DC-DC energy feedback aging system based on electrical characteristics of micro-grid

Abstract

The invention discloses a DCDC energy feedback aging system based on micro-grid electrical characteristics, and relates to the technical field of power supply and distribution devices. The system comprises an experiment module which is used for carrying out DCDC energy feedback aging tests on aging states of a micro-grid under various test working conditions to obtain aging rates of the micro-grid under the various test working conditions; the comparison simulation module is used for dividing the historical operation time period of the micro-grid into a plurality of historical working condition time periods with the same operation state according to the continuously acquired operation parameters of various types of electrical characteristics of the micro-grid; matching each historical working condition time period of the micro-grid with the test working conditions to obtain a plurality of test working conditions matched with each historical working condition time period of the micro-grid; and obtaining the aging degree of the micro-grid according to the aging rate of the test working condition matched with each historical working condition time period of the micro-grid. According to the invention, the accurate aging degree of the micro-grid is detected in a non-off-line state.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply and distribution devices, and in particular relates to a DCDC energy feedback aging system based on the electrical characteristics of a microgrid. Background Art

[0002] With the rapid development of microgrid technology, the reliability of power electronic equipment (such as DCDC converters, energy storage converters, etc.) has become the key to the stable operation of the system. Such equipment is easily affected by factors such as temperature and load fluctuations during long-term operation, and component performance degradation may occur, resulting in a decrease in the overall efficiency of the microgrid or even failure.

[0003] DCDC energy feedback aging mainly involves the recovery and reuse of energy from the DCDC converter during the aging test. However, due to the unstable characteristics of both the power generation and consumption ends of the microgrid, some electrical characteristic parameters are unstable during the operation of the microgrid. At the same time, energy feedback recovery and reuse will continue the instability of the microgrid itself. This requires that the electrical variation characteristics of the microgrid itself be fully considered during the aging test. Summary of the invention

[0004] The purpose of the present invention is to provide a DCDC energy feedback aging system based on the electrical characteristics of a microgrid, which fully considers the operating status of the microgrid, performs detection and comparison through the operating parameters of the microgrid, and thus detects and obtains the accurate aging degree of the microgrid without being offline.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention provides a DCDC energy feedback aging system based on microgrid electrical characteristics, comprising: The experimental module is used to set various test conditions of the microgrid and obtain the operating parameters of various types of electrical characteristics of the microgrid under various test conditions; The DCDC energy feedback aging test is performed on the aging state of the microgrid under various test conditions to obtain the aging rate of the microgrid under various test conditions; A monitoring module, used to continuously obtain the operating parameters of various types of electrical characteristics of the microgrid at each operating moment; A comparison simulation module is used to divide the historical operation period of the microgrid into a plurality of historical operating condition periods with the same operation status according to the operation parameters of various types of electrical characteristics of the microgrid that are continuously obtained; Matching each historical operating condition period of the microgrid with the test operating condition to obtain a plurality of test operating conditions that match each historical operating condition period of the microgrid; The aging degree of the microgrid is obtained according to the aging rate of the test operating condition matched with each historical operating period of the microgrid.

[0006] The present invention tests the aging rate of the microgrid under various test conditions through an experimental module, then continuously monitors the microgrid in operation, and evaluates the operation aging status of each historical operating period, thereby achieving more accurate aging detection when the microgrid is in continuous operation.

[0007] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 A schematic diagram of functional units and information flows of a DCDC energy feedback aging system based on microgrid electrical characteristics in one embodiment of the present invention; Figure 2 A schematic diagram of the steps of a DCDC energy feedback aging system based on microgrid electrical characteristics in one embodiment of the present invention; Figure 3 is a schematic diagram of step S4 of the present invention in an embodiment; Figure 4 is a schematic diagram of step S42 of the present invention in an embodiment; Figure 5 is a schematic diagram of step S5 of the present invention in an embodiment; Figure 6 is a schematic diagram of step S6 of the present invention in an embodiment; In the accompanying drawings, the components represented by the reference numerals are listed as follows: 1-Experimental module, 2-Monitoring module, 3-Comparison simulation module. DETAILED DESCRIPTION

[0010] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0011] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0012] Microgrids are usually used in small-scale independent scenarios such as isolated islands and oases, and will also use a large number of unstable power generation equipment such as wind power and photovoltaic power. At the same time, due to the small scale of power consumption equipment, it is easy to cause grid fluctuations. If the microgrid frequently disconnects the grid equipment for aging measurement during the aging detection process, it will affect the normal operation of the grid. However, if sufficient detection is not performed, accurate aging monitoring cannot be achieved. In view of this, the present invention provides the following solution.

[0013] See also Figures 1 to 2 As shown, the present invention provides a DCDC energy feedback aging system based on the electrical characteristics of a microgrid, which is divided into an experimental module 1, a monitoring module 2 and a comparison simulation module 3 from the functional unit. The experimental module 1 in this system is used to fully detect and count the DCDC energy feedback aging rate of the microgrid under different test conditions, and then the monitoring module 2 collects the operating parameters of the power grid in the operating state, and then the simulation module 3 combines the results of the experimental module 1 and the monitoring module 2 to estimate the aging degree of the microgrid in the current operating state.

[0014] In the specific operation process of this system, the experimental module first executes step S1 to set various test conditions of the microgrid, and obtains the operating parameters of various types of electrical characteristics of the microgrid under various test conditions; the types of electrical characteristics include voltage and current harmonic characteristics, DC bus voltage fluctuations, impedance characteristics, power bidirectional flow characteristics, fault ride-through characteristics, time scale dynamics, efficiency-load rate curve, energy feedback phase synchronization rate and high impedance fault (HIF) characteristics. Then, step S2 can be executed to perform DCDC energy feedback aging test on the aging state of the microgrid under various test conditions to obtain the aging rate of the microgrid under various test conditions. The operating components of the microgrid can be set with a monitoring module 2 to execute step S3 to continuously obtain the operating parameters of various types of electrical characteristics of the microgrid at each operating moment.

[0015] See also Figures 1 to 3As shown, during the operation, the comparison simulation module 3 can first execute step S4 to divide the historical operation time period of the microgrid into multiple historical operating time periods with the same operation state according to the operation parameters of various types of electrical characteristics of the microgrid that are continuously obtained. Specifically, step S41 can be first executed to calculate the operation state difference rate of the microgrid at different operation times according to the operation parameters of various types of electrical characteristics of the microgrid at different operation times. The operation state difference rate can be obtained by calculating the cumulative value of the difference between the operation parameters of various types of electrical characteristics at different operation times as the operation state difference rate of the microgrid at different operation times.

[0016] See also Figure 3 and 4 As shown, after the difference in operating status at different operating moments is quantitatively calculated, step S42 can be executed to obtain multiple operating moment combinations with the same operating state of the microgrid according to the operating state difference rate between the microgrid at different operating moments. Specifically, step S421 can be first executed to select multiple operating moments as marked operating moments in the historical operating period of the microgrid. Next, step S422 can be executed to calculate and obtain the operating state difference rate between each marked operating moment of the microgrid and other operating moments. Next, step S423 can be executed to divide each other operating moment other than the marked operating moment and the marked operating moment with the smallest operating state difference rate into the same operating moment combination.

[0017] Of course, since the microgrid states between each operating moment in the divided operating moment combination may not be consistent, it is necessary to determine whether the operating states of all operating moments included in each operating moment combination are the same. Specifically, step S424 can be first executed to calculate the mean operating parameter of each type of electrical feature of all operating moments included in the operating moment combination in each operating moment combination. Next, step S425 can be executed to calculate whether the operating moment with the smallest operating state difference rate between the mean operating parameter of each type of electrical feature of all operating moments in each operating moment combination and that of all operating moments is the marked operating moment.

[0018] If so, it means that the operating states of all the operating moments included in each operating moment combination are the same, so step S426 can be executed next to obtain multiple operating moment combinations with the same operating state of the microgrid. If not, it means that the operating states of all the operating moments included in each operating moment combination are not the same, so step S427 can be executed next to take the operating moment with the smallest operating state difference rate between the operating parameter mean of each type of electrical characteristics in each operating moment combination and all operating moments as the iterated marked operating moment. Next, steps S422 to S423 can be executed to re-divide the iterated operating moment combinations according to the iterated marked operating moments. Next, steps S424 to S425 can be executed to determine whether the operating states of all the operating moments included in each iterated operating moment combination are the same. That is, through continuous optimization in a step-by-step iterative manner, a more consistent operating moment combination can be obtained. In order to supplement the implementation process of the above steps S421 to S427, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of power grid operation data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0019] #include <iostream> #include <vector> #include <cmath> #include <algorithm> #include <limits> #include <numeric> using namespace std; / / Electrical characteristics data structure struct ElectricalCharacteristics { double voltage; / / voltage (V) double current; / / Current (A) vector <double>voltageHarmonics; / / voltage harmonic components double dcBusVoltage; / / DC bus voltage (V) double impedance; / / equivalent impedance (Ω) / / Constructor to initialize default values ElectricalCharacteristics() : voltage(0), current(0), dcBusVoltage(0), impedance(0) { voltageHarmonics.resize(50, 0); / / Initialize 50th harmonic } }; / / Runtime data struct OperationMoment { int id; / / Runtime ID time_t timestamp; / / timestamp ElectricalCharacteristics ec; / / Electrical characteristics }; / / Runtime combination struct MomentCluster { int clusterId; / / combination ID vector <int>momentIds; / / Contains the runtime ID ElectricalCharacteristics centroid; / / Centroid (feature mean) int representativeId; / / represents the runtime ID (with the smallest difference) }; class MomentClusterer { private: / / Calculate the difference between two electrical characteristics (0-1) double calculateDifferenceRate(const ElectricalCharacteristics&ec1, const ElectricalCharacteristics&ec2) const { / / Simple implementation: weighted average of feature differences double voltageDiff = fabs(ec1.voltage - ec2.voltage) / max(ec1.voltage, ec2.voltage); double currentDiff = fabs(ec1.current - ec2.current) / max(ec1.current, ec2.current); double dcBusDiff = fabs(ec1.dcBusVoltage - ec2.dcBusVoltage) / max(ec1.dcBusVoltage, ec2.dcBusVoltage); double impedanceDiff = fabs(ec1.impedance - ec2.impedance) / max(ec1.impedance, ec2.impedance); / / Calculate the harmonic difference (THD) auto calcTHD = [](const vector <double>&harmonics) double sum = 0; for (size_t i = 1; i <harmonics.size(); i++) { sum += harmonics[i] * harmonics[i]; } return sqrt(sum); }; double thd1 = calcTHD(ec1.voltageHarmonics); double thd2 = calcTHD(ec2.voltageHarmonics); double harmonicDiff = fabs(thd1 - thd2) / max(thd1, thd2); / / Weighted average (weights can be adjusted according to actual conditions) return (voltageDiff * 0.3 + currentDiff * 0.3 + dcBusDiff * 0.1 + impedanceDiff * 0.1 + harmonicDiff * 0.2); } / / Calculate the mean value of electrical characteristics (centroid) ElectricalCharacteristics calculateCentroid(const vector <operationmoment>&moments) const { ElectricalCharacteristics centroid; if (moments.empty()) return centroid; / / Initialize the accumulator double voltageSum = 0, currentSum = 0, dcBusSum = 0, impedanceSum =0; vector <double>harmonicSums(moments[0].ec.voltageHarmonics.size(), 0); / / Accumulate each eigenvalue for (const auto&moment : moments) { voltageSum += moment.ec.voltage; currentSum += moment.ec.current; dcBusSum += moment.ec.dcBusVoltage; impedanceSum += moment.ec.impedance; for (size_t i = 0; i <moment.ec.voltageHarmonics.size(); i++) { harmonicSums[i] += moment.ec.voltageHarmonics[i]; } } / / Calculate the average double count = moments.size(); centroid.voltage = voltageSum / count; centroid.current = currentSum / count; centroid.dcBusVoltage = dcBusSum / count; centroid.impedance = impedanceSum / count; for (size_t i = 0; i <harmonicSums.size(); i++) { centroid.voltageHarmonics[i] = harmonicSums[i] / count; } return centroid; } / / Find the running time with the smallest difference rate with the center of mass in the combination int findRepresentativeMoment(const vector <operationmoment>&moments, const ElectricalCharacteristics¢roid) const { if (moments.empty()) return -1; int representativeId = moments[0].id; double minDiffRate = numeric_limits <double>::max(); for (const auto& moment : moments) { double diffRate = calculateDifferenceRate(moment.ec, centroid); if (diffRate < minDiffRate) { minDiffRate = diffRate; representativeId = moment.id; } } return representativeId; } public: / / Divide the combination of running moments vector <momentcluster>clusterMoments(const vector <operationmoment>&allMoments, const vector <int>&initialMarkers, int maxIterations = 10) const { vector <momentcluster>clusters; / / 1. Initialize the combination (centered on marking the running time) for (size_t i = 0; i <initialMarkers.size(); i++) { int markerId = initialMarkers[i]; auto it = find_if(allMoments.begin(), allMoments.end(), [markerId](const OperationMoment&m) { return m.id == markerId;}); if (it != allMoments.end()) { MomentCluster cluster; cluster.clusterId = i + 1; cluster.representativeId = markerId; cluster.centroid = it->ec; clusters.push_back(cluster); } } / / Iterative optimization bool changed; int iteration = 0; do { changed = false; / / 2. Clear the running time of each combination (keep the center of mass and representative) for (auto&cluster : clusters) { cluster.momentIds.clear(); } / / 3. Assign each running moment to the combination with the smallest difference rate for (const auto&moment : allMoments) { double minDiffRate = numeric_limits <double>::max(); int bestClusterId = -1; / / Find the combination with the smallest difference rate for (const auto&cluster : clusters) { double diffRate = calculateDifferenceRate(moment.ec,cluster.centroid); if (diffRate <minDiffRate) { minDiffRate = diffRate; bestClusterId = cluster.clusterId; } } / / Add to best combination if (bestClusterId != -1) { auto it = find_if(clusters.begin(), clusters.end(), [bestClusterId](const MomentCluster&c) { return c.clusterId == bestClusterId; }); if (it != clusters.end()) { it->momentIds.push_back(moment.id); } } } / / 4. Update the centroid and representative running time of each combination for (auto&cluster : clusters) { / / Collect all running times in the combination vector <operationmoment>clusterMoments; for (int momentId : cluster.momentIds) { auto it = find_if(allMoments.begin(), allMoments.end(), [momentId](const OperationMoment&m) { return m.id == momentId; }); if (it != allMoments.end()) { clusterMoments.push_back(*it); } } / / Add the representative running time (if not in the group) auto repIt = find_if(allMoments.begin(), allMoments.end(), [cluster](const OperationMoment&m) { return m.id == cluster.representativeId; }); if (repIt != allMoments.end()&& find(cluster.momentIds.begin(), cluster.momentIds.end(),cluster.representativeId) == cluster.momentIds.end()) { clusterMoments.push_back(*repIt); } if (!clusterMoments.empty()) { / / Calculate the new centroid ElectricalCharacteristics newCentroid = calculateCentroid(clusterMoments); / / Find the new representative running time int newRepresentative = findRepresentativeMoment(clusterMoments,newCentroid); / / Check if there is any change if (newRepresentative != cluster.representativeId || calculateDifferenceRate(newCentroid, cluster.centroid)>0.01) { changed = true; } / / Update combination information cluster.centroid = newCentroid; cluster.representativeId = newRepresentative; } } iteration++; } while (changed&&iteration <maxIterations); / / 5. Finally determine the running time of each combination for (auto&cluster : clusters) { cluster.momentIds.clear(); for (const auto&moment : allMoments) { double minDiffRate = numeric_limits <double>::max(); int bestClusterId = -1; for (const auto&c : clusters) { double diffRate = calculateDifferenceRate(moment.ec, c.centroid); if (diffRate <minDiffRate) { minDiffRate = diffRate; bestClusterId = c.clusterId; } } if (bestClusterId == cluster.clusterId) { cluster.momentIds.push_back(moment.id); } } } return clusters; } }; / / Example usage int main() { / / Create the runtime combiner MomentClusterer clusterer; / / Example generates runtime data (which should be obtained from the monitoring system in actual applications) vector <operationmoment>moments; time_t baseTime = time(nullptr); / / Generate runtime data in three different states for (int i = 0; i<30; i++) { OperationMoment moment; moment.id = i + 1; moment.timestamp = baseTime + i * 60; / / Three different states appear alternately int state = (i / 10) % 3; switch (state) { case 0: / / State 1 moment.ec.voltage = 400.0 + (rand() % 10 - 5) * 0.1; moment.ec.current = 50.0 + (rand() % 10 - 5) * 0.1; break; case 1: / / State 2 moment.ec.voltage = 380.0 + (rand() % 10 - 5) * 0.1; moment.ec.current = 60.0 + (rand() % 10 - 5) * 0.1; break; case 2: / / State 3 moment.ec.voltage = 420.0 + (rand() % 10 - 5) * 0.1; moment.ec.current = 45.0 + (rand() % 10 - 5) * 0.1; break; } / / Set other electrical characteristics... moments.push_back(moment); } / / Select the initial markup runtime (here we simply select the first three) vector <int>initialMarkers = {1, 2, 3}; / / Divide the running time combination auto clusters = clusterer.clusterMoments(moments, initialMarkers); / / Output the division result cout<<"Run time combination partition result:"< <endl; for (const auto&cluster : clusters) { cout<<"combination"< <cluster.clusterId<<":"<<endl; cout<<" represents the runtime ID: "< <cluster.representativeId<<endl; cout<<" contains the running time number: "< <cluster.momentIds.size()<<endl; cout<<"Average voltage: "< <cluster.centroid.voltage<<"V"<<endl; cout<<"Average current: "< <cluster.centroid.current<<"A"<<endl; } return 0; } This code implements a complete algorithm for the combination division of microgrid operation time. During the operation process, the initial mark selection is first performed, and multiple operation times are selected from the historical operation period as the initial marked operation time. Then iterative combination division is performed. By calculating the difference rate between each operation time and the marked operation time, the operation time is divided into the combination with the smallest difference rate. Next, the centroid and representative are updated. The mean of the electrical characteristics is calculated as the centroid in each combination, and the operation time with the smallest difference rate with the centroid is found as the new representative. Finally, iterative optimization and result output are performed. Through multiple iterative optimizations, it is ensured that the operation times in each combination are highly similar. Finally, the divided operation time combination is output, including the representative operation time of each combination, the number of operation times included, and the average electrical characteristics.

[0020] The algorithm can effectively identify the similarity of microgrid operating states and divide historical operating data into multiple combinations with similar states, providing a basis for subsequent aging analysis and performance evaluation. The accuracy and reliability of the division results are ensured through an iterative optimization process.

[0021] Please continue reading Figures 1 to 3 As shown, after obtaining multiple operating time combinations with the same operating status, step S43 can be finally executed to treat the time period of continuous operating time distribution in each operating time combination as a historical operating period to obtain multiple historical operating periods during the operation of the microgrid.

[0022] Please continue reading Figure 1 , 2 As shown in Figure 5, after dividing the historical operating time periods with the same operating state of the microgrid, step S5 can be executed next to match each historical operating time period of the microgrid with the test condition to obtain several test conditions that match each historical operating time period of the microgrid. Specifically, for each historical operating time period of the microgrid, step S51 can be first executed to calculate and obtain the mean value of the operating parameters of each type of electrical characteristics at each operating moment in the historical operating time period as the typical operating parameters of each type of electrical characteristics in the historical operating time period. Next, step S52 can be executed to respectively calculate the operating state difference rate between the typical operating parameters of each type of electrical characteristics in the historical operating time period and the operating parameters of each type of electrical characteristics under each test condition, and finally step S53 can be executed to select several test conditions whose operating state difference rate with the historical operating time period is less than a set value as several test conditions that match the historical operating time period of the microgrid. The above process can scientifically and reasonably evaluate the cumulative aging degree of the microgrid under various operating conditions. By considering the matching similarity and duration of different operating conditions, it ensures the accuracy and reliability of the evaluation results, providing an important basis for the maintenance and life prediction of the microgrid.

[0023] Please continue reading Figure 1 , 2 As shown in Figure 6, after matching the corresponding test conditions for each historical operating period, step S6 can be executed to obtain the aging degree of the microgrid according to the aging rate of the test conditions matched for each historical operating period of the microgrid. Specifically, step S61 can be first executed to calculate the proportional coefficient between the operating state difference rate of the historical operating period and each matched test condition for each historical operating period of the microgrid, and then step S62 can be executed to calculate the weighted average of several test conditions matched for the historical operating period to obtain the deemed aging rate of the microgrid for the historical operating period. Finally, step S63 can be executed to obtain the aging degree of the microgrid according to the duration of each historical operating period of the microgrid and the corresponding deemed aging rate. That is, through a scientific weighted algorithm and a rigorous calculation process, an accurate assessment of the aging degree of the microgrid is achieved, providing reliable data support for equipment maintenance and life prediction.

[0024] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0025] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0026] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0027] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.< / int> < / operationmoment> < / double> < / operationmoment> < / double> < / momentcluster> < / int> < / operationmoment> < / momentcluster> < / double> < / operationmoment> < / double> < / operationmoment> < / double> < / int> < / double> < / numeric> < / limits> < / algorithm> < / cmath> < / vector> < / iostream>

Claims

1. A DCDC energy feedback aging system based on microgrid electrical characteristics, characterized in that: include, The experimental module is used to set various test conditions of the microgrid and obtain the operating parameters of various types of electrical characteristics of the microgrid under various test conditions; The DCDC energy feedback aging test is performed on the aging state of the microgrid under various test conditions to obtain the aging rate of the microgrid under various test conditions; A monitoring module, used to continuously obtain the operating parameters of various types of electrical characteristics of the microgrid at each operating moment; A comparison simulation module is used to divide the historical operation period of the microgrid into a plurality of historical operating condition periods with the same operation status according to the operation parameters of various types of electrical characteristics of the microgrid that are continuously obtained; Matching each historical operating condition period of the microgrid with the test operating condition to obtain a plurality of test operating conditions that match each historical operating condition period of the microgrid; The aging degree of the microgrid is obtained according to the aging rate of the test operating condition matched with each historical operating period of the microgrid.

2. The system according to claim 1, characterized in that include, Types of electrical characteristics include voltage and current harmonic characteristics, DC bus voltage fluctuations, impedance characteristics, power bidirectional flow characteristics, fault ride-through characteristics, time scale dynamics, efficiency-load rate curves, energy feedback phase synchronization rate and / or high impedance fault (HIF) characteristics.

3. The system according to claim 1, characterized in that The step of dividing the historical operation time period of the microgrid into a plurality of historical operating time periods with the same operation state according to the continuously acquired operation parameters of various types of electrical characteristics of the microgrid includes: The operating state difference rate of the microgrid at different operating times is calculated according to the operating parameters of various types of electrical characteristics of the microgrid at different operating times; According to the difference rate of the operating state of the microgrid at different operating times, a plurality of operating time combinations with the same operating state of the microgrid are obtained; The time period of the continuous operating time distribution in each operating time combination is taken as a historical operating condition period, so as to obtain a plurality of historical operating condition periods in the operation process of the microgrid.

4. The system according to claim 3, characterized in that: The step of calculating the operating state difference rate of the microgrid at different operating times according to the operating parameters of various types of electrical characteristics of the microgrid at different operating times includes: The accumulated value of the difference of the operating parameters of each type of electrical characteristics at different operating times is calculated and obtained as the operating state difference rate of the microgrid at different operating times.

5. The system according to claim 3, characterized in that The step of obtaining a plurality of operation time combinations of the microgrid with the same operation state according to the operation state difference rate between the microgrids at different operation times comprises: Selecting multiple operating moments in the historical operating period of the microgrid as marked operating moments; Calculate and obtain the operating state difference rate between each marked operating moment of the microgrid and other operating moments; Classify each other running time other than the marked running time and the marked running time with the smallest difference rate between the running states into the same running time combination; Determine whether the running states of all running times included in each running time combination are the same; If so, multiple operation time combinations with the same microgrid operation state are obtained; If not, iteratively re-partition until a combination of operating time points with the same operating state of the microgrid is obtained.

6. The system according to claim 5, characterized in that The step of determining whether the running states of all running times included in each running time combination are the same, include, In each operating time combination, calculating the operating parameter mean value of each type of electrical characteristics of all operating times included in the operating time combination; Calculate and obtain whether the operating time at which the operating state difference rate between the operating parameter mean value of each type of electrical characteristics in each operating time combination and all operating times is the smallest is the marked operating time; If yes, it is determined that the running states of all running times included in each running time combination are the same; If not, it is determined that the running states of all the running times included in each running time combination are different.

7. The system according to claim 5, characterized in that The step of iteratively re-dividing until a combination of operating time points with the same operating state of the microgrid is obtained, include, The running time with the smallest running state difference rate between the running parameter mean of each type of electrical characteristics in each running time combination and all running times is used as the marked running time after iteration; Re-dividing the combination of the running time after the iteration according to the marked running time after the iteration; It is determined whether the running states of all the running times included in the running time combination after each iteration are the same.

8. The system according to claim 1, characterized in that The step of matching each historical operating condition period of the microgrid with the test operating condition to obtain a plurality of test operating conditions that match each historical operating condition period of the microgrid, include, For each historical operating period of the microgrid, perform the following steps respectively: Calculate and obtain the mean operating parameter of each type of electrical characteristics at each operating moment in the historical operating period as the typical operating parameter of each type of electrical characteristics in the historical operating period. The operating state difference rate between the typical operating parameters of each type of electrical characteristics in the historical operating condition period and the operating parameters of each type of electrical characteristics under each test condition is calculated respectively. A plurality of test operating conditions whose operating state difference rate with the historical operating condition period is less than a set value are selected as a plurality of test operating conditions matching the historical operating condition period of the microgrid.

9. The system according to claim 1, characterized in that The step of obtaining the aging degree of the microgrid according to the aging rate of the test operating condition matched with each historical operating condition period of the microgrid includes: The average of the aging rates of several test conditions matching each historical operating period of the microgrid is taken as the deemed aging rate of each historical operating period of the microgrid; The aging degree of the microgrid is obtained by accumulating the duration of each historical operating period of the microgrid and the corresponding deemed aging rate.

10. The system according to claim 9, characterized in that The step of taking the average of the aging rates of several test operating conditions matching each historical operating period of the microgrid as the aging rate of each historical operating period of the microgrid, comprises: For each historical operating period of the microgrid, the proportional coefficient between the operating state difference rate of the historical operating period and each matching test operating condition is calculated, and the weighted average of several test operating conditions matching the historical operating period is calculated to obtain the deemed aging rate of the microgrid for the historical operating period.

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