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        改進(jìn)的粒子群優(yōu)化算法對(duì)斷路器儲(chǔ)能彈簧的優(yōu)化設(shè)計(jì)

        2019-08-01 01:48:57石麗莉夏克文戴水東鞠文哲
        計(jì)算機(jī)應(yīng)用 2019年5期
        關(guān)鍵詞:鯰魚效應(yīng)云模型粒子群優(yōu)化算法

        石麗莉 夏克文 戴水東 鞠文哲

        摘 要:針對(duì)斷路器儲(chǔ)能彈簧傳統(tǒng)經(jīng)驗(yàn)試算的設(shè)計(jì)方法易導(dǎo)致彈簧結(jié)構(gòu)參數(shù)不合理、斷路器的體積大及分?jǐn)嘈阅懿畹膯栴},應(yīng)用一種結(jié)合鯰魚效應(yīng)改進(jìn)的云粒子群優(yōu)化算法對(duì)斷路器的儲(chǔ)能彈簧參數(shù)進(jìn)行優(yōu)化設(shè)計(jì)。首先,根據(jù)儲(chǔ)能彈簧的工作原理,推導(dǎo)儲(chǔ)能彈簧的數(shù)學(xué)優(yōu)化設(shè)計(jì)模型以及彈簧參數(shù)設(shè)計(jì)的約束條件;然后,根據(jù)優(yōu)化模型對(duì)算法進(jìn)行改進(jìn),在傳統(tǒng)粒子群優(yōu)化算法的基礎(chǔ)上,引入鯰魚效應(yīng)策略產(chǎn)生多樣候選解,避免算法陷入局部最優(yōu)值,并結(jié)合云模型適時(shí)調(diào)整尋優(yōu)速度權(quán)重因子,以加快算法的收斂和提高全局搜索能力;最后,采用改進(jìn)算法對(duì)斷路器的儲(chǔ)能彈簧優(yōu)化模型進(jìn)行仿真及相應(yīng)的彈簧參數(shù)計(jì)算。實(shí)驗(yàn)結(jié)果表明,可以應(yīng)用改進(jìn)的粒子群優(yōu)化算法對(duì)斷路器儲(chǔ)能彈簧進(jìn)行優(yōu)化設(shè)計(jì),設(shè)計(jì)結(jié)果更加小型化、分?jǐn)嘈阅芨鼉?yōu)。

        關(guān)鍵詞:儲(chǔ)能彈簧;粒子群優(yōu)化算法;云模型;鯰魚效應(yīng)

        中圖分類號(hào):TP213

        文獻(xiàn)標(biāo)志碼:A

        Abstract: In the traditional way to design the energy storage spring of the circuit breaker the method of experience trial calculation is mainly adopted, which may easily lead to unreasonable parameters of the spring structure, large volume of circuit breaker and poor breaking performance. Therefore, An improved cloud particle swarm optimization algorithm combined with catfish effect was applied to optimize the parameters of energy storage spring of circuit breaker. Firstly, according to the working principle of energy storage springs, the mathematical optimization design model of the energy storage springs and the constraints of the spring parameter design were deduced. Then, improving the algorithm based on the optimization model, on the basis of the traditional particle swarm optimization algorithm, catfish effect strategy was introduced to produce various candidate solutions, avoiding the algorithm falling into local optimal value and the optimization speed weighting factor was adjusted combined with the cloud model to speed up the convergence of the algorithm and improve the ability of global search solutions. Finally, the improved algorithm was used to simulate the optimization model of the energy storage spring of circuit breakers and calculate the corresponding spring parameters. The results show that the improved particle swarm optimization algorithm can achieve miniaturization and better breaking performance of circuit breakers.

        0 引言

        在新能源領(lǐng)域與智能電網(wǎng)的快速發(fā)展大趨勢(shì)下,供配電市場(chǎng)規(guī)模不斷擴(kuò)大,電網(wǎng)的可靠性運(yùn)行要求也越來越高[1]。斷路器作為常見的開關(guān)器件,用于接通和分?jǐn)嚯娏?,以保護(hù)電氣設(shè)施、配電線路免于由短路引起的過電流受損及過欠壓破壞[2]。隨著日常用電量增多,為確保電網(wǎng)能夠安全工作,對(duì)斷路器的優(yōu)化要求日異嚴(yán)苛[3]。其中,斷路器優(yōu)化主要體現(xiàn)在節(jié)能化、快速分?jǐn)?、小型化、可通信等方面[4-5], 因此,設(shè)計(jì)高效、穩(wěn)定、安全的斷路器是目前研究的熱點(diǎn)、難點(diǎn)[6-8]。

        在斷路器小型化、快速分?jǐn)喾矫娴膬?yōu)化,儲(chǔ)能彈簧是斷路器的首要優(yōu)化對(duì)象[9]。儲(chǔ)能彈簧設(shè)計(jì)時(shí),彈簧力不宜過大從而可以減少機(jī)械磨損、減小設(shè)計(jì)體積;彈簧力也不宜過小從而觸頭可以快速閉合、分?jǐn)嚯娏? 此外,儲(chǔ)能彈簧的設(shè)計(jì)還存在諸多復(fù)雜約束,主要包括:剪切強(qiáng)度約束、疲勞強(qiáng)度約束、彈簧剛度約束、細(xì)長(zhǎng)比約束、共振約束以及彈簧旋繞比約束等[10]。而傳統(tǒng)的斷路器儲(chǔ)能彈簧設(shè)計(jì)方法通常采用經(jīng)驗(yàn)估算、反復(fù)試算、生產(chǎn)大量樣機(jī)測(cè)試實(shí)驗(yàn)等方式,使得斷路器自身體積設(shè)計(jì)過大、設(shè)計(jì)粗糙導(dǎo)致斷路器分?jǐn)嘈阅懿?、壽命短。因此,須結(jié)合當(dāng)今先進(jìn)的仿真優(yōu)化技術(shù),并提出科學(xué)、可靠的斷路器優(yōu)化設(shè)計(jì)方案。

        粒子群優(yōu)化(Particle Swarm Optimization, PSO)算法常用來解決具有非線性、多條件、不可微和多極值等特征的工程優(yōu)化問題[11]; 同時(shí),由于PSO算法操作便捷、適用性強(qiáng),該算法得以在工程設(shè)計(jì)、生命科學(xué)演化、電網(wǎng)優(yōu)化、集成測(cè)試等方面大量應(yīng)用[12-16]。然而,對(duì)于不同實(shí)際問題的應(yīng)用,PSO算法的性能都需依情況進(jìn)行調(diào)整。傳統(tǒng)的PSO算法在迭代之初,速度慣性系數(shù)較大,有利于全局尋優(yōu),此時(shí)如果粒子群已經(jīng)在最優(yōu)值范圍附近搜索,但多數(shù)粒子對(duì)最優(yōu)值不敏感,會(huì)產(chǎn)生盲目尋優(yōu)、算法性能下降等問題;在迭代后期,尋優(yōu)慣性系數(shù)減小有利于局部尋優(yōu),但多數(shù)粒子又可能陷入局部最優(yōu)、粒子多樣性差,從而得不到最優(yōu)解[17]。針對(duì)PSO算法還存在的收斂慢、易陷入局部最優(yōu)問題,算法應(yīng)進(jìn)行必要的改進(jìn)才能適應(yīng)各種復(fù)雜多約束的優(yōu)化問題,如陳大鵬等[18]在傳統(tǒng)PSO算法中采用慣性權(quán)重因子呈指數(shù)下降的策略,并引入人工免疫思想,形成免疫PSO算法,來增加粒子多樣性,避免粒子陷入局部最優(yōu);范成禮等[19]針對(duì)傳統(tǒng)PSO算法在求解高維空間的復(fù)雜問題時(shí)易陷入局部最優(yōu)的問題,提出了一種帶反向預(yù)測(cè)和斥力因子的改進(jìn)PSO算法。而對(duì)于PSO算法的早熟問題,黃松等[20]則提出了一種自適應(yīng)變異概率PSO算法,研究通過考察粒子聚集度動(dòng)態(tài)調(diào)節(jié)每代粒子的變異概率,并對(duì)全局尋優(yōu)進(jìn)行高斯和柯西緩和變異、對(duì)最差個(gè)體最優(yōu)位置進(jìn)行小波變異,最后證明了改進(jìn)算法具有較高的收斂精度。此外,李國(guó)棟等[21]還提出一種用于定性與定量信息轉(zhuǎn)換的云模型,其中,正態(tài)云模型可將定性的概念通過定量表示,并可以和PSO算法結(jié)合。

        綜上,本文將針對(duì)萬(wàn)能式斷路器儲(chǔ)能彈簧設(shè)計(jì)中,彈簧結(jié)構(gòu)參數(shù)設(shè)計(jì)粗糙、試算方法復(fù)雜低效等問題,提出應(yīng)用結(jié)合鯰魚效應(yīng)改進(jìn)的云粒子群優(yōu)化算法,對(duì)萬(wàn)能式斷路器的儲(chǔ)能彈簧進(jìn)行優(yōu)化仿真設(shè)計(jì)。即先推導(dǎo)儲(chǔ)能彈簧優(yōu)化目標(biāo)函數(shù)數(shù)學(xué)模型與彈簧約束條件,再根據(jù)優(yōu)化的數(shù)學(xué)模型及約束條件對(duì)粒子群優(yōu)化算法加以改進(jìn),最后采用改進(jìn)的算法優(yōu)化設(shè)計(jì)儲(chǔ)能彈簧,并計(jì)算出相應(yīng)的彈簧設(shè)計(jì)參數(shù)。

        4 結(jié)語(yǔ)

        通過采用改進(jìn)粒子群優(yōu)化算法優(yōu)化設(shè)計(jì)的斷路器儲(chǔ)能彈簧結(jié)構(gòu)參數(shù),可得到如下結(jié)論:

        首先,根據(jù)斷路器的儲(chǔ)能彈簧設(shè)計(jì)要求,在滿足彈簧相應(yīng)的工作強(qiáng)度下,采用試算的方式設(shè)計(jì)可以得到一組彈簧參數(shù),但試算方式所得結(jié)果相對(duì)粗糙,設(shè)計(jì)的彈簧體積較大。

        而對(duì)斷路器儲(chǔ)能彈簧可進(jìn)行優(yōu)化建模,并推導(dǎo)約束條件不等式;再采用PSO算法,根據(jù)斷路器相應(yīng)的設(shè)計(jì)要求,對(duì)算法的求解速度與精度兩方面進(jìn)行深度改進(jìn)。其中,引入云模型以加快求解速度,引入鯰魚效應(yīng)策略增加了候選解的多樣性,使得算法求解精度更高。

        最后,應(yīng)用改進(jìn)后的PSO算法設(shè)計(jì)得到的斷路器儲(chǔ)能彈簧質(zhì)量、體積及其他相關(guān)參數(shù),可以在給定參數(shù)設(shè)計(jì)范圍內(nèi)快速求解,與試算方式求得結(jié)果進(jìn)行比較,得到儲(chǔ)能彈簧更小的設(shè)計(jì)參數(shù)、質(zhì)量和體積,從而減小儲(chǔ)能彈簧的設(shè)計(jì)體積與實(shí)現(xiàn)斷路器的快速分?jǐn)?,并提高了設(shè)計(jì)效率。

        此外,CECPSO算法不僅可用于儲(chǔ)能彈簧的優(yōu)化設(shè)計(jì),還可以用于斷路器其他零部件及結(jié)構(gòu)的優(yōu)化設(shè)計(jì),以取代傳統(tǒng)的試算設(shè)計(jì)方法。

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