A method and system for measuring dynamic electric energy with high accuracy

Abstract

The present invention provides a high-accuracy dynamic electric energy metering method and system, comprising: synchronizing voltage and current sampling signals; dynamically adjusting the window length of each sampling signal based on the active power stability of the sampling signal using an adaptive non-overlapping window algorithm, and calculating the real-time power of the sampling signal; and adaptively adjusting the power threshold based on the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal, thereby achieving accurate dynamic electric energy metering. This improves the accuracy of electric energy metering.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grid and electric energy metering, and in particular to a method and system for metering dynamic electric energy with high accuracy. Background Art

[0002] In recent years, with the large-scale integration of renewable energy sources such as wind and solar power, and the rapid development of new loads such as electric vehicles and energy storage devices, the operating environment of power systems has become significantly more dynamic and nonlinear. The intermittent and fluctuating nature of renewable energy generation, coupled with the widespread integration of power electronics, has led to increased harmonic content and frequent frequency offsets in the voltage and current signals in the power grid. This poses significant challenges to the accuracy of traditional energy metering algorithms under dynamic operating conditions.

[0003] In traditional electricity metering technology, sliding window algorithms and non-overlapping algorithms are the two mainstream methods. The sliding window algorithm calculates power point by point by sliding a fixed-length window. Although it can ensure metering accuracy under steady-state signals, its fixed window design makes it difficult to adapt to the rapid changes in dynamic signals, resulting in fundamental errors in power mutations or high-frequency harmonics. The non-overlapping algorithm reduces the amount of calculation by using a jumping window, but it tends to ignore signal mutation information at the window boundary and lacks adaptability to non-stationary signals. In addition, existing algorithms generally rely on fixed active power thresholds for energy accumulation judgment. The static setting of the threshold is easily affected by noise interference or signal fluctuations, resulting in the accumulation of metering errors, especially in the low-power range, which may cause creeping or omission problems.

[0004] Furthermore, in actual operation, due to factors such as grid frequency fluctuations and sampling clock deviations, the sampling process of the energy meter is often difficult to fully synchronize with the grid signal, resulting in asynchronous sampling errors. To adapt to the development needs of new power systems, a new energy metering algorithm is urgently needed that can comprehensively consider factors such as steady-state performance, dynamic response, threshold effects, and asynchronous sampling. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for measuring dynamic electric energy with high accuracy, the method comprising:

[0006] Synchronize the sampling signals of voltage and current;

[0007] Based on an adaptive non-overlapping window algorithm, dynamically adjusting the window length of each sampling signal according to the active power stability of the sampling signal, and calculating the real-time power of the sampling signal;

[0008] The power threshold is adaptively adjusted according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal to achieve accurate measurement of dynamic electric energy.

[0009] Furthermore, the sampling signals of voltage and current are synchronized, including:

[0010] The second-order Lagrange interpolation algorithm is used to convert the frequency of the sampling signal into a signal sequence that is a multiple of the preset fundamental frequency and synchronized with the grid signal. The specific expression of the synchronization processing is:

[0011] Assume that the original sampling sequence is , the corresponding time point is , the synchronization time is t,

[0012]

[0013] Then the synchronized signal value x(t) is:

[0014]

[0015] The value of n is:

[0016]

[0017] is the sampling frequency of the signal.

[0018] Furthermore, based on an adaptive non-overlapping window algorithm, the window length of each sampling signal is dynamically adjusted according to the active power stability of the sampling signal, including:

[0019] Keep the sampling signal windows non-overlapping, assuming the sampling signal window length is L(n), and when the active power of the sampling signal fluctuates, adaptively shorten the window length L(n);

[0020] When the active power of the sampling signal is relatively stable, adaptively extending the window length L(n);

[0021] The adaptive adjustment expression of the window length L(n) is:

[0022]

[0023] Among them, 256 is the number of sampling points in the fundamental wave period. is the mapping function of the window length L(n).

[0024] Furthermore, the mapping function of the window length L(n) is The expression is:

[0025]

[0026] P is the average power of each window, where K is related to the maximum window length. The relationship between K and the maximum window length is as follows:

[0027]

[0028] Among them, 256 is the number of sampling points in the fundamental wave period. is the maximum window length.

[0029] Furthermore, the power threshold is adaptively adjusted according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal, including:

[0030] Determine the accuracy level of the energy meter and the maximum threshold value of the starting current ;

[0031] When the frequency of dynamic changes in the real-time power of the sampled signal is greater than a preset fluctuation threshold, reducing the power threshold;

[0032] When the real-time power dynamic change frequency of the sampling signal is less than the preset fluctuation threshold, the power threshold is increased;

[0033] Power threshold The expression is:

[0034]

[0035] in, is the variance of the real-time power signal output by the system, is the expected value of the real-time power signal output by the system.

[0036] The present invention also provides a high-accuracy dynamic electric energy metering system, comprising:

[0037] A synchronization processing module is used to synchronize the sampling signals of voltage and current;

[0038] An adaptive non-overlapping window processing module, configured to dynamically adjust the window length of each sampling signal based on an adaptive non-overlapping window algorithm and the active power stability of the sampling signal, and calculate the real-time power of the sampling signal;

[0039] The power adaptive adjustment module is used to adaptively adjust the power threshold according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal to achieve accurate measurement of dynamic electric energy.

[0040] Furthermore, the synchronization processing module includes:

[0041] The signal sequence synchronization submodule is used to convert the frequency of the sampling signal into a signal sequence that is a multiple of the preset fundamental frequency and synchronized with the grid signal using a second-order Lagrange interpolation algorithm. The specific expression for the synchronization processing is:

[0042] Assume that the original sampling sequence is , the corresponding time point is , the synchronization time is t,

[0043]

[0044] Then the synchronized signal value x(t) is:

[0045]

[0046] The value of n is:

[0047]

[0048] is the sampling frequency of the signal.

[0049] Furthermore, the adaptive non-overlapping window processing module includes:

[0050] a first window length adaptive adjustment submodule, configured to maintain non-overlap between sampling signal windows, assuming the window length of the sampling signal is L(n), and adaptively shortening the window length L(n) when the active power of the sampling signal fluctuates;

[0051] A second window length adaptive adjustment submodule, configured to adaptively extend the window length L(n) when the active power of the sampling signal is relatively stable;

[0052] The adaptive adjustment expression of the window length L(n) is:

[0053]

[0054] Among them, 256 is the number of sampling points in the fundamental wave period. is the mapping function of the window length L(n).

[0055] Furthermore, the mapping function of the window length L(n) is The expression is:

[0056]

[0057] P is the average power of each window, where K is related to the maximum window length. The relationship between K and the maximum window length is as follows:

[0058]

[0059] Among them, 256 is the number of sampling points in the fundamental wave period. is the maximum window length.

[0060] Furthermore, the power adaptive adjustment module includes:

[0061] The level and maximum threshold determination submodule is used to determine the accuracy level of the energy meter and the maximum threshold of the starting current. ;

[0062] A first power threshold adjustment submodule, configured to reduce the power threshold when the frequency of dynamic changes in the real-time power of the sampling signal is greater than a preset fluctuation threshold;

[0063] A first power threshold adjustment submodule, configured to increase the power threshold when the real-time power dynamic change frequency of the sampling signal is less than a preset fluctuation threshold;

[0064] Power threshold The expression is:

[0065]

[0066] in, is the variance of the real-time power signal output by the system, is the expected value of the real-time power signal output by the system.

[0067] The present invention provides a high-accuracy dynamic electric energy metering method and system that synchronizes sampled signals to reduce metering errors caused by asynchronous sampling. An improved adaptive non-overlapping window algorithm dynamically adjusts the length of each window based on signal characteristics while maintaining non-overlap between windows, enabling more accurate capture of signal changes and reducing dynamic errors. The threshold is adjusted based on real-time power fluctuations in the power grid, achieving adaptive threshold adjustment and addressing metering errors caused by active power thresholds. This improves the accuracy of electric energy metering. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of a method for measuring high-accuracy dynamic electric energy provided by an embodiment of the present invention;

[0069] Figure 2 This is a block diagram of an implementation of a high-accuracy electric energy meter provided by an embodiment of the present invention;

[0070] Figure 3 This is a structural diagram of a high-accuracy dynamic electric energy metering system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following description sets forth numerous specific details to facilitate a thorough understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific implementations disclosed below.

[0072] Example 1

[0073] The present invention provides a method for improving the measurement of dynamic electric energy. It comprehensively considers the various technical problems faced by electric energy measurement in the new power system environment, and makes systematic improvements and innovations on the basis of traditional measurement algorithms. The method provided by the present invention is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the specific steps include:

[0074] Step S101 : performing synchronization processing on the sampling signals of voltage and current.

[0075] To reduce measurement errors caused by asynchronous sampling, the present invention first synchronizes the voltage and current sampling signals. Specifically, the fundamental frequency f is determined by performing zero-crossing detection on the voltage signal over a longer period of time. A second-order Lagrange interpolation algorithm is then used to convert the sampling signal frequency into a signal sequence that is a multiple of the preset fundamental frequency and synchronized with the grid signal. The preset fundamental frequency multiple can be 256.

[0076] The second-order Lagrange interpolation algorithm is a classic algorithm based on polynomial interpolation. It has the advantages of high accuracy and stable calculation, and can effectively improve the synchronization of signals. The specific expression of synchronization processing is:

[0077] Assume that the original sampling sequence is , the corresponding time point is , the synchronization time is t,

[0078]

[0079] Then the synchronized signal value x(t) is:

[0080]

[0081] The value of n is:

[0082]

[0083] is the sampling frequency of the signal.

[0084] Step S102 : Based on an adaptive non-overlapping window algorithm, dynamically adjust the window length of each sampling signal according to the active power stability of the sampling signal, and calculate the real-time power of the sampling signal.

[0085] In the power system, power calculation is usually processed in real time using a sliding window method. Although the traditional sliding window method has a simple process, there are principle errors for high-frequency signals when considering startup and creep, and the computational complexity is relatively high. The design of a fixed window length is also difficult to adapt to the characteristic changes of dynamic signals. Although the non-overlapping algorithm reduces the amount of calculation through a jumping window, it tends to ignore signal mutation information at the window boundary, has poor adaptability to non-stationary signals, and lacks a dynamic adjustment mechanism for the window length. It cannot be flexibly changed according to the actual changes in the signal, resulting in it being difficult to accurately calculate power in a complex and changeable power grid signal environment. To this end, the present invention proposes an improved adaptive non-overlapping window algorithm, the core of which is to combine adaptive variable window length with non-overlapping windows. On the basis of maintaining no overlap between windows, the length of each window is dynamically adjusted according to the signal characteristics. Specifically, to maintain non-overlap between sampling signal windows, assume that the window length of the sampling signal is L(n). When the active power of the sampling signal fluctuates, the window length L(n) is adaptively shortened; when the active power of the sampling signal is relatively stable, the window length L(n) is adaptively extended. The adaptive adjustment expression of the window length L(n) is:

[0086]

[0087] 256 is the number of sampling points within the fundamental wave period. The window length is adjusted using the variance of active power and the second-order moment. When the grid signal is stable, the variance of active power is close to 0, and the maximum window length is used. When the grid signal fluctuates the most, the variance of active power is close to the second-order moment, and the minimum window length is used, which is the number of sampling points within a single fundamental wave period.

[0088] The mapping function of the window length L(n) The expression is:

[0089]

[0090] P is the average power of each window, where K is related to the maximum window length. The relationship between K and the maximum window length is as follows:

[0091]

[0092] Among them, 256 is the number of sampling points in the fundamental wave period. is the maximum window length.

[0093] Step S103 , adaptively adjusting the power threshold according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal, so as to achieve accurate measurement of dynamic electric energy.

[0094] In order to solve the problem that the fixed power threshold in the traditional algorithm may introduce errors, the present invention proposes an adaptive threshold judgment method. First, the accuracy level of the energy meter is determined, and the maximum threshold is determined by the starting current. Then, the threshold is adjusted according to the fluctuation of the real-time power of the power grid to achieve adaptive power threshold adjustment. When the frequency of dynamic changes in the real-time power of the sampling signal is greater than the preset fluctuation threshold, the power threshold is reduced; when the frequency of dynamic changes in the real-time power of the sampling signal is less than the preset fluctuation threshold, the power threshold is increased.

[0095] Power threshold The expression is:

[0096]

[0097] in, is the variance of the real-time power signal output by the system, It is the expected value of the real-time power signal output by the system, or the average value of the real-time power signal output by the system.

[0098] Generally, when the real-time power changes frequently, the threshold is reduced to avoid the loss of some energy metering caused by the threshold. When the real-time power is relatively stable, the threshold is increased to minimize the error caused by thermal noise in energy metering.

[0099] According to the above power threshold It can be seen that when the dynamic change frequency of the real-time power of the sampling signal is less than the preset fluctuation threshold, that is, when the real-time power is stable, the standard deviation of the power signal is close to 0, and the power threshold is close to the maximum value;

[0100] When the dynamic change frequency of the real-time power of the sampling signal is greater than the preset fluctuation threshold, that is, when the real-time power fluctuates violently, the standard deviation of the real-time power is close to the expected value. At this time, the threshold is close to 0 to avoid the loss of the electric energy to be measured.

[0101] Example 2

[0102] The present invention provides a method for measuring high-accuracy dynamic electric energy to achieve high-accuracy electric energy meter measurement, such as Figure 2As shown, first, the fundamental frequency f is obtained by using the zero-crossing detection method on the sampled voltage signal. Then, an interpolation algorithm is used to synchronize the voltage sampling signal. Based on the adaptive non-overlapping window algorithm, the window length of each sampling signal is dynamically adjusted according to the active power stability of the sampling signal, and the real-time power of the sampling signal is calculated. When the real-time power changes frequently, the threshold is reduced to avoid the loss of some electric energy metering caused by the threshold. When the real-time power is relatively stable, the threshold is increased to minimize the error caused by thermal noise in the electric energy metering. Finally, the dynamic electric energy is calculated by processing the above-mentioned sampling signals.

[0103] Example 3

[0104] Based on the same inventive concept, the present invention also provides a high-accuracy dynamic electric energy metering system 300, such as Figure 3 Shown, including:

[0105] A synchronization processing module 310 is used to synchronize the voltage and current sampling signals;

[0106] An adaptive non-overlapping window processing module 320 is configured to dynamically adjust the window length of each sampled signal based on an adaptive non-overlapping window algorithm and the active power stability of the sampled signal, and calculate the real-time power of the sampled signal;

[0107] The power adaptive adjustment module 330 is used to adaptively adjust the power threshold according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal to achieve accurate measurement of dynamic electric energy.

[0108] Furthermore, the synchronization processing module includes:

[0109] The signal sequence synchronization submodule is used to convert the frequency of the sampling signal into a signal sequence that is a multiple of the preset fundamental frequency and synchronized with the grid signal using a second-order Lagrange interpolation algorithm. The specific expression for the synchronization processing is:

[0110] Assume that the original sampling sequence is , the corresponding time point is , the synchronization time is t,

[0111]

[0112] Then the synchronized signal value x(t) is:

[0113]

[0114] The value of n is:

[0115]

[0116] is the sampling frequency of the signal.

[0117] Furthermore, the adaptive non-overlapping window processing module includes:

[0118] a first window length adaptive adjustment submodule, configured to maintain non-overlap between sampling signal windows, assuming the window length of the sampling signal is L(n), and adaptively shortening the window length L(n) when the active power of the sampling signal fluctuates;

[0119] A second window length adaptive adjustment submodule, configured to adaptively extend the window length L(n) when the active power of the sampling signal is relatively stable;

[0120] The adaptive adjustment expression of the window length L(n) is:

[0121]

[0122] Among them, 256 is the number of sampling points in the fundamental wave period. is the mapping function of the window length L(n).

[0123] Furthermore, the mapping function of the window length L(n) is The expression is:

[0124]

[0125] P is the average power of each window, where K is related to the maximum window length. The relationship between K and the maximum window length is as follows:

[0126]

[0127] Among them, 256 is the number of sampling points in the fundamental wave period. is the maximum window length.

[0128] Furthermore, the power adaptive adjustment module includes:

[0129] The level and maximum threshold determination submodule is used to determine the accuracy level of the energy meter and the maximum threshold of the starting current. ;

[0130] A first power threshold adjustment submodule, configured to reduce the power threshold when the frequency of dynamic changes in the real-time power of the sampling signal is greater than a preset fluctuation threshold;

[0131] A first power threshold adjustment submodule, configured to increase the power threshold when the real-time power dynamic change frequency of the sampling signal is less than a preset fluctuation threshold;

[0132] Power threshold The expression is:

[0133]

[0134] in, is the variance of the real-time power signal output by the system, It is the expected value of the real-time power signal output by the system, or the average value of the real-time power signal output by the system.

[0135] The present invention provides a method and system for measuring dynamic electric energy with high accuracy. The method synchronizes sampling signals to reduce measurement errors caused by asynchronous sampling. An improved adaptive non-overlapping window algorithm dynamically adjusts the length of each window according to signal characteristics while maintaining non-overlap between windows, thereby more accurately capturing signal changes and reducing dynamic errors. The method also adjusts the threshold according to the fluctuation of the real-time power of the power grid to achieve adaptive threshold regulation, thereby resolving the measurement error problem caused by the active power threshold.

[0136] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0137] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0138] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0139] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modifications or equivalents that do not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for measuring dynamic electric energy with high accuracy, characterized in that: include: Synchronize the sampling signals of voltage and current; Based on an adaptive non-overlapping window algorithm, dynamically adjusting the window length of each sampling signal according to the active power stability of the sampling signal, and calculating the real-time power of the sampling signal; Adaptively adjust the power threshold according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal to achieve accurate measurement of dynamic electric energy; Based on an adaptive non-overlapping window algorithm, the window length of each sampling signal is dynamically adjusted according to the active power stability of the sampling signal, including: Keep the sampling signal windows non-overlapping, assuming the sampling signal window length is L(n), and when the active power of the sampling signal fluctuates, adaptively shorten the window length L(n); When the active power of the sampling signal is relatively stable, adaptively extending the window length L(n); The adaptive adjustment expression of the window length L(n) is: Wherein, 256 is the number of sampling points in the fundamental wave period, which is the mapping function of the window length L(n); The mapping function of the window length L(n) The expression is: P is the average power of each window, where K is related to the maximum window length. The relationship between K and the maximum window length is as follows: Among them, 256 is the number of sampling points in the fundamental wave period. is the maximum window length; Adaptively adjusting the power threshold according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal includes: Determine the accuracy level of the energy meter and the maximum threshold value of the starting current ; When the real-time power dynamic change frequency of the sampling signal is greater than a preset fluctuation threshold, the power threshold is reduced; When the real-time power dynamic change frequency of the sampling signal is less than the preset fluctuation threshold, the power threshold is increased; Power threshold The expression is: in, is the standard deviation of the real-time power signal output by the system, is the expected value of the real-time power signal output by the system.

2. The method according to claim 1, characterized in that Perform synchronization processing on the sampling signals of voltage and current, including: The second-order Lagrange interpolation algorithm is used to convert the frequency of the sampling signal into a signal sequence that is a multiple of the preset fundamental frequency and synchronized with the grid signal. The specific expression of the synchronization processing is: Assume that the original sampling sequence is , the corresponding time point is , the synchronization time is t, Then the synchronized signal value x(t) is: The value of n is: is the sampling frequency of the signal.

3. A high-accuracy dynamic electric energy metering system, characterized in that: include: A synchronization processing module is used to synchronize the sampling signals of voltage and current; An adaptive non-overlapping window processing module, configured to dynamically adjust the window length of each sampling signal based on an adaptive non-overlapping window algorithm and the active power stability of the sampling signal, and calculate the real-time power of the sampling signal; A power adaptive adjustment module, configured to adaptively adjust the power threshold according to the accuracy level of the electric energy meter and the fluctuation of the real-time power of the sampling signal, thereby achieving accurate measurement of dynamic electric energy; Adaptive non-overlapping window processing module, including: a first window length adaptive adjustment submodule, configured to maintain non-overlap between sampling signal windows, assuming the window length of the sampling signal is L(n), and adaptively shortening the window length L(n) when the active power of the sampling signal fluctuates; A second window length adaptive adjustment submodule, configured to adaptively extend the window length L(n) when the active power of the sampling signal is relatively stable; The adaptive adjustment expression of the window length L(n) is: Wherein, 256 is the number of sampling points in the fundamental wave period, which is the mapping function of the window length L(n); The mapping function of the window length L(n) The expression is: P is the average power of each window, where K is related to the maximum window length. The relationship between K and the maximum window length is as follows: Among them, 256 is the number of sampling points in the fundamental wave period. is the maximum window length; Power adaptive adjustment module, including: The level and maximum threshold determination submodule is used to determine the accuracy level of the energy meter and the maximum threshold of the starting current. ; A first power threshold adjustment submodule, configured to reduce the power threshold when the frequency of dynamic changes in the real-time power of the sampling signal is greater than a preset fluctuation threshold; A first power threshold adjustment submodule, configured to increase the power threshold when the real-time power dynamic change frequency of the sampling signal is less than a preset fluctuation threshold; Power threshold The expression is: in, is the standard deviation of the real-time power signal output by the system, is the expected value of the real-time power signal output by the system.

4. The system according to claim 3, characterized in that Synchronization processing module, including: The signal sequence synchronization submodule is used to convert the frequency of the sampled signal into a signal sequence that is a multiple of the preset fundamental frequency and synchronized with the grid signal using a second-order Lagrange interpolation algorithm. The specific expression for the synchronization processing is: Assume that the original sampling sequence is , the corresponding time point is , the synchronization time is t, Then the synchronized signal value x(t) is: The value of n is: is the sampling frequency of the signal.

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