CN116070107A - Analysis Method of Load Current Waveform and Excitation Characteristics Based on Binary Ride Sequence - Google Patents

Abstract

The invention relates to a load current waveform and excitation characteristic analysis method based on a binary game sequence, which includes collecting power dynamic load signals for preprocessing; extracting the waveform data of the power dynamic load signals, and performing fluctuation characteristic analysis; according to the signal waveform data Establish a dual-feature modulation model of the dynamic load signal, obtain a random function sequence, and extract the amplitude random characteristic parameter and run-length random characteristic parameter of the current signal; analyze the relationship between the filter signal and the amplitude random characteristic parameter of the binary run sequence of the current signal, And based on the current signal waveform data, the amplitude of the current signal and the random characteristic index of the run are compared and analyzed. The present invention can more accurately test the influence of the random fluctuation characteristic of the run length of the actual dynamic load current signal on the dynamic error of the electric energy meter by analyzing the amplitude random fluctuation characteristic and the run length random fluctuation characteristic of the load current signal, and has important practical value for reducing the measurement loss .

Description

Analysis Method of Load Current Waveform and Excitation Characteristics Based on Binary Ride Sequence

technical field

The invention relates to the technical field of smart grids, in particular to a method for analyzing load current waveforms and excitation characteristics based on a binary game sequence.

Background technique

With the introduction of new energy sources and the widespread access of high-power dynamic load equipment to the grid, the smart grid presents the characteristics of diversified energy generation, diversified load components, and complex load characteristics. As an important information collection device in the smart grid measurement system, the operating status of the smart energy meter under complex power consumption conditions, that is, accuracy and reliability, is related to the common interests of power companies and power users. Dynamic load is a special nonlinear electric load, whose power (including active power and reactive power) changes rapidly with the voltage at the end of the load, the frequency of the system and the operating state of the load, especially in AC steelmaking. High-voltage, high-power dynamic load equipment such as electric arc furnaces, electrolytic aluminum, rolling mills, electrified railway traction locomotives, electric vehicle charging stations, electronic computer tomography machines and digital X-ray inspection machines, as well as the combined effects of temperature, humidity and lightning strikes , showing fast random fluctuation characteristics. Compared with traditional electric loads, typical dynamic loads not only have the characteristics of harmonic interference, distortion, and voltage flicker, but also have frequent rapid and large fluctuations in the current amplitude on a small time scale (millisecond level). The load power may reach dozens of times the power under normal working conditions. These typical dynamic loads and complex power consumption conditions make the operating electrical environment of the smart energy meter present complex characteristics, resulting in a large amount of electricity generated by the smart energy meter when metering and charging. The measurement is out of tolerance.

In the research direction of random fluctuation characteristics of dynamic loads, many domestic and foreign researchers in related fields have conducted long-term research and achieved instructive research results, mainly including: analysis of load voltage fluctuations and deviations through short-term voltage data , three-phase voltage imbalance, etc., use high-speed sampling of short-term (second-level) voltage data to analyze the power quality characteristics of grid nonlinear loads such as voltage flicker, swell, sag, and harmonics. Improving power quality is of great significance. So far, the published literature has neither analyzed the nonlinear load current and active power in the transient (second, millisecond level) fast random change characteristics, nor analyzed the millisecond level of the power load current in each power frequency cycle. The random fluctuation characteristics on the time scale, and the influence of this characteristic on the dynamic error of the electric energy of the smart energy meter has not been analyzed. For this reason, in the face of impact loads with different run lengths, the existing verification methods cannot accurately verify the error of the electric energy meter under complex working conditions.

Contents of the invention

For this reason, the technical problem to be solved by the present invention is to overcome the technical defects existing in the prior art, and propose a load current waveform and excitation characteristic analysis method based on the binary game sequence, which can analyze the instantaneous voltage signal of a typical dynamic load On the basis of collecting and preprocessing the instantaneous current signal, analyzing the amplitude random fluctuation characteristics and run length random fluctuation characteristics of the load current signal, it is possible to more accurately test the influence of the run length random fluctuation characteristics of the actual dynamic load current signal on the dynamic error of the electric energy meter. Therefore, timely detection of out-of-tolerance electric energy meters has important practical value in reducing measurement losses.

In order to solve the above-mentioned technical problems, the present invention provides a load current waveform and excitation characteristic analysis method based on binary tour sequence, including:

Collect power dynamic load signals;

Preprocessing the power dynamic load signal;

Extract the current signal waveform data and voltage signal waveform data of the preprocessed power dynamic load signal, and analyze the fluctuation characteristics according to the current signal waveform data and voltage signal waveform data;

A dual-feature modulation model of the dynamic load signal is established according to the current signal waveform data and the voltage signal waveform data, and a random function sequence is obtained based on the dual-feature modulation model of the dynamic load signal;

Extracting an amplitude random characteristic parameter and a run length random characteristic parameter of the current signal based on the random function sequence;

Analyze the relationship between the filtered signal and the amplitude random characteristic parameter of the binary run sequence of the current signal, and compare and analyze the amplitude of the current signal and the random characteristic index of the run length based on the current signal waveform data.

In one embodiment of the present invention, the method for collecting power dynamic load signals includes:

Use a high-speed sampling and storage recorder to collect single-phase instantaneous voltage signals and instantaneous current signals of AC steelmaking electric arc furnaces and electrified railway traction locomotives for a period of time on site.

In one embodiment of the present invention, before performing preprocessing on the electric power dynamic load signal, data format conversion is performed on the electric power dynamic load signal.

In one embodiment of the present invention, the method for preprocessing the power dynamic load signal includes:

The sliding of the window function on the time axis is used to truncate the signal to extract the fundamental wave components of the current signal and the voltage signal.

In one embodiment of the present invention, the method of extracting the current signal waveform data and the voltage signal waveform data of the preprocessed power dynamic load signal, and performing fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data includes:

Extract the fundamental wave and envelope of the dynamic load signal of the electric power, obtain the fundamental voltage envelope waveform diagram and the current fundamental wave envelope waveform diagram of the typical load signal, and analyze the fluctuation and cycle characteristics of the dynamic load voltage and current signal.

In one embodiment of the present invention, the method for obtaining the random function sequence based on the dual-feature modulation model of the dynamic load signal includes:

On each subinterval [nT, (n+1)T], use the rectangular window function g(t-nT) to truncate the dual-feature modulation model of the dynamic load signal to obtain a sequence of random functions, where the rectangular window function g( t-nT) is expressed as:

Among them, t represents a time variable, and the value range of t is t=[0,(L+1)T], L represents the length of the dynamic load signal, and T represents the power frequency period.

In addition, the present invention also provides a load current waveform and excitation characteristic analysis system based on a binary game sequence, including:

A signal acquisition module, the signal acquisition module is used to acquire power dynamic load signals;

A signal preprocessing module, the signal preprocessing module is used to preprocess the electric power dynamic load signal;

A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to extract the current signal waveform data and the voltage signal waveform data of the preprocessed power dynamic load signal, and perform fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data;

A modulation model establishment module, the modulation model establishment module is used to establish a dual-feature modulation model of the dynamic load signal according to the current signal waveform data and the voltage signal waveform data, and obtain a random function sequence based on the dual-feature modulation model of the dynamic load signal;

A random characteristic parameter extraction module, the random characteristic parameter extraction module is used to extract the amplitude random characteristic parameter and the run length random characteristic parameter of the current signal based on the random function sequence;

A run characteristic analysis module, the run characteristic analysis module is used to analyze the relationship between the filtered signal of the binary run sequence of the current signal and the random characteristic parameter of the amplitude, and based on the current signal waveform data, the random characteristic index of the amplitude of the current signal and the run Conduct comparative analysis.

In one embodiment of the present invention, in the signal acquisition module, a high-speed sampling storage recorder is used to collect on-site single-phase instantaneous voltage signals and instantaneous current signals of AC steelmaking electric arc furnaces and electrified railway traction locomotives within a period of time.

In one embodiment of the present invention, before the signal preprocessing module preprocesses the electric power dynamic load signal, data format conversion is performed on the electric power dynamic load signal.

In one embodiment of the present invention, the modulation model building module is also used to obtain a random function sequence based on the dual-feature modulation model of the dynamic load signal, including on each subinterval [nT, (n+1)T], using The rectangular window function g(t-nT) truncates the dual-feature modulation model of the dynamic load signal to obtain a random function sequence, where the rectangular window function g(t-nT) is expressed as:

Among them, t represents a time variable, and the value range of t is t=[0,(L+1)T], L represents the length of the dynamic load signal, and T represents the power frequency cycle.

The above technical solution of the present invention has the following advantages compared with the prior art:

1. A load signal waveform and excitation characteristic analysis method based on a binary game sequence according to the present invention, which analyzes the load current on the basis of collecting and preprocessing the instantaneous voltage signal and instantaneous current signal of a typical dynamic load The amplitude random fluctuation characteristics and run length random fluctuation characteristics of the signal can more accurately test the influence of the run length random fluctuation characteristics of the actual dynamic load current signal on the dynamic error of the electric energy meter, so as to find out-of-tolerance electric energy meters in time, which is very important for reducing measurement losses. Practical value;

2. A load current waveform and excitation characteristic analysis method based on a binary run sequence according to the present invention, which conducts matching research on the relevant characteristics of the excitation signal through the selection of the dynamic load run, and simulates the impact load so that the power source becomes a The linear fluctuating power source provides conditions for the verification of electric energy meters under complex working conditions.

Description of drawings

In order to make the content of the present invention more clearly understood, the present invention will be further described in detail below according to the specific embodiments of the present invention and in conjunction with the accompanying drawings.

Fig. 1 is a schematic flowchart of a load signal waveform and excitation characteristic analysis method based on a binary game sequence according to the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the present invention.

Please refer to FIG. 1 , the embodiment of the present invention provides a load current waveform and excitation characteristic analysis method based on a binary sequence sequence, the method includes the following steps:

Step S1: collecting electric power dynamic load signals;

Step S2: Preprocessing the electric power dynamic load signal;

Step S3: extracting the current signal waveform data and the voltage signal waveform data of the preprocessed power dynamic load signal, and performing fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data;

Step S4: Establish a dual-feature modulation model of the dynamic load signal according to the current signal waveform data and the voltage signal waveform data, and obtain a random function sequence based on the dual-feature modulation model of the dynamic load signal;

Step S5: Extracting amplitude random characteristic parameters and run length random characteristic parameters of the current signal based on the random function sequence;

Step S6: Analyze the relationship between the filtered signal of the binary run sequence of the current signal and the random characteristic parameter of the amplitude, and compare and analyze the amplitude of the current signal and the random characteristic index of the run based on the waveform data of the current signal.

A load signal waveform and excitation characteristic analysis method based on a binary game sequence according to the present invention, which analyzes the load current signal on the basis of collecting and preprocessing the instantaneous voltage signal and instantaneous current signal of a typical dynamic load Random amplitude fluctuation characteristics and run length random fluctuation characteristics can more accurately test the influence of the random fluctuation characteristics of the actual dynamic load current signal on the dynamic error of the electric energy meter, so as to find out-of-tolerance electric energy meters in time, which has important practical value for reducing measurement losses .

Among them, in step S1, take AC steel-making electric arc furnace and electrified railway traction locomotive as an example. These types of loads often have some characteristic random characteristics, which are typical dynamic loads with high power, high voltage, and fast random fluctuation characteristics. They are widely used, consume a lot of electricity, and have a significant impact on the error of the energy meter. Therefore, in this embodiment, under normal working conditions, a high-speed sampling and storage recorder is used to collect single-phase instantaneous voltage signals and instantaneous current signals of AC steelmaking electric arc furnaces and electrified railway traction locomotives within a period of time as the analysis objects of load characteristics. .

The A-phase voltage signal data and current signal data collected on site above are in MEM format, which is not convenient for data processing and load characteristic analysis. It is necessary to convert the data format into .mat format data suitable for data analysis in MATLAB software, in order to facilitate subsequent Random fluctuation characteristic analysis and run length characteristic analysis of dynamic load signal.

Among them, in step S2, the complex and random fluctuation characteristics of the dynamic load signal in the actual power grid make the electric energy meter that has passed the verification in the static environment of the laboratory often have a small error in electric energy measurement under steady-state conditions, while under dynamic conditions, Sometimes there will be a large energy measurement error. The national standard GB/T 14549 stipulates that the total harmonic distortion rate of the voltage at the common connection point of the load shall not exceed 5%. From the perspective of error testing, the cumulative electric energy of the harmonic is relatively small compared to the cumulative electric energy of the fundamental wave. The impact of errors is limited. Under dynamic conditions, the random characteristic of the fundamental wave of the load current signal is still the main factor affecting the measurement error of the electric energy meter. Therefore, preprocessing is carried out on the dynamic load voltage and current signals collected on site, and the amplitude signal of its fundamental component is extracted. Influence of the random fluctuation characteristics of the current signal and the fundamental wave component of the voltage signal on the measurement error of the electric energy meter. The high-power dynamic load signals collected in the field are often time-varying non-stationary random signals, while the traditional method based on Fourier transform to extract the fundamental wave of the signal, its basis function is a complex sine, which cannot reflect the time domain and frequency of the load at the same time. The local information of the domain is not suitable for non-stationary random signals. Therefore, this embodiment adopts the short-time Fourier transform method proposed based on Fourier transform, that is, the signal is truncated by sliding the window function on the time axis to realize the time-frequency positioning function, thereby extracting the load current signal and The fundamental component of a voltage signal.

Wherein, in step S3, for the A-phase instantaneous voltage and instantaneous current signals of AC electric arc furnaces and electrified railway traction locomotives collected on site, the preprocessing method of dynamic load signals is used, and the program is written based on the MATLAB platform for processing and analysis . By analyzing the instantaneous voltage and current signal waveforms, the fundamental envelope voltage and the fundamental envelope current signal waveforms of the dynamic load, the fluctuation and cycle characteristics of the dynamic load voltage and current signals are analyzed. Specifically, by performing fundamental wave extraction and envelope extraction on the signals of AC steelmaking electric arc furnaces and electrified railway traction locomotives collected on site, the voltage and current fundamental wave envelope signal waveforms and current fundamental wave of two typical load signals can be obtained. Envelope waveform diagram. From the perspective of the impact on the dynamic error test of the electric energy meter, the characteristics of the load signal are summarized as follows:

① From the perspective of error testing, the cumulative electric energy of harmonic power accounts for a small proportion of the electric energy accumulated by fundamental wave power, which has little influence on the measurement error of the electric energy meter. Under dynamic conditions, the random characteristic of the fundamental wave of the load current signal is still the main factor affecting the measurement error of the electric energy meter.

②Because people's life and production have certain time rules, during the normal working process of high-power dynamic load equipment such as AC steelmaking electric arc furnace and electrified railway traction locomotive, the load current signal fluctuates rapidly and randomly with time and often presents a certain Cyclic. From the point of view of the error test of the electric energy meter, the periodic component of the load current signal changes slowly with time, which has little influence on the error of the electric energy measurement, and is the secondary factor of the error.

③The fluctuation range of the voltage signal of the dynamic load is small around the per-unit value. From the perspective of energy meter error testing, the small-scale fluctuation of the voltage has little influence on the error. The model of the error test signal can be considered to be approximately steady state signal.

④ The current signal of the dynamic load has a large fluctuation range around the per unit value, and has a significant fast random fluctuation characteristic.

Among them, in steps S4-S5, first establish the dual-feature modulation model and random function sequence of the dynamic load signal; then, extract the amplitude random characteristic parameter and run length random characteristic parameter of the current signal; secondly, use the correlation analysis method to analyze the load current The relationship between the filtered signal and the amplitude characteristic parameters of the binary run sequence of the signal; finally, based on the dynamic load current signal waveform data collected on site, the run characteristic analysis method is used to compare the amplitude of the load current signal with the random characteristic index of the run analyze.

①Double-feature modulation model of load signal

分钟的负荷运行周期内，现场连续高速采集负荷正常工作状态下的三相瞬时电压与瞬时电流信号波形数据。设t为采样时间点，在正实数集上取值，釆集的负荷波形数据的本质是对电网中连续时间随机信号X(t')＝{x(t')}，t’表示连续时间变量，以f_s＝50kHz的釆样率，即Ts＝1/fs＝2μs的釆样时间间隔，釆样而得到的一个离散随机样本序列X(t)中的{x(t)＝x(t)_t＝t'Ts,t'∈R⁺}，t＝t'Ts表示离散时间变量。所以，根据随机过程理论，建立动态负荷信号的离散时间调制模型如下：For typical dynamic loads such as AC steelmaking electric arc furnaces and electrified railway traction locomotives, a high-speed sampling device is used.

During the 1-minute load operation period, the on-site continuous high-speed acquisition of three-phase instantaneous voltage and instantaneous current signal waveform data under the normal working state of the load. Let t be the sampling time point, take a value on the set of positive real numbers, the essence of the collected load waveform data is the continuous time random signal X(t')={x(t')} in the power grid, t' represents the continuous time variable, {x ₍ t)=x( t) _t=t'Ts ,t'∈R ⁺ }, t=t'Ts represents a discrete time variable. Therefore, according to the stochastic process theory, the discrete-time modulation model of the dynamic load signal is established as follows:

②Double-characteristic random function sequence of load signal

Considering that the actual dynamic load signals are all causal signals, the value interval of the time variable t is t=[0,(L+1)T], L∈N, L is the length of the dynamic load signal, and T=0.02s is Power frequency cycle. On each sub-interval [nT, (n+1)T], use the rectangular window function g(t-nT) to truncate the dual-feature modulation model of the dynamic load signal, and the rectangular window function g(t-nT) is expressed as :

Thus, a random function sequence of the dynamic load voltage signal and current signal is obtained. This model is used to further establish the binary run-length function sequence model of the load signal, and extract the amplitude random characteristic parameters and run-length random characteristic parameters of the dynamic load signal.

Among them, in step S6, the correlation analysis of load current signal amplitude and its run length:

①Because the amplitude mean value and amplitude variance reflect the fluctuation of the load current signal amplitude, and the run length mean value and run length variance reflect the fluctuation change of the load current signal amplitude speed, the smaller the run length mean value and the run length variance, the faster the load current fluctuation speed . Therefore, the current signal of AC steelmaking electric arc furnace is reflected as a transient impact load, and the current signal of an electrified railway traction locomotive is reflected as a fast fluctuating load.

②The run length patterns of the two types of dynamic load current signals are dominated by transient run lengths, indicating that both types of loads have obvious impact characteristics and fast random fluctuation characteristics; while the proportion of long-term run lengths of AC steelmaking electric arc furnace current signals is much smaller than that of Electrified railway traction substations show that the former load current signal has stronger impulse characteristics.

③ The difference between the number of positive run lengths and the number of negative run lengths of the two types of dynamic load current signals is 1 or 0; at the same time, as the data length increases sufficiently, the probability of occurrence of the run length elements "+1" and "-1" are equal, indicating that the binary game sequence of the amplitude of the dynamic load current signal is balanced.

④ The smaller the run length fluctuation range, the greater the total number of run lengths within a certain signal length, and the faster the fluctuation speed of the load current signal amplitude, further verifying that the former load current signal has stronger impact characteristics.

In summary, the present invention relates to the following technical contents:

(1) Through the digital characteristic analysis of the run length of the dynamic load current signal, the correlation characteristics between the run length of the dynamic load current signal and the fast random fluctuation of the load are studied;

(2) Through the analysis of run-length modal characteristics and run-length random fluctuation characteristics of dynamic load current signal, the binary run sequence of dynamic load current signal amplitude is studied;

(3) Study the asymptotic Gaussian distribution characteristics and binary autocorrelation characteristics of the amplitude of the dynamic load current signal and its run length through the analysis of the amplitude characteristic function and run length characteristic function of the dynamic load current signal;

(4) Through the selection of the dynamic load run, it is classified according to the duration of the fluctuation, including transient run length (1-5 cycles), short-term run length (6-64 cycles) and long-term run length (65-500 period) and other typical run-length fluctuation modes, carry out matching research on the relevant characteristics of the excitation signal, simulate the impact load to make the power source a nonlinear fluctuating power source, and provide conditions for the verification of the electric energy meter under complex working conditions.

The following is an introduction to a load current waveform and excitation characteristic analysis system based on a binary game sequence disclosed in the embodiment of the present invention. A load current waveform and excitation characteristic analysis system based on a binary game sequence described below is the same as the above The described analysis method of load current waveform and excitation characteristics based on binary game sequence can be referred to each other.

The present invention also provides a load current waveform and excitation characteristic analysis system based on a binary game sequence, including:

A signal acquisition module, the signal acquisition module is used to acquire power dynamic load signals;

A signal preprocessing module, the signal preprocessing module is used to preprocess the electric power dynamic load signal;

A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to extract the current signal waveform data and the voltage signal waveform data of the preprocessed power dynamic load signal, and perform fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data;

A modulation model establishment module, the modulation model establishment module is used to establish a dual-feature modulation model of the dynamic load signal according to the current signal waveform data and the voltage signal waveform data, and obtain a random function sequence based on the dual-feature modulation model of the dynamic load signal;

A random characteristic parameter extraction module, the random characteristic parameter extraction module is used to extract the amplitude random characteristic parameter and the run length random characteristic parameter of the current signal based on the random function sequence;

A run characteristic analysis module, the run characteristic analysis module is used to analyze the relationship between the filtered signal of the binary run sequence of the current signal and the random characteristic parameter of the amplitude, and based on the current signal waveform data, the random characteristic index of the amplitude of the current signal and the run Conduct comparative analysis.

A load signal waveform and excitation characteristic analysis system based on a binary game sequence according to the present invention, which analyzes the load current signal on the basis of collecting and preprocessing the instantaneous voltage signal and instantaneous current signal of a typical dynamic load Random amplitude fluctuation characteristics and run length random fluctuation characteristics can more accurately test the influence of the random fluctuation characteristics of the actual dynamic load current signal on the dynamic error of the electric energy meter, so as to find out-of-tolerance electric energy meters in time, which has important practical value for reducing measurement losses .

In one embodiment of the present invention, in the signal acquisition module, a high-speed sampling storage recorder is used to collect on-site single-phase instantaneous voltage signals and instantaneous current signals of AC steelmaking electric arc furnaces and electrified railway traction locomotives within a period of time.

In one embodiment of the present invention, before the signal preprocessing module preprocesses the electric power dynamic load signal, data format conversion is performed on the electric power dynamic load signal.

In one embodiment of the present invention, the modulation model building module is also used to obtain a random function sequence based on the dual-feature modulation model of the dynamic load signal, including on each subinterval [nT, (n+1)T], using The rectangular window function g(t-nT) truncates the dual-feature modulation model of the dynamic load signal to obtain a random function sequence, where the rectangular window function g(t-nT) is expressed as:

Among them, t represents a time variable, and the value range of t is t=[0,(L+1)T], L represents the length of the dynamic load signal, and T represents the power frequency cycle.

A load current waveform and excitation characteristic analysis system based on a binary run sequence according to the present invention, which conducts matching research on the relevant characteristics of the excitation signal through the selection of the dynamic load run, and simulates the impact load so that the power source becomes a nonlinear fluctuation The power source provides conditions for the verification of electric energy meters under complex working conditions.

The load current waveform and excitation characteristic analysis system based on the binary sequence sequence of this embodiment is used to realize the aforementioned load current waveform and excitation characteristic analysis method based on the binary sequence sequence, so the specific implementation of the system can be seen in the foregoing The embodiment part of the load current waveform and excitation characteristic analysis method based on the binary game sequence, so its specific implementation can refer to the descriptions of the corresponding embodiments of each part, and will not be introduced here.

In addition, because the load current waveform and excitation characteristic analysis system based on the binary game sequence of this embodiment is used to realize the aforementioned load current waveform and excitation characteristic analysis method based on the binary game sequence, its function is similar to that of the above method. Corresponding functions, no more details here.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

Apparently, the above-mentioned embodiments are only examples for clear description, and are not intended to limit the implementation. For those of ordinary skill in the art, on the basis of the above description, other changes or changes in various forms can also be made. It is not necessary and impossible to exhaustively list all the implementation manners here. However, the obvious changes or changes derived therefrom are still within the scope of protection of the present invention.

Claims (10)

1. A load current waveform and excitation characteristic analysis method based on a binary run sequence is characterized in that: comprising the following steps:

collecting an electric dynamic load signal;

preprocessing an electric dynamic load signal;

extracting current signal waveform data and voltage signal waveform data of the preprocessed power dynamic load signal, and carrying out fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data;

establishing a dual-characteristic modulation model of a dynamic load signal according to current signal waveform data and voltage signal waveform data, and obtaining a random function sequence based on the dual-characteristic modulation model of the dynamic load signal;

extracting amplitude random characteristic parameters and run random characteristic parameters of the current signal based on the random function sequence;

and analyzing the relation between the filtered signal of the binary run sequence of the current signal and the random characteristic parameter of the amplitude, and comparing and analyzing the amplitude of the current signal and the random characteristic index of the run based on the waveform data of the current signal.

2. The method for analyzing load current waveform and excitation characteristics based on binary run-length sequence according to claim 1, wherein: the method for collecting the power dynamic load signal comprises the following steps:

the method comprises the steps of using a high-speed sampling storage recorder, and collecting single-phase instantaneous voltage signals and instantaneous current signals of an alternating-current steelmaking electric arc furnace and an electrified railway traction locomotive in a field.

3. The method for analyzing load current waveform and excitation characteristics based on binary run-length sequence according to claim 1, wherein: before preprocessing the electric dynamic load signal, the electric dynamic load signal is subjected to data format conversion.

4. A method of analysis of load current waveforms and excitation characteristics based on binary run-length sequences according to claim 1 or 3, characterized in that: the method for preprocessing the electric dynamic load signal comprises the following steps:

the signal is truncated using sliding of the window function on the time axis to extract the fundamental components of the current signal and the voltage signal.

5. The method for analyzing load current waveform and excitation characteristics based on binary run-length sequence according to claim 1, wherein: the method for extracting the current signal waveform data and the voltage signal waveform data of the preprocessed power dynamic load signal and carrying out fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data comprises the following steps:

and carrying out fundamental wave extraction and envelope extraction on the dynamic load signal of the electric power to obtain a voltage fundamental wave envelope waveform diagram and a current fundamental wave envelope waveform diagram of a typical load signal, and analyzing the fluctuation and the cycle characteristics of the dynamic load voltage and the current signal.

6. The method for analyzing load current waveform and excitation characteristics based on binary run-length sequences according to claim 1 or 5, wherein: the method for obtaining the random function sequence based on the dual-feature modulation model of the dynamic load signal comprises the following steps:

on each subinterval [ nT, (n+1) T ], a dual-feature modulation model of the dynamic load signal is truncated by adopting a rectangular window function g (T-nT), so as to obtain a random function sequence, wherein the rectangular window function g (T-nT) is expressed as:

wherein T represents a time variable, the value interval of T is t= [0, (L+1) T ], L represents the length of a dynamic load signal, and T represents a power frequency period.

7. A system for analyzing load current waveform and excitation characteristics based on binary run-length sequences, characterized in that: comprising the following steps:

the signal acquisition module is used for acquiring an electric dynamic load signal;

the signal preprocessing module is used for preprocessing the dynamic power load signal;

the fluctuation characteristic analysis module is used for extracting current signal waveform data and voltage signal waveform data of the preprocessed power dynamic load signal and carrying out fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data;

the modulation model building module is used for building a dual-characteristic modulation model of the dynamic load signal according to the current signal waveform data and the voltage signal waveform data, and obtaining a random function sequence based on the dual-characteristic modulation model of the dynamic load signal;

the random characteristic parameter extraction module is used for extracting amplitude random characteristic parameters and run random characteristic parameters of the current signal based on the random function sequence;

the run characteristic analysis module is used for analyzing the relation between the filtered signal of the binary run sequence of the current signal and the random characteristic parameter of the amplitude, and comparing and analyzing the amplitude of the current signal and the random characteristic index of the run based on the waveform data of the current signal.

8. The binary run-length based load current waveform and excitation characteristics analysis system of claim 7, wherein: in the signal acquisition module, a high-speed sampling storage recorder is used, and a single-phase instantaneous voltage signal and an instantaneous current signal of an alternating-current steelmaking electric arc furnace and an electrified railway traction locomotive are collected in the field for a period of time.

9. The binary run-length based load current waveform and excitation characteristics analysis system of claim 7, wherein: and before the signal preprocessing module preprocesses the electric dynamic load signal, carrying out data format conversion on the electric dynamic load signal.

10. The binary run-length based load current waveform and excitation characteristics analysis system of claim 7, wherein: the modulation model building module is further configured to obtain a random function sequence based on a dual-feature modulation model of the dynamic load signal, and includes, on each subinterval [ nT, (n+1) T ], truncating the dual-feature modulation model of the dynamic load signal by using a rectangular window function g (T-nT), to obtain the random function sequence, where the rectangular window function g (T-nT) is expressed as:

wherein T represents a time variable, the value interval of T is t= [0, (L+1) T ], L represents the length of a dynamic load signal, and T represents a power frequency period.

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