipped with an output rate-of-change limit to ensure smooth changes in the control command. Simultaneously, the system is also equipped with an anti-saturation mechanism to prevent integral saturation under large deviations.

[0052] In one possible implementation, generating the energy distribution control signal in step S4 further includes a feedforward control step based on predicted data: S411: Calculate the feedforward compensation amount based on the predicted solar power output, wind power output, and available energy of the energy storage system in step S2; S412: Superimpose the feedforward compensation onto the energy distribution control signal generated based on the multi-energy complementary optimization parameters; S413: Employs a fuzzy logic control algorithm to dynamically adjust the strength of feedforward compensation based on the real-time state of the system.

[0053] This step introduces a feedforward control mechanism to improve system response speed. The calculation of the feedforward compensation amount is based on energy output forecast data. The system pre-calculates the amount of power to be compensated based on the predicted trends in solar and wind power generation. During the calculation, the response characteristics and regulation capabilities of each energy source are considered, and energy sources with fast response speeds are preferentially selected as the execution unit for feedforward compensation. The feedforward compensation signal and the feedback control signal are combined using a vector superposition method, and the mixing ratio of the two signals is dynamically adjusted according to the real-time operating status of the system. The fuzzy logic controller optimizes the strength parameters of the feedforward compensation online based on system stability indicators and energy availability indicators, ensuring that the feedforward control is both fast and accurate.

[0054] In one possible implementation, the feedback mechanism in step S5 further includes an adaptive adjustment step: S511: Real-time calculation of voltage fluctuation spectrum to identify dominant oscillation modes; S512: Adaptively adjust the calculation parameters of the voltage regulation index in step S3 according to the frequency and amplitude of the dominant oscillation mode; S513: Based on the adjusted voltage regulation index, correct the generation strategy of the energy distribution control signal in step S4 online.

[0055] This step enables adaptive adjustment of control parameters. The system performs spectral analysis on the voltage fluctuation signal using Fast Fourier Transform (FFT) to identify the dominant oscillation mode and its corresponding frequency characteristics. Based on the identified oscillation characteristics, the system automatically adjusts the weighting coefficients in the voltage regulation index calculation, making the index calculation more focused on the most critical voltage issue. Simultaneously, the system online corrects the parameter settings of the control strategy, making energy distribution control more tailored to the actual needs of the current system. This adaptive mechanism ensures that the control system can adapt to different operating conditions and external factors, always maintaining optimal control performance.

[0056] The specific steps for real-time calculation of voltage fluctuation spectrum are as follows: The system continuously acquires the voltage deviation signal of the key bus at a sampling frequency of not less than 1kHz. The system extracts a 1-second time window of data (containing 1000 sampling points). After applying a Hanning window function to this time window, a Fast Fourier Transform (FFT) is performed to convert the time-domain signal to the frequency-domain signal. The dominant oscillation mode is the frequency point corresponding to the highest amplitude spectral component in the 1Hz to 100Hz frequency range after the FFT transformation. The system identifies this dominant frequency. and its amplitude Then, based on a preset adjustment mapping table, the weighting coefficients in the voltage regulation index calculation will be adaptively adjusted. For example, when a high frequency of the dominant oscillation mode is identified, such as... When, automatically increase the coefficient of the differential term. The weights are adjusted to enhance the control system's ability to suppress high-frequency disturbances.

[0057] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: (1) In this invention, by innovatively proposing and calculating the energy complementarity coefficient and voltage stability index, the first accurate and real-time quantitative assessment of the multi-energy synergy effect and system voltage stability risk is realized, providing a clear and reliable basis for control decision-making, overcoming the ambiguity of traditional methods that rely on human experience judgment, and improving the scientificity and effectiveness of control from the source. (2) In this invention, by constructing a composite control architecture that integrates feedforward prediction and feedback regulation, and by using fuzzy logic to dynamically adjust the feedforward strength, the system can not only quickly compensate for the voltage deviation that has occurred, but also actively predict and offset potential power imbalances based on ultra-short-term new energy power generation predictions, which significantly improves the system's ability to suppress drastic load changes and energy fluctuations and its response speed. (3) In this invention, by introducing an adaptive adjustment mechanism based on real-time voltage spectrum analysis, the dominant oscillation mode of the system can be automatically identified and key control parameters can be corrected online, so that the entire control system has the ability to self-optimize and adjust, adapt to different operating conditions and changes in the external environment, always maintain the optimal control performance, and enhance the robustness and applicability of the system.

[0058] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0059] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0060] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0061] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0062] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity, characterized in that, include: S1: Real-time monitoring of the power grid parameters of the urban rail transit power supply system, including voltage, current, frequency, and the output status of various energy sources, including solar energy, wind energy, and energy storage systems; S2: Based on historical data and real-time monitoring data, predict the power generation output of the various energy sources, including solar power generation output, wind power generation output, and available energy from energy storage systems; S3: Calculate the multi-energy complementarity optimization parameters, including the energy complementarity coefficient and the voltage regulation index, to evaluate the energy complementarity effect and voltage stability requirements. The energy complementarity coefficient reflects the degree of coordination of multi-energy output, and the voltage regulation index reflects the severity of voltage deviation. S4: Based on the multi-energy complementary optimization parameters, generate an energy distribution control signal and adjust the power supply output through the energy conversion device to achieve voltage stability; S5: Monitor the power supply voltage in real time through a feedback mechanism and dynamically adjust the energy distribution control signal to maintain voltage stability.

2. The method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 1, characterized in that, The prediction of power generation output from multiple energy sources in step S2 includes: S201: Collect data on solar irradiance, wind speed, and ambient temperature; S202: Predict solar power output using a time series analysis model based on historical irradiance data and weather forecast; S203: Predict wind power generation output using a wind energy prediction model, which is based on historical wind speed data and a turbulence model; S204: Based on the current state of charge, charge / discharge efficiency, and historical data of the energy storage system, predict the available energy of the energy storage system.

3. The method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 1, characterized in that, The method for calculating the energy complementarity coefficient in step S3 includes: S301: Obtaining solar power generation Wind power generation capacity and energy storage system output power These power values ​​are either real-time or predicted values; S302: Calculate the Energy Complementarity Factor (ECC). The calculation formula is as follows: in, The number of sampling points. For the first Solar power generation at each sampling point For the first Wind power generation at each sampling point For the first The output power of the energy storage system at each sampling point The standard deviation of solar power generation. The standard deviation of wind power generation capacity. The standard deviation of the output power of the energy storage system; S303: Evaluate the energy complementarity effect based on the ECC value. The higher the ECC value, the better the complementarity effect.

4. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity as described in claim 1, characterized in that, The method for calculating the voltage regulation index in step S3 includes: S311: Monitor actual voltage value and rated voltage value ; S312: Calculate voltage deviation ; S313: Calculate the voltage regulation index VSI using the following formula: in, For time windows, the unit is seconds; These are weighting coefficients, preset according to system characteristics, and are dimensionless. For time t Voltage deviation at that time, in volts; The rate of change of voltage deviation, in volts per second; S314: Determine voltage stability requirements based on VSI value. A higher VSI value indicates a more unstable voltage.

5. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity as described in claim 1, characterized in that, The generation of the energy distribution control signal in step S4 includes: S401: Determine the type and proportion of energy to be used preferentially based on the energy complementarity coefficient and voltage regulation index; S402: Generates control signals to adjust the output power of solar inverters, wind power converters, and energy storage system converters; S403: Sends control signals to the energy conversion device via the communication module.

6. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 5, characterized in that, Adjusting the output power in step S402 includes: S4021: When the voltage regulation index exceeds the threshold, prioritize increasing the output of the energy storage system or reducing the output of high-fluctuation energy. S4022: When the energy complementarity coefficient is low, increase the charging and discharging of the energy storage system to balance the energy output.

7. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 1, characterized in that, The feedback mechanism in step S5 includes: S501: Real-time sampling of power supply voltage value and comparison with rated voltage value; S502: Dynamically adjusts the energy distribution control signal based on voltage deviation; S503: Regularly update multi-energy complementary optimization parameters to adapt to system changes.

8. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 7, characterized in that, The dynamic adjustment in step S502 includes: S5021: Uses a PID controller to generate an adjustment signal based on voltage deviation; S5022: Integrates the adjustment signal into the energy distribution control signal; S5023: Limit adjustment speed to prevent system oscillation.

9. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 1, characterized in that, The generation of the energy distribution control signal in step S4 also includes a feedforward control step based on prediction data: S411: Calculate the feedforward compensation amount based on the predicted solar power output, wind power output, and available energy of the energy storage system in step S2; S412: Superimpose the feedforward compensation onto the energy distribution control signal generated based on the multi-energy complementary optimization parameters; S413: Employs a fuzzy logic control algorithm to dynamically adjust the strength of feedforward compensation based on the real-time state of the system.

10. A method for stabilizing power supply in urban rail transit based on multi-energy complementarity according to claim 1, characterized in that, The feedback mechanism in step S5 also includes an adaptive adjustment step: S511: Real-time calculation of voltage fluctuation spectrum to identify dominant oscillation modes; S512: Adaptively adjust the calculation parameters of the voltage regulation index in step S3 according to the frequency and amplitude of the dominant oscillation mode; S513: Based on the adjusted voltage regulation index, correct the generation strategy of the energy distribution control signal in step S4 online.

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