CN108333504A  A kind of extra high voltage line phase selection based on Adaptive Neurofuzzy Inference closes performance prediction algorithm  Google Patents
A kind of extra high voltage line phase selection based on Adaptive Neurofuzzy Inference closes performance prediction algorithm Download PDFInfo
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 CN108333504A CN108333504A CN201810113553.6A CN201810113553A CN108333504A CN 108333504 A CN108333504 A CN 108333504A CN 201810113553 A CN201810113553 A CN 201810113553A CN 108333504 A CN108333504 A CN 108333504A
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 G—PHYSICS
 G01—MEASURING; TESTING
 G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
 G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
 G01R31/327—Testing of circuit interrupters, switches or circuitbreakers
Abstract
Description
Technical field
The invention belongs to intelligent switch appliance fields, are related to a kind of extrahigh voltage based on Adaptive Neurofuzzy Inference Circuit phase selection closes performance prediction algorithm.
Background technology
One of the principal element for influencing UHV transmission line Insulation Coordination is the switching overvoltage of transmission line of electricity.Due to spy Highpressure system connects low reactance, new transformer fe core material, the measures such as breaker of not restriking using neutral point, will cut off nonloaded line Road, excision noload transformer and offtheline overvoltage are inhibited or are reduced one by one, so that nonloaded line is closed a floodgate and overlapped Lock rises to principal contradiction, and as the deciding factor of selection extrahigh voltage system operatio impact dielectric level.
Controlled switching technology closes phase combined floodgate by controlling breaker in target, can inhibit nonloaded line from principle Closing overvoltage.The determination of optimum phase is one of the key problem of controlled switching technology, and influences the master of best switchingon phase It includes prebreakdown and the mechanical scatter of breaker to want factor, thus by phase control techniques be applied to extrahigh voltage system in when need examine Consider influence of the breaker performance parameter to failure rate, solves the Quantitatively mapping relationship of breaker performance parameter and line failure rate.
Manufacturer for phased breaker and user, it is to have to assess control effect before device fabrication and use very much Meaning.In order to improve manufacture efficiency, reduce energy consumption, raising product quality, manufacturer needs according to different user to failure rate Requirement adjust switch performance parameter in time.User can also be under the premise of known switching characteristic parameter, and forecast assessment is phased The spot effect of breaker, and then select suitable product.
Invention content
The technical problem to be solved by the present invention is to the relationships for extra high voltage line phase selection switch technology and line failure rate Extra high voltage line failure rate model is established, to realize breaker performance parameter, insulator chain operation wave withstanding voltage and circuit event The Quantitatively mapping relationship of barrier rate.
Due to can not directly obtain line failure rate f with close coefficient k, 3 σ of mechanical scatter and insulator chain operation wave it is resistance to By voltage u_{It is resistance to}Between analytical expression, the present invention proposes a kind of mathematical model that can characterize relation above  adaptive god Through fuzzy inference system (adaptivenetworkbased fuzzy inference system, ANFIS).Based on ANFIS It establishes one three and inputs then Sugeno fuzzy models of requiring to report his or her problems within a prescribed time and in a prescribed place, pass through rule and membership function that gridding method generates fuzzy system. Using in MATLAB softwares Mathworks and anfisedit complete the foundation of all models, coefficient k, machine are closed with breaker Tool 3 σ of dispersibility and insulator chain operation wave withstanding voltage u_{It is resistance to}It is real using line failure rate f as output parameter as input parameter The Quantitatively mapping relationship of existing breaker performance parameter, insulator chain operation wave withstanding voltage and line failure rate.
Technical scheme of the present invention:
A kind of extra high voltage line phase selection based on Adaptive Neurofuzzy Inference closes performance prediction algorithm, and step is such as Under：
The first step：The foundation of extra high voltage line failure rate model
1st layer：Three input variables are blurred, three input variables are to close coefficient k, 3 σ of mechanical scatter and insulation Substring operates wave withstanding voltage u_{It is resistance to}, the input of this layer is the sample data set of actual load, exports being subordinate to for each input variable Degree, output form are：
Wherein：
O_{11}Indicate the output of the 1st layer of the 1st node；
O_{12}Indicate the output of the 1st layer of the 2nd node；
O_{13}Indicate the output of the 1st layer of the 3rd node；
O_{14}Indicate the output of the 1st layer of the 4th node；
O_{15}Indicate the output of the 1st layer of the 5th node；
O_{16}Indicate the output of the 1st layer of the 6th node；
And P_{i}It is three corresponding Fuzzy Linguistic Variables of input variable；
Function mu is the membership function for the condition that meets, and membership function uses Gauss (Gauss) function, expression formula point It is not：
Wherein：Fuzzy membership function { a_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}Be the ith node layer parameter set；a_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}Referred to as premise parameter,The membership function of coefficient k is closed for input variable；For the person in servitude of 3 σ of mechanical scatter Category degree function；μ_{Pi}(z) it is that insulator chain operates wave withstanding voltage u_{It is resistance to}Membership function；
2nd layer：Realize that the rule intensity of the 2nd layer of ith of node of the 1st layer of product calculation respectively exported is ω_{i}, the 2nd layer Ith of node, which exports, is：
3rd layer：To the rule intensity ω of the 2nd layer of the 1st node_{1}With the rule intensity ω of the 2nd layer of the 2nd node_{2}Returned One change is handled, the output O of ith of node of third layer_{3i}For：
4th layer：4th layer of each node i is the adaptive node with node function, node function f_{i}It is linear Function, f_{i}=p_{i}x+q_{i}y+s_{i}z+t_{i}, the 4th layer of ith of node output O_{4i}For：
Wherein：Fuzzy parameter p_{i}、q_{i}、s_{i}And t_{i}For consequent parameter, i.e. TakagiSugeno linear equations parameter, pass through The leastsquares estimation algorithm of ANFIS determines.
5th layer：This layer is the stationary nodes for carrying ∑, and function is ith of node output O for calculating the 4th layer_{4i}'s Summation, using total output O as system_{5}, i.e. line failure rate f：
Second step：Premise parameter a is determined by hybrid algorithm_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}With consequent parameter p_{i}, q_{i}, s_{i}, t_{i}；Tool Steps are as follows for body：
1) empirical value of three input variables is substituted into extra high voltage line failure rate model, it is in each iteration, first solid Determine premise parameter, the 1st layer of output continues to transmit along network forward direction, until the 4th layer, then use leastsquares estimation algorithm pair Consequent parameter carries out calculating adjustment, obtains the consequent parameter for meeting the requirements value, and the 4th layer of output continuation is forward propagated to along network Up to total output O_{5}；
2) by known output empirical value and obtained total output O_{5}Subtract each other to obtain error amount, error amount will reversely be passed along network It broadcasts, fixed consequent parameter adjusts l layers of premise parameter using gradient descent method；
3) first two steps are repeated, until error amount meets the requirements value, the premise parameter a that will be obtained_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}And knot By parameter p_{i}, q_{i}, s_{i}, t_{i}It substitutes into formula (6), obtains complete extra high voltage line failure rate model；
4) it closes coefficient k and 3 σ of mechanical scatter is substituted into complete extra high voltage line failure rate model, calculate total defeated Go out O_{5}, i.e. failure rate f.
Third walks：Different line failure rates require the performance parameter requirement of lower phase selection breaker
According to the complete extra high voltage line failure rate model of foundation, to the breaker under the requirement of circuit different faults rate Performance parameter is analyzed.
1) insulator chain operation wave withstanding voltage u is set_{It is resistance to}For certain value, breaker is closed into coefficient k in a certain section N parts are divided into, 3 σ of mechanical scatter of breaker is divided into M parts, obtains the input matrix of N × M.
2) using the matrix as input, backwards calculation is carried out based on complete extra high voltage line failure rate model, is worked as Corresponding breaker closes 3 σ of coefficient k and mechanical scatter when line failure rate f variations.
A large amount of parallel and distributed process is provided the beneficial effects of the invention are as follows the model system proposed, extensive attribute, Especially learn attribute, and optimizes network using backpropagation algorithm.ANFIS frameworks can not only use language message, also Itself can be adapted to using numerical data, to realize that better extra high voltage line phase selection closes failure rate estimated performance, and And have the characteristics that the calculating time is short.
Description of the drawings
Fig. 1 is that three inputs are required to report his or her problems within a prescribed time and in a prescribed place the ANFIS structures of then Sugeno fuzzy models.
Fig. 2 is the relationship that line failure rate and breaker close coefficient, mechanical scatter.
Specific embodiment
Below in conjunction with technical solution and attached drawing, the implementation process of this patent is illustrated.
As shown in Figure 1 and Figure 2, a kind of extra high voltage line phase selection based on Adaptive Neurofuzzy Inference closes Performance prediction algorithm, steps are as follows：
The first step：The foundation of extra high voltage line failure rate model
1st layer：Three input variables are blurred, three input variables close coefficient k, 3 σ of mechanical scatter and insulator String operation wave withstanding voltage u_{It is resistance to}, the input of this layer is the sample data set of actual load, is exported as the degree of membership of each input variable, Output form is：
Wherein：O_{11}Indicate the output of the 1st layer of the 1st node；O_{12}Indicate the output of the 1st layer of the 2nd node；O_{13}Indicate the 1st The output of the 3rd node of layer；O_{14}Indicate the output of the 1st layer of the 4th node；O_{15}Indicate the output of the 1st layer of the 5th node；O_{16}Table Show the output of the 1st layer of the 6th node；φ_{ki}、φ_{σi}And P_{i}Be close coefficient k, 3 σ of mechanical scatter and insulator chain operation wave it is resistance to By voltage u_{It is resistance to}The corresponding Fuzzy Linguistic Variable of these three nodes；
Function mu is the membership function for the condition that meets, and membership function uses Gauss (Gauss) function, expression formula point It is not：
Wherein：Fuzzy membership function { a_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}Be the ith node layer parameter set；a_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}Referred to as premise parameter,The membership function of coefficient k is closed for input variable；For the person in servitude of 3 σ of mechanical scatter Category degree function；μ_{Pi}(z) it is that insulator chain operates wave withstanding voltage u_{It is resistance to}Membership function；
2nd layer：Realize that the rule intensity of the 2nd layer of ith of node of the 1st layer of product calculation respectively exported is ω_{i}, the 2nd layer Ith of node, which exports, is：
3rd layer：To the rule intensity ω of the 2nd layer of the 1st node_{1}With the rule intensity ω of the 2nd layer of the 2nd node_{2}Returned One change is handled, the output O of ith of node of third layer_{3i}For：
4th layer：4th layer of each node i is the adaptive node with node function, node function f_{i}It is linear Function, f_{i}=p_{i}x+q_{i}y+s_{i}z+t_{i}, the 4th layer of ith of node output O_{4i}For：
Wherein：Fuzzy parameter p_{i}、q_{i}、s_{i}And t_{i}For consequent parameter, i.e. TakagiSugeno linear equations parameter, pass through The leastsquares estimation algorithm of ANFIS determines.
5th layer：This layer is the stationary nodes for carrying ∑, and function is ith of node output O for calculating the 4th layer_{4i}'s Summation, using total output O as system_{5}, i.e. line failure rate f：
Second step：Premise parameter a is determined by hybrid algorithm_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}With consequent parameter p_{i}, q_{i}, s_{i}, t_{i}；Tool Steps are as follows for body：
1) empirical value of three input variables is substituted into extra high voltage line failure rate model, it is in each iteration, first solid Determine premise parameter, the 1st layer of output continues to transmit along network forward direction, until the 4th layer, then use leastsquares estimation algorithm pair Consequent parameter carries out calculating adjustment, obtains determining that the consequent parameter for meeting the requirements value, the 4th layer of output continue to pass along network forward direction It is multicast to up to total output O_{5}；
2) by known output empirical value and obtained total output O_{5}Subtract each other to obtain error amount, error amount will reversely be passed along network It broadcasts, fixed consequent parameter adjusts l layers of premise parameter using gradient descent method；
3) first two steps are repeated, until error amount meets the requirements value, the premise parameter a that will be obtained_{i}, b_{i}, c_{i}, d_{i}, e_{i}, g_{i}And knot By parameter p_{i}, q_{i}, s_{i}, t_{i}It substitutes into formula (6), obtains complete extra high voltage line failure rate model；
4) it closes coefficient k and 3 σ of mechanical scatter is substituted into complete extra high voltage line failure rate model, calculate total defeated Go out O_{5}, i.e. failure rate f.
Third walks：Different line failure rates require the performance parameter requirement of lower phase selection breaker
According to the complete extra high voltage line failure rate model of foundation, to the breaker under the requirement of circuit different faults rate Performance parameter is analyzed.
1) insulator chain operation wave withstanding voltage u is set_{It is resistance to}For certain value, breaker is closed into coefficient k in a certain section N parts are divided into, 3 σ of mechanical scatter of breaker is divided into M parts, obtains the input matrix of N × M.
2) using the matrix as input, backwards calculation is carried out based on complete extra high voltage line failure rate model, is worked as Corresponding breaker closes 3 σ of coefficient k and mechanical scatter when line failure rate f variations.
By taking 1000kV UHV transmission lines as an example, under being required using the ANFIS model analysis different faults rates of foundation The performance parameter of breaker obtains as shown in Fig. 2 as a result, obtaining as drawn a conclusion：
(1) when closing maximum coefficient k, 3 σ minimums of mechanical scatter, line failure rate is preferably minimized；
(2) when closing coefficient k and rising to 0.7, the downward trend of failure rate slows down, this value is considered as closing The minimum value of coefficient k.
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US20150188415A1 (en) *  20131230  20150702  King Abdulaziz City For Science And Technology  Photovoltaic systems with maximum power point tracking controller 

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周沛洪 等: "取消750 kV线路断路器合闸电阻的研究", 《电网与水力发电进展》 * 
林圣 等: "基于ANFIS的特高压输电线路故障分类识别方法", 《西南交通大学学报》 * 
谢将剑 等: "基于永磁操动机构的同步关合关键技术的研究", 《高压电器》 * 
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