Power system adjusting method and system and related equipment

Abstract

The invention relates to the technical field of intelligent power grids, in particular to a power system adjusting method and system and related equipment, and the method comprises the steps: clustering all power generation nodes in a power grid to obtain a node cluster; initial trigger angles of all power generation nodes in the power grid are obtained, new trigger angles of all the power generation nodes are generated and optimized and iterated by using a particle swarm optimization algorithm, and node cluster adjustment weights of all node clusters and node cluster association degrees among the node clusters are calculated in the iteration process; taking a comprehensive result of the node cluster adjustment weight and the node cluster correlation degree as the fitness of a particle swarm optimization algorithm; when iteration is terminated, outputting an optimal trigger angle control vector; and controlling and adjusting a thyristor in a static var compensator of each power generation node in the power grid by using the optimal trigger angle control vector. According to the method, the node voltage balance is ensured, the interference between different power generation nodes is reduced, and the flexible adjustment performance of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly relates to a power system regulation method, system and related devices. Background Art

[0002] At present, multiple clean energy power generation nodes have been connected to the power grid on a large scale and are increasing year by year, forming a complex power network system. Moreover, there are more and more interactive facilities such as distributed energy, making the power grid system change from a passive network to an active network, and the power system power flow has changed from a unidirectional flow to a two-way interaction, which puts forward higher requirements for the optimal configuration ability of the power system. By introducing a variety of power electronic conversion, compensation and control technical means, a smart grid can effectively achieve flexible regulation of the power system and support the grid operation requirements of frequent changes in the power system operation mode and frequent conversion of the power flow direction brought about by the large-scale centralized access of clean energy power generation nodes such as wind energy and solar energy.

[0003] The related technology of the flexible alternating current transmission system is an advanced power transmission and distribution technology commonly used in the power industry. It combines communication technology, power electronics and intelligent control by using power electronic devices, and can effectively cope with the power flow control, reactive power compensation and power quality problems of the power system, and achieve flexible regulation of the power system. The static var compensator adjusts the equivalent impedance of the system by the variable transmission susceptance inside, thereby adjusting the system voltage and improving the system stability. It is a widely used flexible alternating current transmission controller at present. At present, when a smart grid controls the node voltage of clean energy power generation nodes through a flexible alternating current transmission system, it only independently considers the voltage regulation of a single static var compensator on the clean energy power generation nodes, ignoring the interaction effects between multiple static var compensators in the cross-regional power system, resulting in enhanced interference between different power generation nodes, reducing the stability of the power system, and further leading to a decline in the flexible regulation performance of the power system. Summary of the Invention

[0004] The present invention provides a power system regulation method, system and related devices to solve the existing problems.

[0005] The power system regulation method, system and related devices of the present invention adopt the following technical solutions: An embodiment of the present invention provides a power system regulation method, which includes the following steps: Obtain the node voltage sequences of each power generation node in the power grid and the distances between the power generation nodes; Cluster all the power generation nodes in the power grid by using the node difference value obtained from the distances between different power generation nodes, the overall difference of the node voltages, and the correlation between the node voltage sequences to obtain several node clusters; Obtain the initial trigger angles of all power generation nodes in the power grid, randomly generate new trigger angles for each power generation node, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angles. During the iteration process, calculate the node cluster adjustment weights of each node cluster and the node cluster correlation degrees between node clusters. The node cluster adjustment weight is determined based on the difference between the node voltage and the rated voltage and the change amount of the trigger angle. The node cluster correlation degree is determined based on the node difference value and the new trigger angle. Take the comprehensive result of the node cluster adjustment weight and the node cluster correlation degree as the fitness in the particle swarm optimization algorithm. When the iteration terminates, output the optimal trigger angle control vector. Use the optimal trigger angle control vector to control and adjust the thyristors in the static var compensator of each power generation node in the power grid.

[0006] Preferably, the specific method for obtaining the node cluster is as follows: Calculate the node difference value between any two power generation nodes according to the distance between any two power generation nodes, the overall difference of the node voltages, and the correlation between the voltage smoothing sequences corresponding to the two power generation nodes respectively after fitting all the node voltage sequences. Use the node difference value between different power generation nodes in the power grid as the distance metric method of the DBSCAN algorithm, and use the DBSCAN algorithm to cluster all the power generation nodes in the smart grid to obtain several clustering clusters, and record any clustering cluster as a node cluster.

[0007] Preferably, the specific method for obtaining the voltage smoothing sequence is as follows: Normalize the node voltages in each node voltage sequence of all power generation nodes by using the maximum-minimum normalization method to obtain the voltage normalization sequence. And take the voltage normalization sequence as the input of the polynomial fitting algorithm, output the fitting curve of the voltage normalization sequence, and form a new sequence with the fitting values of the voltage normalization sequence on the fitting curve, which is recorded as the voltage smoothing sequence.

[0008] Preferably, the method of randomly generating new trigger angles for each power generation node and using the particle swarm optimization algorithm to optimize and iterate all the new trigger angles includes the following specific method: Randomly take values in the interval to form a vector with the same length as the initial trigger angle control vector, which is recorded as the new trigger angle control vector of the power grid. Denote the elements in the new trigger angle control vector as new trigger angles, and obtain new trigger angle control vectors as the initial population, and use the particle swarm optimization algorithm to optimize and iterate the initial population. Output the new trigger angle control vector corresponding to the iteration order for each optimization iteration, where , and are the preset first parameter, second parameter, and third parameter respectively.

[0009] Preferably, the specific method for obtaining the node cluster adjustment weight is as follows: During the optimization iteration of the initial population by the particle swarm optimization algorithm, analyze the numerical level difference between the node voltage sequences of all power generation nodes in any node cluster and the rated voltage of the power grid, as well as the angular difference between the initial trigger angle control vector and the new trigger angle control vector at the corresponding iteration order, to obtain the node cluster adjustment weight after any optimization iteration of the node cluster. Both the numerical level difference and the angular difference are positively correlated with the node cluster adjustment weight.

[0010] Preferably, the specific method for obtaining the node cluster correlation is as follows: During the optimization iteration of the initial population by the particle swarm optimization algorithm, calculate the node cluster correlation between different node clusters after any optimization iteration according to the node difference value between power generation nodes in different node clusters and the new trigger angle output by the optimization iteration. The node difference value is negatively correlated with the node cluster correlation, and the new trigger angle output by the optimization iteration is positively correlated with the node cluster correlation.

[0011] Preferably, the specific method for obtaining the fitness is as follows: During the optimization iteration of the initial population by the particle swarm optimization algorithm, weight all node cluster adjustment weights against the node cluster correlation to obtain the fitness of the new trigger angle control vector after any optimization iteration. Both the node cluster adjustment weight and the node cluster correlation are positively correlated with the fitness.

[0012] Preferably, the specific method for controlling and adjusting the thyristors in the static var compensator of each power generation node in the power grid using the optimal trigger angle control vector includes the following: Obtain each element in the optimal trigger angle control vector as the optimal trigger angle of the corresponding power generation node. Install static var compensators at each power generation node, and control the thyristors in the static var compensator through the optimal trigger angle of the power generation node to change the equivalent impedance of the thyristor-controlled reactor.

[0013] A power system regulation system adopts any one of the power system regulation methods described above. This system includes the following modules: Data acquisition module: used to obtain several node voltage sequences of each power generation node in the power grid and the distances between the power generation nodes; Node clustering module: used to cluster all power generation nodes in the power grid using the node difference value obtained from the distances between different power generation nodes, the overall difference in node voltages, and the correlation between node voltage sequences to obtain several node clusters; Optimization Iteration Module: It is used to obtain the initial trigger angles of all power generation nodes in the power grid, randomly generate new trigger angles for each power generation node, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angles. During the iteration process, the node cluster adjustment weight and the node cluster correlation degree between node clusters are calculated. The node cluster adjustment weight is determined based on the difference between the node voltage and the rated voltage and the trigger angle change amount, and the node cluster correlation degree is determined based on the node difference value and the new trigger angle. The comprehensive result of the node cluster adjustment weight and the node cluster correlation degree is used as the fitness in the particle swarm optimization algorithm. When the iteration terminates, the optimal trigger angle control vector is output. Power Regulation Module: It is used to control and regulate the thyristors in the static var compensator of each power generation node in the power grid by using the optimal trigger angle control vector.

[0014] A power system regulation device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the power system regulation methods described above.

[0015] The beneficial effects of the technical solution of the present invention are as follows: Through the analysis of the correlation of the node voltage sequence and distance clustering, node clusters that can reflect the topological characteristics of the power grid are formed. Compared with the traditional global unified regulation strategy, the partition optimization based on the node cluster difference value can accurately identify the voltage weak links, making the trigger angle regulation of power generation nodes more targeted. At the same time, based on the fitness function obtained by combining the particle swarm algorithm with the two dimensions of the node cluster adjustment weight and the correlation degree, the collaborative optimization of the trigger angle control is realized. The node cluster adjustment weight quantifies the voltage deviation and the trigger angle change cost, avoiding excessive adjustment of a single node. The node cluster correlation degree ensures the coordination between adjacent clusters, reduces the interaction effect between multiple static var compensators in the cross-regional power system, while ensuring the node voltage balance, reduces the interference between different power generation nodes, and improves the flexible regulation performance of the power system. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is the flowchart of the steps of a power system regulation method of the present invention; Figure 2 It is the structural block diagram of a power system regulation system of the present invention. Detailed Embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details a power system regulation method, system, and related equipment proposed according to the present invention, including its specific implementation manner, structure, features, and effects as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solutions of a power system regulation method, system, and related equipment provided by the present invention with reference to the accompanying drawings.

[0021] Please refer to Figure 1 , which shows a flowchart of the steps of a power system regulation method provided by an embodiment of the present invention. The method includes the following steps: Step S001: Collect the node voltage sequence through a voltage sensor and obtain the distances between different clean energy power generation nodes.

[0022] It should be noted that there are a large number of clean energy power generation nodes in the power grid system. However, when using smart grid technology to control the node voltage of clean energy power generation nodes through a flexible AC transmission system, only the voltage regulation of a single static var compensator for clean energy power generation nodes is considered independently, ignoring the interaction effects among multiple static var compensators in the cross-regional power system, resulting in enhanced interference between different power generation nodes, decreased stability of the power system, and further decreased flexible regulation performance of the power system. Therefore, the embodiment of the present invention proposes a power system regulation method to solve this technical problem.

[0023] Specifically, to implement the power system regulation method proposed in this embodiment, it is first necessary to collect the node voltage sequences of all clean energy power generation nodes in the power grid system and the distances between clean energy power generation nodes. The specific process is as follows: First, the clean energy power generation nodes in the power grid system are simply referred to as power generation nodes. Through voltage sensors installed at each power generation node, the node voltage data of each clean energy power generation node is collected in real time, and at the same time, the distances between all different clean energy power generation nodes are obtained.

[0024] Then, the node voltage data collected from each clean energy power generation node is segmented to obtain the node voltage sequences measured by the power generation node at different previous time periods, where the length of the node voltage sequence is , where is a preset length parameter.

[0025] It should be noted that in the embodiments of the present invention, according to experience, the data acquisition frequency of the node voltage is preset to 100 Hz, and the length parameter takes a value of 50, which can be adjusted according to actual situations in other embodiments, and the embodiments of the present invention do not make specific limitations.

[0026] So far, the node voltage sequences of each power generation node and the distances between different power generation nodes are obtained through the above method.

[0027] Step S002: Cluster all the power generation nodes in the power grid by using the distances between different power generation nodes, the overall difference of the node voltages, and the node difference values obtained from the correlation between the node voltage sequences to obtain several node clusters.

[0028] It should be noted that due to the large-scale access of clean energy power to the smart grid, there are multiple static var compensators participating in the control of the flexible AC transmission system in the cross-regional power system composed of clean energy power generation nodes at different locations. There will be a strong interaction between clean energy power generation nodes with strong electrical coupling and low short-circuit capacity, resulting in mechanical and electrical oscillations and transient disturbances in the power system, and reducing its flexible regulation performance.

[0029] Furthermore, it should be noted that the power generation output change of clean energy is strongly correlated with the region where it is located. For example, similar light intensity and wind intensity changes in the same region make the solar and wind power generation nodes in this region have similar power generation output characteristics. When the static var compensator adjusts the node voltage, the electrical coupling between the nodes in the adjacent region is strong, and the control interaction between the two is large; at the same time, the triggering angle changes of its static var compensator are similar, affecting the equivalent impedance of the power generation node, and further affecting the short-circuit capacity and flexible regulation performance of the power system in this region. In order to distinguish the strength of the interaction between power generation nodes in different regions and facilitate the subsequent optimization of the flexible regulation of the static var compensator in the smart grid, the embodiments of the present invention cluster the power generation nodes according to the regional change characteristics of the power generation output of clean energy power generation nodes to obtain different node clusters.

[0030] Specifically, as a preferred embodiment, the specific method for obtaining the node clusters is as follows: First, calculate the node difference values between any two power generation nodes according to the distance between any two power generation nodes, the overall difference of the node voltages, and the correlation between the voltage smoothing sequences respectively corresponding to the two power generation nodes after fitting all the node voltage sequences.

[0031] As an optional embodiment, the specific method for obtaining the voltage smoothing sequence is as follows: Normalize the node voltages in each node voltage sequence of all power generation nodes by using the maximum-minimum normalization method to obtain a voltage normalization sequence; and use the voltage normalization sequence as the input of the polynomial fitting algorithm, output the fitting curve of the voltage normalization sequence, and form a new sequence with the fitting values of the voltage normalization sequence on the fitting curve, which is denoted as the voltage smoothing sequence.

[0032] It should be noted that considering that the power generation nodes in the same region have similar output power variations, but there are differences in the degree of power generation output. For example, affected by clouds, the output power variations of solar power generation nodes in the same region are similar, but the power generation node in the center of the cloud cover has a smaller output power degree, while the power generation node located at the edge of the cloud cover is affected by a stronger light intensity and has a larger output power degree. At the same time, short-term and drastic natural factor changes will occur in a relatively small location range in the same region, such as an instantaneous increase in wind power, etc., resulting in instantaneous fluctuations in the node voltages of different power generation nodes; Therefore, in the embodiment of the present invention, the influence of the output power degree of different power generation nodes on the subsequent judgment of the similarity degree of output power variations is eliminated by normalizing the node voltage sequence, and further, the voltage smoothing sequence is obtained by curve fitting for the normalized sequence, effectively reducing the influence of the instantaneous fluctuations of the node voltage on the similarity degree of the output power variations of the power generation nodes.

[0033] In addition, it should be noted that since the fluctuations of clean energy power generation output are random in a relatively short period of time, while the overall temperature, light, and wind power in adjacent regions change relatively stably in a relatively long period of time, therefore, according to the above method, the voltage smoothing sequences of several node voltage sequences of each clean energy power generation node before the current measurement moment are obtained, so as to analyze the power generation output variations of different power generation nodes in a relatively long period of time.

[0034] As an optional embodiment, the specific calculation method of the node difference value is as follows: Among them, represents the distance between the th and the th power generation nodes; represents the number of voltage smoothing sequences participating in the calculation of the node difference value; and respectively represent the average values of the node voltages in the th and the th power generation nodes in the th node voltage sequence; and respectively represent the th and the th power generation nodes in the a voltage smoothing sequence; representing a cosine function; is a preset first constant value; representing the th and th node difference value of the generating nodes in the smart grid; representing an absolute value function.

[0035] It should be noted that in the embodiment of the present invention, the preset value is 500, and the first constant value is preset to 2. The purpose is to map the cosine similarity to a positive value. In the embodiment of the present invention, and can be adjusted according to the actual situation, and the embodiment of the present invention does not make specific limitations.

[0036] It should be noted that the node difference value between generating nodes reflects the difference degree of the output of two generating nodes during the power generation process; on the one hand, the smaller the distance between generating nodes, the closer the influence degree of natural factors, and the smaller the difference degree of node power generation output; at the same time, the smaller the impedance value of the transmission line between the two, the greater the electrical coupling degree between the two, the closer the change of node voltage, and the smaller the calculated node difference value. On the other hand, the voltage smoothing sequence reflects the change trend of the power generation output of different nodes. The greater the cosine similarity between the two, the smaller the difference degree of the power generation output, and in the same area, the smaller the difference between the average values of node voltages, the closer the power generation outputs of the two, and the smaller the node difference value.

[0037] Then, using the node difference value between different generating nodes in the power grid as the distance metric method of the DBSCAN algorithm, and clustering all generating nodes in the smart grid by using the DBSCAN algorithm to obtain several clustering clusters, and any clustering cluster is denoted as a node cluster.

[0038] It should be noted that in the embodiment of the present invention, the neighborhood radius value in the DBSCAN algorithm is the average value of the node difference values of all different generating nodes, and the minimum number of clustering points of the core object is preset to 5, which can be adjusted according to the actual situation.

[0039] It should be further noted that for the outliers after clustering, they are separately regarded as node clusters each containing only one power generation node. By analyzing the regional variation characteristics of the clean energy power generation output, calculating the voltage smoothing sequence, eliminating the influence of the output levels of different power generation nodes and the instantaneous voltage fluctuations on the subsequent judgment of the similarity of output changes, further calculating the node difference value, and using the DBSCAN algorithm to cluster the nodes with the same power generation output change characteristics into node clusters, which is convenient for subsequent flexible regulation optimization. While ensuring the voltage balance of each node within the node cluster, the short-circuit capacity between the node clusters with a relatively large electrical coupling degree is increased, thereby improving the stability of the power system.

[0040] Thus, several node clusters are obtained through the above method.

[0041] Step S003: Obtain the initial trigger angles of all power generation nodes in the power grid, randomly generate new trigger angles for each power generation node, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angles. During the iteration process, calculate the node cluster adjustment weight of each node cluster and the node cluster correlation degree between node clusters. The node cluster adjustment weight is determined based on the difference between the node voltage and the rated voltage and the change amount of the trigger angle. The node cluster correlation degree is determined based on the node difference value and the new trigger angle. Take the comprehensive result of the node cluster adjustment weight and the node cluster correlation degree as the fitness in the particle swarm optimization algorithm. When the iteration terminates, output the optimal trigger angle control vector.

[0042] It should be noted that the initial trigger angles obtained by the flexible AC transmission system through PI control only independently consider a single power generation node in the power system. However, there are interactive effects between multiple static var compensators in the cross-regional power system, which may seriously deteriorate the voltage control performance of the static var compensator and even lead to the instability of the power system in severe cases. Therefore, the embodiments of the present invention consider the interactive effects of the power generation nodes within the node clusters in the smart grid and the interactive effects between different node clusters, and use the particle swarm optimization algorithm to optimize the trigger angles of different power generation nodes in the smart grid, improving the stability of the power system and its flexible regulation performance.

[0043] Specifically, as a preferred embodiment, the method for obtaining the optimal trigger angle control vector includes: First, obtain the initial trigger angles of each power generation node in the power grid, and denote the vector formed by the initial trigger angles of all power generation nodes as the initial trigger angle control vector. Obtain several new trigger angle control vectors by random generation, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angle control vectors.

[0044] As an alternative embodiment, the method for obtaining the initial firing angle of each power generation node in the power grid and denoting the vector formed by the initial firing angles of all power generation nodes as the initial firing angle control vector specifically includes: Perform PI parameter tuning on the static var compensator of the power generation node by the Ziegler-Nichols method. Adopt the PI control algorithm, use the difference between the node voltage obtained by real-time measurement and the rated voltage as the input, and output the initial firing angle of the power generation node.

[0045] It should be noted that the flexible AC transmission system uses the PI control method to obtain the firing angle of the thyristor of a single static var compensator and adjusts its equivalent impedance to achieve the purpose of stabilizing the voltage of a single node. Therefore, the specific process of obtaining the initial firing angle of the power generation node is well known to those skilled in the art and will not be elaborated here.

[0046] As an alternative embodiment, the method for obtaining several new firing angle control vectors by random generation and performing optimization iteration on all new firing angle control vectors by the particle swarm optimization algorithm specifically includes: Randomly take values in the interval to form a vector with the same length as the initial firing angle control vector, which is denoted as the new firing angle control vector of the power grid. Denote the elements in the new firing angle control vector as new firing angles, and so on, to obtain new firing angle control vectors as the initial population, and adopt the particle swarm optimization algorithm to perform optimization iteration on the initial population. Each time of optimization iteration outputs the new firing angle control vector corresponding to the iteration order, where 、 and are the preset first parameter, second parameter, and third parameter respectively.

[0047] It should be noted that generally, the value range of the firing angle is in the interval Therefore, in the embodiment of the present invention, the preset interval is , that is, the preset first parameter is , the second parameter is ; in addition, in the embodiment of the present invention, the third parameter is preset as 30 according to experience, and the first parameter , the second parameter and the third parameter can be adjusted according to actual situations, and the embodiment of the present invention does not make specific limitations.

[0048] Further, it should be noted that in the embodiments of the present invention, the purpose of the same length between the initial trigger angle control vector and the new trigger angle control vector is that since the elements in both vectors represent trigger angles, the embodiments of the invention ensure that there is always a corresponding relationship between the trigger angle and the power generation nodes by making the number of elements in the vectors the same. This is to facilitate that each element representing the optimal trigger angle in the optimal trigger angle control vector obtained through continuous optimization and iteration still has a corresponding relationship with each power generation node, thereby realizing the effective control of each power generation node in the power grid.

[0049] Then, in the process of optimizing and iterating the initial population by the particle swarm optimization algorithm, analyze the numerical level difference between the node voltage sequences of all power generation nodes in any node cluster and the rated voltage of the power grid, as well as the angle difference between the initial trigger angle control vector and the new trigger angle control vector at the corresponding iteration order, to obtain the node cluster adjustment weight after any optimization iteration of the node cluster. Both the numerical level difference and the angle difference are positively correlated with the node cluster adjustment weight.

[0050] It should be noted that when optimizing and iterating the trigger angles of the power generation nodes within a node cluster, the influence degrees of the clean energy power generation nodes in different node clusters by natural factors are different, and the differences between their node voltages and the rated voltage are different. Therefore, in order to ensure the stability of the node voltages of different power generation nodes, the embodiments of the present invention adjust the optimization and iteration process of the trigger angles by obtaining the node cluster adjustment weight of the node cluster, thereby ensuring the voltage stability of different power generation nodes.

[0051] As an optional embodiment, the specific calculation method of the node cluster adjustment weight of the node cluster is as follows: Wherein, represents the number of power generation nodes in the th node cluster; represents the average value of the node voltages in the currently measured node voltage sequence of the th power generation node; represents the rated voltage of the power grid; and respectively represent the initial trigger angle of the th power generation node and the trigger angle output after the th optimization iteration; represents the logarithmic function with the natural constant as the base; represents the th node cluster at the th optimization iteration; represents the absolute value symbol.

[0052] It should be noted that the adjustment weight of the node cluster reflects the degree of influence of the adjustment of the thyristor firing angle of the static var compensator in the corresponding node cluster on the power system. In the specific calculation method, the logarithmic function is used to control the size of the adjustment weight of the node cluster; the smaller the difference between the average node voltage of the clean energy generation node and the rated voltage, the smaller the weight of the corresponding node cluster in the subsequent calculation of fitness, and the smaller the calculated adjustment weight of the node cluster, so that its firing angle can be adjusted in a larger range, while ensuring the voltage stability of the corresponding generation node, improving the short-circuit capacity of the power system in the area where the node cluster is located. In order to balance the node voltage of a single generation node, the greater the difference between the firing angle output by the optimization iteration and the initial firing angle, the smaller the adjustment range for it, the greater the calculated adjustment weight of the node cluster, and the greater the fitness obtained by calculating the subsequent corresponding firing angle, so as to ensure the stability of the node voltage. In addition, the larger the firing angle of the thyristor of the static var compensator, the greater its equivalent impedance, the smaller the short-circuit capacity of the power system, and the greater the degree of interaction between different generation nodes. Therefore, when the absolute value of the difference between the firing angle and the initial firing angle is equal, the influence of the firing angle greater than the initial firing angle on the power system stability is greater. The logarithmic function is used to represent this influence characteristic, so that the weight set at this time is greater, in order to reduce the adjustment range of the subsequent corresponding firing angle.

[0053] Secondly, in the process of optimizing and iterating the initial population by the particle swarm optimization algorithm, according to the node difference value between the generation nodes in different node clusters and the new firing angle output by the optimization iteration, calculate the node cluster correlation degree after any optimization iteration between different node clusters. The node difference value is negatively correlated with the node cluster correlation degree, and the new firing angle output by the optimization iteration is positively correlated with the node cluster correlation degree.

[0054] It should be noted that the generation nodes in different node clusters are in different regions. Affected by the impedance of the transmission lines between different regions and the natural factors on the power generation output, there are differences in the electrical coupling degree of the cross-regional power system. At the same time, the firing angle of the thyristor of the static var compensator of the generation node is negatively correlated with the short-circuit capacity of the power system, and in a strongly coupled cross-regional power system with a higher short-circuit capacity, the interaction between its different generation nodes is weaker. Therefore, the embodiments of the present invention analyze the electrical coupling degree between the generation nodes of different node clusters and adjust their firing angles to reduce the interaction in the flexible AC transmission system.

[0055] As an optional embodiment, the specific calculation method of the node cluster correlation degree is: Wherein, and respectively represent the number of generation nodes in the th and the th node clusters; and respectively represent the new triggering angle of the -th generating node in the -th node cluster output after the -th optimization iteration, and the new triggering angle of the -th generating node in the -th node cluster; represents the node difference value between the -th generating node in the -th node cluster and the -th generating node in the -th node cluster; is a preset second constant value; represents the node cluster correlation degree under the triggering angle control of the -th node cluster and the -th node cluster output in the -th optimization iteration.

[0056] It should be noted that the node cluster correlation degree reflects the short-circuit capacity of the power system in the areas where the two node clusters are located and their electrical coupling degree. When is equal to , it indicates the degree of interactive influence of the electrical coupling degree between different generating nodes in the same node cluster on the power system in the area where the node cluster is located. Among them, when is equal to , takes the value of 0.

[0057] It should be noted that according to experience, the preset value of the second constant is 1, aiming to prevent the denominator from being zero and at the same time control the magnitude of the node cluster correlation degree.

[0058] Furthermore, it should be noted that the node difference value reflects the difference degree of the output of two clean energy generating nodes. The smaller the node difference value between generating nodes, the stronger their electrical coupling degree, the greater the correlation degree between the two node clusters, the greater the node cluster correlation degree obtained by calculation, and the greater the degree of adjustment required for the triggering angle of the generating nodes in the cross-regional power system to adjust its short-circuit capacity and reduce the interactive influence of its internal nodes. Therefore, the greater the fitness obtained by subsequent calculation. In addition, the larger the triggering angle of the static var compensator of the generating nodes in different node clusters, the smaller the short-circuit capacity of the cross-regional power system, the stronger the interactive influence received, the greater the node cluster correlation degree obtained by calculation, the greater the triggering angle fitness calculated subsequently, and the worse the performance of the corresponding triggering angle for flexible regulation of the power system.

[0059] Further, in the optimization iteration process of the particle swarm optimization algorithm for the initial population, all node cluster adjustment weights are used to weight the node cluster correlation degree, and the fitness of the new trigger angle control vector after any optimization iteration is obtained. Both the node cluster adjustment weight and the node cluster correlation degree are positively correlated with the fitness.

[0060] As an optional embodiment, the specific calculation method for the fitness of the trigger angle control vector is as follows: Among them, represents the number of node clusters in the smart grid; and respectively represent the node cluster adjustment weights of the th node cluster and the th node cluster in the th optimization iteration; represents the node cluster correlation degree under the trigger angle control output by the th node cluster and the th node cluster in the th optimization iteration; represents the fitness of the trigger angle control vector in the th optimization iteration.

[0061] It should be noted that the fitness of the trigger angle control vector during the optimization iteration process reflects the quality of its flexible adjustment performance for the smart grid cross-regional power system. On the one hand, the node cluster adjustment weight reflects the influence degree of the adjustment of the static var compensator trigger angle within the node cluster on the power system. The larger the node cluster adjustment weight, the greater the influence of the current trigger angle of the generating nodes within the node cluster on the power system, and the larger the calculated fitness, indicating that its flexible adjustment performance is worse. On the other hand, the node cluster correlation degree reflects the short-circuit capacity of the power systems in the regions where the two node clusters are located and their electrical coupling degree. The larger the node cluster correlation degree, the greater the electrical coupling degree of the cross-regional power system where the two node clusters are located, and the lower the short-circuit capacity. The interaction effects between different generating nodes in the power system are greater, resulting in a decrease in the stability of the power system, and the larger the fitness of the trigger angle control vector obtained by calculation.

[0062] Finally, the fitness is used as the fitness in the particle swarm optimization algorithm. After the number of iterations of the particle swarm optimization algorithm reaches the maximum number of iterations, the trigger angle control vector obtained in the last iteration is used as the optimal trigger angle control vector, where the maximum number of iterations of the particle swarm optimization algorithm is where is a preset fourth parameter.

[0063] It should be noted that in the embodiments of the present invention, the fourth parameter is 30. It should be emphasized that the third parameter and the fourth parameter do not necessarily have to be equal; the value of the fourth parameter can be adjusted according to the actual situation, and the embodiments of the present invention do not make specific limitations.

[0064] Thus, the optimal trigger angle control vector of the power grid is obtained.

[0065] Step S004: Use the optimal trigger angle control vector to control and adjust the thyristors in the static var compensator of each power generation node in the power grid.

[0066] Specifically, as a specific embodiment, the method of using the optimal trigger angle control vector to control and adjust the thyristors in the static var compensator of each power generation node in the power grid specifically includes: Obtain each element in the optimal trigger angle control vector as the optimal trigger angle of the corresponding power generation node. Install static var compensators at each power generation node, and control the thyristors in the static var compensator through the optimal trigger angle of the power generation node to change the equivalent impedance of the thyristor-controlled reactor, so as to achieve the power compensation effect on the power grid, balance the node voltage at the power generation node, and thus achieve the regulation of the power grid power system.

[0067] It should be noted that in the embodiments of the present invention, by utilizing the regional characteristics of the power generation output of clean energy power generation nodes, all power generation nodes in the cross-regional power system are divided into different node clusters. By analyzing the short-circuit capacity of each power generation node within the node cluster and the coupling degree between different node clusters, the particle swarm optimization algorithm is used to optimize the trigger angles of the static var compensators of each clean energy power generation node, reduce the interaction effects between multiple static var compensators in the cross-regional power system, while ensuring the balance of node voltage, reduce the interference between different power generation nodes, and improve the flexible regulation performance of the power system.

[0068] Through the above steps, the flexible regulation performance of the power system is completed.

[0069] Please refer to Figure 2 , which shows the structural block diagram of a power system regulation system provided by an embodiment of the present invention. The system includes the following modules: Data acquisition module: used to obtain the node voltage sequences of each power generation node in the power grid and the distances between power generation nodes; Node clustering module: used to cluster all power generation nodes in the power grid by using the node difference value obtained from the distances between different power generation nodes, the overall difference of node voltages, and the correlation between node voltage sequences, to obtain several node clusters; Optimization Iteration Module: It is used to obtain the initial trigger angles of all generating nodes in the power grid, randomly generate new trigger angles for each generating node, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angles. During the iteration process, calculate the node cluster adjustment weight of each node cluster and the node cluster correlation degree between node clusters. The node cluster adjustment weight is determined based on the difference between the node voltage and the rated voltage and the change amount of the trigger angle. The node cluster correlation degree is determined based on the node difference value and the new trigger angle; use the comprehensive result of the node cluster adjustment weight and the node cluster correlation degree as the fitness in the particle swarm optimization algorithm; output the optimal trigger angle control vector when the iteration terminates; Power Regulation Module: It is used to control and regulate the thyristors in the static var compensator of each generating node in the power grid by using the optimal trigger angle control vector.

[0070] In another embodiment of the present invention, there is also provided a power system regulation device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps from step S001 to step S004 in the described power system regulation method.

[0071] Furthermore, in an optional embodiment, the above-mentioned memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. The memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0072] The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory may be a Random Access Memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, Static RAM (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Sync Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0073] In an alternative embodiment, the above-mentioned processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processing (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor that supports the Advanced RISC Machines (ARM) architecture.

[0074] All or part of the processes in the above-described method embodiments of the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0075] In the embodiment of the present invention, through the correlation analysis of the node voltage sequence and distance clustering, node clusters that can reflect the topological characteristics of the power grid are formed. Compared with the traditional global unified regulation strategy, the partition optimization based on the difference value of the node clusters can accurately identify the voltage weak links, making the trigger angle regulation of the power generation nodes more targeted. At the same time, based on the fitness function obtained by combining the particle swarm algorithm with the two dimensions of the node cluster regulation weight and the correlation degree, the collaborative optimization of the trigger angle control is realized. The node cluster regulation weight quantifies the voltage deviation and the cost of trigger angle change to avoid over-regulation of a single node, and the node cluster correlation degree ensures the coordination between adjacent clusters, reducing the interaction effect between multiple static var compensators in the cross-regional power system. While ensuring the node voltage balance, it reduces the interference between different power generation nodes and improves the flexible regulation performance of the power system.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A power system regulation method, characterized in that, The method includes the following steps: Obtain a number of node voltage sequences of each power generation node in the power grid and the distances between the power generation nodes; Cluster all the power generation nodes in the power grid using the node difference values obtained from the distances between different power generation nodes, the overall difference in node voltages, and the correlation between node voltage sequences to obtain several node clusters; Obtain the initial trigger angles of all the power generation nodes in the power grid, randomly generate new trigger angles for each power generation node, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angles. During the iteration process, calculate the node cluster adjustment weights and the node cluster association degrees between node clusters. The node cluster adjustment weights are determined based on the difference between the node voltage and the rated voltage and the change amount of the trigger angle, and the node cluster association degrees are determined based on the node difference values and the new trigger angles; use the comprehensive result of the node cluster adjustment weights and the node cluster association degrees as the fitness in the particle swarm optimization algorithm; output the optimal trigger angle control vector when the iteration terminates; Use the optimal trigger angle control vector to control and adjust the thyristors in the static var compensator of each power generation node in the power grid.

2. The method for adjusting a power system according to claim 1, wherein The specific method for obtaining the node clusters is as follows: Calculate the node difference values between any two power generation nodes according to the distances between any two power generation nodes, the overall difference in node voltages, and the correlation between the voltage smoothing sequences corresponding to the two power generation nodes respectively after fitting all the node voltage sequences; Use the node difference values between different power generation nodes in the power grid as the distance metric method of the DBSCAN algorithm, and use the DBSCAN algorithm to cluster all the power generation nodes in the smart grid to obtain several clustering clusters, and record any clustering cluster as a node cluster.

3. The method for regulating a power system according to claim 2, wherein The specific method for obtaining the voltage smoothing sequence is as follows: Normalize the node voltages in each node voltage sequence of all the power generation nodes using the maximum-minimum normalization method to obtain a voltage normalization sequence; and use the voltage normalization sequence as the input of the polynomial fitting algorithm, output the fitting curve of the voltage normalization sequence, and form a new sequence with the fitting values of the voltage normalization sequence on the fitting curve, which is recorded as the voltage smoothing sequence.

4. The method for adjusting a power system according to claim 1, wherein, The specific method for randomly generating new trigger angles for each power generation node and using the particle swarm optimization algorithm to optimize and iterate all the new trigger angles includes: Randomly select values within the interval to form a vector with the same length as the initial trigger angle control vector, denoted as the new trigger angle control vector of the power grid. Denote the elements in the new trigger angle control vector as new trigger angles, and obtain new trigger angle control vectors as the initial population, and use the particle swarm optimization algorithm to optimize and iterate the initial population. Each optimization iteration outputs the new trigger angle control vector corresponding to the iteration order, where , and are the preset first parameter, second parameter, and third parameter respectively.

5. The method for regulating a power system according to claim 4, wherein, The specific method for obtaining the node cluster adjustment weights is as follows: During the optimization and iteration process of the particle swarm optimization algorithm for the initial population, analyze the numerical level difference between the node voltage sequences of all the power generation nodes in any node cluster and the rated voltage of the power grid, and the angular difference between the initial trigger angle control vector and the new trigger angle control vector at the corresponding iteration order, to obtain the node cluster adjustment weight of the node cluster after any optimization iteration. Both the numerical level difference and the angular difference are positively correlated with the node cluster adjustment weight.

6. The method for adjusting a power system according to claim 4, characterized in that The specific method for obtaining the node cluster association degrees is as follows: During the optimization iteration process of the initial population by the particle swarm optimization algorithm, according to the node difference values between the power generation nodes within different node clusters and the new trigger angles output by the optimization iteration, calculate the node cluster correlation degrees between different node clusters after any optimization iteration. The node difference value is negatively correlated with the node cluster correlation degree, and the new trigger angle output by the optimization iteration is positively correlated with the node cluster correlation degree.

7. The method for adjusting a power system according to claim 1, wherein The specific method for obtaining the fitness is as follows: During the optimization iteration process of the initial population by the particle swarm optimization algorithm, weight the node cluster correlation degrees with all node cluster adjustment weights to obtain the fitness of the new trigger angle control vector after any optimization iteration. Both the node cluster adjustment weight and the node cluster correlation degree are positively correlated with the fitness.

8. The method for adjusting a power system according to claim 1, wherein The specific method for controlling and adjusting the thyristors in the static var compensator of each power generation node in the power grid by using the optimal trigger angle control vector includes: Obtain each element in the optimal trigger angle control vector as the optimal trigger angle of the corresponding power generation node. Install static var compensators at each power generation node, and control the thyristors in the static var compensator through the optimal trigger angle of the power generation node to change the equivalent impedance of the thyristor controlled reactor.

9. A power system regulation system, which adopts a power system regulation method as described in any one of claims 1-8, characterized in that, The system includes the following modules: Data acquisition module: used to obtain a plurality of node voltage sequences of each power generation node in the power grid and the distances between the power generation nodes; Node clustering module: used to cluster all the power generation nodes in the power grid by using the node difference values obtained from the distances between different power generation nodes, the overall difference of the node voltages, and the correlation between the node voltage sequences to obtain several node clusters; Optimization iteration module: used to obtain the initial trigger angles of all the power generation nodes in the power grid, randomly generate new trigger angles for each power generation node, and use the particle swarm optimization algorithm to optimize and iterate all the new trigger angles. During the iteration process, calculate the node cluster adjustment weights and the node cluster correlation degrees between the node clusters. The node cluster adjustment weight is determined based on the difference between the node voltage and the rated voltage and the trigger angle change amount, and the node cluster correlation degree is determined based on the node difference value and the new trigger angle; use the comprehensive result of the node cluster adjustment weight and the node cluster correlation degree as the fitness in the particle swarm optimization algorithm; output the optimal trigger angle control vector when the iteration terminates; Power regulation module: used to control and adjust the thyristors in the static var compensator of each power generation node in the power grid by using the optimal trigger angle control vector.

10. A power system regulating device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a power system regulation method according to any one of claims 1 to 8.

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