CN116502922B - Power grid stability analysis system based on group intelligent algorithm - Google Patents
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Abstract
The invention relates to the technical field of power grid analysis, in particular to a power grid stability analysis system based on a group intelligent algorithm, which comprises the following components: the device comprises a data acquisition part, a data conversion part, a steady state calculation part and a stability analysis part; the data acquisition part is configured to acquire equipment parameters of each equipment and operation parameters of each equipment of the power grid; the steady state calculation part is configured to establish a steady state space, and calculate to obtain an individual optimal position and a global optimal position of each group of transformation data sets in the steady state space; the stability analysis part is configured to perform difference analysis on the individual optimal positions of the standard data set and the individual optimal positions of all the transformation data sets, compare the final difference value with a preset stability threshold interval, and judge the stability of the power grid. The stability analysis accuracy and efficiency are improved, adaptability and intelligence are provided, and accurate stability assessment and decision support are provided for power grid managers.
Description
Technical Field
The disclosure relates to the technical field of power grid analysis, and in particular relates to a power grid stability analysis system based on a group intelligent algorithm.
Background
The stability of the power system is one of the important indicators of the safety and reliability of the operation of the power grid. Traditional power grid stability analysis methods rely mainly on mathematical modeling and simulation techniques, using dynamic stability equations and power system models to simulate and evaluate the stability of the power grid. However, conventional approaches present challenges in handling large-scale power grids, accounting for uncertainty, and improving computational efficiency.
Currently, new techniques and methods have emerged to improve grid stability analysis. One common approach is based on a combination of tidal current calculation and transient stability analysis. The power flow calculation is used for evaluating the static stability of the power grid, and the stability of the system is judged by analyzing the power flow distribution and the node voltage condition of the power grid. The transient stability analysis is then used to evaluate the dynamic stability of the power grid, which is analyzed by simulating the transient process of the power grid and taking into account the dynamic response of the power equipment. The combination method can comprehensively consider the static and dynamic characteristics of the power grid, and improve the accuracy of stability analysis.
Another prior art is a method of grid stability analysis based on machine learning and artificial intelligence. Machine learning algorithms, such as neural networks, support vector machines, decision trees, and the like, can learn patterns and rules of the power grid through training models, so as to predict the stability of the power grid. The method can automatically learn and extract the characteristics from a large amount of data, and quickly and accurately evaluate the stability of the power grid. However, these methods still face challenges in data acquisition and model training, such as data quality, number and diversity of training samples, and the like.
Despite the advances made in grid stability analysis in the prior art, there are still some problems and challenges. First, the conventional method has high computational complexity in handling large-scale power grids, resulting in low analysis efficiency. Secondly, the traditional method has limited processing capacity on uncertainty and complexity, and is difficult to accurately evaluate the stability of the power grid. In addition, the prior art still has room for improvement in the aspects of model establishment, parameter optimization, algorithm design and the like, so as to improve the accuracy and reliability of the power grid stability analysis.
Disclosure of Invention
The system for analyzing the stability of the power grid based on the group intelligent algorithm improves the accuracy and efficiency of the stability analysis, has adaptability and intelligence, and provides accurate stability assessment and decision support for power grid managers.
In order to solve the problems, the technical scheme of the invention is realized as follows:
a system for grid stability analysis based on a swarm intelligence algorithm, the system comprising: the device comprises a data acquisition part, a data conversion part, a steady state calculation part and a stability analysis part; the data acquisition part is configured to acquire equipment parameters of each equipment of the power grid and operation parameters of each equipment, and perform normalization pretreatment on the operation parameters of each equipment by combining the equipment parameters to obtain the equipment operation parameters; the data conversion part is configured to perform data conversion on the collected equipment operation parametersThe method specifically comprises the following steps: taking the equipment operation data of each equipment as standard data, and forming a standard data set by all the equipment operation data; setting a conversion section in which each device operation data in the standard data group is subjected toSub-random transformation to obtain->Group transformation data group, each time randomly transforming to obtain a transformation data of the device operation data, wherein ∈10>The method comprises the steps of carrying out a first treatment on the surface of the Each group of transformed data sets comprises +.>Personal transformation data->The amount of data for the device operation; the range of values of the transformation data is within a transformation interval; the steady state calculation part is configured to establish a steady state space, firstly, the standard data set is placed in the steady state space, the individual optimal position and the global optimal position of the standard data set in the steady state space are calculated, then, each group of transformation data sets are placed in sequence according to the random transformation sequence, and the individual optimal position and the global optimal position of each group of transformation data sets in the steady state space are calculated; the individual optimal position characterizes the optimal position of each transformation data in each group of transformation data or each standard data in the standard data group; the global optimal position characterizes the optimal position of the whole of each group of transformation data groups or standard data groups; the stability analysis part is configured to perform differential analysis on the individual best positions of the standard data sets and the individual best positions of all the transformation data sets to obtain a first differential value, and perform differential analysis on the overall best position of each transformation data set and the overall best position of the standard data sets to obtain a second differential value based on the first differential value and the second differential valueAnd calculating based on a preset weighting coefficient to obtain a final difference value, and comparing the final difference value with a preset stability threshold interval to judge the stability of the power grid.
Further, the device parameters include: generator parameters, transformer parameters, transmission line parameters, switchgear parameters, and generator regulator parameters; the generator parameters include: generator capacity, power factor, and inertia constant; the transformer parameters include: transformer capacity, rated voltage ratio, short circuit impedance, and conversion efficiency; the transmission line parameters include: resistance and reactance; the switchgear parameters include: rated current; the generator regulator parameters include: tuning range, steady-state gain, and response time.
Further, the operating parameters include: generator operating parameters, transformer operating parameters, transmission line operating parameters, switch operating parameters, and generator regulator operating parameters; the generator operating parameters include: real-time voltage, real-time frequency, real-time active power, real-time reactive power and real-time rotational speed; the transformer operating parameters include: real-time input voltage, real-time output voltage and real-time temperature; the transmission line operation parameters include: real-time power transmission current, real-time power transmission voltage, real-time power loss and real-time current phase angle; the switch operating parameters include: switching current and switching voltage; the generator regulator parameters include: real-time steady-state gain and real-time response time.
Further, the method for obtaining the operation parameters of the equipment by carrying out normalization pretreatment on the operation parameters of each equipment by combining the parameters of the equipment by the data acquisition part comprises the following steps: converting the operating parameters of each device to dimensionless, uniform range of device operating parameters based on the device parameters of each device using the following formula:
；
wherein ,for the operating parameters +.>Is an equipment operation parameter; />For operating parameters->The device parameter mean value of the corresponding device is calculated using the following formula:
；
wherein ,for operating parameters->Device parameters of the corresponding device->For operating parameters->The number of categories of device parameters of the corresponding device.
Further, the steady state calculating part establishes a steady state space, the standard data set is firstly put in the steady state space, and the method for calculating the individual optimal position and the global optimal position of the standard data set in the steady state space comprises the following steps: treating each standard data in the standard data group as a particle; if the standard data set containsThe number of particles is set to +.>The method comprises the steps of carrying out a first treatment on the surface of the Randomly initializing the position and velocity for each particle, denoted +.> and />, wherein />An index representing the particles; definition of fitness function->For evaluating the stability of each particle; wherein (1)>Is a parameter vector; the iterative updating particle specifically comprises the following steps: updating the speed and the position of the particles, and iteratively updating the individual optimal position and the global optimal position of the particles; judging whether to terminate the iterative updating particles according to a preset stopping condition; outputting the global optimum position and the individual optimum position to obtain the individual optimum position +.>And global best position->。
Further, the method for calculating the individual optimal position and the global optimal position of each group of transformation data in the steady state space by the steady state calculation part sequentially puts each group of transformation data in the random transformation sequence comprises the following steps: establishing a steady-state space, and sequentially placing each group of transformation data sets in the steady-state space according to the random transformation sequence; treating each of the transformed data in the transformed data set as a particle; if the transformation data set containsThe number of particles is set to +.>The method comprises the steps of carrying out a first treatment on the surface of the Randomly initializing the position and velocity for each particle, denoted +.> and />, wherein />An index representing the particles; definition of fitness function->For evaluating the stability of each particle; wherein (1)>Is a parameter vector; the iterative updating particle specifically comprises the following steps: updating the speed and the position of the particles, and iteratively updating the individual optimal position and the global optimal position of the particles; judging whether to terminate the iterative updating particles according to a preset stopping condition; outputting the global optimum position and the individual optimum position to obtain the individual optimum position +.>And global best position->。
Further, when updating the velocity and position of the particles, the following formula is used:
；
wherein ,is particle->Speed at the current moment, +.>Is particle->At the speed of the next moment in time,is particle->Position at the current moment,/->Is particle->At the next moment in time,/->Is inertial weight, ++> and />Is acceleration constant, +.> and />Is a random number +.>Is particle->Is the best position of the individual->Is the global best location.
Further, when iteratively updating the individual best position and the global best position of the particles, the following formula is used:
；
wherein ,is particle->Is characterized by the individual best position of each criterion in the criterion dataset,/for each criterion>Is the global optimal position for the standard dataset.
Further, the stop condition reaches a maximum number of iterations.
Further, the stability analysis section performs a difference analysis on the individual best positions of the standard data set and the individual best positions of all the transformation data sets, and when a first difference value is obtained, the following formula is used:
；
wherein ,for distance calculation, add->For the first difference value, +.>And performing difference analysis on the optimal position of the whole transformation data set of each group and the optimal position of the whole standard data set to obtain a second difference value, wherein the second difference value is obtained by using the following formula:
；
wherein ,is the second difference value.
The power grid stability analysis system based on the group intelligent algorithm has the following beneficial effects:
accuracy of power grid stability analysis is improved: the invention adopts a steady state calculation and stability analysis method based on a group intelligent algorithm, and fully considers the complex relation between each equipment parameter and operation parameter of the power grid. Through carrying out normalization pretreatment and data transformation on the equipment parameters, the dimensional difference among the parameters is effectively eliminated, and the value range of the parameters is unified. Therefore, the steady state of the power grid can be analyzed more accurately, and the optimal position in the steady state space is obtained, so that the accuracy of the stability analysis of the power grid is improved.
And the efficiency of power grid stability analysis is improved: conventional power grid stability analysis methods typically require extensive calculations and simulations, which are time-consuming and labor-consuming. The invention can quickly converge to the optimal solution or the position close to the optimal solution by iteratively updating the position and the speed of the particles by utilizing the parallel searching capability of the swarm intelligence algorithm. Therefore, the efficiency of power grid stability analysis is greatly improved, and the calculation time and the resource consumption are reduced.
Possesses adaptability and intelligence: the invention adopts a swarm intelligent algorithm, wherein the particle swarm optimization algorithm has self-adaptability and intelligence. In the iterative updating process, the particles can realize autonomous searching and adjustment according to the self position and speed and the guidance of the individual and global optimal positions. Therefore, the algorithm can adapt to different power grid conditions and problem demands, has certain self-adaptive capacity, can globally search steady-state space, and improves the intelligence and adaptability of power grid stability analysis.
Drawings
Fig. 1 is a schematic system structure diagram of a power grid stability analysis system based on a swarm intelligence algorithm according to an embodiment of the present invention;
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present disclosure more clear and obvious, the present disclosure is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present disclosure and are not intended to limit the present disclosure. .
Referring to fig. 1, a system for analyzing grid stability based on a swarm intelligence algorithm, the system comprising: the device comprises a data acquisition part, a data conversion part, a steady state calculation part and a stability analysis part; the data acquisition part is configured to acquire equipment parameters of each equipment of the power grid and operation parameters of each equipment, and perform normalization pretreatment on the operation parameters of each equipment by combining the equipment parameters to obtain the equipment operation parameters; the data transformation part is configured to perform data transformation on the collected equipment operation parameters, and specifically comprises the following steps: taking the equipment operation data of each equipment as standard data, and forming a standard data set by all the equipment operation data; setting a conversion section in which each device operation data in the standard data group is subjected toSub-random transformation to obtain->Group transformation data group, each time randomly transforming to obtain a transformation data of the device operation data, wherein ∈10>The method comprises the steps of carrying out a first treatment on the surface of the Each group of transformed data sets comprises +.>Personal transformation data->The amount of data for the device operation; the range of values of the transformation data is within a transformation interval; the steady state calculation part is configured to establish a steady state space, firstly place the standard data set in the steady state space, calculate to obtain the individual optimal position and the global optimal position of the standard data set in the steady state space, then place each group of transformation data set in sequence according to the random transformation order, calculate to obtain the individual optimal position and the global optimal position of each group of transformation data set in the steady state spaceA good position; the individual optimal position characterizes the optimal position of each transformation data in each group of transformation data or each standard data in the standard data group; the global optimal position characterizes the optimal position of the whole of each group of transformation data groups or standard data groups; the stability analysis part is configured to perform difference analysis on the individual optimal positions of the standard data set and the individual optimal positions of all the transformation data sets to obtain a first difference value, perform difference analysis on the overall optimal position of each transformation data set and the overall optimal position of the standard data set to obtain a second difference value, calculate based on a preset weighting coefficient and the first difference value and the second difference value to obtain a final difference value, and compare the final difference value with a preset stability threshold interval to judge the stability of the power grid.
In particular, there may be some uncertainty in the operating environment and parameters of the power grid, such as load fluctuations, equipment failures, etc. The uncertainty can be simulated by introducing randomness through data transformation, so that the system has better adaptability. Different devices in the power grid may have different characteristics and operating conditions. The data transformation may take this diversity into account by generating different data variants. Therefore, various working states of the power grid can be covered more comprehensively, and the analysis capability of the system on various conditions of the power grid is improved. The data transformation may generate multiple sets of data by random transformation, thereby increasing the number of samples of data. More data samples are helpful for improving the accuracy and reliability of the stability of the power grid, so that the analysis result is more representative. The data transformation can help the system overcome noise and abnormal conditions in the data, and the robustness of the system is improved. By introducing a plurality of groups of transformation data, the system can better resist interference and abnormal values in the data, and the analysis accuracy of the stability of the power grid is improved.
Specifically, when the steady-state space is established, the steady-state space needs to be initialized, which specifically includes: a spatial range is defined and a certain number of particles are initialized. These particles represent individuals searching for the optimal solution in steady state space. In steady state space, an objective function needs to be defined to evaluate the fitness of each particle. The choice of the objective function is typically related to an evaluation index of the grid stability, and may be a function of stability differences, energy losses, etc.
During steady state calculation, each particle moves according to the current position and velocity. The updating of the velocity and position of the particles follow the principles of a swarm intelligence algorithm, such as a velocity update formula and a position update formula in a particle swarm optimization algorithm. Each particle will record its own individual best position, i.e. the best solution reached at the current position. Meanwhile, the system can track the global optimal position, namely the optimal solution achieved in the whole particle swarm. In a continuous iterative manner, the population of particles searches for an optimal solution in steady state space. In the iterative process, the particles are updated according to the individual optimal positions and the global optimal positions of the particles, and continue to move. The number of iterations depends on the algorithm settings or termination conditions. The search process for steady-state space is typically iterated a number of times until convergence conditions are met. The convergence condition may be that the number of iterations reaches a preset value, or that the objective function value reaches a certain accuracy range.
Preferably, the device parameters include: generator parameters, transformer parameters, transmission line parameters, switchgear parameters, and generator regulator parameters; the generator parameters include: generator capacity, power factor, and inertia constant; the transformer parameters include: transformer capacity, rated voltage ratio, short circuit impedance, and conversion efficiency; the transmission line parameters include: resistance and reactance; the switchgear parameters include: rated current; the generator regulator parameters include: tuning range, steady-state gain, and response time.
Specifically, the generator parameters: a generator is one of the core components in the electrical grid, the parameters of which have a significant impact on the stability of the electrical grid. The generator parameters include generator capacity, power factor, and inertia constant. The generator capacity: refers to the rated output power of the generator, typically in Megawatts (MW). The size of the generator capacity affects the power supply capacity and stability of the grid. Power factor: the power factor of a generator represents the relationship between the generator output power and the apparent power (sum of real and reactive power). The power factor has an influence on the aspects of active power and reactive power balance, voltage stabilization and the like of the power grid. Inertia constant: the inertia constant reflects the rotational mass and inertial characteristics of the generator. It has an important role in the short-term stability and frequency response of the grid.
Parameters of the transformer: transformers are used in the power grid for the transmission and conversion of electrical energy, the parameters of which also play an important role in the stability of the power grid. The transformer parameters include transformer capacity, rated voltage ratio, short circuit impedance, and conversion efficiency. Transformer capacity: refers to the rated power transfer capability of the transformer. The size of the transformer capacity determines the limitations and capabilities of the transformer in power transfer. Rated voltage ratio: the rated voltage ratio of a transformer represents the voltage conversion ratio between the input and output terminals of the transformer. The transformer realizes the transmission and adjustment of electric energy by changing the voltage proportion. Short circuit impedance: the short-circuit impedance of a transformer refers to the ratio between the voltage drop at the input of the transformer and the short-circuit current in a short-circuit condition. The short circuit impedance reflects the electrical characteristics of the transformer and the ability to respond to grid faults. Conversion efficiency: the conversion efficiency of a transformer represents the energy loss of the transformer in the power transmission. The efficient transformer can reduce energy loss in power transmission. Parameters of the transmission line: the power transmission line is used for long-distance transmission of electric energy, and parameters of the power transmission line have important influence on the transmission capacity and stability of a power grid. The transmission line parameters include resistance and reactance. Resistance: the resistance of the transmission line refers to the resistance loss caused by the line itself. The resistor can cause heat loss of electric energy in the transmission process, and the transmission efficiency and stability are affected. Reactance: the reactance of a transmission line mainly comprises inductance and capacitance. The inductance corresponds to the inductive reactance of the line and the capacitance corresponds to the capacitive reactance of the line. Reactance affects the transmission characteristics of electrical energy in a transmission line, such as voltage stability and power transmission capability. Switching device parameters: switching devices are used in the power grid to control the flow of current and the distribution of electrical energy. The switchgear parameters include rated current. Rated current: the rated current of the switching device represents the maximum current value that the device can withstand. The rated current is used for the type selection and protection setting of the equipment to ensure the normal operation of the equipment and the stability of the power grid. Generator regulator parameters: the generator regulator is used for regulating the output power and voltage of the generator so as to maintain the stability of the power grid. The generator regulator parameters include regulation range, steady-state gain, and response time. The adjusting range is as follows: the regulation range of the generator regulator means the range in which it is possible to adjust the generator output power and voltage. The regulation range determines the regulation capacity of the generator and the response capacity to the grid frequency and voltage. Steady state gain: the steady state gain of a generator regulator is indicative of the extent to which the regulator responds to changes in generator output power or voltage. The greater the steady state gain, the more significant the effect of the generator regulator on grid stability. Response time: the response time of the generator regulator indicates the time the regulator has changed from receiving the control signal to the output. The speed of the response time determines the ability of the generator regulator to respond quickly to grid disturbances.
Preferably, the operating parameters include: generator operating parameters, transformer operating parameters, transmission line operating parameters, switch operating parameters, and generator regulator operating parameters; the generator operating parameters include: real-time voltage, real-time frequency, real-time active power, real-time reactive power and real-time rotational speed; the transformer operating parameters include: real-time input voltage, real-time output voltage and real-time temperature; the transmission line operation parameters include: real-time power transmission current, real-time power transmission voltage, real-time power loss and real-time current phase angle; the switch operating parameters include: switching current and switching voltage; the generator regulator parameters include: real-time steady-state gain and real-time response time.
Preferably, the data acquisition part performs normalization preprocessing on the operation parameters of each device in combination with the device parameters, and the method for obtaining the operation parameters of the device comprises the following steps: converting the operating parameters of each device to dimensionless, uniform range of device operating parameters based on the device parameters of each device using the following formula:
；
wherein ,for the operating parameters +.>Is an equipment operation parameter; />For operating parameters->The device parameter mean value of the corresponding device is calculated using the following formula:
；
wherein ,for operating parameters->Device parameters of the corresponding device->For operating parameters->The number of categories of device parameters of the corresponding device.
The optimized normalization preprocessing method considers the mean value of the equipment parameters under different conditionsBy performing different scaling operations on the operating parameters, the operating parameters are better able to adapt to equipment parameters of different ranges and varying degrees. When->When smaller (less than or equal to 1), the operation parameters are amplified by 100 times and +.>Multiplying; when->Between 1 and 10, directly with +.>Multiplying; when->When the value is larger (10 or more), the operation parameters are compared with those based on 10Is subjected to an exponential operation on the logarithmic absolute value of (a).
When (when)When 1 or less: this indicates that the mean value of the device parameter is small, indicating that the range of variation of the device parameter is relatively small. To amplify the difference in operating parameters, the operating parameters are amplified by a factor of 100 and +.>Multiplying to obtain normalized equipment operation parameter +.>. The purpose of this is to amplify the operating parameters so that they more reflect the differences in the operating parameters of the device over a normalized range.
When (when)Between 1 and 10: this represents a more moderate mean value of the device parameters and a more even range of variation of the device parameters. Directly associate the operating parameters with->Multiplying to obtain normalized equipment operation parameter +.>. This isThe purpose of this is to linearly scale the running parameter to the mean of the plant parameters, maintaining its relative relationship to the plant parameters.
When (when)When the ratio is more than or equal to 10: this means that the mean value of the device parameters is large and the range of variation of the device parameters is wide. The operating parameters are compared with 10-base +.>Carrying out exponential operation on the logarithmic absolute value to obtain normalized equipment operation parameters +.>. The purpose of this is to amplify the operating parameters to a greater extent within the normalized range by exponential operation to better reflect the differences in the operating parameters of the device.
Preferably, the steady state calculating part establishes a steady state space, the standard data set is firstly put in the steady state space, and the method for calculating the individual best position and the global best position of the standard data set in the steady state space comprises the following steps: treating each standard data in the standard data group as a particle; if the standard data set containsThe number of particles is set to +.>The method comprises the steps of carrying out a first treatment on the surface of the Randomly initializing the position and velocity for each particle, denoted +.> and />, wherein />An index representing the particles; definition of fitness function->For evaluating the stability of each particle; wherein (1)>Is a parameter vector; the iterative updating particle specifically comprises the following steps: updating the speed and the position of the particles, and iteratively updating the individual optimal position and the global optimal position of the particles; judging whether to terminate the iterative updating particles according to a preset stopping condition; outputting the global optimum position and the individual optimum position to obtain the individual optimum position +.>And global best position->。
Specifically, the invention adopts the idea of Particle Swarm Optimization (PSO) algorithm. The PSO algorithm is a random optimization algorithm for simulating the foraging behavior of the bird group, and an optimal solution is found by simulating the movement and information communication of particles in a search space.
First, each standard data is regarded as one particle, and the position and velocity of each particle are randomly initialized. The position of the particle represents the current value of the operating parameter of the device and the velocity represents the direction and rate of movement of the particle in the search space.
Using fitness functionsThe stability of each particle at the current position is evaluated. The specific form of the fitness function depends on the goal of the steady state calculation and the requirements of the stability analysis. The smaller the value of the fitness function, the better the stability of the particles.
The velocity and position of the particles are updated according to the principles of the PSO algorithm. The update of the velocity takes into account the relationship between the velocity of the particles themselves, the individual best position and the global best position. According to a certain weight coefficient and a random factor, the speed of the particles can be attracted and influenced by the individual and global optimal positions, so that information sharing and collaborative searching are realized. And updating the current position of the particle through the updated speed.
In each iterative update, the individual best position and the global best position of the particle are updated. The individual best position is the position of the particle itself that historically has the best fitness, while the global best position is the position of the whole particle population that has the best fitness. The individual best position and the global best position are updated by comparing the current fitness with the historical best fitness.
And judging whether to terminate the iterative updating particles according to a preset stopping condition. A common stopping condition may be that a preset number of iterations is reached, or that the objective function value reaches a certain accuracy range.
And after the iterative updating is finished, outputting the global optimal position and the individual optimal position to obtain the individual optimal position and the global optimal position of the standard data set in the steady-state space.
Preferably, the steady state calculating part sequentially puts each group of transformation data into the random transformation sequence, and the method for calculating the individual optimal position and the global optimal position of each group of transformation data in the steady state space comprises the following steps: establishing a steady-state space, and sequentially placing each group of transformation data sets in the steady-state space according to the random transformation sequence; treating each of the transformed data in the transformed data set as a particle; if the transformation data set containsThe number of particles is set to +.>The method comprises the steps of carrying out a first treatment on the surface of the Randomly initializing the position and velocity for each particle, denoted +.> and />, wherein />Watch (watch)Index of particles; definition of fitness function->For evaluating the stability of each particle; wherein (1)>Is a parameter vector; the iterative updating particle specifically comprises the following steps: updating the speed and the position of the particles, and iteratively updating the individual optimal position and the global optimal position of the particles; judging whether to terminate the iterative updating particles according to a preset stopping condition; outputting the global optimum position and the individual optimum position to obtain the individual optimum position +.>And global best position->。
Preferably, the following formula is used when updating the velocity and position of the particles:
；
wherein ,is particle->Speed at the current moment, +.>Is particle->At the speed of the next moment in time,is particle->At the position ofPosition at the current moment,/->Is particle->At the next moment in time,/->Is inertial weight, ++> and />Is acceleration constant, +.> and />Is a random number +.>Is particle->Is the best position of the individual->Is the global best location.
In each iteration, the velocity and position of the particle are updated according to the relationship between the velocity of the particle itself, the individual best position and the global best position. The first term in the equationRepresenting inertial weights, maintains the inertia of the particles in the search space, balancing the ability of global and local searches. Second item->Representing the attraction of particles to the individual's optimal location, historically optimizing the particles toward themselvesThe position moves. Third itemIndicating that the particles are attracted by the global optimum position, causing the particles to move towards the historically optimum position for the whole population of particles.
Preferably, the following formula is used when iteratively updating the individual best position and the global best position of the particles:
；
wherein ,is particle->Is characterized by the individual best position of each criterion in the criterion dataset,/for each criterion>Is the global optimal position for the standard dataset.
In particular, the core of this process is to simulate the movement of particle swarms in the search space and the communication of information. Each particle continuously adjusts its own position and velocity according to its own position and velocity, and the guidance of the individual and global optimal positions. And gradually approaching the whole particle swarm to the global optimal position through updating the individual optimal position and the global optimal position, so that the optimal solution or the position close to the optimal solution is found.
In the velocity and position update formula, inertial weightsThe degree of inertia of the particles in the search space is controlled. The difference between the individual best position and the global best position is determined by the acceleration constant +.> and />Weighting to adjust the balance of particles between individual and global positions. Random number-> and />Random factors are introduced, so that the diversity of the algorithm is increased, and the situation that a local optimal solution is trapped is avoided.
Through the iterative updating process, the particle swarm constantly searches and adjusts its own position and speed to find the best individual and global position in the steady-state space. The iterative updating process can globally search the steady-state space, improves the optimizing capability, and is hopeful to find the optimal position of the standard data set, thereby providing more accurate results for the stability analysis of the power grid.
Preferably, the stop condition reaches a maximum number of iterations.
Preferably, the stability analysis section performs a difference analysis on the individual best positions of the standard data set and the individual best positions of all the transformation data sets, and when the first difference value is obtained, the following formula is used:
；
wherein ,for distance calculation, add->For the first difference value, +.>And performing difference analysis on the optimal position of the whole transformation data set of each group and the optimal position of the whole standard data set to obtain a second difference value, wherein the second difference value is obtained by using the following formula:
；
wherein ,is the second difference value.
In calculating the first difference value, differences between the individual best positions of the standard data sets and the individual best positions of the corresponding positions in each of the transformed data sets are first calculated, and then these differences are summed. The first difference value finally obtainedThe degree of the integrated difference between the individual best positions of the standard data sets and the individual best positions of all the transformation data sets is represented.
In calculating the second difference value, the difference between the optimal position of the whole of each set of transformation data sets and the optimal position of the whole of standard data sets is first calculated, and these differences are then summed and averaged. The second difference value finally obtainedThe average degree of difference between the optimal position of the whole of each set of transformation data and the optimal position of the whole of standard data is shown.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the present disclosure. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the present disclosure shall fall within the scope of the claims of the present disclosure.
Claims (8)
1. A system for analyzing stability of a power grid based on a swarm intelligence algorithm, the system comprising: the device comprises a data acquisition part, a data conversion part, a steady state calculation part and a stability analysis part; the data acquisition part is configured forAcquiring equipment parameters of each equipment of a power grid and operation parameters of each equipment, and carrying out normalization pretreatment on the operation parameters of each equipment by combining the equipment parameters to obtain the equipment operation parameters; the data transformation part is configured to perform data transformation on the collected equipment operation parameters, and specifically comprises the following steps: taking the equipment operation data of each equipment as standard data, and forming a standard data set by all the equipment operation data; setting a conversion section in which each device operation data in the standard data group is subjected toSub-random transformation to obtain->Group transformation data group, each time randomly transforming to obtain a transformation data of the device operation data, wherein ∈10>The method comprises the steps of carrying out a first treatment on the surface of the Each group of transformed data sets comprises +.>Personal transformation data->The amount of data for the device operation; the range of values of the transformation data is within a transformation interval; the steady state calculation part is configured to establish a steady state space, firstly, the standard data set is placed in the steady state space, the individual optimal position and the global optimal position of the standard data set in the steady state space are calculated, then, each group of transformation data sets are placed in sequence according to the random transformation sequence, and the individual optimal position and the global optimal position of each group of transformation data sets in the steady state space are calculated; the individual optimal position characterizes the optimal position of each transformation data in each group of transformation data or each standard data in the standard data group; the global optimum position characterizes the optimum bit of each set of transformed data sets or the whole standard data setPlacing; the stability analysis part is configured to perform difference analysis on the individual optimal positions of the standard data sets and the individual optimal positions of all the transformation data sets to obtain a first difference value, perform difference analysis on the overall optimal position of each transformation data set and the overall optimal position of the standard data sets to obtain a second difference value, calculate based on a preset weighting coefficient and the first difference value and the second difference value to obtain a final difference value, compare the final difference value with a preset stability threshold interval, and judge the stability of the power grid; the steady state calculation part establishes a steady state space, the standard data set is firstly put in the steady state space, and the method for calculating the individual optimal position and the global optimal position of the standard data set in the steady state space comprises the following steps: treating each standard data in the standard data group as a particle; if the standard data set contains->The number of particles is set to +.>The method comprises the steps of carrying out a first treatment on the surface of the Randomly initializing the position and velocity for each particle, denoted +.> and />, wherein />An index representing the particles; definition of fitness function->For evaluating the stability of each particle; wherein (1)>Is a parameter vector; the iterative updating particle specifically comprises the following steps: updating the velocity of particlesAnd a location, iteratively updating an individual best location and a global best location of the particles; judging whether to terminate the iterative updating particles according to a preset stopping condition; outputting the global optimum position and the individual optimum position to obtain the individual optimum position +.>And global best position->The method comprises the steps of carrying out a first treatment on the surface of the The steady state calculation part sequentially puts each group of transformation data into the random transformation sequence, and the method for calculating the individual optimal position and the global optimal position of each group of transformation data in the steady state space comprises the following steps: establishing a steady-state space, and sequentially placing each group of transformation data sets in the steady-state space according to the random transformation sequence; treating each of the transformed data in the transformed data set as a particle; if the transformation data set contains->The number of particles is set to +.>The method comprises the steps of carrying out a first treatment on the surface of the Randomly initializing the position and velocity for each particle, denoted +.> and />, wherein />An index representing the particles; definition of fitness function->For evaluating the stability of each particle; wherein (1)>Is a parameter vector; the iterative updating particle specifically comprises the following steps: updating the speed and the position of the particles, and iteratively updating the individual optimal position and the global optimal position of the particles; judging whether to terminate the iterative updating particles according to a preset stopping condition; outputting the global optimum position and the individual optimum position to obtain the individual optimum position +.>And global best position->。
2. The swarm intelligence algorithm-based grid stability analysis system of claim 1, wherein said device parameters include: generator parameters, transformer parameters, transmission line parameters, switchgear parameters, and generator regulator parameters; the generator parameters include: generator capacity, power factor, and inertia constant; the transformer parameters include: transformer capacity, rated voltage ratio, short circuit impedance, and conversion efficiency; the transmission line parameters include: resistance and reactance; the switchgear parameters include: rated current; the generator regulator parameters include: tuning range, steady-state gain, and response time.
3. The swarm intelligence algorithm-based grid stability analysis system of claim 2, wherein said operating parameters include: generator operating parameters, transformer operating parameters, transmission line operating parameters, switch operating parameters, and generator regulator operating parameters; the generator operating parameters include: real-time voltage, real-time frequency, real-time active power, real-time reactive power and real-time rotational speed; the transformer operating parameters include: real-time input voltage, real-time output voltage and real-time temperature; the transmission line operation parameters include: real-time power transmission current, real-time power transmission voltage, real-time power loss and real-time current phase angle; the switch operating parameters include: switching current and switching voltage; the generator regulator parameters include: real-time steady-state gain and real-time response time.
4. The grid stability analysis system based on a swarm intelligence algorithm according to claim 3, wherein the method for normalizing the operation parameters of each device by the data acquisition part in combination with the device parameters to obtain the device operation parameters comprises the following steps: converting the operating parameters of each device to dimensionless, uniform range of device operating parameters based on the device parameters of each device using the following formula:
；
wherein ,for the operating parameters +.>Is an equipment operation parameter; />For operating parameters->The device parameter mean value of the corresponding device is calculated using the following formula:
；
wherein ,for operating parameters->Device parameters of the corresponding device->For operating parameters->The number of categories of device parameters of the corresponding device.
5. The grid stability analysis system based on a swarm intelligence algorithm according to claim 4, wherein the following formula is used when updating the speed and position of the particles:
；
wherein ,is particle->Speed at the current moment, +.>Is particle->Speed at the next moment, +.>Is particle->Position at the current moment,/->Is particle->At the next moment in time,/->Is inertial weight, ++> and />Is acceleration constant, +.> and />Is a random number +.>Is particle->Is the best position of the individual->Is the global best location.
6. The grid stability analysis system based on a swarm intelligence algorithm according to claim 5, wherein the following formula is used when iteratively updating the individual best positions and the global best positions of the particles:
；
wherein ,is particle->Is characterized by the individual best position of each criterion in the criterion dataset,/for each criterion>Is the global optimal position for the standard dataset.
7. The swarm intelligence algorithm-based grid stability analysis system of claim 6, wherein the stopping condition reaches a maximum number of iterations.
8. The system according to claim 7, wherein the stability analysis section performs a difference analysis on the individual best positions of the standard data set and the individual best positions of all the transformation data sets to obtain the first difference value using the following formula:
；
wherein ,for distance calculation, add->For the first difference value, +.>And performing difference analysis on the optimal position of the whole transformation data set of each group and the optimal position of the whole standard data set to obtain a second difference value, wherein the second difference value is obtained by using the following formula:
；
wherein ,is the second difference value.
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Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103823433A (en) * | 2014-01-08 | 2014-05-28 | 国家电网公司 | Method for realizing relay protection equipment on-line monitoring by use of communication process analysis |
| CN104978493A (en) * | 2015-07-13 | 2015-10-14 | 广东美的制冷设备有限公司 | Power estimation method and device for PTC (positive temperature coefficient) electric heater in air-conditioning system |
| CN105429134A (en) * | 2015-12-08 | 2016-03-23 | 河海大学 | Grid voltage stability prediction method based on power big data |
| CN106558874A (en) * | 2015-09-23 | 2017-04-05 | 国网天津市电力公司 | A kind of transformer station's reliability comprehensive estimation method towards management and running |
| CN108198091A (en) * | 2017-12-25 | 2018-06-22 | 贵州电网有限责任公司电力调度控制中心 | A kind of reactive optimization method of wind power system based on improvement particle cluster algorithm |
| CN112198385A (en) * | 2020-09-30 | 2021-01-08 | 国网山西省电力公司晋中供电公司 | Non-invasive load monitoring method |
| AU2020104000A4 (en) * | 2020-12-10 | 2021-02-18 | Guangxi University | Short-term Load Forecasting Method Based on TCN and IPSO-LSSVM Combined Model |
| WO2021038779A1 (en) * | 2019-08-29 | 2021-03-04 | Nec Corporation | Signal analysis method, and apparatus for three-phase system, and program |
| CN112865075A (en) * | 2021-01-12 | 2021-05-28 | 许继集团有限公司 | Optimization method for alternating current-direct current hybrid micro-grid |
| EP3876169A1 (en) * | 2020-03-06 | 2021-09-08 | Robert Bosch GmbH | Preprocessing method for providing homogeneous data blocks |
| CN114123190A (en) * | 2021-11-30 | 2022-03-01 | 广东电网有限责任公司 | Method and device for determining target region to which ammeter belongs, electronic equipment and storage medium |
| CN114386659A (en) * | 2021-12-06 | 2022-04-22 | 西安交通大学 | Pump pipe optimization method for water system of nuclear power plant |
| CN114884082A (en) * | 2022-06-14 | 2022-08-09 | 河北工业大学 | Active power distribution network reconstruction and reactive power cooperative optimization method |
| CN115395553A (en) * | 2022-04-15 | 2022-11-25 | 国网黑龙江省电力有限公司电力科学研究院 | Power grid reactive power optimization and voltage control method based on gravity energy storage |
| CN115438932A (en) * | 2022-08-23 | 2022-12-06 | 国网河南省电力公司焦作供电公司 | Optimized scheduling and immune particle swarm solving method for comprehensive energy distribution network |
| WO2023014719A1 (en) * | 2021-08-02 | 2023-02-09 | Schlumberger Technology Corporation | Modular hydrocarbon facility placement planning system with machine learning well trajectory optimization |
| CN115833199A (en) * | 2022-12-01 | 2023-03-21 | 国网辽宁省电力有限公司阜新供电公司 | Mobile energy storage scheduling method, system and storage medium |
| WO2023060815A1 (en) * | 2021-10-12 | 2023-04-20 | 广西电网有限责任公司电力科学研究院 | Energy storage capacity optimization configuration method for improving reliability of power distribution network |
| CN116167277A (en) * | 2022-12-29 | 2023-05-26 | 国网冀北电力有限公司承德供电公司 | Elman network-based power equipment fault diagnosis method, system and medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11742666B2 (en) * | 2019-01-24 | 2023-08-29 | King Fahd University Of Petroleum And Minerals | Method for synchronizing an active load with a microgrid using a proportional integral controller |
-
2023
- 2023-06-26 CN CN202310754566.2A patent/CN116502922B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103823433A (en) * | 2014-01-08 | 2014-05-28 | 国家电网公司 | Method for realizing relay protection equipment on-line monitoring by use of communication process analysis |
| CN104978493A (en) * | 2015-07-13 | 2015-10-14 | 广东美的制冷设备有限公司 | Power estimation method and device for PTC (positive temperature coefficient) electric heater in air-conditioning system |
| CN106558874A (en) * | 2015-09-23 | 2017-04-05 | 国网天津市电力公司 | A kind of transformer station's reliability comprehensive estimation method towards management and running |
| CN105429134A (en) * | 2015-12-08 | 2016-03-23 | 河海大学 | Grid voltage stability prediction method based on power big data |
| CN108198091A (en) * | 2017-12-25 | 2018-06-22 | 贵州电网有限责任公司电力调度控制中心 | A kind of reactive optimization method of wind power system based on improvement particle cluster algorithm |
| WO2021038779A1 (en) * | 2019-08-29 | 2021-03-04 | Nec Corporation | Signal analysis method, and apparatus for three-phase system, and program |
| EP3876169A1 (en) * | 2020-03-06 | 2021-09-08 | Robert Bosch GmbH | Preprocessing method for providing homogeneous data blocks |
| CN112198385A (en) * | 2020-09-30 | 2021-01-08 | 国网山西省电力公司晋中供电公司 | Non-invasive load monitoring method |
| AU2020104000A4 (en) * | 2020-12-10 | 2021-02-18 | Guangxi University | Short-term Load Forecasting Method Based on TCN and IPSO-LSSVM Combined Model |
| CN112865075A (en) * | 2021-01-12 | 2021-05-28 | 许继集团有限公司 | Optimization method for alternating current-direct current hybrid micro-grid |
| WO2023014719A1 (en) * | 2021-08-02 | 2023-02-09 | Schlumberger Technology Corporation | Modular hydrocarbon facility placement planning system with machine learning well trajectory optimization |
| WO2023060815A1 (en) * | 2021-10-12 | 2023-04-20 | 广西电网有限责任公司电力科学研究院 | Energy storage capacity optimization configuration method for improving reliability of power distribution network |
| CN114123190A (en) * | 2021-11-30 | 2022-03-01 | 广东电网有限责任公司 | Method and device for determining target region to which ammeter belongs, electronic equipment and storage medium |
| CN114386659A (en) * | 2021-12-06 | 2022-04-22 | 西安交通大学 | Pump pipe optimization method for water system of nuclear power plant |
| CN115395553A (en) * | 2022-04-15 | 2022-11-25 | 国网黑龙江省电力有限公司电力科学研究院 | Power grid reactive power optimization and voltage control method based on gravity energy storage |
| CN114884082A (en) * | 2022-06-14 | 2022-08-09 | 河北工业大学 | Active power distribution network reconstruction and reactive power cooperative optimization method |
| CN115438932A (en) * | 2022-08-23 | 2022-12-06 | 国网河南省电力公司焦作供电公司 | Optimized scheduling and immune particle swarm solving method for comprehensive energy distribution network |
| CN115833199A (en) * | 2022-12-01 | 2023-03-21 | 国网辽宁省电力有限公司阜新供电公司 | Mobile energy storage scheduling method, system and storage medium |
| CN116167277A (en) * | 2022-12-29 | 2023-05-26 | 国网冀北电力有限公司承德供电公司 | Elman network-based power equipment fault diagnosis method, system and medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于深度学习的短期电力负荷预测;姚栋方;吴瀛;罗磊;阎帅;武文广;丁宏;;国外电子测量技术(第01期);全文 * |
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