CN110350510A - A kind of power distribution network service restoration method considering failure disturbance degree - Google Patents
A kind of power distribution network service restoration method considering failure disturbance degree Download PDFInfo
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Abstract
The invention discloses a kind of power distribution network service restoration methods for considering failure disturbance degree, pass through analysis distribution network failure impact evaluation failure disturbance degree caused by load and grid structure, it divides failure menace level and matches service restoration model, execute failure recovering algorithm and generate service restoration scheme.The present invention passes through the intrinsic contradictions between the assessment of introducing failure disturbance degree and Controlling model Adaptive matching mechanism coordination service restoration algorithm speed and tactful quality, in power grid occurrence of large-area power loss, system regard the power supply of fast quick-recovery load as unique objects, power supply strategy is turned using particle swarm algorithm search, exits search after obtaining the feasible solution for restoring global load power supply；The load loss of outage caused by electric network fault is relatively light and when will not influence important load power supply, it regard the runnability after load power loss amount, control cost, reconfiguration of electric networks as evaluation index simultaneously, optimal case is searched using multi-objective particle swarm algorithm, promotes the tactful quality of failure recovering algorithm.
Description
Technical field
The invention belongs to power distribution network service restoration technical field and power system automation technology fields, are related to power distribution network
Service restoration, specifically it is a kind of consider failure disturbance degree power distribution network service restoration method.
Background technique
Along with the development of social economy, the highly reliable operation problem of power distribution network is in widespread attention.Service restoration is to match
The core function of power grid self-healing control is completed to turn power supply to power loss load by the form of reconstructed network after electric network fault,
It is of great significance to power grid power supply reliability is improved.
Power distribution network service restoration need to consider power loss load, switch number of operations, counterweight balance, quality of voltage, net as a whole
The composite factors such as damage, are a complicated multiple target multiple constraint nonlinear combinatorial optimization problems.In recent years, domestic and foreign scholars surround
Power distribution network service restoration project carried out a large amount of research work, the solution of proposition mainly includes that traditional mathematics optimization is calculated
Method, heuritic approach, intelligent algorithm etc..Intelligent algorithm is got the attention with its particular advantages, such as Yu Juanya
Deng (Yu Juanya, Wang Zengping, Sun Jie, Yang Guosheng are based on branch exchange --- and the distribution network failure of particle swarm algorithm restores [J] electricity
Force system is protected and control, 2014,42 (13): 95-99.) thought of simulated annealing is incorporated into radix-2 algorithm inertia weight
In dynamic adjustment, and judge that the similitude of particle effectively avoids particle progress TSP question using population's fitness variance
Particle swarm algorithm easily " precocity " the problem of；In addition, particle swarm algorithm is combined with branch exchange method, population is effectively improved
The search speed of algorithm.(power distribution network multiple target power supply of Dai Zhihui, Chong Zhiqiang, the Jiao Yanjun containing distributed generation resource is extensive by Dai Zhihui etc.
Multiple [J] electric power network technique, 2014,38 (7): 1959-1965.) it is obtained using binary particle swarm algorithm search Pareto non-domination solution
Service restoration scheme, method produce specific service restoration scheme by dispatcher's according to circumstances flexible choice.
There are intrinsic contradictions between the solving speed of power distribution network service restoration algorithm and the quality of optimal solution, increase Controlling model
Evaluation index by using sacrifice strategy formation speed as cost, excessively pursuit algorithm the speed of service also not can guarantee strategy
Reasonability.The relationship of said two devices is not considered in research at this stage as a whole, and system generates control using unified mathematical model
Strategy is often attended to one thing and lose sight of another in the choice of speed and quality, and service restoration function is caused to be difficult to meet practical power distribution network difference
Turn power reguirements under fault condition.
Summary of the invention
The technical problems to be solved by the present invention are: proposing a kind of power distribution network service restoration side for considering failure disturbance degree
Method, by introducing the assessment of failure disturbance degree and Controlling model Adaptive matching mechanism coordination service restoration algorithm speed and strategy
Intrinsic contradictions between quality optimize service restoration to the adaptability of power grid difference operating status and fault condition.
The technical solution that the present invention is taken in order to solve the technical problem is as follows:
A kind of power distribution network service restoration method considering failure disturbance degree, includes the following steps:
(1) distribution network failure disturbance degree is assessed: from failure loss of outage caused by load and to the destruction of grid structure
The fault severity level of two level analyzing power distribution networks of situation；
(2) service restoration model adaptation matches: being based on failure disturbance degree, the failure of power distribution network is divided into I, II, III
Grade, according to the grade preference of service restoration evaluation index and fault level match control model；
(3) failure recovering algorithm is executed, service restoration scheme is generated.
In step (1), the distribution network failure disturbance degree assessment determines that distribution network failure influences using following method
Degree: using power loss load as load loss of outage in short-term, with the characterization grid structure destruction of faulty line institute on-load number of nodes
Degree determines the serious value of failure with the mode of weighting summation, failure is seriously worth tight with the failure under most serious fault condition
Weight values are divided by, and failure disturbance degree is obtained.
In step (2), the service restoration model adaptation matching is to match fault recovery mould based on fault level
For type, for I grades of failures, using power loss load as evaluation index, service restoration model is that single goal nonlinear combination is excellent
Change problem turns power supply strategy using binary particle swarm algorithm search, can restore the feasible of all power loss load power supplies when obtaining
Output is final result when solution；For II grades of failures, consider control cost, using power loss load and switch number of operations as
Evaluation index uses the mode of weighting summation by service restoration model conversation for single-object problem, using binary system particle
Group's algorithm, which generates, turns power supply strategy；For III level failure, consider the runnability of power grid after fault recovery, by power loss load,
Number of operations, counterweight balance, quality of voltage, network loss are switched as evaluation index, service restoration model is multi-objective optimization question,
Optimal power recovery policy is searched using multi-objective particle swarm algorithm.
The beneficial effects of the present invention are:
The invention proposes a kind of power distribution network service restoration methods for considering failure disturbance degree, by introducing failure disturbance degree
Intrinsic lance between assessment and Controlling model Adaptive matching mechanism effective coordination service restoration algorithm speed and tactful quality
Fail-over policy quickly generating in power grid catastrophe failure has been effectively ensured in shield, optimizes service restoration function to electricity
Net the adaptability of different operating statuses and fault condition.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is that the distribution network failure based on binary particle swarm algorithm restores flow chart；
Fig. 3 is that the distribution network failure based on multi-objective particle swarm algorithm restores flow chart；
Fig. 4 is the test circuit simplification figure of power distribution network service restoration.
Specific embodiment
The present invention will be further explained with reference to the accompanying drawing and explanation:
It is as shown in Fig. 1 a kind of process for the power distribution network service restoration method for considering failure disturbance degree disclosed by the invention
Figure.The present invention is by introducing the assessment of failure disturbance degree and Controlling model Adaptive matching mechanism, effective coordination service restoration algorithm
Intrinsic contradictions between speed and tactful quality, assessment failure disturbance degree divides fault level after electric network fault, extensive according to powering
The grade preference of multiple evaluation index matches fault recovery model with fault level, executes corresponding failure recovering algorithm and generates power supply
Recovery scheme.The specific implementation of each step is described below:
1, distribution network failure disturbance degree is assessed:
(1) load loss of outage:
Active power of the load loss of outage using the load in moment t characterizes, and mathematic(al) representation is as follows:
In formula, n is power loss load number；PNiIt is the standard value that the active power of load i obtains after normalized, PNi
Calculation formula are as follows:
In formula, PiFor the active power of load, PmaxFor the active power of user maximum in network, PminFor minimum wattful power
Rate.
(2) extent of the destruction of the failure to grid structure:
Failure is related with the quantity of faulty line institute on-load node to the extent of the destruction of grid structure, faulty line institute band
Load bus quantity is more, and the route is more important in a network, more serious to the destruction of rack after breaking down.
Calculation formula of the failure to the extent of the destruction IMP of grid structure are as follows:
In formula: NkFor the quantity of route k institute on-load node, N is the sum of load bus.
(3) distribution network failure disturbance degree calculates:
Distribution network failure influence value is characterized as to the weighting of load loss of outage and rack extent of the destruction in short-term, is calculated public
Formula is shown below:
fk=α Lloss+βIMPk
In formula: LlossFor load loss of outage in short-term；IMPkIt is faulty line k to the extent of the destruction of grid structure；α is with β
Weight coefficient, alpha+beta=1.
Failure influence value is made into standardization and normalized, calculation formula are shown below:
In formula: f%For the failure influence value after standardization；fmaxFor most serious failure influence value.
2, service restoration model adaptation matching mechanisms:
Construction and operation experience based on power distribution network, analyze power distribution network service restoration objective function in each evaluation index it is preferential
Grade sequence selects evaluation index, coordinate fault recovery algorithms strategy generating speed and control quality according to electric network fault disturbance degree
Between contradiction.
(1) mathematical model of power distribution network service restoration:
The objective function of power distribution network service restoration model specifically includes that power loss load f1, switch number of operations f2, load
Balance f3, quality of voltage f4, network loss f5；
The constraint condition for needing to meet includes: the radial topological constraints of 1. power distribution network；2. line current and transformer hold
Amount constraint；3. the node voltage of power distribution network constrains；
In conjunction with power distribution network service requirement and practical experience, provide the control priority of objective function evaluation index: power loss is negative
Lotus amount f1The system of reflecting fails to turn by service restoration function for loss caused by load, is most important evaluation index；
Switch number of operations f2It is the control indicator of costs, the number of operations for reducing switch to the maximum extent is to promote power distribution network self-healing control
The key of system cruising ability；Counterweight balance f3, quality of voltage f4, network loss f5It is the running performance index of power grid after fault recovery,
Priority is lower than f1With f2。
(2) the Adaptive matching rule of service restoration model:
Distribution network failure grade is divided according to the failure disturbance degree evaluation result of step 1, according to fault level and target letter
Number evaluation index simplifies principle in the same direction and determines service restoration control mode:
I grades of fault level (most serious situation): the failure disturbance degree f that step 1 determines%Value range: 70% < f%≤
100%, distribution network failure is more serious at this time, need to accelerate service restoration, system is to restore power loss load power supply f1Refer to for evaluation
Mark, power distribution network service restoration model at this time is single goal nonlinear combinatorial optimization problem, is searched using binary particle swarm algorithm
Service restoration strategy is sought, and i.e. output is final power supply when searching the feasible solution that can restore all power loss load power supplies
Recovery policy, the arithmetic speed of boosting algorithm；
II grades of fault level: the failure disturbance degree f that step 1 determines%Value range: 55% < f%≤ 70%, match at this time
Electric network fault degree is serious not as good as I grades, control cost is considered, by power loss load f1With switch number of operations f2Evaluation is selected as to refer to
Mark, but be simplified control model, above-mentioned two index is handled by the way of weighting summation, it will power distribution network service restoration model
It is considered as the combinatorial optimization problem of single goal, service restoration strategy is searched using binary particle swarm algorithm；
Fault level III level: the failure disturbance degree f that step 1 determines%Value range: f%≤ 55%, power distribution network at this time
Fault degree is most light, it may be considered that the runnability of power grid after fault recovery, by above-mentioned f1~f5It is used as evaluation index, distribution
Net service restoration model is the nonlinear combinatorial optimization problem of multiple target, and it is extensive to search optimal power using multi-objective particle swarm algorithm
Multiple strategy.
3, failure recovering algorithm:
As described in step 2, according to fault degree difference, the present invention use two different failure recovering algorithms, for I,
II grades of failures, Controlling model is single goal nonlinear optimal problem, using binary particle swarm algorithm；For III level failure, control
Simulation is multi-objective nonlinear optimization problem, obtains service restoration scheme using multi-objective particle swarm algorithm.
(1) binary particle swarm algorithm:
Binary Particle Swarm Optimization (BPSO), algorithm indicate the position letter of particle with binary variable (0 and 1)
Breath indicates that particle position is 1 probability with particle rapidity size.Particle rapidity is bigger, then the probability that particle position is 1 is got over
Greatly；Speed is smaller, then the probability that particle position is 1 is also just smaller.Particle rapidity and location update formula are as follows:
In formula:WithRespectively indicate speed and position letter of the particle j in+1 iteration of kth on m-dimensional space
Breath；ω indicates inertia coeffeicent；c1And c2It is accelerator coefficient；r1And r2Indicate a random number between section [0,1]；Indicate position of the individual optimal value of particle j on m-dimensional space in preceding k iteration；For in preceding kth time
In iteration, population optimal value is in the position of m-dimensional space；S (x) is Sigmoid function, and rand indicates single order vector, each
Dimension component is the random number between [0,1].
It is as shown in Fig. 2 that distribution network failure based on binary particle swarm algorithm restores flow chart.
Specific solution procedure:
Step1: setting algorithm parameter, and input initial data (network structure and parameter, fault zone and power loss region)；
Step2: initialization of population obtains N number of feasible solution for meeting radial structure requirement, and calculates particle fitness letter
Numerical value；
Step3: according to formula, the speed of more new particle and position；
Step4: the radial judgement of network calculates the fitness function value for meeting the particle of radial structure；
Step5: judge whether population optimal value gbest is feasible solution (whether having restored global power), if feasible, directly
It connects output scheme and executes；Otherwise, continue Step6；
Step6: according to the relationship between fitness value, the optimal pbest of more new individual and global optimum gbest；
Step7: judging whether to reach preset stopping criterion (being usually arranged as maximum number of iterations), stops if meeting
It only requires, then terminates, otherwise, return to Step3.
(2) multi-objective particle swarm algorithm:
Multi-objective particle swarm algorithm (MOPSO) stores the Pareto non-domination solution in search process using external particles group,
And it constantly updates so that storing Pareto optimal solution in external particles group's Chinese style, while assessing outside using adaptive mesh method
The spatial position distribution density of non-domination solution in population selects the smallest Pareto non-domination solution of position distribution density as grain
The gbest of subgroup guides particle flight, while as often as possible saving Pareto non-domination solution, so that before Pareto is optimal
Edge is uniformly distributed.
Internal particle location update formula are as follows:
In formula: ω is inertia weight；c1And c2For accelerator coefficient；Random number of the rand between section [0,1]；Round is
Round up operator；ωmaxAnd ωminRespectively inertia weight maximum value and minimum value；tmaxFor maximum number of iterations.
It is as shown in Fig. 3 that distribution network failure based on multi-objective particle swarm algorithm restores flow chart.
Specific step is as follows:
Step1: setting algorithm parameter, and input initial data (network structure and parameter, fault zone and power loss region)；
Step2: internal initialization of population obtains the M feasible solutions for meeting radial structure requirement；
Step3: the particle for meeting radial requirement fitness value calculation: is substituted into multiple fitness functions (objective function)
In, calculate the fitness value of each particle；
Step4: external population recruitment: judging interparticle Pareto dominance relation in internal population according to fitness value,
Non-domination solution (noninferior solution) is put into external population；
Step5: the smallest particle of particle distribution density global optimum and individual optimal selection: is selected from external population
As global optimum gbest, the individual optimal value pbest of wherein each particle is determined in internal population；
Step6: internal population recruitment: by particle rapidity, position calculation formula, update in internal population the speed of particle and
Position；
Step7: judging whether to reach preset stopping criterion (being usually arranged as maximum number of iterations), stops if meeting
It only requires, then terminates, otherwise, return to Step3.
It is described in further detail with reference to the accompanying drawings of the specification with technical solution of the embodiment to the application.
Power distribution network simulation model as shown in Fig. 4, the system have 3 power supply nodes, 16 distribution lines, 13 load sections
Point.
Example 1:
Route 5 breaks down, after Fault Isolation 8., 9., 10.,WithLoad power loss, remaining is normal power supply area.
Step 1: distribution network failure disturbance degree assessment.Calculate loss of outage in short-term, power loss load is shared 8., 9., 10.,
With5 loads, by payload PiGauge load value P is obtained after doing normalizedNi, by load level coefficient ciWith PNi
It substitutes into formula and calculates loss of outage in short-term, obtain final result Lloss=1.9068.Failure is calculated to the destruction journey of grid structure
IMP is spent, 2. administrative supply district does the supply path that depth-first traversal determines load bus to feeder line, the load section in network
Point sum be N is 5,5 synteny of faulty line have 8., 9., 10.,With5 loads, N5It is 5, then faulty line 5 is to rack knot
The extent of the destruction IMP of structure are as follows: IMP5=N5/ N=1.Failure disturbance degree is calculated, α=0.6 is enabled, β=0.4 calculates failure influence value
f5=α Lloss+βIMP5=1.5441；The fault condition of power grid most serious is under unit feeder powering mode, and feeder line outlet occurs
Failure causes the power failure of all loads in all areas, and failure influence value in this case is 2.0143, therefore 5 failure of route
Disturbance degree are as follows: 76.66%.
Step 2: fault recovery model adaptation matching.Based on failure disturbance degree, electric network fault belongs to I grades of failures, needs
Acceleration restores electricity, and system is to restore power loss load power supply f1For evaluation index, power distribution network service restoration model at this time is single
Target nonlinear combinatorial optimization problem.Service restoration strategy is searched using binary particle swarm algorithm, and can be extensive when searching
Output is final service restoration strategy when the feasible solution of multiple all power loss load power supplies.
Step 3: service restoration algorithm is executed.Using binary particle swarm algorithm to service restoration problem solving (binary system
The parameter of population is write), when algorithm iteration proceeds to the 2nd time, obtain feasible service restoration scheme are as follows: open-circuit line 6
Switch, the switch of closed circuit 14 and 15 at this time due to the power supply of the recovered all power loss loads of the program, therefore stop searching
Rope, output program control respective switch opening and closing, completes to turn power supply to power loss region.
Route 2 breaks down, and 6. and 7. load power loss after Fault Isolation, remaining is normal power supply area.
Step 1: distribution network failure disturbance degree assessment.Calculate loss of outage in short-term, power loss load it is shared 6. and 7. 2 it is negative
Lotus, by payload PiGauge load value P is obtained after doing normalizedNi, by load level coefficient ciWith PNiSubstitute into formula meter
Loss of outage in short-term is calculated, final result L is obtainedloss=1.1258.Failure is calculated to the extent of the destruction IMP of grid structure, to feedback
1. administrative supply district does the supply path that depth-first traversal determines load bus to line, and the load bus sum in network is N
It is 5,1 synteny of faulty line has 6. and 7. 2 loads, N1It is 2, then extent of the destruction IMP of the faulty line 5 to grid structure are as follows:
IMP2=N2/ N=0.4.Failure disturbance degree is calculated, α=0.6 is enabled, β=0.4 calculates failure influence value f2=α Lloss+βIMP2=
0.8355；Failure influence value under the fault condition of power grid most serious is 2.2095, therefore 2 failure disturbance degree of route are as follows:
37.81%.
Step 2: fault recovery model adaptation matching.Electric network fault belongs to III level failure, at this time distribution network failure journey
It spends most light, it may be considered that the runnability of power grid after fault recovery, by above-mentioned f1~f5It is used as evaluation index, power distribution network power supply
Restoration model is the nonlinear combinatorial optimization problem of multiple target, searches optimal power using multi-objective particle swarm algorithm and restores plan
Slightly.
Step 3: service restoration algorithm is executed.Using multi-objective particle swarm algorithm to service restoration problem solving, one is obtained
Series P areto non-domination solution, as shown in table 1.
The final non-domination solution of table 1
By table 1 it can be found that the switch number of operations of scheme 1 is minimum, but quality of voltage is worst；The power load distributing of scheme 4
The most balanced, quality of voltage is best, but it is most to switch number of operations；Scheme 2 and each target of scheme 3 are more balanced.
Claims (3)
1. a kind of power distribution network service restoration method for considering failure disturbance degree, which is characterized in that described method includes following steps:
(1) distribution network failure disturbance degree is assessed: from failure loss of outage caused by load and to the destruction situation of grid structure
The fault severity level of two level analyzing power distribution networks；
(2) service restoration model adaptation matches: being based on failure disturbance degree, the failure of power distribution network is divided into I, II, III grade, root
According to the grade preference and fault level match control model of service restoration evaluation index；
(3) failure recovering algorithm is executed, service restoration scheme is generated.
2. a kind of power distribution network service restoration method for considering failure disturbance degree according to claim 1, it is characterised in that:
In step (1), the distribution network failure disturbance degree assessment uses following method to determine distribution network failure disturbance degree: negative with power loss
Lotus amount characterizes grid structure extent of the destruction as load loss of outage in short-term, with faulty line institute on-load number of nodes, with weighting
The mode of addition determines the serious value of failure, and by failure, seriously value with the failure under most serious fault condition seriously be divided by by value, obtains
Obtain failure disturbance degree.
3. a kind of power distribution network service restoration method for considering failure disturbance degree according to claim 1, it is characterised in that:
In step (2), the service restoration model adaptation matching is matched for fault recovery model based on fault level, right
In I grades of failures, using power loss load as evaluation index, service restoration model is single goal nonlinear combinatorial optimization problem, is used
Binary particle swarm algorithm search turns power supply strategy, exports when obtaining the feasible solution that can restore the power supply of all power loss loads and is
Final result；For II grade of failure, control cost is considered, using power loss load and switch number of operations as evaluation index, adopt
By service restoration model conversation it is single-object problem with the mode of weighting summation, is generated and turned using binary particle swarm algorithm
Power supply strategy；For III grade of failure, consider the runnability of power grid after fault recovery, by power loss load, switch number of operations,
Counterweight balance, quality of voltage, network loss are as evaluation index, and service restoration model is multi-objective optimization question, using multiple target grain
Swarm optimization searches optimal power recovery policy.
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