Electric energy quality monitoring method and system based on intelligent fusion terminal

Abstract

The invention relates to the field of power quality monitoring, in particular to a power quality monitoring method and system based on an intelligent fusion terminal, and the method comprises the steps: obtaining the historical power quality data of the intelligent fusion terminal, and carrying out the preprocessing; performing curve fitting on the preprocessed electric energy quality data, extracting a maximum value sequence and a minimum value sequence from the fitted curve, calculating the periodic probability of a preset number of extreme points in a corresponding time period, and judging the periodic characteristics of the electric energy quality signal; constructing a period window, calculating a period index according to the consistency of the power quality data in the period window, analyzing the trend of the period index changing along with the period length, obtaining the optimal feature point, determining the optimal period length and the optimal period wave band, and completing the monitoring of the power quality. According to the method, the nonlinear load problem of the power grid is effectively solved by dynamically adjusting the length of the period window, fitting the spline curve and analyzing the period index, and the accuracy and the processing efficiency of period identification are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power quality monitoring. In particular, it relates to a power quality monitoring method and system based on intelligent fusion terminal. BACKGROUND

[0002] Power system is an important infrastructure in modern society, and stable operation is crucial for ensuring social production and life. With the continuous expansion of power grid scale and the increase of complexity, power quality monitoring has become a key link to ensure the reliable operation of power system. In recent years, intelligent fusion terminal has gradually attracted attention in power quality monitoring, which can integrate multiple functions to realize real-time monitoring and data collection of power quality, providing strong support for stable operation of power system.

[0003] In the process of power quality monitoring, accurately identifying the periodic characteristics of power quality data is an important link to ensure the stable operation of power system. Monitoring of power cycle waveform can timely discover abnormal conditions in power grid, such as voltage fluctuation, harmonic interference, etc., so as to take corresponding measures for adjustment and optimization. With the increasing complexity of power grid environment, power cycle waveform is disturbed by various factors such as harmonics, interharmonics, voltage fluctuation, etc., which makes accurate identification of power cycle waveform more important.

[0004] In power quality monitoring, a large amount of data needs to be processed to identify periodic characteristics, resulting in slower response of monitoring system. Traditional methods are easily affected by noise and other interference factors, making it difficult to effectively filter out power quality signals with periodic characteristics, resulting in misjudgment or omission. The power cycle waveform in power system also changes dynamically with load changes, equipment operating state and power grid topology changes, making it difficult for traditional methods to quickly and accurately identify the periodic waveform in power quality data and dynamically adapt to these changes, which cannot meet the needs of real-time monitoring and fast response of power quality monitoring. SUMMARY

[0005] To solve the problem of slow response caused by processing a large amount of data in power quality monitoring, misjudgment or omission due to noise and interference, and difficulty in adapting to dynamically changing power cycle waveform, which cannot meet the needs of real-time monitoring and fast response, the present application provides solutions in the following aspects.

[0006] In a first aspect, a power quality monitoring method based on an intelligent fusion terminal includes: obtaining historical power quality data of the intelligent fusion terminal and preprocessing the data; fitting a curve to the preprocessed power quality data, extracting a maximum value sequence and a minimum value sequence from the fitted curve, calculating a periodic probability of a preset number of extreme points in a corresponding time period, and determining a periodic characteristic of the power quality signal; constructing a periodic window according to the periodic characteristic, calculating a periodic index based on the consistency of the power quality data in the periodic window, analyzing the trend of the periodic index with respect to the periodic length, obtaining a feature point that best reflects the periodicity, determining an optimal periodic length of the feature point, thereby identifying a periodic waveband, and completing accurate identification of the power cycle waveform.

[0007] The maximum value and minimum value sequences are extracted by fitting the power curve, the periodic probability in the relevant time period is calculated, and the periodic characteristic of the power quality signal is preliminarily determined. Further, the periodic window is constructed and the periodic index is calculated, the trend of the periodic index with respect to the periodic length is analyzed, the optimal periodic length is determined, and the periodic waveband is identified. Finally, accurate identification of the power cycle waveform is completed. By accurately extracting the key features of the power quality signal and analyzing the periodic changes, the accuracy and efficiency of the identification are significantly improved, and the adaptability to complex power grid environments and interference factors is enhanced, meeting the needs of real-time monitoring and rapid response.

[0008] Preferably, the calculation method of the periodic probability includes: The preprocessed power quality data is fitted using Matlab to obtain the maximum value sequence and the minimum value sequence of the power curve, and the nearest extreme point at the current time is taken as the starting point, and the time period corresponding to the preset number of maximum values and minimum values is extracted along the time sequence; Taking the first extreme value of any time period as the target extreme value, the time difference between the next extreme value adjacent to the target extreme value is calculated, and an energy fluctuation vector is constructed in the order of extreme value, time difference, extreme value, and time difference; Taking any time period as a target time period, two time periods, a first time period and a second time period, are traced back, the similarity of the energy fluctuation vector between the target time period and the first time period is calculated to obtain a first periodic probability, and the similarity of the energy fluctuation vector between the target time period and the second time period is calculated to obtain a second periodic probability.

[0009] By using Matlab to perform curve fitting on the preprocessed power quality data, the maximum and minimum value sequences of the power curve are accurately extracted, and the most recent extreme value point is taken as the starting point to intercept a time period corresponding to a preset number of extreme value points. By constructing a power fluctuation vector containing extreme values and time differences, the similarity of power fluctuation vectors in different time periods is calculated and compared, and the first cycle probability and the second cycle probability are obtained, thereby quantitatively evaluating the periodic characteristics of the power quality signal, improving the accuracy and efficiency of cycle identification, and enhancing the adaptability to changes in complex power grid environment and interference factors.

[0010] Preferably, in response to the first cycle probability being greater than or equal to a first preset threshold and the second cycle probability being less than a second preset threshold, it is considered that the power quality signal enters a new cycle; In response to the first cycle probability being less than the first preset threshold and the second cycle probability being greater than or equal to the second preset threshold, it is considered that there is no new cycle in the power quality signal; In response to the first cycle probability being less than the first preset threshold and the second cycle probability being less than the second preset threshold, it is considered that there is no new cycle in the power quality signal; In response to the first cycle probability being greater than or equal to the first preset threshold and the second cycle probability being greater than or equal to the second preset threshold, it is considered that there is no new cycle in the power quality signal.

[0011] By comparing the relationship between the first cycle probability and the second cycle probability and the preset threshold, whether the power quality signal has periodicity or periodicity disappears can be effectively identified, and the accuracy and reliability of periodicity identification are significantly improved.

[0012] Preferably, the calculation method of the cycle index comprises: Initialize the cycle window, set the initial length to 1, and set the starting point of the cycle window to the starting time of the time sequence. Each time point in the time sequence is traversed, the standard deviation of the power quality data at each time point in the cycle is calculated, the standard deviations of all time points are accumulated to obtain the fluctuation sum of the power quality data in the entire cycle, and the reciprocal of the sum of the fluctuation sum of the power quality data and a constant term is multiplied by the reciprocal of the cycle length as the cycle index.

[0013] Preferably, the step of obtaining the optimal cycle length comprises: The initial length of the cycle window is set to the minimum unit 1, and the initial cycle window is constructed from the starting point of the data sequence. The cycle index of each data point in the initial cycle window is calculated, the cycle window is iterated, the length of the cycle window is increased by 1 each time, and the cycle index is recalculated in each new cycle window until the cycle window covers the entire data sequence or reaches a preset maximum cycle length limit, forming a cycle index sequence, and the cycle length corresponding to the first maximum value in the cycle index sequence is selected as the optimal cycle length.

[0014] By setting the initial length of the cycle window and iteratively increasing, the obtained cycle index sequence is calculated and analyzed to determine the optimal cycle length, which can accurately identify the optimal cycle characteristics of the power quality signal, improve the calculation efficiency and adapt to the characteristics of different data sequences, and further provide reliable periodic information for in-depth analysis and practical application of the power quality signal.

[0015] Preferably, the cycle index acquisition method can also be replaced by: Initialize the cycle window, set the initial length to 1, and set the starting point of the cycle window to the starting time of the time sequence. Each time point in the time sequence is traversed, the standard deviation of the power quality data corresponding to each time point in the cycle is calculated, and the standard deviations of all time points are accumulated. The sum of the fluctuations of the power quality data in the entire cycle is added to the constant term, and the reciprocal of the sum is obtained. The cycle index is obtained.

[0016] By initializing the cycle window length to 1 and starting from the starting point of the time sequence, the standard deviation of the power quality data of each time point in the sequence is calculated, the sum of the fluctuations of the power quality data in the cycle is accumulated, and the reciprocal of the sum of the small constant is taken. The cycle index is calculated to identify the cycle characteristics in the power quality signal.

[0017] Preferably, the optimal cycle length acquisition method can also be replaced by: Initialize the cycle window, set the initial length to 1, and increase the cycle length by 1. At each length, use the spline curve fitting function of Matlab to construct a mapping function with the cycle length as the horizontal coordinate and the cycle index as the vertical coordinate. Calculate the first derivative and second derivative of the mapping function. On the second derivative curve, the first minimum point is obtained. The position corresponding to the minimum value point is the maximum change point, and the cycle length corresponding to the maximum change point is determined as the optimal cycle length.

[0018] The second aspect is a power quality monitoring system based on an intelligent fusion terminal, which comprises a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the power quality monitoring method based on the intelligent fusion terminal is realized.

[0019] The present application has the following effects: 1. The present application can effectively handle the nonlinear load and device configuration problems in the power grid by dynamically adjusting the cycle window length and using the spline curve fitting technology of Matlab. In addition, by analyzing the cycle index sequence, the present application can identify the optimal feature point of the periodic change in the power grid, thereby enhancing the adaptability to power grid fluctuations and the robustness of the algorithm.

[0020] 2、The present application ensures data quality through a preprocessing step, quickly identifies periodic characteristics through periodic probability calculation, and accurately determines the optimal period length through periodic index analysis. Not only does this improve the accuracy of period identification, but it also improves processing efficiency by reducing unnecessary calculations, making the method suitable for real-time monitoring and fast response application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a method flowchart of steps S1-S3 in an embodiment of the power quality monitoring method based on the intelligent fusion terminal.

[0022] Figure 2 is a structural block diagram of an embodiment of the power quality monitoring system based on the intelligent fusion terminal. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments.

[0024] Referring to Figure 1 , a power quality monitoring method based on an intelligent fusion terminal includes steps S1-S3, as follows: S1: Obtain historical power quality data of the intelligent fusion terminal and perform preprocessing.

[0025] The intelligent fusion terminal collects electrical parameters such as current and voltage through sensors such as current transformers and voltage transformers. These sensors convert analog signals into digital signals, which are then transmitted to the data acquisition module. The present application performs quality monitoring on different power quality data.

[0026] Preprocessing includes data cleaning to remove outliers and fill in missing values to ensure data integrity, removing noise through methods such as filtering or wavelet denoising, normalizing or standardizing power quality data to unify data scales, adjusting the sampling rate as needed, and removing long-term trends to focus on periodic changes.

[0027] Further explanation, data is collected from multiple devices or multiple locations of the same device, and the synchronization of data collection needs to be ensured to facilitate subsequent data integration and analysis.

[0028] In the power system, when the nonlinear load such as frequency converter, rectifier and switching power supply and other equipment are running, due to the nonlinear relationship between current and voltage, it will periodically inject harmonic current into the power grid. For example, the diode in the rectifier circuit is unidirectional, which causes the current to be intermittent and generates characteristic harmonics such as 5th and 7th harmonics, which is the theoretical result based on Fourier analysis. In addition, the high-frequency switching action (kHz level) of power electronic devices such as IGBT (Insulated Gate Bipolar Transistor) and inverter will cause voltage mutation and high-frequency oscillation, forming periodic glitches or high-frequency harmonics. Unreasonable device configuration, such as capacitor parameter mismatch or neutral line current imbalance, may exacerbate the harmonic superposition effect and affect the accuracy of the periodic waveform of electric energy.

[0029] Directly calculating the periodic waveform of electric energy not only has a large amount of calculation, but also the result may not be accurate. Therefore, preliminary screening is needed to reduce the amount of calculation and confirm the periodic characteristics of real-time power quality signals. The specific steps of the intelligent screening method for quickly identifying the periodic characteristics of power quality signals are as follows: S2: fitting curve on the preprocessed power quality data, extracting the maximum value sequence and the minimum value sequence from the fitting curve, calculating the periodic probability of a preset number of extreme points in the corresponding time period, and judging the periodic characteristics of the power quality signal.

[0030] Periodic probability includes: Using Matlab to fit the curve of the preprocessed power quality data, obtaining the maximum value sequence and the minimum value sequence of the electric energy curve, taking the nearest extreme point at the current time as the starting point, and intercepting the time period corresponding to the preset number of maximum values and minimum values along the time sequence; Taking the first extreme value of any time period as the target extreme value, calculating the time difference between the next extreme value adjacent to the target extreme value, and constructing the electric energy fluctuation vector in the order of extreme value, time difference, extreme value, time difference; Taking any time period as the target time period, tracing back two time periods, the first time period and the second time period, respectively, calculating the similarity of the electric energy fluctuation vector between the target time period and the first time period to obtain the first periodic probability, and calculating the similarity of the electric energy fluctuation vector between the target time period and the second time period to obtain the second periodic probability.

[0031] By calculating the time interval between two consecutive extreme values, the time span between the two extreme values is reflected, which is a key feature for periodic analysis, because one of the significant features of periodic signals is the existence of fixed or approximately fixed time intervals. The electric energy fluctuation vector can capture the change characteristics of the power quality signal in a period.

[0032] Further, the time period corresponding to the preset number of maximum values and minimum values is intercepted in the time sequence, for example, the time points containing 2 maximum values and 2 minimum values. The purpose of such interception is to capture the change characteristics of the power quality signal in a complete cycle, and the data contained in the time period is sufficient to reflect the periodic behavior of the power quality signal.

[0033] By calculating the first cycle probability and the second cycle probability, the periodic characteristics of the power quality signal are analyzed in depth, so as to identify and evaluate the behavior patterns of the power quality signal in different time periods. The first cycle probability measures the similarity of the current time period and the immediately preceding time period of the power fluctuation pattern, the higher the similarity, the more consistent the power behavior in the two adjacent time periods, which reflects the stability of the periodic signal. The second cycle probability measures the similarity of the current time period and the time period earlier, the lower the similarity, the more changeable the power behavior in a longer time span, which helps to find the evolution of the periodic pattern. By comprehensively considering the two probability values, the periodic characteristics of the power quality signal can be more accurately identified, and the consistency and change trend of the periodic waveform can be analyzed, which is crucial for application scenarios such as monitoring of power systems, analysis of equipment status and diagnosis of electrical faults.

[0034] The periodic characteristics of the power quality signal are determined, including: In response to the first cycle probability being greater than or equal to the first preset threshold and the second cycle probability being less than the second preset threshold, it is considered that the power quality signal enters a new cycle; the high first cycle probability indicates that the power fluctuation pattern in the current time period is very similar to the previous time period, which is usually a characteristic of periodic power quality signals. While the low second cycle probability indicates that the power fluctuation pattern in the current time period is significantly different from the previous two time periods, which means that the power fluctuation pattern in the current time period is a new cycle, rather than a repetition of the previous cycle pattern.

[0035] In response to the first cycle probability being less than the first preset threshold and the second cycle probability being greater than or equal to the second preset threshold, it is considered that there is no new cycle in the power quality signal; the low first cycle probability indicates that the power fluctuation pattern in the current time period is not similar to the previous time period, while the high second cycle probability indicates that the power fluctuation pattern in the current time period is similar to the previous two time periods. This indicates that the power fluctuation pattern in the current time period is a repetition of the previous cycle, rather than a new cycle.

[0036] In response to the first cycle probability being less than the first preset threshold and the second cycle probability being less than the second preset threshold, it is considered that there is no new cycle in the power quality signal; the low first cycle probability and the low second cycle probability indicate that the power fluctuation pattern in the current time period is not similar to the previous time period and the previous two time periods. This may be because the power quality signal is disturbed or changes greatly, but it is not enough to judge as a new cycle.

[0037] In response to the first cycle probability being greater than or equal to a first preset threshold and the second cycle probability being greater than or equal to a second preset threshold, it is considered that there is no new cycle in the power quality signal. A high first cycle probability indicates that the power fluctuation pattern in the current period is very similar to that in the previous period, and a high second cycle probability indicates that the power fluctuation pattern in the current period is also similar to that in the previous two periods. This indicates that the power fluctuation pattern in the current period is a repetition of the previous cycle, rather than a new cycle.

[0038] For example, the first threshold is 0.9 and the second threshold is 0.7, which can be adjusted according to specific circumstances.

[0039] The appearance of a new cycle may indicate an anomaly or fault in the power system. For example, the nonlinear characteristics of equipment, disturbances in the power grid, or sudden changes in load can all cause periodic changes in the power quality signal. By detecting the appearance of a new cycle in a timely manner, potential faults or anomalies can be quickly identified, and appropriate measures can be taken to repair or adjust, avoiding further damage.

[0040] By identifying a new cycle, the operating parameters of the equipment can be adjusted, the load distribution can be optimized, and the efficiency and reliability of the system can be improved. In addition, the analysis of periodic changes can also help to predict power demand and optimize the scheduling and management of the power grid.

[0041] S3: Construct a cycle window according to the cycle characteristics, calculate a cycle index based on the consistency of the power quality data in the cycle window, analyze the trend of the cycle index with the change of the cycle length, obtain a feature point that reflects the best periodicity, determine the optimal cycle length of the feature point, and thus determine the cycle band, and complete the accurate identification of the power cycle waveform.

[0042] The cycle window is constructed to focus on a specific time period of the power quality data, so as to more accurately analyze the cycle characteristics of the power quality signal. By traversing the length of the cycle window and calculating the cycle index and fitting curve, the optimal cycle length can be found, so as to ensure that the analyzed data segment is representative and can capture the change characteristics of the power quality signal in a complete cycle.

[0043] The cycle index includes: Initialize the cycle window, set its initial length to 1, and set the start point of the cycle window to the starting time of the time series. Traverse each time point in the time series, calculate the standard deviation of the power quality data at each time point in each cycle, accumulate the standard deviations of all time points to obtain the total fluctuation of the power quality data in the entire cycle, and multiply the reciprocal of the sum of the constant term by the reciprocal of the cycle length as the cycle index.

[0044] Specifically, the cycle index satisfies the following relationship: ; In the formula, represents the cycle index, represents the cycle length, represents the th cycle, th power quality data in the th cycle, represents the standard deviation function.

[0045] That is, the cycle length refers to the number of power quality data contained in a cycle. The smaller the sum of current fluctuation amplitudes at all times obtained by accumulation, the higher the repetition of power in the cycle. By introducing , the cycle length is ensured not to increase indefinitely, while maintaining the rationality of the cycle index .

[0046] The trend of the cycle index with the cycle length is analyzed to identify the optimal cycle length, which best reflects the periodic characteristics of the power quality signal. By observing the trend of the cycle index, the stability and consistency of the power quality signal under different cycle lengths are determined. The specific steps are as follows: The optimal cycle length includes: Set the initial length of the cycle window to the smallest unit 1, and construct the initial cycle window from the starting point of the data sequence. Calculate the cycle index of each data point in the initial cycle window. Iterate the cycle window, increase the cycle window length by 1 each time, and recalculate the cycle index in each new cycle window until the cycle window covers the entire data sequence or reaches the preset maximum cycle length limit. Form a cycle index sequence, and select the cycle length corresponding to the first maximum value in the cycle index sequence as the optimal cycle length.

[0047] Increase the cycle length by 1 each time, and obtain a cycle length after each increase. Obtain a cycle index sequence. The first maximum value in the cycle index sequence corresponds to the optimal cycle length, i.e., the number of power quality data in the power quality cycle window.

[0048] Set the maximum cycle window length to , and stop calculating the cycle index when the cycle length increases to . The value of can be determined according to the actual power quality data sampling frequency and monitoring requirements. For example, for a 50Hz power grid frequency, a cycle is 20ms, and if the sampling frequency is 10kHz, there are 500 sampling points in a cycle. You can set to 500 or adjust it appropriately according to actual requirements.

[0049] By finding the cycle length with the highest cycle index, it indicates that the periodicity of the power quality signal at this length is the strongest. By analyzing the trend of the cycle index, the stability of the power quality signal at different cycle lengths can be understood. By selecting the appropriate cycle length, the problem of overfitting or underfitting caused by too long or too short cycle length can be avoided.

[0050] In addition, another embodiment also includes: Initialize the cycle window with an initial length of 1 and set the start point of the cycle window to the starting time of the time series. Traverse each time point in the time series, calculate the standard deviation of the power quality data at each time point in the cycle, and accumulate the standard deviations of all time points. The sum of the fluctuations of the power quality data and the constant term is taken as the reciprocal to obtain the cycle index.

[0051] Initialize the cycle window with an initial length of 1 and increase the cycle length by 1 at a time. At each length, use the spline curve fitting function of Matlab to construct a mapping function with the cycle length as the horizontal coordinate and the cycle index as the vertical coordinate. Calculate the first and second derivatives of the mapping function. On the second derivative curve, the first minimum point is obtained, and the position corresponding to the minimum point is the maximum change point. The cycle length corresponding to the maximum change point is determined as the optimal cycle length.

[0052] In the second embodiment, the cycle length is not directly limited, but the optimal cycle length is determined through subsequent fitting and second derivative analysis. The latter is more suitable for scenarios with high precision requirements and sufficient computing resources.

[0053] The optimal cycle length is determined, and the cycle window is constructed based on it. This window length can reflect the best periodic characteristics of the power quality signal, so the part of the power quality signal in the window is considered to be a representative periodic waveform. In short, the part of the power quality signal in the cycle window is the periodic waveform we identified, which solves the problem of dynamically adapting to different waveform changes and meets the needs of real-time monitoring and fast response.

[0054] Further explanation, key features can be extracted from the cycle window for further analysis of power quality problems, such as: harmonic content: calculate the amplitude and phase of each harmonic to evaluate the degree of harmonic pollution; voltage deviation: monitor the effective value of the voltage to determine whether there is a high or low voltage; frequency deviation: measure the change of the grid frequency to ensure it is within the allowed range; classify the extracted features to identify different types of power quality problems.

[0055] The application also provides an intelligent fusion terminal-based power quality monitoring system. As Figure 2As shown, the system comprises a processor and a memory, the memory stores computer program instructions, when the computer program instructions are executed by the processor, a kind of power quality monitoring method based on intelligent fusion terminal according to the first aspect of the present application is realized.The system also includes communication bus and communication interface and other components well known to those skilled in the art, its setting and function are known in the art, thus not repeated here.

[0056] It should be noted that for those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.

Claims

1. A power quality monitoring method based on an intelligent fusion terminal, characterized in that, include: Acquire historical power quality data from smart converged terminals and perform preprocessing; The preprocessed power quality data is fitted with a curve, and the maximum and minimum value sequences are extracted from the fitted curve. The periodic probability of a preset number of extreme points in the corresponding time period is calculated to determine the periodic characteristics of the power quality signal. A periodic window is constructed based on the periodic characteristics. The periodic index is calculated based on the consistency of power quality data within the periodic window. The trend of the periodic index changing with the period length is analyzed to obtain the feature point that best reflects the periodicity. The optimal period length of the feature point is determined, thereby clarifying the periodic band and completing the accurate identification of the power periodic waveform.

2. The power quality monitoring method based on an intelligent fusion terminal according to claim 1, characterized in that, The calculation method for the periodic probability includes: Matlab was used to perform curve fitting on the preprocessed power quality data to obtain the maximum and minimum value sequences of the power curve. The most recent extreme point at the current time was taken as the starting point, and the time periods corresponding to the maximum and minimum values ​​were extracted forward along the time series by a preset number of times. Using the first extreme value of any time period as the target extreme value, calculate the time difference between the next extreme value adjacent to the target extreme value, and construct the power fluctuation vector in the order of extreme value, time difference, extreme value, time difference; Taking any time period as the target time period, trace back two time periods, namely the first time period and the second time period. Calculate the similarity of the power fluctuation vector between the target time period and the first time period to obtain the first cycle probability, and calculate the similarity of the power fluctuation vector between the target time period and the second time period to obtain the second cycle probability.

3. The power quality monitoring method based on an intelligent fusion terminal according to claim 2, characterized in that, If the probability of the first cycle is greater than or equal to the first preset threshold and the probability of the second cycle is less than the second preset threshold, then the power quality signal is considered to have entered a new cycle. If the probability of the first cycle is less than the first preset threshold and the probability of the second cycle is greater than or equal to the second preset threshold, then it is considered that there is no new cycle in the power quality signal. If the probability of the first cycle is less than the first preset threshold and the probability of the second cycle is less than the second preset threshold, then it is considered that there is no new cycle in the power quality signal. If the probability of the first cycle is greater than or equal to the first preset threshold and the probability of the second cycle is greater than or equal to the second preset threshold, then it is considered that there is no new cycle in the power quality signal.

4. The power quality monitoring method based on an intelligent fusion terminal according to claim 1, characterized in that, The calculation method for the cycle index includes: Initialize the period window with an initial length of 1, and set the starting point of the period window to the start time of the time series. Iterate through each time point in the time series, calculate the standard deviation of the power quality data at the corresponding time point in each period, and sum the standard deviations of all time points to obtain the total fluctuation of the power quality data in the entire period. Multiply the reciprocal of the sum of the fluctuation of the power quality data and the constant term by the reciprocal of the period length to obtain the period exponent.

5. The power quality monitoring method based on an intelligent fusion terminal according to claim 1, characterized in that, The steps for obtaining the optimal period length include: Set the initial length of the periodic window to the minimum unit of 1, and construct the initial periodic window from the starting point of the data sequence. Calculate the periodic index of each data point within the initial periodic window, iterate the periodic window, increment the periodic window length by 1 each time, and recalculate the periodic index within each new periodic window until the periodic window covers the entire data sequence or reaches the preset maximum periodic length limit, forming a periodic index sequence. Select the periodic length corresponding to the first maximum value as the optimal periodic length.

6. The power quality monitoring method based on an intelligent fusion terminal according to claim 1, characterized in that, The method for obtaining the cycle index can also be replaced by: Initialize the period window with an initial length of 1, and set the starting point of the period window to the start time of the time series. Iterate through each time point in the time series, calculate the standard deviation of the power quality data at the corresponding time point in each period, and accumulate the standard deviations of all time points. The accumulated result reflects the total fluctuation of the power quality data in the entire period. Add the sum of the fluctuations of the power quality data to the sum of the constant term and take the reciprocal to obtain the period index.

7. The power quality monitoring method based on an intelligent fusion terminal according to claim 1, characterized in that, The method for obtaining the optimal period length can also be replaced by: Initialize the period window with an initial length of 1. Increment the period length sequentially with a step size of 1. At each length, use Matlab's spline curve fitting function to construct a mapping function with the period length as the x-axis and the period exponent as the y-axis. Calculate the first and second derivatives of the mapping function. Obtain the first minimum point on the second derivative curve. The position corresponding to the minimum point is the maximum change point. Determine the period length corresponding to the maximum change point as the optimal period length.

8. A power quality monitoring system based on an intelligent fusion terminal, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the power quality monitoring method based on a smart fusion terminal according to any one of claims 1-7.

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