CN108767870B - Integrated distributed self-adaptive reactive voltage automatic control method - Google Patents
Integrated distributed self-adaptive reactive voltage automatic control method Download PDFInfo
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
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Abstract
The invention relates to an integrated distributed self-adaptive reactive voltage automatic control method, which comprises an automatic local regulation side reactive voltage control system AVC and more than one automatic county regulation side reactive voltage control systems AVC; when the power grid network is normal, performing centralized operation and unified decision on the local dispatching side AVC service; when the system is disconnected, the model is adaptively adjusted, and AVC (automatic voltage control) on each county side is in distributed operation and distributed decision; when the power grid network is recovered, the intelligent synchronization of control and locking protection data is realized, and meanwhile, the centralized operation and unified decision of the local dispatching side AVC service are recovered, so that the reliability of the system is effectively improved; the AVC service of the local dispatching side and the AVC service of the county dispatching side are operated and decided in a mode of combining a genetic algorithm and sensitivity analysis. The invention can ensure the safe, efficient and stable operation of the power grid, simultaneously improve the voltage qualification rate, reduce the grid loss, prolong the service life of electrical equipment and reduce the labor intensity of regulating and controlling personnel.
Description
Technical Field
The invention relates to the field of electric power, in particular to an integrated distributed self-adaptive reactive voltage automatic control method.
Background
The voltage is an important index of the quality of electric energy, and the voltage quality has important influence on the safe and economic operation of a power system, the safe production of users, the product quality and the safety and the service life of electrical equipment. Reactive compensation and reactive balance of the power system are basic conditions for ensuring voltage quality. Effective voltage control and reasonable reactive compensation can not only ensure the voltage quality, but also improve the stability and the safety of the operation of the power system and fully exert the economic benefit.
Along with the completion of national grid, place and county integrated construction, the scale of a power grid dispatching technical support system accessing a station is explosively increased, and the pressure of monitoring personnel for voltage and reactive power regulation is very high, so that the reliability, stability and availability of AVC (automatic reactive voltage control) are very important. However, in the current AVC system, all stations are integrated together, which may cause the whole AVC system to be disabled when the topology verification of an individual station is unsuccessful. Therefore, research and development of a set of wide-area, region and county integrated distributed self-adaptive intelligent reactive voltage automatic control system with region and county integrated intelligent maintenance and high-availability reactive voltage control strategies is not slow enough.
Disclosure of Invention
In view of the above, the invention aims to provide an integrated distributed adaptive reactive voltage automatic control method, which can ensure safe, efficient and stable operation of a power grid, improve voltage qualification rate, reduce grid loss, prolong the service life of electrical equipment, and reduce labor intensity of regulation and control personnel.
The invention is realized by adopting the following scheme: an integrated distributed self-adaptive reactive voltage automatic control method comprises an automatic local regulation side reactive voltage control system AVC and more than one automatic county regulation side reactive voltage control systems AVC;
when the power grid network is normal, performing centralized operation and unified decision on the local dispatching side AVC service;
when the system is disconnected, the model is adaptively adjusted, and AVC (automatic voltage control) on each county side is in distributed operation and distributed decision;
when the power grid network is recovered, the intelligent synchronization of control and locking protection data is realized, and meanwhile, the centralized operation and unified decision of the local dispatching side AVC service are recovered, so that the reliability of the system is effectively improved;
the AVC service of the local dispatching side and the AVC service of the county dispatching side are operated and decided in a mode of combining a genetic algorithm and sensitivity analysis.
Furthermore, the plant stations related to the local side AVC comprise local plant stations, county plant stations and wind farms, and are used for performing voltage correction control, power factor correction control and network loss optimization control; the upper-level dispatching is coordinated to finish the voltage reactive layered control by changing the output of a controllable reactive power supply in the power grid, switching of reactive compensation equipment and adjusting of a transformer tap.
Furthermore, the plant stations related to the AVC of the county side comprise the plant stations governed by the county side for performing voltage correction control, power factor correction control and network loss optimization control; the upper-level dispatching is coordinated to finish the voltage reactive layered control by changing the output of a controllable reactive power supply in the power grid, switching of reactive compensation equipment and adjusting of a transformer tap.
Further, the decision is made by combining the AVC service of the local-scale and the AVC service of the county-scale by adopting a genetic algorithm and sensitivity analysis, and the method specifically comprises the following steps:
step S1: defining an initial device: selecting an operable capacitor and a main transformer from all capacitors and main transformers in the out-of-limit gateway;
wherein, the selection conditions of the capacitor are as follows: the method is not forbidden, and the method participates in AVC control, the action times do not exceed the daily action time upper limit, the rated capacity is in a reasonable range, the sensitivity of the capacitor to the out-of-limit power factor is greater than a set sensitivity threshold value, and the sensitivity of the capacitor to the out-of-limit monitoring point voltage is greater than the set sensitivity threshold value;
wherein, the selection condition of the main transformer is as follows: the method is not forbidden, and the method participates in AVC control, the action times do not exceed the daily action time upper limit, the tap positions are reasonable, on-load voltage regulation is carried out, the sensitivity of a main transformer to the out-of-limit power factor is greater than a set sensitivity threshold value, and the sensitivity of the main transformer to the out-of-limit monitoring point voltage is greater than the set sensitivity threshold value;
step S2: population initialization: forming an initial population, setting the population scale to be a fixed value larger than the total number of initial equipment, and taking the operable capacitance switching state and the current gear of a main transformer as control variables; the method for initializing the population comprises the following steps: the current capacitance switching state and the main gear are used as initial values of a first individual, each of other individuals only generates a change of a control variable, the other control variables are the same as the first individual, the change principle of the control variables is a capacitance inversion state, the main gear takes a random number within the range of upper and lower gear limits, and the upper and lower gear limits of the main gear are respectively a current gear lifting gear;
step S3: setting a fitness function:
wherein Fv is the value of the overall voltage off-limit penalty function,
the power factor of the gateway is the out-of-limit penalty function value;
in the formula (I), the compound is shown in the specification,
is a power factor, VimaxAnd ViminRespectively are the upper limit and the lower limit of the voltage,
and
respectively the upper and lower limits of the power factor;
if the gateway power factor is out-of-limit seriously or a new gateway power factor is out-of-limit due to the voltage adjusting scheme, the voltage out-of-limit penalty function value Fv is increased by 100; if the scheme of adjusting the power factor causes the voltage of the monitoring point under the gateway to be increased beyond the limit, the voltage in the dead zone becomes the out-of-limit, the voltage is newly generated beyond the limit, and the power factor penalty function value
Increased by 100.
Step S4: selecting the individual with the minimum fitness value to be inherited to the next generation group, and assigning the fitness extreme value of the corresponding individual to the global fitness extreme value; then, carrying out cross and variation iteration operations in sequence, and recalculating the fitness value;
wherein, the convergence speed of the genetic algorithm is directly influenced by the quality of the crossover operator. The invention adopts an improved crossover operator similarity adjustment method as a crossover operator, determines whether to carry out crossover operation according to the similarity between individuals, and the crossover operation process is as follows: each individual r1 in each generation of population has the chance of crossing with other individuals, the crossing probability is set to be 1, a random function generates the r2 th individual, and the crossing object determined as r1 is r 2. The similarity s of the individuals r1 and r2 is calculated as l/n, where l is the length of the longest common string of control variables of r1 and r2, and n is the length of the string of control variables. Given the threshold P: 0.5, two individuals can be crossed only if their similarity s is less than P. The crossing method comprises the following steps: randomly determining a crossing position d by adopting multipoint crossing, and if d is at the capacitance state position of the control variable string, crossing all variables behind the positions d of r1 and r 2; if d is in the main shift position of the control variable string, all variables r1 and r2 preceding position d cross each other.
The variation operation is performed by adopting an automatic fuzzy adjustment method, so that the maximum fitness value, the minimum fitness value and the average fitness value in a group after one iteration are facilitated, a variation adaptation factor H1 is solved, a threshold Cr of the variation probability is set according to H1, the variation probability p of a capacitor and the variation probability q of a main gear are determined by a random number, when p is smaller than Cr, single-point variation of the capacitor is performed, and when q is smaller than Cr, single-point variation of the main gear is performed.
Step S5: judging whether a termination condition is met: the iteration times exceed a preset value or the optimal fitness value reaches 0; if so, obtaining the optimal individual, and entering the step S6, otherwise, returning to the step S1;
step S6: and (4) the equipment with the minimum fitness value is quitted from running to obtain an optimal control scheme.
Further, in step S1,
the sensitivity of the device to the out-of-limit power factor is calculated using the following equation:
fOptCos＝fk1×((cosmean-cosdes)2-(cosnew-cosdes)2)；
in the formula, cosmeanRepresents the mean value of the current gateway power factor, cosnewRepresents the gateway power factor value after the operation of the equipment: cos (chemical oxygen demand)new＝cosmean+ deltacos, where deltacos represents the amount of change in power factor after device operation; cos (chemical oxygen demand)desRepresenting the gateway power factor target value: cos (chemical oxygen demand)des＝cosdnlnt+fk5×(cosuplnt-cosdnlnt)，fk5＝(Pnow-minP)/(maxP-minP); wherein, PnowIndicating the current active power of the whole networkThe rate, maxP, represents the maximum value of total net work in the historical data of the last three days, cosuplntRepresenting the upper limit of the current gateway power factor; cos (chemical oxygen demand)dnlntRepresenting the lower limit of the current gateway power factor; fk1 power factor index coefficient;
the sensitivity of the device to the out-of-limit monitor point voltage is calculated using the following equation:
wherein, fOptVsignal＝fk2×((Vmean-Vdes)2-(Vnew-Vdes)2)；
In the formula, Vnes＝Vmean+ deltaV, representing the voltage value of the monitoring point bus after the equipment is operated; vmeanThe average voltage value of the bus of the current monitoring point is represented, deltaV represents the voltage variation of the bus of the current monitoring point after the equipment is operated, and nWpNum represents the number of monitoring points under the gateway; vdes＝Vdnlnt+fk5×(Vuplnt-Vdnlnt)，fk5＝(Pnow-minP)/(maxP-minP)，PnowRepresenting the current active power of the whole network, wherein maxP represents the maximum value of the active power of the whole network in the historical data of the last three days, and minP represents the minimum value of the active power of the whole network in the historical data of the last three days; fk2 denotes a power factor index coefficient; vuplntRepresents the upper limit of the current gateway voltage, VdnlntRepresents the current gateway voltage lower limit;
wherein the equipment is a capacitor or a main transformer.
Preferably, the sensitivity analysis is used for solving the problem of prevention control, and the grouping calculation is carried out according to the type and the capacity of the control equipment, wherein considering that the capacity of the capacitor is large and the influence of the capacitor on the power factor and the network loss has strong nonlinearity, the sensitivity analysis of the capacitor adopts one-by-one projection/cutting scanning calculation; the main transformer is grouped and only adopts ascending or descending group scanning calculation, and simultaneously, the synchronous adjustment of the parallel operation transformers is considered.
Preferably, in consideration of the characteristics of regional reactive voltage control, a sensitivity analysis method is selected and adopted, and grouping calculation is carried out according to the type and the capacity of control equipment, so that the calculation speed can be increased, and the requirement of a system on the speed can be met. The sensitivity analysis method is simple, the calculation speed is high, the convergence problem does not exist, and the real-time requirement of prevention and control is met.
Preferably, the method further comprises correcting the bus voltage, correcting the gateway power factor and optimizing the network loss.
The bus voltage correction specifically comprises the following steps:
step S21: clearing the scheme generated last time;
step S22: checking the voltage out-of-limit condition of a plant which participates in AVC control and has a working mode other than exit;
step S23: finding out-of-limit monitoring points, traversing all capacitors, reactors, magnetically controlled reactors in the subsystem, transformers of the station and a higher station of the station, and checking the availability of the equipment;
step S24: if available equipment exists, filtering out the equipment causing out-of-limit after operation, and calculating the comprehensive index of each equipment; otherwise, alarming; wherein the calculation of the comprehensive index adopts the following formula:
fOptCoef＝fOptCos+fOptV-fDeltaLoss+1/fFee；
in the formula, fOptCos represents a power factor index value, fOptV is a voltage index value, fDeltaLoss is the network loss of the equipment after operation, and fFee is the control cost of the equipment;
step S25: and sequencing the comprehensive indexes from large to small, generating a correction voltage scheme, and returning to the step S21.
Wherein, the correcting gateway power factor specifically comprises the following steps:
step S31: traversing all the sub-gateways to find out the out-of-limit gateway;
step S32: traversing all capacitors, reactors and magnetically controlled reactors in the out-of-limit gate; and judging the availability of the equipment;
step S33: if no available equipment exists, alarming; otherwise, filtering out the equipment out of limit after operation, calculating the comprehensive indexes of the other equipment, and sequencing the comprehensive indexes from large to small;
step S34: if operable equipment exists, generating a power factor capacitance or reactance scheme of the correction gateway; otherwise, selecting a capacitor, a reactor or a magnetically controlled reactor with the comprehensive index arranged at the head, and searching for a transformer capable of forming combined operation with the capacitor, the reactor or the magnetically controlled reactor;
step S35: finding an available transformer from transformers which can form combined operation with capacitors, reactors or magnetically controlled reactors arranged at the head position, and generating a correction gateway power factor combination scheme; and if the available transformer cannot be found, alarming.
The method for optimizing the network loss specifically comprises the following steps:
step S41: traversing all the sub-gateways, traversing all the capacitors, the reactors and the magnetically controlled reactors in the gateways, and judging the availability of the equipment;
step S42: if no available equipment exists, returning to the step S41 for re-traversal; otherwise, adding the available equipment into the temporary array;
step S43: filtering out the equipment which is out of limit after operation, and calculating the comprehensive indexes of other equipment; if no available equipment exists after the out-of-limit equipment after the filtering operation, returning to the step S41; otherwise, sorting the comprehensive indexes from big to small;
step S44: and selecting the equipment with the maximum comprehensive index to generate an optimized network loss scheme.
Compared with the prior art, the invention has the following beneficial effects:
1. when the power grid network is normal, the local dispatching side AVC service performs centralized operation and unified decision; when the system is disconnected, the model is adaptively adjusted, and AVC distributed operation and distributed decision are carried out in each county; when the power grid network is recovered, the control and locking protection data are intelligently synchronized, and the reliability of the system can be effectively improved.
2. The invention adopts a practical algorithm combining an optimized control algorithm based on a sensitivity matrix analysis method and a reactive voltage automatic control algorithm based on a genetic algorithm, and can ensure the generation of a reactive voltage automatic control strategy which is feasible and high in usability.
Drawings
Fig. 1 is a schematic system deployment diagram according to an embodiment of the present invention.
FIG. 2 is a flow chart of a method combining genetic algorithm and sensitivity analysis according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of correcting the bus voltage according to the embodiment of the present invention.
Fig. 4 is a schematic flow chart of correcting gateway power factor in the embodiment of the present invention.
Fig. 5 is a schematic flow chart of optimizing the network loss in the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides an integrated distributed adaptive reactive voltage automatic control method, which includes a local-regulation-side reactive voltage automatic control system AVC and more than one county-regulation-side reactive voltage automatic control systems AVC;
when the power grid network is normal, performing centralized operation and unified decision on the local dispatching side AVC service;
when the system is disconnected, the model is adaptively adjusted, and AVC (automatic voltage control) on each county side is in distributed operation and distributed decision;
when the power grid network is recovered, the intelligent synchronization of control and locking protection data is realized, and meanwhile, the centralized operation and unified decision of the local dispatching side AVC service are recovered, so that the reliability of the system is effectively improved;
the AVC service of the local dispatching side and the AVC service of the county dispatching side are operated and decided in a mode of combining a genetic algorithm and sensitivity analysis.
In this embodiment, the plant stations related to the local side AVC include a local plant station, each county plant station, and an electric farm, and are used for performing voltage correction control, power factor correction control, and network loss optimization control; the upper-level dispatching is coordinated to finish the voltage reactive layered control by changing the output of a controllable reactive power supply in the power grid, switching of reactive compensation equipment and adjusting of a transformer tap.
In this embodiment, the plant stations related to the AVC at the county side include the plant stations governed by the county side, and are used for performing voltage correction control, power factor correction control and network loss optimization control; the upper-level dispatching is coordinated to finish the voltage reactive layered control by changing the output of a controllable reactive power supply in the power grid, switching of reactive compensation equipment and adjusting of a transformer tap.
As shown in fig. 2, in this embodiment, the deciding of the AVC at local-level and AVC at county-level side by combining the genetic algorithm and the sensitivity analysis specifically includes the following steps:
step S1: defining an initial device: selecting an operable capacitor and a main transformer from all capacitors and main transformers in the out-of-limit gateway;
wherein, the selection conditions of the capacitor are as follows: the method is not forbidden, and the method participates in AVC control, the action times do not exceed the daily action time upper limit, the rated capacity is in a reasonable range, the sensitivity of the capacitor to the out-of-limit power factor is greater than a set sensitivity threshold value, and the sensitivity of the capacitor to the out-of-limit monitoring point voltage is greater than the set sensitivity threshold value;
wherein, the selection condition of the main transformer is as follows: the method is not forbidden, and the method participates in AVC control, the action times do not exceed the daily action time upper limit, the tap positions are reasonable, on-load voltage regulation is carried out, the sensitivity of a main transformer to the out-of-limit power factor is greater than a set sensitivity threshold value, and the sensitivity of the main transformer to the out-of-limit monitoring point voltage is greater than the set sensitivity threshold value;
step S2: population initialization: forming an initial population, setting the population scale to be a fixed value larger than the total number of initial equipment, and taking the operable capacitance switching state and the current gear of a main transformer as control variables; the method for initializing the population comprises the following steps: the current capacitance switching state and the main gear are used as initial values of a first individual, each of other individuals only generates a change of a control variable, the other control variables are the same as the first individual, the change principle of the control variables is a capacitance inversion state, the main gear takes a random number within the range of upper and lower gear limits, and the upper and lower gear limits of the main gear are respectively a current gear lifting gear;
step S3: setting a fitness function:
wherein Fv is the value of the overall voltage off-limit penalty function,
the power factor of the gateway is the out-of-limit penalty function value;
in the formula (I), the compound is shown in the specification,
is a power factor, VimaxAnd ViminRespectively are the upper limit and the lower limit of the voltage,
and
respectively the upper and lower limits of the power factor;
if the gateway power factor is out-of-limit seriously or a new gateway power factor is out-of-limit due to the voltage adjusting scheme, the voltage out-of-limit penalty function value Fv is increased by 100; if the scheme of adjusting the power factor causes the voltage of the monitoring point under the gateway to be increased beyond the limit, the voltage in the dead zone becomes the out-of-limit, the voltage is newly generated beyond the limit, and the power factor penalty function value
Increased by 100.
Step S4: selecting the individual with the minimum fitness value to be inherited to the next generation group, and assigning the fitness extreme value of the corresponding individual to the global fitness extreme value; then, carrying out cross and variation iteration operations in sequence, and recalculating the fitness value;
wherein, the convergence speed of the genetic algorithm is directly influenced by the quality of the crossover operator. The invention adopts an improved crossover operator similarity adjustment method as a crossover operator, determines whether to carry out crossover operation according to the similarity between individuals, and the crossover operation process is as follows: each individual r1 in each generation of population has the chance of crossing with other individuals, the crossing probability is set to be 1, a random function generates the r2 th individual, and the crossing object determined as r1 is r 2. The similarity s of the individuals r1 and r2 is calculated as l/n, where l is the length of the longest common string of control variables of r1 and r2, and n is the length of the string of control variables. Given the threshold P: 0.5, two individuals can be crossed only if their similarity s is less than P. The crossing method comprises the following steps: randomly determining a crossing position d by adopting multipoint crossing, and if d is at the capacitance state position of the control variable string, crossing all variables behind the positions d of r1 and r 2; if d is in the main shift position of the control variable string, all variables r1 and r2 preceding position d cross each other.
The variation operation is performed by adopting an automatic fuzzy adjustment method, so that the maximum fitness value, the minimum fitness value and the average fitness value in a group after one iteration are facilitated, a variation adaptation factor H1 is solved, a threshold Cr of the variation probability is set according to H1, the variation probability p of a capacitor and the variation probability q of a main gear are determined by a random number, when p is smaller than Cr, single-point variation of the capacitor is performed, and when q is smaller than Cr, single-point variation of the main gear is performed.
Step S5: judging whether a termination condition is met: the iteration times exceed a preset value or the optimal fitness value reaches 0; if so, obtaining the optimal individual, and entering the step S6, otherwise, returning to the step S1;
step S6: and (4) the equipment with the minimum fitness value is quitted from running to obtain an optimal control scheme.
Further, in step S1,
the sensitivity of the device to the out-of-limit power factor is calculated using the following equation:
fOptCos＝fk1×((cosmean-cosdes)2-(cosnew-cosdes)2)；
in the formula, cosmeanRepresents the mean value of the current gateway power factor, cosnewRepresents the gateway power factor value after the operation of the equipment: cos (chemical oxygen demand)new＝cosmean+ deltacos, where deltacos represents the amount of change in power factor after device operation; cos (chemical oxygen demand)desRepresenting the gateway power factor target value: cos (chemical oxygen demand)des＝cosdnlnt+fk5×(cosuplnt-cosdnlnt)，fk5＝(Pnow-minP)/(maxP-minP); wherein, PnowRepresents the current active power of the whole network, maxP represents the maximum value of the active power of the whole network in the historical data of the last three days, cosuplntRepresenting the upper limit of the current gateway power factor; cos (chemical oxygen demand)dnlntRepresenting the lower limit of the current gateway power factor; fk1 power factor index coefficient;
the sensitivity of the device to the out-of-limit monitor point voltage is calculated using the following equation:
wherein, fOptVsignal＝fk2×((Vmean-Vdes)2-(Vnew-Vdes)2)；
In the formula, Vnes＝Vmean+ deltaV, representing the voltage value of the monitoring point bus after the equipment is operated; vmeanThe average voltage value of the bus of the current monitoring point is represented, deltaV represents the voltage variation of the bus of the current monitoring point after the equipment is operated, and nWpNum represents the number of monitoring points under the gateway; vdes＝Vdnlnt+fk5×(Vuplnt-Vdnlnt)，fk5＝(Pnow-minP)/(maxP-minP)，PnowRepresenting the current active power of the whole network, wherein maxP represents the maximum value of the active power of the whole network in the historical data of the last three days, and minP represents the minimum value of the active power of the whole network in the historical data of the last three days; fk2 denotes a power factor index coefficient; vuplntRepresents the upper limit of the current gateway voltage, VdnlntRepresents the current gateway voltage lower limit;
wherein the equipment is a capacitor or a main transformer.
Preferably, in this embodiment, the sensitivity analysis is used to solve the problem of preventive control, and the group calculation is performed according to the type and capacity of the control device, wherein considering that the capacity of the capacitor is relatively large and the influence of the capacitor on the power factor and the network loss is relatively strong non-linear, the sensitivity analysis of the capacitor adopts one-by-one projection/cut scanning calculation; the main transformer is grouped and only adopts ascending or descending group scanning calculation, and simultaneously, the synchronous adjustment of the parallel operation transformers is considered.
Preferably, in this embodiment, in consideration of the characteristics of regional reactive voltage control, a sensitivity analysis method is selected and adopted, and group calculation is performed according to the type and capacity of the control equipment, so that the calculation speed can be increased, and the requirement of the system on the speed can be met. The sensitivity analysis method is simple, the calculation speed is high, the convergence problem does not exist, and the real-time requirement of prevention and control is met.
Preferably, the present embodiment further comprises rectifying the bus voltage, rectifying the gateway power factor, and optimizing the network loss.
As shown in fig. 3, the step of correcting the bus voltage specifically includes the following steps:
step S21: clearing the scheme generated last time;
step S22: checking the voltage out-of-limit condition of a plant which participates in AVC control and has a working mode other than exit;
step S23: finding out-of-limit monitoring points, traversing all capacitors, reactors, magnetically controlled reactors in the subsystem, transformers of the station and a higher station of the station, and checking the availability of the equipment;
step S24: if available equipment exists, filtering out the equipment causing out-of-limit after operation, and calculating the comprehensive index of each equipment; otherwise, alarming; wherein the calculation of the comprehensive index adopts the following formula:
fOptCoef＝fOptCos+fOptV-fDeltaLoss+1/fFee；
in the formula, fOptCos represents a power factor index value, fOptV is a voltage index value, fDeltaLoss is the network loss of the equipment after operation, and fFee is the control cost of the equipment;
step S25: and sequencing the comprehensive indexes from large to small, generating a correction voltage scheme, and returning to the step S21.
As shown in fig. 4, the correcting gateway power factor specifically includes the following steps:
step S31: traversing all the sub-gateways to find out the out-of-limit gateway;
step S32: traversing all capacitors, reactors and magnetically controlled reactors in the out-of-limit gate; and judging the availability of the equipment;
step S33: if no available equipment exists, alarming; otherwise, filtering out the equipment out of limit after operation, calculating the comprehensive indexes of the other equipment, and sequencing the comprehensive indexes from large to small;
step S34: if operable equipment exists, generating a power factor capacitance or reactance scheme of the correction gateway; otherwise, selecting a capacitor, a reactor or a magnetically controlled reactor with the comprehensive index arranged at the head, and searching for a transformer capable of forming combined operation with the capacitor, the reactor or the magnetically controlled reactor;
step S35: finding an available transformer from transformers which can form combined operation with capacitors, reactors or magnetically controlled reactors arranged at the head position, and generating a correction gateway power factor combination scheme; and if the available transformer cannot be found, alarming.
As shown in fig. 5, the optimizing the network loss specifically includes the following steps:
step S41: traversing all the sub-gateways, traversing all the capacitors, the reactors and the magnetically controlled reactors in the gateways, and judging the availability of the equipment;
step S42: if no available equipment exists, returning to the step S41 for re-traversal; otherwise, adding the available equipment into the temporary array;
step S43: filtering out the equipment which is out of limit after operation, and calculating the comprehensive indexes of other equipment; if no available equipment exists after the out-of-limit equipment after the filtering operation, returning to the step S41; otherwise, sorting the comprehensive indexes from big to small;
step S44: selecting the equipment with the maximum comprehensive index to generate an optimized network loss scheme
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (4)
1. An integrated distributed self-adaptive reactive voltage automatic control method is characterized by comprising the following steps: the system comprises an automatic local-regulation-side reactive voltage control system AVC and more than one automatic county-regulation-side reactive voltage control systems AVC;
when the power grid network is normal, performing centralized operation and unified decision on the local dispatching side AVC service;
when the system is disconnected, the model is adaptively adjusted, and AVC (automatic voltage control) on each county side is in distributed operation and distributed decision;
when the power grid network is recovered, the intelligent synchronization of control and locking protection data is realized, and meanwhile, the centralized operation and unified decision of the local dispatching side AVC service are recovered, so that the reliability of the system is effectively improved;
the AVC service of the local dispatching side and the AVC service of the county dispatching side are operated and decided in a mode of combining a genetic algorithm and sensitivity analysis;
AVC of local tone side and AVC service of county tone side are adopted
The method for making the decision by combining the genetic algorithm and the sensitivity analysis specifically comprises the following steps:
step S1: defining an initial device: selecting an operable capacitor and a main transformer from all capacitors and main transformers in the out-of-limit gateway;
wherein, the selection conditions of the capacitor are as follows: the method is not forbidden, and the method participates in AVC control, the action times do not exceed the daily action time upper limit, the rated capacity is in a reasonable range, the sensitivity of the capacitor to the out-of-limit power factor is greater than a set sensitivity threshold value, and the sensitivity of the capacitor to the out-of-limit monitoring point voltage is greater than the set sensitivity threshold value;
wherein, the selection condition of the main transformer is as follows: the method is not forbidden, and the method participates in AVC control, the action times do not exceed the daily action time upper limit, the tap positions are reasonable, on-load voltage regulation is carried out, the sensitivity of a main transformer to the out-of-limit power factor is greater than a set sensitivity threshold value, and the sensitivity of the main transformer to the out-of-limit monitoring point voltage is greater than the set sensitivity threshold value;
step S2: population initialization: forming an initial population, setting the population scale to be a fixed value larger than the total number of initial equipment, and taking the operable capacitance switching state and the current gear of a main transformer as control variables; the method for initializing the population comprises the following steps: the current capacitance switching state and the main gear are used as initial values of a first individual, each of other individuals only generates a change of a control variable, the other control variables are the same as the first individual, the change principle of the control variables is a capacitance inversion state, the main gear takes a random number within the range of upper and lower gear limits, and the upper and lower gear limits of the main gear are respectively a current gear lifting gear;
step S3: setting a fitness function:
wherein Fv is the value of the overall voltage off-limit penalty function,
the power factor of the gateway is the out-of-limit penalty function value;
in the formula (I), the compound is shown in the specification,
is a power factor, VmaxAnd VminRespectively are the upper limit and the lower limit of the voltage,
and
respectively the upper and lower limits of the power factor;
step S4: selecting the individual with the minimum fitness value to be inherited to the next generation group, and assigning the fitness extreme value of the corresponding individual to the global fitness extreme value; then, carrying out cross and variation iteration operations in sequence, and recalculating the fitness value;
step S5: judging whether a termination condition is met: the iteration times exceed a preset value or the optimal fitness value reaches 0; if so, obtaining the optimal individual, and entering the step S6, otherwise, returning to the step S1;
step S6: the equipment with the minimum fitness value is quitted from running to obtain an optimal control scheme;
in step S1, the sensitivity of the device to the out-of-limit power factor is calculated using the following equation:
fOptCos＝fk1×((cosmean-cosdes)2-(cosnew-cosdes)2)；
in the formula, cosmeanRepresents the mean value of the current gateway power factor, cosnewRepresents the gateway power factor value after the operation of the equipment: cos (chemical oxygen demand)new＝cosmean+ deltacos, where delta cos represents the amount of change in power factor after device operation; cos (chemical oxygen demand)desRepresenting the gateway power factor target value: cos (chemical oxygen demand)des＝cosdnlnt+fk5×(cosuplnt-cosdnlnt)，fk5＝(Pnow-minP)/(maxP-minP); wherein, PnowRepresents the current active power of the whole network, maxP represents the maximum value of the active power of the whole network in the historical data of the last three days, cosuplntRepresenting the upper limit of the current gateway power factor; cos (chemical oxygen demand)dnlntRepresenting the lower limit of the current gateway power factor; fk1 power factor index coefficient;
the sensitivity of the device to the out-of-limit monitor point voltage is calculated using the following equation:
wherein, fOptVsignal＝fk2×((Vmean-Vdes)2-(Vnew-Vdes)2)；
In the formula, Vnew＝Vmean+ deltaV, representing the voltage value of the monitoring point bus after the equipment is operated; vmeanThe average voltage value of the bus of the current monitoring point is represented, deltaV represents the voltage variation of the bus of the current monitoring point after the equipment is operated, and nWpNum represents the number of monitoring points under the gateway; vdes＝Vdnlnt+fk5×(Vuplnt-Vdnlnt)，fk5＝(Pnow-minP)/(maxP-minP)，PnowRepresenting the current active power of the whole network, wherein maxP represents the maximum value of the active power of the whole network in the historical data of the last three days, and minP represents the minimum value of the active power of the whole network in the historical data of the last three days; fk2 denotes a power factor index coefficient; vuplntRepresents the upper limit of the current gateway voltage, VdnlntRepresents the current gateway voltage lower limit;
wherein the equipment is a capacitor or a main transformer.
2. The integrated distributed adaptive reactive voltage automatic control method according to claim 1, characterized in that: the stations related to the local side AVC comprise local stations, county stations and wind farms, and are used for performing voltage correction control, power factor correction control and network loss optimization control; the upper-level dispatching is coordinated to finish the voltage reactive layered control by changing the output of a controllable reactive power supply in the power grid, switching of reactive compensation equipment and adjusting of a transformer tap.
3. The integrated distributed adaptive reactive voltage automatic control method according to claim 1, characterized in that: the factory stations related to the AVC of the county side comprise factory stations governed by the county side and are used for performing voltage correction control, power factor correction control and network loss optimization control; the upper-level dispatching is coordinated to finish the voltage reactive layered control by changing the output of a controllable reactive power supply in the power grid, switching of reactive compensation equipment and adjusting of a transformer tap.
4. The integrated distributed adaptive reactive voltage automatic control method according to claim 1, characterized in that: the sensitivity analysis of the capacitor adopts one-by-one projection/cutting scanning calculation; the main transformer is grouped and only adopts ascending or descending group scanning calculation, and simultaneously, the synchronous adjustment of the parallel operation transformers is considered.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104410077A (en) * | 2014-12-18 | 2015-03-11 | 积成电子股份有限公司 | Improved genetic algorithm based multithreading voltage and reactive power optimization control method for electric power system |
| CN105515011A (en) * | 2015-12-04 | 2016-04-20 | 国网浙江省电力公司绍兴供电公司 | Coordination control method for combined operation of regional and county automatic voltage control (AVC) systems |
| CN105633975A (en) * | 2016-03-03 | 2016-06-01 | 国网安徽省电力公司芜湖供电公司 | AVC control system for distributed county power grid |
| JP5979404B1 (en) * | 2016-04-06 | 2016-08-24 | 富士電機株式会社 | Distributed power control method and control apparatus |
| CN106655207A (en) * | 2017-03-21 | 2017-05-10 | 国网山东省电力公司枣庄供电公司 | Power distribution network reactive power optimization system and method based on multi-data analysis |
-
2018
- 2018-06-28 CN CN201810687664.8A patent/CN108767870B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104410077A (en) * | 2014-12-18 | 2015-03-11 | 积成电子股份有限公司 | Improved genetic algorithm based multithreading voltage and reactive power optimization control method for electric power system |
| CN105515011A (en) * | 2015-12-04 | 2016-04-20 | 国网浙江省电力公司绍兴供电公司 | Coordination control method for combined operation of regional and county automatic voltage control (AVC) systems |
| CN105633975A (en) * | 2016-03-03 | 2016-06-01 | 国网安徽省电力公司芜湖供电公司 | AVC control system for distributed county power grid |
| JP5979404B1 (en) * | 2016-04-06 | 2016-08-24 | 富士電機株式会社 | Distributed power control method and control apparatus |
| CN106655207A (en) * | 2017-03-21 | 2017-05-10 | 国网山东省电力公司枣庄供电公司 | Power distribution network reactive power optimization system and method based on multi-data analysis |
Non-Patent Citations (1)
| Title |
|---|
| "基于EMS的分布式地县电网AVC控制策略";苏志朋 宋铭敏 汤大伟 赵晓莉;《电力***保护与控制》;20170316;137-141 * |
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