A Harmonic Measurement and Evaluation Method and System Based on Time-Domain Statistics

Abstract

This invention belongs to the field of power quality measurement technology, specifically disclosing a harmonic measurement and evaluation method and system based on time-domain statistics. The method includes: acquiring the time-domain waveform signal of the power signal containing harmonic information for each sliding window; calculating the single-window harmonic energy variance index using statistical calculation methods; grouping the discretized single-window harmonic energy variance index according to the required frequency resolution; determining whether each group of data exhibits a centrally symmetrical distribution within acceptable error limits; if so, calculating the harmonic energy variance index for harmonic distortion evaluation with a preset period length; and establishing the correspondence between the harmonic energy variance index and the total harmonic distortion rate index, thereby intuitively evaluating the harmonic distortion of the power system. This invention enables lightweight data calculation, improves data processing efficiency, and can meet the needs of online monitoring to a certain extent.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power quality measurement, and in particular to a harmonic measurement evaluation method and system based on time domain statistics. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] In modern power systems, with the vigorous development of smart grids, distributed energy, electric vehicle charging piles and other new power equipment and systems, a large number of nonlinear power equipment such as power electronic devices, electric arc furnaces, frequency converters, switching power supplies, etc. are widely used in power systems. These devices will generate a large amount of harmonics during operation, causing the harmonic pollution of the power grid to increase day by day. Harmonics not only increase power grid losses and reduce power transmission efficiency, but also can cause relay protection devices to malfunction, power equipment to overheat and damage, and even interfere with communication systems, seriously threatening the safe and stable operation of the power system and power quality. Therefore, accurate measurement and evaluation of power system harmonic components are key links to ensure reliable operation of the power system and improve power quality.

[0004] At present, commonly used harmonic measurement methods, such as Fast Fourier Transform (FFT) method and its modified algorithm, instantaneous reactive power theory method, wavelet transform method, etc., are mostly based on frequency domain calculation principles and have many limitations. For example: the FFT method and its modified algorithm require the collection of multiple cycle signal data, and through discrete Fourier transform, the time domain signal is converted to the frequency domain for analysis. When dealing with non-stationary signals, if the signal frequency changes or there is frequency leakage, the measurement accuracy will be greatly affected, and the instantaneous change characteristics of the harmonic signal cannot be quickly captured. At the same time, a large number of complex operations make the calculation complexity high, making it difficult to meet the real-time requirements. The instantaneous reactive power theory method performs coordinate transformation on the voltage and current of a three-phase circuit in the frequency domain to detect harmonic current, but it is highly dependent on the balanced state of the three-phase system. In a three-phase unbalanced system, the measurement error is large, the measurement accuracy of high-order harmonics and interharmonics is insufficient, and the complex coordinate transformation also increases the calculation amount. Although the wavelet transform method can perform time-frequency localization analysis on the signal, it is essentially a conversion and processing between the frequency domain and the time domain. Due to the lack of unified standards for selecting wavelet basis functions, different selections will increase the uncertainty of the measurement results, and the complex multi-resolution analysis calculation makes the real-time performance poor, making it difficult to apply to power system harmonic measurement scenarios with high real-time requirements.

[0005] As power systems continue to expand in scale and become increasingly complex, harmonic components are becoming more diverse, with interharmonics, subharmonics, and harmonic modulation products constantly emerging. These traditional methods, which are based on frequency domain calculations, rely on multi-period data, and are computationally complex and lack real-time performance, are no longer able to accurately analyze and evaluate harmonic conditions in complex harmonic environments, and cannot meet the needs of modern power systems for high-precision, real-time harmonic measurement and evaluation. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a harmonic measurement and evaluation method and system based on time-domain statistics. By modifying the statistical calculation method, the single-window harmonic energy variance index is calculated, thereby obtaining smoothed data representing the average harmonic variation within the corresponding period. A correspondence is established between the smoothed data and the traditional THD monitoring threshold, thus providing an intuitive assessment of the harmonic distortion of the power system.

[0007] In some implementations, the following technical solutions are adopted: A harmonic measurement and evaluation method based on time-domain statistics includes: The power signal monitored by the measuring equipment is acquired in real time through a sliding window, and the time-domain waveform signal of the power signal containing harmonic information in each sliding window is obtained. The single-window harmonic energy variance index was calculated using statistical methods and then discretized. According to the required frequency resolution, the discrete single-window harmonic energy variance index is grouped. Determine whether the data in each group are centrally symmetrically distributed within the allowable error range. If so, calculate the harmonic energy variance index for harmonic distortion evaluation with a preset period length. Establish the correspondence between the harmonic energy variance index and the total harmonic distortion rate index to intuitively assess the harmonic distortion of the power system.

[0008] As a further solution, real-time data acquisition of the power signal monitored by the measuring device is performed through a sliding window, wherein the length of the sliding window is a complete fundamental frequency period and the sliding step size is half the length of the sliding window.

[0009] As a further approach, the time-domain waveform of the power signal containing harmonic information for each sliding window is obtained, specifically: ; ; in, It is a time-domain waveform signal; It is an ideal time-domain waveform signal; This is a noise signal; This represents the moment the waveform signal was generated. The first in the waveform signal One harmonic; This represents the total number of harmonics contained in the waveform signal; For the first The amplitude of each harmonic; For the first The number of harmonics; For the first The phase of each harmonic relative to the fundamental wave.

[0010] As a further approach, the single-window harmonic energy variance index is calculated using statistical methods, specifically: ; in, This is a single-window harmonic energy variance index. The length of the sliding window. For the first The start time of each sliding window.

[0011] As a further approach, the discretized single-window harmonic energy variance index is grouped according to the required frequency resolution, specifically as follows: The number of cycles is determined to be M according to the required frequency resolution, and each set consists of 2M discrete single-window harmonic energy variance indices.

[0012] As a further approach, the harmonic energy variance index for harmonic distortion evaluation with a preset period length is calculated, specifically as follows: Establish M Harmonic energy variance index for evaluating harmonic distortion based on period length: ; in, The harmonic energy variance index is used for harmonic distortion evaluation. 2 selected for measurement M The index value is the number of the single-window harmonic energy variance index; M is the number of periods.

[0013] As a further approach, a correspondence is established between the harmonic energy variance index and the total harmonic distortion rate index, thereby providing a direct assessment of the harmonic distortion of the power system. Specifically: A model is constructed to model the correspondence between the harmonic energy variance index and the monitoring threshold specified by the traditional total harmonic distortion (THD), thereby obtaining a mapping model between the harmonic energy variance index and the traditional THD monitoring threshold. This transforms the time-domain statistics into intuitively comparable THD monitoring thresholds, thus providing an intuitive assessment of the harmonic distortion of the power system.

[0014] In other embodiments, the following technical solutions are adopted: A harmonic measurement and evaluation system based on time-domain statistics includes: The data acquisition module is configured to collect real-time data of the power signal monitored by the measuring device through a sliding window, and acquire the time-domain waveform signal of the power signal containing harmonic information for each sliding window; The single-window index calculation module is configured to calculate the single-window harmonic energy variance index using statistical calculation methods and then discretize it. The single-window index grouping module is configured to group the discrete single-window harmonic energy variance index according to the required frequency resolution requirements. The group index calculation module is configured to determine whether the data of each group are centrally symmetrically distributed under the allowable error conditions. If so, it calculates the harmonic energy variance index of the harmonic distortion evaluation with a preset period length. The harmonic assessment module is configured to establish the correspondence between the harmonic energy variance index and the total harmonic distortion rate index, thereby intuitively assessing the harmonic distortion of the power system.

[0015] In other embodiments, the following technical solutions are adopted: A terminal device includes a processor and a memory, the processor being used to implement instructions; the memory being used to store multiple instructions adapted to be loaded and executed by the processor to perform the aforementioned harmonic measurement and evaluation method based on time-domain statistics.

[0016] In other embodiments, the following technical solutions are adopted: A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described harmonic measurement and evaluation method based on time-domain statistics.

[0017] Compared with the prior art, the beneficial effects of the present invention are: (1) Currently, the sampling method for harmonic data is to perform Fourier decomposition on data in groups of ten (or more) cycles, which will result in spectral leakage in the frequency domain. This invention adopts a sliding window sampling strategy with a sliding interval of half a cycle, covering the entire sampling period. This allows for more comprehensive and detailed acquisition of power signal data and richer harmonic information. Compared with traditional acquisition methods, this improves the quality and efficiency of data acquisition.

[0018] (2) In the field of signal processing, statistics are quantitative indicators that describe the probability distribution characteristics of signals. This invention transforms the original frequency domain analysis into time domain calculation by calculating the single-window harmonic energy variance index. It is less affected by frequency fluctuations and avoids errors caused by spectrum leakage in frequency domain indicators. At the same time, it does not require preprocessing steps such as windowing. The algorithm is simple and can achieve lightweight data calculation, improve data processing efficiency, and meet the needs of online monitoring to a certain extent.

[0019] The harmonic energy variance index is finally calculated based on the single-window harmonic energy variance index. Then, a mapping relationship between the harmonic energy variance index and the monitoring threshold specified by the traditional THD is established, which facilitates the understanding and application of power engineering technicians. This invention evaluates the harmonic problems existing in the power system from the time domain perspective, enriches the theoretical system of harmonic assessment, and can form a complementary verification relationship with THD, improving the reliability of the assessment and providing accurate and reliable data support for harmonic control and optimization of the power system.

[0020] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] Figure 1 This is a flowchart of the harmonic measurement and evaluation method based on time-domain statistics in an embodiment of the present invention; Figure 2 This is a schematic diagram of domain waveform signal acquisition in an embodiment of the present invention; Figure 3 This is a diagram showing the average variation of harmonics within period M in an embodiment of the present invention; Figure 4 This is a schematic diagram showing the mapping relationship between the new harmonic distortion index and the traditional index monitoring threshold in an embodiment of the present invention. Detailed Implementation

[0022] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. 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.

[0024] Example 1 In one or more embodiments, a harmonic measurement and evaluation method based on time-domain statistics is disclosed, combined with Figure 1 Specifically, it includes the following process: S101: Real-time data acquisition of power signals monitored by the measuring equipment is performed through a sliding window to obtain the time-domain waveform signal of the power signal containing harmonic information for each sliding window.

[0025] Among them, power signals refer to the original electrical signals in the power grid that can be directly collected by measuring equipment and reflect the changes of power parameters over time, that is, the original discrete signal data with time as the horizontal axis and voltage amplitude or current amplitude as the vertical axis.

[0026] Existing technologies mostly sample harmonic data by performing Fourier decomposition on data in groups of ten (or more) cycles, and then calculate indicators such as THD to evaluate harmonic distortion. This can lead to spectral leakage in the frequency domain, affecting the accuracy of harmonic evaluation.

[0027] To address the aforementioned issues, this embodiment employs a sampling window consisting of a complete cycle, sliding half a cycle at a time to achieve continuous data acquisition. Figure 2 As shown, this allows for the acquisition of power signal data containing rich harmonic information.

[0028] Specifically, firstly, based on the fundamental frequency of the power system Define sampling parameters, including the sampling window length. Sliding step size Sampling frequency and the number of window sampling points .

[0029] (1) Define the sampling window length parameter as the complete fundamental period, i.e.: (1) In the formula: This is the fundamental frequency of the power system; The fundamental frequency period of the power system; This is the sampling window length parameter.

[0030] (2) Define the sliding step size parameter as half the length of the sampling window, that is: (2) In the formula: This is the sliding step size parameter.

[0031] (3) Define the sampling frequency parameter to satisfy the Nyquist sampling theorem, that is: (3) In the formula: This refers to the sampling frequency parameter; This refers to the highest harmonic frequency to be analyzed in the power system.

[0032] (4) Define the number of sampling points in the window as: (4) In the formula: The number of sampling points in the window is a parameter determined by the sampling frequency. Decide.

[0033] Using the parameters defined above, a sliding window discretization data acquisition process is implemented to continuously capture the time-domain waveform of power signals containing harmonic components.

[0034] Each sampling window is one cycle long, and each time it slides by half a cycle. If M cycles are sampled, the amount of data obtained is 2M.

[0035] No. The start time and time range of each sampling window are as follows: (5) (6) In the formula: For the first The start time of each sampling window; This indicates that the length of each sampling window is... .

[0036] S102: The single-window harmonic energy variance index is calculated using statistical methods and then discretized.

[0037] In this embodiment, the data collected in each sampling window are processed and calculated. By using an improved statistical calculation method, the single-window harmonic energy variance index is calculated to obtain the index result reflecting the harmonic changes within that period, providing a basis for harmonic analysis.

[0038] Specifically, by acquiring data in real time using the measurement equipment from the previous step, the time-domain waveform signal for each sampling window is obtained: (7) in, (8) In the formula: This represents the moment the waveform signal was generated. The first in the waveform signal One harmonic; This represents the total number of harmonics contained in the waveform signal; For the first The amplitude of each harmonic; For the first The number of harmonics; For the first The phase of each harmonic relative to the fundamental wave; It is a time-domain waveform signal; It is an ideal waveform signal; This is a noise signal.

[0039] In existing technologies, the variance of is mostly calculated using the following methods: If the selected signal calculation duration is not an integer multiple of the harmonic period it contains, it will lead to differences in the calculation results, and the results will be affected by the harmonic order. If there are other power quality problems, the calculation results will be significantly different.

[0040] This embodiment improves the method of calculating variance by setting the average data value to 0, thus obtaining the single-window harmonic energy variance index. Specifically: (9) Discretize it to fit the data collected in the computation window: (10) In the formula: To The form after discretization; For sampling data points; The time-domain waveform data value obtained from sampling.

[0041] S103: According to the required frequency resolution, the discretized single-window harmonic energy variance index is grouped.

[0042] The specific relationships between the harmonics corresponding to the smallest frequency interval that can be analyzed by the selected signal length are as follows: (11) In the formula: M The number of cycles selected to meet the required frequency resolution.

[0043] Select an appropriate frequency resolution based on the actual needs of the project, and then select the corresponding period for calculation.

[0044] In this embodiment, the calculated single-window harmonic energy variance index data is divided into 2... M They are grouped into sets of 1 for processing.

[0045] S104: Determine whether the data in each group are centrally symmetrically distributed within the allowable error range. If so, calculate the harmonic energy variance index for harmonic distortion evaluation with a preset period length.

[0046] In this embodiment, a matching analysis is performed on the data using harmonic problem criteria. Based on the required frequency resolution, data groups are selected for mean harmonic energy variance index calculation, resulting in smoothed data representing the average harmonic variation within the corresponding period, such as... Figure 3 As shown, this improves data stability and reliability, and achieves data lightweighting.

[0047] The specific criteria for harmonic problems are as follows: determine whether each set of data satisfies the requirement of a centrally symmetrical distribution within the allowable error range. If it does, it indicates that the data is suitable for the harmonic distortion index calculation method proposed in this embodiment and can be used to calculate the degree of harmonic distortion. At this time, the harmonic energy variance index for harmonic distortion evaluation with a preset period length is calculated. If it does not satisfy the requirement, it indicates that there are other power quality problems with the data, and it is impossible to confirm whether harmonic distortion exists. Therefore, it cannot be used for calculation by the method of this embodiment.

[0048] In this embodiment, the single-window harmonic energy variance index is further processed, and a harmonic energy variance index for harmonic distortion evaluation with a period length of M is established according to the required frequency resolution: (12) In the formula: Harmonic energy variance index for evaluating harmonic distortion; 2 selected for measurement M Each single-window harmonic energy variance index is numbered. The index result calculated from a set of data represents the degree of harmonic distortion within that set.

[0049] This embodiment eliminates variables containing harmonic order and phase by setting the average value to 0, retaining only terms related to harmonic content. This ensures that the final calculation result is unaffected by harmonic order and phase while maintaining lightweight data calculation.

[0050] S105: Establish the correspondence between the harmonic energy variance index and the total harmonic distortion rate index, so as to intuitively evaluate the harmonic distortion of the power system.

[0051] Because the traditional harmonic distortion rate (THD) index has specific monitoring thresholds in the national standard, which are the upper limits of harmonics allowed under each level of power grid; if it exceeds this range, it does not meet the power quality standards and needs to be addressed.

[0052] In this embodiment, a correspondence model is established between the harmonic energy variance index VHD for harmonic distortion evaluation and the monitoring threshold specified in the traditional THD, thus obtaining the mapping relationship between VHD and the traditional THD monitoring threshold; for example Figure 4 As shown, Figure 4On the left, A1, A2, and A3 refer to several THD limits specified in the national standard, while on the right, B1, B2, and B3 are the calculated values ​​of the indicators proposed in this embodiment under the corresponding relationship.

[0053] In this embodiment, the two indicators are linearized separately. Since VHD and the traditional THD indicator have the same trend, only the slope of the curve is different, a point-to-point mapping relationship can be established between the two curves. After verification, VHD and the traditional THD indicator have a positive proportional relationship.

[0054] This embodiment establishes a correspondence between harmonic energy variance index and THD, transforming time-domain statistics into intuitive and comparable THD monitoring thresholds. This allows for an intuitive assessment of the harmonic distortion of the power system, providing data support for harmonic mitigation and optimization.

[0055] Example 2 In one or more embodiments, a harmonic measurement and evaluation system based on time-domain statistics is disclosed, comprising: The data acquisition module is configured to collect real-time data of the power signal monitored by the measuring device through a sliding window, and acquire the time-domain waveform signal of the power signal containing harmonic information for each sliding window; The single-window index calculation module is configured to calculate the single-window harmonic energy variance index using statistical calculation methods and then discretize it. The single-window index grouping module is configured to group the discrete single-window harmonic energy variance index according to the required frequency resolution requirements. The group index calculation module is configured to determine whether the data of each group are centrally symmetrically distributed under the allowable error conditions. If so, it calculates the harmonic energy variance index of the harmonic distortion evaluation with a preset period length. The harmonic assessment module is configured to establish the correspondence between the harmonic energy variance index and the total harmonic distortion rate index, thereby intuitively assessing the harmonic distortion of the power system.

[0056] It should be noted that the specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.

[0057] Example 3 In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded by the processor and executed by the harmonic measurement and evaluation method based on time-domain statistics as described in Embodiment 1.

[0058] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0059] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0060] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0061] Example 4 In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the harmonic measurement and evaluation method based on time-domain statistics described in Embodiment 1.

[0062] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A harmonic measurement and evaluation method based on time-domain statistics, characterized in that, include: The power signal monitored by the measuring equipment is acquired in real time through a sliding window, and the time-domain waveform signal of the power signal containing harmonic information in each sliding window is obtained. The single-window harmonic energy variance index was calculated using statistical methods and then discretized. According to the required frequency resolution, the discrete single-window harmonic energy variance index is grouped. Determine whether the data in each group are centrally symmetrically distributed within the allowable error range. If so, calculate the harmonic energy variance index for harmonic distortion evaluation with a preset period length. Establish the correspondence between the harmonic energy variance index and the total harmonic distortion rate index to intuitively assess the harmonic distortion of the power system.

2. The harmonic measurement and evaluation method based on time-domain statistics as described in claim 1, characterized in that, Real-time data acquisition of power signals monitored by the measuring equipment is performed through a sliding window. The length of the sliding window is a complete fundamental frequency period, and the sliding step size is half the length of the sliding window.

3. The harmonic measurement and evaluation method based on time-domain statistics as described in claim 1, characterized in that, Obtain the time-domain waveform of the power signal, including harmonic information, for each sliding window, specifically: ； ； in, It is a time-domain waveform signal; It is an ideal time-domain waveform signal; This is a noise signal; This represents the moment the waveform signal was generated. The first in the waveform signal One harmonic; This represents the total number of harmonics contained in the waveform signal; For the first The amplitude of each harmonic; For the first The number of harmonics; For the first The phase of each harmonic relative to the fundamental wave.

4. The harmonic measurement and evaluation method based on time-domain statistics as described in claim 3, characterized in that, The single-window harmonic energy variance index is calculated using statistical methods, specifically as follows: ； in, This is a single-window harmonic energy variance index. The length of the sliding window. For the first The start time of each sliding window.

5. The harmonic measurement and evaluation method based on time-domain statistics as described in claim 1, characterized in that, According to the required frequency resolution, the discretized single-window harmonic energy variance index is grouped and processed as follows: The number of cycles is determined to be M according to the required frequency resolution, and each set consists of 2M discrete single-window harmonic energy variance indices.

6. The harmonic measurement and evaluation method based on time-domain statistics as described in claim 1, characterized in that, The harmonic energy variance index for harmonic distortion evaluation at a preset period length is calculated as follows: Establish M Harmonic energy variance index for evaluating harmonic distortion based on period length: ； in, The harmonic energy variance index is used for harmonic distortion evaluation. 2 selected for measurement M The index value is the number of the single-window harmonic energy variance index; M is the number of periods.

7. The harmonic measurement and evaluation method based on time-domain statistics as described in claim 1, characterized in that, A correspondence is established between the harmonic energy variance index and the total harmonic distortion rate index to intuitively assess the harmonic distortion of the power system. Specifically: A model is constructed to model the correspondence between the harmonic energy variance index and the monitoring threshold specified by the traditional total harmonic distortion (THD), thereby obtaining a mapping model between the harmonic energy variance index and the traditional THD monitoring threshold. This transforms the time-domain statistics into intuitively comparable THD monitoring thresholds, thus providing an intuitive assessment of the harmonic distortion of the power system.

8. A harmonic measurement and evaluation system based on time-domain statistics, characterized in that, include: The data acquisition module is configured to collect real-time data of the power signal monitored by the measuring device through a sliding window, and acquire the time-domain waveform signal of the power signal containing harmonic information for each sliding window; The single-window index calculation module is configured to calculate the single-window harmonic energy variance index using statistical calculation methods and then discretize it. The single-window index grouping module is configured to group the discrete single-window harmonic energy variance index according to the required frequency resolution requirements. The group index calculation module is configured to determine whether the data of each group are centrally symmetrically distributed under the allowable error conditions. If so, it calculates the harmonic energy variance index of the harmonic distortion evaluation with a preset period length. The harmonic assessment module is configured to establish the correspondence between the harmonic energy variance index and the total harmonic distortion rate index, thereby intuitively assessing the harmonic distortion of the power system.

9. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed as described in any one of claims 1-7, for harmonic measurement and evaluation based on time-domain statistics.

10. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded by the processor of a terminal device and executed as described in any one of claims 1-7, the harmonic measurement and evaluation method based on time-domain statistics.

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