An Internet of Things-based power quality governance monitoring system and method

Abstract

The present invention relates to the technical field of power grid monitoring, and specifically to a power quality governance monitoring system and method based on the Internet of Things. The monitoring method includes: Step S1, obtaining evaluation index data of the monitoring area through the perception layer; Step S2, evaluating the power quality of the monitoring area; Step S3, when the evaluation result is abnormal, obtaining the coverage range of the regulating devices already connected in the monitoring area and the fault information occurring in the monitoring area, and judging whether new regulating devices need to be connected; Step S4, if new regulating devices need to be connected, outputting the area where the regulating devices are recommended to be connected. Through the analysis and calculation of the intelligent evaluation records of electric energy, the present invention can accurately judge whether new regulating devices need to be connected; and at the same time, it will also recommend the area where the regulating devices are connected, so as to timely improve the power quality, ensure the regulation effect, reduce the damage of electrical equipment, realize all-round intelligent monitoring, and solve the problem of power pollution.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and specifically to a power quality governance monitoring system and method based on the Internet of Things. Background Art

[0002] Power quality is not only related to the safe and economic operation of power grid enterprises, but also affects the safe operation of users and the quality of products. The power quality monitoring system uses power quality monitoring terminals installed on the power grid side or the user side to transmit monitoring data back to the monitoring center (monitoring master station or substation) through the network, realizing simultaneous monitoring of multiple locations and publishing power quality-related information, which is an effective means for power quality monitoring and evaluation.

[0003] Currently, the methods for governing the power quality of the distribution network are mostly to add some regulating devices or to improve the distribution network lines as a whole. Among them, the method of adding new regulating devices is fast and is the choice of most people. However, which area of the distribution network to add the regulating device to and when to add the regulating device are problems that are neglected to be solved. If the area where the regulating device is connected is inappropriate, the expected regulating effect cannot be achieved. And if the regulating device is not connected in time, it will cause damage to electrical equipment. Based on this, this solution provides a power quality governance monitoring system and method based on the Internet of Things. Summary of the Invention

[0004] The purpose of the present invention is to provide a power quality governance monitoring system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A power quality governance monitoring method based on the Internet of Things, the monitoring method includes the following steps:

[0006] Step S1: Obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer;

[0007] Step S2: The monitoring master station center retrieves the evaluation index data, evaluates the power quality of the monitoring area, and obtains an evaluation result, and the evaluation result includes normal and abnormal;

[0008] Step S3: When the evaluation result is abnormal, obtain the coverage range of the regulating devices already connected in the monitoring area and the fault information occurring in the monitoring area, and judge whether a new regulating device needs to be connected;

[0009] Step S4: If a new regulating device needs to be connected, output the area where the regulating device is recommended to be connected.

[0010] Further, the step S1 includes:

[0011] Divide the power equipment powered by the same substation into the same monitoring area;

[0012] Obtain the evaluation index data in the monitoring area through the power quality monitoring equipment, and the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase unbalance, voltage fluctuation, and voltage flicker;

[0013] The perception layer further includes a current sensor, a voltage sensor, a frequency meter, and an oscilloscope;

[0014] The current sensor is used to monitor the current data of the power equipment, and the voltage sensor is used to monitor the voltage data of the power equipment; the frequency meter is used to monitor the frequencies of the current and voltage, and the oscilloscope is used to display the waveforms of the current and voltage;

[0015] Upload the monitored evaluation index data to the data layer for storage.

[0016] Further, the step S2 includes:

[0017] Obtain the threshold range of each evaluation index data, score each evaluation index data according to the threshold range to form an array [A, B, C, D, E, F], where A is the evaluation index data score of the frequency deviation, B is the evaluation index data score of the harmonic distortion, C is the evaluation index data score of the voltage deviation, D is the evaluation index data score of the three-phase unbalance, E is the evaluation index data score of the voltage fluctuation, and F is the evaluation index data score of the voltage flicker;

[0018] Draw a radar chart, represent each evaluation index data with an axis, all axes extend from the same point, mark the nodes on the corresponding axes according to the score of each evaluation index data, and connect all the nodes with lines to form a closed polygon;

[0019] Calculate the area of the polygon and denote it as S1, obtain the set threshold area as S2. If the area S1 is greater than the area S2, output that the evaluation result of the current power quality is abnormal; if the area S1 is less than the area S2, output that the evaluation result of the current power quality is normal.

[0020] Further, the step S3 includes:

[0021] When the evaluation result is abnormal in step S301, retrieve the top view of the monitoring area, establish a coordinate system, mark the positions of the existing adjustment devices in the monitoring area on the coordinate system as reference points, and at the same time mark the coverage ranges of the adjustment devices on the coordinate system. Take the closed figure formed by the intersection of the monitoring area and the coverage ranges of the adjustment devices as the adjustment area, and mark the area remaining in the monitoring area after removing the adjustment area as the area to be adjusted;

[0022] In step S302, take the time interval between two adjacent acquisitions of the evaluation result as the marked time period, and obtain the fault information that occurred in the monitoring area during the marked time period. The fault information includes fault device information and fault index information. The fault device information is the information of the power equipment that has a fault; the fault index information is the fault type and the occurrence time. The fault type includes one or more of the faults caused by frequency deviation, waveform distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker;

[0023] Mark the positions of all power equipment that has had a fault on the coordinate system and use them as secondary reference points. Extract all the secondary reference points distributed in the area to be adjusted as the analysis targets, obtain the number of faults that occurred to the power equipment corresponding to the analysis targets during the marked time period, and mark one fault occurrence as a tertiary reference point. Count the number of tertiary reference points in the adjustment area;

[0024] At the same time, establish a new coordinate system. Take the time when the power equipment corresponding to the tertiary reference point starts to have a fault as the abscissa, and take the duration of the power equipment having a fault as the ordinate, and mark the tertiary reference points in the adjustment area in the new coordinate system;

[0025] In step S303, when the evaluation result of this item is abnormal, record the number of tertiary reference points in the area to be adjusted as Q1; when the evaluation result of the previous item is abnormal, record the number of tertiary reference points in the area to be adjusted as Q2, and calculate the diffusion value between the two evaluation results. The calculation formula is W = [(Q2 - Q1) / (Q1 + Q2)] / T, where T represents the value quantified by the time difference between this evaluation result and the previous evaluation result. When the diffusion value W is greater than the set threshold, output the result that a new adjustment device needs to be connected; when the diffusion value W is less than the set threshold, output the result that a new adjustment device does not need to be connected.

[0026] Furthermore, step S4 includes:

[0027] Taking the boundary line of the adjustment area as the boundary, divide the unconnected non-adjustment areas, mark them as {Non-adjustment Area 1, Non-adjustment Area 2,..., Non-adjustment Area N}, extract the three-level reference points corresponding to each non-adjustment area, calculate the dispersion degree of the three-level reference points in each non-adjustment area, and output the non-adjustment area corresponding to the smallest dispersion degree as the area recommended to access the new adjustment device.

[0028] An Internet of Things-based power quality governance monitoring system, which is applied to an Internet of Things-based power quality governance monitoring method. The monitoring system includes: a collection module, an evaluation module, a judgment module, and a recommendation module;

[0029] The collection module is used to obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer;

[0030] The evaluation module is used to retrieve the evaluation index data from the monitoring master station center, evaluate the power quality of the monitoring area, and obtain an evaluation result. The evaluation result includes normal and abnormal;

[0031] The judgment module is used to, when the evaluation result is abnormal, obtain the coverage range of the adjustment devices already connected in the monitoring area and the fault information occurring in the monitoring area, and judge whether a new adjustment device needs to be connected;

[0032] The recommendation module is used to, when a new adjustment device needs to be connected, output the area recommended to access the adjustment device.

[0033] Further, the collection module includes an evaluation index unit, and the evaluation index unit is used to obtain the evaluation index data in the monitoring area through a power quality monitoring device. The evaluation index data includes frequency deviation, harmonic distortion, voltage deviation, three-phase unbalance, voltage fluctuation, and voltage flicker.

[0034] Further, the evaluation module includes a quantization unit, a radar scoring unit, and a result output unit;

[0035] The quantization unit is used to obtain the threshold range of each evaluation index data and score each evaluation index data according to the threshold range;

[0036] The radar scoring unit is used to draw a radar chart, represent each evaluation index data with an axis, all axes extend from the same point, mark nodes on the corresponding axes according to the score of each evaluation index data, and connect all the nodes with lines to form a closed polygon;

[0037] The result output unit is used to output the evaluation result of the power quality this time. The evaluation result includes normal and abnormal.

[0038] Further, the judgment module includes a region division unit and a calculation unit;

[0039] The region division unit is used to divide the monitoring region into an adjustment region and a non-adjustment region;

[0040] The calculation unit is used to calculate the change in the diffusion value between two adjacent evaluation results. When the diffusion value W is greater than the set threshold, it outputs the result that a new adjustment device needs to be connected; when the diffusion value W is less than the set threshold, it outputs the result that a new adjustment device does not need to be connected.

[0041] Further, the recommendation module is used to divide the unconnected non-adjustment regions with the boundary line of the adjustment region as the boundary, mark them as {non-adjustment region one, non-adjustment region two,..., non-adjustment region N}, extract the corresponding three-level reference points of each non-adjustment region, calculate the dispersion degree of the three-level reference points in each non-adjustment region, and output the non-adjustment region corresponding to the smallest dispersion degree as the region recommended to connect a new adjustment device.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. Through the analysis and calculation of the intelligent evaluation records of electric energy, the present invention can accurately judge whether a new adjustment device needs to be connected; and at the same time, it will also recommend the region to connect the adjustment device, so as to timely improve the power quality, ensure the adjustment effect, reduce the damage of electrical equipment, realize all-round intelligent monitoring, and solve the problem of power pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic flow chart of a power quality governance monitoring method based on the Internet of Things according to the present invention;

[0045] Figure 2 It is a schematic diagram of the module connection of a power quality governance monitoring system based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] Embodiment 1: As Figure 1 shown, the present invention provides a power quality governance monitoring method based on the Internet of Things. The monitoring method includes the following steps:

[0048] Step S1: Obtain the evaluation index data of the monitoring area through the perception layer, and transmit the evaluation index data to the data layer;

[0049] Specifically, step S1 includes:

[0050] Divide the power equipment powered by the same substation into the same monitoring area;

[0051] Obtain the evaluation index data in the monitoring area through the power quality monitoring equipment, and the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase unbalance, voltage fluctuation, and voltage flicker;

[0052] The perception layer further includes a current sensor, a voltage sensor, a frequency meter, and an oscilloscope;

[0053] The current sensor is used to monitor the current data of the power equipment, and the voltage sensor is used to monitor the voltage data of the power equipment; the frequency meter is used to monitor the frequencies of the current and voltage, and the oscilloscope is used to display the waveforms of the current and voltage;

[0054] Upload the monitored evaluation index data to the data layer for storage.

[0055] Step S2: The monitoring master station center retrieves the evaluation index data, evaluates the power quality of the monitoring area, and obtains an evaluation result, where the evaluation result includes normal and abnormal;

[0056] Specifically, step S2 includes:

[0057] Obtain the threshold range of each evaluation index data, score each evaluation index data according to the threshold range to form an array [A, B, C, D, E, F], where A is the score of the evaluation index data of the frequency deviation, B is the score of the evaluation index data of the harmonic distortion, C is the score of the evaluation index data of the voltage deviation, D is the score of the evaluation index data of the three-phase unbalance, E is the score of the evaluation index data of the voltage fluctuation, and F is the score of the evaluation index data of the voltage flicker;

[0058] Draw a radar chart, represent each evaluation index data with an axis, all axes extend from the same point, mark the nodes on the corresponding axes according to the score of each evaluation index data, and connect all the nodes with lines to form a closed polygon;

[0059] Calculate the area of the polygon and record it as S1, obtain the set threshold area as S2. If the area S1 is greater than the area S2, output the evaluation result of the power quality this time as abnormal. If the area S1 is less than the area S2, output the evaluation result of the power quality this time as normal.

[0060] Step S3: When the evaluation result is abnormal, obtain the coverage range of the adjustment devices already connected in the monitoring area and the fault information that occurred in the monitoring area, and determine whether new adjustment devices need to be connected;

[0061] Specifically, the step S3 includes:

[0062] Step S301: When the evaluation result is abnormal, retrieve the top view of the monitoring area, establish a coordinate system, mark the positions of the existing adjustment devices in the monitoring area on the coordinate system as reference points, and at the same time mark the coverage range of the adjustment devices on the coordinate system. Mark the closed figure where the monitoring area intersects with the coverage range of the adjustment devices as the adjustment area, and mark the remaining area in the monitoring area after excluding the adjustment area as the area to be adjusted;

[0063] Step S302: Take the time interval between two adjacent obtained evaluation results as the marked time period, and obtain the fault information that occurred in the monitoring area during the marked time period. The fault information includes fault device information and fault index information. The fault device information is the information of the power equipment that has failed; the fault index information is the fault type and the occurrence time. The fault type includes one or more of the faults caused by frequency deviation, waveform distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker;

[0064] Mark the positions of all power equipment that has failed on the coordinate system and use them as secondary reference points. Extract all secondary reference points distributed in the area to be adjusted as the analysis targets, obtain the number of faults that occurred to the power equipment corresponding to the analysis targets during the marked time period, and mark one fault occurrence as a tertiary reference point. Count the number of tertiary reference points in the adjustment area;

[0065] At the same time, establish a new coordinate system. Take the time when the power equipment corresponding to the tertiary reference point starts to fail as the abscissa, and take the duration of the power equipment failure as the ordinate, and mark the tertiary reference points in the adjustment area in the new coordinate system;

[0066] Step S303: Denote the number of tertiary reference points in the area to be adjusted when this evaluation result is abnormal as Q1; denote the number of tertiary reference points in the area to be adjusted when the previous evaluation result is abnormal as Q2, and calculate the diffusion value between the two evaluation results. Its calculation formula is W = [(Q2 - Q1) / (Q1 + Q2)] / T, where T represents the value quantified by the time difference between this evaluation result and the previous evaluation result. When the diffusion value W is greater than the set threshold, output the result that new adjustment devices need to be connected; when the diffusion value W is less than the set threshold, output the result that new adjustment devices do not need to be connected.

[0067] Step S4: If a new adjustment device needs to be connected, output the area recommended for connecting the adjustment device.

[0068] Specifically, the step S4 includes:

[0069] Taking the boundary line of the adjustment area as the boundary, divide the unconnected non-adjustment areas, mark them as {non-adjustment area one, non-adjustment area two,..., non-adjustment area N}, extract the three-level reference points corresponding to each non-adjustment area, calculate the dispersion degree of the three-level reference points in each non-adjustment area, and output the non-adjustment area corresponding to the minimum dispersion degree as the area recommended for connecting the new adjustment device;

[0070] Specifically, the dispersion degree of the three-level reference points can be calculated using the mean difference, and its calculation formula is: ;

[0071] Where represents the dispersion degree, Q represents the number of three-level reference points corresponding to a certain non-adjustment area, represents each data point, represents the mean value of the data.

[0072] Embodiment 2: As Figure 2 shown, a power quality governance monitoring system based on the Internet of Things, which is applied to a power quality governance monitoring method based on the Internet of Things. The monitoring system includes: a collection module, an evaluation module, a judgment module, and a recommendation module;

[0073] The collection module is used to obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer;

[0074] Specifically, the collection module includes an evaluation index unit, and the evaluation index unit is used to obtain the evaluation index data in the monitoring area through the power quality monitoring device. The evaluation index data includes frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker.

[0075] The evaluation module is used to retrieve the evaluation index data from the monitoring master station center, evaluate the power quality of the monitoring area, and obtain an evaluation result. The evaluation result includes normal and abnormal;

[0076] Specifically, the evaluation module includes a quantization unit, a radar scoring unit, and a result output unit;

[0077] The quantization unit is used to obtain the threshold range of each evaluation index data and score each evaluation index data according to the threshold range;

[0078] The radar scoring unit is used to draw a radar chart. Each evaluation index data is represented by an axis, and all axes extend from the same point. According to the score value of each evaluation index data, nodes are marked on the corresponding axis, and all nodes are connected by lines to form a closed polygon.

[0079] The result output unit is used to output the evaluation result of the power quality this time, and the evaluation result includes normal and abnormal.

[0080] The judgment module is used to, when the evaluation result is abnormal, obtain the coverage range of the regulating devices already connected in the monitoring area and the fault information occurring in the monitoring area, and judge whether new regulating devices need to be connected.

[0081] Specifically, the judgment module includes a region division unit and a calculation unit.

[0082] The region division unit is used to divide the monitoring area into a regulating area and a non-regulating area.

[0083] The calculation unit is used to calculate the change in the diffusion value between two adjacent evaluation results. When the diffusion value W is greater than the set threshold, it outputs the result that new regulating devices need to be connected; when the diffusion value W is less than the set threshold, it outputs the result that new regulating devices do not need to be connected.

[0084] The recommendation module is used to, when new regulating devices need to be connected, output the area recommended for connecting the regulating devices.

[0085] Specifically, the recommendation module is used to divide the unconnected non-regulating areas with the boundary line of the regulating area as the boundary, mark them as {non-regulating area one, non-regulating area two,..., non-regulating area N}, extract the corresponding third-level reference points of each non-regulating area, calculate the dispersion degree of the third-level reference points in each non-regulating area, and output the non-regulating area corresponding to the minimum dispersion degree as the area recommended for connecting new regulating devices.

[0086] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A power quality governance monitoring method based on the Internet of Things, characterized in that: The monitoring method includes the following steps: Step S1: Obtain the evaluation index data of the monitoring area through the perception layer, and transmit the evaluation index data to the data layer; Step S2: The monitoring master station center retrieves the evaluation index data, evaluates the power quality of the monitoring area, and obtains an evaluation result, where the evaluation result includes normal and abnormal; Step S3: When the evaluation result is abnormal, obtain the coverage range of the adjustment devices already connected in the monitoring area and the fault information that occurred in the monitoring area, and determine whether a new adjustment device needs to be connected; Step S4: If a new adjustment device needs to be connected, output the area where the adjustment device is recommended to be connected; The said Step S3 includes: Step S301: When the evaluation result is abnormal, retrieve the top view of the monitoring area, establish a coordinate system, mark the positions of the existing adjustment devices in the monitoring area on the coordinate system, and use them as reference points. At the same time, mark the coverage range of the adjustment devices on the coordinate system. The closed figure formed by the intersection of the monitoring area and the coverage range of the adjustment devices is used as the adjustment area, and the area remaining in the monitoring area after removing the adjustment area is marked as the area to be adjusted; Step S302: Use the time interval between two adjacent evaluations as the marked time period, and obtain the fault information that occurred in the monitoring area during the marked time period. The fault information includes fault device information and fault index information. The fault device information is the information of the power equipment that has failed; the fault index information is the fault type and the occurrence time, and the fault type includes one or more of the faults caused by frequency deviation, waveform distortion, voltage deviation, three-phase imbalance, voltage fluctuation, and voltage flicker; Mark the positions of all power equipment that has failed on the coordinate system and use them as secondary reference points. Extract all secondary reference points distributed in the area to be adjusted as the analysis targets, obtain the number of faults that occurred to the power equipment corresponding to the analysis targets during the marked time period, and mark one fault occurrence as a tertiary reference point, and count the number of tertiary reference points in the adjustment area; At the same time, establish a new coordinate system, use the time when the power equipment corresponding to the tertiary reference point starts to fail as the abscissa, and use the duration of the power equipment failure as the ordinate, and mark the tertiary reference points in the adjustment area in the new coordinate system; Step S303: When the evaluation result of this item is abnormal, record the number of tertiary reference points in the area to be adjusted as Q1; when the evaluation result of the previous item is abnormal, record the number of tertiary reference points in the area to be adjusted as Q2, and calculate the diffusion value between the two evaluation results. The calculation formula is W = [(Q2 - Q1) / (Q1 + Q2)] / T, where T represents the value quantified by the time difference between this evaluation result and the previous evaluation result. When the diffusion value W is greater than the set threshold, output the result that a new adjustment device needs to be connected; when the diffusion value W is less than the set threshold, output the result that a new adjustment device does not need to be connected.

2. The method for monitoring power quality governance based on the Internet of Things according to claim 1, characterized in that: The said Step S1 includes: Power equipment powered by the same substation is divided into the same monitoring area; Evaluation index data in the monitoring area is obtained through power quality monitoring equipment, and the evaluation index data includes one or more of frequency deviation, harmonic distortion, voltage deviation, three-phase unbalance, voltage fluctuation, and voltage flicker; The perception layer further includes a current sensor, a voltage sensor, a frequency meter, and an oscilloscope; The current sensor is used to monitor the current data of power equipment, and the voltage sensor is used to monitor the voltage data of power equipment; the frequency meter is used to monitor the frequencies of current and voltage, and the oscilloscope is used to display the waveforms of current and voltage; The monitored evaluation index data is uploaded to the data layer for storage.

3. The method for monitoring power quality governance based on the Internet of Things according to claim 1, characterized in that: The step S2 includes: Obtain the threshold range of each evaluation index data, score each evaluation index data according to the threshold range, and form an array [A, B, C, D, E, F], where A is the score of the evaluation index data of frequency deviation, B is the score of the evaluation index data of harmonic distortion, C is the score of the evaluation index data of voltage deviation, D is the score of the evaluation index data of three-phase unbalance, E is the score of the evaluation index data of voltage fluctuation, and F is the score of the evaluation index data of voltage flicker; Draw a radar chart, represent each evaluation index data with an axis, and all axes extend from the same point. Mark nodes on the corresponding axes according to the score of each evaluation index data, and connect all the nodes with lines to form a closed polygon; Calculate the area of the polygon and denote it as S1, obtain the set threshold area as S2. If the area S1 is greater than the area S2, output that the evaluation result of the current power quality is abnormal; if the area S1 is less than the area S2, output that the evaluation result of the current power quality is normal.

4. A power quality governance monitoring method based on the Internet of Things according to claim 1, characterized in that: The step S4 includes: Taking the boundary line of the adjustment area as the boundary, divide the non-connected non-adjustment areas, mark them as {non-adjustment area one, non-adjustment area two,..., non-adjustment area N}, extract the three-level reference points corresponding to each non-adjustment area, calculate the dispersion degree of the three-level reference points in each non-adjustment area, and output the non-adjustment area corresponding to the minimum dispersion degree as the area recommended for connecting new adjustment equipment.

5. A power quality governance monitoring system based on the Internet of Things, which is applied to a power quality governance monitoring method based on the Internet of Things described in any one of claims 1-4, and is characterized in that: The monitoring system includes: a collection module, an evaluation module, a judgment module, and a recommendation module; The collection module is used to obtain the evaluation index data of the monitoring area through the perception layer and transmit the evaluation index data to the data layer; The evaluation module is used to retrieve the evaluation index data from the monitoring main station center, evaluate the power quality of the monitoring area, and obtain an evaluation result, where the evaluation result includes normal and abnormal; The judgment module is used to, when the evaluation result is abnormal, obtain the coverage range of the adjustment equipment already connected in the monitoring area and the fault information occurring in the monitoring area, and judge whether a new adjustment equipment needs to be connected; The recommendation module is used to output the area recommended for connecting the adjustment equipment when a new adjustment equipment needs to be connected.

6. The power quality governance monitoring system based on the Internet of Things according to claim 5, wherein: The acquisition module includes an evaluation index unit, which is used to obtain evaluation index data in the monitoring area through a power quality monitoring device. The evaluation index data includes frequency deviation, harmonic distortion, voltage deviation, three-phase imbalance, voltage fluctuation and voltage flicker.

7. The power quality governance monitoring system based on the Internet of Things according to claim 5, characterized in that: The evaluation module includes a quantization unit, a radar scoring unit and a result output unit; The quantization unit is used to obtain the threshold range of each evaluation index data and score each evaluation index data according to the threshold range; The radar scoring unit is used to draw a radar chart, represent each evaluation index data with an axis, and all axes extend from the same point. According to the score of each evaluation index data, mark the nodes on the corresponding axis and connect all the nodes with lines to form a closed polygon; The result output unit is used to output the evaluation result of the power quality this time. The evaluation result includes normal and abnormal.

8. An Internet of Things-based power quality governance monitoring system according to claim 5, characterized in that: The judgment module includes a region division unit and a calculation unit; The region division unit is used to divide the monitoring area into an adjustment area and a non-adjustment area; The calculation unit is used to calculate the change in the diffusion value between two adjacent evaluation results. When the diffusion value W is greater than the set threshold, output the result that a new adjustment device needs to be connected; when the diffusion value W is less than the set threshold, output the result that a new adjustment device does not need to be connected.

9. The power quality governance monitoring system based on the Internet of Things according to claim 5, characterized in that: The recommendation module is used to divide the unconnected non-adjustment areas with the boundary line of the adjustment area as the boundary, mark them as {non-adjustment area 1, non-adjustment area 2,..., non-adjustment area N}, extract the corresponding third-level reference points of each non-adjustment area, calculate the dispersion degree of the third-level reference points in each non-adjustment area, and output the non-adjustment area with the smallest dispersion degree as the area recommended to connect a new adjustment device.

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