Current transient fusion and steady-state balance method based on intelligent control

Abstract

The application relates to the technical field of power system steady-state balance, and discloses a current transient fusion and steady-state balance method based on intelligent control, which comprises the following steps: based on a transient current fluctuation signal in a power system, the current fluctuation amplitude, the voltage fluctuation amplitude and the frequency are obtained; the change rate of the fluctuation amplitude and the frequency is calculated through the change amplitude of the current and the voltage, and is compared with a preset threshold value to generate a transient fluctuation state signal; in the application, the particle swarm optimization algorithm is used to obtain the transient fluctuation state signal, the distribution of the current load of each node is accurately calculated, fine adjustment is carried out, the load balance is ensured, the stability and the response speed of the system under transient disturbance are improved, the language type fuzzy system is used, the optimal fusion mode is intelligently selected according to the actual energy distribution state when the transient and steady-state currents are fused, the efficient utilization and distribution of energy are realized, and the long-term operation stability and safety of the power system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system steady-state balance, in particular to a current transient fusion and steady-state balance method based on intelligent control. BACKGROUND

[0002] The core of the steady-state balance technology is to ensure that the current, voltage and power of each part of the power system remain in a stable state during normal operation, avoiding power grid instability or equipment damage caused by unbalanced load or other disturbances. It is applied to power transmission, power distribution systems and various industrial automation fields, aiming to improve the reliability and efficiency of the power system.

[0003] The current transient fusion and steady-state balance method based on intelligent control aims to effectively fuse the transient current in the power system and balance the power load under steady-state conditions through intelligent control means, aiming to improve the operational stability of the power system, prevent the power grid from becoming unstable due to transient disturbances, and ensure the balance of power distribution in long-term operation, enhance the overall safety and operational efficiency of the power system, and reduce potential risks caused by unstable current or unbalanced load.

[0004] When dealing with transient fluctuations in the power system, traditional methods rely on preset rules and empirical formulas to adjust current load, lack dynamic response capability and high-precision adjustment means, resulting in uneven load distribution, which may cause power grid instability or equipment overload. Using fixed paths or limited optimization strategies for current path selection lacks dynamic adaptability to changing conditions, resulting in low energy distribution efficiency, so that traditional methods cannot achieve intelligent adjustment according to real-time energy status when fusing transient and steady-state currents, resulting in low energy utilization rate, affecting long-term stability of the system, increasing operation and maintenance costs, and causing serious power failures in extreme conditions. SUMMARY

[0005] The purpose of the present application is to solve the shortcomings in the prior art, and to propose a current transient fusion and steady-state balance method based on intelligent control.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] The current transient fusion and steady-state balance method based on intelligent control comprises the following steps:

[0008] Step 1, based on the transient current fluctuation signal in the power system, the current fluctuation amplitude, voltage fluctuation amplitude and frequency are obtained, the fluctuation amplitude and frequency change rate are calculated by measuring the change amplitude of current and voltage, and compared with the preset threshold value, and the transient fluctuation state signal is generated;

[0009] Step 2, based on the transient fluctuation state signal, using particle swarm optimization algorithm, obtain steady-state current load distribution and transient load adjustment parameters, obtain the current load of each node, by comparing the relationship between the current load of each node and the transient fluctuation state, adjust the load until the load distribution reaches the balance state, generate the balance load adjustment signal;

[0010] Step 3, based on the balance load adjustment signal, obtain steady-state current load parameters and transient current parameters, calculate the weighted average value of each node current, select the current path by judging the weighted average current value, and determine the new current path, generate the current path planning result;

[0011] Step 4, based on the current path planning result, obtain the current path and voltage path parameters, adjust the current path to meet the optimization requirements, correct the voltage path and confirm, generate the current voltage path adjustment result;

[0012] Step 5, based on the current voltage path adjustment result, using language fuzzy system, obtain the fusion state of transient and steady-state current, select the appropriate fusion mode by judging whether the energy distribution is balanced, switch the transient and steady-state current energy circuit to the selected mode, generate the optimized energy distribution result;

[0013] Step 6, based on the optimized energy distribution result, obtain the energy storage module parameters and system energy state, select the appropriate energy storage, adjust the working state of the storage to realize the best energy storage, generate the energy storage adjustment signal.

[0014] Further, the transient fluctuation state signal includes current fluctuation amplitude, voltage fluctuation amplitude, and fluctuation frequency; the balance load adjustment signal includes node current adjustment parameter, load redistribution parameter, and balance state indication; the optimized current path signal includes new current path selection, load optimization path, and adjusted path parameter; the current voltage path adjustment result includes corrected current path, corrected voltage path, and path confirmation information; the fusion energy distribution signal includes energy harvesting mode, energy distribution strategy, and fusion state indication; the energy storage adjustment signal includes storage working state, energy distribution parameter, and energy storage state indication.

[0015] Further, in step 1, generating the transient fluctuation state signal includes the following steps:

[0016] Step 1.1, based on the transient current fluctuation signal in the power system, perform initial measurement of current and voltage, obtain the change amplitude by point-by-point sampling and record it, generate the initial voltage and current change data;

[0017] Step 1.2, based on the voltage current initial change data, the fluctuation amplitude is calculated, the frequency change rate is extracted by comparing the amplitude change between adjacent sampling points, and the data is integrated to generate the fluctuation amplitude and frequency parameter set;

[0018] Step 1.3, based on the fluctuation amplitude and frequency parameter set, compare the parameter set with the preset threshold, judge whether the data is out of the safe range, and mark the abnormal data, generate the transient fluctuation state signal.

[0019] Further, in step 2, the balance load adjustment signal is generated, including the following steps:

[0020] Step 2.1, based on the transient fluctuation state signal, the current load of each node in the power system is obtained, the load distribution is extracted by scanning the node data set, and the node load distribution data is generated;

[0021] Step 2.2, based on the node load distribution data, the particle swarm optimization algorithm is used to associate and compare with the transient load adjustment parameters, the load adjustment demand is analyzed node by node, and the node to be adjusted is identified, and the node load adjustment instruction is generated;

[0022] Step 2.3, based on the node load adjustment instruction, the load of each node is adjusted step by step, the node parameters are updated in real time, the overall load is evenly distributed, and the balance load adjustment signal is generated.

[0023] Further, the particle swarm optimization algorithm is calculated according to the following formula:

[0024]

[0025] Wherein: Indicates the particle Velocity in the first Generation; Inertia weight; Indicates the particle Velocity in the first Generation; Individual acceleration factor; Random number in [0, 1]; The best position of the particle In the current iteration; The position of the particle In the first Generation; Group acceleration factor; Random number in [0, 1]; Global optimal position of the whole population; Load balancing adjustment coefficient; Time step; is a node a current load value; is an average load value of all nodes; is a load adjustment response coefficient; is a node a power fluctuation value representing the fluctuation of power of the node under the current load; is a node a temperature value.

[0026] Further, in step 3, the current path planning result is generated, including the following steps:

[0027] Step 3.1, based on the balanced load adjustment signal, obtain the steady-state current load parameter and the transient current parameter, read the instantaneous current value of each node, and sample the timing data, and calculate the weighted average value of the current of each node to generate the weighted average current value;

[0028] Step 3.2, based on the weighted average current value, judge whether the node current exceeds the preset threshold, analyze and compare the node current distribution, select the path with the smallest current load, and generate an optimized current path signal;

[0029] Step 3.3, based on the optimized current path signal, adjust the current distribution of the selected path, re-plan the current flow direction in the path, and generate a current path planning result.

[0030] Further, in step 4, the current-voltage path adjustment result is generated, including the following steps:

[0031] Step 4.1, based on the current path planning result, obtain the current voltage path parameter, adjust the voltage distribution in the voltage path, and perform voltage fluctuation balancing processing to generate a voltage path adjustment result;

[0032] Step 4.2, based on the voltage path adjustment result, link and adjust the current path and the voltage path, perform synchronous correction to ensure the coordination between the paths, and generate a current-voltage path synchronization result;

[0033] Step 4.3, based on the current-voltage path synchronization result, check the current and voltage stability between paths, perform path verification, confirm the path configuration, and generate a current-voltage path adjustment result.

[0034] Further, in step 5, the optimized energy distribution result is generated, including the following steps:

[0035] Step 5.1, based on the current voltage path adjustment result, obtain the fusion state of transient and steady-state current, detect the instantaneous value of transient current and the average value of steady-state current, judge the energy difference, and generate current fusion state result combined with voltage fluctuation analysis;

[0036] Step 5.2, based on the current fusion state result, use language fuzzy system to select the optimal current fusion mode, adjust the energy taking circuit of transient current and steady-state current, switch the circuit to the selected mode, and monitor the stability of current switching, generate fusion energy distribution signal;

[0037] Step 5.3, based on the fusion energy distribution signal, detect the load distribution in the current path, adjust the current output proportion of each path, balance the distributed energy, and confirm the adjustment result, generate the optimized energy distribution result.

[0038] Further, the language fuzzy system is calculated according to the following formula:

[0039]

[0040] Wherein: represents the optimal current fusion mode finally calculated; represents the membership function of the th fuzzy rule; represents the weight of the th fuzzy rule; represents the time sequence correlation coefficient of the th fuzzy rule; represents the environmental correction coefficient of current change; represents the total number of fuzzy rules.

[0041] Further, in step 6, the energy storage adjustment signal is generated, including the following steps:

[0042] Step 6.1, based on the optimized energy distribution result, obtain the voltage and current parameters and temperature value of the energy storage module, record the current storage state, analyze the energy storage amount and change trend, and generate the energy storage state result;

[0043] Step 6.2, based on the energy storage state result, select the appropriate energy storage device, adjust the charging voltage and discharging rate of the storage device, optimize the efficiency of energy storage by adjusting the working mode of the storage device, and generate the initial energy storage adjustment signal;

[0044] Step 6.3, based on the initial energy storage adjustment signal, check the running status of the energy storage device, record the key parameters, verify the stability of the storage device, confirm the energy storage configuration, and generate the final energy storage adjustment signal.

[0045] Compared with the prior art, the application has the advantages and positive effects that:

[0046] In the application, the transient fluctuation state signal is obtained through the particle swarm optimization algorithm, the distribution of the current load of each node is accurately calculated, and fine adjustment is performed to ensure load balancing and improve the stability and response speed of the system under transient disturbance. In the dynamic adjustment process of load distribution, the current path can be quickly optimized according to the real-time working condition to avoid the problem of power grid instability caused by transient disturbance, improve the overall efficiency of the power system, and use the language type fuzzy system to intelligently select the optimal fusion mode according to the actual energy distribution state when the transient and steady-state currents are fused, thereby realizing efficient utilization and distribution of energy and enhancing the long-term operation stability and safety of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0048] Figure 1 The present application is a schematic diagram of the main steps. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments obtained by those skilled in the art without creative labor based on the embodiments in the present application are within the scope of protection of the present application.

[0050] The present embodiment provides a current transient fusion and steady-state balancing method based on intelligent control, as shown in the figure, the current transient fusion and steady-state balancing method based on intelligent control comprises the following steps: Figure 1

[0051] Step 1, based on the transient current fluctuation signal in the power system, the current fluctuation amplitude, voltage fluctuation amplitude and frequency are obtained; by measuring the change amplitude of current and voltage, the change rate of fluctuation amplitude and frequency is calculated, and compared with the preset threshold value, to generate a transient fluctuation state signal.

[0052] Among them, the transient fluctuation state signal includes current fluctuation amplitude, voltage fluctuation amplitude, and fluctuation frequency.

[0053] The specific steps for generating the transient fluctuation state signal are:

[0054] ​Step 1.1, based on the transient current fluctuation signal in the power system, the initial measurement of current and voltage is carried out, the change amplitude is obtained by point-by-point sampling and recorded, and the initial change data of voltage and current is generated.

[0055] Step 1.2, based on the initial change data of voltage and current, the fluctuation amplitude is calculated; by comparing the amplitude change between adjacent sampling points, the frequency change rate is extracted and the data is integrated, and the fluctuation amplitude and frequency parameter set is generated.

[0056] Step 1.3, based on the fluctuation amplitude and frequency parameter set, compare the parameter set with the preset threshold; judge whether the data is out of the safe range, and mark the abnormal data, and generate the transient fluctuation state signal.

[0057] Based on the transient current fluctuation signal in the power system, the initial measurement of current and voltage is carried out; the current and voltage are sampled by using point-by-point sampling method, and the interval time between each sampling point is 1 millisecond; the A / D converter is used to convert the sampled analog signal into digital signal, and the sampling precision is set to 16 bits; the sampling data is transmitted to the central processing unit through the data bus, and the change amplitude is recorded, the current and voltage values of each sampling point are stored as double-precision floating point numbers during recording, and the data is stored in temporary memory, and finally the initial change data of voltage and current is generated.

[0058] Based on the initial change data of voltage and current, the fluctuation amplitude is calculated; the difference algorithm is used to calculate the amplitude difference of current and voltage data between adjacent sampling points, and the difference value obtained by calculation is stored in an array; then the fast Fourier transform algorithm is used to analyze the frequency of the differentiated data, and the fast Fourier transform of 1024 points is used, and the frequency resolution is set to 1 hertz; the analysis result is integrated into a frequency parameter set, and the fluctuation amplitude and frequency parameter set is generated.

[0059] Based on the fluctuation amplitude and frequency parameter set, compare the parameter set with the preset threshold; through the conditional judgment statement, the upper limit of the preset threshold is current fluctuation ± 5% rated value, voltage fluctuation ± 3% rated value, and the preset threshold of frequency change rate is ± 0.5 hertz; each item of data in the parameter set is looped and traversed, and whether it is out of the preset threshold range is judged.

[0060] If a certain data in the parameter set is out of the threshold range, mark the data as abnormal data, and mark it with a Boolean variable, and generate the transient fluctuation state signal.

[0061] Step 1 achieves real-time monitoring and abnormal state recognition of transient current and voltage fluctuations in the power system through detailed sampling and analysis process. First, the initial change data of voltage and current are obtained by point-by-point sampling, and then the fluctuation amplitude and frequency change are calculated. This method can effectively extract dynamic characteristics and ensure accurate monitoring of transient fluctuations in the power system. By comparing with the preset threshold, abnormal conditions beyond the safe range can be identified in time, improving the stability and safety of the power system, and providing reliable basis for subsequent fault handling and decision-making.

[0062] Step 2, based on the transient fluctuation state signal, adopts particle swarm optimization algorithm to obtain steady-state current load distribution and transient load adjustment parameters; obtains the current load of each node, compares the relationship between the current load of each node and the transient fluctuation state, adjusts the load until the load distribution reaches a balanced state, and generates a balanced load adjustment signal.

[0063] Among them, the balanced load adjustment signal includes node current adjustment parameters, load redistribution parameters, and balanced state indication.

[0064] The specific steps of generating the balanced load adjustment signal are as follows:

[0065] Step 2.1, based on the transient fluctuation state signal, obtains the current load of each node in the power system; by scanning the node data set, the load distribution is extracted, and the node load distribution data is generated.

[0066] Step 2.2, based on the node load distribution data, adopts particle swarm optimization algorithm to associate and compare with transient load adjustment parameters; through node-by-node analysis of load adjustment requirements, the nodes that need to be adjusted are identified, and node load adjustment instructions are generated.

[0067] Step 2.3, based on the node load adjustment instruction, gradually adjusts the load of each node; by updating the node parameters in real time, the overall load is evenly distributed, and the balanced load adjustment signal is generated.

[0068] Based on the transient fluctuation state signal, the current load of each node in the power system is obtained; the data acquisition module is used to scan the current load data of all nodes in the power system in real time, and the scanning frequency is set to 10 times per second; by reading the real-time current value in the node data set, the load information of each node is extracted; the collected data is stored in the central database and classified and archived according to the node number, and the node load distribution data is generated.

[0069] Based on the node load distribution data, the particle swarm optimization algorithm is used to associate and compare with the transient load adjustment parameters; the population size is set to 50 and the maximum iteration number is set to 100 when initializing the particle swarm. The position vector of the particle represents the load distribution of the node, and the speed vector represents the change rate of the load adjustment; the fitness function is used to evaluate the load adjustment effect of each particle, and the fitness value is calculated according to the transient load adjustment parameters. In each iteration process, the particle speed and position are updated, and the load is adjusted according to the global optimal solution and individual optimal solution. The load adjustment demand of each node is analyzed, the nodes needing adjustment are identified, and the node load adjustment instruction is generated.

[0070] Based on the node load adjustment instruction, the load of each node is adjusted step by step; the central control system gradually issues adjustment commands, and the commands are transmitted to each node control unit through the communication network of the power system; each node modifies the load parameters of the node according to the received adjustment instruction. The real-time updated node parameters include the current load value and the power factor, and the parameter update frequency is set to 5 times per second. The system will record the current node state after each update and store it in the temporary database, and generate the balanced load adjustment signal.

[0071] The particle swarm optimization algorithm is calculated according to the following formula:

[0072]

[0073] Wherein: represents the particle speed in the first generation; is the inertia weight; represents the particle speed in the first generation; is the individual acceleration factor; is a random number in the range of [0, 1]; is the best position of the particle in the current iteration; is the position of the particle in the first generation; is the group acceleration factor; is a random number in the range of [0, 1]; is the global optimal position of the entire population; is the load balancing adjustment coefficient; is the time step; is the current load value of the node ; is the average load value of all nodes; is the load adjustment response coefficient; is the current load value of the node power fluctuation value of the node, indicating the fluctuation of the power of the node under the current load; temperature value of the node .

[0074] Execution process:

[0075] First, initialize the speed of each node , maintain a certain speed continuity through the inertia weight ; adjust the global and local search ability of the particle, respectively use the individual acceleration factor and the group acceleration factor , combined with random numbers and , make the particle tend to its own historical best position and the global optimal position .

[0076] Next, introduce the time step in the formula, and the difference between the current load value of the node and the average load value of all nodes , and weight it with the load balance adjustment coefficient , which is used to dynamically adjust the distribution of node load in the current transient process, and ensure load balance; introduce the ratio of node power fluctuation value and node temperature value , and weight it through the load adjustment response coefficient , considering the influence of thermal stability and power change of the node.

[0077] Finally, calculate the optimal adjustment position of each node under the current load state , generate the corresponding node load adjustment instruction, and realize the fusion of current transient and the balance of steady state.

[0078] Step 2 effectively manages the power system load through particle swarm optimization algorithm, ensuring the balanced distribution of node current load. First, through real-time data acquisition and analysis, the load state of each node is accurately obtained, and the nodes that need to be adjusted are identified. Second, particle swarm optimization algorithm is used to develop load adjustment instructions to achieve accurate and efficient load redistribution. This process not only improves the stability and efficiency of the power system, but also responds to transient fluctuations in time and avoids waste of power resources. The final balanced load adjustment signal provides a scientific basis for subsequent load management decisions, improving the overall operation quality of the power system.

[0079] Step 3, based on the balanced load adjustment signal, obtain the steady-state current load parameter and transient current parameter, and calculate the weighted average value of the current of each node; select the current path by judging the weighted average current value, determine the new current path, and generate the current path planning result.

[0080] wherein the current path planning result comprises a new current path selection, a load optimized path, and an adjusted path parameter;

[0081] The specific steps for generating the current path planning result are as follows:

[0082] Step 3.1, based on the balanced load adjustment signal, obtain the steady-state current load parameter and the transient current parameter; read the instantaneous current value of each node and sample the timing data, and calculate the weighted average value of the current of each node to generate the weighted average current value.

[0083] Step 3.2, based on the weighted average current value, determine whether the node current exceeds the preset threshold; analyze and compare the node current distribution, select the path with the smallest current load, and generate an optimized current path signal.

[0084] Step 3.3, based on the optimized current path signal, adjust the current distribution of the selected path, re-plan the current flow direction in the path, and generate a current path planning result.

[0085] Based on the balanced load adjustment signal, obtain the steady-state current load parameter and the transient current parameter; read the instantaneous current value of each node using a sampling method, set the sampling frequency to 10 milliseconds, convert the analog current signal to a digital signal through an A / D converter; the read timing data is stored in a time series database, and the sliding window technology is used to process the timing data, and the window size is set to 100 sampling points; the weighted average current value is generated by weighted calculation on the data in the window, and the weight value is dynamically adjusted and calculated according to the importance of the node and the current load condition.

[0086] Based on the weighted average current value, determine whether the node current exceeds the preset threshold; analyze and compare the node current distribution using the preset safety current threshold, and the preset threshold is set according to the system design parameters, wherein the current threshold range is ±10% of the rated value; compare the weighted average current value of each node using a conditional judgment statement, traverse all nodes through a for loop, identify the nodes whose current exceeds the preset threshold, analyze the current load condition, and select the path with the smallest current load through a dynamic programming algorithm; the path selection process is calculated according to the geographical location of the node and the resistance value of the line to generate an optimized current path signal.

[0087] Based on the optimized current path signal, the current distribution of the selected path is adjusted; the network flow algorithm is used to re-plan the current flow direction in the path, and the source node and sink node are set as the current inflow and outflow points; the minimum cost maximum flow algorithm is used to allocate the flow of each line in the path, considering the resistance, voltage drop and heat loss of the line and other parameters, dynamically adjusting the current distribution of each line, updating the current flow direction data in the path in real time, and storing the data in the control center database of the power system, generating the current path planning result.

[0088] Step 3 improves the load balancing and safety of the power system by dynamically optimizing the current path. With the help of the weighted average of steady-state and transient current, the current situation of each node is monitored and analyzed in real time, which can identify nodes exceeding the set threshold in time to avoid overload risk. At the same time, through network flow algorithm and dynamic programming technology, the current flow direction is adjusted efficiently, the current distribution is optimized, the energy loss is reduced, and the overall operation efficiency of the system is improved. This process helps to prolong the service life of equipment, reduce operation and maintenance cost, and realize intelligent management of power system.

[0089] Step 4, based on the current path planning result, obtains the current current path and voltage path parameters; by adjusting the current path to meet the optimization requirements, correcting the voltage path and confirming, generating the current voltage path adjustment result.

[0090] Among them, the current voltage path adjustment result includes the corrected current path, the corrected voltage path, and the path confirmation information;

[0091] The specific steps of generating the current voltage path adjustment result are as follows:

[0092] Step 4.1, based on the current path planning result, obtains the current voltage path parameters, adjusts the voltage distribution in the voltage path, and performs voltage fluctuation balancing, generating the voltage path adjustment result.

[0093] Step 4.2, based on the voltage path adjustment result, link adjustment of current path and voltage path, synchronous correction, ensure the coordination between the paths, generate the current voltage path synchronization result.

[0094] Step 4.3, based on the current voltage path synchronization result, check the current and voltage stability between paths, perform path verification, confirm path configuration, generate the current voltage path adjustment result.

[0095] Based on the current path planning result, the current voltage path parameters are obtained; the data acquisition module is used to monitor the voltage of each node in the voltage path in real time, the sampling frequency is set to 100 times per second, and the collected voltage signal is converted into a digital signal through an A / D converter; the read voltage data is stored in the voltage path database, the voltage distribution in the voltage path is adjusted, and a distributed control algorithm is used to calculate the voltage distribution of each node; the voltage of each node is adjusted by the voltage regulator, the adjustment range is ±5% of the rated voltage, and the voltage value of each node is ensured within the set range, and the voltage path adjustment result is generated.

[0096] Based on the initial voltage path adjustment result, the current path and the voltage path are adjusted in linkage; a synchronous correction algorithm is used to adjust the current path and the voltage path in linkage, the difference value between the two paths is calculated by reading the latest current and voltage path data; the difference value is calculated using the Euclidean distance formula, the current and voltage difference value between the paths is used as the input parameter, and the step parameter of the correction algorithm is adjusted to realize synchronous correction, and the path data is dynamically updated during the correction process; the adjustment frequency is set to 50 times per second, and the current and voltage path synchronization result is generated.

[0097] Based on the current and voltage path synchronization result, the current and voltage stability between the paths is checked; the path stability checking algorithm is used to check the current and voltage paths one by one, the real-time data of the current and voltage paths are read, the stability between the paths is analyzed, and the path verification module is used to comprehensively verify the difference between the paths; the verification standard includes voltage deviation, current deviation and path stability coefficient, after the path verification is completed, the configuration of each path is confirmed, and the current and voltage path adjustment result is generated.

[0098] Step 4 ensures that the distribution of current and voltage reaches the best stability and balance through systematic current and voltage path adjustment. Through linkage adjustment, not only the voltage path is effectively monitored and corrected, but also the current path is optimized through real-time data acquisition and synchronous correction algorithm. The finally generated current and voltage path adjustment result provides accurate path verification and confirmation information, enhances the reliability of the power system, reduces the impact of voltage fluctuation on equipment, improves the overall system performance, and effectively supports the safe and efficient operation of power equipment.

[0099] Step 5, based on the current and voltage path adjustment result, a language fuzzy system is used to obtain the fusion state of transient and steady-state current; by judging whether the energy distribution is balanced, selecting the appropriate fusion mode, and switching the transient and steady-state current energy taking circuit to the selected mode, an optimized energy distribution result is generated.

[0100] Among them, the generation of the optimized energy distribution result includes energy taking mode, energy distribution strategy, and fusion state indication;

[0101] The specific steps for generating the optimized energy distribution result are:

[0102] Step 5.1, based on the current-voltage path adjustment result, obtain the fusion state of transient and steady-state currents; detect the instantaneous value of transient current and the average value of steady-state current, judge the energy difference, and generate the current fusion state result combined with voltage fluctuation analysis.

[0103] Step 5.2, based on the current fusion state result, use the linguistic fuzzy system to select the optimal current fusion mode, adjust the energy taking circuit of transient and steady-state currents, switch the circuit to the selected mode, and monitor the stability of current switching to generate the fusion energy distribution signal.

[0104] Step 5.3, based on the fusion energy distribution signal, detect the load distribution in the current path, adjust the current output proportion of each path, balance the distributed energy, and confirm the adjustment result to generate the optimized energy distribution result.

[0105] Based on the current-voltage path adjustment result, obtain the fusion state of transient and steady-state currents; use the data acquisition module to detect the instantaneous value of transient current in real time, the sampling frequency is set to 1000 times per second, the instantaneous value is converted to digital signal by A / D converter and stored in the transient current database; at the same time, read the historical data of steady-state current, calculate its average value, the average value calculation uses sliding window technology, the window size is set to 100 data points; use the calculated instantaneous value and average value, compare the difference value of the two through energy difference calculation module, and combine with voltage fluctuation data for comprehensive analysis to generate the current fusion state result.

[0106] Based on the current fusion state result, use the linguistic fuzzy system to select the optimal current fusion mode; the linguistic fuzzy system uses fuzzy rule base for reasoning:

[0107] First, the current fusion state result is fuzzified, including energy difference, voltage fluctuation, etc.; after fuzzification, the rules in the fuzzy rule base are used for reasoning, the rules in the rule base are pre-set by expert system, and after reasoning, the defuzzification is performed; the defuzzification uses the maximum membership degree method, the defuzzified result is used to select the optimal fusion mode of transient and steady-state currents; then use the switching controller to adjust the energy taking circuit, switch the circuit to the selected mode, and monitor the stability of current switching through the real-time monitoring module to generate the fusion energy distribution signal.

[0108] Based on the fusion energy distribution signal, the load distribution in the current path is detected; the real-time load of the current path is monitored by the load detection module, and the sampling frequency is set to 500 times per second; the load data is transmitted to the control center through the power system communication network, the current output proportion of each path is adjusted, and the proportional integral control algorithm is used to dynamically adjust the current output of each path; the control parameters include proportional gain and integral time constant, the control algorithm adjusts the current output in real time through the regulator, balances the energy distribution of each path, and uses the verification module to confirm the adjusted result to generate the optimized energy distribution result.

[0109] The linguistic fuzzy system calculates according to the following formula:

[0110]

[0111] Wherein: represents the final calculation of the optimal current fusion mode; represents the membership function of the th fuzzy rule; represents the weight of the th fuzzy rule; represents the time sequence correlation coefficient of the th fuzzy rule; represents the environmental correction coefficient of current change; represents the total number of fuzzy rules.

[0112] Execution process:

[0113] According to the current current fusion state, the membership function in the fuzzy logic system calculates the membership value of each fuzzy rule, reflecting the matching degree of the current current state and each fuzzy rule, and the weight of each fuzzy rule is multiplied, and the weight value represents the importance of each fuzzy rule in the whole decision-making process, the time sequence correlation coefficient is introduced, the influence of the change of current characteristics with time on fuzzy rules is evaluated by analyzing historical data, the fuzzy rules at different time points are dynamically adjusted, the environmental correction coefficient is introduced, based on external environmental parameters including temperature, humidity, electromagnetic interference, calculation is performed to correct the influence of external environment on current fusion mode; through global adjustment of the contribution value of each fuzzy rule, and weighted summation of the contribution value of the corrected fuzzy rule, the optimal current fusion mode is obtained, the circuit switching and balance of transient and steady state current are realized, and the efficient distribution of energy and smooth switching of current are realized.

[0114] Step 5 ensures efficient energy distribution through real-time monitoring and analysis of current and voltage, combining transient and steady-state currents. A linguistic fuzzy system is used to dynamically select the optimal current fusion mode based on energy differences and voltage fluctuations, ensuring smooth circuit switching. Additionally, real-time monitoring of load distribution and dynamic adjustment of current output ratios optimize energy distribution across paths, improving system stability and energy efficiency. This comprehensive approach effectively improves energy utilization and reduces energy loss.

[0115] Step 6: Based on the optimized energy distribution results, obtain energy storage module parameters and system energy state; select appropriate energy storage devices and adjust their working states to achieve optimal energy storage, generating energy storage adjustment signals.

[0116] The energy storage adjustment signal includes storage working state, energy distribution parameters, and energy storage state indication.

[0117] The specific steps for generating energy storage adjustment signals are as follows:

[0118] Step 6.1: Based on the optimized energy distribution results, obtain voltage and current parameters and temperature values of the energy storage module, record the current storage state, and analyze the energy storage amount and trend, generating energy storage state results.

[0119] Step 6.2: Based on the energy storage state results, select appropriate energy storage devices, adjust the charging voltage and discharging rate of the storage devices, optimize the efficiency of energy storage by adjusting the working mode of the storage devices, and generate initial energy storage adjustment signals.

[0120] Step 6.3: Based on the initial energy storage adjustment signals, check the operating conditions of the energy storage devices, record key parameters, verify the stability of the storage devices, confirm the energy storage configuration, and generate final energy storage adjustment signals.

[0121] Based on the optimized energy distribution results, obtain voltage and current parameters and temperature values of the energy storage module; use a sensor network to monitor key parameters of the energy storage module in real time, including voltage, current, and temperature, with a sampling frequency of 200 times per second. The collected data is converted into digital signals by an analog-to-digital converter and transmitted to a data processing unit. The current storage state is recorded, and real-time data is stored and managed using a time series database. The energy storage amount and trend are analyzed. During the analysis process, linear regression algorithm is used to fit historical data, calculate the energy storage change rate, and generate energy storage state results.

[0122] Based on the energy storage state result, an appropriate energy storage device is selected, and a decision tree algorithm is used to evaluate multiple energy storage devices; the input variables of the decision tree include voltage, current and temperature parameters, and the output is the appropriate energy storage device model; the algorithm generates a decision tree through recursion, selects the optimal path to determine the storage device, and adjusts the charging voltage and discharge rate of the storage device; the charging voltage and discharge rate are adjusted in real time through a fuzzy control algorithm, and the fuzzy control algorithm generates an initial energy storage adjustment signal based on preset fuzzy rules and membership functions.

[0123] Based on the initial energy storage adjustment signal, the operating condition of the energy storage device is checked; the state monitoring system is used to comprehensively check the operating state of the energy storage device, including voltage stability, discharge current smoothness and temperature fluctuation range, the state monitoring system uses a timer set interval of 5 seconds to automatically collect each parameter and record to the central database, and verifies the stability of the storage device; the execution unit verifies each key parameter of the storage device to confirm whether the storage device is in a safe and stable operating state, and generates a final energy storage adjustment signal.

[0124] Through step 6, efficient management and optimization of the energy storage module can be achieved. Real-time monitoring and data analysis are used to ensure that the energy storage device operates in the best working condition, improving energy storage efficiency. Combined with the decision tree and fuzzy control algorithm, the appropriate energy storage device is intelligently selected and the charging and discharging parameters are adjusted in real time, thereby optimizing energy distribution and storage. At the same time, the key parameters of the storage device are checked regularly to ensure its stability and safety. This series of measures will improve the energy management capability of the system, prolong the service life of the equipment, and reduce energy consumption.

[0125] In summary, the current transient fusion and steady-state balance method based on intelligent control provided in the embodiment uses the particle swarm optimization algorithm to obtain the transient fluctuation state signal, accurately calculates the distribution of current load at each node, and finely adjusts to ensure load balancing, improve the stability and response speed of the system under transient disturbance, and quickly optimize the current path during dynamic adjustment of load distribution, avoiding the problem of unstable power grid caused by transient disturbance, improving the overall efficiency of the power system, and using the language type fuzzy system to intelligently select the optimal fusion mode according to the actual energy distribution state during transient and steady-state current fusion, realizing efficient utilization and distribution of energy, and enhancing the long-term operation stability and safety of the power system.

[0126] The above is only the preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can make equivalent replacement or change according to the technical solution and inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for current transient fusion and steady state balance based on intelligent control, characterized in that, The method comprises the following steps: Step 1, based on the transient current fluctuation signal in the power system, the current fluctuation amplitude, the voltage fluctuation amplitude and the frequency are obtained, the change rate of the fluctuation amplitude and the frequency is calculated by measuring the change amplitude of the current and the voltage, and compared with the preset threshold to generate a transient fluctuation state signal; Step 2, based on the transient fluctuation state signal, the particle swarm optimization algorithm is used to obtain the steady-state current load distribution and the transient load adjustment parameter, the current load of each node is obtained, the load adjustment is carried out by comparing the relationship between the current load of each node and the transient fluctuation state, until the load distribution reaches the balance state, and a balanced load adjustment signal is generated; Step 3, based on the balanced load adjustment signal, the steady-state current load parameter and the transient current parameter are obtained, the weighted average value of the current of each node is calculated, the current path is selected by judging the weighted average current value, and the new current path is determined, and a current path planning result is generated; Step 4, based on the current path planning result, the current path and voltage path parameters are obtained, the current path is adjusted to meet the optimization requirements, the voltage path is corrected and confirmed, and a current voltage path adjustment result is generated; Step 5, based on the current voltage path adjustment result, a language fuzzy system is used to obtain the fusion state of the transient and steady-state currents, the energy distribution is judged whether it is balanced, the appropriate fusion mode is selected, the transient and steady-state current energy taking circuit is switched to the selected mode, and an optimized energy distribution result is generated; The generation of the optimized energy distribution result comprises the following steps: Step 5.1, based on the current voltage path adjustment result, the fusion state of the transient and steady-state currents is obtained, the instantaneous value of the transient current and the average value of the steady-state current are detected, the energy difference is judged, and the voltage fluctuation analysis is combined to generate a current fusion state result; Step 5.2, based on the current fusion state result, a language fuzzy system is used to select the optimal current fusion mode, adjust the transient current and the steady-state current energy taking circuit, switch the circuit to the selected mode, and monitor the stability of the current switching, and generate a fusion energy distribution signal; Step 5.3, based on the fusion energy distribution signal, the load distribution in the current path is detected, the current output proportion of each path is adjusted, the energy is evenly distributed, and the adjustment result is confirmed, and an optimized energy distribution result is generated; The language fuzzy system is calculated according to the following formula: wherein: represents the final calculated optimal current fusion pattern; represents the membership function of the th fuzzy rule; represents the weight of the th fuzzy rule; represents the time sequence correlation coefficient of the th fuzzy rule; represents the environmental correction coefficient of the current change; represents the total number of fuzzy rules; Step 6, based on the optimized energy distribution result, the energy storage module parameters and the system energy state are obtained, the appropriate energy storage device is selected, the working state of the storage device is adjusted to realize the best energy storage, and an energy storage adjustment signal is generated.

2. The method of claim 1, wherein the method is based on intelligent control of current transient fusion and steady state balance. The transient fluctuation state signal includes current fluctuation amplitude, voltage fluctuation amplitude, and fluctuation frequency; the balanced load adjustment signal includes node current adjustment parameter, load reallocation parameter, and balanced state indication; the current path planning result includes new current path selection, load optimization path, and adjusted path parameter; the current-voltage path adjustment result includes corrected current path, corrected voltage path, and path confirmation information; the fusion energy distribution signal includes energy acquisition mode, energy distribution strategy, and fusion state indication; and the energy storage adjustment signal includes storage working state, energy distribution parameter, and energy storage state indication.

3. The intelligent control based current transient fusion and steady state balancing method of claim 1, wherein: In step 1, the transient fluctuation state signal is generated, including the following steps: In step 1.1, initial measurement of current and voltage is performed based on the transient current fluctuation signal in the power system, the change amplitude is obtained by point-by-point sampling and recorded, and voltage and current initial change data are generated; In step 1.2, fluctuation amplitude calculation is performed based on the voltage and current initial change data, the frequency change rate is extracted by comparing the amplitude change between adjacent sampling points, and the data is integrated to generate a fluctuation amplitude and frequency parameter set; In step 1.3, based on the fluctuation amplitude and frequency parameter set, the parameter set is compared with the preset threshold value to determine whether the data is out of the safe range, and the abnormal data is marked to generate the transient fluctuation state signal.

4. The intelligent control based current transient fusion and steady state balancing method of claim 1, wherein: In step 2, the balanced load adjustment signal is generated, including the following steps: In step 2.1, based on the transient fluctuation state signal, the current load of each node in the current power system is obtained, the load distribution is extracted by scanning the node data set, and node load distribution data is generated; In step 2.2, based on the node load distribution data, a particle swarm optimization algorithm is used to associate and compare with the transient load adjustment parameter, the load adjustment demand is analyzed node by node, and the nodes that need to be adjusted are identified to generate node load adjustment instructions; In step 2.3, based on the node load adjustment instructions, the load of each node is adjusted, the node parameters are updated in real time, the overall load is evenly distributed, and the balanced load adjustment signal is generated.

5. The intelligent control based current transient fusion and steady state balancing method of claim 4, wherein: The particle swarm optimization algorithm is calculated according to the following formula: where: represents the velocity of the particle in the i-th generation; at the current iteration; is the inertia weight; represents the velocity of the particle in the i-th generation; at the current iteration; is the individual acceleration factor; is a random number in the range [0, 1]; is the best position of the particle in the current iteration; is the position of the particle in the i-th generation; at the current iteration; is the swarm acceleration factor; is a random number in the range [0, 1]; is the global optimal position of the entire population; is the load balancing adjustment coefficient; is the time step; is the current load value of the node ; is the average load value of all nodes; is the load adjustment response coefficient; is the power fluctuation value of the node , indicating the fluctuation of the power of the node under the current load; is the temperature value of the node .

6. The intelligent control based current transient fusion and steady state balancing method of claim 1, wherein: In step 3, the current path planning result is generated, including the following steps: In step 3.1, based on the balanced load adjustment signal, the steady-state current load parameter and the transient current parameter are obtained, the instantaneous current value of each node is read, the time series data is obtained by sampling, and the weighted average current value of each node current is calculated, and the weighted average current value is generated; In step 3.2, based on the weighted average current value, it is judged whether the node current exceeds the preset threshold value, the node current distribution is analyzed and compared, the path with the smallest current load is selected, and the optimized current path signal is generated; In step 3.3, based on the optimized current path signal, the current distribution of the selected path is adjusted, the current flow direction in the path is re-planned, and the current path planning result is generated.

7. The intelligent control based current transient fusion and steady state balancing method of claim 1, wherein: In step 4, the current-voltage path adjustment result is generated, including the following steps: Step 4.1, based on the current path planning result, obtain the current voltage path parameter, adjust the voltage distribution in the voltage path, and perform voltage fluctuation balancing to generate a voltage path adjustment result; Step 4.2, based on the voltage path adjustment result, link and adjust the current path and the voltage path, perform synchronous correction, ensure the coordination between the paths, and generate a current-voltage path synchronization result; Step 4.3, based on the current-voltage path synchronization result, check the current and voltage stability between the paths, perform path verification, confirm the path configuration, and generate a current-voltage path adjustment result.

8. The intelligent control based current transient fusion and steady state balancing method of claim 1, wherein: In step 6, the energy storage adjustment signal is generated, including the following steps: Step 6.1, based on the optimized energy distribution result, obtain the voltage and current parameters and temperature value of the energy storage module, record the current storage state, analyze the energy storage amount and change trend, and generate an energy storage state result; Step 6.2, based on the energy storage state result, select an appropriate energy storage device, adjust the charging voltage and discharging rate of the storage device, optimize the efficiency of energy storage by adjusting the working mode of the storage device, and generate an initial energy storage adjustment signal; Step 6.3, based on the initial energy storage adjustment signal, check the operating condition of the energy storage device, record the key parameters, verify the stability of the storage device, confirm the energy storage configuration, and generate a final energy storage adjustment signal.

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