Automatic control system for high voltage switchgear in box-type substation

Abstract

The present invention discloses an automatic control system for high-voltage switchgear of a box-type substation, and relates to the technical field of power control. The present invention includes a sensor and a control terminal, and obtains the typical range of each type of electrical parameters and internal environmental parameters in the abnormal state and normal state of the substation according to several types of electrical parameters of the box-type substation in the abnormal state and normal state of the substation and several types of internal environmental parameters in the box-type substation; the substation state and abnormal degree coefficient of each box-type substation at its sampling time are determined according to the typical range of each type of electrical parameters and internal environmental parameters in the abnormal state and normal state of the substation; the sampling time of each box-type substation in the next sampling cycle is allocated at the end of the current sampling cycle, and the sampling plan in the next sampling cycle is compiled and continuously executed. The present invention can timely and effectively discover abnormal substations and respond to them.

Description

Technical Field

[0001] The invention belongs to the technical field of power control, and in particular relates to an automatic control system for high-voltage switchgear in a box-type transformer substation. Background Art

[0002] With the acceleration of urbanization and the growth of industrial electricity demand, the power system has continuously increased its requirements for the automation, reliability and efficient operation of substations. Box-type substations are widely used in urban power grids and industrial power supply systems due to their compact structure, easy installation, simple operation and maintenance, etc., to achieve the conversion of distribution voltage and the distribution of electricity. However, the control of high-voltage switchgear in traditional box-type substations mainly relies on manual operation and basic semi-automatic control, which has problems such as slow response speed, high risk of misoperation, and difficult troubleshooting. At the same time, due to the limited level of automation of the system and the lack of intelligent analysis and processing of multi-dimensional data such as environmental conditions and load conditions, the box-type substation has weak adaptive ability in emergency situations and cannot respond quickly to abnormal conditions. Summary of the invention

[0003] The object of the present invention is to provide an automatic control system for high-voltage switchgear of a box-type substation, which can timely and effectively discover abnormal substations and respond by summarizing and analyzing the operating parameters of each box-type substation.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention provides a box-type substation high-voltage switchgear automation control system, comprising:

[0006] Sensors are used to collect and obtain several types of electrical parameters of the box-type substation in abnormal and normal substation conditions and several types of internal environmental parameters in the box-type substation;

[0007] The control end is used to obtain the total number of sampling times in each sampling cycle;

[0008] According to several types of electrical parameters of the box-type substation in the abnormal state of substation and the normal state of substation and several types of internal environmental parameters in the box-type substation, typical ranges of each type of electrical parameters and internal environmental parameters in the abnormal state of substation and the normal state of substation are obtained;

[0009] During this round of sampling cycle, each type of electrical parameters and internal environmental parameters of each box-type substation at its sampling time are obtained according to the sampling plan;

[0010] According to the typical range of each type of electrical parameters and internal environmental parameters under abnormal substation state and normal substation state, the substation state and abnormality degree coefficient of each box-type substation at its sampling time are determined, wherein the substation state includes abnormal substation state and normal substation state;

[0011] For box-type substations in abnormal substation status, control the disconnection of high-voltage switchgear;

[0012] At the end of this sampling cycle, the total number of sampling times in the next sampling cycle will be allocated according to the substation status and abnormality coefficient of each box-type substation to obtain the sampling time of each box-type substation in the next sampling cycle, and the sampling plan for the next sampling cycle will be compiled and continuously executed.

[0013] The present invention collects electrical parameters and internal environmental parameters of box-type substations through various types of sensors, and then analyzes the historical records of electrical parameters and internal environmental parameters of each box-type substation through the control end to analyze the typical range of each type of parameters in abnormal substation state and normal substation state, and judges the substation state of each box-type substation at its sampling time in the current sampling cycle, and formulates a sampling plan in the next sampling cycle according to the abnormal state. In this process, abnormal substations can be discovered in a timely and effective manner and respond to them.

[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 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.

[0016] Figure 1 A schematic diagram of functional units and information flow of an automatic control system for high-voltage switchgear of a box-type substation according to the present invention in one embodiment;

[0017] Figure 2 A schematic diagram of the flow of interaction steps between the sensor and the control end in one embodiment of the present invention;

[0018] Figure 3 This is a schematic diagram of the step flow of step S2 in one embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of the step flow of step S24 in one embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of a step flow chart of step S242 in an embodiment of the present invention;

[0021] Figure 6 This is a schematic diagram of the step flow of step S4 in one embodiment of the present invention;

[0022] Figure 7 A schematic diagram of a step flow chart of step S47 of an embodiment of the present invention;

[0023] Figure 8 This is a schematic diagram of a step flow chart of step S473 in an embodiment of the present invention;

[0024] Fig. 9 This is a schematic diagram of the step flow of step S6 in one embodiment of the present invention;

[0025] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0026] 1-Sensor, 2-control end. DETAILED DESCRIPTION

[0027] 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.

[0028] 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.

[0029] A box-type substation is an integrated power supply facility, usually used for power distribution and voltage transformation. Box-type substations usually adopt a closed box structure, which can effectively save space. However, due to the compact structure, once heat dissipation blockage, electrical component damage, high-voltage or low-voltage electrical fluctuations and other factors continue to occur, the substation may be irreversibly damaged and even affect the normal operation of the power grid. In view of this, the present invention provides the following solution.

[0030] See also Figures 1 to 2As shown, the present invention provides an automatic control system for high-voltage switchgear of a box-type substation, including a sensor 1 and a control terminal 2. The sensor 1 in this solution is arranged in a box-type substation. In practical applications, the sensor 1 includes a series of sensors of various types. During the operation of the system, step S011 can be executed to collect and obtain several types of electrical parameters of the box-type substation in the abnormal state of substation and the normal state of substation and several types of internal environmental parameters in the box-type substation. The types of electrical parameters include electrical parameters related to the operation of the electrical subsystem of the substation, such as high-voltage terminal voltage, high-voltage terminal current, low-voltage terminal voltage, low-voltage terminal current and substation frequency. The types of internal environmental parameters include temperature, humidity, nitrogen oxide concentration, charged ion concentration and other characteristic substances generated when the electrical components of the substation are working.

[0031] The control terminal 2 in this system is limited by the hardware and software conditions, and its information receiving and sending processing capacity in a unit time is limited, so the total number of sampling times of the control terminal 2 in each sampling cycle is limited. Therefore, it is necessary to obtain the total number of sampling times in each sampling cycle in step S1, which is usually input and set by the management personnel. Next, step S2 can be executed to obtain the typical range of each type of electrical parameters and internal environmental parameters in the abnormal state and normal state of the substation according to several types of electrical parameters of the box-type substation in the abnormal state and normal state of the substation, and several types of internal environmental parameters in the box-type substation, so as to serve as a reference standard for subsequent abnormal judgment.

[0032] See also Figures 1 to 3As shown, since the parameters calibrated by the staff are isolated data, but the typical values ​​of the various state ranges of the box-type substation are an interval, in order to obtain the typical range of each type of electrical parameters and internal environmental parameters in the abnormal state of the substation and the normal state of the substation, step S21 can be first performed to calibrate several groups of electrical parameters and internal environmental parameters of each type in the abnormal state of the substation as the calibration parameter combination in the abnormal state of the substation. Next, step S22 can be performed to calibrate several groups of electrical parameters and internal environmental parameters of each type in the normal state of the substation as the calibration parameter combination in the normal state of the substation. Next, step S23 can be performed to use the electrical parameters and internal environmental parameters of each type in other groups of abnormal state of the substation and the normal state of the substation as the uncalibrated parameter combination in the abnormal state of the substation and the normal state of the substation. Next, step S24 can be performed to calculate and obtain the corresponding type parameter difference between each calibration parameter combination in the abnormal state of the substation and the abnormal state of the substation and each uncalibrated parameter combination in the abnormal state of the substation and the abnormal state of the substation, and thereby obtain a set of several parameter combinations in the abnormal state of the substation and the normal state of the substation. Finally, step S25 may be executed to respectively use the sets of several parameter combinations in the abnormal substation state and the normal substation state as the typical ranges of each type of electrical parameters and internal environment parameters in the abnormal substation state and the normal substation state.

[0033] See also Figure 4 and 5As shown, to define the elements of the corresponding set of each state from a large number of parameter combinations of each state, it is necessary to combine the differences between the various parameter combinations and compare them one by one. In view of this, in the specific implementation process of the above step S24, firstly, step S241 can be executed for each uncalibrated parameter combination in the abnormal state of the substation and the normal state of the substation, and the cumulative value of the corresponding type parameter difference between each uncalibrated parameter combination and each calibrated parameter combination is calculated as the parameter difference between each uncalibrated parameter combination and each calibrated parameter combination. Next, step S242 can be executed to classify and group all parameter combinations according to the parameter difference between each uncalibrated parameter combination and each calibrated parameter combination to obtain a set of several parameter combinations in the abnormal state of the substation and the normal state of the substation, wherein the substation state corresponding to the parameter combination in each set is the same. Specifically, step S2421 can be executed first for each uncalibrated parameter combination, and it and the calibrated parameter combination with the smallest parameter difference are divided into the same set, and a set of several parameter combinations with the calibrated parameter combination as the core is obtained. Next, step S2422 can be executed to determine whether the substation states corresponding to the parameter combinations in each set are exactly the same. If so, it means that the sets of each parameter combination have been fully distinguished, so step S2423 can be executed next to obtain a set of several parameter combinations in the substation abnormal state and the substation normal state. If not, it means that the set classification of parameter combinations has not yet ended, and there are parameter combinations with mixed states in each set, so step S2424 can be executed next to calculate the mean parameter combination of all parameter combinations in each set. For each set, step S2425 can be executed next to take the parameter combination with the smallest parameter difference with the mean parameter combination in the set as the updated calibration parameter combination. Then iteratively execute steps S2421 to S2425 to recalculate the set of parameter combinations based on the updated calibration parameter combination and make a judgment.

[0034] In order to supplement the implementation process of the above steps S241 to S242, 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 data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0035] #include <iostream>

[0036] #include <vector>

[0037] #include <string>

[0038] #include <map>

[0039] #include <cmath>

[0040] #include <limits>

[0041] using namespace std;

[0042] / / Parameter structure, including electrical parameters and environmental parameters

[0043] struct Parameters {

[0044] double voltage_high;

[0045] double current_high;

[0046] double voltage_low;

[0047] double current_low;

[0048] double frequency;

[0049] double temperature;

[0050] double humidity;

[0051] double nox_concentration;

[0052] double ion_concentration;

[0053] / / Parameter mean calculation

[0054] static Parameters calculateMean(const vector <parameters>¶m_group){

[0055] Parameters mean = {0};

[0056] int size = param_group.size();

[0057] for (const auto&p : param_group) {

[0058] mean.voltage_high += p.voltage_high;

[0059] mean.current_high += p.current_high;

[0060] mean.voltage_low += p.voltage_low;

[0061] mean.current_low += p.current_low;

[0062] mean.frequency += p.frequency;

[0063] mean.temperature += p.temperature;

[0064] mean.humidity += p.humidity;

[0065] mean.nox_concentration += p.nox_concentration;

[0066] mean.ion_concentration += p.ion_concentration;

[0067] }

[0068] mean.voltage_high / = size;

[0069] mean.current_high / = size;

[0070] mean.voltage_low / = size;

[0071] mean.current_low / = size;

[0072] mean.frequency / = size;

[0073] mean.temperature / = size;

[0074] mean.humidity / = size;

[0075] mean.nox_concentration / = size;

[0076] mean.ion_concentration / = size;

[0077] return mean;

[0078] }

[0079] };

[0080] / / Calculate the difference degree between two parameter combinations

[0081] double calculateParameterDifference(const Parameters&p1, constParameters&p2) {

[0082] return abs(p1.voltage_high - p2.voltage_high) +

[0083] abs(p1.current_high - p2.current_high) +

[0084] abs(p1.voltage_low - p2.voltage_low) +

[0085] abs(p1.current_low - p2.current_low) +

[0086] abs(p1.frequency - p2.frequency) +

[0087] abs(p1.temperature - p2.temperature) +

[0088] abs(p1.humidity - p2.humidity) +

[0089] abs(p1.nox_concentration - p2.nox_concentration) +

[0090] abs(p1.ion_concentration - p2.ion_concentration);

[0091] }

[0092] / / Divide the set and update the calibration parameters

[0093] void processParameterGroups(vector <parameters>&calibrated_params,

[0094] vector <parameters>&uncalibrated_params,

[0095] vector <bool>&calibrated_states,

[0096] vector <bool>&uncalibrated_states) {

[0097] bool converged = false;

[0098] while (!converged) {

[0099] map<int, vector <int>>clusters; / / Each calibration parameter combination is the core collection index

[0100] / / Assign uncalibrated parameter combinations to the nearest calibrated parameter combinations

[0101] for (size_t i = 0; i <uncalibrated_params.size(); ++i) {

[0102] double min_difference = numeric_limits <double>::max();

[0103] int closest_index = -1;

[0104] for (size_t j = 0; j < calibrated_params.size(); ++j) {

[0105] double difference = calculateParameterDifference(uncalibrated_params[i], calibrated_params[j]);

[0106] if (difference < min_difference) {

[0107] min_difference = difference;

[0108] closest_index = j;

[0109] }

[0110] }

[0111] clusters[closest_index].push_back(i);

[0112] }

[0113] / / Check the consistency of states within each set

[0114] converged = true;

[0115] for (const auto& cluster : clusters) {

[0116] bool base_state = calibrated_states[cluster.first];

[0117] for (int idx : cluster.second) {

[0118] if (uncalibrated_states[idx] != base_state) {

[0119] converged = false;

[0120] break;

[0121] }

[0122] }

[0123] if (!converged) {

[0124] / / Calculate the mean parameter in the set

[0125] vector <parameters>group_params;

[0126] group_params.push_back(calibrated_params[cluster.first]);

[0127] for (int idx : cluster.second) {

[0128] group_params.push_back(uncalibrated_params[idx]);

[0129] }

[0130] Parameters mean_params = Parameters::calculateMean(group_params);

[0131] / / Update the calibration parameter combination to the parameter combination closest to the mean parameter combination

[0132] double min_difference = numeric_limits <double>::max();

[0133] Parameters updated_calibrated;

[0134] for (const auto¶ms : group_params) {

[0135] double difference = calculateParameterDifference(params, mean_params);

[0136] if (difference<min_difference) {

[0137] min_difference = difference;

[0138] updated_calibrated = params;

[0139] }

[0140] }

[0141] calibrated_params[cluster.first] = updated_calibrated;

[0142] break;

[0143] }

[0144] }

[0145] }

[0146] }

[0147] int main() {

[0148] / / Simulate calibrated and uncalibrated parameters

[0149] vector <parameters>calibrated_params = {

[0150] {240, 80, 220, 60, 50, 25, 50, 80, 10},

[0151] {230, 70, 210, 50, 45, 20, 40, 70, 15}

[0152] };

[0153] vector <parameters>uncalibrated_params = {

[0154] {245, 85, 225, 65, 52, 27, 55, 85, 12},

[0155] {235, 75, 215, 55, 48, 22, 45, 75, 18},

[0156] {250, 90, 230, 70, 53, 28, 60, 90, 14}

[0157] };

[0158] / / Simulation of substation status: true indicates abnormality, false indicates normality

[0159] vector <bool>calibrated_states = {true, false};

[0160] vector <bool>uncalibrated_states = {true, false, true};

[0161] / / Execute the division and update of parameter combinations

[0162] processParameterGroups(calibrated_params, uncalibrated_params,calibrated_states, uncalibrated_states);

[0163] / / Output results

[0164] cout<<"Updated calibration parameter combination:"< <endl;

[0165] for (const auto¶ms : calibrated_params) {

[0166] cout<<"High voltage: "< <params.voltage_high

[0167] <<",High Voltage Current: "< <params.current_high

[0168] <<",Low Voltage: "< <params.voltage_low

[0169] <<",Low Voltage Current: "< <params.current_low

[0170] <<",Frequency: "< <params.frequency

[0171] <<",Temperature: "< <params.temperature

[0172] <<",Humidity: "< <params.humidity

[0173] <<",Nitrogen oxide concentration: "< <params.nox_concentration

[0174] <<",ion concentration: "< <params.ion_concentration<<endl;

[0175] }

[0176] return 0;

[0177] }

[0178] During the operation, the above code first calculates the parameter difference. For each uncalibrated parameter combination, the difference with all calibrated parameter combinations is calculated. Then, set partitioning is performed, and the uncalibrated parameter combination is divided into the set where the calibrated parameter combination with the smallest difference is located. Next, consistency judgment is performed to check whether the substation status of the parameter combination in each set is consistent. The calibration parameters are then updated. For inconsistent sets, the set mean is calculated and the calibration parameter combination is updated. Finally, the final calibration parameter combination is output to ensure that the parameter status in all sets is consistent. The algorithm improves the accuracy of parameter classification through iterative optimization, and dynamically adjusts the calibration parameters to make them more consistent with the data distribution characteristics in actual operation.

[0179] Please continue reading Figure 1 and 2 As shown, in the process of real-time monitoring of the cabinet-type substations within the monitoring range during this round of sampling cycle, step S3 can be first executed to obtain each type of electrical parameters and internal environmental parameters of each box-type substation at its sampling time according to the sampling plan during this round of sampling cycle.

[0180] In the application, the state of the box-type substation can be a normal substation state or an abnormal substation state. The abnormal substation state can be specifically divided into substation capacity overload, substation line short circuit, input and output overvoltage or undervoltage, ambient temperature is too high or too low, humidity is too high resulting in reduced insulation performance of equipment, abnormal discharge of equipment, etc. Next, step S4 can be executed to determine the substation state and abnormality degree coefficient of each box-type substation at its sampling time according to the typical range of each type of electrical parameters and internal environmental parameters under the abnormal substation state and the normal substation state.

[0181] See also Figure 1 , 2 As shown in Figure 6, since the monitoring capability of the control terminal 2 is limited, different monitoring resources need to be invested in different box-type substations with different abnormality levels according to local conditions. In view of this, the above-mentioned step S4 needs to evaluate the abnormal state of the substation in the abnormal state of the substation to obtain the abnormality degree coefficient during operation. Specifically, first, step S41 can be executed for each parameter combination set in the abnormal state of the substation and the normal state of the substation, and the numerical range of each type of electrical parameters and internal environmental parameters can be counted respectively to obtain the numerical range of each type of electrical parameters and internal environmental parameters of each set in the abnormal state of the substation and the normal state of the substation. Next, step S42 can be executed to determine whether each type of electrical parameters and internal environmental parameters of each box-type substation at its sampling time fall into the numerical range of each type of electrical parameters and internal environmental parameters of any set. If so, it means that the box-type substation is in a certain certain substation state, so next step S43 can be executed to use the substation state corresponding to the set that falls into as the substation state of the box-type substation at its sampling time. Also, because the box-type substation is in a certain substation state, the abnormality degree coefficient corresponding to the abnormal substation state can be defined as 1 and the abnormality degree coefficient corresponding to the normal substation state can be defined as 0. Otherwise, it means that the box-type substation does not belong to the normal substation state, so the next step S46 can be executed. The substation state of the box-type substation at its sampling time is the abnormal substation state, but it also means that the box-type substation does not belong to any of the abnormal substation states previously classified, so its abnormality degree coefficient needs to be supplemented by calculation, that is, step S47 is executed to obtain the abnormality degree coefficient of the box-type substation at its sampling time according to the difference between the numerical range of each type of electrical parameters and internal environmental parameters of the box-type substation at its sampling time and each type of electrical parameters and internal environmental parameters of each set in the abnormal substation state and the normal substation state.

[0182] See also Figure 7 and 8 As shown, since the box-type substation does not belong to any of the previously defined abnormal substation states, it is still necessary to calculate its abnormality coefficient, which requires it to be compared and calculated with each abnormal substation state. Specifically, step S471 can be first executed to form the parameter combination of the box-type substation at its sampling time with each type of electrical parameters and internal environment parameters. Next, step S472 can be executed to calculate and obtain the parameter combination with the smallest parameter difference with the parameter combination of the box-type substation at its sampling time as the comparison parameter combination for each parameter combination set, and obtain the corresponding parameter difference as the comparison parameter difference. Finally, step S473 can be executed to obtain the abnormality coefficient of the box-type substation at its sampling time according to the comparison parameter difference of each set of the parameter combination of the box-type substation at its sampling time, and the substation state of each set. Specifically, step S4731 can be first executed to accumulate the comparison parameter difference corresponding to the set of abnormal substation states as the abnormal comparison parameter difference. Next, step S4732 can be executed to accumulate the control parameter differences corresponding to the set of normal substation states as the normal control parameter differences. Next, step S4733 can be executed to use the accumulated value of the abnormal control parameter differences and the normal control parameter differences as the overall control parameter differences. Finally, step S4734 can be executed to use the ratio of the abnormal control parameter differences to the overall control parameter differences as the abnormal degree coefficient of the box-type substation at its sampling time.

[0183] In order to supplement the implementation process of the above steps S471 to S473, 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 data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0184] #include <iostream>

[0185] #include <vector>

[0186] #include <cmath>

[0187] #include <map>

[0188] #include <limits>

[0189] #include <string>

[0190] using namespace std;

[0191] / / Parameter structure, including electrical parameters and environmental parameters

[0192] struct Parameters {

[0193] double voltage_high;

[0194] double current_high;

[0195] double voltage_low;

[0196] double current_low;

[0197] double frequency;

[0198] double temperature;

[0199] double humidity;

[0200] double nox_concentration;

[0201] double ion_concentration;

[0202] };

[0203] / / Calculate the difference in parameters

[0204] double calculateDifference(const Parameters&p1, const Parameters&p2){

[0205] double diff = 0.0;

[0206] diff += pow(p1.voltage_high - p2.voltage_high, 2);

[0207] diff += pow(p1.current_high - p2.current_high, 2);

[0208] diff += pow(p1.voltage_low - p2.voltage_low, 2);

[0209] diff += pow(p1.current_low - p2.current_low, 2);

[0210] diff += pow(p1.frequency - p2.frequency, 2);

[0211] diff += pow(p1.temperature - p2.temperature, 2);

[0212] diff += pow(p1.humidity - p2.humidity, 2);

[0213] diff += pow(p1.nox_concentration - p2.nox_concentration, 2);

[0214] diff += pow(p1.ion_concentration - p2.ion_concentration, 2);

[0215] return sqrt(diff); / / Calculate Euclidean distance

[0216] }

[0217] / / Get the minimum difference and corresponding parameter combination

[0218] pair<Parameters, double> findClosestMatch(

[0219] const Parameters&sample,

[0220] const vector <parameters>¶m_group) {

[0221] double min_diff = numeric_limits <double>::max();

[0222] Parameters closest;

[0223] for (const auto&p : param_group) {

[0224] double diff = calculateDifference(sample, p);

[0225] if (diff<min_diff) {

[0226] min_diff = diff;

[0227] closest = p;

[0228] }

[0229] }

[0230] return {closest, min_diff};

[0231] }

[0232] / / Calculate the anomaly degree coefficient

[0233] double calculateAnomalyCoefficient(

[0234] const Parameters&sample,

[0235] const vector <parameters>&abnormal_group,

[0236] const vector <parameters>&normal_group) {

[0237] double abnormal_sum = 0.0; / / Accumulation of the difference between the control parameters of the abnormal state

[0238] double normal_sum = 0.0; / / Accumulation of the difference between the control parameters in the normal state

[0239] / / Traverse the exception collection

[0240] for (const auto&abnormal : abnormal_group) {

[0241] abnormal_sum += findClosestMatch(sample, abnormal_group).second;

[0242] }

[0243] / / Traverse the normal collection

[0244] for (const auto&normal : normal_group) {

[0245] normal_sum += findClosestMatch(sample, normal_group).second;

[0246] }

[0247] / / Overall comparison parameter difference

[0248] double total_sum = abnormal_sum + normal_sum;

[0249] if (total_sum == 0.0) {

[0250] / / Avoid division by zero

[0251] return 0.0;

[0252] }

[0253] / / Abnormality coefficient = abnormal difference accumulation / overall difference accumulation

[0254] return abnormal_sum / total_sum;

[0255] }

[0256] int main() {

[0257] / / Simulation abnormal state parameter set

[0258] vector <parameters>abnormal_group = {

[0259] {245, 85, 225, 65, 52, 27, 55, 85, 12},

[0260] {250, 90, 230, 70, 53, 28, 60, 90, 14}

[0261] };

[0262] / / Simulate normal state parameter set

[0263] vector <parameters>normal_group = {

[0264] {230, 75, 215, 55, 48, 22, 45, 75, 18},

[0265] {235, 80, 220, 60, 50, 25, 50, 80, 20}

[0266] };

[0267] / / Parameter combination at sampling time

[0268] Parameters sample = {248, 87, 228, 67, 51, 26, 56, 88, 13};

[0269] / / Calculate the abnormality coefficient

[0270] double anomaly_coefficient = calculateAnomalyCoefficient(sample,abnormal_group, normal_group);

[0271] / / Output results

[0272] cout<<"Abnormality coefficient: "< <anomaly_coefficient<<endl;

[0273] return 0;

[0274] }

[0275] During the operation of the above code, the parameter difference is first calculated, and the difference between two parameter combinations is calculated using the Euclidean distance formula. Then the minimum difference matching is performed to find the control parameter combination with the minimum difference from the current sampling parameter combination from the abnormal and normal sets. Then the differences are accumulated, and the control parameter differences of the abnormal and normal sets are accumulated respectively. Finally, the normalization calculation and applicable scenarios are performed, and the ratio of the abnormal difference to the overall difference is used as the abnormal degree coefficient, and the normalization result is 0,1. This algorithm supports the dynamic evaluation of the abnormal degree of substations by the automated power system, providing an important basis for abnormal handling.

[0276] Continue reading Figures 1 to 2 As shown, the control end 2 can then execute step S5 to control the high-voltage switchgear to disconnect the box-type substation in the abnormal transformation state. In actual application, the variable capacity can also be reduced to try to restore the box-type substation to a normal state.

[0277] See also Fig. 9 As shown, in order to continuously monitor all box-type transformers, step S6 can be executed next. At the end of this round of sampling cycle, the total number of sampling times in the next round of sampling cycle is allocated according to the substation state and abnormality coefficient of each box-type substation to obtain the sampling time of each box-type substation in the next round of sampling cycle, and the sampling plan in the next round of sampling cycle is compiled and continuously executed. Specifically, step S61 can be executed first to reserve the basic sampling times for each box-type substation in the normal state of substation, and obtain the sampling times of each box-type substation in the normal state of substation in the next round of sampling cycle. Next, step S62 can be executed to obtain the adjustable sampling times in the next round of sampling cycle by subtracting the basic sampling times reserved by all box-type substations in the normal state of substation. Next, step S63 can be executed to allocate the adjustable sampling times in the next round of sampling cycle according to the ratio between the abnormality coefficients of the box-type substations in the abnormal state of substation to obtain the sampling times of each box-type substation in the abnormal state of substation in the next round of sampling cycle. Finally, step S64 can be executed to evenly distribute the sampling times of each box-type substation in the next sampling cycle to obtain the sampling time of each box-type substation in the next sampling cycle. Since the sampling monitoring in this scheme is distributed according to the abnormality degree of each box-type substation, the abnormal state of substation can be discovered and processed in time and effectively.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] 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.< / parameters> < / parameters> < / parameters> < / parameters> < / double> < / parameters> < / string> < / limits> < / map> < / cmath> < / vector> < / iostream> < / bool> < / bool> < / parameters> < / parameters> < / double> < / parameters> < / double> < / int> < / bool> < / bool> < / parameters> < / parameters> < / parameters> < / limits> < / cmath> < / map> < / string> < / vector> < / iostream>

Claims

1. A box-type substation high-voltage switchgear automation control system, characterized in that: include, A sensor for collecting and acquiring several types of electrical parameters of a box-type substation in an abnormal state and a normal state of substation and several types of internal environmental parameters in the box-type substation, wherein the types of electrical parameters include high-voltage terminal voltage, high-voltage terminal current, low-voltage terminal voltage, low-voltage terminal current and / or substation frequency, and the types of internal environmental parameters include temperature, humidity, nitrogen oxide concentration and / or charged ion concentration; The control end is used to obtain the total number of sampling times in each sampling cycle; According to several types of electrical parameters of the box-type substation in the abnormal state of substation and the normal state of substation and several types of internal environmental parameters in the box-type substation, typical ranges of each type of electrical parameters and internal environmental parameters in the abnormal state of substation and the normal state of substation are obtained; During this round of sampling cycle, each type of electrical parameters and internal environmental parameters of each box-type substation at its sampling time are obtained according to the sampling plan; According to the typical range of each type of electrical parameters and internal environmental parameters under abnormal substation state and normal substation state, the substation state and abnormality degree coefficient of each box-type substation at its sampling time are determined, wherein the substation state includes abnormal substation state and normal substation state; For box-type substations in abnormal substation status, control the disconnection of high-voltage switchgear; At the end of this round of sampling cycle, the total number of sampling times in the next round of sampling cycle is allocated according to the substation status and abnormality coefficient of each box-type substation to obtain the sampling time of each box-type substation in the next round of sampling cycle, and the sampling plan in the next round of sampling cycle is compiled and continuously executed; The step of obtaining the typical range of each type of electrical parameters and internal environmental parameters in the abnormal state and the normal state of the substation according to several types of electrical parameters of the box-type substation in the abnormal state and the normal state of the substation and several types of internal environmental parameters in the box-type substation includes: Calibrate several groups of electrical parameters and internal environment parameters of each type under abnormal substation conditions as calibration parameter combinations under abnormal substation conditions; Calibrate several groups of electrical parameters and internal environment parameters of each type under the normal state of the substation as calibration parameter combinations under the normal state of the substation; The electrical parameters and internal environment parameters of each type in the other groups of substation abnormal state and substation normal state are respectively used as the uncalibrated parameter combination in the substation abnormal state and substation normal state; Calculate and obtain the corresponding type parameter difference between each calibrated parameter combination in the substation abnormal state and the substation abnormal state and each uncalibrated parameter combination in the substation abnormal state and the substation abnormal state, and thereby obtain a set of several parameter combinations in the substation abnormal state and the substation normal state; The sets of several parameter combinations in the abnormal state of the substation and the normal state of the substation are respectively used as the typical ranges of each type of electrical parameters and internal environment parameters in the abnormal state of the substation and the normal state of the substation; The step of allocating the total number of sampling times in the next sampling cycle according to the substation status and abnormality coefficient of each box-type substation to obtain the sampling time of each box-type substation in the next sampling cycle includes: In order to retain the basic sampling times for each box-type substation in a normal substation state, the sampling times of each box-type substation in a normal substation state in the next sampling cycle are obtained; The adjustable sampling number in the next sampling cycle is obtained by subtracting the basic sampling number reserved for the box-type substations in which all substations are in normal state from the total sampling number in the next sampling cycle; The adjustable sampling times in the next sampling cycle are distributed according to the ratio between the abnormality degree coefficients of the box-type substations in the abnormal substation state to obtain the sampling times of each box-type substation in the abnormal substation state in the next sampling cycle; The sampling times of each box-type substation are evenly distributed in the next sampling cycle to obtain the sampling time of each box-type substation in the next sampling cycle.

2. The system according to claim 1, characterized in that The step of calculating and obtaining the corresponding type parameter difference between each calibrated parameter combination in the abnormal substation state and the abnormal substation state and each uncalibrated parameter combination in the abnormal substation state and the abnormal substation state, and thereby obtaining a set of several parameter combinations in the abnormal substation state and the normal substation state, includes: For each uncalibrated parameter combination in the abnormal state of the substation and the normal state of the substation, the cumulative value of the corresponding type parameter difference between each uncalibrated parameter combination and each calibrated parameter combination is calculated as the parameter difference between each uncalibrated parameter combination and each calibrated parameter combination; According to the parameter difference between each uncalibrated parameter combination and each calibrated parameter combination, all parameter combinations are classified and grouped to obtain sets of several parameter combinations under abnormal substation state and normal substation state, wherein the substation state corresponding to the parameter combinations in each set is the same.

3. The system according to claim 2, characterized in that The step of classifying and grouping all parameter combinations according to the parameter difference between each uncalibrated parameter combination and each calibrated parameter combination to obtain a set of several parameter combinations in the abnormal state of substation and the normal state of substation includes: For each uncalibrated parameter combination, group it with the calibrated parameter combination with the smallest parameter difference into the same set, and obtain a plurality of sets of parameter combinations with the calibrated parameter combination as the core; Determine whether the substation states corresponding to the parameter combinations in each set are exactly the same; If so, a set of several parameter combinations under abnormal substation state and normal substation state is obtained; If not, the mean parameter combination of all parameter combinations in each set is calculated; For each set, the parameter combination with the smallest parameter difference from the mean parameter combination in the set is used as the updated calibration parameter combination; The set of parameter combinations is recalculated and judged based on the updated calibration parameter combination.

4. The system according to claim 1, characterized in that The step of determining the substation state and abnormality degree coefficient of each box-type substation at the sampling time according to the typical range of each type of electrical parameters and internal environmental parameters under abnormal substation state and normal substation state includes: For each set of parameter combinations in the abnormal state and the normal state of the substation, the numerical range of each type of electrical parameters and internal environmental parameters is counted respectively, and the numerical range of each type of electrical parameters and internal environmental parameters of each set in the abnormal state and the normal state of the substation is obtained; Determine whether each type of electrical parameter and internal environmental parameter of each box-type substation at its sampling time falls within the numerical range of each type of electrical parameter and internal environmental parameter of any set; If yes, then the substation state corresponding to the set that falls into it is taken as the substation state of the box-type substation at its sampling time. The abnormality degree coefficient corresponding to the abnormal state of substation is defined as 1. The abnormality degree coefficient corresponding to the normal state of substation is defined as 0; If not, the substation status of the box-type substation at the sampling time is abnormal. The abnormality degree coefficient of the box-type substation at its sampling time is obtained according to the difference between the numerical range of each type of electrical parameters and internal environmental parameters of the box-type substation at its sampling time and the numerical range of each type of electrical parameters and internal environmental parameters of each set in the abnormal state and the normal state of the substation.

5. The system according to claim 4, characterized in that The step of obtaining the abnormality degree coefficient of the box-type substation at its sampling time according to the difference between the numerical range of each type of electrical parameters and internal environmental parameters of the box-type substation at its sampling time and each type of electrical parameters and internal environmental parameters of each set in the abnormal state and the normal state of the substation includes: Combining each type of electrical parameters and internal environment parameters of the box-type substation at its sampling time into a parameter combination of the box-type substation at its sampling time; For each set of parameter combinations, the parameter combination with the smallest parameter difference from the parameter combination of the box-type substation at its sampling time is calculated and obtained as the reference parameter combination, and the corresponding parameter difference is obtained as the reference parameter difference; The abnormality degree coefficient of the box-type substation at its sampling time is obtained according to the difference between the parameter combination of the box-type substation at its sampling time and the control parameter of each set, as well as the substation state of each set.

6. The system according to claim 5, characterized in that The step of obtaining the abnormality degree coefficient of the box-type substation at its sampling time according to the difference between the parameter combination of the box-type substation at its sampling time and the control parameter of each set, and the substation state of each set, includes: The comparison parameter differences corresponding to the set of abnormal substation states are accumulated as the abnormal comparison parameter difference; The control parameter differences corresponding to the set of normal substation states are accumulated as the normal control parameter differences; The cumulative value of the abnormal control parameter difference and the normal control parameter difference is taken as the overall control parameter difference; The ratio of the abnormal comparison parameter difference to the overall comparison parameter difference is taken as the abnormal degree coefficient of the box-type substation at its sampling time.

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