Intelligent power distribution cabinet and power distribution cabinet electric energy quality detection system

Abstract

The invention belongs to the technical field of power distribution cabinet electric energy detection, and provides an intelligent power distribution cabinet and a power distribution cabinet electric energy quality detection system, and the system comprises a loop classification module which obtains static design attribute data and dynamic operation data of each power supply loop, and divides the power supply loops in the power distribution cabinet into key loops and non-key loops; the data acquisition module is used for acquiring running state data of a non-critical loop in real time to form a real-time running state data table; and the detection optimization module is used for cooperatively evaluating the interruption influence risk and the detection efficiency when the power quality detection is carried out on the non-key loop based on the operation state data of the non-key loop. Therefore, a stable closed loop of a detection task is ensured, the problems that segmentation and loop detection depends on manpower, and risks and efficiency are unbalanced are solved finally, low-interruption-tolerance scenes such as a data center and industrial production are adapted, and the detection targets of safe priority of key loops and efficient balance of non-key loops are achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution cabinet electric energy detection, and particularly relates to an intelligent power distribution cabinet and a power distribution cabinet electric energy quality detection system. BACKGROUND

[0002] Power distribution cabinet electric energy quality detection is a core link for guaranteeing stable operation of a power system and avoiding equipment damage and business interruption risks;

[0003] Detection strategies need to closely adapt to power supply reliability requirements in different scenarios, such as data centers and industrial production scenarios, which have extremely low tolerance to power supply interruption, so detection needs to be completed without interrupting power supply. At present, the industry generally adopts a segmented and loop detection method, that is, all loops of a power distribution cabinet are not detected at one time, but are gradually promoted to ensure continuous power supply for critical loads.

[0004] However, when using the segmented and loop detection method, the monitoring sequence and the number of simultaneous detections are mostly dependent on manual experience, which leads to the inability to coordinate interruption impact risks and detection efficiency in the detection process. For example, when detecting non-critical loops, if the number of parallel detections is set only by subjective judgment, it is easy to cause extreme situations such as insufficient efficiency (such as detecting only one loop at a time, resulting in a long detection period for a large number of non-critical loops) or risk out of control (such as blindly increasing the number of parallel detections, ignoring the chain impact that may be caused by non-critical loop failures during high-load periods).

[0005] Therefore, the application provides an intelligent power distribution cabinet and a power distribution cabinet electric energy quality detection system. SUMMARY

[0006] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0007] The technical scheme adopted by the application to solve the technical problems is: a power distribution cabinet electric energy quality detection system, comprising:

[0008] A loop classification module: obtains static design attribute data and dynamic operation data of each power supply loop, and divides the power supply loops in the power distribution cabinet into critical loops and non-critical loops;

[0009] A data acquisition module: real-time acquisition of operation state data of non-critical loops to form a real-time operation state data table;

[0010] A detection optimization module: based on the operation state data of non-critical loops, cooperatively evaluates the interruption impact risk and detection efficiency when performing electric energy quality detection on non-critical loops, balances the interruption impact risk and the detection efficiency as an optimization target, and dynamically calculates the optimal number of parallel electric energy quality detections on the non-critical loops in a single detection period;

[0011] Detection and control module: Based on the optimal number of parallel power quality detections, schedules power quality detection devices to perform synchronous detection on a corresponding number of non-critical circuits.

[0012] An intelligent power distribution cabinet includes: a cabinet body, which internally houses a main busbar compartment, a branch circuit compartment, a cable compartment, and an intelligent control compartment;

[0013] It also includes: a power supply circuit system located in the branch circuit room, a power quality detection device integrated in the cable room, and an intelligent control system installed in the intelligent control room.

[0014] The beneficial effects of this invention are as follows:

[0015] This invention forms a collaborative closed loop with its loop classification module, data acquisition module, detection optimization module, and detection control module. Through dual-judgment, it accurately distinguishes between critical and non-critical loops, laying the foundation for differentiated detection and reducing the risk of critical loop interruptions. It collects real-time, reliable operational data with a high sampling rate and standardized processes, providing precise input for decision-making. It dynamically calculates the optimal number of parallel detections for non-critical loops, replacing manual experience to achieve a dynamic balance between risk and efficiency. It precisely schedules detection devices and monitors the execution process in real time, ensuring a stable closed loop for the detection task. Ultimately, it optimizes the problem of segmented loop detection relying on manual labor and the imbalance between risk and efficiency, adapting to low-interruption-tolerance scenarios such as data centers and industrial production, achieving the detection goal of prioritizing the safety of critical loops and achieving a high-efficiency balance for non-critical loops. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a modular architecture diagram of a power quality detection system for a power distribution cabinet according to the present invention;

[0018] Figure 2 This is a flowchart of the steps of a power quality detection method for a power distribution cabinet according to the present invention;

[0019] Figure 3 This is a structural diagram of an intelligent power distribution cabinet according to the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] Example 1

[0022] Please see Figure 1As shown in the embodiment of the present invention, a power quality detection system for a distribution cabinet addresses the problem that when using a segmented, circuit-based detection method, the power quality detection of the distribution cabinet without interrupting power supply relies on manual experience to formulate parallel detection strategies, which are mostly single-circuit detection strategies. This leads to the inability to coordinate and optimize detection efficiency and the risk of power outages. By classifying circuits according to their importance to focus on optimization objectives, the system dynamically calculates the optimal number of parallel detections to balance efficiency and risk under different detection cycles. Thus, without interrupting power supply, a dynamic balance is achieved between maximizing detection efficiency and minimizing operational risk. The system specifically includes:

[0023] Circuit classification module: used to classify power supply circuits in the distribution cabinet into critical circuits and non-critical circuits;

[0024] The execution process of this module is as follows: Data is obtained from two independent systems through data interfaces, including:

[0025] Retrieve static design attribute data for each power supply circuit from the metadata database of the power distribution management system (PMS);

[0026] Retrieve dynamic operating data of each power supply circuit from the historical database of the Supervisory Control and Data Acquisition (SCADA) system;

[0027] Specifically, static design attribute data includes: allowable voltage sag duration, taken from the minimum voltage sag tolerance value explicitly defined in the technical manual of the connected load equipment;

[0028] Interruption impact rating: This is a string type. According to the load classification in the power supply and distribution system design specifications, the power supply circuit is divided into: Class A (corresponding to first-level loads, interruption of power supply will lead to significant economic losses or complete shutdown), Class B (corresponding to second-level loads, interruption of power supply will lead to relatively large economic losses or partial shutdown), and Class C (corresponding to third-level loads, interruption of power supply has a minor or no impact).

[0029] Dynamic operating data consists of load rate samples recorded every 15 minutes for the power supply circuit to be determined over the past 30 calendar days. The load rate sample value is calculated as (real-time power of the circuit / rated capacity of the circuit) × 100%.

[0030] Perform classification based on static design attribute data, and for each power supply circuit in the distribution cabinet, perform the following judgments:

[0031] Condition 1: If the allowable voltage sag duration is less than the first preset sag duration (20ms) or the power supply circuit interruption impact level is classified as Level A, then the corresponding power supply circuit will be marked as a critical circuit.

[0032] Condition 2: If the allowable voltage sag duration is less than or equal to the second preset sag duration (100ms) and the power supply circuit interruption impact level is classified as Level C, then the corresponding power supply circuit will be marked as a non-critical circuit.

[0033] If a power supply circuit does not meet conditions one and two above, the corresponding power supply circuit will be initially classified as a circuit to be determined, and classification based on dynamic operating data will be performed.

[0034] For any undetermined loop, calculate the historical peak load rate and the average daily load fluctuation.

[0035] Historical peak load rate: The maximum value of all load rate samples in the dynamic operation data;

[0036] Average daily load fluctuation: Calculate the daily load fluctuation value of the circuit to be determined each day and perform an average calculation to obtain the average daily load fluctuation.

[0037] Among them, the daily load fluctuation value is the difference between the maximum and minimum values ​​of the daily load sampling values;

[0038] If the historical peak load rate is greater than or equal to the preset peak (60%) or the daily average load fluctuation is greater than the preset average fluctuation (10%), the corresponding undetermined circuit will be marked as a critical circuit.

[0039] If the historical peak load rate is less than the preset peak (60%) and the average daily load fluctuation is less than or equal to the preset average fluctuation (10%), then the corresponding undetermined circuit will be marked as a non-critical circuit.

[0040] By using both static design attributes and dynamic operational data to determine critical and non-critical circuits, we can ensure that critical circuits are given priority in receiving continuous power during the testing process.

[0041] The aforementioned preset values ​​are all based on industry standards.

[0042] The classification results provide data support for the subsequent detection optimization module. A single-loop detection method is used for critical loops, while for non-critical loops, the optimal number of parallel detections can be dynamically calculated based on the classification results, so as to achieve differentiated detection that prioritizes the safety of critical loops and balances the efficiency and risk of non-critical loops.

[0043] Furthermore, based on the classification of critical and non-critical circuits, the protection sequence can prioritize critical loads, first detecting non-critical circuits and then critical circuits. This reduces the risk of disturbances and interruptions to critical circuits caused by detection operations, minimizes the premature exposure of critical circuits to detection wiring, equipment connection, and other operations, reduces voltage dips or momentary interruptions in critical circuits caused by human error or compatibility issues with detection equipment, and adapts to the low interruption tolerance characteristics of critical loads.

[0044] Data acquisition module: used to collect real-time operating status data of non-critical loops and form a real-time operating status data table;

[0045] The module is executed as follows: it is executed by the intelligent power quality monitoring terminal installed on each non-critical circuit. The core component of the terminal is a dedicated metering chip, which is integrated with an Ethernet or RS-485 communication interface.

[0046] The intelligent power quality monitoring terminal uses internal voltage / current sensors to synchronously sample the original voltage and current waveforms of the circuit at a sampling rate of 5120 points per second.

[0047] Real-time acquisition of operating status data for non-critical circuits, including: real-time load rate, total harmonic distortion of current, and current power factor;

[0048] Specifically, the real-time load rate acquisition process is as follows: every 3 seconds is a calculation cycle. Within a calculation cycle, the instantaneous values ​​of voltage and current are collected and calculated to obtain the instantaneous value of active power. The arithmetic mean of the absolute values ​​is taken as the average active power of the corresponding calculation cycle. The percentage of the average active power to the rated capacity of the circuit is calculated as the real-time load rate.

[0049] The process of obtaining the total harmonic distortion rate of the current is as follows: with every 10 power frequency cycles (i.e., 200 milliseconds) as a data window, the current waveform is analyzed by Fast Fourier Transform (FFT) to extract the effective values ​​of the 2nd to 50th harmonic currents, the sum of squares of the effective values ​​of each harmonic current is calculated, the arithmetic square root of the sum of squares is calculated to obtain the total effective value of the harmonic current, and the percentage of the total effective value of the harmonic current to the effective value of the fundamental current is calculated as the total harmonic distortion rate of the current.

[0050] The current power factor is obtained as follows: within the same 3-second calculation cycle, the average apparent power and the average active power are calculated simultaneously, and the ratio of the average active power to the average apparent power is calculated as the current power factor.

[0051] The calculated real-time load rate and total harmonic distortion rate of the current are filtered and the filtered result is output. The three processed operating status data are packaged together with the unique number of the corresponding power supply circuit and the data timestamp into a standardized data frame.

[0052] The data frames are temporarily cached in the local memory queue of the monitoring terminal, with a queue depth of 100 records;

[0053] At a 1-second interval, the system polls and reads the latest data frames from the memory queues of each monitoring terminal using the Modbus TCP protocol.

[0054] After a successful read, the main control unit parses the running status data from the data frame and updates it to the real-time running status data table in the system memory.

[0055] The detection optimization module communicates with the loop classification module and the data acquisition module. Based on the operating status data of non-critical loops, it collaboratively evaluates the risk of interruption and detection efficiency when performing power quality detection on non-critical loops. With the optimization goal of balancing the risk of interruption and the detection efficiency, it dynamically calculates the optimal number of parallel power quality detections performed on the non-critical loops in a single detection cycle.

[0056] The module executes as follows: It reads the latest operating status data for each non-critical item from the real-time operating status data table, including the real-time load rate, total harmonic distortion of current, and current power factor.

[0057] The process for assessing the risk of disruption is as follows:

[0058] For any non-critical circuit to be evaluated, the real-time load rate is multiplied by the total harmonic distortion rate of the current, and then the calculated product is multiplied by the reciprocal of the absolute value of the current power factor to obtain the comprehensive risk factor.

[0059] It should be noted that, for the power supply circuit, the real-time load is the main contributor to the risk. The total harmonic distortion rate of the current is used to assess the additional impact of harmonic pollution on the power supply stability of the distribution cabinet. The reciprocal of the absolute value of the current power factor is used to characterize the additional pressure of reactive power on the power supply of the distribution cabinet. The comprehensive risk factor comprehensively reflects the degree of impact caused by the interruption of the power supply circuit in the current state.

[0060] Based on a preset number of parallel detections, where the preset number of parallel detections is a temporary assumption made during the calculation process if N loops are detected simultaneously, i.e., N=1,2,3...;

[0061] Iterate through each parallel detection count and calculate the corresponding interruption impact risk coefficient;

[0062] The interruption impact risk coefficient is the average of the comprehensive risk factors of all non-critical loops detected simultaneously, multiplied by the average comprehensive risk factor and the risk amplification coefficient, and output as the interruption impact risk coefficient for the corresponding number of parallel detections.

[0063] Among them, the risk method coefficient is a constant greater than 1, which is used to amplify the risk under high load, for example, it is set to 2.0;

[0064] The process for evaluating detection efficiency is as follows:

[0065] Iterate through each parallel detection count and calculate the corresponding efficiency loss coefficient;

[0066] For each number of parallel detections, the ratio of the baseline detection time for a single non-critical loop to the number of parallel detections is calculated to obtain the equivalent single loop detection time shortened due to parallel processing.

[0067] The benchmark duration for testing a single non-critical circuit was obtained by those skilled in the art based on power quality testing standards and power quality testing terminal standards.

[0068] Calculate the ratio of the total number of non-critical loops to be inspected in the current inspection cycle to the number of parallel inspections, and use this as the theoretical number of inspection batches required to complete the inspection of non-critical loops.

[0069] The product of the equivalent single-loop detection time and the theoretical detection batch is output as the efficiency loss coefficient for the corresponding number of parallel detections.

[0070] It should be noted that the efficiency loss coefficient is essentially the total virtual detection time required to complete the detection of all non-critical loops. It correlates the number of parallel detections with the total number of loops and optimizes two mutually restrictive factors: single detection speed (improved through parallel processing) and detection batches (the more parallel processing, the fewer batches, but the risk per detection may increase). Therefore, the efficiency loss coefficient reflects a system-level, trade-off efficiency perspective, guiding the optimization algorithm to find the optimal number that can both accelerate the single detection through parallel processing and control the total number of detection batches.

[0071] The optimal number of parallel power quality sensors to be performed is determined using the Pareto optimal frontier screening method. The process is as follows:

[0072] Iterate through all candidate parallel detection counts. Each count N corresponds to a two-dimensional vector (E, R) consisting of an efficiency loss coefficient E and an interruption impact risk coefficient R. Based on all two-dimensional vectors, construct a candidate solution set.

[0073] A Pareto optimal solution (or non-dominated solution) is selected from the candidate solution set. A solution is called Pareto optimal if and only if there is no other solution that is better than it in both efficiency loss and interruption risk, or in at least one of the indicators that is strictly better than it while the other is not worse.

[0074] The selection logic is as follows: For a candidate solution N1 (E1, R1), if there is no other candidate solution N2 (E2, R2) that can simultaneously satisfy E2≤E1 and R2≤R1, and at least one of them is strictly less than, then N1 is a Pareto optimal solution, and all solutions that satisfy this condition constitute the Pareto optimal frontier.

[0075] The final solution is determined based on the Pareto optimal frontier. On the curve formed by the Pareto optimal solutions, the most reasonable compromise point is selected, and the shortest distance ideal point method is used.

[0076] In a two-dimensional coordinate system, the ideal point is (E_min, R_min), which is the point formed by taking the minimum value of the efficiency loss coefficient and the minimum value of the interruption impact risk coefficient among all candidate solutions. This point is usually unattainable in practice and is a perfect ideal target.

[0077] Calculate the Euclidean distance between the point corresponding to each solution on the Pareto optimal front and the ideal point, and select the number of parallel detections with the shortest Euclidean distance to the ideal point as the final optimal number of parallel detections.

[0078] It should be noted that the calculated optimal number of parallel detections is the optimal solution dynamically calculated for the current detection cycle, rather than a fixed global parameter. It will change with the system state in different detection cycles, which constitutes the core of dynamic adaptive optimization in this embodiment.

[0079] The direct reason and basis for the difference in the optimal quantity for each testing cycle is as follows:

[0080] Firstly, the real-time load rate, total harmonic distortion rate of current, and current power factor are all dynamic parameters that change over time. For example, during peak load periods in the power distribution system, the real-time load rate of non-critical circuits is generally higher, and the calculated risk coefficient of interruption impact will increase significantly. In this case, in order to control the risk, the optimization algorithm will tend to select a smaller optimal number of parallel detections. Conversely, during off-peak load periods, the carrying capacity is stronger, and in order to improve detection efficiency, the algorithm may output a larger optimal number of parallel detections.

[0081] Secondly, the total number of non-critical loops to be inspected may vary in each inspection cycle. Loops that have completed the inspection of the previous cycle will be removed from the inspection list, while loops newly included in the maintenance plan will be added. The change in the total number of loops to be inspected directly affects the calculation result of the efficiency loss coefficient, which will inevitably lead to the shift of the global optimal solution.

[0082] Third, since the input data (risk and efficiency) of the above two dimensions are different in different detection cycles, the form of the solution space composed of all candidate parallel quantities, as well as the shape and position of the Pareto optimal front selected from it, are unique in each detection cycle. Therefore, the optimal compromise point found by the shortest distance ideal point method, that is, the optimal number of parallel detections, is also for the data of the current cycle.

[0083] By acquiring the loop classification results and reading the real-time operating status data of non-critical loops, data support is provided for risk and efficiency assessment, reducing the possibility of decisions being divorced from the actual operating status.

[0084] By analyzing the risks of interruptions, we can clarify the overall risk level under different numbers of parallel detections and reduce the risk accumulation caused by blindly increasing the number of parallel detections.

[0085] By analyzing detection efficiency, the weighting of parallel acceleration and detection batches is quantified to reflect the efficiency level under different parallel detection quantities. Furthermore, the efficiency assessment is combined with the change in the total number of circuits to be detected, ensuring that the efficiency calculation adapts to dynamic scenarios of increasing or decreasing circuits, and reducing the problem of excessively long actual detection cycles caused by fixed efficiency standards.

[0086] Non-dominated solutions are screened using the Pareto optimal frontier screening method, and then the shortest distance ideal point method is used to find the best trade-off between risk and efficiency, determining the optimal number of parallel operations for the current detection cycle, replacing the fixed number set manually in the traditional way. At the same time, the optimal number is dynamically adjusted with the detection cycle to adapt to the increase or decrease of the total number of loops to be detected, ensuring that the decision in each cycle is in line with the real-time system state, avoiding risk loss of control or low efficiency caused by a one-size-fits-all approach.

[0087] The calculated optimal number of parallel circuits is output to the detection control module, providing a clear execution basis for the synchronous detection of non-critical circuits. This ensures that the detection control module can schedule the detection devices and implement a differentiated strategy of detecting non-critical circuits with the optimal number of parallel circuits and detecting critical circuits in a single-circuit manner. Ultimately, this optimizes the problem of efficiency and risk not being able to be optimized in a coordinated manner in segmented and segmented circuit detection.

[0088] The detection control module communicates with the detection optimization module and schedules the power quality detection devices to perform synchronous detection on a corresponding number of non-critical circuits based on the optimal number of parallel power quality detections.

[0089] The execution process of this module is as follows: It receives the optimal parallel detection quantity instruction from the detection optimization module in real time;

[0090] Obtain the identifier list of all non-critical loops from the classification list generated by the loop classification module;

[0091] Based on the optimal number of parallel detections, a round-robin scheduling algorithm is used to select the appropriate number of loops from the list of non-critical loops in sequence to form the batch list for this detection.

[0092] Send a device status query command to the connected power quality detection device pool to verify the availability of each detection device;

[0093] Check the system resource usage and confirm that the communication bandwidth and data processing unit load indicators are all below the preset thresholds (e.g., CPU utilization ≤70%, memory utilization ≤80%).

[0094] Based on the number of batches in this test, allocate a corresponding number of testing devices from the available equipment pool, and assign a specific loop identifier to each device for testing.

[0095] A standardized testing task instruction package is issued to each assigned testing device, containing:

[0096] Target loop identifier;

[0097] Detection parameter configuration (sampling rate: 256 points / cycle, detection duration: 300 seconds, measurement items: voltage, current, harmonics, flicker, etc.);

[0098] Data upload format specifications;

[0099] Start the distributed task monitor and poll the task status of each detection device every 1 second.

[0100] Monitor the detection progress of each loop in real time, and record the start time, percentage of completion, and abnormal alarm information;

[0101] Receive test result data uploaded by each testing device and generate a batch report for this test, including: the number of successfully tested circuits, the number of failed circuits and their reasons, the test time, and statistics of key power quality indicators;

[0102] Send the completion status of this detection task to the task scheduling center and remove the loops that have successfully completed the detection from the queue of non-critical loops to be detected;

[0103] Update the last detection timestamp of the corresponding loop in the real-time operation status data table.

[0104] This embodiment forms a collaborative closed loop through a loop classification module, a data acquisition module, a detection optimization module, and a detection control module. Its core effects are: precise differentiation of critical and non-critical loops through dual-judgment, laying the foundation for differentiated detection and reducing the risk of critical loop interruptions; real-time and reliable operational data acquisition with high sampling rates and standardized processes, providing accurate input for decision-making; dynamic calculation of the optimal number of parallel detections for non-critical loops, replacing manual experience to achieve a dynamic balance between risk and efficiency; precise scheduling of detection devices and real-time monitoring of the execution process, thereby ensuring a stable closed loop for the detection task. Ultimately, it optimizes the problem of segmented and loop-based detection relying on manual labor and the imbalance between risk and efficiency, adapting to low-interruption-tolerance scenarios such as data centers and industrial production, achieving the detection goal of prioritizing the safety of critical loops and achieving a high-efficiency balance between non-critical loops.

[0105] Example 2

[0106] Based on the same inventive concept as the power quality detection system for a distribution cabinet in the foregoing embodiments, such as Figure 2 As shown, this application provides a method for detecting the power quality of a power distribution cabinet, wherein the method specifically includes the following steps:

[0107] Step 1: Divide the power supply circuits in the distribution cabinet into at least critical circuits and non-critical circuits;

[0108] Step 2: Collect real-time operating status data of non-critical circuits to form a real-time operating status data table;

[0109] Step 3: Based on the operating status data of non-critical circuits, collaboratively assess the risk of interruption and detection efficiency when performing power quality testing on non-critical circuits. With the optimization objective of balancing the risk of interruption and the detection efficiency, dynamically calculate the optimal number of parallel power quality tests to be performed on the non-critical circuits in a single detection cycle.

[0110] Step 4: Based on the optimal number of parallel power quality tests, schedule the power quality testing devices to perform synchronous testing on the corresponding number of non-critical circuits.

[0111] Example 3

[0112] Based on the foregoing embodiments, such as Figure 3 As shown, this application provides an intelligent power distribution cabinet, including:

[0113] The cabinet contains a main busbar compartment, a branch circuit compartment, a cable compartment, and an intelligent control compartment.

[0114] The power supply circuit system is located in the branch circuit room and includes at least one critical circuit and multiple non-critical circuits. Each circuit is equipped with a circuit breaker, contactor and current transformer.

[0115] The power quality detection device is integrated into the cable room and includes multiple distributed power quality monitoring terminals, which are installed on the outgoing side of each non-critical circuit.

[0116] The intelligent control system, installed in the intelligent control room, includes: a main control unit, which adopts a multi-core processor and is pre-installed with the power quality detection system of the power distribution cabinet described in Example 1;

[0117] The data storage unit is equipped with a solid-state drive for storing historical operating data and test results;

[0118] The communication gateway supports multiple industrial protocols such as Modbus TCP and OPC UA, and is used for data interaction with power distribution management systems (PMS) and power distribution monitoring systems (SCADA).

[0119] The human-machine interface is equipped with a 7-inch touchscreen to display real-time operating status, detection progress and alarm information;

[0120] The power management unit provides reliable purified power to all intelligent components and has overvoltage and overcurrent protection functions.

[0121] Safety isolation components include opto-isolators installed at the critical loop testing interface to ensure the power supply safety of critical loads during testing.

[0122] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power quality detection system for an electrical switchgear cabinet, characterized by: Comprise: Loop classification module: obtain the static design attribute data and dynamic operation data of each power supply loop, and divide the power supply loop in the power distribution cabinet into key loop and non-key loop; Data acquisition module: real-time acquisition of the running state data of non-key loop, forming real-time running state data table; Detection optimization module: based on the running state data of non-key loop, cooperatively evaluate the interruption influence risk and detection efficiency when detecting the power quality of non-key loop, balance the interruption influence risk and the detection efficiency as the optimization goal, dynamically calculate the optimal number of parallel power quality detection of non-key loop in a single detection cycle; Detection control module: according to the optimal number of parallel power quality detection, dispatch power quality detection device to execute synchronous detection on corresponding number of non-key loop.

2. The power quality detection system for power distribution cabinet according to claim 1, characterized in that: The process of dividing into key loop and non-key loop is: Perform classification based on static design attribute data, initially classify each power supply loop in the power distribution cabinet, divide non-key loop, key loop and pending loop; Classify the pending loop based on dynamic operation data, divide into key loop and non-key loop.

3. The power quality detection system for power distribution cabinet according to claim 2, characterized in that: The classification process based on static design attribute data is: The static design attribute data includes allowable voltage sag duration and interruption influence level, and the interruption influence level includes A level, B level and C level; For each power supply loop in the power distribution cabinet, the following judgment is performed: If the allowable voltage sag duration is less than the first preset sag duration or the power supply loop interruption influence level is divided into A level, it is marked as key loop; If the allowable voltage sag duration is less than or equal to the second preset sag duration and the power supply loop interruption influence level is divided into C level, it is marked as non-key loop; If the power supply loop does not meet the above judgment, it is marked as pending loop.

4. The power quality detection system for power distribution cabinet according to claim 2, characterized in that: The classification process based on dynamic operation data is: Take the maximum value of all load rate sampling values in the dynamic operation data as the historical load rate peak value; Calculate the daily load fluctuation value of the pending loop day by day, and calculate the mean value to get the daily load fluctuation mean value; If the historical load rate peak value is greater than or equal to the preset peak value or the daily load fluctuation mean value is greater than the preset fluctuation mean value, it is marked as key loop; If the historical load rate peak value is less than the preset peak value and the daily load fluctuation mean value is less than or equal to the preset fluctuation mean value, it is marked as non-key loop.

5. The power quality detection system for power distribution cabinet according to claim 1, characterized in that: The acquisition process of real-time running state data table is: Real-time acquisition of real-time load rate, current total harmonic distortion rate and current power factor of non-key loop; Filter the calculated real-time load rate and current total harmonic distortion rate, pack the processed data with the corresponding power supply loop number and data timestamp into data frame; Poll the latest data frame in the memory queue of each monitoring terminal, the master unit analyzes the data in the data frame, and updates to the real-time running state data table.

6. The power distribution cabinet power quality detection system of claim 5, characterized in that: In the calculation period, the instantaneous values of the collected voltage and current are operated to obtain the instantaneous value of the active power, and the arithmetic average value of the absolute value is taken as the average active power. The percentage of the average active power to the rated capacity of the loop is calculated as the real-time load rate; In the data window, the current waveform is analyzed by fast Fourier transform to extract the effective values of the 2nd to 50th harmonic currents. The square sum of the effective values of each harmonic current is calculated, and the arithmetic square root of the square sum is obtained as the total effective value of the harmonic current. The percentage of the total effective value of the harmonic current to the effective value of the fundamental current is calculated as the total harmonic distortion rate of the current. In the same calculation period, the average apparent power and the average active power are calculated synchronously, and the ratio of the average active power to the average apparent power is calculated as the current power factor.

7. The power quality detection system for power distribution cabinet according to claim 1, characterized in that: The Pareto optimal frontier screening method is used to determine the optimal number of parallel power quality detection; All candidate parallel detection numbers are traversed, and each parallel detection number N corresponds to a two-dimensional vector (E, R) composed of the efficiency loss coefficient E and the interruption impact risk coefficient R, forming a candidate solution set; The Pareto optimal solution is selected from the candidate solution set. The selection logic is: for a candidate solution N1 (E1, R1), if there is no other candidate solution N2 (E2, R2) that can simultaneously satisfy E2≤E1 and R2≤R1, and at least one is strictly less than the relationship, then N1 is a Pareto optimal solution. All solutions that satisfy this condition form the Pareto optimal frontier. Based on the Pareto optimal frontier, the final solution is determined. On the curve composed of the Pareto optimal solution, a most reasonable compromise point is selected, and the shortest distance ideal point method is used. In the two-dimensional coordinate system, the ideal point is a point composed of the minimum value of the efficiency loss coefficient and the minimum value of the interruption impact risk coefficient in all candidate solutions. The Euclidean distance between each point on the Pareto optimal frontier and the ideal point is calculated, and the parallel detection number with the shortest Euclidean distance from the ideal point is selected as the final optimal parallel detection number.

8. The power quality detection system for power distribution cabinet according to claim 7, characterized in that: The process of the interruption impact risk coefficient is as follows: For any non-critical loop to be evaluated, the real-time load rate is multiplied by the total harmonic distortion rate of the current, and the product value is multiplied by the absolute value of the reciprocal of the current power factor to obtain the comprehensive risk factor. Traverse each parallel detection number, calculate the average of the comprehensive risk factors of all non-critical loops detected simultaneously under the parallel detection number, and multiply the average of the comprehensive risk factors by the risk amplification coefficient to output the interruption impact risk coefficient under the corresponding parallel detection number.

9. The power quality detection system for power distribution cabinet according to claim 7, characterized in that: The process of obtaining the efficiency loss coefficient is as follows: For each parallel detection number, the single non-critical loop detection reference time is divided by the parallel detection number to obtain the equivalent single loop detection time shortened due to parallel processing; The total number of non-critical loops to be detected in the current detection period is multiplied by the parallel detection number to obtain the theoretical detection batch required to complete the non-critical loop detection; The product of the equivalent single loop detection time and the theoretical detection batch is output as the efficiency loss coefficient corresponding to the parallel detection number.

10. An intelligent power distribution cabinet characterized by, The system for performing any one of the above claims 1-9 comprises: a cabinet body, internally provided with a main busbar chamber, a branch circuit chamber, a cable chamber and an intelligent control chamber; Further comprising: a power supply circuit system arranged in the branch circuit chamber, an electric energy quality detection device integrated in the cable chamber, and an intelligent control system installed in the intelligent control chamber.

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