Electric energy quality treatment method and equipment with quick fault removal function

Abstract

The invention relates to the technical field of power quality management of a power distribution network, in particular to a power quality management method and device with a rapid fault removal function, and the method specifically comprises the steps: obtaining the comprehensive weight of a three-phase imbalance degree, a reactive loss degree and a harmonic distortion degree through the data characteristics of each node in a power distribution network system; analyzing the operation cost when the power distribution network system is subjected to three-phase imbalance treatment, reactive treatment and harmonic treatment, and determining an optimization objective function; and based on the optimization objective function, combining a particle swarm optimization algorithm, dynamically adjusting the inertia weight during each iteration, obtaining an optimal configuration scheme, and performing power quality management of the power distribution network system. The probability that particles fall into a local optimal solution in the iteration process is effectively reduced, and a better optimal configuration scheme of the electric energy management equipment can be obtained to carry out electric energy quality management on the power distribution network system, so that electric power faults occurring in the power distribution network system can be rapidly removed.

Description

Technical Field

[0001] This application relates to the technical field of power quality governance in distribution networks, and specifically to a power quality governance method and device with a function of quickly removing faults. Background Art

[0002] With the increasing proportion of high-power density distributed generation connected to the low-voltage distribution network, its own volatility and intermittency characteristics, as well as the access of a large number of power electronic devices, will cause serious power quality problems in the low-voltage distribution network. And power quality governance devices usually have the function of quickly removing faults, can quickly compensate when a power grid fault occurs, and can realize governance functions such as reduction of power grid harmonics, reactive power compensation, and balance of power grid loads, thereby ensuring the stable operation of the power system.

[0003] However, the common configuration scheme of power quality governance devices is point-to-point nearby compensation. This governance scheme has problems such as low compensation efficiency and high governance costs. Therefore, how to make full use of the compensation ability of power quality governance devices and reasonably allocate power quality governance devices in the distribution network is an urgent problem to be solved at present. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a power quality governance method and device with a function of quickly removing faults. The specific technical solutions adopted are as follows: In the first aspect, an embodiment of this application provides a power quality governance method with a function of quickly removing faults. The method includes the following steps: In the distribution network system simulation model, based on the data characteristics of each distribution network node, analyze the three-phase unbalance degree, reactive power shortage degree, and harmonic distortion degree of the distribution network system, and combine with the weight distribution algorithm to obtain the corresponding comprehensive weight; Based on the operating conditions of each distribution network node, analyze the operating costs of the distribution network system during three-phase unbalance governance, reactive power governance, and harmonic governance; Based on the three-phase unbalance degree, reactive power shortage degree, and harmonic distortion degree, combine with the corresponding comprehensive weight and the operating cost to obtain the optimization objective function and the corresponding constraint conditions; Based on the optimization objective function and the corresponding constraint conditions, combine with the particle swarm algorithm to perform particle swarm iteration. Based on the change conditions of the three-phase unbalance degree, reactive power shortage degree, and harmonic distortion degree corresponding to each particle in the historical iteration times, calculate the trend eigenvalue of each particle in each iteration; Based on the movement conditions of the particle positions in the historical iteration times, calculate the movement eigenvalue of each particle in each iteration, and combine with the trend eigenvalue to determine the inertia weight adjustment value of each particle in each iteration; Based on the adjusted inertia weight value and combined with the particle swarm optimization algorithm, power quality governance of the distribution network system is carried out.

[0005] In one embodiment, the process of obtaining the three-phase unbalance degree, reactive power deficiency degree, and harmonic distortion degree is as follows: The average value of the voltage unbalance degrees of all distribution network nodes is denoted as the three-phase unbalance degree of the distribution network system; Calculate the average value of the line losses of all distribution network nodes, calculate the average value of the voltage deviations of all distribution network nodes, and calculate the average value of the power factors of all distribution network nodes; divide the product of the average value of the line losses and the average value of the voltage deviations by the average value of the power factors, and the obtained calculation result is denoted as the reactive power deficiency degree of the distribution network system; Calculate the average value of the sum of the total voltage harmonic distortion rate and the voltage harmonic content rate of all distribution network nodes and denote it as the harmonic distortion degree of the distribution network system.

[0006] In one embodiment, the process of obtaining the corresponding comprehensive weight is as follows: Use the normalized values of the three-phase unbalance degree, the reactive power deficiency degree, and the harmonic distortion degree as the input of the analytic hierarchy process, and the output is the subjective weights of the three-phase unbalance degree, the reactive power deficiency degree, and the harmonic distortion degree; obtain the objective weights of the three-phase unbalance degree, the reactive power deficiency degree, and the harmonic distortion degree through the entropy weight method; Denote the average value of the subjective weight and the objective weight of the three-phase unbalance degree as the comprehensive weight of the three-phase unbalance degree; based on the subjective weights and objective weights of the reactive power deficiency degree and the harmonic distortion degree, use the same acquisition method as the comprehensive weight of the three-phase unbalance degree to obtain the comprehensive weights of the reactive power deficiency degree and the harmonic distortion degree.

[0007] In one embodiment, the process of obtaining the operating costs during the three-phase unbalance governance, reactive power governance, and harmonic governance is as follows: Denote the operating cost during the three-phase unbalance governance as The expression of is: represents the switching action state of the intelligent phase-changing switch at the m-th distribution network node. If the switch at the m-th distribution network node does not perform phase change, the switching action state is denoted as 0; if it performs phase change, the switching action state is denoted as 1; M represents the total number of distribution network nodes; Denote the operating cost during the reactive power governance as The expression of is: represents the reactive power compensation capacity to be compensated at the m-th distribution network node; Represents the rated capacity of the intelligent capacitor used for the reactive power compensation device corresponding to the distribution network system; Represents the minimum allowable compensated reactive power capacity; Is the ceiling function; Denote the operating cost during harmonic governance as The expression of is: , where, Represents the effective value of the current to be compensated at the mth distribution network node; Represents the rated compensation capacity of the active filter used for the harmonic compensation device corresponding to the distribution network system; Represents the minimum compensation capacity of the current allowed to input the active filter.

[0008] In one embodiment, the obtaining process of the optimization objective function and the corresponding constraint conditions is as follows: Denote the optimization objective function as The expression of is: , where, Respectively represent the three-phase unbalance degree, reactive power shortage degree and harmonic distortion degree of the distribution network system; Respectively represent the comprehensive weights of the three-phase unbalance degree, reactive power shortage degree and harmonic distortion degree; Respectively represent the operating costs during three-phase unbalance governance, reactive power governance and harmonic governance; Represents a preset weight coefficient; Obtain the value ranges of each parameter required for calculating the three-phase unbalance degree, reactive power shortage degree, harmonic distortion degree, and the operating costs during three-phase unbalance governance, reactive power governance and harmonic governance, and determine the constraint conditions of the optimization objective function.

[0009] In one embodiment, the iteration of the particle swarm based on the optimization objective function and the corresponding constraint conditions is specifically as follows: Take the optimization objective function and its constraint conditions as the objective function and the corresponding constraint conditions of the particle swarm optimization algorithm to obtain a particle swarm, and iterate the position vectors of each particle in the particle swarm through the particle swarm algorithm.

[0010] In one embodiment, the obtaining process of the trend eigenvalue of each particle during each iteration is as follows: Denote the set composed of the position vectors of the nth particle in the particle swarm after the i-th iteration and all previous iterations as the position vector set of the nth particle at the i-th iteration; Calculate the three-phase unbalance degree corresponding to each position vector through the elements in each position vector in the position vector set; use the three-phase unbalance degree corresponding to each position vector as the abscissa and the iteration number corresponding to each position vector as the ordinate, and perform linear fitting using a fitting algorithm to obtain a fitting line. Denote the exponentialized result of the slope of the fitting line as the change coefficient of the three-phase unbalance degree of the position vector set. Based on the elements in each position vector in the position vector set, obtain the change coefficients of the reactive power deficiency degree and the harmonic distortion degree of the position vector set, as well as the change coefficients of the operating costs during three-phase unbalance governance, reactive power governance, and harmonic governance, using the same acquisition method as the change coefficient of the three-phase unbalance degree. Calculate the mean value of the change coefficients of the three-phase unbalance degree, reactive power deficiency degree, and harmonic distortion degree of the position vector set, and denote it as the first mean value; calculate the mean value of the change coefficients of the operating costs during three-phase unbalance governance, reactive power governance, and harmonic governance of the position vector set, and denote it as the second mean value; denote the ratio of the second mean value to the first mean value as the trend eigenvalue of the first i iterations of the nth particle, where n is the particle number and i is the iteration number sequence.

[0011] In one embodiment, the process of obtaining the inertia weight adjustment value for each iteration of each particle is as follows: Take the metric distance between the position vector with iteration number I in the position vector set and the position vector with iteration number as the moving distance of the position vector with iteration number I. Take the sequence composed of the moving distances of all position vectors in the position vector set as the input of the exponentially weighted moving average algorithm, and denote the output result as the moving eigenvalue of the first i iterations of the nth particle. Denote the normalized value of the ratio between the trend eigenvalue and the moving eigenvalue of the first i iterations of the nth particle as the inertia weight adjustment value of the nth particle at its i-th iteration.

[0012] In one embodiment, the power quality governance of the distribution network system is specifically as follows: Take the inertia weight adjustment values of each particle in each iteration as the inertia weight values of each particle in its next iteration, perform particle swarm optimization to obtain the optimal configuration plan of the power governance equipment, and perform power quality governance on the distribution network system according to the obtained optimal configuration plan.

[0013] In a second aspect, an embodiment of the present application further provides a power quality governance device with a fast fault removal function, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0014] The embodiments of the present application at least have the following beneficial effects: By obtaining the comprehensive weights of the three-phase unbalance degree, reactive power deficiency degree, and harmonic distortion degree from the data characteristics of each node in the distribution network system, the present application can accurately evaluate the importance of the three-phase unbalance governance effect, reactive power governance effect, and harmonic governance effect of the power quality governance device in the power quality governance of the distribution network system, thereby improving the subsequent governance effect of the overall power quality of the distribution network system; based on the three-phase unbalance degree, reactive power deficiency degree, harmonic distortion degree, their comprehensive weights, and operating costs, an optimization objective function of the power quality governance device is constructed, which can effectively reduce the total investment cost of the power quality governance device on the premise of meeting various power quality standards of the distribution network system; based on the inertia weight adjustment value obtained from the trend eigenvalue and the moving eigenvalue, the inertia weight in the particle swarm optimization algorithm is dynamically adjusted. Compared with directly using the standard particle swarm optimization algorithm, it can effectively reduce the possibility of particles falling into the local optimal solution during their iteration process and can improve the convergence accuracy of particles converging to the global optimal solution. Furthermore, it can obtain a better optimal configuration plan of the power governance device to conduct power quality governance on the distribution network system, so as to quickly remove the power faults occurring in the distribution network system and achieve three-phase load balance, reactive power compensation, and harmonic suppression of the distribution network system. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of the steps of a power quality governance method with a fast fault removal function provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the acquisition process of the trend eigenvalue of each particle during each iteration. Detailed Embodiments

[0017] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of the power quality management method and device with a fast fault removal function proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] 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 this application belongs.

[0019] The following specifically describes the specific solutions of the power quality management method and device with a fast fault removal function provided by this application in conjunction with the accompanying drawings.

[0020] Please refer to Figure 1 , which shows the step flowchart of the power quality management method with a fast fault removal function provided by one embodiment of this application. The method includes the following steps: Step S1, in the distribution network system simulation model, analyze the three-phase unbalance degree, reactive power shortage degree, and harmonic distortion degree of the distribution network system based on the data characteristics of each distribution network node, and combine the weight distribution algorithm to obtain the corresponding comprehensive weight.

[0021] (1) This application uses the IEEE-33 node system simulation model to simulate the distribution network system. For the setting of the line parameters in this simulation model, preferably, in the embodiments of this application, the line parameters are set as: base voltage 10 kV, base capacity 20 MVA, total active power load 1665 kW, and total reactive power load 2300 kvar. As other embodiments of this application, implementers can set each line parameter according to the actual situation. Thyristors are used to simulate the nonlinear loads in the distribution network, and resistors are used to simulate the three-phase unbalance and reactive power loads.

[0022] (2) The power quality management device in this application includes a three-phase unbalance management module, a reactive power management module, and a harmonic management module, which can respectively achieve the three power quality management functions of three-phase load balance, reactive power compensation, and harmonic suppression of the distribution network. The three-phase unbalance management module uses a three-phase unbalance adjustment device to balance the three-phase load, and the three-phase unbalance adjustment device is an intelligent phase change switch; the reactive power management module uses a reactive power compensation device for reactive power compensation, and the reactive power compensation device is composed of multiple intelligent capacitor banks with the same rated capacity; the harmonic management module uses a harmonic compensation device to suppress harmonics. In this application, the harmonic compensation device is composed of multiple active filters with the same rated compensation capacity S.

[0023] To accurately evaluate the power quality improvement effects of each improvement module in the power quality improvement equipment in the distribution network system, the following processing is performed: Calculate the three-phase unbalance degree of each distribution network node in the simulation model respectively, and denote the average value of the voltage unbalance degrees of all nodes as the three-phase unbalance degree of the distribution network system , which is used to evaluate the three-phase unbalance improvement effect of the power quality improvement equipment on the distribution network system The smaller it is, the better the three-phase unbalance improvement effect. Among them, the calculation of the three-phase unbalance degree is a well-known technology, and the specific process will not be elaborated

[0024] Furthermore, calculate the line loss, voltage deviation and power factor of each distribution network node in the simulation model respectively; denote the average value of the line losses of all nodes as r1, denote the average value of the voltage deviations of all nodes as r2, and denote the average value of the power factors of all nodes as r3, which are used to evaluate the overall reactive power loss situation of the distribution network system; divide the product of the average line loss r1 and the average voltage deviation r2 by the average power factor r3, and denote the obtained calculation result as the reactive power deficiency degree of the distribution network system , which is used to evaluate the reactive power improvement effect of the power quality improvement equipment on the distribution network system The smaller it is, the better the reactive power improvement effect. Among them, the calculations of the line loss, voltage deviation and power factor are all well-known technologies, and the specific processes will not be elaborated

[0025] Furthermore, calculate the total voltage harmonic distortion rate and voltage harmonic content rate of each distribution network node in the simulation model, and denote the average value of the sum of the total voltage harmonic distortion rates and voltage harmonic content rates of all nodes as the harmonic distortion degree of the distribution network system , which is used to evaluate the harmonic improvement effect of the power quality improvement equipment on the distribution network system The smaller it is, the better the reactive power improvement effect. Among them, the calculations of the total voltage harmonic distortion rate and voltage harmonic content rate are all well-known technologies, and the specific processes will not be elaborated

[0026] (3) Since the influence degrees of harmonic distortion, reactive power deficiency and three-phase unbalance in the distribution network system on the power quality in the distribution network system are usually different, in order to accurately evaluate the importance degrees of the three-phase unbalance improvement effect, reactive power improvement effect and harmonic improvement effect of the power quality improvement equipment in the power quality improvement of the distribution network system, the following processing is performed: Use the Z-score normalization method for the three-phase unbalance degree , reactive power deficiency degree and harmonic distortion degree to perform normalization processing, and for the three-phase unbalance degree , reactive power deficiency degree and the degree of harmonic distortion The normalized values are used as the input of the analytic hierarchy process, and the output is the degree of three-phase imbalance , the degree of reactive power shortage and the degree of harmonic distortion The subjective weights are used to evaluate the importance of the three-phase imbalance control effect, reactive power control effect, and harmonic control effect of the power quality control equipment in the power quality control of the distribution network system; at the same time, the entropy weight method is used to calculate the degree of three-phase imbalance , the degree of reactive power shortage and the degree of harmonic distortion The objective weights are used to improve the objectivity of the evaluation results; The mean value of the subjective weight and the objective weight of the degree of three-phase imbalance is recorded as the comprehensive weight of the degree of three-phase imbalance Based on the degree of reactive power shortage and the degree of harmonic distortion The subjective weights and objective weights are used to obtain the comprehensive weights of the degree of reactive power shortage and the degree of harmonic distortion in the same way as the comprehensive weight of the degree of three-phase imbalance and the degree of harmonic distortion The above three comprehensive weights are used to characterize the importance of the three-phase imbalance control effect, reactive power control effect, and harmonic control effect of the power quality control equipment in the power quality control of the distribution network system. Among them, the Z-score normalization method, the analytic hierarchy process, and the entropy weight method are all well-known technologies, and the specific process will not be elaborated.

[0027] Step S2, analyze the operating costs of the distribution network system during three-phase imbalance control, reactive power control, and harmonic control based on the operating conditions of each distribution network node.

[0028] Due to the service life of the switching loss device of the intelligent phase-changing switch, the fewer the switching actions of the intelligent phase-changing switch, the lower its operating cost. In the distribution network, the reactive power control and harmonic control usually use the number of reactive power compensation devices and harmonic compensation devices used as their operating costs. The reactive power control module and harmonic control module in the power quality control equipment in this application use a reactive power compensation device composed of multiple intelligent capacitor banks with the same rated capacity and a harmonic compensation device composed of multiple active filters with the same rated compensation capacity.

[0029] Based on the above analysis, calculate the operating costs of the power quality control equipment during three-phase imbalance control, reactive power control, and harmonic control of the distribution network system. The expressions are as follows: , where It represents the operating cost when conducting three-phase unbalance control on the distribution network system; It represents the switching operation state of the intelligent phase-changing switch at the m-th distribution network node in the simulation model. If the switch at the m-th distribution network node does not perform phase change, the corresponding switching operation state of the m-th distribution network node is recorded as 0. If phase change is performed, the corresponding switching operation state is recorded as 1; M represents the total number of distribution network nodes in the simulation model; , where, It represents the operating cost when conducting reactive power control on the distribution network system; It represents the reactive power compensation capacity to be compensated at the m-th distribution network node; It represents the rated capacity of the intelligent capacitor used by the reactive power compensation device; It represents the minimum allowable reactive power compensation capacity; is the ceiling function; among them, the process of obtaining the minimum allowable reactive power compensation capacity is a well-known technology, and the specific process will not be elaborated; , where, It represents the operating cost when conducting harmonic control on the distribution network system; It represents the effective value of the current to be compensated at the m-th distribution network node; It represents the rated compensation capacity of the active filter used by the harmonic compensation device; It represents the minimum compensation capacity of the current allowed to input the active filter. Among them, the process of obtaining the minimum compensation capacity of the current is a well-known technology, and the specific process will not be elaborated.

[0030] Step S3: Based on the three-phase unbalance degree, reactive power deficiency degree, and harmonic distortion degree, combined with the corresponding comprehensive weights and the operating cost, obtain the optimization objective function and the corresponding constraint conditions.

[0031] Based on the power quality control effect and operating cost of the power quality control equipment in the distribution network system, the optimization objective function of the power quality control equipment is obtained, and the expression is: , where, It represents the optimization objective function of the power quality control equipment, respectively represent the three-phase unbalance degree, reactive power deficiency degree, and harmonic distortion degree of the distribution network system; respectively represent the comprehensive weights of the three-phase unbalance degree, reactive power deficiency degree, and harmonic distortion degree; respectively represent the operating costs when the power quality control equipment conducts three-phase unbalance control, reactive power control, and harmonic control on the distribution network system; Indicates the weight coefficients, which respectively represent the importance degrees of the power quality improvement effect and the operation cost of the power quality improvement equipment in the distribution network system. They can be set by the implementer. Preferably, in the embodiments of the present application, are respectively set to 0.5 and 0.5.

[0032] The better the power quality improvement effect of the power quality improvement equipment in the distribution network system and the smaller the operation cost, the smaller the optimization objective function F.

[0033] Furthermore, the calculation of the three-phase unbalance degree , the reactive power deficiency degree , the harmonic distortion degree and the operation cost are respectively obtained through the national power quality standard and the cost of the power quality equipment to be invested, that is, the value ranges of the respective parameters required when calculating the three-phase unbalance degree, line loss, voltage deviation, power factor, total voltage harmonic distortion rate, voltage harmonic content rate, reactive power capacity to be compensated and the effective value of the current to be compensated of the distribution network nodes in the simulation model are respectively obtained, so as to obtain the constraint conditions of the optimization objective function F.

[0034] Step S4: Based on the optimization objective function and the corresponding constraint conditions, combined with the particle swarm algorithm, perform the iteration of the particle swarm. Based on the change situations of the three-phase unbalance degree, reactive power deficiency degree and harmonic distortion degree corresponding to each particle in the historical iteration times, calculate the trend eigenvalue of each particle in each iteration.

[0035] The particle swarm optimization algorithm has become a commonly used optimization algorithm in various fields due to its fast convergence speed and short operation time. Among them, the inertia weight in the particle swarm optimization algorithm decreases linearly, which makes the global search ability of the algorithm stronger in the initial stage of iteration and the local search ability of the algorithm stronger in the later stage. However, different configuration schemes of the power quality improvement equipment have different impacts on its different power quality improvement effects and different operation costs when performing power quality improvement in the distribution network system. When directly using the particle swarm optimization algorithm to optimize the configuration scheme of the power quality improvement equipment, the linearly decreasing inertia weight in the algorithm will cause the particle swarm optimization algorithm to easily converge too quickly to a local search space that simultaneously reduces multiple power quality improvement effects of the power quality improvement equipment and increases multiple operation costs during its iteration process. Furthermore, it causes the gradually decreasing inertia weight in the subsequent iteration process to be difficult to adjust out of this local search space within a limited number of iterations, and thus cannot provide an optimal configuration scheme that balances all power quality improvement effects and all operation costs of the power quality improvement equipment.

[0036] During the iterative process of particles in the particle swarm optimization algorithm, if there is a significant decreasing trend in multiple power quality governance effects of the power quality governance device and a significant increasing trend in multiple operating costs, and the moving distance of the particle in its subsequent iterations is smaller, it indicates that the particle may have fallen into the local space that increases the result of the optimization objective function of the power quality governance device. Moreover, it is difficult for the particle to jump out of the local space in its subsequent iterations. In this case, the inertia weight of the particle should be larger to help the particle jump out of its local optimal solution. On the contrary, if there is a significant increasing trend in multiple power quality governance effects of the power quality governance device and a significant decreasing trend in multiple operating costs, and the moving distance of the particle in its subsequent iterations is larger, it indicates that the particle may have entered the local space that reduces the result of the optimization objective function of the power quality governance device. Moreover, the particle may break away from the local space in its subsequent iterations. In this case, the inertia weight of the particle should be smaller to help the particle approach its global optimal solution.

[0037] (1) Take the optimization objective function F and its constraints as the objective function and corresponding constraints of the particle swarm optimization algorithm, and denote the obtained particle swarm as the population , and the search space of the population . In the embodiments of the present application, the population size, the number of iterations, and the initial parameters in the particle swarm optimization algorithm all take default values. As other embodiments of the present application, the implementer can set the population size, the number of iterations, and the initial parameters in the particle swarm optimization algorithm according to the actual situation. The particle swarm optimization algorithm is a well-known technology, and the specific process will not be elaborated.

[0038] (2) The initial position vectors of each particle can be generated through the particle swarm algorithm. Among them, the initial position vector of each particle is a vector composed of the preset three-phase unbalance degree, line loss, voltage deviation, power factor, total voltage harmonic distortion rate, voltage harmonic content rate, reactive power compensation capacity to be compensated, and effective value of the current to be compensated of all nodes. Generating the initial position vectors of each particle through the particle swarm algorithm is a well-known technology, and the specific process will not be elaborated.

[0039] When performing particle swarm optimization on the population , taking the nth particle in the population as an example of the i-th iteration, obtain the position vector of the particle at the end of each iteration. Denote the set composed of the position vectors of the i-th and all previous iterations of the particle as the position vector set of the particle at the i-th iteration, and obtain the iteration number corresponding to each position vector in the position vector set .

[0040] Further, taking the three-phase unbalance degree in the optimization objective function F as an example, through the elements in each position vector in the position vector set , calculate the three-phase unbalance degree corresponding to the position vector; taking the three-phase unbalance degrees corresponding to the position vectors in the position vector set as the abscissa and the iteration times corresponding to each position vector as the ordinate, use the least squares fitting algorithm to perform linear fitting to obtain a fitting line, take the slope of the fitting line as the exponent of the exponential function with the natural constant e as the base, and record the calculation result of the exponential function as the change coefficient of the three-phase unbalance degree of the position vector set , which is used to characterize the change trend of the three-phase unbalance degree of the distribution network system during the first i iterations of the particle . The least squares fitting algorithm is a well-known technology, and the specific process will not be elaborated here.

[0041] It should be noted that for the linear fitting of the three-phase unbalance degrees of all position vectors in the position vector set , the present application only provides a linear fitting method. There are many existing linear fitting methods, and implementers can also use other linear fitting algorithms to perform linear fitting on the three-phase unbalance degrees of all position vectors in the position vector set , and the present application does not make specific restrictions.

[0042] Based on the elements in each position vector in the position vector set , adopt the same acquisition method as the change coefficient of the three-phase unbalance degree of the position vector set to obtain the change coefficients of the reactive power shortage degree and the harmonic distortion degree of the position vector set , as well as the change coefficients of the operating costs during three-phase unbalance control, reactive power control, and harmonic control.

[0043] Further, for the first i iterations of the particle , calculate the mean value of the change coefficients of the three-phase unbalance degree, the reactive power shortage degree, and the harmonic distortion degree of the position vector set , and record it as the first mean value ; calculate the mean value of the change coefficients of the operating costs during three-phase unbalance control, reactive power control, and harmonic control of the position vector set , and record it as the second mean value ; record the ratio of the second mean value to the first mean value as the trend eigenvalue of the first i iterations of the particle , which is used to characterize the situation of the particle During the first i iterations, the possibility that the power quality governance effects of the power quality governance device show a significant decreasing trend and the operating costs show a significant increasing trend.

[0044] Step S5: Calculate the movement characteristic value of each particle at each iteration based on the movement of the particle positions in the historical iteration times, and determine the inertia weight adjustment value of each particle at each iteration in combination with the trend characteristic value.

[0045] (1) Taking the position vector with the iteration number I in the position vector set as an example, where , take the Euclidean distance between the position vector with the iteration number I in the position vector set and the position vector with the iteration number as the movement distance of the position vector , which is used to characterize the movement distance of the particle in the search space of the population during its nd and I-th iterations.

[0046] By calculating the movement distance of each position vector with an iteration number greater than 1 in the position vector set , sort the movement distances of all the position vectors in this set in ascending order according to the iteration numbers of the position vectors corresponding to the movement distances, and use the obtained sequence as the input of the exponentially weighted moving average algorithm. Denote the output result as the movement characteristic value of the first i iterations of the particle , which is used to evaluate the magnitude of the movement distance of the particle in the search space of the population during its subsequent iterations. Among them, the exponentially weighted moving average algorithm is a well-known technology, and the specific process will not be elaborated.

[0047] (2) Denote the ratio between the trend characteristic value and the movement characteristic value of the first i iterations of the particle as the inertia weight adjustment coefficient of the particle at its i-th iteration, which is used to adjust the value of the inertia weight of the particle at its iteration; normalize the inertia weight adjustment coefficient of the particle at its i-th iteration, specifically: Calculate the inertia weight adjustment coefficient of each particle in the population at each iteration during its first i iterations, and use all the inertia weight adjustment coefficients as the input of the maximum-minimum normalization method to obtain the normalized value of the inertia weight adjustment coefficient of the particle at its i-th iteration.

[0048] It should be noted that for the normalization of the inertia weight adjustment coefficient, this application only provides one normalization method. There are many existing normalization methods, and implementers can also use other normalization algorithms to normalize the inertia weight adjustment coefficient. This application does not make specific restrictions.

[0049] Use this normalized value as the particle 's inertia weight adjustment value for the i-th iteration and use it as the particle 's inertia weight value during its iteration. This is because the value range of the inertia weight in the particle swarm optimization algorithm is (0, 1).

[0050] Step S6: Based on the inertia weight adjustment value, combined with the particle swarm optimization algorithm, perform power quality governance on the distribution network system.

[0051] Use the particle 's inertia weight adjustment value for the i-th iteration as the particle 's inertia weight value during its iteration, and use the particle swarm optimization algorithm to complete the subsequent optimization process to obtain the best configuration plan for the power governance device. The best configuration plan is the number of active filters, intelligent capacitors, and the load switch phase sequence of the intelligent phase-changing switch used at each distribution network node, and perform power quality governance on the distribution network system according to the obtained best configuration plan.

[0052] The schematic diagram of the acquisition process of the trend characteristic value of each particle during each iteration is as Figure 2 shown.

[0053] Based on the same inventive concept as the above method, the embodiment of this application also provides a power quality governance device with a fast fault removal function, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above power quality governance methods with a fast fault removal function.

[0054] In summary, the embodiment of the present application provides a power quality governance method with a fast fault removal function. By obtaining the comprehensive weights of the three-phase unbalance degree, reactive power shortage degree, and harmonic distortion degree from the data characteristics of each node in the distribution network system, it can accurately evaluate the importance of the three-phase unbalance governance effect, reactive power governance effect, and harmonic governance effect of the power quality governance equipment in the power quality governance of the distribution network system, thereby improving the subsequent governance effect of the overall power quality of the distribution network system; based on the three-phase unbalance degree, reactive power shortage degree, harmonic distortion degree, their comprehensive weights, and operating costs, an optimization objective function of the power quality governance equipment is constructed, which can effectively reduce the total investment cost of the power quality governance equipment on the premise of meeting various power quality standards of the distribution network system; based on the inertia weight adjustment value obtained from the trend eigenvalue and the moving eigenvalue, the inertia weight in the particle swarm optimization algorithm is dynamically adjusted. Compared with directly using the standard particle swarm optimization algorithm, it can effectively reduce the possibility of particles falling into the local optimal solution during its iteration process and can improve the convergence accuracy of particles converging to the global optimal solution, thereby being able to obtain a better optimal configuration plan of the power governance equipment to conduct power quality governance on the distribution network system, so as to quickly remove the power faults occurring in the distribution network system and achieve three-phase load balance, reactive power compensation, and harmonic suppression of the distribution network system.

[0055] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be beneficial.

[0056] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0057] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power quality control method with a rapid fault removal function, characterized in that: The method comprises the following steps: In the distribution network system simulation model, the three-phase imbalance degree, reactive power loss degree and harmonic distortion degree of the distribution network system are analyzed based on the data characteristics of each distribution network node, and the corresponding comprehensive weight is obtained by combining the weight allocation algorithm; Analyze the operating cost of the distribution network system during three-phase unbalance control, reactive power control and harmonic control based on the operating conditions of each distribution network node; Based on the degree of three-phase imbalance, the degree of reactive power loss and the degree of harmonic distortion, combined with the corresponding comprehensive weights and the operating cost, an optimization objective function and corresponding constraints are obtained; Based on the optimization objective function and the corresponding constraints, the particle swarm algorithm is combined to iterate the particle swarm, and based on the changes in the three-phase imbalance degree, reactive power loss degree and harmonic distortion degree corresponding to each particle in the historical iteration number, the trend characteristic value of each particle in each iteration is calculated; Calculate the movement characteristic value of each particle at each iteration based on the movement of the particle position in the historical iteration number, and determine the inertia weight adjustment value of each particle at each iteration in combination with the trend characteristic value; Based on the inertia weight adjustment value and combined with the particle swarm optimization algorithm, power quality management of the distribution network system is performed.

2. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The acquisition process of the three-phase imbalance degree, reactive power loss degree and harmonic distortion degree is as follows: The average value of the voltage imbalance of all distribution network nodes is recorded as the three-phase imbalance degree of the distribution network system; Calculate the mean of the line loss of all distribution network nodes, calculate the mean of the voltage deviation of all distribution network nodes, and calculate the mean of the power factor of all distribution network nodes; divide the product of the mean of the line loss and the mean of the voltage deviation by the mean of the power factor, and record the calculated result as the reactive power loss degree of the distribution network system; The average value of the sum of the total voltage harmonic distortion rate and the voltage harmonic content rate of all distribution network nodes is calculated and recorded as the harmonic distortion degree of the distribution network system.

3. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The process of obtaining the corresponding comprehensive weight is: The normalized values ​​of the three-phase unbalance degree, the reactive power loss degree and the harmonic distortion degree are used as inputs of a hierarchy analysis method, and the outputs are the subjective weights of the three-phase unbalance degree, the reactive power loss degree and the harmonic distortion degree; Obtaining objective weights of the three-phase imbalance degree, the reactive power loss degree and the harmonic distortion degree by using an entropy weight method; The average of the subjective weight and the objective weight of the three-phase imbalance degree is recorded as the comprehensive weight of the three-phase imbalance degree; Based on the subjective weight and objective weight of the reactive power shortage degree and the harmonic distortion degree, the comprehensive weight of the reactive power shortage degree and the harmonic distortion degree is obtained by adopting the same acquisition method as the comprehensive weight of the three-phase imbalance degree.

4. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The process of obtaining the operating cost during the three-phase unbalance control, reactive power control and harmonic control is as follows: The operating cost of three-phase unbalance control is recorded as , The expression is: = , where represents the switch action state of the intelligent phase-changing switch at the mth distribution network node. If the switch at the mth distribution network node does not perform phase-changing, the switch action state is recorded as 0. If the switch at the mth distribution network node performs phase-changing, the switch action state is recorded as 1. M represents the total number of distribution network nodes. The operating cost of reactive power control is recorded as , The expression is: , where represents the reactive capacity to be compensated of the mth distribution network node; Indicates the rated capacity of the smart capacitor used in the reactive power compensation device of the distribution network system; Indicates the minimum allowable compensation reactive capacity; is the ceiling rounding function; The operating cost of harmonic control is recorded as , The expression is: , where represents the effective value of the current to be compensated at the mth distribution network node; Indicates the rated compensation capacity of the active filter used in the corresponding harmonic compensation device of the distribution network system; Indicates the minimum compensation capacity of the current allowed to be put into the active filter.

5. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The process of obtaining the optimization objective function and the corresponding constraint conditions is as follows: The optimization objective function is recorded as , The expression is: , where , , They respectively represent the three-phase imbalance degree, reactive power loss degree and harmonic distortion degree of the distribution network system; , , Respectively represent the comprehensive weights of three-phase imbalance degree, reactive power loss degree and harmonic distortion degree; , , Respectively represent the operating costs of three-phase unbalance control, reactive power control and harmonic control; , Indicates the preset weight coefficient; Obtain the value ranges of various parameters required for calculating the degree of three-phase imbalance, reactive power loss, harmonic distortion, and the operating costs of three-phase imbalance control, reactive power control, and harmonic control, and determine the constraints of the optimization objective function.

6. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The particle swarm iteration based on the optimization objective function and the corresponding constraints is performed in combination with the particle swarm algorithm, specifically: The optimization objective function and its constraints are used as the objective function and corresponding constraints of the particle swarm optimization algorithm to obtain a particle swarm, and the position vector of each particle in the particle swarm is iterated through the particle swarm algorithm.

7. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The process of obtaining the trend characteristic value of each particle at each iteration is as follows: The set of position vectors of the nth particle in the particle swarm after the i-th iteration and all iterations before the i-th iteration is recorded as the position vector set of the nth particle at the i-th iteration; The three-phase imbalance degree corresponding to each position vector in the position vector set is calculated by using the elements in each position vector in the position vector set; the three-phase imbalance degree corresponding to each position vector is used as the horizontal coordinate, and the number of iterations corresponding to each position vector is used as the vertical coordinate, and a straight line is fitted by using a fitting algorithm to obtain a fitting straight line, and the exponential result of the slope of the fitting straight line is recorded as the coefficient of variation of the three-phase imbalance degree of the position vector set; Based on the elements in each position vector in the position vector set, the variation coefficients of the reactive power loss degree and harmonic distortion degree of the position vector set, as well as the variation coefficients of the operating costs during three-phase imbalance control, reactive power control, and harmonic control are obtained in the same manner as the variation coefficient of the three-phase imbalance degree; Calculate the mean of the coefficients of variation of the three-phase imbalance degree, reactive power loss degree and harmonic distortion degree of the position vector set, and record it as the first mean; calculate the mean of the coefficients of variation of the operating cost during the three-phase imbalance control, reactive power control and harmonic control of the position vector set, and record it as the second mean; record the ratio of the second mean to the first mean as the trend characteristic value of the previous i iterations of the nth particle, where n is the sequence number of the particle and i is the sequence number of the iteration number.

8. The power quality control method with fault rapid removal function as claimed in claim 7, characterized in that: The process of obtaining the inertia weight adjustment value of each particle in each iteration is as follows: The position vector with the iteration number I in the position vector set is combined with the position vector with the iteration number The metric distance between the position vectors is taken as the moving distance of the position vector with the iteration number I; Using the sequence composed of the moving distances of all position vectors in the position vector set as the input of the exponentially weighted moving average algorithm, and recording the output result as the moving characteristic value of the previous i iterations of the nth particle; The normalized value of the ratio between the trend characteristic value and the movement characteristic value of the nth particle in the previous i iterations is recorded as the inertia weight adjustment value of the nth particle at its i-th iteration.

9. The power quality control method with fault rapid removal function according to claim 1, characterized in that: The power quality management of the distribution network system is specifically as follows: The inertia weight adjustment value of each particle in each iteration is used as the inertia weight value of each particle in its next iteration, and particle swarm optimization is performed to obtain the optimal configuration scheme of the power management equipment. The power quality management of the distribution network system is carried out according to the obtained optimal configuration scheme.

10. A power quality management device with a fault rapid removal function, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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