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      改進的粒子群優(yōu)化算法對斷路器儲能彈簧的優(yōu)化設計

      2019-08-01 01:48:57石麗莉夏克文戴水東鞠文哲
      計算機應用 2019年5期
      關鍵詞:鯰魚效應云模型粒子群優(yōu)化算法

      石麗莉 夏克文 戴水東 鞠文哲

      摘 要:針對斷路器儲能彈簧傳統(tǒng)經(jīng)驗試算的設計方法易導致彈簧結構參數(shù)不合理、斷路器的體積大及分斷性能差的問題,應用一種結合鯰魚效應改進的云粒子群優(yōu)化算法對斷路器的儲能彈簧參數(shù)進行優(yōu)化設計。首先,根據(jù)儲能彈簧的工作原理,推導儲能彈簧的數(shù)學優(yōu)化設計模型以及彈簧參數(shù)設計的約束條件;然后,根據(jù)優(yōu)化模型對算法進行改進,在傳統(tǒng)粒子群優(yōu)化算法的基礎上,引入鯰魚效應策略產(chǎn)生多樣候選解,避免算法陷入局部最優(yōu)值,并結合云模型適時調(diào)整尋優(yōu)速度權重因子,以加快算法的收斂和提高全局搜索能力;最后,采用改進算法對斷路器的儲能彈簧優(yōu)化模型進行仿真及相應的彈簧參數(shù)計算。實驗結果表明,可以應用改進的粒子群優(yōu)化算法對斷路器儲能彈簧進行優(yōu)化設計,設計結果更加小型化、分斷性能更優(yōu)。

      關鍵詞:儲能彈簧;粒子群優(yōu)化算法;云模型;鯰魚效應

      中圖分類號:TP213

      文獻標志碼: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

      在新能源領域與智能電網(wǎng)的快速發(fā)展大趨勢下,供配電市場規(guī)模不斷擴大,電網(wǎng)的可靠性運行要求也越來越高[1]。斷路器作為常見的開關器件,用于接通和分斷電流,以保護電氣設施、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,對斷路器的優(yōu)化要求日異嚴苛[3]。其中,斷路器優(yōu)化主要體現(xiàn)在節(jié)能化、快速分斷、小型化、可通信等方面[4-5], 因此,設計高效、穩(wěn)定、安全的斷路器是目前研究的熱點、難點[6-8]。

      在斷路器小型化、快速分斷方面的優(yōu)化,儲能彈簧是斷路器的首要優(yōu)化對象[9]。儲能彈簧設計時,彈簧力不宜過大從而可以減少機械磨損、減小設計體積;彈簧力也不宜過小從而觸頭可以快速閉合、分斷電流; 此外,儲能彈簧的設計還存在諸多復雜約束,主要包括:剪切強度約束、疲勞強度約束、彈簧剛度約束、細長比約束、共振約束以及彈簧旋繞比約束等[10]。而傳統(tǒng)的斷路器儲能彈簧設計方法通常采用經(jīng)驗估算、反復試算、生產(chǎn)大量樣機測試實驗等方式,使得斷路器自身體積設計過大、設計粗糙導致斷路器分斷性能差、壽命短。因此,須結合當今先進的仿真優(yōu)化技術,并提出科學、可靠的斷路器優(yōu)化設計方案。

      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,由于PSO算法操作便捷、適用性強,該算法得以在工程設計、生命科學演化、電網(wǎng)優(yōu)化、集成測試等方面大量應用[12-16]。然而,對于不同實際問題的應用,PSO算法的性能都需依情況進行調(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)粒子對最優(yōu)值不敏感,會產(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PSO算法還存在的收斂慢、易陷入局部最優(yōu)問題,算法應進行必要的改進才能適應各種復雜多約束的優(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]針對傳統(tǒng)PSO算法在求解高維空間的復雜問題時易陷入局部最優(yōu)的問題,提出了一種帶反向預測和斥力因子的改進PSO算法。而對于PSO算法的早熟問題,黃松等[20]則提出了一種自適應變異概率PSO算法,研究通過考察粒子聚集度動態(tài)調(diào)節(jié)每代粒子的變異概率,并對全局尋優(yōu)進行高斯和柯西緩和變異、對最差個體最優(yōu)位置進行小波變異,最后證明了改進算法具有較高的收斂精度。此外,李國棟等[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算法結合。

      綜上,本文將針對萬能式斷路器儲能彈簧設計中,彈簧結構參數(shù)設計粗糙、試算方法復雜低效等問題,提出應用結合鯰魚效應改進的云粒子群優(yōu)化算法,對萬能式斷路器的儲能彈簧進行優(yōu)化仿真設計。即先推導儲能彈簧優(yōu)化目標函數(shù)數(shù)學模型與彈簧約束條件,再根據(jù)優(yōu)化的數(shù)學模型及約束條件對粒子群優(yōu)化算法加以改進,最后采用改進的算法優(yōu)化設計儲能彈簧,并計算出相應的彈簧設計參數(shù)。

      4 結語

      通過采用改進粒子群優(yōu)化算法優(yōu)化設計的斷路器儲能彈簧結構參數(shù),可得到如下結論:

      首先,根據(jù)斷路器的儲能彈簧設計要求,在滿足彈簧相應的工作強度下,采用試算的方式設計可以得到一組彈簧參數(shù),但試算方式所得結果相對粗糙,設計的彈簧體積較大。

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

      最后,應用改進后的PSO算法設計得到的斷路器儲能彈簧質(zhì)量、體積及其他相關參數(shù),可以在給定參數(shù)設計范圍內(nèi)快速求解,與試算方式求得結果進行比較,得到儲能彈簧更小的設計參數(shù)、質(zhì)量和體積,從而減小儲能彈簧的設計體積與實現(xiàn)斷路器的快速分斷,并提高了設計效率。

      此外,CECPSO算法不僅可用于儲能彈簧的優(yōu)化設計,還可以用于斷路器其他零部件及結構的優(yōu)化設計,以取代傳統(tǒng)的試算設計方法。

      參考文獻 (References)

      [1] ??? GHOLIAN A, MOHSENIANRAD H, HUA Y B. Optimal industrial load control in smart grid[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2305-2316.

      [2] ??? LIU F, LIU W J, ZHA X M. Solidstate circuit breaker snubber design for transient overvoltage suppression at bus fault interruption in lowvoltage DC microgrid [J]. IEEE Transactions on Power Electronics, 2017, 32(4): 3007-3021.

      [3] ??? MAQSOOD A, OVERSTREET A, CORZINE K. Modified zsource DC circuit breaker topologies[J]. IEEE Transactions on Power Electronics, 2016, 31(10): 7394-7403.

      [4] ??? 朱童,余占清,曾嶸,等.混合式直流斷路器模型及其操作暫態(tài)特性研究[J].中國電機工程學報,2016,36(1):18-30.(ZHU T, YU Z Q, ZENG R, et al. Transient model and operation characteristics researches of hybrid DC circuit breaker [J]. Proceedings of the CSEE, 2016, 36(1): 18-30.)

      [5] ??? PEI X Z, CWIKOWSKI O, SMITH A C. Design and experimental tests of a superconducting hybrid DC circuit breaker [J]. IEEE Transactions on Applied Superconductivity, 2018, 28(3): 1-5.

      [6] ??? 田園,胡炎.計及保護和斷路器動作不確定性的隱性故障檢測模型[J].電網(wǎng)技術,2016,40(9):2896-2903.(TIAN Y, HU Y. Analytic model of hidden failure detection model considering uncertainty of protection and circuit breaker tripping [J]. Power System Technology, 2016, 40(9): 2896-2903.)

      [7] ??? MAJI T K, ACHARJEE P. Multiple solutions of optimal pmu placement using exponential binary PSO algorithm for smart grid applications [J]. IEEE Transactions on Industry Applications, 2017, 53(3): 2550-2559.

      [8] ??? LIU C L, WEI D, ZHANG B. On novel methods for characterizing the arc/contact movement and its relation with the current/voltage in lowvoltage circuit breaker [J]. IEEE Transactions on Plasma Science, 2017, 45(5): 882-888.

      [9] ??? 李鵬飛,周文俊,曾國,等.高壓斷路器合閘彈簧動態(tài)特性及儲能狀態(tài)檢測方法[J].電工技術學報,2016,31(3):104-112.(LI P F, ZHOU W J, ZENG G, et al. The dynamic characteristics and energy storage state detection method of highvoltage circuit breaker closing spring [J]. Transactions of China Electrotechnical Society, 2016, 31(3): 104-112.)

      [10] ?? 趙思洋,汪安本,周文俊,等.基于模糊綜合評判的斷路器操動機構彈簧儲能狀態(tài)評估[J].高壓電器,2016,52(6):187-192.(ZHAO S Y, WANG A B, ZHOU W J, et al. State assessment of circuit breaker actuators spring based on fuzzy comprehensive evaluation [J]. High Voltage Apparatus, 2016, 52(6): 187-192.)

      [11] ?? BONYADI M R, MICHALEWICZ Z. Stability analysis of the particle swarm optimization without stagnation assumption [J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5): 814-819.

      [12] ?? BANERJEE S, GHOSH A, RANA N. An improved interleaved boost converter with PSO based optimal typeIII controller [J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2017, 5(1): 323-337.

      [13] ?? 王皓,歐陽海濱,高立群.一種改進的全局粒子群優(yōu)化算法[J].控制與決策,2016,31(7):1161-1168.(WANG H, OUYANG H B, GAO L Q. An improved global particle swarm optimization algorithm [J]. Control and Decision, 2016, 31(7): 1161-1168.)

      [14] ?? JIANG S Y, YANG S X. An improved multiobjective optimization evolutionary algorithm based on decomposition for complex pareto fronts [J]. IEEE Transactions on Cybernetics, 2016, 46(2): 421-437.

      [15] ?? 張棟華,李征,蔡旭.基于量子行為粒子群優(yōu)化算法的為電網(wǎng)優(yōu)化配置[J].計算機仿真,2014,31(8):120-124,208.(ZHANG D H, LI Z, CAI X. Microgrid optimization allocation problem based on quantumbehaved particle swarm optimization [J]. Computer Simulation, 2014, 31(8): 120-124, 208.)

      [16] ?? 周海鵬,高芹,蔣豐千,等.自適應混沌量子粒子群算法及其在WSN覆蓋優(yōu)化中的應用[J].計算機應用,2018,38(4):1064-1071.(ZHOU H P, GAO Q, JIANG F Q, et al. Application of selfadaptive chaotic quantum particle swarm algorithm in coverage optimization of wireless sensor network [J]. Journal of Computer Applications, 2018, 38(4): 1064-1071.)

      [17] ?? FONG S M, WONG R, VASILAKOS A V. Accelerated PSO swarm search feature selection for data stream mining big data [J]. IEEE Transactions on Services Computing, 2016, 9(1): 33-45.

      [18] ?? 陳大鵬,張九根,梁星.基于免疫粒子群算法的中央空調(diào)冷凍水系統(tǒng)優(yōu)化控制[J].計算機應用,2017,37(9):2717-2721.(CHEN D P, ZHANG J G, LIANG X. Optimal control of chilled water system in central airconditioning based on artificial immune and particle swarm optimization algorithm [J]. Journal of Computer Applications, 2017,37(9): 33-45.)

      [19] ?? 范成禮,邢清華,李響,等.帶反向預測及斥力因子的改進粒子群優(yōu)化算法[J].控制與決策,2015,30(2):311-315.(FAN C L, XING Q H, LI X, et al. Improved particle swarm optimization algorithm with reverse forecast and repulsion [J]. Control and Decision, 2015, 30(2): 311-315.)

      [20] ?? 黃松,田娜,紀志成.基于自適應概率粒子群優(yōu)化算法的研究[J].系統(tǒng)仿真學報,2016,28(4):874-879.(HUANG S, TIAN N, JI Z C. Study of modified particle swarm optimization algorithm based on adaptive mutation probability [J]. Journal of System Simulation, 2016, 28(4): 874-879.)

      [21] ?? 李國棟,胡建平,夏克文.基于云PSO的RVM入侵檢測[J].控制與決策,2015,30(4):698-702.(LI G D, HU J P, XIA K W. Intrusion detection using relevance vector machine based on cloud particle swarm optimization [J]. Control and Decision, 2015, 30(4): 698-702.)

      [22] ?? MASDARI M, SALEHI F, JALALI M, et al. A survey of PSObased scheduling algorithms in cloud computing [J]. Journal of Network and Systems Management, 2017, 25(1): 122-158.

      [23] ?? CHEN S M, CHIOU C H. Multiattribute decision making based on intervalvalued intuitionistic fuzzy sets, PSO techniques, and evidential reasoning methodology [J]. IEEE Transactions on Fuzzy Systems, 2015, 23(6): 1905-1916.

      [24] ?? 邱飛岳,莫雷平,江波,等.基于大規(guī)模變量分解的多目標粒子群優(yōu)化算法研究[J].計算機學報,2016,39(12):2598-2613.(QIU F Y, MO L P, JIANG B, et al. Multiobjective particle swarm optimization algorithm using large scale variable decomposition [J]. Chinese Journal of Computers, 2016, 39(12): 2598-2613.)

      [25] ?? 鞠文哲,夏克文,戴水東.改進的云粒子群優(yōu)化算法及其斷路器優(yōu)化應用[J].計算機應用研究, 2018, 25(7):2084-2087.(JU W Z, XIA K W, DAI S D. Improved cloud particle swarm optimization algorithm and its application in circuit breaker optimization[J].Application Research of Computers, 2018, 25(7):2084-2087.)

      [26] ?? 王生生,楊娟娟,柴勝.基于混沌鯰魚效應的人工蜂群算法及應用[J].電子學報,2014,42(9):1731-1737.(WANG S S, YANG J J, CHAI S. Artificial bee colony algorithm with chaotic catfish effect and its application [J]. Acta Electronica Sinica, 2014, 42(9): 1731-1737.)

      [27] ?? 劉藝,刁興春,曹建軍,等.求解子集問題的鯰魚效應蝙蝠蟻群優(yōu)化[J].系統(tǒng)工程與電子技術,2016,38(10):2441-2448.(LIU Y, DIAO X C, CAO J J, et al. Catfish bat algorithmant colony optimization for subset problems [J]. Systems Engineering and Electronics, 2016, 38(10): 2441-2448.)

      [28] ?? GE H W, SUN L, TAN G Z. Cooperative hierarchical PSO with two stage variable interaction reconstruction for large scale optimization [J]. IEEE Transactions on Cybernetics, 2017, 47(9): 2809-2823.

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