Harmonic analysis early warning method and system based on ammeter real-time monitoring data

Abstract

The invention discloses a harmonic analysis early warning method and system based on real-time monitoring data of an electric meter, and relates to the technical field of electric meter detection.When the system operates, a harmonic resonance problem possibly occurring in an electric power system can be captured by constructing a harmonic detection data vector NEHMDV, and the harmonic resonance problem is processed by a data preprocessing module, so that the real-time monitoring data of the electric meter is obtained. According to the method, the accuracy and consistency of data are ensured, a harmonic risk calculation module further calculates a real-time harmonic resonance frequency Rf (t) and a harmonic resonance risk Pr (t), the real-time harmonic resonance frequency Rf (t) and the harmonic resonance risk Pr (t) are integrated into a risk factor vector EHRFV (t), based on this, a harmonic risk assessment module can quickly fit a harmonic risk assessment index EHRAI (t), and the harmonic risk assessment index EHRAI (t) is matched with a preset harmonic risk assessment threshold XZ, so that the harmonic risk assessment accuracy is improved. And a targeted risk assessment matching strategy is generated, so that the potential harmonic risk is effectively prevented, and serious consequences such as equipment damage and energy waste caused by the harmonic problem are avoided as much as possible.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric meter detection, in particular to a harmonic analysis and early warning method and system based on real-time monitoring data of electric meters. BACKGROUND

[0002] The power system is the infrastructure of modern society, covering multiple key areas such as power generation, transmission, and distribution. In these areas, power quality management is an important part of ensuring the stability and efficiency of the power system. With the increasing complexity of the power system, especially in long transmission lines and large-scale industrial power grids, harmonic phenomena have become one of the main problems affecting power quality. Harmonics not only cause the efficiency of power equipment to decline, but also can cause more serious system failures. In the management of power system harmonics, harmonic resonance is a special and potentially dangerous phenomenon, especially in long-distance transmission lines or large-scale industrial power grids.

[0003] The invention disclosed in Chinese Patent Publication No. CN202210166168.4, a harmonic analysis method and system based on real-time monitoring data of electric meters, obtains real-time monitoring data through intelligent electric meter acquisition, and sends the real-time monitoring data to the harmonic analysis system; pre-process the real-time monitoring data, filter the interference signals in the real-time monitoring data acquisition process, and obtain the processed monitoring data; classify the processed monitoring data based on the pre-set classification requirements to obtain a monitoring data classification set; respectively apply Fourier transform operation to all monitoring data sets to obtain a harmonic voltage set and a harmonic current set; perform trend analysis on the monitoring data set according to the harmonic voltage set and the harmonic current set to obtain a monitoring prediction information set, and perform power consumption evaluation according to the monitoring prediction information set to determine a circuit control scheme. It solves the technical problem that the accuracy of harmonic analysis in guiding the use of the circuit is not high in the prior art, and the actual power consumption of the user is affected.

[0004] It can be seen that the current power system monitoring mainly focuses on basic harmonic voltage and current monitoring. Although these monitoring can provide certain power quality information, the potential threat of harmonic resonance is often overlooked. Harmonic resonance usually occurs when the system impedance matches the harmonic frequency, resulting in harmonic current or voltage amplification effect, thereby causing system instability or even equipment damage. However, the existing harmonic monitoring system usually lacks real-time detection capability for harmonic resonance, and cannot identify and warn such phenomenon in advance, which makes the power system face greater resonance risk. In addition, due to the complex impedance characteristics of long transmission lines and industrial power grids, harmonic resonance phenomenon is more difficult to predict and prevent. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a harmonic analysis early warning method and system based on real-time monitoring data of an electric meter, which solves the problems mentioned in the background art.

[0006] To achieve the above object, the present application is implemented by the following technical solutions: a harmonic analysis early warning system based on real-time monitoring data of an electric meter, comprising a data acquisition module, a data preprocessing module, a harmonic risk calculation module, a harmonic risk assessment module, and a response generation module;

[0007] The data acquisition module acquires voltage data, current data, frequency data, and power consumption in real time through an electric meter integrated sensor group, and constructs a data vector to form a harmonic detection data vector NEHMDV.

[0008] The data preprocessing module pre-processes the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, time alignment preprocessing, and data consistency verification preprocessing, to form a harmonic detection data vector NEHMDV(t) after processing at time t.

[0009] The harmonic risk calculation module calculates the harmonic resonance frequency state of the harmonic detection data vector NEHMDV(t), obtains the real-time harmonic resonance frequency R f (t), and then performs integral operation on the harmonic resonance risk state to obtain the real-time harmonic resonance risk P r (t), and synchronously forms a risk factor vector EHRFV(t) with the real-time harmonic resonance frequency R f (t) and the harmonic resonance risk P r (t).

[0010] The harmonic risk assessment module fits according to the risk factor vector EHRFV(t), obtains a harmonic risk assessment index EHRAI(t), and matches a preset harmonic risk assessment threshold XZ to obtain a harmonic risk assessment matching strategy scheme.

[0011] The response generation module specifically executes according to the content of the harmonic risk assessment matching strategy scheme.

[0012] Preferably, the data acquisition module comprises a sensor acquisition unit.

[0013] The sensor acquisition unit acquires running state data, including voltage data, current data, frequency data, and power consumption, in real time through the sensor group integrated in the electric meter, synchronously marks them as voltage V(t), current I(t), frequency F(t), and power consumption E(t), and constructs a data vector to form a harmonic detection data vector NEHMDV.

[0014] Preferably, the data preprocessing module comprises a data preprocessing unit and a data verification unit.

[0015] The data preprocessing unit preprocesses the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing and time alignment preprocessing, wherein the normalization preprocessing includes processing using the min-max normalization method, thereby eliminating the scale difference between different data dimensions and enabling the harmonic detection data vector NEHMDV to be processed under the same scale; the noise preprocessing includes processing using the moving average filtering method, thereby performing noise filtering and removal of the harmonic detection data vector NEHMDV and removing high-frequency noise and abnormal points in the data; and the time alignment preprocessing includes processing using the linear interpolation method, thereby enabling the data in the harmonic detection data vector NEHMDV to be aligned at the same time point t.

[0016] The data verification unit performs consistency verification preprocessing on the data to form the processed harmonic detection data vector NEHMDV(t) at time t.

[0017] Preferably, the harmonic risk calculation module comprises a frequency calculation unit, a risk calculation unit and an integration unit.

[0018] The frequency calculation unit calculates the harmonic resonance frequency state of the harmonic detection data vector NEHMDV(t) to obtain the real-time harmonic resonance frequency R f (t).

[0019] Preferably, the risk calculation unit performs integral operation on the harmonic resonance risk state of the real-time harmonic resonance frequency R f (t) and the harmonic detection data vector NEHMDV(t) to obtain the real-time harmonic resonance risk P r (t).

[0020] Preferably, the integration unit integrates the real-time harmonic resonance frequency R f (t) and the harmonic resonance risk P r (t) to form a risk factor vector EHRFV(t).

[0021] Preferably, the harmonic risk assessment module comprises a risk fitting unit and a risk matching unit.

[0022] The risk fitting unit fits the risk factor vector EHRFV(t) to obtain a harmonic risk assessment index EHRAI(t).

[0023] The risk matching unit matches the preset harmonic risk assessment threshold XZ with the harmonic risk assessment index EHRAI(t), obtains a risk matching result Match(t), and generates a corresponding harmonic risk assessment matching strategy scheme according to the risk matching result Match(t).

[0024] Preferably, the risk matching result Match(t) is obtained by the following matching mode:

[0025] When the harmonic risk assessment index EHRAI(t) is greater than or equal to the harmonic risk assessment threshold XZ, 1 is returned to Match(t), and the risk matching result Match(t) is marked as 1;

[0026] When the harmonic risk assessment index EHRAI(t) is less than the harmonic risk assessment threshold XZ, 0 is returned to Match(t), and the risk matching result Match(t) is marked as 0;

[0027] The harmonic risk assessment matching strategy scheme is generated by the following matching mode:

[0028] When the risk matching result Match(t) is 1, a harmonic risk assessment abnormal strategy scheme is generated, including starting a response warning mechanism and executing a preset response measure, including a load adjustment response, a device switching adjustment response, a power adjustment response, and a related warning prompt response; at the same time, a timestamp and the risk matching result Match(t) are recorded, and a warning monitoring mechanism is executed, and the number of risk matching results Match(t) in a fixed period is counted and marked as a warning monitoring total Y jzs (t);

[0029] When the warning monitoring total Y jzs (t) is greater than or equal to a preset warning total evaluation threshold YZ, a harmonic risk assessment warning strategy scheme is generated, including starting a harmonic suppression device, cutting off related devices, notifying related operation and maintenance personnel to handle and controlling device power operation state;

[0030] When the risk matching result Match(t) is 0, a harmonic risk assessment abnormal strategy scheme is generated, and a response warning mechanism is not started and a preset response measure is not executed.

[0031] Preferably, the response generation module generates corresponding response measures according to the content of the harmonic risk assessment matching strategy scheme, including harmonic risk assessment abnormal strategy scheme response measures and harmonic risk assessment warning strategy scheme response measures, and performs specific execution according to the response measures, including controlling the start and stop of a filter device, the running power, and the notification of related personnel.

[0032] A harmonic analysis and warning method based on real-time monitoring data of an electric meter, comprising the following steps:

[0033] Step one: the data acquisition module collects voltage data, current data, frequency data and power consumption in real time through the electric meter integrated sensor group, and constructs a data vector to form a harmonic detection data vector NEHMDV;

[0034] Step two: the data preprocessing module pre-processes the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, time alignment preprocessing and data consistency verification preprocessing, to form a harmonic detection data vector NEHMDV(t) after processing at time t;

[0035] Step three: the harmonic risk calculation module calculates the harmonic resonance frequency state of the harmonic detection data vector NEHMDV(t), obtains the real-time harmonic resonance frequency Rf(t), and then performs integral operation on the harmonic resonance risk state to obtain the real-time harmonic resonance risk Pr(t), and synchronously forms a risk factor vector EHRFV(t) with the real-time harmonic resonance frequency Rf(t) and the harmonic resonance risk Pr(t); f r f r (t) and Pr(t);

[0036] Step four: the harmonic risk assessment module fits according to the risk factor vector EHRFV(t), obtains a harmonic risk assessment index EHRAI(t), and matches a preset harmonic risk assessment threshold XZ to obtain a harmonic risk assessment matching strategy scheme;

[0037] Step five: the response generation module specifically executes according to the content of the harmonic risk assessment matching strategy scheme.

[0038] The present application provides a harmonic analysis and early warning method and system based on real-time monitoring data of electric meters, which has the following beneficial effects:

[0039] (1) When the system is running, the harmonic detection data vector NEHMDV is constructed, which can capture the harmonic resonance problems that may occur in the power system. After processing by the data preprocessing module, including normalization, noise filtering, time alignment and data consistency verification steps, the accuracy and consistency of the data are ensured. The harmonic risk calculation module further calculates the real-time harmonic resonance frequency Rf(t) and the harmonic resonance risk Pr(t), and integrates them into the risk factor vector EHRFV(t). Based on this, the harmonic risk assessment module can quickly fit the harmonic risk assessment index EHRAI(t), and generate a targeted risk assessment matching strategy by matching with the preset harmonic risk assessment threshold XZ. Finally, the response generation module takes specific execution and control measures according to the strategy scheme content, effectively preventing potential harmonic risks. The present application realizes precise control of harmonic risks and avoids serious consequences such as equipment damage and energy waste caused by harmonic problems as much as possible.​​​

[0040] (2) The overall and dynamic monitoring of the harmonic risk of the power system can be realized, and accurate risk assessment can be provided through the cooperation of the frequency calculation unit, the risk calculation unit and the integration unit. Firstly, the frequency calculation unit calculates the harmonic resonance frequency R f (t) in real time, reflects the interaction of voltage data and frequency data, and can identify the harmonic resonance in time. Subsequently, the risk calculation unit integrates the calculated current data and power consumption data to calculate the harmonic resonance risk P r (t), which quantifies the cumulative risk from the initial time to the current time. Finally, the integration unit integrates the two indicators into the risk factor vector EHRFV(t), so that the risk assessment is more comprehensive, the harmonic resonance frequency and risk can be captured in real time, and more accurate risk prediction can be provided through dynamic accumulation, thereby effectively improving the stability and safety of the power system.

[0041] (3) According to the risk factor vector EHRFV(t), the harmonic risk assessment index EHRAI(t) is obtained, and the harmonic risk assessment threshold XZ is matched to obtain the risk matching result Match(t), and the corresponding harmonic risk assessment matching strategy scheme is generated according to the risk matching result Match(t), and is specifically executed to cope with the potential risk. In the case of risk matching result Match(t) being 0, the early warning mechanism is not started, and the system is kept running normally. This method not only can respond and handle the harmonic risk in time, improve the stability of the system, but also can ensure long-term tracking and management of the risk through real-time monitoring and statistical analysis, and significantly enhance the safety and reliability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The figure is a block diagram of the harmonic analysis and early warning system based on the real-time monitoring data of the electric meter.

[0043] Figure 2 The figure is a block diagram of the harmonic analysis and early warning system based on the real-time monitoring data of the electric meter. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0045] Embodiment 1

[0046] The application provides a harmonic analysis early warning system based on real-time monitoring data of an electric meter, and refers to Figure 1 , including a data acquisition module, a data preprocessing module, a harmonic risk calculation module, a harmonic risk assessment module and a response generation module.

[0047] The data acquisition module collects voltage data, current data, frequency data and power consumption in real time through an electric meter integrated sensor group, and constructs a data vector to form a harmonic detection data vector NEHMDV.

[0048] The data preprocessing module pre-processes the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, time alignment preprocessing and data consistency verification preprocessing, to form a harmonic detection data vector NEHMDV(t) after processing at time t.

[0049] The harmonic risk calculation module calculates the harmonic resonance frequency state of the harmonic detection data vector NEHMDV(t), obtains the real-time harmonic resonance frequency R f (t), and then performs integral operation on the harmonic resonance risk state to obtain the real-time harmonic resonance risk P r (t), and synchronously forms a risk factor vector EHRFV(t) with the real-time harmonic resonance frequency R f (t) and the harmonic resonance risk P r (t).

[0050] The harmonic risk assessment module fits according to the risk factor vector EHRFV(t), obtains a harmonic risk assessment index EHRAI(t), and matches a preset harmonic risk assessment threshold XZ to obtain a harmonic risk assessment matching strategy scheme.

[0051] The response generation module specifically executes according to the content of the harmonic risk assessment matching strategy scheme.

[0052] In this embodiment, the voltage, current, frequency and power consumption parameters are acquired in real time by the data acquisition module, and the harmonic detection data vector NEHMDV is constructed, which can capture the harmonic resonance problem that may occur in the power system. After the data preprocessing module processes, including normalization, noise filtering, time alignment and data consistency verification steps, the accuracy and consistency of the data are ensured. The harmonic risk calculation module further calculates the real-time harmonic resonance frequency Rf(t) and the harmonic resonance risk Pr(t), and integrates them into the risk factor vector EHRFV(t). Based on this, the harmonic risk assessment module can quickly fit the harmonic risk assessment index EHRAI(t), and through the matching of the preset harmonic risk assessment threshold XZ, the targeted risk assessment matching strategy is generated. Finally, the response generation module takes specific execution and control measures according to the strategy scheme content, effectively preventing potential harmonic risks. The precise control of harmonic risks is realized, the safety and stability of the power system are improved, and serious consequences such as equipment damage and energy waste caused by harmonic problems are avoided.

[0053] Embodiment 2

[0054] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 , specifically: the data acquisition module includes a sensor acquisition unit;

[0055] The sensor acquisition unit acquires running state data in real time through a sensor group integrated with an electric meter, including voltage data, current data, frequency data and power consumption, which are labeled as voltage V(t), current I(t), frequency F(t) and power consumption E(t) respectively, to construct data vectors and form the harmonic detection data vector NEHMDV;

[0056] Among them, the harmonic detection data vector NEHMDV={V(t), I(t), F(t), E(t)}.

[0057] The data preprocessing module includes a data preprocessing unit and a data verification unit;

[0058] The data preprocessing unit preprocesses the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing and time alignment preprocessing. The normalization processing includes using the minimum and maximum normalization method for processing, thereby eliminating the scale difference between different data dimensions, so that the harmonic detection data vector NEHMDV is processed under the same scale. The noise preprocessing includes using the sliding average filtering method for processing, to filter and remove the noise of the harmonic detection data vector NEHMDV, thereby removing the high-frequency noise and abnormal points in the data. The time alignment preprocessing includes using the linear interpolation method for processing, thereby aligning the data in the harmonic detection data vector NEHMDV at the same time point t.

[0059] The data checking unit pre-processes the data by consistency checking, and forms a harmonic detection data vector NEHMDV(t) of time t after processing;

[0060] The harmonic detection data vector NEHMDV(t) is specifically NEHMDV(t) = {V norm (t), I norm (t), F norm (t), E norm (t)}, wherein V norm (t), I norm (t), F norm (t) and E norm (t) represent voltage data, current data, frequency data and electric energy consumption data after normalization processing respectively.

[0061] Embodiment 3

[0062] This embodiment is an explanation and description in embodiment 2, please refer to Figure 1 , specifically: the harmonic risk calculation module includes a frequency calculation unit, a risk calculation unit and an integration unit;

[0063] The frequency calculation unit calculates the harmonic resonance frequency state of the harmonic detection data vector NEHMDV(t), and obtains the real-time harmonic resonance frequency R f (t);

[0064] The risk calculation unit performs integral operation on the harmonic resonance risk state of the real-time harmonic resonance frequency R f (t) and the harmonic detection data vector NEHMDV(t), and obtains the real-time harmonic resonance risk P r (t);

[0065] The integration unit integrates the calculated real-time harmonic resonance frequency R f (t) and the harmonic resonance risk P r (t) to form a risk factor vector EHRFV(t);

[0066] The risk factor vector EHRFV(t) is specifically EHRFV(t) = {P r (t), R f (t)}.

[0067] The harmonic resonance frequency R f (t) is obtained by the following calculation formula:

[0068]

[0069] In the formula, R f(t) represents the harmonic resonance frequency at time t, and specifically represents the interaction of voltage data V norm (t) and frequency data F norm (t) and the resonance situation of the interaction of frequency data F norm (t) represents the frequency data at time t, V norm (t) represents the voltage data at time t, represents the preset resonance frequency constant, represents the frequency data F norm (t) and the preset resonance frequency constant , and specifically reflects the proportion of resonance occurrence.

[0070] The harmonic resonance risk P r (t) is obtained by the following calculation formula:

[0071]

[0072] In the formula, P r (t) represents the cumulative harmonic resonance risk from time t0 to the current time t, and specifically quantifies the real-time harmonic resonance frequency R f (t) at time t0 to time t, current data I norm (t) and energy consumption data E norm (t) is obtained, τ represents the integral variable, and specifically represents the change value of the time dimension, which is accumulated from time t0 to time t, and also includes the risk accumulation from the initial time to the current time, R f (τ) represents the harmonic resonance frequency at time τ, I norm (τ) represents the current data at time τ, E norm (τ) represents the energy consumption data at time τ.

[0073] In this embodiment, comprehensive and dynamic monitoring of the harmonic risk of the power system can be achieved, and accurate risk assessment can be provided through the cooperation of the frequency calculation unit, the risk calculation unit and the integration unit. First, the frequency calculation unit calculates the harmonic resonance frequency R f (t) in real time, which reflects the interaction of voltage data and frequency data, and can identify the harmonic resonance situation in time. Then, the risk calculation unit integrates the calculated current data and energy consumption data to calculate the harmonic resonance risk P r (t), which quantifies the cumulative risk from the initial time to the current time. Finally, the integration unit integrates the two indicators into the risk factor vector EHRFV(t), so that the risk assessment is more comprehensive, and the harmonic resonance frequency and risk can be captured in real time. In addition, more accurate risk prediction can be provided through dynamic accumulation, thereby effectively improving the stability and safety of the power system.

[0074] Embodiment 4

[0075] This embodiment is an explanation and illustration in embodiment 3, please refer to Figure 1 , specifically: the harmonic risk assessment module includes a risk fitting unit and a risk matching unit;

[0076] The risk fitting unit is fitted according to the risk factor vector EHRFV(t), and the harmonic risk assessment index EHRAI(t) is obtained;

[0077] The harmonic risk assessment index EHRAI(t) is obtained by the following calculation formula:

[0078]

[0079] In the formula, n represents the total number of risk factors, specifically the length of the risk factor vector EHRFV(t), w i i represents the weight value of the i-th risk factor in the risk factor vector EHRFV(t), EHRFV i (t) represents the i-th risk factor in the risk factor vector EHRFV(t), specifically including the harmonic resonance frequency R f (t) and the harmonic resonance risk P r (t) risk factor;

[0080] The risk matching unit matches the harmonic risk assessment threshold XZ with the harmonic risk assessment index EHRAI(t) through the preset harmonic risk assessment threshold XZ, obtains the risk matching result Match(t), and generates the corresponding harmonic risk assessment matching strategy scheme according to the risk matching result Match(t).

[0081] The risk matching result Match(t) is obtained by the following matching method:

[0082]

[0083] When the harmonic risk assessment index EHRAI(t) is greater than or equal to the harmonic risk assessment threshold XZ, 1 is returned to Match(t), and the risk matching result Match(t) is marked as 1;

[0084] When the harmonic risk assessment index EHRAI(t) is less than the harmonic risk assessment threshold XZ, 0 is returned to Match(t), and the risk matching result Match(t) is marked as 0;

[0085] The harmonic risk assessment matching strategy scheme is generated by the following matching method:

[0086] When the risk matching result Match(t) = 1, a harmonic risk assessment abnormal strategy scheme is generated, including starting a response warning mechanism and executing a preset response measure, including a load adjustment response, a device switching adjustment response, a power adjustment response and a related warning prompt response; at the same time, a timestamp and the risk matching result Match(t) are recorded, and a warning monitoring mechanism is executed, and the number of risk matching results Match(t) in a fixed period is counted, which is marked as a warning monitoring total Y jzs (t);

[0087] When the warning monitoring total Y jzs (t)≥preset warning total evaluation threshold YZ, a harmonic risk assessment warning strategy scheme is generated, including starting a harmonic suppression device, cutting off related devices, notifying related operation and maintenance personnel to handle and regulating the power operation state of the device;

[0088] When the risk matching result Match(t) = 0, a harmonic risk assessment abnormal strategy scheme is generated, including starting a response warning mechanism and executing a preset response measure.

[0089] The response generation module generates corresponding response measures according to the harmonic risk assessment matching strategy scheme content, including harmonic risk assessment abnormal strategy scheme response measures and harmonic risk assessment warning strategy scheme response measures, and performs specific execution according to the response measures, including regulating the start-stop of the filter device, the running power and the notification of the related personnel.

[0090] In this embodiment, the harmonic risk assessment index EHRAI(t) is obtained by fitting according to the risk factor vector EHRFV(t), and is matched with the preset harmonic risk assessment threshold XZ to obtain the risk matching result Match(t), and the corresponding harmonic risk assessment matching strategy scheme is generated according to the risk matching result Match(t) and is specifically executed to cope with potential risks. In the case of risk matching result Match(t) = 0, the warning mechanism is not started, and the system is kept normal operation. This method not only responds to and handles the harmonic risk in time, improves the stability of the system, but also ensures the long-term tracking and management of the risk through real-time monitoring and statistical analysis, significantly enhances the safety and reliability of the power system.

[0091] Embodiment 5

[0092] A harmonic analysis warning method based on real-time monitoring data of an electric meter, please refer to Figure 2 , specifically: including the following steps:

[0093] Step 1: The data acquisition module acquires voltage data, current data, frequency data and power consumption in real time through the electric meter integrated sensor group, and constructs a data vector to form a harmonic detection data vector NEHMDV;

[0094] Step two: the data preprocessing module pre-processes the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, time alignment preprocessing and data consistency verification preprocessing, to form the processed harmonic detection data vector NEHMDV(t) at time t;

[0095] Step three: the harmonic risk calculation module calculates the harmonic resonance frequency state of the harmonic detection data vector NEHMDV(t), to obtain the real-time harmonic resonance frequency R f (t), and then performs integral operation on the harmonic resonance risk state to obtain the real-time harmonic resonance risk P r (t), and synchronously forms the risk factor vector EHRFV(t) with the real-time harmonic resonance frequency R f (t) and the harmonic resonance risk P r (t);

[0096] Step four: the harmonic risk assessment module fits the risk factor vector EHRFV(t) to obtain the harmonic risk assessment index EHRAI(t), and matches the harmonic risk assessment threshold XZ preset to obtain the harmonic risk assessment matching strategy scheme;

[0097] Step five: the response generation module specifically executes according to the content of the harmonic risk assessment matching strategy scheme.

[0098] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and modifications can be made to these embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A harmonic analysis and early warning system based on real-time monitoring data of electricity meters, characterized in that: It includes a data acquisition module, a data preprocessing module, a harmonic risk calculation module, a harmonic risk assessment module, and a response generation module; The data acquisition module collects voltage, current, frequency, and energy consumption data in real time through the integrated sensor group of the electricity meter, and constructs a harmonic detection data vector NEHMDV. The data preprocessing module preprocesses the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, time alignment preprocessing and data consistency verification preprocessing, to form the harmonic detection data vector NEHMDV(t) at time t after processing. The harmonic risk calculation module calculates the harmonic resonance frequency state from the harmonic detection data vector NEHMDV(t) and obtains the real-time harmonic resonance frequency R. f (t), then perform integration to obtain the harmonic resonance risk state and obtain the real-time harmonic resonance risk P. r (t), synchronously transmitting the real-time harmonic resonance frequency R f (t) Risk of harmonic resonance P r (t) forms the risk factor vector EHRFV(t); The harmonic risk assessment module fits the risk factor vector EHRFV(t) to obtain the harmonic risk assessment index EHRAI(t), and matches it with the preset harmonic risk assessment threshold XZ to obtain a harmonic risk assessment matching strategy scheme. The response generation module executes the specific strategy based on the harmonic risk assessment matching scheme.

2. The harmonic analysis and early warning system based on real-time monitoring data of electricity meters according to claim 1, characterized in that: The data acquisition module includes a sensor acquisition unit; The sensor acquisition unit collects real-time operating status data, including voltage data, current data, frequency data, and energy consumption, through the sensor group integrated in the meter. These data are synchronously labeled as voltage V(t), current I(t), frequency F(t), and energy consumption E(t), and then used to construct a data vector to form the harmonic detection data vector NEHMDV.

3. The harmonic analysis and early warning system based on real-time monitoring data of electricity meters according to claim 1, characterized in that: The data preprocessing module includes a data preprocessing unit and a data verification unit; The data preprocessing unit preprocesses the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, and time alignment preprocessing. Normalization preprocessing uses minimum-maximum normalization to eliminate scale differences between different data dimensions, ensuring the NEHMDV data vector is processed at the same scale. Noise preprocessing uses moving average filtering to filter and remove noise from the NEHMDV data vector, thereby removing high-frequency noise and outliers. Time alignment preprocessing uses linear interpolation to align the data in the NEHMDV data vector at the same time point t. The data verification unit performs consistency verification preprocessing on the data to form a harmonic detection data vector NEHMDV(t) at time t after processing.

4. The harmonic analysis and early warning system based on real-time monitoring data of electricity meters according to claim 1, characterized in that: The harmonic risk calculation module includes a frequency calculation unit, a risk calculation unit, and an integration unit; The frequency calculation unit calculates the harmonic resonance frequency state from the harmonic detection data vector NEHMDV(t) and obtains the real-time harmonic resonance frequency R. f (t).

5. A harmonic analysis and early warning system based on real-time monitoring data of an electricity meter according to claim 4, characterized in that: The risk calculation unit calculates the real-time harmonic resonance frequency R. f (t) and harmonic detection data vector NEHMDV(t) are used to perform integration calculations on the harmonic resonance risk state, and the real-time harmonic resonance risk P is obtained. r (t).

6. A harmonic analysis and early warning system based on real-time monitoring data of an electricity meter according to claim 4, characterized in that: The integration unit calculates and obtains the real-time harmonic resonance frequency R. f (t) Risk of harmonic resonance P r The factors (t) are integrated to form the risk factor vector EHRFV(t).

7. A harmonic analysis and early warning system based on real-time monitoring data of an electricity meter according to claim 6, characterized in that: The harmonic risk assessment module includes a risk fitting unit and a risk matching unit; The risk fitting unit fits the risk factor vector EHRFV(t) to obtain the harmonic risk assessment index EHRAI(t); The risk matching unit matches the preset harmonic risk assessment threshold XZ with the harmonic risk assessment index EHRAI(t) to obtain the risk matching result Match(t), and generates a corresponding harmonic risk assessment matching strategy based on the risk matching result Match(t).

8. A harmonic analysis and early warning system based on real-time monitoring data of an electricity meter according to claim 7, characterized in that: The risk matching result Match(t) is obtained through the following matching method: When the harmonic risk assessment index EHRAI(t) ≥ harmonic risk assessment threshold XZ, 1 is returned to Match(t), and the risk matching result Match(t) is marked as 1; When the harmonic risk assessment index EHRAI(t) < harmonic risk assessment threshold XZ, 0 is returned to Match(t), and the risk matching result Match(t) is marked as 0; The harmonic risk assessment matching strategy is generated through the following matching method: When the risk matching result Match(t) = 1, a harmonic risk assessment anomaly strategy plan is generated, including activating the response early warning mechanism and executing preset response measures, including load adjustment response, equipment switching adjustment response, power adjustment response, and related early warning prompt response; at the same time, the timestamp and the risk matching result Match(t) are recorded, and the early warning monitoring mechanism is executed, and the number of risk matching results Match(t) is counted within a fixed period and marked as the total number of early warning monitoring Y. jzs (t); When the total number of early warning monitoring Y jzs When (t) ≥ the preset total number of warnings assessment threshold YZ, a harmonic risk assessment and warning strategy scheme is generated, including starting harmonic suppression equipment, cutting off related equipment, notifying relevant operation and maintenance personnel to handle and adjust the power operation status of equipment; When the risk matching result Match(t) = 0, a harmonic risk assessment strategy without anomalies is generated, but the response warning mechanism is not activated and the preset response measures are not executed.

9. A harmonic analysis and early warning system based on real-time monitoring data of an electricity meter according to claim 1, characterized in that: The response generation module generates corresponding response measures based on the content of the harmonic risk assessment matching strategy scheme, including response measures for the harmonic risk assessment anomaly strategy scheme and response measures for the harmonic risk assessment early warning strategy scheme, and executes the response measures in detail, including adjusting the start and stop of the filtering equipment, the operating power, and notifying relevant personnel.

10. A harmonic analysis and early warning method based on real-time electricity meter monitoring data, comprising the harmonic analysis and early warning system based on real-time electricity meter monitoring data as described in any one of claims 1 to 9, characterized in that: Includes the following steps: Step 1: The data acquisition module collects voltage, current, frequency, and energy consumption data in real time through the integrated sensor group of the electricity meter, and constructs a harmonic detection data vector NEHMDV. Step 2: The data preprocessing module preprocesses the harmonic detection data vector NEHMDV, including normalization preprocessing, noise preprocessing, time alignment preprocessing, and data consistency verification preprocessing, to form the harmonic detection data vector NEHMDV(t) at time t after processing. Step 3: The harmonic risk calculation module calculates the harmonic resonance frequency state from the harmonic detection data vector NEHMDV(t) and obtains the real-time harmonic resonance frequency R. f (t), then perform integration to obtain the harmonic resonance risk state and obtain the real-time harmonic resonance risk P. r (t), synchronously transmitting the real-time harmonic resonance frequency R f (t) Risk of harmonic resonance P r (t) forms the risk factor vector EHRFV(t); Step 4: The harmonic risk assessment module fits the risk factor vector EHRFV(t) to obtain the harmonic risk assessment index EHRAI(t), and matches it with the preset harmonic risk assessment threshold XZ to obtain the harmonic risk assessment matching strategy scheme. Step 5: The response generation module executes the specific strategy plan based on the harmonic risk assessment.

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