Wide-area measurement and analysis method and device for harmonic waves of electrified railway

Abstract

The invention provides an electrified railway harmonic wave wide area measurement analysis method and device. The method comprises the following steps: acquiring historical operation parameters of each traction substation; determining an electrical coupling relationship between different traction substations based on the historical operation parameters to construct a harmonic coupling model; acquiring real-time electrical data by using anti-interference sensors arranged among the substations; and inputting the electrical data into the harmonic coupling model to output a harmonic analysis result. According to the electrified railway harmonic wave wide-area measurement analysis method and device provided by the invention, a coupling model capable of accurately describing harmonic wave interaction between stations in an electrified railway system is established by collecting interaction data between traction substations. The harmonic coupling model can effectively identify and evaluate a harmonic coupling phenomenon generated by interaction between traction substations, so that a scientific basis is provided in design and optimization processes.

Description

Technical Field

[0001] The present invention relates to the technical field of railway traction power supply system analysis and control, and in particular to a method and device for wide-area measurement and analysis of harmonics in electrified railways. Background Art

[0002] Electrified railway systems are a vital component of modern transportation. Harmonics generated during their operation pose a serious threat to system stability and reliability. Existing methods for analyzing and addressing harmonics primarily rely on decentralized measurement equipment and simple mathematical models. However, these methods exhibit numerous shortcomings in practical applications and are unable to meet the demands of complex electrified railway systems.

[0003] First, existing technologies lack effective modeling methods when dealing with the problem of harmonic coupling between traction substations. The interaction between traction substations will cause complex changes in the propagation path and intensity of harmonics. Existing methods cannot accurately describe this coupling relationship, resulting in huge deviations in the harmonic analysis results. Secondly, when multiple trains are running at the same time, the harmonic superposition effect is extremely complex due to the differences in the load characteristics of different trains. Existing technologies are unable to effectively separate and quantify these superposition effects, resulting in a significant decrease in the accuracy and reliability of harmonic analysis. In addition, the impedance mismatch problem of the traction network is particularly prominent in long-distance power transmission. Existing technologies lack the ability to monitor and dynamically adjust impedance parameters in real time. Harmonic reflection and amplification phenomena occur frequently, further exacerbating the instability of the system.

[0004] More seriously, existing measurement equipment has extremely weak anti-interference capabilities in complex power grid environments. External electromagnetic interference, temperature fluctuations, and other factors can significantly reduce measurement accuracy, distorting harmonic data and making it impossible to provide a reliable basis for subsequent analysis. Existing technologies lack the ability to dynamically adjust harmonic distortion correction strategies in the event of grid voltage fluctuations, exacerbating harmonic distortion issues and potentially even causing systemic failures.

[0005] In summary, existing technologies have serious deficiencies in harmonic coupling modeling, harmonic superposition analysis, impedance parameter monitoring, anti-interference capabilities, and dynamic correction strategies. These deficiencies fail to meet the high-precision and high-reliability harmonic analysis requirements of electrified railway systems. These issues not only limit the application scope of harmonic analysis technology but also pose a significant threat to the safety and stability of electrified railway systems. Summary of the Invention

[0006] In view of this, the present invention provides a method and apparatus for wide-area measurement and analysis of harmonics in electrified railways to solve at least one of the above-mentioned problems.

[0007] In order to achieve the above object, the present invention adopts the following scheme:

[0008] According to a first aspect of the present invention, a method for wide-area measurement and analysis of harmonics in electrified railways is provided, the method comprising: obtaining historical operating parameters of each traction substation; determining the electrical coupling relationship between different traction substations based on the historical operating parameters to construct a harmonic coupling model; collecting real-time electrical data using anti-interference sensors installed between the substations; and inputting the electrical data into the harmonic coupling model to output harmonic analysis results.

[0009] As an embodiment of the present invention, the above method determines the electrical coupling relationship between different traction substations based on the historical operating parameters to construct a harmonic coupling model, including: calculating the electrical coupling coefficient between each substation based on the historical operating parameters; determining the coupling strength level according to the electrical coupling coefficient; adjusting the coupling matrix of the harmonic coupling model using the coupling strength level; and iteratively optimizing the coupling matrix, so that the optimized coupling matrix can accurately reflect the electrical coupling relationship between different traction substations, thereby completing the construction of the harmonic coupling model.

[0010] As an embodiment of the present invention, the above method also includes: collecting real-time operating parameters of each substation through an online monitoring system; correcting the electrical coupling coefficient based on the real-time operating parameters; and adjusting the parameters in the harmonic coupling model based on the corrected electrical coupling coefficient.

[0011] As an embodiment of the present invention, the above method also includes: obtaining electrical quality and energy data of each traction substation under different working load conditions; based on the coupling differences between the electrical quality and energy data to different substations, the coupling differences are used to evaluate the impact of different loads on harmonic coupling; and optimizing the parameter settings of the harmonic coupling model based on the coupling differences.

[0012] As an embodiment of the present invention, the above method further includes: obtaining the coupling voltage of the power system using a power system coupling voltage identification method based on fuzzy logic; and adjusting the harmonic analysis result output by the harmonic coupling model based on the coupling voltage.

[0013] As an embodiment of the present invention, after the above method uses anti-interference sensors installed between each substation to collect real-time electrical data, the method also includes: performing preliminary filtering on the collected electrical data to separate DC and AC components to effectively remove high-frequency noise; and performing deep purification on the electrical data after preliminary filtering through a digital filter to further eliminate residual noise.

[0014] According to a second aspect of the present invention, there is provided a device for wide-area measurement and analysis of harmonics in an electrified railway, the device comprising: a parameter acquisition unit for acquiring historical operating parameters of each traction substation; a model construction unit for determining the electrical coupling relationship between different traction substations based on the historical operating parameters to construct a harmonic coupling model; a real-time data acquisition unit for collecting real-time electrical data using anti-interference sensors installed between the substations; and a harmonic analysis unit for inputting the electrical data into the harmonic coupling model to output a harmonic analysis result.

[0015] As an embodiment of the present invention, the above-mentioned model construction unit includes: a coupling coefficient calculation module, which is used to calculate the electrical coupling coefficient between each substation based on the historical operating parameters; a coupling level determination module, which is used to determine the coupling strength level according to the electrical coupling coefficient; a coupling matrix adjustment module, which is used to adjust the coupling matrix of the harmonic coupling model using the coupling strength level; and an iterative optimization module, which is used to perform iterative optimization calculation on the coupling matrix. The optimized coupling matrix can accurately reflect the electrical coupling relationship between different traction substations, thereby completing the construction of the harmonic coupling model.

[0016] As an embodiment of the present invention, the above-mentioned device also includes: an online acquisition unit, used to collect real-time operating parameters of each substation through an online monitoring system; a coefficient correction unit, used to correct the electrical coupling coefficient based on the real-time operating parameters; and a parameter adjustment unit, used to adjust the parameters in the harmonic coupling model based on the corrected electrical coupling coefficient.

[0017] As an embodiment of the present invention, the above-mentioned device also includes: an electric quality energy acquisition unit, used to obtain the electric quality energy data of each traction substation under different working load conditions; a coupling difference acquisition unit, used to obtain the coupling difference between different substations based on the electric quality energy data, and the coupling difference is used to evaluate the impact of different loads on harmonic coupling; a parameter optimization unit, used to optimize the parameter settings of the harmonic coupling model based on the coupling difference.

[0018] As an embodiment of the present invention, the above-mentioned device also includes: a coupling voltage acquisition unit, which is used to obtain the coupling voltage of the power system using a power system coupling voltage identification method based on fuzzy logic; and an analysis result adjustment unit, which is used to adjust the harmonic analysis result output by the harmonic coupling model based on the coupling voltage.

[0019] As an embodiment of the present invention, the above-mentioned device also includes: a preliminary filtering unit, which is used to perform preliminary filtering on the collected electrical data to separate the DC and AC components to effectively remove high-frequency noise; and a digital filtering unit, which is used to deeply purify the electrical data after preliminary filtering through a digital filter to further eliminate residual noise.

[0020] According to a third aspect of the present invention, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0021] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0022] According to a fifth aspect of the present invention, there is provided a computer program product comprising a computer program / instructions, which implement the steps of the above method when executed by a processor.

[0023] As can be seen from the above technical solution, the method and apparatus for wide-area measurement and analysis of electrified railway harmonics provided in this application collects interaction data between traction substations to establish a coupling model that accurately describes the harmonic interactions between stations in the electrified railway system. This harmonic coupling model can effectively identify and evaluate the harmonic coupling phenomena caused by interactions between traction substations, providing a scientific basis for design and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the 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 work. In the drawings:

[0025] Figure 1 This is a flow chart of a method for wide-area measurement and analysis of harmonics in electrified railways provided in an embodiment of the present application;

[0026] Figure 2 This is a schematic diagram of the process of constructing a harmonic coupling model provided in an embodiment of the present application;

[0027] Figure 3 1 is a flow chart of denoising collected real-time electrical data provided by an embodiment of the present application;

[0028] Figure 4 1 is a flow chart of adjusting parameters in a harmonic coupling model based on a corrected electrical coupling coefficient according to an embodiment of the present application;

[0029] Figure 5 1 is a flow chart of parameter setting of the harmonic coupling model based on coupling difference optimization provided in an embodiment of the present application;

[0030] Figure 6 This is a flow chart of adjusting the output result of the harmonic coupling model based on the coupling voltage provided in an embodiment of the present application;

[0031] Figure 7 This is a schematic diagram of a process for obtaining a coupling voltage of a power system provided in an embodiment of the present application;

[0032] Figure 8 It is a Mandani fuzzy logic block diagram provided in the embodiment of the present application;

[0033] Figure 9 This is a schematic structural diagram of a device for wide-area measurement and analysis of harmonics in electrified railways provided in an embodiment of the present application;

[0034] Figure 10 It is a structural diagram of the model building unit provided in the embodiment of the present application;

[0035] Figure 11 2 is a schematic structural diagram of a device for wide-area measurement and analysis of harmonics in electrified railways provided by another embodiment of the present application;

[0036] Figure 12 2 is a schematic structural diagram of a device for wide-area measurement and analysis of harmonics in electrified railways provided by another embodiment of the present application;

[0037] Figure 13 2 is a schematic structural diagram of a device for wide-area measurement and analysis of harmonics in electrified railways provided by another embodiment of the present application;

[0038] Figure 14 This is a schematic block diagram of the system structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0040] like Figure 1 FIG. 1 is a flow chart of a method for wide-area measurement and analysis of harmonics in an electrified railway according to an embodiment of the present application. This embodiment describes the present application from the perspective of a central processing system. The method includes the following steps:

[0041] Step S101: Acquire historical operating parameters of each traction substation.

[0042] The historical operating parameters in this step include, but are not limited to, key electrical parameters such as traction current, voltage, phase, and power. These parameters can be collected from a traction substation's historical record database, an online monitoring system, or other data acquisition equipment. For example, in a specific electrified railway scenario, intelligent monitoring equipment installed in a traction substation can regularly collect and upload these historical operating parameters to a central processing system.

[0043] These historical operating parameters should cover various operating conditions, such as peak and valley loads, to ensure comprehensive and representative data. Subsequent analysis of this historical operating data will identify the electrical interactions between traction substations and provide accurate input parameters for the subsequent construction of harmonic coupling models.

[0044] Step S102: determining the electrical coupling relationship between different traction substations based on the historical operating parameters to construct a harmonic coupling model.

[0045] Traction substations are interconnected through the traction grid, power lines, and loads, and their electrical parameters influence each other. This influence can be quantified using electrical coupling relationships, such as the coupling coefficient, which describes the coupling strength between two substations. Harmonics are high-frequency components in electrical systems caused by nonlinear loads or other factors. Their propagation path and intensity are directly affected by these electrical coupling relationships, which directly determine the propagation direction and amplitude of harmonics in the system. Therefore, the electrical coupling relationships obtained in this step form the basis for the laws governing harmonic propagation.

[0046] Preferably, Figure 2 As shown, this step may further include the following sub-steps:

[0047] Step S1021: Calculating the electrical coupling coefficients between the substations based on the historical operating parameters.

[0048] First, the electrical coupling coefficient between traction substations can be calculated using the following formula (1):

[0049]

[0050] In the above formula, A represents the current amplitude of substation 1, which generally ranges from tens of amperes to thousands of amperes, and B represents the voltage amplitude of substation 2, which generally ranges from thousands of volts to tens of thousands of volts. There is no fixed standard for the optimal values ​​of A and B, and they can be adjusted according to the actual application scenario. The above formula (1) is calculated by dividing the product of the electrical parameters of the two substations by the square root of their vector sum, with the purpose of quantifying the degree of mutual influence between the two substations. This setting can better reflect the complexity of the actual electrical system and avoid deviation from the actual situation due to the influence of a single aspect.

[0051] In practical applications, the electrical coupling relationship between traction substations may involve multiple parameters (such as power factor, frequency, and impedance). However, introducing too many parameters increases computational complexity and reduces real-time performance. Therefore, this embodiment uses only current and voltage amplitudes to simplify the model and meet real-time computation requirements. Other parameters can be used to modify the electrical coupling coefficient in subsequent steps.

[0052] Step S1022: determining a coupling strength level according to the electrical coupling coefficient.

[0053] Next, this embodiment can determine the coupling strength level based on the value of the electrical coupling coefficient. Generally speaking, the larger the electrical coupling coefficient value, the higher the coupling strength between the two substations. The coupling strength level can be divided into three levels, such as weak, medium, and strong. The following is an example of the classification standard:

[0054] (1) 0≤C<0.3: weak coupling;

[0055] (2) 0.3≤C<0.7: medium coupling;

[0056] (3)0.7≤C≤1: strong coupling.

[0057] The above coupling strength levels can be adjusted based on actual application scenarios and system requirements. This step helps to categorize and manage electrical interactions of different strengths, facilitating subsequent model adjustments and optimization.

[0058] Step S1023: adjusting the coupling matrix of the harmonic coupling model using the coupling strength level.

[0059] The coupling matrix is ​​a mathematical model used to describe the interactions between substations. Each element in the matrix represents the strength of the coupling between the corresponding substations. Adjusting the coupling matrix means updating the values ​​of the matrix elements based on the different levels of coupling strength to more accurately reflect the actual situation. For example, if the coupling strength between two substations is rated as strong, the corresponding value in the matrix can be increased, thereby increasing the model's sensitivity to strong coupling areas.

[0060] Step S1024: performing iterative optimization calculation on the coupling matrix. The optimized coupling matrix can accurately reflect the electrical coupling relationship between different traction substations, thereby completing the construction of the harmonic coupling model.

[0061] The specific operation involves setting an initial matrix and continuously performing numerical simulations. By comparing the simulation results with the actual measured data, the values ​​of the matrix elements are gradually adjusted until the optimal fit is achieved. In one embodiment, assuming a power system with three traction substations, the electrical parameters between them are obtained based on initial measurements. After calculating the correlation coefficient and determining the coupling level, the coupling matrix is ​​adjusted and optimized through multiple iterations, ultimately achieving higher model accuracy and reliability.

[0062] Through these sub-steps, the electrical coupling coefficient is calculated using historical operating parameters, and the coupling matrix is ​​adjusted and optimized based on the coupling strength level. Ultimately, a harmonic coupling model is constructed that accurately reflects the electrical coupling relationship between traction substations. This process ensures the scientific nature and accuracy of the model, providing a solid foundation for harmonic analysis and system optimization.

[0063] Step S103: collect real-time electrical data using anti-interference sensors installed between each substation.

[0064] This embodiment can employ high-precision, anti-interference sensors to collect real-time electrical data between substations. These sensors are installed between traction substations on electrified railways to monitor and record electrical parameters, such as current and voltage, in real time. Their high precision and anti-interference properties ensure data accuracy and reliability, providing a foundation for subsequent data processing. For example, in one embodiment, high-precision, anti-interference sensors are installed at the input and output ports of a traction substation to monitor changes in incoming and outgoing current and voltage.

[0065] Preferably, Figure 3 As shown: After collecting real-time electrical data in this step, the method of the present application may further include the following steps:

[0066] Step S301: performing preliminary filtering on the collected electrical data to separate DC and AC components to effectively remove high-frequency noise.

[0067] Specifically, the preliminary filtering here can be performed using the following formula (2):

[0068] H=A+B·sin(C·t+D) (2)

[0069] In the above formula, H represents the electrical data after preliminary filtering, A and B represent the DC and AC components of the signal, respectively, C represents the frequency, D represents the phase offset, and t represents the time variable. A can vary from -10 to 10, B can vary from 0 to 10, C can vary from 0 to 1000 Hz, and D can vary from -π to π. This formula (1) aims to separate the DC and AC components in the original signal and represent the AC component in the form of a sine function, thereby performing preliminary filtering on the noise signal. Specifically, this method can effectively filter out high-frequency noise while retaining useful low-frequency signal components.

[0070] Step S302: Deeply purify the preliminarily filtered electrical data through a digital filter to further eliminate the remaining noise.

[0071] A digital filter is typically an algorithm used to remove residual noise from data, ensuring cleaner and more reliable data. The selection and design of a digital filter depends on the specific application scenario and noise characteristics. For example, a commonly used digital filter type is a low-pass filter, which effectively removes high-frequency noise while retaining useful low-frequency signals. Specifically, in the present invention, a low-pass filter can be used to further purify the data and improve the accuracy of the model.

[0072] The combination of steps S301 and S302 can significantly improve the quality of electrical data, provide high-precision input data for the construction and analysis of the harmonic coupling model, and thus enhance the analysis and prediction capabilities of the entire system.

[0073] Step S104: inputting the electrical data into the harmonic coupling model to output harmonic analysis results.

[0074] Finally, the processed data is applied to the input of the harmonic coupling model. This data reflects the interactions between traction substations. By analyzing this data, the harmonic coupling model can reveal the propagation patterns of harmonics in the electrified railway system, providing a basis for system optimization. For example, in one embodiment, the processed electrical data is input into the harmonic coupling model. Through simulation analysis, it can be determined how current changes in a specific substation affect the harmonic levels of other substations, thereby formulating effective control strategies to reduce the impact of harmonics on the system.

[0075] As can be seen from the above, the wide-area harmonic measurement and analysis method for electrified railways provided in this application collects interaction data between traction substations to establish a coupling model that accurately describes the harmonic interactions between stations in the electrified railway system. This harmonic coupling model can effectively identify and evaluate the harmonic coupling phenomena caused by the interactions between traction substations, providing a scientific basis for design and optimization.

[0076] Preferably, in the process of applying the above harmonic coupling model, this embodiment can also continuously optimize and adjust the model, such as Figure 4 As shown, the wide-area measurement and analysis method for electrified railway harmonics of this embodiment may further include the following steps:

[0077] Step S401: Collect the real-time operating parameters of each substation through the online monitoring system.

[0078] The real-time operating parameters collected in this step include key electrical parameters such as voltage, current, power factor, frequency, and load status. The data collection covers all traction substations to ensure the comprehensiveness and consistency of the system. In this embodiment, the operating data of each substation can be recorded in real time through an online monitoring system (such as smart sensors, data collectors, etc.). At the same time, multi-point synchronous sampling technology can be used to ensure the time consistency and accuracy of the collected data. For example, in a certain electrified railway system, the online monitoring system can collect voltage and current data of each substation once a minute and upload it to the central processing system to provide real-time data support for subsequent model optimization.

[0079] Step S402: Correcting the electrical coupling coefficient based on the real-time operating parameters.

[0080] In this step, the electrical coupling coefficient can be corrected according to the following formula (3):

[0081] F = k × C (3)

[0082] In the above formula, F represents the corrected electrical coupling coefficient, C represents the uncorrected electrical coupling coefficient, and k represents the correction factor. The value range of the correction factor k is usually between 0 and 1. Specifically, the correction factor k is used to adjust the coupling coefficient deviation caused by traction network, load changes, etc. The calculation of the correction factor k can be based on the changes in real-time operating parameters, such as load fluctuations, grid voltage fluctuations, etc. The significance of this formula is that by correcting the coupling coefficient, the model can more accurately reflect the actual operating conditions. For example, in actual applications, if the initial coupling coefficient of a traction substation is 0.85, the changes in current amplitude and voltage amplitude are monitored in real time, causing the initial coupling coefficient C to deviate from the actual value, and the correction factor k is obtained as 0.9 through analysis, then the corrected coupling coefficient F = 0.9*0.85 = 0.765.

[0083] Step S403: adjusting the parameters in the harmonic coupling model based on the corrected electrical coupling coefficient.

[0084] In this step, the corrected electrical coupling coefficient is used to update the relevant parameters in the coupling model, thereby improving the accuracy and reliability of the model. The specific adjustment method is: recalculate the model parameters, such as the harmonic propagation path and intensity, based on the corrected coupling coefficient; use numerical optimization algorithms (such as gradient descent method, genetic algorithm, etc.) to iteratively adjust the model parameters to ensure that the model output is consistent with the actual operating status. For example, in a certain electrified railway system, the parameters of the harmonic coupling model were adjusted using the corrected coupling coefficient, and it was found that the harmonic propagation path of a certain substation had changed. The model optimized the prediction results accordingly, providing a more reliable basis for the system's harmonic suppression.

[0085] Further preferably, after step S403, this embodiment can also regularly verify and update the harmonic coupling model. In order to ensure the long-term effectiveness and adaptability of the harmonic coupling model, it is necessary to regularly verify and update the harmonic coupling model. Verification can be performed by comparing the model prediction results with the actual measurement data. If a large deviation is found, the model parameters should be adjusted or the coupling coefficient should be recalculated in a timely manner. For example, a comprehensive model verification and parameter update can be performed every month or every quarter to ensure that the model always maintains a high level of accuracy and predictive ability. In this way, the entire system can operate continuously and efficiently, effectively suppressing the adverse effects of harmonics.

[0086] Through steps S401 to S403, this embodiment achieves dynamic optimization and adjustment of the harmonic coupling model. The online monitoring system provides real-time operating parameters, and the corrected electrical coupling coefficient reflects the actual system status. Ultimately, adjusting model parameters improves the accuracy and adaptability of the harmonic coupling model. This approach effectively addresses the complex operating environment of electrified railway systems and enhances the accuracy and efficiency of harmonic analysis and control.

[0087] Preferably, Figure 5 As shown, in the process of applying the above harmonic coupling model, this embodiment may further include the following steps:

[0088] Step S501: Acquire the electrical quality and energy data of each traction substation under different workload conditions.

[0089] The power quality data in this step includes key power quality indicators such as voltage, current, power factor, and harmonic content. This data reflects the operating status of the traction substation under different load conditions. The load conditions in this step should include peak load, valley load, and dynamic load changes. For example, the railway system may have different train traffic at different times of the day, resulting in load changes. All of this data should be recorded.

[0090] Step S502: Based on the coupling differences between the electric quality energy data and different substations, the coupling differences are used to evaluate the impact of different loads on harmonic coupling.

[0091] Coupling difference refers to the degree of difference in power quality between different traction substations. In this embodiment, it is used to quantify the impact of load on harmonic coupling. In this embodiment, the coupling difference between different substations can be obtained by the following formula (4):

[0092] D=|E1-E2| (4)

[0093] In the above formula, E represents the coupling difference. E1 and E2 represent the power quality indicators of substation 1 and substation 2 under specific load conditions, respectively.

[0094] Specifically, the coupling difference D between each pair of substations can be calculated based on the collected electrical quality and energy data. The impact of load changes on harmonic coupling can then be evaluated by comparing the D values ​​under different load conditions. The larger the D value, the more significant the coupling difference between the two substations, and the greater the impact of the load on harmonic coupling; conversely, the smaller the impact. Therefore, by analyzing the changing trend of the D value, this embodiment can identify which load conditions have the most significant impact on harmonic coupling. For example, within a certain railway section, analysis found that the coupling difference D between two substations increased significantly during peak load, indicating that load changes have a strong impact on harmonic coupling.

[0095] Step S503: Optimizing parameter settings of the harmonic coupling model based on the coupling difference.

[0096] In this step, the parameters of the model are adjusted according to the calculated coupling difference D to improve the prediction accuracy and stability of the model. This can be achieved by using an optimization algorithm, such as a gradient descent method or a particle swarm optimization algorithm. Specifically, assuming that the coupling difference predicted by the preliminary model under a certain parameter setting deviates significantly from the actual data, the parameters can be further adjusted according to the new coupling difference D until the error between the model prediction result and the actual data is minimized. In one embodiment, through multiple iterative optimizations, it was found that when a key parameter was adjusted from an initial value of 0.5 to 0.7, the accuracy of the model prediction result was significantly improved.

[0097] Further preferably, after step S503, this embodiment can also verify the effectiveness of the model through experimental data. This embodiment can apply the optimized model to a real experimental environment to compare the consistency of the harmonic coupling results predicted by the model with the actual measured data. The verification process should include multiple experimental test points and different working conditions to ensure the robustness and reliability of the model under various conditions. Specifically, sensors can be arranged between multiple traction substations in the railway system to collect actual data under a series of different load conditions, and then these data can be input into the model for verification. For example, after verification, the coupling difference D predicted by the model is in good agreement with the measured data in multiple time periods, indicating that the model has high accuracy.

[0098] The above-mentioned process of steps S501-S503 can effectively deal with the harmonic coupling problem under complex load conditions, and provide reliable technical support for harmonic analysis and control of electrified railway systems.

[0099] Preferably, Figure 6 As shown, in the process of applying the above harmonic coupling model, this embodiment may further include the following steps:

[0100] Step S601: obtaining the coupling voltage of the power system by using a power system coupling voltage identification method based on fuzzy logic.

[0101] To accurately identify and obtain power system coupling voltage, this embodiment proposes a fuzzy logic-based power system coupling voltage identification method. This method uses an extended Prony algorithm on a micro-broadband device to identify coupling voltage parameters. Specifically, this method analyzes the characteristics of the power system coupling voltage—active power, zero-sequence current components, and the product of voltage and power factor—and employs Mamdani fuzzy logic to identify the coupling voltage. This overcomes the limitation of identifying oscillations based on a precise and absolute fixed value, thereby making coupling voltage identification more accurate.

[0102] like Figure 7 As shown, this step may specifically include the following sub-steps:

[0103] Step S6011: Calculate the active power (P), voltage and power factor product (Ucosφ) and current sequence component ratio based on the three-phase voltage and current broadband data calculated by the synchronous broadband measurement device.

[0104] Step S6012: Use Mandani fuzzy logic to determine whether coupling voltage occurs.

[0105] like Figure 8 The figure shows the Mandani fuzzy logic block diagram provided by this embodiment, wherein active power P, Ucosφ, current sequence component ratio is the input variable of the Mamdani fuzzy reasoning. Since when coupling voltage occurs, the power fluctuation and Ucosφ are large, and the zero-negative sequence component of the current is small, the fuzzy reasoning rules are as follows:

[0106] (1) IF power fluctuation is large AND Ucosφ is large AND current sequence component ratio is small;

[0107] THEN oscillation occurs.

[0108] (2) IF power fluctuation is small OR Ucosφ is small OR current sequence component ratio is large;

[0109] THEN no oscillation occurs.

[0110] In the above fuzzy inference rules, each fuzzy logic input variable, such as large power fluctuation, small power fluctuation, and large Ucosφ, adopts the trapezoidal membership function defined as follows:

[0111]

[0112] Where x is the input variable, (a, b, c, d) is the value range of the trapezoidal membership function; the membership function value for a large active power P is (0.02, 0.2, 0.6, 1); the value for a large power factor product Ucosφ is (0.02, 0.2, 0.6, 1); the value for a large current sequence component ratio is (0.1, 0.4, 0.6, 1); the value for a small active power P is (0, 0, 0.01, 0.04); the value for a small power factor product Ucosφ is (0, 0, 0.01, 0.04); the value for a small current sequence component ratio is (0, 0, 0.2, 0.6).

[0113] Step S6013: When a coupling voltage occurs, the Prony algorithm is used on the micro broadband device to perform parameter identification to calculate the amplitude, frequency, attenuation factor, and damping ratio of the coupling voltage, thereby achieving local online identification of the coupling voltage.

[0114] In this embodiment, coupling voltage parameter identification is implemented on a micro-broadband device, fully utilizing the device's high-density raw data, reducing coupling voltage parameter identification time and improving parameter identification accuracy. Because the coupling voltage frequency typically ranges from 0.1 to 2 Hz, the active power data must be low-pass filtered to remove high-frequency components before using the Prony algorithm to identify the coupling voltage parameters. The Prony algorithm is implemented based on a high-performance DSP processor (SHARK series). Because coupling voltage determination and analysis require caching large amounts of data and performing numerous matrix operations, the Prony algorithm's associated software has been optimized to incorporate the DSP's unique features (such as single-instruction, multiple-data execution and ring buffering), improving the algorithm's execution efficiency in the micro-broadband device.

[0115] Step S602: adjusting the harmonic analysis result output by the harmonic coupling model based on the coupling voltage.

[0116] This means that the coupling voltage calculated using the above formula will be promptly fed back into the coupling model to dynamically correct the model and better reflect the harmonic state of the actual power grid. Specifically, after the coupling voltage is calculated, the system can adjust the harmonic analysis algorithm based on the new coupling voltage to ensure that the results output by the harmonic coupling model are more consistent with the actual operating conditions of the power grid. For example, in a typical electrified railway scenario, once the coupling voltage is calculated to be 27.5V, this value will be updated in real time to the harmonic coupling model, which will then adjust its prediction results accordingly to make the harmonic analysis more accurate.

[0117] Further preferably, after the above step S602, this embodiment can also compare the optimization and adjustment effects through multiple experiments, that is, verify the effectiveness and reliability of the model through experiments. By comparing and analyzing the performance of the model under different working conditions, the optimal parameter configuration and adjustment strategy can be found. In a specific experiment, it may be necessary to conduct tests under different grid load conditions, record the coupling voltage of each test and its impact on the model performance, and determine the optimal parameter combination through statistical analysis. For example, it can be found through multiple experimental comparisons that under a certain specific line impedance, the prediction accuracy and response speed of the model are the best, which provides a reliable parameter basis for subsequent applications.

[0118] Through the above steps S601-S602, the complex impact of grid voltage fluctuations on harmonic propagation can be effectively addressed, providing more accurate technical support for harmonic analysis and control of electrified railway systems.

[0119] like Figure 9The figure shows a schematic diagram of the structure of a wide-area harmonic measurement and analysis device for electrified railways provided by an embodiment of the present application. The device includes: a parameter acquisition unit 910, a model building unit 920, a real-time data acquisition unit 930, and a harmonic analysis unit 940, which are sequentially connected.

[0120] The parameter acquisition unit 910 is used to acquire the historical operating parameters of each traction substation.

[0121] The model building unit 920 is configured to determine the electrical coupling relationship between different traction substations based on the historical operating parameters to build a harmonic coupling model.

[0122] The real-time data acquisition unit 930 is used to collect real-time electrical data using anti-interference sensors installed between each substation.

[0123] The harmonic analysis unit 940 is configured to input the electrical data into the harmonic coupling model to output harmonic analysis results.

[0124] Preferably, Figure 10 As shown, the model building unit 920 includes:

[0125] The coupling coefficient calculation module 921 is used to calculate the electrical coupling coefficients between the substations based on the historical operating parameters.

[0126] The coupling level determination module 922 is configured to determine a coupling strength level according to the electrical coupling coefficient.

[0127] The coupling matrix adjustment module 923 is configured to adjust the coupling matrix of the harmonic coupling model using the coupling strength level.

[0128] The iterative optimization module 924 is used to perform iterative optimization calculations on the coupling matrix. The optimized coupling matrix can accurately reflect the electrical coupling relationship between different traction substations, thereby completing the construction of the harmonic coupling model.

[0129] Preferably, Figure 11 As shown, the above device also includes:

[0130] The online collection unit 1110 is used to collect real-time operating parameters of each substation through an online monitoring system.

[0131] The coefficient correction unit 1120 is configured to correct the electrical coupling coefficient based on the real-time operating parameters.

[0132] The parameter adjustment unit 1130 is configured to adjust the parameters in the harmonic coupling model based on the corrected electrical coupling coefficient.

[0133] Figure 11The various units are connected in sequence, and the parameter adjustment unit 1130 is connected to the model construction unit 920.

[0134] Preferably, Figure 12 As shown, the above device also includes:

[0135] The electric quality energy acquisition unit 1210 is used to obtain the electric quality energy data of each traction substation under different working load conditions;

[0136] A coupling difference obtaining unit 1220 is configured to obtain coupling differences between different substations based on the electric quality energy data, wherein the coupling differences are used to evaluate the impact of different loads on harmonic coupling;

[0137] The parameter optimization unit 1230 is configured to optimize parameter settings of the harmonic coupling model based on the coupling difference.

[0138] Figure 12 The various units are connected in sequence, and the parameter optimization unit 1230 is connected to the model construction unit 920.

[0139] Preferably, Figure 13 As shown, the above device also includes:

[0140] The coupling voltage acquisition unit 1320 is configured to obtain the coupling voltage of the power system by using a power system coupling voltage identification method based on fuzzy logic.

[0141] The analysis result adjustment unit 1330 is configured to adjust the harmonic analysis result output by the harmonic coupling model based on the coupling voltage.

[0142] Figure 13 The various units are connected in sequence, and the analysis result adjustment unit 1330 is connected to the harmonic analysis unit 940.

[0143] Preferably, the above device further comprises:

[0144] A preliminary filtering unit, configured to perform preliminary filtering on the collected electrical data to separate DC and AC components to effectively remove high-frequency noise;

[0145] The digital filtering unit is used to further eliminate the remaining noise by deeply purifying the electrical data after preliminary filtering through a digital filter.

[0146] As can be seen from the above technical solution, the method and apparatus for wide-area measurement and analysis of electrified railway harmonics provided in this application collects interaction data between traction substations to establish a coupling model that accurately describes the harmonic interactions between stations in the electrified railway system. This harmonic coupling model can effectively identify and evaluate the harmonic coupling phenomena caused by interactions between traction substations, providing a scientific basis for design and optimization.

[0147] An embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the above method is implemented when the processor executes the program.

[0148] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the above method when the computer program / instruction is executed by a processor.

[0149] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above method.

[0150] like Figure 14 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 14 In addition, the electronic device 600 may also include all components shown in Figure 14 For components not shown, reference may be made to the prior art.

[0151] like Figure 14 As shown, the central processing unit 100 is sometimes also referred to as a controller or an operation control unit, and may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operations of various components of the electronic device 600 .

[0152] Memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information and may also store programs that execute the relevant information. The CPU 100 may execute the programs stored in memory 140 to implement information storage or processing.

[0153] The input unit 120 provides input to the CPU 100. The input unit 120 may be, for example, a keypad or touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0154] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), or a SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application programs and function programs or processes for executing the operations of the electronic device 600 via the central processing unit 100.

[0155] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0156] The communication module 110 is a transmitter / receiver that transmits and receives signals via the antenna 111. The communication module (transmitter / receiver) is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0157] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby implementing common telecommunication functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 130 is coupled to the central processing unit 100, enabling local recording via the microphone 132 and playback of stored audio via the speaker 131.

[0158] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0162] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for wide-area measurement and analysis of harmonics in electrified railways, characterized in that: The method comprises: Obtain historical operating parameters of each traction substation; Determining electrical coupling relationships between different traction substations based on the historical operating parameters to construct a harmonic coupling model; Collect real-time electrical data using anti-interference sensors installed between each substation; The electrical data is input into the harmonic coupling model to output harmonic analysis results.

2. The method for wide-area measurement and analysis of harmonics in electrified railways according to claim 1, wherein: The step of determining the electrical coupling relationship between different traction substations based on the historical operating parameters to construct a harmonic coupling model includes: Calculating the electrical coupling coefficients between the substations based on the historical operating parameters; determining a coupling strength level according to the electrical coupling coefficient; adjusting a coupling matrix of a harmonic coupling model using the coupling strength level; The coupling matrix is ​​iteratively optimized and calculated, and the optimized coupling matrix can accurately reflect the electrical coupling relationship between different traction substations, thereby completing the construction of the harmonic coupling model.

3. The method for wide-area measurement and analysis of harmonics in electrified railways according to claim 2, wherein: The method further comprises: Collect real-time operating parameters of each substation through the online monitoring system; Correcting the electrical coupling coefficient based on the real-time operating parameters; Parameters in the harmonic coupling model are adjusted based on the corrected electrical coupling coefficient.

4. The method for wide-area measurement and analysis of harmonics in electrified railways according to claim 2, wherein: The method further comprises: Obtaining electrical quality and energy data for each traction substation under different workload conditions; Based on the coupling difference between the electric quality energy data and different substations, the coupling difference is used to evaluate the impact of different loads on harmonic coupling; Parameter settings of the harmonic coupling model are optimized based on the coupling difference.

5. The method for wide-area measurement and analysis of harmonics in electrified railways according to claim 1, wherein: The method further comprises: The coupling voltage of the power system is obtained by using the power system coupling voltage identification method based on fuzzy logic; A harmonic analysis result output by the harmonic coupling model is adjusted based on the coupling voltage.

6. The method for wide-area measurement and analysis of harmonics in electrified railways according to claim 1, wherein: After collecting real-time electrical data using anti-interference sensors installed between the substations, the method further includes: Performing preliminary filtering on the collected electrical data to separate DC and AC components to effectively remove high-frequency noise; The electrical data after preliminary filtering is deeply purified by digital filters to further eliminate the remaining noise.

7. A device for wide-area measurement and analysis of harmonics in electrified railways, characterized in that: The device comprises: A parameter acquisition unit, used to obtain historical operating parameters of each traction substation; a model building unit, configured to determine the electrical coupling relationship between different traction substations based on the historical operating parameters to build a harmonic coupling model; Real-time data acquisition unit, used to collect real-time electrical data using anti-interference sensors installed between each substation; A harmonic analysis unit is used to input the electrical data into the harmonic coupling model to output a harmonic analysis result.

8. The electrified railway harmonic wide-area measurement and analysis device according to claim 7, characterized in that: The model building unit includes: A coupling coefficient calculation module, configured to calculate the electrical coupling coefficients between the substations based on the historical operating parameters; a coupling level determination module, configured to determine a coupling strength level according to the electrical coupling coefficient; a coupling matrix adjustment module, configured to adjust a coupling matrix of a harmonic coupling model using the coupling strength level; The iterative optimization module is used to perform iterative optimization calculation on the coupling matrix. The optimized coupling matrix can accurately reflect the electrical coupling relationship between different traction substations, thereby completing the construction of the harmonic coupling model.

9. The device for wide-area measurement and analysis of harmonics in electrified railways according to claim 8, characterized in that: The device further comprises: Online data collection unit, used to collect real-time operating parameters of each substation through the online monitoring system; a coefficient correction unit, configured to correct the electrical coupling coefficient based on the real-time operating parameters; A parameter adjustment unit is used to adjust parameters in the harmonic coupling model based on the corrected electrical coupling coefficient.

10. The device for wide-area measurement and analysis of harmonics in electrified railways according to claim 8, characterized in that: The device further comprises: The electric quality energy acquisition unit is used to obtain the electric quality energy data of each traction substation under different workload conditions; a coupling difference obtaining unit, configured to obtain coupling differences between different substations based on the electric quality energy data, wherein the coupling differences are used to evaluate the effects of different loads on harmonic coupling; A parameter optimization unit is used to optimize parameter settings of the harmonic coupling model based on the coupling difference.

11. The device for wide-area measurement and analysis of harmonics in electrified railways according to claim 7, characterized in that: The device further comprises: A coupling voltage acquisition unit, configured to obtain the coupling voltage of the power system by using a power system coupling voltage identification method based on fuzzy logic; An analysis result adjustment unit is configured to adjust a harmonic analysis result output by the harmonic coupling model based on the coupling voltage.

12. The device for wide-area measurement and analysis of harmonics in electrified railways according to claim 7, wherein: The device further comprises: A preliminary filtering unit, configured to perform preliminary filtering on the collected electrical data to separate DC and AC components to effectively remove high-frequency noise; The digital filtering unit is used to further eliminate the remaining noise by deeply purifying the electrical data after preliminary filtering through a digital filter.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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