A DCDC energy feedback aging system based on microgrid electrical characteristics

Abstract

The present invention discloses a DC / CDC energy feedback aging system based on the electrical characteristics of a microgrid, relating to the technical field of power supply and distribution devices. The present invention includes an experimental module for performing DC / CDC energy feedback aging tests on the aging status of a microgrid under various test conditions to obtain the aging rate of the microgrid under each test condition; a comparison simulation module for dividing the microgrid's historical operating period into multiple historical operating condition periods with the same operating status based on continuously acquired operating parameters of various types of electrical characteristics of the microgrid; matching each historical operating condition period of the microgrid with a test condition to obtain a number of test conditions matching each historical operating condition period of the microgrid; and obtaining the aging degree of the microgrid based on the aging rate of the test conditions matching each historical operating condition period of the microgrid. The present invention detects and obtains the accurate aging degree of the microgrid without going offline.

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 electronics (such as DC-DC converters and energy storage inverters) has become critical to stable system operation. Over long-term operation, these devices are susceptible to component performance degradation due to factors such as temperature and load fluctuations, leading to a decrease in overall microgrid efficiency and even failure.

[0003] DCDC energy feedback aging primarily involves recovering and reusing the energy generated by the DCDC converter during aging testing. However, due to the unstable nature of both the power generation and consumption ends of a microgrid, some electrical characteristic parameters become unstable during microgrid operation. Furthermore, energy feedback recovery and reuse can perpetuate the inherent instability of the microgrid. This necessitates that the electrical fluctuations inherent in the microgrid be fully considered during aging testing. 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 and performs detection and comparison through the operating parameters of the microgrid, so as to detect and obtain the accurate aging degree of the microgrid without being offline.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention provides a DCDC energy feedback aging system based on the electrical characteristics of a microgrid, comprising:

[0007] 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;

[0008] 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;

[0009] A monitoring module is used to continuously obtain the operating parameters of various types of electrical characteristics of the microgrid at each operating moment;

[0010] A comparison simulation module is used to divide the historical operation period of the microgrid into multiple historical operating condition periods with the same operation status according to the continuously acquired operation parameters of various types of electrical characteristics of the microgrid;

[0011] 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;

[0012] The aging degree of the microgrid is obtained according to the aging rate of the test operating condition matched with each historical operating condition period of the microgrid.

[0013] 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 operating aging status of each historical operating period, thereby achieving more accurate aging detection when the microgrid is continuously operating.

[0014] 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

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of the functional units and information flow of a DCDC energy feedback aging system based on microgrid electrical characteristics in one embodiment of the present invention;

[0017] Figure 2 This is a schematic flow chart of the steps of an embodiment of a DCDC energy feedback aging system based on microgrid electrical characteristics according to the present invention;

[0018] Figure 3 is a schematic diagram of step S4 according to an embodiment of the present invention;

[0019] Figure 4 is a schematic diagram of step S42 according to an embodiment of the present invention;

[0020] Figure 5 is a schematic diagram of step S5 according to an embodiment of the present invention;

[0021] Figure 6 is a schematic diagram of step S6 according to an embodiment of the present invention;

[0022] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0023] 1-Experimental module, 2-Monitoring module, 3-Comparison and simulation module. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0025] It should be noted that the terms "first," "second," and the like in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are 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. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application as detailed in the appended claims.

[0026] Microgrids are often used in small, independent scenarios such as isolated islands and oases. They also utilize a large amount of unstable power generation equipment, such as wind and photovoltaic power. Furthermore, the small size of these devices can easily cause grid fluctuations. Frequently disconnecting grid equipment for aging testing during microgrid aging testing can affect normal grid operation. However, accurate aging monitoring cannot be achieved without sufficient testing. To address this issue, the present invention provides the following solution.

[0027] 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. The system functionally comprises an experimental module 1, a monitoring module 2, and a comparative simulation module 3. The experimental module 1 in this system is used to fully detect and statistically analyze the DCDC energy feedback aging rate of the microgrid under different test conditions. The monitoring module 2 then collects the operating parameters of the grid during operation. The simulation module 3 then combines the results of the experimental module 1 and the monitoring module 2 to estimate the aging degree of the microgrid in its current operating state.

[0028] During the specific operation of this system, the experimental module first executes step S1 to set up various test conditions for the microgrid and obtain the operating parameters of various electrical characteristics under these test conditions. These electrical characteristics include voltage and current harmonics, DC bus voltage fluctuations, impedance characteristics, bidirectional power flow characteristics, fault ride-through characteristics, timescale dynamics, efficiency-load ratio curves, energy feedback phase synchronization rate, and high-impedance fault (HIF) characteristics. Next, step S2 is executed to conduct a DC-DC energy feedback aging test on the microgrid under various test conditions to obtain the microgrid aging rate under each test condition. A monitoring module 2 can be configured for the operating components of the microgrid to execute step S3 to continuously obtain the operating parameters of various electrical characteristics of the microgrid at each operating moment.

[0029] See also Figures 1 to 3As shown, during operation, the comparison simulation module 3 may first execute step S4 to divide the historical operating period of the microgrid into a plurality of historical operating condition periods with the same operating status according to the continuously acquired operating parameters of various types of electrical characteristics of the microgrid. Specifically, step S41 may first be executed to calculate the operating status difference rate of the microgrid at different operating moments according to the operating parameters of various types of electrical characteristics of the microgrid at different operating moments. The operating status difference rate may be obtained by calculating the cumulative value of the difference between the operating parameters of various types of electrical characteristics at different operating moments as the operating status difference rate of the microgrid at different operating moments.

[0030] See also Figure 3 and 4 As shown, after quantitatively calculating the operating state differences at different operating moments, step S42 can be executed to obtain multiple operating moment combinations with the same microgrid operating state based on the operating state difference rates between the microgrid's different operating moments. Specifically, step S421 can be executed to select multiple operating moments from the microgrid's historical operating period as marked operating moments. Next, step S422 can be executed to calculate the operating state difference rate between each marked operating moment of the microgrid and other operating moments. Next, step S423 can be executed to group each operating moment other than the marked operating moment with the marked operating moment having the smallest operating state difference rate into the same operating moment combination.

[0031] Of course, since the microgrid states at each operating moment within a defined operating moment combination may not be consistent, it is necessary to determine whether the operating states of all operating moments within each operating moment combination are the same. Specifically, step S424 can be performed to first calculate the mean operating parameter of each type of electrical characteristic for all operating moments within each operating moment combination. Next, step S425 can be performed to determine whether the operating moment with the smallest operating state difference between the mean operating parameter of each type of electrical characteristic for each operating moment within each operating moment combination and all operating moments is the marked operating moment.

[0032] 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 different, so step S427 can be executed next to use the operating moment with the smallest operating state difference rate between the operating parameters of each type of electrical characteristics in each operating moment combination and all operating moments as the marked operating moment after iteration. Next, steps S422 to S423 can be executed to re-divide the iterated operating moment combination according to the iterated marked operating moment. 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 is obtained.

[0033] To supplement the implementation of steps S421 to S427, the source code for some functional modules is provided, with cross-references and explanations provided in the comments. To prevent the leakage of grid operation data involving commercial secrets, data that does not affect the implementation of the solution is desensitized. The same applies below.

[0034] #include <iostream>

[0035] #include <vector>

[0036] #include <cmath>

[0037] #include <algorithm>

[0038] #include <limits>

[0039] #include <numeric>

[0040] using namespace std;

[0041] / / Electrical characteristics data structure

[0042] struct ElectricalCharacteristics {

[0043] double voltage; / / voltage (V)

[0044] double current; / / Current (A)

[0045] vector <double>voltageHarmonics; / / voltage harmonic components

[0046] double dcBusVoltage; / / DC bus voltage (V)

[0047] double impedance; / / equivalent impedance (Ω)

[0048] / / Constructor to initialize default values

[0049] ElectricalCharacteristics() : voltage(0), current(0), dcBusVoltage(0), impedance(0) {

[0050] voltageHarmonics.resize(50, 0); / / Initialize 50th harmonic

[0051] }

[0052] };

[0053] / / Runtime data

[0054] struct OperationMoment {

[0055] int id; / / Runtime ID

[0056] time_t timestamp; / / timestamp

[0057] ElectricalCharacteristics ec; / / Electrical characteristics

[0058] };

[0059] / / Runtime combination

[0060] struct MomentCluster {

[0061] int clusterId; / / combination ID

[0062] vector <int>momentIds; / / Contains the runtime ID

[0063] ElectricalCharacteristics centroid; / / Centroid (feature mean)

[0064] int representativeId; / / represents the runtime ID (with the smallest difference)

[0065] };

[0066] class MomentClusterer {

[0067] private:

[0068] / / Calculate the difference between two electrical characteristics (0-1)

[0069] double calculateDifferenceRate(const ElectricalCharacteristics&ec1,

[0070] const ElectricalCharacteristics&ec2) const {

[0071] / / Simple implementation: weighted average of feature differences

[0072] double voltageDiff = fabs(ec1.voltage - ec2.voltage) / max(ec1.voltage, ec2.voltage);

[0073] double currentDiff = fabs(ec1.current - ec2.current) / max(ec1.current, ec2.current);

[0074] double dcBusDiff = fabs(ec1.dcBusVoltage - ec2.dcBusVoltage) / max(ec1.dcBusVoltage, ec2.dcBusVoltage);

[0075] double impedanceDiff = fabs(ec1.impedance - ec2.impedance) / max(ec1.impedance, ec2.impedance);

[0076] / / Calculate the harmonic difference (THD)

[0077] auto calcTHD = [](const vector <double>&harmonics)

[0078] double sum = 0;

[0079] for (size_t i = 1; i <harmonics.size(); i++) {

[0080] sum += harmonics[i] * harmonics[i];

[0081] }

[0082] return sqrt(sum);

[0083] };

[0084] double thd1 = calcTHD(ec1.voltageHarmonics);

[0085] double thd2 = calcTHD(ec2.voltageHarmonics);

[0086] double harmonicDiff = fabs(thd1 - thd2) / max(thd1, thd2);

[0087] / / Weighted average (weights can be adjusted according to actual conditions)

[0088] return (voltageDiff * 0.3 + currentDiff * 0.3 + dcBusDiff * 0.1 +

[0089] impedanceDiff * 0.1 + harmonicDiff * 0.2);

[0090] }

[0091] / / Calculate the mean of electrical characteristics (center of mass)

[0092] ElectricalCharacteristics calculateCentroid(const vector <operationmoment>&moments) const {

[0093] ElectricalCharacteristics centroid;

[0094] if (moments.empty()) return centroid;

[0095] / / Initialize the accumulator

[0096] double voltageSum = 0, currentSum = 0, dcBusSum = 0, impedanceSum =0;

[0097] vector <double>harmonicSums(moments[0].ec.voltageHarmonics.size(), 0);

[0098] / / Accumulate each eigenvalue

[0099] for (const auto&moment : moments) {

[0100] voltageSum += moment.ec.voltage;

[0101] currentSum += moment.ec.current;

[0102] dcBusSum += moment.ec.dcBusVoltage;

[0103] impedanceSum += moment.ec.impedance;

[0104] for (size_t i = 0; i<moment.ec.voltageHarmonics.size(); i++) {

[0105] harmonicSums[i] += moment.ec.voltageHarmonics[i];

[0106] }

[0107] }

[0108] / / Calculate the average value

[0109] double count = moments.size();

[0110] centroid.voltage = voltageSum / count;

[0111] centroid.current = currentSum / count;

[0112] centroid.dcBusVoltage = dcBusSum / count;

[0113] centroid.impedance = impedanceSum / count;

[0114] for (size_t i = 0; i <harmonicSums.size(); i++) {

[0115] centroid.voltageHarmonics[i] = harmonicSums[i] / count;

[0116] }

[0117] return centroid;

[0118] }

[0119] / / Find the running time with the smallest difference from the center of mass in the combination

[0120] int findRepresentativeMoment(const vector <operationmoment>&moments,

[0121] const ElectricalCharacteristics¢roid) const {

[0122] if (moments.empty()) return -1;

[0123] int representativeId = moments[0].id;

[0124] double minDiffRate = numeric_limits <double>::max();

[0125] for (const auto& moment : moments) {

[0126] double diffRate = calculateDifferenceRate(moment.ec, centroid);

[0127] if (diffRate < minDiffRate) {

[0128] minDiffRate = diffRate;

[0129] representativeId = moment.id;

[0130] }

[0131] }

[0132] return representativeId;

[0133] }

[0134] public:

[0135] / / Divide the combination of running moments

[0136] vector <momentcluster>clusterMoments(const vector <operationmoment>&allMoments,

[0137] const vector <int>&initialMarkers,

[0138] int maxIterations = 10) const {

[0139] vector <momentcluster>clusters;

[0140] / / 1. Initialize the combination (centered around marking runtime)

[0141] for (size_t i = 0; i <initialMarkers.size(); i++) {

[0142] int markerId = initialMarkers[i];

[0143] auto it = find_if(allMoments.begin(), allMoments.end(),

[0144] [markerId](const OperationMoment&m) { return m.id == markerId;});

[0145] if (it != allMoments.end()) {

[0146] MomentCluster cluster;

[0147] cluster.clusterId = i + 1;

[0148] cluster.representativeId = markerId;

[0149] cluster.centroid = it->ec;

[0150] clusters.push_back(cluster);

[0151] }

[0152] }

[0153] / / Iterative optimization

[0154] bool changed;

[0155] int iteration = 0;

[0156] do {

[0157] changed = false;

[0158] / / 2. Clear the running time of each combination (keep the center of mass and representative)

[0159] for (auto&cluster : clusters) {

[0160] cluster.momentIds.clear();

[0161] }

[0162] / / 3. Assign each running moment to the combination with the smallest difference rate

[0163] for (const auto&moment : allMoments) {

[0164] double minDiffRate = numeric_limits <double>::max();

[0165] int bestClusterId = -1;

[0166] / / Find the combination with the smallest difference rate

[0167] for (const auto&cluster : clusters) {

[0168] double diffRate = calculateDifferenceRate(moment.ec,cluster.centroid);

[0169] if (diffRate <minDiffRate) {

[0170] minDiffRate = diffRate;

[0171] bestClusterId = cluster.clusterId;

[0172] }

[0173] }

[0174] / / Add to the best combination

[0175] if (bestClusterId != -1) {

[0176] auto it = find_if(clusters.begin(), clusters.end(),

[0177] [bestClusterId](const MomentCluster&c) {

[0178] return c.clusterId == bestClusterId;

[0179] });

[0180] if (it != clusters.end()) {

[0181] it->momentIds.push_back(moment.id);

[0182] }

[0183] }

[0184] }

[0185] / / 4. Update the center of mass and representative running time of each combination

[0186] for (auto&cluster : clusters) {

[0187] / / Collect all running times in the group

[0188] vector <operationmoment>clusterMoments;

[0189] for (int momentId : cluster.momentIds) {

[0190] auto it = find_if(allMoments.begin(), allMoments.end(),

[0191] [momentId](const OperationMoment&m) {

[0192] return m.id == momentId;

[0193] });

[0194] if (it != allMoments.end()) {

[0195] clusterMoments.push_back(*it);

[0196] }

[0197] }

[0198] / / Add the representative running time (if not in the group)

[0199] auto repIt = find_if(allMoments.begin(), allMoments.end(),

[0200] [cluster](const OperationMoment&m) {

[0201] return m.id == cluster.representativeId;

[0202] });

[0203] if (repIt != allMoments.end()&&

[0204] find(cluster.momentIds.begin(), cluster.momentIds.end(),cluster.representativeId) == cluster.momentIds.end()) {

[0205] clusterMoments.push_back(*repIt);

[0206] }

[0207] if (!clusterMoments.empty()) {

[0208] / / Calculate the new centroid

[0209] ElectricalCharacteristics newCentroid = calculateCentroid(clusterMoments);

[0210] / / Find the new representative running time

[0211] int newRepresentative = findRepresentativeMoment(clusterMoments,newCentroid);

[0212] / / Check if changes have occurred

[0213] if (newRepresentative != cluster.representativeId ||

[0214] calculateDifferenceRate(newCentroid, cluster.centroid)>0.01) {

[0215] changed = true;

[0216] }

[0217] / / Update combination information

[0218] cluster.centroid = newCentroid;

[0219] cluster.representativeId = newRepresentative;

[0220] }

[0221] }

[0222] iteration++;

[0223] } while (changed&&iteration <maxIterations);

[0224] / / 5. Finally determine the running time of each combination

[0225] for (auto&cluster : clusters) {

[0226] cluster.momentIds.clear();

[0227] for (const auto&moment : allMoments) {

[0228] double minDiffRate = numeric_limits <double>::max();

[0229] int bestClusterId = -1;

[0230] for (const auto&c : clusters) {

[0231] double diffRate = calculateDifferenceRate(moment.ec, c.centroid);

[0232] if (diffRate <minDiffRate) {

[0233] minDiffRate = diffRate;

[0234] bestClusterId = c.clusterId;

[0235] }

[0236] }

[0237] if (bestClusterId == cluster.clusterId) {

[0238] cluster.momentIds.push_back(moment.id);

[0239] }

[0240] }

[0241] }

[0242] return clusters;

[0243] }

[0244] };

[0245] / / Example usage

[0246] int main() {

[0247] / / Create the runtime combiner

[0248] MomentClusterer clusterer;

[0249] / / This example generates runtime data (which should be obtained from the monitoring system in actual applications)

[0250] vector <operationmoment>moments;

[0251] time_t baseTime = time(nullptr);

[0252] / / Generate runtime data in three different states

[0253] for (int i = 0; i<30; i++) {

[0254] OperationMoment moment;

[0255] moment.id = i + 1;

[0256] moment.timestamp = baseTime + i * 60;

[0257] / / Three different states appear alternately

[0258] int state = (i / 10) % 3;

[0259] switch (state) {

[0260] case 0: / / State 1

[0261] moment.ec.voltage = 400.0 + (rand() % 10 - 5) * 0.1;

[0262] moment.ec.current = 50.0 + (rand() % 10 - 5) * 0.1;

[0263] break;

[0264] case 1: / / State 2

[0265] moment.ec.voltage = 380.0 + (rand() % 10 - 5) * 0.1;

[0266] moment.ec.current = 60.0 + (rand() % 10 - 5) * 0.1;

[0267] break;

[0268] case 2: / / State 3

[0269] moment.ec.voltage = 420.0 + (rand() % 10 - 5) * 0.1;

[0270] moment.ec.current = 45.0 + (rand() % 10 - 5) * 0.1;

[0271] break;

[0272] }

[0273] / / Set other electrical characteristics...

[0274] moments.push_back(moment);

[0275] }

[0276] / / Select the initial markup run time (here we simply select the first three)

[0277] vector <int>initialMarkers = {1, 2, 3};

[0278] / / Divide the runtime combinations

[0279] auto clusters = clusterer.clusterMoments(moments, initialMarkers);

[0280] / / Output the division result

[0281] cout<<"Runtime combination division result:"< <endl;

[0282] for (const auto&cluster : clusters) {

[0283] cout<<"combination"< <cluster.clusterId<<":"<<endl;

[0284] cout<<" represents the runtime ID: "< <cluster.representativeId<<endl;

[0285] cout<<" contains the number of running times: "< <cluster.momentIds.size()<<endl;

[0286] cout<<"Average voltage: "< <cluster.centroid.voltage<<"V"<<endl;

[0287] cout<<"Average current: "< <cluster.centroid.current<<"A"<<endl;

[0288] }

[0289] return 0;

[0290] }

[0291] This code implements a complete algorithm for partitioning microgrid operating time combinations. During operation, initial label selection is performed, selecting multiple operating times from historical operating periods as the initial labeled operating times. Iterative combination partitioning is then performed, calculating the difference between each operating time and the labeled operating time, and partitioning the operating times into combinations with the smallest difference. Next, centroid and representative updates are performed. Within each combination, the mean electrical signature is calculated as the centroid, and the operating time with the smallest difference from the centroid is selected as the new representative. Finally, iterative optimization and result output are performed. Through multiple iterations of optimization, the operating times within each combination are ensured to be highly similar. The final output is the partitioned operating time combinations, including each combination's representative operating time, the number of included operating times, and the average electrical signature.

[0292] The algorithm effectively identifies similarities in microgrid operating states and divides historical operating data into multiple groups with similar states, providing a foundation for subsequent aging analysis and performance evaluation. The accuracy and reliability of the division results are ensured through an iterative optimization process.

[0293] 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.

[0294] Please continue reading Figure 1 、 2 As shown in step S5, after dividing the historical operating period with the same operating state of the microgrid, step S5 can be executed to match each historical operating period of the microgrid with the test operating condition to obtain several test operating conditions that match each historical operating period of the microgrid. Specifically, for each historical operating period of the microgrid, step S51 can be executed first 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 period as the typical operating parameters of each type of electrical characteristics in the historical operating 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 period and the operating parameters of each type of electrical characteristics under each test condition. Finally, step S53 can be executed to select several test operating conditions whose operating state difference rate with the historical operating period is less than a set value as several test operating conditions that match the historical operating period of the microgrid. The above process can scientifically and rationally 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.

[0295] 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 based on 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 based on the duration of each historical operating period of the microgrid and the corresponding deemed aging rate. That is, through a scientific weighting 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.

[0296] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and the part for the module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be performed substantially in parallel, and they can sometimes also be performed in the opposite order, depending on the function involved.

[0297] It should also be noted that each box in the block diagram and / or flowchart, and combinations 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.

[0298] Although the present invention has been 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 examining 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. The fact that certain measures are recorded in different dependent claims does not mean that these measures cannot be combined to produce good results.

[0299] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons skilled 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 by: 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 is 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 operating period of the microgrid into multiple historical operating condition periods with the same operating status according to the operating parameters of various types of electrical characteristics of the microgrid that are continuously obtained; 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. Calculate the difference between the typical operating parameters of each type of electrical characteristics during the historical operating period and the operating parameters of each type of electrical characteristics under each test operating condition. Selecting a number of test operating conditions whose operating state difference rate with the historical operating condition period is less than a set value as a number of test operating conditions matching the historical operating condition period of the microgrid; For each historical operating period of the microgrid, calculate the proportional coefficient between the operating state difference rate of the historical operating period and each matching test operating condition, and calculate the weighted average of several test operating conditions matching the historical operating period to obtain the deemed aging rate of the microgrid for the historical operating period; The aging degree of the microgrid is obtained by accumulating the duration of each historical operating period of the microgrid and the corresponding aging rate.

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

3. The system according to claim 1, wherein: The step of dividing the historical operating period of the microgrid into a plurality of historical operating period with the same operating state according to the continuously acquired operating 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 based on the operating parameters of various types of electrical characteristics of the microgrid at different operating times; According to the difference rate of the operating states of the microgrid at different operating moments, a plurality of operating moment combinations with the same operating state of the microgrid are obtained; The time period of the temporally continuous operating time distribution within each operating time combination is taken as a historical operating condition period, and multiple historical operating condition periods in the operation process of the microgrid are obtained.

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 based on the operating parameters of various types of electrical characteristics of the microgrid at different operating times includes: The accumulated value of the difference between the operating parameters of various types of electrical characteristics at different operating moments is calculated and obtained as the operating state difference rate of the microgrid at different operating moments.

5. The system according to claim 3, wherein: The step of obtaining a plurality of operation time combinations of the microgrid having the same operation state according to the operation state difference rate between the microgrid at different operation times includes: Selecting multiple operating moments from 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 status into the same running time combination; Determine whether the running statuses of all running times included in each running time combination are the same; If so, multiple operating time combinations with the same microgrid operating state are obtained; If not, iteratively re-partition until a combination of operating moments with the same microgrid operating state is obtained.

6. The system according to claim 5, characterized in that The step of determining whether the operating states of all the operating moments included in each operating moment combination are the same, include, In each operating time combination, calculating the average value of the operating parameters of each type of electrical characteristics of all operating times included in the operating time combination; Calculate whether the operating time at which the operating state difference rate between the operating parameter mean value of each type of electrical characteristic in each operating time combination and all operating times is the smallest is the marked operating time; If so, 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 moments 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 post-iteration running time combination according to the post-iteration marked running time; It is determined whether the running states of all the running moments included in the running moment combination after each iteration are the same.

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