A line power frequency parameter identification method and system based on broadband information

Abstract

The present disclosure belongs to the technical field of power systems, and particularly relates to a line power frequency parameter identification method and system based on wideband information, comprising: obtaining measurement data of two ends of a line to be identified, wherein the measurement data comprises power frequency data and wideband data; based on the obtained measurement data, constructing a line state variable set under power frequency and a line state variable set under wideband; based on the constructed line state variable set under power frequency and the line state variable set under wideband, establishing a parameter identification model with power frequency information and wideband information; obtaining a measurement error equation according to the obtained parameter identification model and the measurement data; and iteratively solving the obtained measurement error equation to complete identification of line power frequency parameters.

Description

Technical Field

[0001] This disclosure belongs to the field of power system technology, specifically relating to a method and system for identifying line power frequency parameters based on broadband information. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Advances in condition monitoring and data analytics technologies are supporting the improvement of power system automation levels and playing an increasingly important role in the safe and economical operation of the power grid. Accurate grid parameters and network topology are prerequisites for the reliable operation of advanced applications.

[0004] The accuracy of power frequency parameters of power grid lines is affected from their initial design to subsequent operation and maintenance, including power flow calculations, state estimation, fault location, relay protection settings, line loss calculations, and selection of power system operation modes. Due to factors such as service life and environment, there can be significant discrepancies between the initial design parameters and the actual parameters of overhead lines after commissioning. Therefore, the line parameters required for routine power grid operation and maintenance need to be measured and calculated in real time. In engineering practice, line parameters are often obtained through theoretical calculations based on empirical formulas, resulting in low accuracy. Furthermore, due to the complexity of actual line structures and the influence of geographical and environmental factors, errors inevitably exist between the theoretically calculated values ​​and the actual operational values ​​of power line parameters. With the development of phasor measurement units (PMUs), the use of PMU measurement data for line parameter identification has been widely applied in transmission networks.

[0005] According to the inventors, during the parameter identification process of distribution network lines, the accuracy of the identification results is low due to the small load current and small phase angle difference between the two ends of the distribution network lines. The identification results differ significantly from the actual parameters, and even when the lines are short or lightly loaded, the Jacobian matrix becomes singular, leading to failure in line parameter identification. With the large-scale integration of new energy power generation into the grid, the number of power electronic devices in the power system is constantly increasing. The harmonics generated by power electronic devices during switching have some negative impacts on the stable operation of the system, but they also introduce a lot of broadband information beyond the power frequency. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a method and system for identifying power frequency parameters of power lines based on broadband information. The broadband information is extracted using a PMU device and incorporated into the parameter identification process, resulting in higher data redundancy than using only power frequency information, thereby improving the accuracy of parameter identification. Furthermore, by utilizing the difference between the attenuation characteristics of broadband information on the line and those of power frequency parameters, the identification of line inductance and susceptance can be achieved even when the line is short or lightly loaded.

[0007] According to some embodiments, the first solution of this disclosure provides a method for identifying line power frequency parameters based on broadband information, which adopts the following technical solution:

[0008] A method for identifying power frequency parameters of power lines based on broadband information includes:

[0009] Acquire measurement data at both ends of the line to be identified, including power frequency data and broadband data;

[0010] Based on the acquired measurement data, a set of line state variables under power frequency and a set of line state variables under broadband were constructed respectively.

[0011] Based on the constructed set of line state variables under power frequency and the set of line state variables under broadband, a parameter identification model combining power frequency information and broadband information is established.

[0012] Based on the obtained parameter identification model and measurement data, the measurement error equation is obtained;

[0013] The obtained measurement error equation is solved iteratively to identify the power frequency parameters of the line.

[0014] As a further technical limitation, the measurement data includes the active power, reactive power, voltage phasor, and current phasor of the transmission line; wherein the reference direction of the current and power is the direction flowing into the line as the positive direction.

[0015] As a further technical limitation, the set of line state variables under power frequency is related to the phase resistance, reactance, susceptance to ground, phase voltage, phase current and current angle of the line under power frequency; the set of line state variables under broadband information is related to the phase resistance, reactance, susceptance to ground, phase voltage, phase current and current angle of the line under broadband information.

[0016] As a further technical limitation, during the iterative solution process, the residuals of the obtained measurement error equations are calculated, and adaptive robustness processing is performed on the obtained residuals to resist the influence of gross error data on the identification results.

[0017] As a further technical limitation, the least squares method is used to iteratively solve the measurement error equation. During the iterative solution process, the median principle is used to estimate the residual sequence distribution parameters, and the robustness threshold of the full function is adaptively adjusted.

[0018] Furthermore, with the goal of minimizing the measurement error, the measurement error equation is solved iteratively using the least squares method until the measurement error is minimized, at which point the iteration stops, and the identification results of the line power frequency parameters are obtained.

[0019] As a further technical limitation, the power frequency parameters of the line include phase resistance, reactance, and susceptance to ground.

[0020] According to some embodiments, the second solution of this disclosure provides a line power frequency parameter identification system based on broadband information, which adopts the following technical solution:

[0021] A power frequency parameter identification system for power lines based on broadband information includes:

[0022] The acquisition module is configured to acquire measurement data at both ends of the line to be identified, the measurement data including power frequency data and broadband data;

[0023] The module is configured to construct a set of line state variables under power frequency and a set of line state variables under broadband based on the acquired measurement data; and to establish a parameter identification model that combines power frequency information and broadband information based on the constructed set of line state variables under power frequency and the set of line state variables under broadband.

[0024] The calculation module is configured to identify the model and measurement data based on the obtained parameters and obtain the measurement error equation.

[0025] The identification module is configured to iteratively solve the obtained measurement error equation to identify the power frequency parameters of the line.

[0026] According to some embodiments, a third aspect of this disclosure provides a computer-readable storage medium, employing the following technical solution:

[0027] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the line power frequency parameter identification method based on broadband information as described in the first aspect of this disclosure.

[0028] According to some embodiments, the fourth solution of this disclosure provides an electronic device that adopts the following technical solution:

[0029] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the line power frequency parameter identification method based on broadband information as described in the first aspect of this disclosure.

[0030] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0031] Compared with traditional power frequency parameter identification methods based on power frequency information, the line power frequency parameter identification method provided in this disclosure can improve the accuracy of line power frequency parameter identification and provide strong support for power grid condition monitoring and data analysis. It can also identify the line power frequency reactance and susceptance parameters when the distribution network line is lightly loaded or the line is short, which leads to the failure of traditional identification methods. Attached Figure Description

[0032] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0033] Figure 1 This is a flowchart of the line power frequency parameter identification method based on broadband information in Embodiment 1 of this disclosure;

[0034] Figure 2 This is a schematic diagram of the π-type lumped parameter circuit model structure in Embodiment 1 of this disclosure;

[0035] Figure 3 This is a flowchart of the identification process for the line power frequency parameter identification method based on broadband information in Embodiment 1 of this disclosure.

[0036] Figure 4 This is a structural block diagram of the line power frequency parameter identification system based on broadband information in Embodiment 2 of this disclosure. Detailed Implementation

[0037] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0038] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0039] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0040] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0041] Example 1

[0042] Embodiment 1 of this disclosure introduces a method for identifying line power frequency parameters based on broadband information.

[0043] To address the issues of low parameter identification accuracy and significant deviations from true values ​​in power frequency parameter identification for short or lightly loaded power lines in distribution networks, this embodiment proposes a power frequency parameter identification method based on broadband information. This method utilizes a PMU device to acquire broadband information from measurement data and incorporates it into the power frequency parameter identification process, thereby increasing measurement redundancy and improving identification accuracy. Furthermore, by leveraging the different response characteristics of broadband voltage and current on line inductance and susceptance compared to power frequency voltage and current, accurate identification of line inductance and susceptance can be achieved even in short or lightly loaded power lines.

[0044] Considering that gross errors may occur during actual data collection and transmission, causing the identification results to deviate from the true values, an adaptive robustness algorithm is also incorporated into the parameter identification process to resist the impact of gross errors on the identification results.

[0045] like Figure 1 The method for identifying line power frequency parameters based on broadband information, as shown, includes:

[0046] Acquire measurement data at both ends of the line to be identified, including power frequency data and broadband data;

[0047] Based on the acquired measurement data, a set of line state variables under power frequency and a set of line state variables under broadband were constructed respectively.

[0048] Based on the constructed set of line state variables under power frequency and the set of line state variables under broadband, a parameter identification model combining power frequency information and broadband information is established.

[0049] Based on the obtained parameter identification model and measurement data, the measurement error equation is obtained;

[0050] The obtained measurement error equation is solved iteratively to identify the power frequency parameters of the line.

[0051] The power transmission lines are arranged according to Figure 2 The diagram shows the π-type equivalent. Wherein, To identify the power frequency voltage phasors at both ends of the line. P represents the phasor of the power frequency current flowing into both ends of the line. 1,2 Q represents the active power flowing into the power frequency line at both ends. 1，2 This refers to the reactive power flowing into both ends of the line at the power frequency. To identify the broadband voltage phasors at both ends of the line, P represents the broadband current phasor flowing into both ends of the line. 1w,2w Q represents the broadband active power flowing into both ends of the line. 1w，2w The broadband reactive power flowing into both ends of the line is represented by R, X, and B, which represent the line phase resistance, reactance, and susceptance to ground at the power frequency, respectively.

[0052] The phase angle of the voltage on line II side is used as the reference phase angle, i.e. According to Kirchhoff's voltage law KVL, Kirchhoff's current law KCL, and the corresponding expressions for active and reactive power, we get:

[0053]

[0054] Then, each power frequency electrical quantity of a line can be represented by a set of state variables υ:

[0055] υ={R,X,B,U2,I2,θ2} (2)

[0056] Where θ2 represents when At that time, current flows into side II. The phase angle.

[0057] Similarly, for each broadband electrical quantity of the line, the set of state variables υ is used. w To indicate:

[0058] υ w ={R w ,X w B w U 2w ,I 2w ,θ 2w} (3)

[0059] Where, θ 2w Indicates when At that time, current flows into side II. The phase angle. R w X w B wThese represent the phase resistance, reactance, and susceptance to ground of the line under broadband conditions. The line parameters under broadband conditions and those under power frequency conditions have the following relationship:

[0060]

[0061] Among them, f w The frequency of a broadband electrical quantity.

[0062] Set of state variables υ w It can be rewritten as:

[0063] υ w ={R,X,B,U 2w ,I 2w ,θ 2w} (5)

[0064] Based on formulas (2) and (5), a mathematical model for parameter identification by combining power frequency and broadband information is established as follows:

[0065]

[0066] Due to measurement errors, the measured voltage amplitude, current amplitude, active power, and reactive power data in actual power systems do not strictly satisfy the above equations. The relationship between actual measurements and estimated values ​​is as follows:

[0067] z=h(υ)+ε (7)

[0068] Where z is the actual quantity measured; h(υ) is the calculated value of the quantity measured from the state quantity; and ε is the difference between the actual quantity measured and the estimated value.

[0069] The measurement errors of each quantity can be expressed by the following error equation:

[0070]

[0071] Among them, electrical quantities containing 'm' in the subscript are actual measured quantities, quantities not containing 'm' in the subscript are measured values ​​calculated from state quantities, electrical quantities containing 'w' in the subscript are broadband electrical quantities, and electrical quantities not containing 'w' in the subscript are power frequency electrical quantities.

[0072] For measurement data with errors in actual power systems, there is always a set of line parameter values ​​R, X, B and {U2,I2,θ2,U...}. 2w ,I 2w ,θ 2w The objective function is to find the combination of state values ​​that minimizes the error between the state values ​​of all line electrical quantities and the actual measured values ​​of the line.

[0073]

[0074] The least squares method is used to solve formula (9), and the optimal line parameters are found by minimizing the objective function (i.e., formula (9)).

[0075] In formula (9), each term is the sum of the power frequency component and the broadband component. For example, the first term can be written as:

[0076]

[0077] Because measurement errors exist and their nature is unknown in actual power systems, using a single data point to identify parameters will result in significant dispersion and unreliability of the identification results. To improve the reliability of parameter identification, the number of data points can be increased to enhance model redundancy, thereby reducing estimation errors caused by measurement errors.

[0078] N data points are selected from the time period [t, t+T]. Since the objective function formula (9) can also be applied to N data points, the objective function Res of this multi-point parameter identification model can be expressed as:

[0079]

[0080] Similarly, each term in formula (11) is the sum of the power frequency component and the broadband component. For example, the first term can be written as:

[0081]

[0082] Where t is the start time of the time window, T is the length of the time window, t+T is the end time of the time window, and k is the index of the data point. The state variable identification value is optimal when the objective function Res is minimized.

[0083] To improve the algorithm's resilience to the impact of bad data on the estimated values, the adaptive robustness method (IGG robustness method) is also incorporated into the parameter identification process. This method fully considers the actual situation of the measurement data and adopts different weight functions and robustness criteria for different measurement data.

[0084] During the least squares iterative solution process, the median principle is used to effectively estimate the distribution parameters of the residual sequence in each iteration. The robustness threshold of the weight function can be adaptively adjusted to ensure the robustness of the algorithm and the reliability of the identification results, thereby improving the algorithm's adaptability to different measurement errors.

[0085] In summary, such as Figure 3 As shown, where n is the number of iterations, R n X n B nThe resistance, reactance, and susceptance generated in the nth iteration are respectively. The specific identification process of the line power frequency parameter identification method based on broadband information is as follows:

[0086] The measurement data at both ends of the line that need to be identified by the PMU device are obtained. The measurement data includes: active power, reactive power, voltage phasor and current phasor of the transmission line; the reference direction of the current and power is the direction of the flow into the line as the positive direction.

[0087] Assign initial values ​​to each variable, and set the number of iterations n = 0;

[0088] According to formula (6), the measurement error equation (i.e. formula (8)) is written in combination with the measurement value. Each data point can write a set of equations. If there are N data points, the N sets of equations are combined to obtain the final identification equation set.

[0089] Calculate the residuals of each equation in the final identified equation system, and perform IGG robustness processing based on the residuals;

[0090] Solving R iteratively using the least squares method n X n B n Ultimately, this minimizes the objective function Res. n X n B n These are the identification values ​​for the line's power frequency resistance, reactance, and susceptance.

[0091] This embodiment provides a method for identifying line power frequency parameters based on broadband information. The broadband information is extracted by a PMU device and incorporated into the parameter identification process, which can achieve higher data redundancy than using only power frequency information, thereby improving the accuracy of parameter identification. By utilizing the fact that the attenuation characteristics of broadband information on the line are different from those of power frequency quantities, the identification of line inductance and susceptance can be achieved when the line is short or lightly loaded.

[0092] Example 2

[0093] Embodiment 2 of this disclosure introduces a line power frequency parameter identification system based on broadband information.

[0094] like Figure 4 The system shown is a line power frequency parameter identification system based on broadband information, comprising:

[0095] The acquisition module is configured to acquire measurement data at both ends of the line to be identified, the measurement data including power frequency data and broadband data;

[0096] The module is configured to construct a set of line state variables under power frequency and a set of line state variables under broadband based on the acquired measurement data; and to establish a parameter identification model that combines power frequency information and broadband information based on the constructed set of line state variables under power frequency and the set of line state variables under broadband.

[0097] The calculation module is configured to identify the model and measurement data based on the obtained parameters and obtain the measurement error equation.

[0098] The identification module is configured to iteratively solve the obtained measurement error equation to identify the power frequency parameters of the line.

[0099] The detailed steps are the same as those of the line power frequency parameter identification method based on broadband information provided in Example 1, and will not be repeated here.

[0100] Example 3

[0101] Embodiment 3 of this disclosure provides a computer-readable storage medium.

[0102] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the line power frequency parameter identification method based on broadband information as described in Embodiment 1 of this disclosure.

[0103] The detailed steps are the same as those of the line power frequency parameter identification method based on broadband information provided in Example 1, and will not be repeated here.

[0104] Example 4

[0105] Embodiment 4 of this disclosure provides an electronic device.

[0106] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the line power frequency parameter identification method based on broadband information as described in Embodiment 1 of this disclosure.

[0107] The detailed steps are the same as those of the line power frequency parameter identification method based on broadband information provided in Example 1, and will not be repeated here.

[0108] The above description is merely a preferred embodiment of this disclosure and is not intended to limit the scope of this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for identifying power frequency parameters of a transmission line based on broadband information, characterized in that, include: Acquire measurement data at both ends of the line to be identified, including power frequency data and broadband data; Based on the acquired measurement data, a set of line state variables under power frequency and a set of line state variables under broadband were constructed respectively. Based on the constructed set of line state variables under power frequency and the set of line state variables under broadband, a parameter identification model combining power frequency information and broadband information is established. Based on the obtained parameter identification model and measurement data, the measurement error equation is obtained; The obtained measurement error equation is solved iteratively to identify the power frequency parameters of the line. During the iterative solution process, the residuals of the obtained measurement error equation are calculated, and adaptive robustness processing is applied to the obtained residuals to resist the influence of gross error data on the identification results. The least squares method is used to iteratively solve the measurement error equation. During the iterative solution process, the median principle is used to estimate the distribution parameters of the residual sequence and adaptively adjust the robustness threshold of the full function.

2. The method for identifying line power frequency parameters based on broadband information as described in claim 1, characterized in that, The measurement data includes the active power, reactive power, voltage phasor, and current phasor of the transmission line; wherein the reference direction for both current and power is the direction flowing into the line as the positive direction.

3. The method for identifying line power frequency parameters based on broadband information as described in claim 1, characterized in that, The set of line state variables under power frequency is related to the phase resistance, reactance, susceptance to ground, phase voltage, phase current, and current angle of the line under power frequency; the set of line state variables under broadband information is related to the phase resistance, reactance, susceptance to ground, phase voltage, phase current, and current angle of the line under broadband information.

4. The method for identifying line power frequency parameters based on broadband information as described in claim 1, characterized in that, With the goal of minimizing measurement error, the measurement error equation is solved iteratively using the least squares method until the measurement error is minimized, at which point the iteration stops, and the identification results of the power frequency parameters of the line are obtained.

5. The method for identifying line power frequency parameters based on broadband information as described in claim 1, characterized in that, The power frequency parameters of the line include phase resistance, reactance, and susceptance to ground.

6. A line power frequency parameter identification system based on broadband information, characterized in that, include: The acquisition module is configured to acquire measurement data at both ends of the line to be identified, the measurement data including power frequency data and broadband data; The module is configured to construct a set of line state variables under power frequency and a set of line state variables under broadband frequency based on the acquired measurement data. Based on the constructed set of line state variables under power frequency and the set of line state variables under broadband, a parameter identification model combining power frequency information and broadband information is established. The calculation module is configured to identify the model and measurement data based on the obtained parameters and obtain the measurement error equation; The identification module is configured to iteratively solve the obtained measurement error equation to identify the power frequency parameters of the line. During the iterative solution process, the residuals of the obtained measurement error equations are calculated, and adaptive robustness processing is performed on the obtained residuals to resist the influence of gross error data on the identification results.

7. The line power frequency parameter identification system based on broadband information as described in claim 6, characterized in that, The measurement data includes the active power, reactive power, voltage phasor, and current phasor of the transmission line; wherein the reference direction for both current and power is the direction flowing into the line as the positive direction.

8. The line power frequency parameter identification system based on broadband information as described in claim 6, characterized in that, The set of line state variables under power frequency is related to the phase resistance, reactance, susceptance to ground, phase voltage, phase current, and current angle of the line under power frequency; the set of line state variables under broadband information is related to the phase resistance, reactance, susceptance to ground, phase voltage, phase current, and current angle of the line under broadband information.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the line power frequency parameter identification method based on broadband information as described in any one of claims 1-5.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the line power frequency parameter identification method based on broadband information as described in any one of claims 1-5.

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