A reactive compensation optimization method based on substation operation data

Abstract

The present invention discloses a reactive compensation optimization method based on substation operation data. First, according to the data obtained by the equipment manufacturer or on-site test, the voltage, active power and reactive power data of the 10kV bus side are obtained; the typical data of bus voltage, active power and reactive power are selected according to the quarter; the minimum residual reactive power is taken as the objective function, and the reactive power is not reversed, the number of switching times and switching intervals of the reactive compensator are taken as constraints. The improved particle swarm algorithm is used for optimization and solution, and the optimal scheme of reactive compensation device grouping and capacity is determined, and the weighted comprehensive evaluation coefficient of the three factors of comprehensive loss, investment and location is established. The comprehensive optimization evaluation coefficients under each optimization scheme are compared, and the scheme with the smallest comprehensive optimization evaluation coefficient is selected as the optimal scheme to achieve reactive compensation optimization. The above method can improve the power factor and voltage level, and improve the economy and reliability of power grid operation while ensuring the safe and stable operation of power supply and use equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality management of power systems, and in particular to a reactive power compensation optimization method based on substation operation data. Background Art

[0002] Insufficient reactive power in the power system will reduce the power factor of the system, thereby increasing the loss of the power supply line and reducing the utilization rate of the power generation and supply equipment. When the power factor is lower than the national standard, the power company will impose fines or even power outages. At present, the purpose of improving the power factor is achieved by equipping reactive power compensation devices. At present, the power network usually adopts the method of centralized compensation in substations, and reactive power compensation is performed on the 10kV bus side. The compensation equipment includes static VAR compensator (SVC), shunt capacitors, synchronous phase condenser and static synchronous compensator (STATCOM). The optimization of reactive power compensation in substations involves many aspects, including the type of reactive power compensation device, reactive power compensation capacity, installation location, and grouping, so that the bus voltage will increase and the power factor will increase after compensation, so that the indicators related to power quality are effectively improved to ensure the safe operation of the system.

[0003] However, the existing technology will encounter various problems in the process of formulating the reactive power compensation plan of the substation. The first is the selection of historical data. Considering that power users are generally seasonal, the load characteristics and power consumption of power users in different seasons are generally different. Therefore, under strict conditions, the reactive operation historical data of the whole year must be used as the reactive power optimization data basis, but the huge historical data will undoubtedly increase the difficulty of reactive power compensation optimization and complicate the formulation of reactive power compensation plans; the second is the selection of reactive power compensation capacity and grouping. The larger the reactive power compensation capacity, the greater the investment; the more groups, the more switches are switched, which reduces the economy and safety and reliability of the reactive power compensation plan; thirdly, it is necessary to ensure that reactive power is not reversed. When the reactive power compensation amount is greater than the reactive power demand, the excess reactive power will be reversed to the power grid, but when calculating the power factor, it is "reversely sent and recorded", which will cause the power factor to drop and bring a series of hazards; in addition, it is necessary to consider the daily switching limit of the reactive power compensator. The overvoltage and impact current generated during the switching process threaten the safe and stable operation of the power grid on the one hand, and accelerate the insulation aging and capacity decay of the capacitor on the other hand, shortening the service life of the capacitor. Summary of the invention

[0004] The purpose of the present invention is to provide a reactive power compensation optimization method based on substation operation data, which can improve the power factor and voltage level, and improve the economy and reliability of power grid operation while ensuring the safe and stable operation of power supply and use equipment.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A reactive power compensation optimization method based on substation operation data, the method comprising:

[0007] Step 1: Obtain the voltage, active power and reactive power data on the 10kV bus side according to the data obtained by the equipment manufacturer or on-site testing;

[0008] Step 2: Select typical data of bus voltage, active power and reactive power in a year, and select a data sequence with a time length of at least one week in each quarter;

[0009] Step 3: Taking the minimum remaining reactive power as the objective function, and taking the non-reverse reactive power, the switching times and switching time intervals of the reactive compensation device as the constraints, an improved particle swarm algorithm is used for optimization and solution to obtain the optimization scheme of reactive compensation device grouping and capacity;

[0010] Step 4: Establish a weighted comprehensive evaluation coefficient of comprehensive loss, investment, and location, compare the comprehensive optimization evaluation coefficients under various optimization schemes, select the scheme with the smallest coefficient as the best scheme, and achieve reactive power compensation optimization.

[0011] It can be seen from the technical solution provided by the present invention that the above method can improve the power factor and voltage level, and improve the economy and reliability of power grid operation while ensuring the safe and stable operation of power supply and use equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0013] Figure 1 A schematic flow chart of a reactive power compensation optimization method based on substation operation data provided by an embodiment of the present invention;

[0014] Figure 2 It is a typical data operation curve of voltage, active power and reactive power in the embodiment of the present invention;

[0015] Figure 3 It is a schematic diagram of the iterative process of the improved particle swarm algorithm in the example of the present invention;

[0016] Figure 4 This is a diagram showing the effect of reactive power compensation in the example given in the present invention;

[0017] Figure 5 This is a diagram showing the effect of the control strategy of the reactive power compensation device in the example given in the present invention. DETAILED DESCRIPTION

[0018] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, which does not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 The figure is a flow chart of a reactive power compensation optimization method based on substation operation data provided by an embodiment of the present invention, the method comprising:

[0020] Step 1: Obtain the voltage, active power and reactive power data on the 10kV bus side according to the data obtained by the equipment manufacturer or on-site testing;

[0021] Step 2: Select typical data of bus voltage, active power and reactive power in a year, and select a data sequence with a time length of at least one week in each quarter;

[0022] Step 3: Taking the minimum remaining reactive power as the objective function, and taking the non-reverse reactive power, the switching times and switching time intervals of the reactive compensation device as the constraints, an improved particle swarm algorithm is used for optimization and solution to obtain the optimization scheme of reactive compensation device grouping and capacity;

[0023] In this step, the calculation formula for the remaining reactive power is:

[0024]

[0025] In the formula, kvarh1 is the reactive power remaining after compensation; N is the number of sampling points; Q1 is the reactive power after compensation; t inter is the sampling time interval.

[0026] In specific implementation, the reactive compensation switching of the reactive compensation device is carried out according to the following principles:

[0027] (1) No reactive power is sent back

[0028] Reactive power is not reversed. It is required that the reactive power compensated by the reactive compensation device does not exceed the original reactive power data of the substation, and no compensation is made when the reactive power of the substation is negative. Therefore, if the original reactive power data of the substation is greater than or equal to the maximum capacity of the reactive compensation device, the maximum capacity is used to compensate for these data. After screening, if the original reactive power data of the substation is greater than or equal to the second largest capacity of the reactive compensation device, the second largest capacity is used to compensate for these data. Screen in sequence... Finally, if the reactive power of the substation is negative, the reactive compensation device performs compensation.

[0029] (2) The switching interval should be at least 30 minutes

[0030] Because the reactive compensation device to be cut off can only complete the charging and discharging process after 30 minutes, it will not affect the next time it is put into operation; therefore, it is required that each group of reactive compensation devices cannot be continuously switched on and off within 30 minutes. Within 30 minutes, if the reactive compensation device is in the switched-on state at the first data sampling point, in the switched-off state at the next data sampling point, and in the switched-on state for the remaining time thereafter, the reactive compensation device is kept in the switched-off state for the remaining time thereafter. Therefore, the switching time interval should meet the following conditions:

[0031] T interval ≥30min

[0032] Where, T interval It is the switching time interval of reactive power compensator;

[0033] (3) The principle that the number of switching on and off of the reactive power compensation device is less than the upper limit

[0034] Frequent switching of reactive compensation devices will damage the switching switches and reduce the service life of capacitors. Therefore, it is necessary to constrain the maximum number of switching times per day for each group of reactive compensation devices. Assuming the number is b, both switching on and off are switching actions of reactive compensation devices. First, find the number of times the reactive compensation device is switched on every day. If the number is less than or equal to b / 2-1, keep the original switching strategy, otherwise find the subscript of the state change point of the reactive compensation device every day; then find the working interval of the reactive compensation device every day based on this, and sort the lengths of these working intervals; finally, because some working intervals may run through two days, it is necessary to select the longest b / 2-1 intervals per day, and make the rest of the time non-working, that is, the cutting state; therefore, the switching times of the reactive compensation device should meet the following conditions:

[0035]

[0036] In the formula, sw i d is the switching times of the i-th group of reactive power compensators on the d-th day; sw max It is the maximum switching times of the reactive power compensator per day, which can be set according to actual needs.

[0037] In addition, the improved particle swarm algorithm is specifically as follows:

[0038] The formula for updating the d-dimensional velocity of particle i is expressed as:

[0039]

[0040] The formula for updating the position of particle i in the dth dimension is expressed as:

[0041]

[0042] Where v is the velocity of the particle; x is the position of the particle; w is the inertia weight; k is the current number of iterations; id is the dth dimension of the ith particle; gbest is the individual extreme value of the particle; zbest is the global extreme value of the population; c1 and c2 are learning factors; r1 and r2 are random numbers between 0 and 1; w is the weight of the particle swarm algorithm.

[0043] In order to speed up the convergence, the particle swarm algorithm weight w is improved as follows:

[0044]

[0045] In the formula, w max 、w min are the maximum and minimum values ​​of the inertia weight respectively; k is the current number of iterations; K is the total number of iterations.

[0046] In the initial iteration, the weight w of the particle swarm algorithm is relatively large, the speed of the particles is also relatively large, and the global search ability is strong; continuous iteration makes the weight w of the particle swarm algorithm decrease, the speed of the particles also decreases, the local search ability is strong, the calculation rate is improved, and the convergence process is accelerated.

[0047] Step 4: Establish a weighted comprehensive evaluation coefficient of comprehensive loss, investment, and location, compare the comprehensive optimization evaluation coefficients under various optimization schemes, select the scheme with the smallest coefficient as the best scheme, and achieve reactive power compensation optimization.

[0048] In this step, the evaluation method with the lowest loss is:

[0049]

[0050] In the formula, kvarh1 is the reactive power remaining after compensation; kvarh is the reactive power before compensation;

[0051] The evaluation method with the smallest investment is:

[0052]

[0053] In the formula, Q c is the total capacity of reactive power compensation; Q max is the maximum reactive power demand before compensation;

[0054] The best evaluation method for location is:

[0055] Z = 0.8 means the location is optimal; Z = 1 means the location is moderate; Z = 1.2 means the location is inappropriate;

[0056] The weighted comprehensive optimization evaluation coefficient F combining the three factors of loss, investment and location is expressed as:

[0057] F=α*X+β*Y+γ*Z

[0058] In the formula, α, β, and γ are weight coefficients. The larger the α, the greater the proportion of compensation effect; the larger the β, the greater the proportion of economic benefit; the larger the γ, the greater the proportion of location selection; the values ​​of α, β, and γ are set according to the specific situation.

[0059] Compare the comprehensive optimization evaluation coefficient F under each optimization scheme. The smaller the F value, the better it is.

[0060] Because the larger the compensation capacity, the greater the investment; the more groups there are, the more switches there are, and the economy is poor; if the location is not selected appropriately, the safety level will be lower.

[0061] It is worth noting that the contents not described in detail in the embodiments of the present invention belong to the prior art known to professional and technical personnel in the field.

[0062] For ease of understanding, the following is explained with specific examples. It should be noted that the values ​​used in the following examples are only examples. Users can make corresponding changes according to actual needs. The following only quotes several solutions for comparison, and selects the best solution for narration. In this example, the data sampling time interval is 15 minutes. The data source is the 10kV bus voltage and 10kV total incoming power of a substation. The reactive compensation plan is formulated based on this data:

[0063] (1) Based on the data obtained from the equipment manufacturer or on-site testing, the voltage u0(t), active power p0(t) and reactive power q0(t) on the 10 kV bus side are obtained.

[0064] (2) Select four weeks of typical data according to the quarter, including the voltage u(t), active power p(t) and reactive power q(t) on the 10kV bus side. The typical data operation curves of voltage, active power and reactive power are as follows: Figure 2 As shown, (1) is the voltage data operation curve, (2) is the power data operation curve, and the voltage and power operation statistics are shown in Tables 1 and 2 below. The maximum reactive power demand of the substation is 6.85 Mvar.

[0065] Table 1 Busbar operating voltage trend statistics report

[0066] I. Maximum value (kV) I.Minimum value (kV) I. Average value (kV) I.95% probability value (kV) II.10.55 II.9.81 II.10.24 II.10.49

[0067] Table 2 Operation power statistics report

[0068]

[0069]

[0070] (3) Taking the minimum residual reactive power as the objective function and the non-reverse reactive power, the number of switching times of the reactive compensator and the switching time interval as the constraints, an improved particle swarm algorithm is used for optimization and solution to obtain the optimal solution for the grouping and capacity of the reactive compensation devices.

[0071] The reactive power compensation devices are grouped into 2, 3, and 4 groups, and the optimal compensation capacity and the comprehensive optimization evaluation coefficient are calculated respectively. The calculation results are shown in Table 3 below. Taking the installation of 2 groups of reactive power compensation devices as an example, Figure 3 The figure shows a schematic diagram of the iterative process of the improved particle swarm algorithm in the example of the present invention. Figure 4 The figure shows the reactive power compensation effect in the example of the present invention, including (1) a comparison diagram of reactive power compensation amount and load reactive power and (2) a comparison diagram before and after power factor compensation. Figure 5 The figure shows the effect of the control strategy of the reactive compensation device in the example of the present invention, including (1) the switching state diagram of each group of reactive compensation devices, (2) the daily switching times of each group of reactive compensation devices, and (3) the daily accumulated operating time of each group of reactive compensation devices.

[0072] (4) Establish a weighted comprehensive evaluation coefficient of the three factors of comprehensive loss, investment, and location, compare the comprehensive optimization evaluation coefficients under various optimization schemes, select the scheme with the smallest coefficient as the best scheme, and achieve reactive power compensation optimization.

[0073] Table 3 Reactive power compensation optimization scheme

[0074]

[0075] The final comprehensive optimization evaluation coefficients of the three schemes in (3) above are F2=1.5068, F3=1.8617, and F4=2.1942, respectively. The value of scheme 1 is the smallest, so scheme 1 has the best compensation effect.

[0076] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or in any form that the information constitutes prior art known to those skilled in the art.

Claims

1. A reactive power compensation optimization method based on substation operation data, characterized in that: The method comprises: Step 1: Obtain the voltage, active power and reactive power data on the 10kV bus side according to the data obtained by the equipment manufacturer or on-site testing; Step 2: Select typical data of bus voltage, active power and reactive power in a year, and select a data sequence with a time length of at least one week in each quarter; Step 3: Taking the minimum remaining reactive power as the objective function, and taking the non-reverse reactive power transmission, the switching times and switching time intervals of the reactive compensation device as the constraints, an improved particle swarm algorithm is used for optimization and solution to obtain the optimization scheme of reactive compensation device grouping and capacity; In step 3, the calculation formula for the remaining reactive power is: In the formula, kvarh1 is the reactive power remaining after compensation; N is the number of sampling points; Q1 is the reactive power after compensation; t inter is the sampling time interval; The reactive power compensation switching of the reactive power compensation device is carried out according to the following principles: (1) No reactive power is sent back Reactive power is not reversed. It is required that the reactive power compensated by the reactive compensation device does not exceed the original reactive power data of the substation, and no compensation is made when the reactive power of the substation is negative. Therefore, if the original reactive power data of the substation is greater than or equal to the maximum capacity of the reactive compensation device, the maximum capacity is used to compensate for these data. After screening, if the original reactive power data of the substation is greater than or equal to the second largest capacity of the reactive compensation device, the second largest capacity is used to compensate for these data. Screen in sequence. Finally, if the reactive power of the substation is negative, the reactive compensation device performs compensation. (2) The switching interval should be at least 30 minutes Because the reactive compensation device to be cut off can only complete the charging and discharging process after 30 minutes, it will not affect the next time it is put into operation; therefore, it is required that each group of reactive compensation devices cannot be continuously switched on and off within 30 minutes. Within 30 minutes, if the reactive compensation device is in the switched-on state at the first data sampling point, in the switched-off state at the next data sampling point, and in the switched-on state for the remaining time thereafter, the reactive compensation device is kept in the switched-off state for the remaining time thereafter. Therefore, the switching time interval should meet the following conditions: T interval ≥30min Where, T interval It is the switching time interval of reactive power compensator; (3) The principle that the number of switching on and off of the reactive power compensation device is less than the upper limit Frequent switching of reactive compensation devices will damage the switching switches and reduce the service life of capacitors. Therefore, it is necessary to constrain the maximum number of switching times per day for each group of reactive compensation devices. Assuming the number is b, both switching on and off are switching actions of reactive compensation devices. First, find the number of times the reactive compensation device is switched on every day. If the number is less than or equal to b / 2-1, keep the original switching strategy, otherwise find the subscript of the state change point of the reactive compensation device every day; then find the working interval of the reactive compensation device every day based on this, and sort the lengths of these working intervals; finally, because some working intervals may run through two days, it is necessary to select the longest b / 2-1 intervals per day, and make the rest of the time non-working, that is, the cutting state; therefore, the switching times of the reactive compensation device should meet the following conditions: In the formula, sw i d is the switching times of the i-th group of reactive power compensators on the d-th day; sw max The maximum number of switching times of the reactive power compensator per day can be set according to actual needs; The improved particle swarm algorithm is specifically as follows: The formula for updating the d-dimensional velocity of particle i is expressed as: The formula for updating the position of particle i in the dth dimension is expressed as: In the formula, v is the velocity of the particle; x is the position of the particle; w is the inertia weight; k is the current iteration number; id is the dth dimension of the ith particle; gbest is the individual extreme value of the particle; zbest is the global extreme value of the population; c1 and c2 are learning factors; r1 and r2 are random numbers between 0 and 1; w is the weight of the particle swarm algorithm; In order to speed up the convergence, the particle swarm algorithm weight w is improved as follows: In the formula, w max 、w min are the maximum and minimum values ​​of inertia weight respectively; k is the current number of iterations; K is the total number of iterations; Step 4: Establish a weighted comprehensive evaluation coefficient of comprehensive loss, investment, and location, compare the comprehensive optimization evaluation coefficients under various optimization schemes, select the scheme with the smallest coefficient as the best scheme, and achieve reactive power compensation optimization.

2. The reactive power compensation optimization method based on substation operation data according to claim 1 is characterized in that: In step 4, the method with the lowest loss is evaluated as: In the formula, kvarh1 is the reactive power remaining after compensation; kvarh is the reactive power before compensation; The evaluation method with the smallest investment is: In the formula, Q c is the total capacity of reactive power compensation; Q max is the maximum reactive power demand before compensation; The best evaluation method for location is: Z = 0.8 means the location is optimal; Z = 1 means the location is moderate; Z = 1.2 means the location is inappropriate; The weighted comprehensive optimization evaluation coefficient F combining the three factors of loss, investment and location is expressed as: F=α*X+β*Y+γ*Z In the formula, α, β, and γ are weight coefficients. The larger α is, the greater the proportion of compensation effect; the larger β is, the greater the proportion of economic benefit; the larger γ is, the greater the proportion of location selection; the values ​​of α, β, and γ are set according to specific circumstances; The smaller the weighted comprehensive evaluation coefficient F value is, the better it is; because the larger the compensation capacity is, the greater the investment is; the more groups there are, the more switches there are, and the poorer the economy is; The less appropriate the location, the lower the security level.

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