Evaluation method of the impact of complex power consumption conditions on the operating status of smart energy meters

Abstract

The present invention relates to an evaluation method for the influence of complex power conditions on the operation state of a smart electric energy meter, including collecting a power dynamic load signal, preprocessing the power dynamic load signal; extracting current signal waveform data and voltage signal waveform data of the preprocessed power dynamic load signal; performing fluctuation characteristic analysis according to the current signal waveform data and the voltage signal waveform data; and evaluating the influence level of the large-amplitude fluctuation characteristic of the dynamic load on the operation state of the smart electric energy meter. The present invention comprehensively evaluates the influence of complex power conditions (dynamic load rapid large-amplitude fluctuation characteristics (stroke length characteristics), climate and environmental characteristics) on the operation state of the smart electric energy meter, and through the state evaluation of the operation reliability of the smart electric energy meter, the severity of the functional failure of the smart electric energy meter and the development trend of the failure can be predicted, thereby making it possible to arrange a maintenance plan in advance and in a targeted manner to avoid more losses in time, providing guidance for the state inspection of the smart electric energy meter.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a method for evaluating the impact of complex power usage conditions on the operating state of a smart electric energy meter. Background Art

[0002] With the introduction of new energy and the widespread access of high-power dynamic load equipment to the power grid, smart grids present 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 smart energy meters under complex power conditions, that is, accuracy and reliability, is related to the common interests of power companies and power users. Dynamic load is a special nonlinear power load, and its power (including active power and reactive power) changes rapidly with the voltage at the load endpoint, the frequency of the system, and the operating status of the load. In particular, high-voltage and high-power dynamic load equipment such as AC steelmaking arc furnaces, electrolytic aluminum, rolling mills, electrified railway traction locomotives, electric vehicle charging stations, electronic computer tomography scanners and digital X-ray inspection machines, as well as the combined effects of temperature, humidity and lightning strikes, present rapid random fluctuation characteristics. Compared with traditional power loads, typical dynamic loads not only have characteristics such as harmonic interference, distortion and voltage flicker, but their current amplitude also frequently fluctuates rapidly and greatly on a small time scale (millisecond level). The load power at the peak may reach dozens of times the power under normal operating conditions. The complex power consumption conditions of these typical dynamic loads make the operating electrical environment of smart electricity meters complex, resulting in large measurement errors when smart electricity meters are used for metering and charging.

[0003] In the research direction of dynamic load random fluctuation characteristics, many domestic and foreign researchers in related fields have conducted long-term research and achieved guiding research results, mainly including: using short-term voltage data to analyze the dynamic load characteristics caused by load voltage fluctuation and deviation, three-phase voltage imbalance, etc., and using short-term (second-level) voltage data with high-speed sampling to analyze the power quality characteristics of nonlinear loads such as voltage flicker, swell, sag and harmonics, which is of great significance to improving power quality. So far, the published literature has neither analyzed the rapid random change characteristics of nonlinear load current and active power in transient (second-level, millisecond-level), nor analyzed the random fluctuation characteristics of power load current on the millisecond-level small time scale of each power frequency cycle, nor analyzed the influence of this characteristic on the dynamic error of smart electric energy meter. For this reason, facing the impact loads with different run lengths, the existing verification methods cannot accurately verify the error of electric energy meters under complex working conditions. Summary of the invention

[0004] To this end, the technical problem to be solved by the present invention is to overcome the technical defects existing in the prior art, and propose an evaluation method for the impact of complex power consumption conditions on the operating state of smart electric energy meters, which comprehensively evaluates the impact of complex power consumption conditions (dynamic load rapid and large fluctuation characteristics (stroke length characteristics), climate and environmental characteristics) on the operating state of smart electric energy meters. By evaluating the operating reliability of smart electric energy meters, the severity of functional failures of smart electric energy meters and the development trend of failures can be predicted, thereby arranging maintenance plans in advance and in a targeted manner to avoid more losses in time, providing guidance for the status inspection work of smart electric energy meters.

[0005] In order to solve the above technical problems, the present invention provides a method for evaluating the impact of complex power consumption conditions on the operating state of a smart electric energy meter, comprising:

[0006] Collect power dynamic load signals and pre-process the power dynamic load signals;

[0007] Extracting current signal waveform data and voltage signal waveform data of the preprocessed power dynamic load signal;

[0008] Perform fluctuation characteristic analysis based on current signal waveform data and voltage signal waveform data;

[0009] Evaluate the impact level of large fluctuation characteristics of dynamic load on the operating status of smart electric energy meters.

[0010] In one embodiment of the present invention, the method for collecting power dynamic load signals includes:

[0011] A high-speed sampling storage recorder is used to collect single-phase instantaneous voltage and current signals of an AC steelmaking arc furnace and an electrified railway traction locomotive over a period of time.

[0012] In one embodiment of the present invention, the method for preprocessing the power dynamic load signal includes:

[0013] The signal is truncated by sliding the window function on the time axis to extract the fundamental components of the current signal and the voltage signal.

[0014] In one embodiment of the present invention, a method for extracting current signal waveform data and voltage signal waveform data of a preprocessed power dynamic load signal includes:

[0015] The fundamental wave and envelope extraction of the power dynamic load signal are performed to obtain the voltage fundamental wave envelope waveform and current fundamental wave envelope waveform of the typical load signal, and analyze the volatility and cyclic characteristics of the dynamic load voltage and current signals.

[0016] In one embodiment of the present invention, a method for evaluating the impact level of dynamic load large-amplitude fluctuation characteristics on the operating state of a smart electric energy meter includes:

[0017] The evaluation index system for the evaluated object is established as follows:

[0018] .

[0019] In one embodiment of the present invention, a method for evaluating the impact level of dynamic load large-amplitude fluctuation characteristics on the operating state of a smart electric energy meter includes:

[0020] The evaluation level set is established as follows:

[0021] .

[0022] In addition, the present invention also provides an evaluation system for the impact of complex power consumption conditions on the operating state of a smart electric energy meter, comprising:

[0023] A signal acquisition preprocessing module, wherein the signal acquisition preprocessing module is used to acquire power dynamic load signals and preprocess the power dynamic load signals;

[0024] A waveform feature extraction module, the waveform feature extraction module is used to extract current signal waveform data and voltage signal waveform data of the pre-processed power dynamic load signal;

[0025] A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to perform fluctuation characteristic analysis based on the current signal waveform data and the voltage signal waveform data;

[0026] The grade evaluation module is used to evaluate the impact level of the large-scale fluctuation characteristics of the dynamic load on the operating state of the smart electric energy meter.

[0027] In one embodiment of the present invention, in the signal acquisition module, a high-speed sampling storage recorder is used to collect single-phase instantaneous voltage signals and instantaneous current signals of an AC steelmaking arc furnace and an electrified railway traction locomotive over a period of time on site.

[0028] In one embodiment of the present invention, in the grade evaluation module, an evaluation index system for the evaluated object is established as follows:

[0029] .

[0030] In one embodiment of the present invention, in the grade evaluation module, the evaluation grade set is established as follows:

[0031] .

[0032] The above technical solution of the present invention has the following advantages compared with the prior art:

[0033] The present invention discloses a method for evaluating the influence of complex power consumption conditions on the operating state of a smart electric energy meter, which comprehensively evaluates the influence of complex power consumption conditions (dynamic load rapid and large amplitude fluctuation characteristics (stroke length characteristics), climate and environmental characteristics) on the operating state of a smart electric energy meter. By evaluating the operating reliability of the smart electric energy meter through the status, the severity of the functional failure of the smart electric energy meter and the development trend of the failure can be predicted, thereby making it possible to arrange maintenance plans in advance and in a targeted manner to avoid more losses in time, thus providing guidance for the status inspection of the smart electric energy meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0035] Figure 1 It is a flow chart of a method for evaluating the influence of complex power usage conditions on the operating state of a smart electric energy meter according to the present invention. DETAILED DESCRIPTION

[0036] The present invention is 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 embodiments are not intended to limit the present invention.

[0037] Please refer to Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the impact of complex power consumption conditions on the operating state of a smart electric energy meter, the method comprising the following steps:

[0038] Step S1: collecting power dynamic load signals and preprocessing the power dynamic load signals;

[0039] Step S2: extracting the current signal waveform data and the voltage signal waveform data of the pre-processed power dynamic load signal;

[0040] Step S3: performing fluctuation characteristic analysis based on the current signal waveform data and the voltage signal waveform data;

[0041] Step S4: Evaluate the impact level of the large-scale fluctuation characteristics of the dynamic load on the operating state of the smart electric energy meter.

[0042] The present invention discloses a method for evaluating the influence of complex power consumption conditions on the operating state of a smart electric energy meter, which comprehensively evaluates the influence of complex power consumption conditions (dynamic load rapid and large amplitude fluctuation characteristics (stroke length characteristics), climate and environmental characteristics) on the operating state of a smart electric energy meter. By evaluating the operating reliability of the smart electric energy meter through the status, the severity of the functional failure of the smart electric energy meter and the development trend of the failure can be predicted, thereby making it possible to arrange maintenance plans in advance and in a targeted manner to avoid more losses in time, thus providing guidance for the status inspection of the smart electric energy meter.

[0043] Among them, in step S1, taking the AC steelmaking arc furnace and the electrified railway traction locomotive as examples, both are typical dynamic load signals in the power grid that are closely related to production and life. Due to the working cycle of the load equipment and the regularity of production activities, this type of load often has certain characteristics of randomness, and both 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 electric energy meter. Therefore, in the normal working state, this embodiment uses a high-speed sampling storage recorder to collect the single-phase instantaneous voltage signal and instantaneous current signal of the AC steelmaking arc furnace and the electrified railway traction locomotive on site within a period of time as the analysis object of the load characteristics.

[0044] The A-phase voltage signal data and current signal data collected on site are in MEM format, which is not convenient for data processing and load characteristic analysis. The data format needs to be converted into .mat format data suitable for data analysis in MATLAB software to facilitate the subsequent dynamic load signal random fluctuation characteristic analysis and run characteristic analysis.

[0045] Among them, in step S1, the complex random fluctuation characteristics of the dynamic load signal in the actual power grid make the electric energy meter that has been calibrated in the static environment of the laboratory often have a small electric energy measurement error under steady-state conditions, but sometimes produce a large electric energy measurement error under dynamic conditions. 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 harmonics accounts for a very small proportion of the cumulative electric energy of the fundamental wave, and has limited impact on the measurement error of the electric energy meter. Under dynamic conditions, the random characteristics of the fundamental wave of the load current signal are still the main factors affecting the measurement error of the electric energy meter. Therefore, the dynamic load voltage and current signals collected on site are preprocessed to extract the amplitude signal of the fundamental wave component. Its role is to ignore the secondary influence of the load signal harmonics on the measurement error of the electric energy meter, so as to focus on analyzing the influence of the random fluctuation characteristics of the fundamental wave component of the dynamic load current signal and voltage signal on the measurement error of the electric energy meter. The high-power dynamic load signals collected on site are often time-varying non-stationary random signals, and the traditional method of extracting the fundamental wave of the signal based on Fourier transform, whose basis function is a complex sine, cannot simultaneously reflect the local information of the load in the time domain and frequency domain, and 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 shortened by sliding the window function on the time axis to realize the time-frequency positioning function, thereby extracting the fundamental wave components of the load current signal and voltage signal.

[0046] Among them, in steps S2 and S3, the instantaneous voltage and instantaneous current signals of phase A of the AC arc furnace and the electrified railway traction locomotive collected on site are processed and analyzed by using the preprocessing method of dynamic load signals and writing programs based on the MATLAB platform. By analyzing the instantaneous voltage and instantaneous current signal waveforms, fundamental envelope voltage and fundamental envelope current signal waveforms of the dynamic load, the volatility and cyclic characteristics of the dynamic load voltage and current signals are analyzed. Specifically, by performing fundamental wave extraction and envelope extraction on the signals of the AC steelmaking arc furnace and the electrified railway traction locomotive collected on site, the voltage and current fundamental wave envelope signal waveforms and current fundamental wave envelope waveforms of two types of typical load signals can be obtained. 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:

[0047] ① From the perspective of error testing, the proportion of electric energy accumulated by harmonic power is very small compared to the electric energy accumulated by fundamental power, and has little impact on the metering error of the energy meter. Under dynamic conditions, the random characteristics of the fundamental wave of the load current signal are still the main factor affecting the metering error of the energy meter.

[0048] ②Since people's lives and production have certain time rules, during the normal operation of high-power dynamic load equipment such as AC steelmaking arc furnaces and electrified railway traction locomotives, the load current signal fluctuates rapidly and randomly over time and often shows a certain periodicity. From the perspective of the error test of the energy meter, the periodic component of the load current signal changes slowly over time, has little effect on the error of energy metering, and is a minor factor of the error.

[0049] ③ The voltage signal of the dynamic load has a small fluctuation range near the per-unit value. From the perspective of the energy meter error test, the small range of voltage fluctuation has little effect on the error. By optimizing the model of the error test signal, the voltage signal can be considered to be approximately a steady-state signal.

[0050] ④ The current signal of the dynamic load has a large fluctuation range near the per-unit value and has a significant fast random fluctuation characteristic.

[0051] Among them, in step S4, according to the current reliability of the operation of the smart energy meter, its operation status can be divided into the following four levels, assuming that the operation index error is φ:

[0052] 1) Normal state: The error of each operating indicator of the smart energy meter is below 1φ, and it is in a state of normal operation, stable performance and normal function;

[0053] 2) Attention status: If the error of one or more operating indicators of the smart energy meter is within the range of (2-5)φ, the smart energy meter is in a relatively normal operation, relatively stable performance and relatively intact function state, and can continue to operate. At this time, it is necessary to ask the staff to strengthen the monitoring of the operation of the smart energy meter;

[0054] 3) Abnormal state: The error of one or more operating indicators of the smart energy meter is within the range of (5-15)φ. In this state, the operating indicator error of the smart energy meter has exceeded the standard error range, the working performance has been reduced, and the function has been slightly damaged. It is necessary to arrange professional personnel to monitor the operation and troubleshoot the smart energy meter;

[0055] 4) Serious status: The error of one or more operating indicators of the smart energy meter exceeds the range of 15φ. In this state, the smart energy meter can no longer work normally, the performance is significantly reduced, the function is lost, or even permanently fails. It is necessary to arrange for staff to replace the energy meter immediately.

[0056] In view of the impact of complex power consumption conditions on the operating status of smart meters, the following analysis model is established:

[0057] Establish an evaluation index system for the evaluated object:

[0058] In complex power consumption conditions, the main influencing factors include the typical dynamic run length fluctuation characteristics and typical climate and environmental characteristics. The index system for constructing the operating status of smart energy meters is:

[0059] .

[0060] Create a rating set:

[0061] The evaluation level of the operation status of the smart energy meter adopts 4 levels, that is, the evaluation level set is:

[0062] .

[0063] According to the corresponding evaluation of U and V, simulation analysis is performed, and the impact of multi-element impact load on the error range of electric energy metering is shown in Table 1 below:

[0064] Table 1

[0065]

[0066] The following is an introduction to an evaluation system for the impact of a complex power consumption condition on the operating state of a smart power meter disclosed in an embodiment of the present invention. The evaluation system for the impact of a complex power consumption condition on the operating state of a smart power meter described below and the evaluation method for the impact of a complex power consumption condition on the operating state of a smart power meter described above can be referenced to each other.

[0067] The present invention also provides an evaluation system for the impact of complex power consumption conditions on the operating state of a smart electric energy meter, comprising:

[0068] A signal acquisition preprocessing module, wherein the signal acquisition preprocessing module is used to acquire power dynamic load signals and preprocess the power dynamic load signals;

[0069] A waveform feature extraction module, the waveform feature extraction module is used to extract current signal waveform data and voltage signal waveform data of the pre-processed power dynamic load signal;

[0070] A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to perform fluctuation characteristic analysis based on the current signal waveform data and the voltage signal waveform data;

[0071] The grade evaluation module is used to evaluate the impact level of the large-scale fluctuation characteristics of the dynamic load on the operating state of the smart electric energy meter.

[0072] In one embodiment of the present invention, in the signal acquisition module, a high-speed sampling storage recorder is used to collect single-phase instantaneous voltage signals and instantaneous current signals of an AC steelmaking arc furnace and an electrified railway traction locomotive over a period of time on site.

[0073] In one embodiment of the present invention, in the grade evaluation module, an evaluation index system for the evaluated object is established as follows:

[0074] .

[0075] In one embodiment of the present invention, in the grade evaluation module, the evaluation grade set is established as follows:

[0076] .

[0077] The above technical solution of the present invention has the following advantages compared with the prior art:

[0078] The evaluation system for the influence of complex power consumption conditions on the operating state of smart electric energy meters described in the present invention comprehensively evaluates the influence of complex power consumption conditions (dynamic load rapid and large amplitude fluctuation characteristics (stroke length characteristics), climate and environmental characteristics) on the operating state of smart electric energy meters. By evaluating the operating reliability of smart electric energy meters through status, the severity of functional failures of smart electric energy meters and the development trend of failures can be predicted, thereby making it possible to arrange maintenance plans in advance and in a targeted manner to avoid more losses in time, thus providing guidance for the status inspection work of smart electric energy meters.

[0079] The evaluation system for the influence of complex power consumption conditions on the operating state of smart power meters in this embodiment is used to implement the aforementioned evaluation method for the influence of complex power consumption conditions on the operating state of smart power meters. Therefore, the specific implementation method of the system can be seen in the embodiment part of the evaluation method for the influence of complex power consumption conditions on the operating state of smart power meters in the previous text. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part, which will not be elaborated here.

[0080] In addition, since the evaluation system for the impact of complex power consumption conditions on the operating status of smart power meters in this embodiment is used to implement the aforementioned evaluation method for the impact of complex power consumption conditions on the operating status of smart power meters, its function corresponds to that of the aforementioned method and will not be repeated here.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0085] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for evaluating the impact of complex power consumption conditions on the operating state of a smart energy meter, characterized in that: include: Collect power dynamic load signals and pre-process the power dynamic load signals, including: The signal is shortened by sliding the window function on the time axis to extract the fundamental components of the current signal and the voltage signal; Extracting the current signal waveform data and the voltage signal waveform data of the pre-processed power dynamic load signal, including: Perform fundamental wave extraction and envelope extraction on the power dynamic load signal to obtain the voltage fundamental wave envelope waveform and current fundamental wave envelope waveform of the typical load signal, and analyze the volatility and cyclic characteristics of the dynamic load voltage and current signals; Perform fluctuation characteristic analysis based on current signal waveform data and voltage signal waveform data; Evaluate the impact of large fluctuations in dynamic load on the operating status of smart energy meters; Methods for evaluating the impact of large fluctuations in dynamic load on the operating status of smart electric energy meters include: The evaluation index system for the evaluated object is established as follows: ； Methods for evaluating the impact of large fluctuations in dynamic load on the operating status of smart electric energy meters include: The evaluation level set is established as follows: 。 2. The method for evaluating the impact of complex power consumption conditions on the operating state of a smart electric energy meter according to claim 1 is characterized in that: Methods for collecting power dynamic load signals include: A high-speed sampling storage recorder is used to collect single-phase instantaneous voltage and current signals of an AC steelmaking arc furnace and an electrified railway traction locomotive over a period of time.

3. An evaluation system for the impact of complex power consumption conditions on the operating status of smart electric energy meters, characterized by: The system is used to implement the evaluation method of the influence of complex power consumption conditions on the operating state of the smart electric energy meter as described in any one of claims 1 to 2, comprising: A signal acquisition preprocessing module, wherein the signal acquisition preprocessing module is used to acquire power dynamic load signals and preprocess the power dynamic load signals; A waveform feature extraction module, the waveform feature extraction module is used to extract current signal waveform data and voltage signal waveform data of the pre-processed power dynamic load signal; A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to perform fluctuation characteristic analysis based on the current signal waveform data and the voltage signal waveform data; The grade evaluation module is used to evaluate the impact level of the large-scale fluctuation characteristics of the dynamic load on the operating state of the smart electric energy meter.

4. The evaluation system for the impact of complex power consumption conditions on the operating state of smart electric energy meters according to claim 3 is characterized by: In the signal acquisition module, a high-speed sampling storage recorder is used to collect single-phase instantaneous voltage signals and instantaneous current signals of the AC steelmaking arc furnace and the electrified railway traction locomotive over a period of time on site.

5. The evaluation system for the impact of complex power consumption conditions on the operating state of smart electric energy meters according to claim 3 is characterized by: In the grade evaluation module, the evaluation index system of the evaluated object is established as follows: 。 6. The evaluation system for the impact of complex power consumption conditions on the operating state of smart electric energy meters according to claim 5 is characterized by: In the grade evaluation module, the evaluation grade set is established as follows: 。

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