Apparatus and method for power detection and for generating power patterns

Abstract

The invention relates to an apparatus for detecting a power in power networks (1) and / or for generating power patterns, comprising a current-measuring device (2) for measuring a mains current present at a measuring point of a power network (1), a voltage-measuring device (3) for measuring a mains voltage applied at the measuring point of the power network (1), a frequency-based A / D converter module (8), which is configured such that the mains current and / or the mains voltage are sampled at specified frequency intervals to give digital frequency-component current data and frequency-component voltage data, a wavelet module (9, 12), which is configured such that frequency-component current data and frequency-component voltage data are multiplied by a specified wavelet function to give wavelet frequency-component current data and wavelet frequency-component voltage data, wherein the A / D converter module (8) can be actuated such that it samples the mains current and the mains voltage in different specified frequency ranges at different sampling rates to give current data and voltage data such that the sampling rates are lower in a low frequency range than in a comparatively higher frequency range, and that a computation module is configured such that the wavelet frequency-component current data and the wavelet frequency-component voltage data multiplied in the wavelet module (9, 12) are added up to give power data and / or energy data.

Description

[0001] Device and method for determining performance and generating performance patterns

[0002] The invention relates to a device for determining power in power grids according to patent claim 1 and to a method therefor according to claim 16.

[0003] Power measurement in multi-wire circuits is defined in Germany according to DIN 401 10-2 and worldwide according to IEE-1459. The calculation methods of these standards form the basis for power calculations using modern measuring instruments. According to the standard, the specified operating range is + / - 1 Hz relative to a nominal frequency of 50 Hz and + / - 1.2 Hz relative to a nominal frequency of 60 Hz. Harmonics are taken into account. In the low-frequency range, they are only taken into account from 15 Hz upwards. Non-linear system behavior of the grid is therefore not taken into account. Since in the future more and more systems based on decentralized converters will be put into operation, which influence the system behavior in the low frequency range, the power result will be falsified if a low-frequency range of the power-relevant variables, namely the grid current and the grid voltage, is not taken into account.The Fourier analysis-based algorithms used today generate errors for low-frequency modulated nominal frequencies that, depending on the technical design (sampling rate, window length), far exceed permissible tolerance values. It should be noted that a fast Fourier transform (FFT) delivers information in the frequency domain with a resolution that depends on the time window (measurement interval). No information about the signal is reproducible. To circumvent this limitation, a "windowed" Fourier transform was introduced to generate a frequency-time spectrum. This variant of the FFT is also known as an SFT short-time Fourier transform. However, this short-time Fourier transform has the disadvantage that a fixed window width must be used. This property has a significant impact on the frequency-time resolution and thus on the accuracy with which a signal can be identified.

[0004] US Patent No. 9,170,986 B2 discloses a power analysis method that determines information on harmonic components based on wavelets, particularly the reconstruction of waveforms that are analyzed and processed in other devices and applications. However, this method eliminates low-frequency components (sag / swell cycles), which fall under the term "flicker" according to power quality standards (DIN-EN 50160).

[0005] The object of the present invention is to provide a device and a method for determining power in power grids that also takes low-frequency signal components into account when determining power. In particular, the device should enable the evaluation of non-stationary, non-linear processes and actively utilize them for the precise, parallel, and time-correct evaluation of power and energy signals. To achieve this object, the invention has the features of patent claim 1.

[0006] According to the invention, predefined wavelets are used for fixed frequency intervals, with the frequency range extending from 0 Hz to a maximum frequency. The frequency range to be examined is divided into frequency intervals in such a way that the frequency intervals currently used in standards are respected and the corresponding current and voltage values ​​are each subjected to the wavelet function (wavelet pattern). The invention takes advantage of the fact that the length of the wavelets depends on the frequency interval or frequency level to be examined. By dividing the current or voltage signals into predefined frequency intervals, a frequency-based parallel analysis or calculation of the power can be carried out. This ensures highly accurate measurement of individual frequencies for all power components, in particular active and reactive power, and in particular for the nominal frequency.

[0007] The invention enables, on the one hand, precise power determination, whereby the exact amplitudes of the frequency components are determined in a first measuring path. On the other hand, grid disturbances can be evaluated based on power patterns, whereby the power components are determined in a second measuring path at a specific, predetermined time across a predetermined frequency range. The superposition of the transmitted power components forms power patterns that can characterize specific grid disturbances. This makes it possible to provide energy efficiency services that subsequently lead to the detection of an ongoing disturbance in the grid. According to the invention, the first measuring path can be used for precise power measurement, and the second measuring path for time-accurate power pattern determination. Alternatively, a combination of both measuring paths can be used to determine exact and simultaneously time-accurate voltage and current quantities.According to a preferred embodiment of the invention, the temporal length of the wavelet patterns differs in predetermined, different frequency ranges. In a frequency range that includes relatively high frequencies, namely, for example, frequencies between 50 Hz and 4 kHz, and in which, for example, the current and voltage values ​​are sampled equidistantly in 25 Hz steps, the wavelet patterns are shorter in time than in another frequency range or frequency band, which lies between 1 Hz and 50 Hz and in which, for example, the current and voltage values ​​are sampled in equidistant 1 Hz frequency bands. Thus, depending on the selected frequency range or frequency band, there is a discontinuous change in the temporal length of the wavelet patterns. The temporal length of the wavelet patterns preferably depends on the Heisenberg uncertainty principle, according to which calculation time is inversely proportional to frequency.

[0008] According to an alternative embodiment of the invention, the temporal lengths of the wavelet patterns can increase continuously and inversely proportional to the sampling frequency. However, the computational effort for this is higher.

[0009] According to a preferred embodiment of the invention, the wavelet pattern is designed as a Daubechies wavelet pattern, in particular as an orthogonal Daubechies wavelet pattern. In particular, if it is a Daubechies wavelet DB 10 with 20 coefficients, the values ​​of the scaling and wavelet function are normalized to 1 so that they can be used directly as root mean square values ​​(RMS) for the current and voltage determination. This simplifies the determination of the effective power, since the frequency-selective current and voltage values ​​can be multiplied and summed as effective values. This makes the power or energy determination effective. No additional conversion is required. Validation of the measured values ​​is easy to perform. According to a further development of the invention, the signals, namely the mains current and the mains voltage, are sampled at equidistant frequency intervals.It is advantageous to have equal amplitude Z frequency component values.

[0010] According to a further development of the invention, a predetermined latency time, with which the wavelet values ​​calculated in different frequency ranges (frequency intervals) are shifted, depends on a minimum frequency. Only after the latency time has elapsed can the signal be displayed and used for further processing. It is taken into account that wavelets have a changing time and frequency resolution. In order to display or measure non-stationary signals, the observation period and the frequency must be adapted to the signal to be observed. In this respect, low-frequency signal components must be observed for longer or a longer wavelet must be used. To determine the exact power, larger time intervals must therefore be used, usually in the range of minutes. This latency time specifies the maximum runtime correction in order to synchronize the respective frequency-dependent or frequency-interval-dependent wavelet values.Using the frequency-based runtime correction module, a "backward transformation" of the time to the observation time takes place, i.e. from when one wants to take into account the exact shift of the wavelet signal in relation to the useful signal (e.g. 50 Hz mains frequency). If the frequency interval is set to 1 Hz, for example, 0.01 Hz can be used as the lowest frequency value, which leads to a latency of 100 seconds. After these 100 seconds have elapsed, the current and voltage signals present at the observation time in the respective frequency intervals (at the beginning of the 100 seconds) can be summed up and the power values ​​at the observation time can be determined by corresponding back-time shifting. According to a development of the invention, a time-based runtime correction module is provided, by means of which a time-precise measurement is made.This allows patterns and time-accurate assignments of frequency components to be determined. For example, rapidly changing dynamics can be detected, such as aging processes, fault detection of machine components, and changes in the operating behavior of systems and devices. The time-based runtime correction module ensures that all wavelet values ​​are measured with precise timing. This makes it possible to reconstruct the original signal from the individual components.

[0011] According to a further development of the invention, a Kl module is provided in which error pattern recognition algorithms are collected in order to be able to detect current errors by comparing them with the currently measured values.

[0012] To solve this problem, the method has the features of patent claim 16.

[0013] Embodiments of the invention are explained in more detail below with reference to the drawings.

[0014] They show:

[0015] Fig. 1 is a block diagram of a measuring device according to the invention,

[0016] Fig. 2 shows a processing of measured power and energy values ​​​​divided according to frequency components, Fig. 3 shows a graph with a function of frequency or time intervals for different time windows based on a Heisenberg uncertainty principle,

[0017] Fig. 4 a representation of wavelets of different resolution,

[0018] Fig. 5a shows two derived test signals,

[0019] Fig. 5b an analysis of the first test signal using Fast Fourier Transformation (FFIT) and Wavelets (WT),

[0020] Fig. 5c an analysis of the second test signal using Fast Fourier Transform (FFIT) and Wavelets (WT),

[0021] Fig. 6 shows the frequency ranges to be examined,

[0022] Fig. 7a shows the determined frequency components of the wavelets for a specific disturbance in the power grid,

[0023] Fig. 7b is an enlarged view of a low-frequency portion of the wavelet amplitudes according to Figure 7a,

[0024] Fig. 8 shows an exemplary representation of wavelet values ​​at different frequencies (frequency components),

[0025] Fig. 9 a representation of a Daubechies wavelet pattern (D20) and

[0026] Fig. 10 is a table of the wavelet coefficients of the Daubechies wavelet pattern according to Figure 9 at an exemplary pattern frequency of fs = 7.680 Hz. A device according to the invention for determining a power in a power grid 1 essentially consists of a current measuring device 2 for measuring a grid current at a measuring point of the power grid 1, a voltage measuring device 3 for measuring a grid voltage at the measuring point of the power grid 1, a sampling device 4 for sampling the measured grid current and the grid voltage, and a processing device 5 for processing the sampled grid current and grid voltage values.

[0027] Preferably, the device additionally comprises a communication / control device 6, which is connected to remotely located communication participants via a communication interface 7.

[0028] The communication interface 7 can be used in particular to forward the determined power patterns that were determined at a specific point in time. The determined power patterns can be used for control and protection applications, for example via an algorithm, in order to control a protective device or to trigger protective devices.

[0029] Preferably, the device has a display 22 for displaying the determined performance results.

[0030] The processing device 5 is divided into two processing sub-devices 5a and 5b, wherein the respective processing sub-devices 5a, 5b can be implemented independently of one another. The first processing sub-device 5a is used to determine an exact power and energy measurement. A second processing sub-device 5b is used to determine a rapid detection of changes and the associated time of the individual frequency components, for example for use in methods for pattern recognition and error analysis (NILM Non-Intrusive Load Monitoring) or predictive maintenance. The power or power components that can be determined using the device according to the invention are shown below. m=im=l

[0031] • Total active power P in watts o P G : Active component of fundamental oscillation u — 1 o P o : Effective component distortion v > 1 o P M : Effective component modulation

[0032] 0 < u < 1 io o P w : Effective part of interactions me (1 ... oo) 0 < v < 1

[0033] • Total reactive power Q in Var consists of the components o Q G : Displacement reactive power fundamental frequency u— .1 o Q o : Displacement reactive power distortion u > lo Q M : Displacement reactive power modulation

[0034] 0 < v < 1 o Displacement reactive power Interactions m _ 0 < o D Q I Deformation reactive power distortion v > 1 o D M : Deformation reactive power modulation

[0035] 0 < v < 1 o D Wm . Deformation reactive power interactions

[0036] (1 -_C9L) _ Q < V < 1 o D M0 : Interaction of D o and D M me

[0037] (1 ... oo) v > ig^:Interaction of Aw and i?w m me (1 ... QQ) .. .U-^1

[0038] OD nM / 7n : Interaction of D„ and D Wlllme (1 ... QQ) .... . - . o U: Unbalanced reactive power The underlined power components are not determined by conventional power measuring devices, but are reserved exclusively for the measuring device according to the invention.

[0039] Figure 5a illustrates the determination of test signals STI and ST2 from two different signals S1 and S2 of different frequencies. Figures 5b and 5c illustrate the analysis based on a fast Fourier transform (FFT) and using wavelet transform. After the Fourier transform, frequency values ​​of different magnitudes are determined, while after the wavelet transform, identical wavelet values ​​are determined, regardless of the composition of the frequency components of the test signal Sn or STZ.

[0040] The signals S1 and S2 are harmonic oscillations, whereby the frequency of the signal S1 is lower than the frequency of the signal S2. The first test signal ST1 results from the superposition or summation of the first signal S1 and the second signal S2. The second test signal ST2 results from the temporal succession of the first signal S1 and the second signal S2. As can be seen from Figure 5a, the second test signal ST2 consists exclusively of the first signal S1 of lower frequency in a first time period and exclusively of the signal S2 of higher frequency in a second time period.

[0041] If the Fast Fourier Transformation FFT is applied to the first test signal Sn, it can be seen from Figure 5b that the test signal STI consists of two frequencies, namely an initially low frequency fi and a higher frequency f2. If the Wavelet Transformation is applied to the first test signal Sn, two values ​​Wi, W2 result, with the first value W1 corresponding to the lower frequency fi and the second value W2 to the higher frequency f2. These two constant values ​​W1 and W2 are generated over the entire time window. It should be noted that wavelets are a wave pattern of a specific time duration. Morlet wavelets and Daubechies wavelets, for example, are well known and are suitable as pattern functions for power measurement. Figure 4, for example, shows a wavelet (wavelet function) WL.In a time window of + / - 20 time units, it exhibits oscillations with an amplitude that increases in the first half and decreases in the second half. With respect to time unit zero, the wavelet is symmetric, meaning that the increasing first half is symmetrical to the decreasing second half of the wavelet and has its maximum amplitude in the middle of the time window.

[0042] If a fast Fourier transformation (FFT) and a wavelet transformation are applied to the second test signal ST2, the result is shown in Figure 5c. After the fast Fourier transformation (FFT), a value for the lower frequency fi is obtained in a first time window in which the first signal Si with a lower frequency fi is present, and a value for the higher frequency fz is obtained in a second time interval in which the signal Sz with a higher frequency fz is present. According to the wavelet transformation, the value Wi, which corresponds to the low frequency fi, is obtained in the first time period Ti, and the value Wz, which corresponds to the higher frequency fz, is obtained in the second time period T2. In addition, the wavelet transformation provides information about when the frequency change takes place, namely at time ti, at which the value Wi jumps to the higher value Wz.This time ti of the change from one wavelet value Wi to the other wavelet value Wz is utilized in the following described time-correct arrangements of the determined power values ​​for different frequencies.

[0043] Figure 2 shows the processing device 5 in more detail. A first processing sub-device 5a of the processing device 5 is frequency-based, while a second processing sub-device 5b of the processing device 5 is time-based.

[0044] The first processing sub-device 5a processes data from a frequency-based A / D converter module 8 of the sampling device 4, which samples the measured mains current and the mains voltage L1, L2, L3, N, earth - 4-phase in parallel at predetermined frequency intervals A, B, C, D, E, F. The frequency ranges are divided as follows:

[0045] Frequency range 0: Direct current (DC)

[0046] Frequency range A: Sampling rate > 2.4 kHz in the range approx. 0 to 55 Hz, or approx. 0 to 66 Hz - depending on the nominal frequency

[0047] Frequency range B: Sampling rate > 24 kHz in the range 47 Hz < 50 Hz < 53 Hz, or 55 Hz < 60 Hz < 66 Hz

[0048] Frequency range C, D: Sampling rate > 40 kHz - typical: 40.96 kHz in the nominal frequency range 50 Hz to 2.5 kHz, or 60 Hz to 3.0 kHz, in range D in intervals of 200 Hz

[0049] Frequency range E: Sampling rate > 400 kHz - typical: 409.6 kHz, in the range from 9 kHz to 150 kHz

[0050] Frequency range F: Sampling rate > 1.5 MHz, in the range 150 kHz to 500 kHz. Preferably, the frequency intervals in the individual ranges are equidistant, for example, with an interval width of 1 Hz in frequency range A. A lowest frequency interval can, for example, run from 0.005 Hz to 1 Hz. The next second-higher frequency interval can run from 1 Hz to 2 Hz, and so on, up to frequencies in the kHz range, for example. In addition, special frequency ranges can be defined if particularly interesting frequency components need to be measured with very high accuracy.

[0051] Figure 6 shows an overview of the various frequency ranges 0, A, B, C, D, E, F. As already described above, the sampling rate in frequency range B is higher than in frequency range A. Within the respective frequency ranges A, B, C, D, E, the sampling rate is preferably constant so that corresponding wavelet values ​​can be determined for time intervals of equal length. Figure 7a shows the result of a wavelet analysis of a disturbance in the power grid carried out according to Figure 2. In addition to frequency components and a relatively high frequency component in operating band B between 47.5 Hz and 52.5 Hz, it was possible to determine in particular low-frequency components with a frequency of 25 Hz and less, which lie in the first frequency band A between 0 Hz and 50 Hz.The magnitude and distribution of the frequency components, i.e. the frequency pattern determined based on the wavelet transformation, allow conclusions to be drawn about the type of damage. In addition, after a minimum latency period, the power at the beginning of the latency period can be determined. A more detailed examination of the frequency components in the low-frequency range is shown in Figure 7b. It can be seen that a low-frequency resonance with sidebands is present, with the resonance frequency being 25 Hz. The sidebands are located at 12.5 Hz and 37.5 Hz, see hatched bar. The detection of such a low-frequency resonance with sidebands can be achieved by modulating electric vehicles and other power electronic loads connected to the power grid.

[0052] The first processing sub-device 5a further comprises a first wavelet module 9. The digitized current and voltage values ​​are multiplied by a predefined wavelet function for each frequency interval and then transferred to a first wavelet propagation time correction module 10 on the signal flow side. Figure 4 shows exemplary wavelet functions WLo, WLsa, and WLsb, which have different signal widths but the same signal height at the time t0 of maximum amplitude. For example, this can be a Morlet wavelet or a Daubechies wavelet, which is particularly suitable for measuring frequency intervals. The wavelet function can be scaled according to requirements, specifically with regard to the signal width and signal height. The signal width depends on the frequency and is adapted to the frequency ranges so that the required accuracy can be achieved.

[0053] The frequency-based first runtime correction module 10 is designed such that, depending on a frequency or frequency interval size, the determined wavelet value is shifted back by a latency time ÖA, 6B, ÖC, ÖD, ÖE, ÖF, so that the current and voltage values ​​for each frequency interval are time-synchronized.

[0054] The first processing sub-device 5a further comprises a processing module 11, by means of which the wavelet values ​​representing the voltage and current values ​​present for the respective frequency intervals are summed for the respective real and imaginary components and calculated to form a power value (active and reactive components). To determine the respective imaginary component, the wavelet values ​​are each shifted by 90°. Thus, first wavelet values ​​for a real component and second wavelet values ​​for an imaginary component of the current and voltage can be determined.

[0055] The specified latency depends on the minimum frequency, which is defined by the lowest frequency interval. According to the Heisenberg uncertainty principle, the lower the frequency, the longer the wait time is required until the complete measurement signal can be correctly captured.

[0056] Figure 3 shows, above the abscissa, the function of frequency over time required to determine the current-voltage values ​​in respective frequency intervals. For example, a 0.1 Hz signal requires a duration of 10 seconds. A 0.01 Hz signal requires 100 seconds. These measured variables in the frequency domain are further processed in the processing module 11 to determine the power data. The second processing sub-device 5b comprises a second wavelet module 12, by means of which the digitized current and voltage data provided by the A / D conversion module 8 in the corresponding frequency intervals A, B, C, D, E, F are calculated using time-based wavelet functions to form wavelet values ​​Δt, Bt, Ct, Dt, Et, Ft. These wavelet functions are dimensioned such that a relatively fast measurement can be determined at the expense of accuracy.

[0057] A second runtime correction module 13 of the second processing sub-device 5b located downstream of the signal carries out runtime correction so that the time-correct values ​​can be superimposed or summed up for the respective frequency intervals.

[0058] At the output of the first runtime correction module 10 or the second runtime correction module 13, the resolved frequency components A, B, C, D, E, F are summed to yield power and energy values ​​for the respective intervals, which can then be used to determine a total energy or power. For this purpose, the processing module 11 has a calculation module 15. The calculation module 15 has a corresponding power calculation routine.

[0059] Figure 8 shows an example of determined wavelet values ​​A1, A2, A3 at different frequencies, namely 0.3 Hz, 29 Hz, and 35 Hz. To determine the correct power, the latency ti_3 for the lowest frequency, namely 0.3 Hz, must be waited for, and then the wavelet values ​​A1, A2, A3 for all frequency ranges must be summed. The latency for the 35 Hz component is shorter with T11 than for the 29 Hz component with T1?. The largest latency in the present embodiment applies to the 0.3 Hz frequency component with T13. The latencies can, for example, correspond to the fixed sampling rates in the respective frequency bands A, B, C, D, E, F.

[0060] Preferably, the amplitudes A1, A2, A3 ...A nThe frequency components are compared with a predefined threshold value S. If the determined amplitudes are smaller than the predefined threshold value S, they are not taken into account in the calculation. In the present exemplary embodiment, the amplitude A1 is smaller than the threshold value, so the 35 Hz component is not taken into account when calculating the power. To determine the power, only the amplitudes A2 and A3 are taken into account. This threshold comparison allows insignificant frequency components, which may extend the measurement time, to be neglected, as they are insignificant for determining the power.

[0061] Figure 8 shows an example of the course of three different wavelets WL1, WL2 and WL3 in a fault situation. All three wavelet responses are above a specified threshold value S. Wavelets below the threshold value S would not be taken into account in the calculation. The first wavelet WL1 refers to a measurement at a frequency of 35 Hz. The second wavelet WL2 refers to a measurement of 29 Hz and the third wavelet WL3 refers to a measurement at a frequency of 0.3 Hz. Accordingly, the corresponding latency times ki, ti2 and ti_3 result, whereby for the power measurement the latency time ti_3 of the lowest frequency must be waited for in order to obtain the power result at time t0 by summing up the measured values ​​for the individual frequencies.

[0062] In addition, a database (not shown) can be provided in which a trainable Kl module is arranged, which is intended to assign corresponding wavelet pattern functions for the characteristic fault constellation, so that the assigned wavelet functions are selected depending on a recorded parameter characterizing a given fault.

[0063] The Kl module can be trained using an algorithm that processes a large number of learning data sets, each containing learning input values ​​for input variables and learning output values ​​for output variables. Using the Kl module, one or more input variables can be mapped to one or more output variables based on the parameters.

[0064] It should be noted that the frequency component current data and the frequency-voltage data are each determined by wavelet function values ​​at time t0 according to Figure 4. The time t0 is always the time at which the maximum amplitude of the wavelet is present.

[0065] The length of a wavelet pattern, as shown in Figure 4, results from the sampling rate. If the sampling rate is relatively high, as in the frequency range C, D, the signal duration of the wavelet is relatively short. However, if the sampling rate is relatively low, as in the frequency range A, the signal duration of the wavelet is relatively long, meaning it is temporally stretched compared to the wavelet with a high sampling rate.

[0066] The invention utilizes a changing time and frequency resolution of wavelet to represent non-stationary signals, with the observation period and frequency being adapted to the signal to be observed.

[0067] The wavelet pattern functions stored in the database correspond to specific faults. If a comparison of the currently determined wavelet pattern or frequency spectrum of the wavelet function reveals a match with a predefined wavelet pattern function, a characteristic fault can be easily identified. This fault can be caused by errors or changed dynamics due to aging processes, fault detection of machine components, or altered operating behavior of power electronic systems and devices. This requires that the wavelet pattern functions for these errors or faults are available or have been empirically determined.

[0068] For example, these wavelet pattern functions can be specified empirically by using corresponding measurement data or learned using the Kl module.

[0069] According to a further embodiment of the invention, a Daubechies wavelet is used as the wavelet pattern, which is designed as an orthogonal wavelet with a scaling function normalized to 1 and a wavelet function normalized to 1.

[0070] The Daubechies wavelet WLD10 shown in Figure 9 is characterized by the fact that the course of the wavelet increases in magnitude with increasing frequency to the value +1 or, as shown in Figure 9, -1 to the desired frequency and then continues to increase the frequency. By comparing it with the respective current and voltage data of different sampling frequencies, applying the Daubechies wavelet to the corresponding sampled current / voltage sampled values, figuratively speaking, a locking of the corresponding current and voltage values ​​for the respective frequencies takes place. Because the Daubechies wavelet is normalized to 1 in terms of both the scaling function and the wavelet function, an effective value can be calculated for both the voltage V and the current I without the need for a frequency-dependent conversion.The following equations show the calculation of the effective value for the voltage V and the current I, where the coefficients c and d correspond to the coefficients of the scaling function and the wavelet function of the Daubechies wavelet WLD10. The Daubechies wavelet WLD10 is known as wavelet D20 and has a total of 20 coefficients.

[0071] Hey

[0072] %Ej = jx 100

[0073] 5

[0074] Percent energy of the wavelet coefficients for each level j frequency band

[0075] F s Sampling frequency

[0076] 15

[0077] Effective value calculation based on Daubechies wavelet:

[0078] Figure 10 shows an example of a wavelet WLD 10 with a pattern frequency of fs = 7.680 Hz. This results in different pseudofrequencies and pseudotime periods for the different wavelet levels di, d2, da, d4, and ds. The smaller the pseudofrequency, for example, at ds = 164.2 Hz, the longer the pseudotime period (0.0061 seconds). The Daubechies wavelet pattern thus advantageously enables the direct determination of the effective value of current and voltage.

[0079] Alternatively, it is also possible to determine the component of current I and voltage V as a function of specified frequencies. If, for example, the power is to be determined for a specific frequency component, only the corresponding frequency component of current I and voltage V is applied or calculated using the Daubechies wavelet pattern. A suitable sampling frequency is used with the A / D converter. Alternatively or additionally, a frequency-selective network impedance can be determined from the determined frequency-selective currents and frequency-selective voltages by calculating a corresponding quotient of frequency-selective voltage and frequency-selective current.The invention advantageously enables, in particular, the determination of voltage and current values ​​in the low-frequency range, i.e. preferably in a range between 0 Hz and 66 Hz, for example 0 Hz to 20 Hz. Due to the property of the wavelets that they have a limited time length in the range from 180 ms to 120 s (frequency interval 0 Hz to 66.0 Hz), the voltage values ​​and current values ​​can be determined relatively quickly in all relevant frequency bands.

[0080] Different frequency ranges with different wavelet time domains are given as examples below.

[0081] It can be seen that for the lowest frequency range A, the wavelet has the largest time domain and the largest time span.

[0082] 5 When using the Daubechies wavelet (DB10 or D20), the effective values ​​of the measured voltages and currents can be determined directly, so that the effective value of the power can be determined directly by multiplying the voltage and current values.

[0083] 10 As can be seen from Figure 9, the Debauchies wavelet pattern has approx.

[0084] 12 oscillations of the respective frequency, for example, 8 to 10 oscillations can be taken into account instead without any significant loss of accuracy.

[0085] : o A computer-readable medium, i.e. a program (software program), can be provided on which program sections executable by a computer unit are stored in order to carry out all steps of the described method for measuring power and / or energy when the program sections are executed by the computer unit. The pro-

[0086] 20 grams can be implemented in the device.

Claims

Patent claims 1. Device for determining a power in power grids (1) and / or for generating power patterns with - a current measuring device (2) for measuring a mains current present at a measuring point of the power grid (1), - a voltage measuring device (3) for measuring a mains voltage applied to the measuring point of the power grid (1), - a frequency-based A / D converter module (8) which is configured such that the mains current and / or the mains voltage are sampled at predetermined frequency intervals (Af) to produce digital frequency component current data and frequency component voltage data, - a wavelet module (9, 12) which is configured such that frequency component current data and frequency component voltage data are multiplied by a predetermined wavelet function to produce wavelet frequency component current data and wavelet frequency component voltage data, characterized in that the A / D converter module (8) is controllable such that it samples the mains current and the mains voltage in predetermined different frequency ranges (A, B, C, D, E) with different sampling rates to produce current data and voltage data, so that in a low frequency range (A) the sampling rates are lower than in a comparatively higher frequency range <B, C, D, E), und dass ein Be- rechnungsmodul (15) derart eingerichtet ist, dass die in dem Wa- velet-Modul (9, 10) multiplizierten Wavelet- Frequency component current data and wavelet frequency component voltage data are summed to form power data and / or energy data.

2. Device according to claim 1, characterized in that a temporal length of the wavelet pattern is selected such that it decreases continuously or discontinuously with increasing frequency of the wavelet frequency component current data and the wavelet frequency component voltage data.

3. Device according to claim 1 or 2, characterized in that the temporal length of the wavelet pattern lies in a range of 180 ms to 120 s for a frequency range between 0 Hz and 66 Hz.

4. Device according to one of claims 1 to 3, characterized in that the wavelet pattern is designed as a Daubechies wavelet pattern, in particular as an orthogonal Daubechies wavelet pattern.

5. Device according to claim 4, characterized in that the Daubechies wavelet pattern is designed such that both a scaling function and a wavelet function of the Daubechies wavelet pattern are normalized to 1 in terms of magnitude.

6. Device according to one of claims 1 to 5, characterized in that a runtime correction module (10) is designed to shift the frequency component current data and the frequency component voltage data as a function of a frequency (f) by a latency time, so that the frequency component Current data and the frequency component voltage data of the frequency intervals are available synchronously for the calculation module (15).

7. Device according to one of claims 1 to 6, characterized in that the A / D converter module (8) is designed so that the mains current and the mains voltage are sampled in equidistant frequency intervals (Af).

8. Device according to one of claims 1 to 7, characterized in that the wavelet module (9) is set up so that the predetermined wavelet function is shifted by 90° in the frequency plane to provide a first wavelet function for a real part and to provide a second wavelet function for an imaginary part of the current or voltage data.

9. Device according to one of claims 1 to 8, characterized in that the predetermined latency time is dependent on the minimum frequency of the frequency component current data or frequency component voltage data.

10. Device according to one of claims 1 to 9, characterized in that the latency time is determined on the basis of a Heisenberg uncertainty principle.

11. Device according to one of claims 1 to 10, characterized in that a second wavelet module (12) is provided such that the frequency component current data and the frequency component voltage data are calculated as a function of a time value are shifted by a latency time, so that the frequency component current data and frequency component voltage data of the respective frequency intervals are available at a predetermined time within a time interval after the occurrence of a fault in the network, wherein a length of the time interval is determined by a smallest frequency component of the current signal or voltage signal to be observed or evaluated.

12. Device according to one of claims 1 to 11, characterized in that the wavelet function is weighted and / or validated depending on the frequency contents.

13. Device according to one of claims 1 to 12, characterized in that only frequency component current data and frequency component voltage data which are above a predetermined threshold value (S) are taken into account in the calculation and / or evaluation. 14 Device according to one of claims 1 to 13, characterized in that a database is provided in which a trainable Kl module is arranged, which is intended to assign corresponding wavelet pattern functions for characteristic fault constellations, so that the assigned wavelet function is selected as a function of a detected parameter characterizing a predetermined fault. 15 Device according to one of claims 1 to 14, characterized in that the Kl module can be trained by means of a algorithm 1, which processes a plurality of read data sets, each containing learning input values ​​for input variables and learning output values ​​for output variables, and which is designed to map one or more input variables to one or more output variables based on the parameters.

16. Method for carrying out a power measurement in power grids, wherein mains current values ​​and mains voltage values ​​are sampled and digitized, characterized in that - that the mains current values ​​and the mains voltage values ​​are sampled in predetermined frequency ranges (A, B, C, D, E) with different sampling rates, wherein in a first frequency range (A), in which relatively low frequency components of the mains current values ​​and the mains voltage values ​​are located, the sampling rate is lower than in a second frequency range (B, C, D, E), in which the frequency components of the mains current values ​​and the mains voltage values ​​are comparatively larger, - that the sampled mains current values ​​and the sampled mains voltage values ​​are each multiplied by a wavelet pattern to form wavelet current data and wavelet voltage data, which each form a wavelet curve (WL1, WL2, WL3), - that the wavelet curves (WL1, WL2, WL3) are synchronized with each other, whereby the wavelet curve (WL3) with the lowest sampling rate serves as a reference for the other wavelet curves (WL1, WL2) with comparatively higher sampling rates, - that the power is calculated by summing the wavelet current data and wavelet voltage data characterizing the wavelet curves (WL1, WL2, WL3).

17. Method according to claim 16, characterized in that the mains current values ​​and the mains voltage values ​​are multiplied by an orthogonal Daubechies wavelet pattern, the scaling function and wavelet function of which are normalized to 1 in terms of magnitude, so that effective values ​​of the current data and the voltage data can be determined directly therefrom.

18. Method according to claim 16, characterized in that the same wavelet function is multiplied with the measured mains current data and mains voltage data at least within the frequency ranges equidistant time intervals 19. Method according to one of claims 16 to 18, characterized in that only those wavelet curves (WL1, WL2, WL3) of mains current values ​​and / or mains voltage values ​​which are above a predetermined threshold value (S) are taken into account in the power calculation.

20. Method according to one of claims 16 to 19, characterized in that wavelet curves (WL1, WL2, WL3) in a frequency range which includes lower frequencies are taken into account in the calculation with a delay as another frequency range with a greater latency time (tus) than the wavelet curve which has been determined at a higher frequency.

21. A computer-readable medium on which program sections executable by a computer unit are stored in order to carry out all steps of the method according to one of claims 16 to 20 when the program sections are executed by the computer unit.

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