Power system monitoring method based on noise-like signal low-frequency oscillation identification

Abstract

The invention discloses a power system monitoring method based on noise-like signal low-frequency oscillation identification, which relates to the technical field of power system dynamic monitoring, and comprises the following steps: configuring detection parameters, and carrying out oscillation mode first detection; counting analysis result samples, and updating oscillation information; and first early warning is carried out, early warning information is sent, and a result is stored and uploaded. According to the electric power system monitoring method based on noise-like signal low-frequency oscillation identification, the real-time performance and precision of low-frequency oscillation monitoring of an existing electric power system are improved, and the problem that a traditional method is insufficient in adaptability in a complex power grid environment is solved. According to the method, the low-frequency oscillation of the power system is measured and online early warning is provided by using the white noise signal in the daily operation of the power grid and combining an ARMA (autoregressive moving average) model, so that the operation safety and stability of the system are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic monitoring of electric power systems, and in particular to an electric power system monitoring method based on low-frequency oscillation recognition of noise-like signals. Background Art

[0002] With the expansion of the scale of power systems, especially the widespread application of large-scale hydropower units and fast excitation systems, the problem of low-frequency oscillation has become more prominent. This type of oscillation not only threatens the safe and stable operation of the power grid, but also significantly limits the power transmission capacity. In recent years, many low-frequency oscillation accidents at home and abroad have fully revealed its harmfulness, and effective monitoring and early warning methods are urgently needed to ensure the safe operation of the power grid.

[0003] At present, online monitoring of low-frequency oscillations in power systems mainly adopts the period-amplitude waveform detection method and the improved Prony algorithm. The period-amplitude detection method quickly identifies single-mode oscillations, while the Prony algorithm is good at analyzing complex oscillation signals. However, when the number of monitoring signals increases, the analysis time of the Prony algorithm is too long and it is difficult to meet the real-time requirements. In addition, most existing methods rely on large disturbance signals in the system, which makes it difficult to accurately evaluate oscillations when the power system is operating normally, resulting in delayed warning timing.

[0004] Based on the spectrum analysis results of different types of signals, the system response signals without obvious disturbances, the system response signals with obvious disturbances and the white noise signals are distinguished. The response signals without obvious disturbances that contain rich information on the dynamic characteristics of the system are defined as noise-like signals.

[0005] In fact, even in the normal operation of the power system, load fluctuations, equipment switching and other operations will generate tiny noise-like signals. Although these signals are small in amplitude, they contain rich oscillation information. Therefore, developing a low-frequency oscillation identification method based on noise-like signals can not only improve the real-time and accuracy of oscillation detection, but also provide more warning time, help optimize scheduling decisions and ensure the stable operation of the system.

[0006] How to analyze the low-frequency oscillation mode of the power system from the large number of micro-disturbance signals that exist during normal system operation, transform the power system monitoring function from real-time alarm to early warning, and provide more time for operation and dispatching decisions has become an urgent problem that needs to be solved. Summary of the invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is: the existing period-amplitude waveform detection method and improved Prony algorithm have the problems of single detection signal and too long analysis time, which makes it difficult to meet the real-time requirements, and how to accurately evaluate the oscillation during the normal operation of the power system.

[0009] To solve the above technical problems, the present invention provides the following technical solutions: a power system monitoring method based on low-frequency oscillation identification of noise-like signals, including detection parameter configuration, performing a first detection of the oscillation mode; statistically analyzing result samples, and updating the oscillation information; performing a first warning, sending warning information, storing and uploading the results.

[0010] As a preferred solution of the power system monitoring method based on low-frequency oscillation identification of noise-like signals described in the present invention, the detection parameter configuration includes configuring parameters of the monitoring system and collecting data.

[0011] As a preferred solution of the power system monitoring method based on low-frequency oscillation identification of noise-like signals described in the present invention, the first detection of the oscillation mode includes measuring each monitoring section, obtaining data of each oscillation mode, and performing the first detection of the oscillation mode.

[0012] As a preferred solution of the power system monitoring method based on noise-like signal low-frequency oscillation identification described in the present invention, the oscillation mode measurement includes using the ARMA method to measure each monitoring section to obtain the frequency and damping ratio of each oscillation mode.

[0013] As a preferred solution of the power system monitoring method based on low-frequency oscillation identification of noise-like signals described in the present invention, the statistical analysis result samples and updating of oscillation information include statistically analyzing the damping ratio calculation result samples within the last 15 minutes, obtaining real-time estimated values ​​of the frequency and damping ratio of the oscillation mode, and refreshing the calculation results to each monitoring section in real time.

[0014] As a preferred solution of the power system monitoring method based on low-frequency oscillation identification of noise-like signals described in the present invention, the first warning and sending of warning information include determining whether the damping ratio of the system oscillation mode is lower than a set warning value.

[0015] As a preferred solution of the power system monitoring method based on low-frequency oscillation identification of noise-like signals described in the present invention, the early warning judgment and sending of early warning information include comparing the damping ratio with the early warning value. If the damping ratio is lower, early warning information of insufficient damping ratio is sent.

[0016] Another object of the present invention is to provide an electric power system monitoring system based on low-frequency oscillation identification of noise-like signals, which can realize real-time monitoring and early warning of low-frequency oscillations of the electric power system through noise-like signals, which not only improves the accuracy and real-time performance of monitoring, but also realizes wide applicability under low-cost conditions, effectively ensuring the safe and stable operation of the electric power system.

[0017] As a preferred solution of the power system monitoring system based on low-frequency oscillation identification of noise-like signals described in the present invention, it includes: a signal acquisition module, an analysis module, and an early warning information module; the signal acquisition module is used to detect parameter configuration and perform oscillation mode measurement; the analysis module is used to statistically analyze result samples and update oscillation information; the early warning information module is used to perform a first early warning, send early warning information, and store and upload results.

[0018] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a power system monitoring method based on low-frequency oscillation identification of noise-like signals.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a power system monitoring method based on low-frequency oscillation identification of noise-like signals.

[0020] Beneficial effects of the invention: The power system monitoring method based on noise-like signal low-frequency oscillation identification provided by the invention improves the real-time performance and accuracy of low-frequency oscillation monitoring of the existing power system, and solves the problem of insufficient adaptability of traditional methods in complex power grid environments. By using white noise signals in the daily operation of the power grid, combined with the ARMA (autoregressive moving average) model, the low-frequency oscillation of the power system is measured and warned to ensure the stability and safety of the system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 An overall flow chart of a power system monitoring method based on low-frequency oscillation identification of noise-like signals provided in the first embodiment of the present invention.

[0023] Figure 2 Another flow chart of a method for monitoring a power system based on low-frequency oscillation identification of noise-like signals provided by the first embodiment of the present invention.

[0024] Figure 3 A cross-section damping ratio result display page diagram of a power system monitoring method based on low-frequency oscillation identification of noise-like signals provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] Example 1, reference Figure 1-Figure 3 , as an embodiment of the present invention, provides a power system monitoring method based on low-frequency oscillation identification of noise-like signals, comprising:

[0027] S1: Detect parameter configuration and perform the first oscillation mode detection;

[0028] Furthermore, the detection parameter configuration includes configuring parameters of the monitoring system and collecting data to collect noise-like signals, where the noise signal is white noise data in the operation of the power grid.

[0029] The wide-area measurement system realizes the synchronous acquisition of real-time steady-state and dynamic information at multiple points in the power system. By observing the measured signals, it is found that in addition to the widely used system response signals after obvious disturbances, when the system has no obvious disturbances, the signal also has a small fluctuation phenomenon similar to noise. With the concept of noise-like signals, based on the spectrum analysis results of different types of signals, the system response signals when there is no obvious disturbance and the system response signals and white noise signals when there is obvious disturbance are distinguished, and this response signal when there is no obvious disturbance, which contains rich information on the dynamic characteristics of the system, is defined as a noise-like signal.

[0030] Furthermore, the first detection of the oscillation mode can be performed by a variety of methods, such as the matrix-pencil method or the autoregressive (AR) model. In this embodiment, daily noise-like data is used to perform oscillation mode detection, and dangerous oscillation modes and damping conditions of the system are discovered in time according to slight load disturbances during daily steady-state operation.

[0031] As a preference, in this embodiment, the first oscillation mode detection adopts oscillation mode detection based on an autoregressive (AR) model, and its principle steps are as follows:

[0032] (1) Assuming that the power system is driven by white noise ε(k) near the steady-state operating point, the linear autoregressive expression of the quantity measured at the kth moment is written as:

[0033]

[0034] Where n is the assumed model order, and appropriate methods need to be studied to select the appropriate n; a i (k) is the time-varying coefficient to be solved, is the actual value of the quantity measured at the kth moment, and y(k) is the predicted value at the kth moment calculated using the values ​​of the previous k-1 moments of the quantity measured.

[0035] (2) Use the recursive least squares algorithm to solve the following target optimization equation, thereby solving the coefficient a of the above AR autoregressive model i (k).

[0036]

[0037] (3) Every Tr time period, solve the root z of the polynomial corresponding to the AR model according to the following formula: i , and the roots of this polynomial correspond to the eigenvalues ​​of the system.

[0038] z n -a 1 (k)z n-1 …-a n-1 (k)za n (k) = 0

[0039] (4) The frequency f of each oscillation mode of the system is calculated from the system eigenvalue according to the following formula: i and damping ξ i .

[0040]

[0041] where a i and ω i Corresponding to the eigenvalue s i =a i +jω i The real and imaginary parts of .

[0042] In another feasible embodiment, the first detection of the oscillation mode adopts a subspace identification method (matrix-pencil method), and its principle steps are as follows:

[0043] The subspace identification method (matrix-pencil method) is adopted, specifically: the modal information of the system is extracted directly from the measured data. Through data-driven analysis of the measured signals, the collected time domain data is organized into a Hankel matrix, that is, a matrix with a special structure, in which each column is a subsequence of the time series data. This structure is conducive to subsequent matrix operations and modal extraction. By translating the Hankel matrix once in a time step, two matrices of the same size are constructed, reflecting the dynamic characteristics of the system. Solve the generalized eigenvalue problem and extract the modal parameters. According to the extracted modal parameters, the oscillation mode of the system is identified, including frequency and damping ratio. The accuracy of the identification results can be verified by comparing with the known system characteristics. The robustness of this method can also meet the use of complex large-scale power systems.

[0044] S2: Statistically analyze the result samples and update the oscillation information.

[0045] It should be noted that in order to ensure the real-time and continuity of monitoring, the rolling window technology is used to perform statistical analysis on the signal data within the last 15 minutes every 2 minutes, and calculate the frequency and damping ratio of the oscillation mode in real time. This ensures the dynamic update of the oscillation mode information and reflects the real-time changes in the power grid oscillation. Each monitoring section can obtain the latest oscillation mode parameters based on the refreshed data.

[0046] S3: Issue the first warning, send warning information, store and upload the results.

[0047] Furthermore, the first warning can be set with a variety of warning judgment methods, which can be warning schemes such as warning mechanisms based on threshold judgment or warning mechanisms based on machine learning. Other methods of intermittent analysis that can respond to rolling windows in a timely manner are also applicable.

[0048] In this embodiment, the early warning threshold of the damping ratio is selected. Generally, the damping ratio reflects the stability of the oscillation mode. When the damping ratio is lower than the set threshold (3%), it indicates that the system may have a greater oscillation risk. At this time, the early warning judgment module will automatically issue an early warning signal based on the actual monitoring data to remind the operator to take corresponding measures to prevent the oscillation from further expanding and affecting the stability and safety of the system.

[0049] In another feasible embodiment, an early warning scheme such as an early warning mechanism based on machine learning is adopted, and a machine learning model (such as a neural network or a support vector machine) is trained using historical operation data and oscillation events. In real-time monitoring, the current system operation parameters and oscillation mode information are input, and the model predicts the oscillation risk level, issues an early warning based on the oscillation level, and generates early warning information including the level.

[0050] It should be noted that the system will send the warning information to the operator or related management system to realize the timely transmission of information.

[0051] Furthermore, the monitoring results of the oscillation mode will be stored and uploaded to each monitoring section for display and recording, which will facilitate subsequent analysis, comparison and data backup.

[0052] It should be noted that through the long-term accumulation and analysis of data, the generation mechanism and influencing factors of low-frequency oscillations in the power system can be further revealed, helping to formulate more effective protection strategies.

[0053] In the monitoring system's section definition table, the active power of the AC lines that make up the section. In the monitoring system's section definition table, the section is used to reflect the power transmission situation in a specific area. The definition of a section usually consists of multiple AC transmission lines, and the active power transmitted by these lines is used to evaluate the operating status of the power grid. In the section, active power is the core indicator that determines the load level and power transmission capacity of the section. By real-time monitoring of the active power in the section, changes in the power grid load can be discovered in a timely manner, and then it can be determined whether the power grid is in normal operation or abnormal.

[0054] Specify the monitoring section to monitor the calculated damping ratio value of the oscillation mode, periodically collect statistics on the damping ratio calculation result samples in the last 15 minutes, and output the estimated values ​​of frequency and damping ratio.

[0055] like Figure 3 The display of damping ratio results is organized according to the list of monitoring sections, and the display page figure has been given. Area 1 on the left is the list of monitoring sections. You can switch monitoring sections by selecting with the mouse. Area 2 is the calendar of the minimum damping ratio of the dominant mode of the selected monitoring section. The daily displayed value is the minimum damping ratio monitored on that day. Area 3 is the statistical value of the oscillation mode detected in the section, which displays the maximum and minimum values ​​of the damping ratio of the oscillation mode on the current or selected date. When there are multiple oscillation modes in the monitoring section, select an oscillation mode with the mouse for detailed analysis. Area 4 shows the change trend of the selected oscillation mode in the past 10 days. Area 5 shows the change trend line of the damping ratio of the selected oscillation mode on that day. Area 6 shows the distribution of the samples calculated for the oscillation mode in the "frequency-damping ratio" coordinate system; Area 7 shows the distribution projection and corresponding estimated value of the number of samples calculated for the oscillation mode on the frequency and damping ratio coordinate axes.

[0056] Example 2, an embodiment of the present invention, provides a power system monitoring method based on low-frequency oscillation identification of noise-like signals. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0057] First, in order to verify the effectiveness and superiority of the present invention, a practical application experiment of power system oscillation mode monitoring and early warning was carried out. The experiment selected a regional power grid as the research object. The wide-area measurement system was parameterized, and 10 key measurement points in the regional power grid were selected, which covered the main transmission lines and load centers. The synchronous sampling frequency was configured to 50Hz to ensure high-precision capture of the dynamic characteristics of the power system. Under continuous operation, the power grid operation data for one week was collected.

[0058] Table 1 Experimental data comparison table

[0059]

[0060] From the data in Table 1, it can be seen that the oscillation frequency and damping ratio of the power system changed significantly between 12:00 and 12:12 on October 1, 2023. The specific analysis is as follows:

[0061] The oscillation frequency gradually increases from 0.45Hz to 0.60Hz, and the damping ratio decreases from 4.5% to 1.0%. This shows that the oscillation mode of the system is becoming unstable, and the increase in oscillation frequency and the decrease in damping ratio are both unfavorable signals.

[0062] The load level and power generation output gradually increased during this period, with the load increasing from 5000MW to 5300MW and the power generation output increasing from 5200MW to 5500MW. This may be one of the factors that led to the deterioration of the oscillation mode. High load and high power generation output will increase the operating stress of the system and reduce the stability of the system.

[0063] The increase in wind speed from 5.0m / s to 7.5m / s may cause fluctuations in wind power output, which in turn affects the dynamic characteristics of the system. The randomness and intermittency of wind power are potential causes of power system oscillations.

[0064] Traditional methods usually rely on response signals after obvious disturbances, and it is difficult to capture the dynamic changes of the system in the absence of obvious disturbances in a timely manner. The present invention uses noise-like signals and successfully detects changes in oscillation modes in the absence of obvious disturbances through the AR model method. This proves that the present invention has higher sensitivity and accuracy in oscillation mode detection and can detect potential stability problems in advance. Traditional oscillation mode detection methods usually require significant system disturbances to work effectively, and cannot identify potential oscillation risks under stable operation. The present invention overcomes this limitation by utilizing noise-like signals and realizes oscillation mode detection in the absence of obvious disturbances.

[0065] Embodiment 3 is an embodiment of the present invention, which provides a power system monitoring system based on low-frequency oscillation identification of noise-like signals, including a signal acquisition module, an analysis module, and an early warning information module.

[0066] Among them, the signal acquisition module is used to detect parameter configuration and measure oscillation mode; the analysis module is used to statistically analyze result samples and update oscillation information; the early warning information module is used to perform the first early warning, send early warning information, store and upload results.

[0067] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0068] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0069] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0070] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power system monitoring method based on low-frequency oscillation identification of noise-like signals, characterized in that: include: Detect parameter configuration and perform the first oscillation mode detection; Statistical analysis result samples and update oscillation information; Issue the first warning, send warning information, store and upload the results.

2. The method for monitoring a power system based on low-frequency oscillation identification of noise-like signals according to claim 1, characterized in that: The detection parameter configuration includes configuring parameters of the monitoring system and collecting data.

3. The method for monitoring a power system based on low-frequency oscillation identification of noise-like signals according to claim 2, characterized in that: The first oscillation mode detection includes measuring each monitoring section, obtaining data of each oscillation mode, and performing the first oscillation mode detection.

4. The method for monitoring a power system based on low-frequency oscillation identification of noise-like signals according to claim 3, characterized in that: The statistical analysis result samples and updating of the oscillation information include statistically analyzing the damping ratio calculation result samples within the last 15 minutes, obtaining the real-time estimated values ​​of the frequency and damping ratio of the oscillation mode, and refreshing the calculation results to each monitoring section in real time.

5. The method for monitoring a power system based on low-frequency oscillation identification of noise-like signals according to claim 4, characterized in that: The performing of the first warning and sending of the warning information includes determining whether the damping ratio of the system oscillation mode is lower than a set warning value.

6. The method for monitoring a power system based on low-frequency oscillation identification of noise-like signals according to claim 5, characterized in that: The performing of the first warning and sending of the warning information includes comparing the damping ratio with the warning value, and if the damping ratio is lower, sending a warning information indicating that the damping ratio is insufficient.

7. The method for monitoring a power system based on low-frequency oscillation identification of noise-like signals according to claim 6, characterized in that: The storing and uploading of the results includes storing and uploading the oscillation mode identification results to each monitoring section.

8. A system using the power system monitoring method based on low-frequency oscillation identification of noise-like signals as claimed in any one of claims 1 to 7, characterized in that: Including signal acquisition module, analysis module, and early warning information module; The signal acquisition module is used to detect parameter configuration and perform oscillation mode measurement; The analysis module is used to statistically analyze the result samples and update the oscillation information; The warning information module is used to perform a first warning, send warning information, and store and upload results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power system monitoring method based on noise-like signal low-frequency oscillation identification according to any one of claims 1 to 7 are implemented.

10. 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 power system monitoring method based on noise-like signal low-frequency oscillation identification according to any one of claims 1 to 7 are implemented.

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