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

Abstract

The invention relates to the technical field of steady-state balance of an electric power system, and discloses a current transient fusion and steady-state balance method based on intelligent control, which comprises the following steps of: based on a transient current fluctuation signal in the electric power system, obtaining a current fluctuation amplitude, a voltage fluctuation amplitude and a frequency, and measuring the current fluctuation amplitude and the voltage fluctuation amplitude by measuring the variation amplitude of the current and the voltage; and calculating the change rate of the fluctuation amplitude and the frequency, and comparing the change rate with a preset threshold value to generate a transient fluctuation state signal. According to the method, transient fluctuation state signals are obtained through a particle swarm optimization algorithm, the distribution condition of current loads of all nodes is accurately calculated, fine adjustment is carried out, load balance is ensured, the stability and response speed of the system under transient disturbance are improved, a language type fuzzy system is adopted, and when transient current and steady current are fused, the stability of the system is improved. According to the actual energy distribution state, the optimal fusion mode is intelligently selected, efficient utilization and distribution of energy are achieved, and the long-term operation stability and safety of a power system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of steady-state balance of electric power systems, and in particular to a current transient fusion and steady-state balance method based on intelligent control. Background Art

[0002] The core of the field of 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 grid instability or equipment damage caused by load imbalance or other disturbances. It is applied to power transmission, distribution systems and various industrial automation fields, with the aim of improving the reliability and efficiency of the power system.

[0003] The current transient fusion and steady-state balance method based on intelligent control aims to achieve effective fusion of transient currents in the power system and balance of power load under steady-state conditions through intelligent control means, aiming to improve the operating 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 operating efficiency of the power system, and reduce potential risks caused by current instability or load imbalance.

[0004] When dealing with transient fluctuations in power systems, traditional methods rely on preset rules and empirical formulas to adjust current loads. They lack dynamic response capabilities and high-precision adjustment methods, resulting in uneven load distribution, which may in turn cause grid instability or equipment overload. Fixed paths or limited optimization strategies are used to select current paths. They lack the ability to dynamically adapt to variable operating conditions, resulting in inefficient energy distribution. When transient and steady-state currents are merged, traditional methods cannot achieve intelligent adjustments based on real-time energy status, resulting in low energy utilization, affecting the long-term stability of the system, increasing operating and maintenance costs, and causing serious power failures under extreme conditions. Summary of the invention

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

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

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

[0008] Step 1, based on the transient current fluctuation signal in the power system, obtain the current fluctuation amplitude, voltage fluctuation amplitude and frequency, calculate the change rate of the fluctuation amplitude and frequency by measuring the change amplitude of the current and voltage, and compare them with the preset threshold value to generate a transient fluctuation state signal;

[0009] Step 2: Based on the transient fluctuation state signal, a particle swarm optimization algorithm is used to obtain the steady-state current load distribution and transient load adjustment parameters, and the current load of each node. By comparing the relationship between the current load of each node and the transient fluctuation state, load adjustment is performed until the load distribution reaches a balanced state, and a balanced load adjustment signal is generated;

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

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

[0012] Step 5: Based on the current and voltage path adjustment results, a linguistic fuzzy system is used to obtain the fusion state of transient and steady-state currents. By judging whether the energy distribution is balanced, a suitable fusion mode is selected, and the transient and steady-state current energy-taking circuits are switched to the selected mode to generate a fusion energy allocation signal.

[0013] Step 6, based on the fused energy allocation signal, obtain the energy storage module parameters and the system energy state, select an adaptive energy storage device, adjust the working state of the storage device to achieve optimal energy storage, and generate an energy storage adjustment signal.

[0014] Furthermore, the transient fluctuation state signal includes current fluctuation amplitude, voltage fluctuation amplitude, and fluctuation frequency; the balanced load adjustment signal includes node current adjustment parameters, load redistribution parameters, and balanced state indication; the optimized current path signal includes new current path selection, load optimization path, and adjusted path parameters; the current and voltage path adjustment results include corrected current path, corrected voltage path, and path confirmation information; the fused energy allocation signal includes energy extraction mode, energy allocation strategy, and fusion state indication; the energy storage adjustment signal includes memory working state, energy allocation parameters, and energy storage state indication.

[0015] Further, in step 1, generating the transient fluctuation state signal comprises 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 sampling point by point and record it, and generate initial voltage and current change data;

[0017] Step 1.2, based on the initial voltage and current change data, calculate the fluctuation amplitude, extract the frequency change rate by comparing the amplitude changes between adjacent sampling points and integrate the data to generate a 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 value, determine whether the data exceeds the safety range, mark the abnormal data, and generate a transient fluctuation state signal.

[0019] Further, in step 2, generating the balanced load adjustment signal includes the following steps:

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

[0021] Step 2.2, based on the node load distribution data, a particle swarm optimization algorithm is used to perform correlation comparison with transient load adjustment parameters, load adjustment requirements are analyzed node by node, nodes that need to be adjusted are identified, and node load adjustment instructions are generated;

[0022] Step 2.3, based on the node load adjustment instruction, gradually adjust the load of each node, evenly distribute the overall load by updating the node parameters in real time, and generate a balanced load adjustment signal.

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

[0024]

[0025] in: represents the velocity of particle i in the k+1th generation; ω is the inertia weight; represents the speed of particle i in the kth generation; c1 is the individual acceleration factor; r1 is a random number in the range of [0, 1]; is the best position of particle i in the current iteration; is the position of particle i in the kth generation; c2 is the population acceleration factor; r2 is a random number in the range [0, 1]; g best is the global optimal position of the entire population; α is the load balancing adjustment coefficient; Δt is the time step; L i is the current load value of node i; L avg is the average load value of all nodes; β is the load adjustment response coefficient; ΔP i is the power fluctuation value of node i, indicating the power fluctuation of the node under the current load; T i is the temperature value of node i.

[0026] Further, in step 3, generating the current path planning result includes the following steps:

[0027] Step 3.1, based on the balanced load adjustment signal, obtain steady-state current load parameters and transient current parameters, read the instantaneous current value of each node, and perform sampling to obtain time series data, and calculate the weighted average value of the current of each node to generate a weighted average current value;

[0028] Step 3.2, based on the weighted average current value, determine whether the node current exceeds a preset threshold, analyze and compare the node current distribution, select a 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, generating the current and voltage path adjustment result includes the following steps:

[0031] Step 4.1, based on the current path planning result, obtaining current voltage path parameters, adjusting the voltage distribution in the voltage path, performing voltage fluctuation equalization processing, and generating a voltage path adjustment result;

[0032] Step 4.2, based on the voltage path adjustment result, the current path and the voltage path are adjusted in linkage, and synchronous correction is performed to ensure the coordination between the paths, and a current and voltage path synchronization result is generated;

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

[0034] Further, in step 5, generating the optimized energy allocation result includes the following steps:

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

[0036] 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 energy extraction circuits of transient current and steady-state current, switch the circuit to the selected mode, monitor the stability of current switching, and generate a fusion energy allocation signal;

[0037] Step 5.3, based on the fused energy distribution signal, detect the load distribution in the current path, adjust the current output ratio of each path, evenly distribute the energy, confirm the adjustment result, and generate the optimized energy distribution result.

[0038] Furthermore, the linguistic fuzzy system is calculated according to the following formula:

[0039]

[0040] Where: u′ represents the optimal current fusion mode finally calculated; μ i (x) represents the membership function of the i-th fuzzy rule; w i represents the weight of the i-th fuzzy rule; k i represents the time series correlation coefficient of the i-th fuzzy rule; λ represents the environmental correction coefficient of current change; n represents the total number of fuzzy rules.

[0041] Further, in step 6, generating the energy storage adjustment signal comprises the following steps:

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

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

[0044] Step 6.3, based on the energy storage adjustment signal, check the operating status of the energy storage, record various key parameters, verify the stability of the storage, confirm the energy storage configuration, and generate an energy storage adjustment signal.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, a transient fluctuation state signal is obtained through a particle swarm optimization algorithm, the distribution of current loads at each node is accurately calculated, and fine adjustments are made to ensure load balancing, thereby improving the stability and response speed of the system under transient disturbances. In the dynamic adjustment process of load distribution, the current path can be quickly optimized according to the real-time working conditions, thereby avoiding the instability of the power grid caused by transient disturbances and improving the overall efficiency of the power system. A language-based fuzzy system is used, and when the transient and steady-state currents are merged, the optimal fusion mode is intelligently selected according to the actual energy distribution state, thereby achieving efficient utilization and distribution of energy and enhancing the long-term operation stability and safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

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

[0049] The following will be combined with the drawings of the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0050] This embodiment provides a current transient fusion and steady-state balancing method based on intelligent control, such as Figure 1 As shown, the current transient fusion and steady-state balancing method based on intelligent control includes the following steps:

[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 the fluctuation amplitude and frequency is calculated, and compared with the preset threshold value to generate a transient fluctuation state signal.

[0052] The transient fluctuation state signal includes current fluctuation amplitude, voltage fluctuation amplitude, and fluctuation frequency.

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

[0054] 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 through point-by-point sampling and record it, and generate initial voltage and current change data.

[0055] Step 1.2, based on the initial change data of voltage and current, calculate the fluctuation amplitude; by comparing the amplitude changes between adjacent sampling points, extract the frequency change rate and integrate the data to generate a fluctuation amplitude and frequency parameter set.

[0056] Step 1.3, based on the fluctuation amplitude and frequency parameter set, compare the parameter set with the preset threshold; determine whether the data exceeds the safety range, mark the abnormal data, and generate a 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 using the point-by-point sampling method, and the interval time of each sampling point is 1 millisecond; the sampled analog signal is converted into a digital signal using an A / D converter, and the sampling accuracy is set to 16 bits; the sampled data is transmitted to the central processing unit through the data bus, and the change amplitude is recorded. When recording, the current and voltage values ​​of each sampling point are stored as double-precision floating point numbers, and the data is stored in temporary memory, and finally the initial voltage and current change data are generated.

[0058] Based on the initial change data of voltage and current, the fluctuation amplitude is calculated; the differential algorithm is used to calculate the amplitude difference of the current and voltage data between adjacent sampling points, and the calculated differential value is stored in an array; then the fast Fourier transform algorithm is used to perform frequency analysis on the differential data, using a 1024-point fast Fourier transform and a frequency resolution of 1 Hz; the analysis results are integrated into a frequency parameter set to generate a fluctuation amplitude and frequency parameter set.

[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 the current fluctuation ±5% of the rated value, the voltage fluctuation ±3% of the rated value, and the preset threshold of the frequency change rate is ±0.5 Hz; loop through each data in the parameter set to determine whether it exceeds the preset threshold range.

[0060] If a certain data in the parameter set exceeds the threshold range, the data is marked as abnormal data. The mark is represented by a Boolean variable to generate a transient fluctuation state signal.

[0061] Step 1 achieves real-time monitoring and abnormal state identification of transient current and voltage fluctuations in the power system through a 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 features and ensure accurate monitoring of transient fluctuations in the power system. By comparing with the preset threshold, abnormal conditions beyond the safety range can be identified in time, improving the stability and safety of the power system, thereby providing a reliable basis for subsequent fault handling and decision-making.

[0062] Step 2: Based on the transient fluctuation state signal, a particle swarm optimization algorithm is used to obtain the steady-state current load distribution and transient load adjustment parameters; the current load condition of each node is obtained, and the load is adjusted by comparing the relationship between the current load of each node and the transient fluctuation state until the load distribution reaches a balanced state, thereby generating a balanced load adjustment signal.

[0063] The balanced load adjustment signal includes a node current adjustment parameter, a load redistribution parameter, and a balanced state indication.

[0064] The specific steps of generating a balanced load adjustment signal are:

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

[0066] Step 2.2, based on the node load distribution data, the particle swarm optimization algorithm is used to perform correlation comparison with the transient load adjustment parameters; the load adjustment requirements are analyzed node by node, and the nodes that need to be adjusted are identified to generate node load adjustment instructions.

[0067] Step 2.3, based on the node load adjustment instruction, gradually adjust the load of each node; by updating the node parameters in real time, evenly distribute the overall load and generate a balanced load adjustment signal.

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

[0069] Based on the node load distribution data, the particle swarm optimization algorithm is used to perform correlation comparison with the transient load adjustment parameters; when initializing the particle swarm, the group size is set to 50 and the maximum number of iterations is 100. The particle position vector represents the load distribution of the node, and the velocity vector represents the rate of change of the load adjustment; the fitness function is used to evaluate the load adjustment effect of each particle, and the fitness value is calculated based on the transient load adjustment parameters. The particle speed and position are updated during each iteration, and the load is adjusted according to the global optimal solution and the individual optimal solution. By analyzing the load adjustment requirements node by node, the nodes that need to be adjusted are identified, and the node load adjustment instructions are generated.

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

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

[0072]

[0073] in: represents the velocity of particle i in the k+1th generation; ω is the inertia weight; represents the speed of particle i in the kth generation; c1 is the individual acceleration factor; r1 is a random number in the range of [0, 1]; is the best position of particle i in the current iteration; is the position of particle i in the kth generation; c2 is the population acceleration factor; r2 is a random number in the range [0, 1]; g best is the global optimal position of the entire population; α is the load balancing adjustment coefficient; Δt is the time step; L i is the current load value of node i; L avg is the average load value of all nodes; β is the load adjustment response coefficient; ΔP i is the power fluctuation value of node i, indicating the power fluctuation of the node under the current load; T i is the temperature value of node i.

[0074] Execution process:

[0075] First, initialize the velocity of each node The inertia weight ω is used to maintain a certain speed continuity; the global and local search capabilities of the particles are adjusted, and the individual acceleration factor c1 and the group acceleration factor c2 are used respectively, combined with the random numbers r1 and r2, so that the particles tend to their best historical position. and the global optimal position g best .

[0076] Next, the time step Δt and the current node load value L are introduced into the formula i The average load value L of all nodes avg The difference is weighted by the load balancing adjustment coefficient α to dynamically adjust the node load distribution during the current transient process to ensure load balancing; the node power fluctuation value ΔP is introduced i and node temperature value T i The ratio is weighted by the load adjustment response factor β, taking into account the thermal stability of the node and the impact of power changes.

[0077] Finally, the optimal adjustment position of each node under the current load state is calculated Generate corresponding node load adjustment instructions to achieve transient current fusion and steady-state balance.

[0078] Step 2 effectively manages the power system load through the particle swarm optimization algorithm to ensure the balanced distribution of the current load at each node. First, through real-time data collection and analysis, the load status of each node is accurately obtained and the nodes that need to be adjusted are identified. Secondly, the particle swarm optimization algorithm is used to formulate 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 a timely manner to avoid waste of power resources. The final generated balanced load adjustment signal provides a scientific basis for subsequent load management decisions and improves the overall operation quality of the power system.

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

[0080] The optimized current path signal includes a new current path selection, a load optimized path, and adjusted path parameters;

[0081] The specific steps to generate the current path planning results are:

[0082] Step 3.1, based on the balanced load adjustment signal, obtain the steady-state current load parameters and transient current parameters; read the instantaneous current value of each node, and sample to obtain timing data, and at the same time calculate the weighted average value of the current of each node to generate a 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, the steady-state current load parameters and transient current parameters are obtained; the instantaneous current value of each node is read by the sampling method, the sampling frequency is set to 10 milliseconds, and the analog current signal is converted into a digital signal through an A / D converter; the read time series data is stored in a time series database, and the sliding window technology is used to process the time series data, and the window size is set to 100 sampling points; by weighted calculation of the data in the window, the weight value is dynamically adjusted and calculated according to the importance of the node and the current load situation, and a weighted average current value is generated.

[0086] Based on the weighted average current value, determine whether the node current exceeds the preset threshold; use the preset safety current threshold to analyze and compare the node current distribution, the preset threshold is set according to the system design parameters, and the current threshold range is ±10% of the rated value; use conditional judgment statements to compare the weighted average current value of each node, traverse all nodes through a for loop, identify nodes whose current exceeds the preset threshold, analyze their current load conditions, and select the path with the smallest current load through a dynamic programming algorithm; the path selection process is calculated based on 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 current flow direction in the path is replanned using the network flow algorithm, 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 distribute the flow to each line in the path, considering parameters such as line resistance, voltage drop and heat loss, 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 to generate the current path planning results.

[0088] Step 3 improves the load balancing and safety of the power system by dynamically optimizing the current path. By using the weighted average value based on steady-state and transient currents, the current conditions of each node are monitored and analyzed in real time, and nodes that exceed the set threshold can be identified in time to avoid overload risks. At the same time, through network flow algorithms and dynamic programming technology, efficient adjustment of current flow is achieved, current distribution is optimized, energy loss is reduced, and the overall operation efficiency of the system is improved. This process helps to extend equipment life, reduce operation and maintenance costs, and realize intelligent management of power systems.

[0089] Step 4, based on the optimized current path signal, obtain the current current path and voltage path parameters; by adjusting the current path to meet the optimization requirements, correcting the voltage path and confirming it, generate the current and voltage path adjustment results.

[0090] The current and voltage path adjustment result includes a corrected current path, a corrected voltage path, and path confirmation information;

[0091] The specific steps to generate the current and voltage path adjustment results are:

[0092] Step 4.1, based on the current path planning result, obtain the current voltage path parameters, adjust the voltage distribution in the voltage path, perform voltage fluctuation equalization processing, and generate the voltage path adjustment result.

[0093] Step 4.2, based on the voltage path adjustment result, the current path and the voltage path are adjusted in conjunction, and synchronous correction is performed to ensure the coordination between the paths and generate the current and voltage path synchronization result.

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

[0095] Based on the current path planning results, 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 the voltage distribution of each node is calculated using a distributed control algorithm; the voltage of each node is adjusted by a voltage regulator, and the adjustment range is ±5% of the rated voltage to ensure that the voltage value of each node is within the set range, and the voltage path adjustment result is generated.

[0096] Based on the voltage path adjustment result, the current path and the voltage path are adjusted in conjunction. A synchronous correction algorithm is used to perform a linkage adjustment of the current path and the voltage path. The difference 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, and the current and voltage difference values ​​between the paths are used as input parameters. The step size parameters of the correction algorithm are adjusted to achieve synchronous correction, and the path data is dynamically updated during the correction process. The adjustment frequency is set to 50 times per second to generate current and voltage path synchronization results.

[0097] Based on the current and voltage path synchronization results, check the current and voltage stability between each path; use the path stability check algorithm to check the current and voltage paths one by one, read the real-time data of the current and voltage paths, perform stability analysis between paths, and use the path verification module to perform comprehensive verification of the differences between paths; the verification standards include voltage deviation, current deviation and path stability coefficient. After the path verification is completed, confirm the configuration of each path and generate the current and voltage path adjustment results.

[0098] Step 4 ensures the optimal stability and balance of current and voltage distribution through systematic current and voltage path adjustment. The linkage adjustment not only effectively monitors and corrects the voltage path, but also optimizes the current path through real-time data acquisition and synchronous correction algorithms. The final generated current and voltage path adjustment results provide accurate path verification and confirmation information, enhance the reliability of the power system, reduce the impact of voltage fluctuations on equipment, improve the overall system performance, and effectively support the safe and efficient operation of power equipment.

[0099] Step 5: Based on the current and voltage path adjustment results, a linguistic fuzzy system is used to obtain the fusion state of transient and steady-state currents; by judging whether the energy distribution is balanced, a suitable fusion mode is selected, and the transient and steady-state current energy-taking circuits are switched to the selected mode to generate a fusion energy distribution signal.

[0100] The fusion energy allocation signal includes an energy extraction mode, an energy allocation strategy, and a fusion status indication;

[0101] The specific steps to generate the fusion energy allocation signal are:

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

[0103] Step 5.2, based on the current fusion state results, a linguistic fuzzy system is used to select the optimal current fusion mode, adjust the energy extraction circuits of transient current and steady-state current, switch the circuit to the selected mode, monitor the smoothness of current switching, and generate a fusion energy distribution signal.

[0104] Step 5.3, based on the fused energy distribution signal, detect the load distribution in the current path, adjust the current output ratio of each path, evenly distribute the energy, confirm the adjustment result, and generate the optimized energy distribution result.

[0105] Based on the current and voltage path adjustment results, the fusion state of transient and steady-state current is obtained; the instantaneous value of the transient current is detected in real time using the data acquisition module, and the sampling frequency is set to 1000 times per second. The instantaneous value is converted into a digital signal through an A / D converter and stored in the transient current database; at the same time, the historical data of the steady-state current is read and its average value is calculated. The average value is calculated using a sliding window technology, and the window size is set to 100 data points; the calculated instantaneous value and average value are used to compare the difference between the two through the energy difference calculation module, and a comprehensive analysis is performed in combination with the voltage fluctuation data to generate the current fusion state result.

[0106] Based on the current fusion state results, a language-based fuzzy system is used to select the optimal current fusion mode; the language-based fuzzy system performs reasoning through a fuzzy rule base:

[0107] Firstly, the current fusion state result is fuzzified, and the fuzzy variables include energy difference, voltage fluctuation, etc.; after fuzzification, the rules in the fuzzy rule base are used for reasoning, and the rules in the rule base are preset by the expert system. After the reasoning is completed, the defuzzification is performed; the defuzzification adopts the maximum membership method, and the defuzzified result is used to select the optimal fusion mode of transient current and steady-state current; then the switching controller is used to adjust the energy-taking circuit, switch the circuit to the selected mode, and monitor the smoothness of the current switching through the real-time monitoring module to generate a fusion energy distribution signal.

[0108] Based on the fused energy distribution signal, the load distribution in the current path is detected; the real-time load of the current path is monitored using 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 ratio of each path is adjusted, and the current output of each path is dynamically adjusted using the proportional-integral control algorithm; the control parameters include proportional gain and integral time constant, and the control algorithm adjusts the current output in real time through the regulator to balance the energy distribution of each path, and uses the verification module to confirm the adjusted results to generate optimized energy distribution results.

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

[0110]

[0111] Where: u′ represents the optimal current fusion mode finally calculated; μ i (x) represents the membership function of the i-th fuzzy rule; w i represents the weight of the i-th fuzzy rule; k i represents the time series correlation coefficient of the i-th fuzzy rule; λ represents the environmental correction coefficient of current change; n represents the total number of fuzzy rules.

[0112] Execution process:

[0113] According to the current fusion state, the membership function μ in the fuzzy logic system is used i (x) Calculate the membership value of each fuzzy rule, which reflects the matching degree between the current state and each fuzzy rule, and the weight w of each fuzzy rule. i Multiplied, the weight value represents the importance of each fuzzy rule in the entire decision-making process, and the time series correlation coefficient k is introduced. i, by analyzing historical data, evaluating the impact of changes in current characteristics over time on fuzzy rules; dynamically adjusting the fuzzy rules at different time points, introducing the environmental correction coefficient λ, and calculating based on external environmental parameters, including temperature, humidity, and electromagnetic interference, to correct the impact of the external environment on the current fusion mode; by globally adjusting the contribution values ​​of each fuzzy rule and performing weighted summation on the corrected fuzzy rule contribution values, the optimal current fusion mode u′ is obtained, circuit switching is performed, and transient and steady-state currents are balanced to achieve efficient energy distribution and smooth current switching.

[0114] Step 5 ensures efficient energy distribution by integrating transient and steady-state currents through real-time monitoring and analysis based on current and voltage. By using a linguistic fuzzy system, the optimal current fusion mode can be dynamically selected based on energy differences and voltage fluctuations, thereby achieving smooth switching of the circuit. In addition, by real-time monitoring of load distribution and dynamically adjusting the current output ratio, the energy distribution of each path is optimized, and the stability and energy efficiency of the system are improved. This comprehensive approach effectively improves the utilization of electrical energy and reduces energy loss.

[0115] Step 6, based on the fused energy allocation signal, obtain the energy storage module parameters and the system energy state; select an adaptive energy storage device, adjust the working state of the storage device to achieve optimal energy storage, and generate an energy storage adjustment signal.

[0116] The energy storage adjustment signal includes a memory operating state, an energy allocation parameter, and an energy storage state indication.

[0117] The specific steps to generate the energy storage adjustment signal are:

[0118] Step 6.1, based on the optimized energy allocation 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.

[0119] Step 6.2, based on the energy storage status result, select an adaptive energy storage device, adjust the charging voltage and discharge rate of the storage device, optimize the efficiency of energy storage by adjusting the working mode of the storage device, and generate an energy storage adjustment signal.

[0120] Step 6.3, based on the energy storage adjustment signal, check the operating status of the energy storage, record various key parameters, verify the stability of the storage, confirm the energy storage configuration, and generate the energy storage adjustment signal.

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

[0122] Based on the energy storage status results, an adaptive energy storage device is selected, and a decision tree algorithm is used to evaluate a variety of energy storage devices. The input variables of the decision tree include voltage, current and temperature parameters, and the output is an adaptive energy storage device model. The algorithm generates a decision tree recursively, 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, which performs reasoning based on preset fuzzy rules and membership functions to generate an energy storage adjustment signal.

[0123] Based on the energy storage adjustment signal, check the operating status of the energy storage; use the status monitoring system to conduct a comprehensive inspection of the operating status of the energy storage, including voltage stability, discharge current stability and temperature fluctuation range. The status monitoring system uses a timer set at an interval of 5 seconds to automatically collect various parameters and record them in the central database to verify the stability of the storage; verify the key parameters of the storage through the execution unit to confirm whether the storage is in a safe and stable operating state, and generate an energy storage adjustment signal.

[0124] Through step 6, efficient management and optimization of energy storage modules can be achieved. Real-time monitoring and data analysis are used to ensure that the energy storage operates in the best working state and improve energy storage efficiency. Combined with decision trees and fuzzy control algorithms, the energy storage is intelligently selected and the charging and discharging parameters are adjusted in real time to optimize energy distribution and storage. At the same time, the key parameters of the storage are regularly checked to ensure its stability and safety. This series of measures will improve the energy management capabilities of the system, extend 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 this embodiment obtains transient fluctuation state signals through a particle swarm optimization algorithm, accurately calculates the distribution of current loads at each node, and performs fine adjustments to ensure load balance, thereby improving the stability and response speed of the system under transient disturbances. In the dynamic adjustment process of load distribution, the current path can be quickly optimized according to the real-time operating conditions, thereby avoiding the instability of the power grid caused by transient disturbances, and improving the overall efficiency of the power system. By using a language-based fuzzy system, when the transient and steady-state currents are merged, the optimal fusion mode is intelligently selected according to the actual energy distribution state, thereby achieving efficient utilization and distribution of energy and enhancing the long-term operation stability and safety of the power system.

[0126] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for current transient fusion and steady-state balance based on intelligent control, characterized in that: The following steps are involved: Step 1, based on the transient current fluctuation signal in the power system, obtain the current fluctuation amplitude, voltage fluctuation amplitude and frequency, calculate the change rate of the fluctuation amplitude and frequency by measuring the change amplitude of the current and voltage, and compare them with the preset threshold value to generate a transient fluctuation state signal; Step 2: Based on the transient fluctuation state signal, a particle swarm optimization algorithm is used to obtain the steady-state current load distribution and transient load adjustment parameters, and the current load of each node. By comparing the relationship between the current load of each node and the transient fluctuation state, load adjustment is performed until the load distribution reaches a balanced state, and a balanced load adjustment signal is generated; Step 3, based on the balanced load adjustment signal, obtain the steady-state current load parameter and the transient current parameter, calculate the weighted average value of the current of each node, select the current path by judging the current value after the weighted average, determine the new current path, and generate the optimized current path signal; Step 4, based on the optimized current path signal, obtain the current current path and voltage path parameters, adjust the current path to meet the optimization requirements, correct the voltage path and confirm it, and generate the current and voltage path adjustment results; Step 5: Based on the current and voltage path adjustment results, a linguistic fuzzy system is used to obtain the fusion state of transient and steady-state currents. By judging whether the energy distribution is balanced, a suitable fusion mode is selected, and the transient and steady-state current energy-taking circuits are switched to the selected mode to generate a fusion energy allocation signal. Step 6, based on the fused energy allocation signal, obtain the energy storage module parameters and the system energy state, select an adaptive energy storage device, adjust the working state of the storage device to achieve optimal energy storage, and generate an energy storage adjustment signal.

2. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1 is characterized in that: The transient fluctuation state signal includes the current fluctuation amplitude, the voltage fluctuation amplitude, and the fluctuation frequency; the balanced load adjustment signal includes the node current adjustment parameter, the load redistribution parameter, and the balance state indication; the optimized current path signal includes the new current path selection, the load optimization path, and the adjusted path parameter; the current and voltage path adjustment result includes the corrected current path, the corrected voltage path, and the path confirmation information; the fused energy allocation signal includes the energy extraction mode, the energy allocation strategy, and the fusion state indication; the energy storage adjustment signal includes the memory working state, the energy allocation parameter, and the energy storage state indication.

3. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1 is characterized in that: In step 1, generating the transient fluctuation state signal includes the following steps: 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 sampling point by point and record it, and generate initial voltage and current change data; Step 1.2, based on the initial voltage and current change data, calculate the fluctuation amplitude, extract the frequency change rate by comparing the amplitude changes between adjacent sampling points and integrate the data to generate a fluctuation amplitude and frequency parameter set; Step 1.3, based on the fluctuation amplitude and frequency parameter set, compare the parameter set with the preset threshold value, determine whether the data exceeds the safety range, mark the abnormal data, and generate a transient fluctuation state signal.

4. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1 is characterized in that: In step 2, generating the load balancing adjustment signal includes the following steps: Step 2.1, based on the transient fluctuation state signal, obtain the current load of each node in the current power system, extract the load distribution by scanning each node data set, and generate node load distribution data; Step 2.2, based on the node load distribution data, a particle swarm optimization algorithm is used to perform correlation comparison with transient load adjustment parameters, load adjustment requirements are analyzed node by node, nodes that need to be adjusted are identified, and node load adjustment instructions are generated; Step 2.3, based on the node load adjustment instruction, gradually adjust the load of each node, evenly distribute the overall load by updating the node parameters in real time, and generate a balanced load adjustment signal.

5. The current transient fusion and steady-state balancing method based on intelligent control according to claim 4 is characterized in that: The particle swarm optimization algorithm is calculated according to the following formula: in: represents the velocity of particle i in the k+1th generation; ω is the inertia weight; represents the speed of particle i in the kth generation; c1 is the individual acceleration factor; r1 is a random number in the range of [0, 1]; is the best position of particle i in the current iteration; is the position of particle i in the kth generation; c2 is the population acceleration factor; r2 is a random number in the range [0, 1]; g best is the global optimal position of the entire population; α is the load balancing adjustment coefficient; Δt is the time step; L i is the current load value of node i; L avg is the average load value of all nodes; β is the load adjustment response coefficient; ΔP i is the power fluctuation value of node i, indicating the power fluctuation of the node under the current load; T i is the temperature value of node i.

6. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1, characterized in that: In step 3, generating the current path planning result includes the following steps: Step 3.1, based on the balanced load adjustment signal, obtain steady-state current load parameters and transient current parameters, read the instantaneous current value of each node, and perform sampling to obtain time series data, and calculate the weighted average value of the current of each node to generate a weighted average current value; Step 3.2, based on the weighted average current value, determine whether the node current exceeds a preset threshold, analyze and compare the node current distribution, select a path with the smallest current load, and generate an optimized current path signal; 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.

7. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1 is characterized in that: In step 4, generating the current and voltage path adjustment result includes the following steps: Step 4.1, based on the current path planning result, obtaining current voltage path parameters, adjusting the voltage distribution in the voltage path, performing voltage fluctuation equalization processing, and generating a voltage path adjustment result; Step 4.2, based on the voltage path adjustment result, the current path and the voltage path are adjusted in linkage, and synchronous correction is performed to ensure the coordination between the paths, and a current and voltage path synchronization result is generated; Step 4.3, based on the current and voltage path adjustment result, check the current and voltage stability between the paths, perform path verification, confirm the path configuration, and generate the current and voltage path adjustment result.

8. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1, characterized in that: In step 5, generating the optimized energy allocation result includes the following steps: Step 5.1, based on the current and voltage path adjustment result, obtain the fusion state of transient and steady-state currents, detect the instantaneous value of the transient current and the average value of the steady-state current, judge the energy difference, and generate the current fusion state result in combination with the voltage fluctuation analysis; 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 energy extraction circuits of transient current and steady-state current, switch the circuit to the selected mode, monitor the stability of current switching, and generate a fusion energy allocation signal; Step 5.3, based on the fused energy distribution signal, detect the load distribution in the current path, adjust the current output ratio of each path, evenly distribute the energy, confirm the adjustment result, and generate the optimized energy distribution result.

9. The current transient fusion and steady-state balancing method based on intelligent control according to claim 8, characterized in that: The linguistic fuzzy system is calculated according to the following formula: Where: u′ represents the optimal current fusion mode finally calculated; μ i (x) represents the membership function of the i-th fuzzy rule; w i represents the weight of the i-th fuzzy rule; k i represents the time series correlation coefficient of the i-th fuzzy rule; λ represents the environmental correction coefficient of current change; n represents the total number of fuzzy rules.

10. The current transient fusion and steady-state balancing method based on intelligent control according to claim 1, characterized in that: In step 6, generating the energy storage adjustment signal comprises the following steps: Step 6.1, based on the optimized energy allocation result, obtain the voltage and current parameters and temperature value of the energy storage module, record the current storage state, and analyze the energy storage amount and change trend to generate the energy storage state result; Step 6.2, based on the energy storage state result, select an adaptive energy storage device, adjust the charging voltage and discharge rate of the storage device, optimize the efficiency of energy storage by adjusting the working mode of the storage device, and generate an energy storage adjustment signal; Step 6.3, based on the energy storage adjustment signal, check the operating status of the energy storage, record various key parameters, verify the stability of the storage, confirm the energy storage configuration, and generate an energy storage adjustment signal.

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