Intelligent power load identification method

Abstract

The invention discloses an intelligent electric power load identification method, relates to the technical field of electric power load identification, and solves the technical problem that electric power load identification cannot be effectively carried out according to extracted features, so that identification errors exist. Secondly, when features are extracted and recognized, effective values corresponding to voltage and current are calculated, a corresponding peak factor is calculated according to the effective value of the current, meanwhile, a corresponding recognition interval is established according to the peak factor, the peak factor is calculated according to the real-time features subsequently, and the accuracy of subsequent use of the data is ensured; according to the method and the system, the dynamic characteristics are analyzed and the power load is identified in the matching process, so that the overall identification accuracy is improved, and the interference of similar characteristics on the identification is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric load identification, and in particular to an intelligent electric load identification method. Background Art

[0002] Power load power consumption monitoring is the first step in carrying out the systematic project of power saving, because only by knowing how electricity is used and consumed can we find effective power saving control measures and more efficient ways of using electricity.

[0003] According to the publication number CN112968519A, a smart power load identification method is disclosed, which does not require the installation of sensors in front of each load, and realizes lossless and low-cost load identification. The method realizes the change of the total load of the circuit by adding or subtracting electrical appliances in the circuit, records the change x(i) of the electrical parameter of the total incoming line of the circuit when the load changes, and records the current circuit load corresponding to x(i) as the graph node data g(i). The graph node data g(i) is subjected to a fast Fourier transform to convert it into a fixed-dimensional graph node frequency domain data h(i), and x(i) and h(i) constitute a training sample. A self-circulating neural network is constructed, and the input of the self-circulating neural network is the change x(i) of the electrical parameter at time i and the graph node frequency domain data h(i-1) at time i-1, and the output is the graph node frequency domain data h(i) at time i; the self-circulating neural network is cyclically trained using the training sample, and the trained self-circulating neural network can be used for smart power load identification.

[0004] The above patent obtains the change in electrical parameters from the total incoming line of the circuit and the current circuit load condition, and uses these data to form training samples to train the self-circulating neural network, so that the self-circulating neural network can reflect the mapping relationship between the change in electrical parameters and the circuit load condition. Through such a deep learning method, there is no need to install sensors in front of each load, and only the total electrical parameter data of the circuit needs to be obtained to realize intelligent identification of the load.

[0005] However, some existing power load identification systems have errors in the subsequent power load identification due to precision errors in the data collection process. On the other hand, the electrical characteristics of some loads may not be obvious or similar, making it difficult to extract effective distinguishing features. For example, some high-efficiency motors and traditional motors may be very similar in power factor, harmonic content, etc., and it is difficult to accurately distinguish them only through these common electrical characteristics. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides an intelligent power load identification method, which solves the problem that the power load cannot be effectively identified based on the extracted features, resulting in errors in identification.

[0007] To achieve the above objectives, the present invention is implemented by the following technical solutions: an intelligent power load identification method, the method specifically comprising the following steps:

[0008] S101: collecting power data of the power system through a variety of sensors, and performing preprocessing operations on the power data to obtain preprocessed data;

[0009] S102: extracting corresponding features from the obtained pre-processed data, calculating the corresponding effective voltage and effective current based on the time domain features, and calculating the peak factor according to the effective current value;

[0010] S103: Analyze historical data, classify loads according to peak factors to obtain characteristics of loads of the same type, generate peak matching intervals according to the characteristics of loads of the same type, and then calculate harmonic values ​​corresponding to the peak matching intervals;

[0011] S104: Calculate the corresponding real-time peak factor according to the acquired real-time features, match it with the peak matching interval to obtain a matching result, and determine the specific load cause according to the number of matching results, and generate load cause information;

[0012] S105: Extract dynamic features based on load cause information, and perform verification analysis based on the current rise rate in the obtained dynamic features, and generate verification results, where the verification results include correct verification results and incorrect verification results, and then perform secondary analysis on the incorrect verification results based on the dynamic features to generate specific cause information.

[0013] As a further solution of the present invention: the specific method of performing preprocessing operation on the power data in step S101 to obtain preprocessed data is:

[0014] The power data including voltage and current are acquired, and the voltage and current are their corresponding time series data, and then the acquired power data is filtered to obtain pre-processed data.

[0015] As a further solution of the present invention: the specific method of calculating the peak factor according to the effective current value in step S102 is:

[0016] Obtain preprocessed data, define a time period T, and obtain the corresponding unit time voltage value and unit time current value within the time period T. Then, obtain the corresponding effective value by calculating the square root of the average value of the square of the current or voltage within the time period t. At the same time, obtain the peak value Fmax corresponding to the current signal within the time period T, and calculate the ratio of the peak value to the current effective value to obtain the peak factor.

[0017] As a further solution of the present invention: the specific method of generating the peak matching interval according to the same type of load characteristics in step S103 is:

[0018] The historical data is acquired, and then the load characteristics corresponding to the load types in the historical data are extracted. At the same time, the load types are classified into the same type to obtain the same type of load characteristics, and the peak factors corresponding to the same type of load characteristics are obtained. Then, the same type of peak matching intervals of the same type of load characteristics are generated according to the peak factors.

[0019] As a further solution of the present invention: the specific method of calculating the harmonic value corresponding to the peak matching interval in step S103 is:

[0020] Determine whether there are multiple groups of the same peak factors in the same type of peak matching intervals. If there are multiple groups of the same peak factors, mark the corresponding same type of peak matching intervals and record them as the intervals to be analyzed. If there are no multiple groups of the same peak factors, no processing is performed.

[0021] All the intervals to be analyzed are obtained, and then the amplitudes and phases of the harmonics of the corresponding load types with the same peak factor in the intervals to be analyzed are calculated, and the calculated amplitudes and phases are used as distinguishing features of the corresponding load types.

[0022] As a further solution of the present invention: the specific method of obtaining the matching result in step S104 is:

[0023] Acquire real-time features, calculate corresponding real-time peak factors according to the real-time features, and match the acquired real-time peak factors with the peak matching interval;

[0024] The real-time peak factor is matched with multiple peak matching intervals, and the corresponding peak matching intervals are screened and recorded as marked intervals. Then, the real-time peak factor is matched with the marked intervals, and the load type with the same real-time peak factor is screened and recorded as the load type to be analyzed.

[0025] As a further solution of the present invention: the specific method of determining the specific load reason and generating the load reason information according to the number of matching results in step S104 is:

[0026] When there is only one load type to be analyzed, the corresponding load type to be analyzed is recorded as the standard load, and load reason information is generated at the same time;

[0027] When there are multiple load types to be analyzed, the corresponding harmonic amplitude and phase are calculated according to the real-time characteristics, and the calculated harmonic amplitude and phase are matched with the amplitude and phase of the load type to be analyzed to obtain the specific load type, and the load cause information is generated at the same time.

[0028] As a further solution of the present invention: the specific method of generating the verification result in step S105 is:

[0029] The load type corresponding to the load reason information is analyzed, and the current rise rate corresponding to the load type is calculated, and the calculation method is:

[0030] Use current transformers and data acquisition equipment to sample the current during the load startup process. Suppose the collected current sequence is i(a), where a=1, 2, ..., a-1, and a is the number of sampling points. The corresponding time series is t(a). Then calculate the current difference between two adjacent sampling points Δi(a)=i(a+1)-i(a), and the time interval between two adjacent sampling points Δt(a)=t(a+1)-t(a). Substitute the calculated current difference and time interval into the formula The current rise rate r(n) is calculated;

[0031] Then the calculated current rise rate is compared with the load cause information. If the current rise rate is the same as the current rise rate corresponding to the load cause information, it means that the load identification is correct, and a correct verification result is generated. Otherwise, if the current rise rate is different from the current rise rate corresponding to the load cause information, it means that the load identification is wrong, and a verification error result is generated. Then, the current rise rate is used as the standard to match the specific load and generate specific cause information.

[0032] The present invention provides an intelligent power load identification method. Compared with the prior art, it has the following beneficial effects:

[0033] The present invention performs preprocessing operations on the acquired power data to ensure the accuracy of the subsequent use of the data. Secondly, when extracting and identifying features, the effective values ​​corresponding to the voltage and current are calculated, and the corresponding peak factor is calculated according to the effective value of the current. At the same time, a corresponding identification interval is established according to the peak factor. Subsequently, the peak factor is calculated according to the real-time characteristics and matched with the identification interval. In the matching process, the dynamic characteristics are comprehensively analyzed to identify the power load, thereby improving the overall identification accuracy and avoiding interference of similar features on the identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] For example, see Figure 1 The present application provides an intelligent power load identification method, which specifically includes the following steps.

[0037] Next, in step S101, power data of the power system is collected by various sensors, and preprocessing operations are performed on the power data to obtain preprocessed data.

[0038] First, power data is acquired, where the power data includes the voltage and current corresponding to the power system, and the voltage and current collected here are the corresponding time series data, which are usually collected using sensors (such as current transformers and voltage transformers) at a higher sampling frequency (for example, hundreds to thousands of samples per second). At the same time, the current data reflects the change in the amount of charge passing through the load, and the voltage data represents the potential difference across the circuit. The obtained power data is then preprocessed. The specific preprocessing operation is mainly to filter the power data. Common filters include low-pass filters, high-pass filters, and band-pass filters. For example, a simple moving average filter or a digital filter (such as a Butterworth filter) is used to smooth the data, and the preprocessed data is obtained after further filtering.

[0039] Next, in step S102, corresponding features are extracted from the obtained pre-processed data, the corresponding effective voltage and effective current are calculated based on the time domain features, and the peak factor is calculated according to the effective current value.

[0040] Get the preprocessed data, define the time period T, and get the corresponding unit time voltage value and unit time current value in the time period T. Then, calculate the square root of the average value of the square of the current or voltage in the time period t to get the corresponding effective value. For example, for the current signal i(t), its effective value is The calculation method for the effective value of voltage is similar to the calculation method for the effective value of current. At the same time, the peak value Fmax corresponding to the current signal within the time period T is obtained, and the peak value is calculated by the ratio of the effective value of current to obtain the peak factor. The specific calculation formula is

[0041] Next, in step S103, the historical data is analyzed, and the loads are classified according to the peak factors to obtain the same type of load characteristics, and the peak matching interval is generated according to the same type of load characteristics, and then the harmonic value corresponding to the peak matching interval is calculated.

[0042] First, historical data is acquired, and the historical data here is the historical records corresponding to all load types. Then, the load characteristics corresponding to the load types in the historical data are extracted. The specific load characteristics represent the peak factor. At the same time, the load types are classified into the same type to obtain the same type of load characteristics, and the peak factors corresponding to the same type of load characteristics are obtained. Then, the same type of peak matching intervals of the same type of load characteristics are generated according to the peak factors, and the peak matching intervals here are the peak intervals composed of the minimum and maximum values ​​corresponding to the same type of load characteristics. For incandescent lamps, their current and voltage are almost in phase, assuming that the peak factors measured under different working conditions are between 1.0-1.1; the motor is a typical inductive load, its current lags behind the voltage, and the measured peak factor is about 1.3-1.5; the current of the capacitive load leads the voltage, and its peak factor is in the range of 1.2-1.4.

[0043] Then, the obtained same type of peak matching intervals are analyzed to determine whether there are multiple groups of identical peak factors in the same type of peak matching intervals, and the multiple groups here represent two or more identical peak factors. If there are multiple groups of identical peak factors, the corresponding same type of peak matching intervals will be marked and recorded as the interval to be analyzed. If there are no multiple groups of identical peak factors, no processing will be performed.

[0044] Get all the intervals to be analyzed, and then calculate the amplitude and phase of the harmonics of the load type corresponding to the same peak factor in the interval to be analyzed. The specific calculation method is:

[0045] First, a data acquisition device (such as an oscilloscope, a power analyzer, etc.) is used to sample the voltage or current signal at a certain sampling frequency to obtain a discrete signal sample sequence X(n), where n=0, 1, 2, ..., n-1, and n is the number of sampling points. The collected discrete signal sequence is subjected to FFT calculation, and FFT here represents the Fourier transform method, and the time domain signal is converted into a frequency domain signal to obtain the spectrum X(k) of the corresponding signal (voltage or current signal), where k=0, 1, 2, ..., n-1;

[0046] Calculate the amplitude A of the harmonics based on the obtained spectrum X(k) k and Phase The specific calculation formula is as follows:

[0047] When k = 0, And |X(k)| represents the modulus of the complex number X(k), Where arg represents the argument of the complex number. The specific argument represents the angle between the vector and the positive direction of the real axis. The calculated amplitude and phase are used as the distinguishing features of the corresponding load type. If a discrete signal sequence is transformed by Fourier transform to obtain X(1)=1+i, then The value calculated here is the modulus value, and the amplitude value is further calculated. Phase The calculated amplitude and phase are used as distinguishing features.

[0048] Next, in step S104, the corresponding real-time peak factor is calculated according to the acquired real-time features, and matched with the peak matching interval to obtain a matching result. At the same time, the specific load cause is determined according to the number of matching results, and load cause information is generated.

[0049] Acquire the real-time features, and calculate the corresponding real-time peak factor according to the real-time features. The calculation method here is the same as the calculation method in step S102. At the same time, match the acquired real-time peak factor with the peak matching interval. The specific matching method is: first, match the real-time peak factor with multiple peak matching intervals, screen and obtain the corresponding peak matching interval and record it as the marked interval, then match the real-time peak factor with the marked interval, screen and record the load type with the same real-time peak factor as the load type to be analyzed;

[0050] In a specific embodiment, (for example, the resistive load peak matching interval [1.0, 1.1], the inductive load peak matching interval [1.3, 1.5], the capacitive load peak matching interval [1.2, 1.4]) are compared one by one, and the peak matching intervals that meet the conditions are screened out and marked as marked intervals. Assuming that the calculated real-time peak factor is 1.4, after comparison with the above three intervals, it meets the peak matching interval [1.2, 1.4] of the capacitive load, then this interval is the marked interval. Then further matching is performed in the marked interval. Since the real-time peak factor 1.4 is within the interval of the capacitive load, it can be determined that the load type is a capacitive load.

[0051] Then, the number of load types to be analyzed is judged. When there is only one load type to be analyzed, the corresponding load type to be analyzed is recorded as the standard load, and load reason information is generated. When there are multiple load types to be analyzed, the corresponding harmonic amplitude and phase are calculated according to the real-time characteristics, and the calculated harmonic amplitude and phase are matched with the amplitude and phase of the load type to be analyzed to obtain the specific load type, and load reason information is generated.

[0052] In a specific embodiment, for example, there are resistive loads (the harmonic amplitude may be 0.5 units and the phase is 30 degrees at certain frequencies), inductive loads (the harmonic amplitude is 0.8 units and the phase is 60 degrees at the corresponding frequencies), and capacitive loads (the harmonic amplitude is 0.6 units and the phase is 45 degrees at this frequency). At this time, the harmonic amplitude calculated according to the real-time characteristics is 0.8 units and the phase is 60 degrees. Then, the calculated harmonic amplitude and phase are matched with the amplitudes and phases of these load types to be analyzed. After comparison, it is found that they match the harmonic amplitude and phase of the inductive load, so as to determine that the specific load type is the inductive load, and at the same time generate load cause information, such as "By matching the characteristics of harmonic amplitude of 0.8 units and phase of 60 degrees, the load type is determined to be the inductive load".

[0053] Next, in step S105, dynamic feature extraction is performed according to the load cause information. At the same time, verification analysis is carried out according to the current rise rate in the obtained dynamic features, and a verification result is generated. The verification result includes a correct verification result and an incorrect verification result. Then, a specific cause information is generated by performing a secondary analysis on the incorrect verification result according to the dynamic features.

[0054] Analyze the corresponding load type in the load cause information. It is judged by calculating the current rise rate corresponding to the load type, and the calculation method of the current rise rate is as follows:

[0055] Use a current transformer and data acquisition equipment (such as an oscilloscope, a power data collector, etc.) to sample the current during the load startup process. Let the collected current sequence be i(a), where a = 1, 2,..., a - 1, and a is the number of sampling points. The corresponding time sequence is t(a). Then calculate the current difference between two adjacent sampling points Δi(a) = i(a + 1) - i(a), and the time interval between two adjacent sampling points Δt(a) = t(a + 1) - t(a);

[0056] Substitute the calculated current difference and time interval into the formula Calculate the current rise rate r(n). By analogy, multiple consecutive current rise rate values are calculated. For example, take the average of the current rise rates from a = 0 to a = b - 1, (b < a), that is

[0057] Then the calculated current rise rate is compared with the load reason information. If the current rise rate is the same as the current rise rate corresponding to the load reason information, it means that the load is correctly identified and a correct verification result is generated. Otherwise, if the current rise rate is different from the current rise rate corresponding to the load reason information, it means that the load is incorrectly identified and a verification error result is generated. Then, the current rise rate is used as the standard to match the specific load and generate specific reason information. Here, the screening is performed based on the current rise rate as the standard.

[0058] Embodiment 2, as embodiment 2 of the present invention, differs from embodiment 1 in that in step S105, the current rising rate is calculated by a continuous function approximate calculation method.

[0059] If the function expression i(t) of the current changing with time during the load startup process is known, the current rise rate r(t) is equal to the derivative of the current function i(t) with respect to time t, that is, For example, for a simple exponential current growth model i(t) = I0(1-e -kt ), where I0 is the steady-state current, k is the time constant, and the current rise rate r(t) = kI0e -kt .

[0060] Embodiment 3, as the embodiment 3 of the present invention, focuses on combining the implementation processes of embodiment 1 and embodiment 2 for implementation.

[0061] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0062] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent power load identification method, characterized in that: The method specifically comprises the following steps: S101: collecting power data of the power system through a variety of sensors, and performing preprocessing operations on the power data to obtain preprocessed data; S102: extracting corresponding features from the obtained pre-processed data, calculating the corresponding effective voltage and effective current based on the time domain features, and calculating the peak factor according to the effective current value; S103: Analyze historical data, classify loads according to peak factors to obtain characteristics of loads of the same type, generate peak matching intervals according to the characteristics of loads of the same type, and then calculate harmonic values ​​corresponding to the peak matching intervals; S104: Calculate the corresponding real-time peak factor according to the acquired real-time features, match it with the peak matching interval to obtain a matching result, and determine the specific load cause according to the number of matching results, and generate load cause information; S105: Extract dynamic features based on load cause information, and perform verification analysis based on the current rise rate in the obtained dynamic features, and generate verification results, where the verification results include correct verification results and incorrect verification results, and then perform secondary analysis on the incorrect verification results based on the dynamic features to generate specific cause information.

2. The intelligent power load identification method according to claim 1, characterized in that: The specific method of performing preprocessing operation on the power data in step S101 to obtain preprocessed data is: The power data including voltage and current are acquired, and the voltage and current are their corresponding time series data, and then the acquired power data is filtered to obtain pre-processed data.

3. The intelligent power load identification method according to claim 1, characterized in that: The specific method of calculating the peak factor according to the effective current value in step S102 is: Obtain preprocessed data, define a time period T, and obtain the corresponding unit time voltage value and unit time current value within the time period T. Then, obtain the corresponding effective value by calculating the square root of the average value of the square of the current or voltage within the time period t. At the same time, obtain the peak value Fmax corresponding to the current signal within the time period T, and calculate the ratio of the peak value to the current effective value to obtain the peak factor.

4. The intelligent power load identification method according to claim 1, characterized in that: The specific method of generating the peak matching interval according to the same type of load characteristics in step S103 is: The historical data is acquired, and then the load characteristics corresponding to the load types in the historical data are extracted. At the same time, the load types are classified into the same type to obtain the same type of load characteristics, and the peak factors corresponding to the same type of load characteristics are obtained. Then, the same type of peak matching intervals of the same type of load characteristics are generated according to the peak factors.

5. The intelligent power load identification method according to claim 1, characterized in that: The specific method of calculating the harmonic value corresponding to the peak matching interval in step S103 is: Determine whether there are multiple groups of the same peak factors in the same type of peak matching intervals. If there are multiple groups of the same peak factors, mark the corresponding same type of peak matching intervals and record them as the intervals to be analyzed. If there are no multiple groups of the same peak factors, no processing is performed. All the intervals to be analyzed are obtained, and then the amplitudes and phases of the harmonics of the corresponding load types with the same peak factor in the intervals to be analyzed are calculated, and the calculated amplitudes and phases are used as distinguishing features of the corresponding load types.

6. The intelligent power load identification method according to claim 1, characterized in that: The specific method of obtaining the matching result in step S104 is: Acquire real-time features, calculate corresponding real-time peak factors according to the real-time features, and match the acquired real-time peak factors with the peak matching interval; The real-time peak factor is matched with multiple peak matching intervals, and the corresponding peak matching intervals are screened and recorded as marked intervals. Then, the real-time peak factor is matched with the marked intervals, and the load type with the same real-time peak factor is screened and recorded as the load type to be analyzed.

7. The intelligent power load identification method according to claim 1, characterized in that: The specific method of determining the specific load reason and generating the load reason information according to the number of matching results in step S104 is: When there is only one load type to be analyzed, the corresponding load type to be analyzed is recorded as the standard load, and load reason information is generated at the same time; When there are multiple load types to be analyzed, the corresponding harmonic amplitude and phase are calculated according to the real-time characteristics, and the calculated harmonic amplitude and phase are matched with the amplitude and phase of the load type to be analyzed to obtain the specific load type, and the load cause information is generated at the same time.

8. The intelligent power load identification method according to claim 1, characterized in that: The specific method of generating the verification result in step S105 is: The load type corresponding to the load reason information is analyzed, and the current rise rate corresponding to the load type is calculated, and the calculation method is: Use current transformers and data acquisition equipment to sample the current during the load startup process. Suppose the collected current sequence is i(a), where a=1, 2, ..., a-1, and a is the number of sampling points. The corresponding time series is t(a). Then calculate the current difference between two adjacent sampling points Δi(a)=i(a+1)-i(a), and the time interval between two adjacent sampling points Δt(a)=t(a+1)-t(a). Substitute the calculated current difference and time interval into the formula The current rise rate r(n) is calculated; Then the calculated current rise rate is compared with the load cause information. If the current rise rate is the same as the current rise rate corresponding to the load cause information, it means that the load identification is correct, and a correct verification result is generated. Otherwise, if the current rise rate is different from the current rise rate corresponding to the load cause information, it means that the load identification is wrong, and a verification error result is generated. Then, the current rise rate is used as the standard to match the specific load and generate specific cause information.

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