本文選題：實數(shù)編碼群體極值優(yōu)化 + 預(yù)測控制��； 參考：《溫州大學(xué)》2017年碩士論文

【摘要】：預(yù)測控制在復(fù)雜工業(yè)過程控制系統(tǒng)中得到了廣泛的應(yīng)用,但現(xiàn)有算法嚴重依賴設(shè)計經(jīng)驗,因此如何采用進化算法對傳統(tǒng)預(yù)測控制方法中的滾動優(yōu)化策略進行改進,從而進一步提升預(yù)測控制方法高效求解約束優(yōu)化控制難題的能力,具有重要的理論意義和工程應(yīng)用價值,已成為學(xué)術(shù)界和工業(yè)界的研究熱點之一。另外,預(yù)測控制方法在電力電子功率變換器和多區(qū)域互聯(lián)電力系統(tǒng)中的應(yīng)用研究探索近年來越來越受到電氣工程領(lǐng)域的關(guān)注,但還處于起步階段。因此,本文從基于實數(shù)編碼群體極值優(yōu)化的新視角研究預(yù)測控制的改進方法及其在典型過程控制系統(tǒng)與多區(qū)域互聯(lián)電力系統(tǒng)中的應(yīng)用。本文的主要研究工作和創(chuàng)新點如下:(1)提出了一種基于多點非均勻變異的實數(shù)編碼群體極值優(yōu)化算法(RCEO),通過對多個典型連續(xù)優(yōu)化測試函數(shù)和電壓自動控制系統(tǒng)的分數(shù)階PID控制器參數(shù)優(yōu)化問題的仿真研究,驗證了 RCEO相比基于實數(shù)編碼的遺傳算法(GA)、粒子群算法(PSO)和基于其它變異操作的極值優(yōu)化等進化算法具有更少的可調(diào)參數(shù)和更佳的優(yōu)化性能;(2)在研究工作(1)的基礎(chǔ)上,提出了基于RCEO的約束廣義預(yù)測控制算法(CGPC-RCEO )和約束動態(tài)矩陣控制算法(CDMC-RCEO),分別通過對熱交換器循環(huán)水溫度控制和連續(xù)攪拌釜式反應(yīng)器溫度控制的仿真研究從而驗證了本文提出的CGPC-RCEO和CDMC-RCEO算法相比經(jīng)典預(yù)測控制算法、基于GA和PSO的預(yù)測控制方法具有更佳的系統(tǒng)輸出響應(yīng)和控制增量響應(yīng)等控制性能;(3)基于研究工作(2),將CDMC-RCEO的基本思想推廣應(yīng)用到多區(qū)域互聯(lián)電力系統(tǒng)中,提出了一種基于CDMC-RCEO的多區(qū)域互聯(lián)電力系統(tǒng)分布式負荷頻率預(yù)測控制方法,對兩區(qū)域和三區(qū)域互聯(lián)電力系統(tǒng)的仿真實驗結(jié)果表明了:本文提出方法相比傳統(tǒng)積分控制算法、比例積分算法、CGPC-RCEO、基于GA和PSO的CDMC算法具有更佳的動態(tài)和穩(wěn)態(tài)控制性能,且具有更強的魯棒性。
[Abstract]:Predictive control has been widely used in complex industrial process control systems, but the existing algorithms rely heavily on design experience, so how to improve the rolling optimization strategy in traditional predictive control methods by evolutionary algorithm. Therefore, it is of great theoretical significance and engineering application value to improve the ability of predictive control method to solve the problem of constrained optimal control efficiently. It has become one of the research hotspots in academia and industry. In addition, the application of predictive control method in power electronic power converters and multi-area interconnected power systems has attracted more and more attention in the field of electrical engineering in recent years, but it is still in its infancy. Therefore, this paper studies the improved predictive control method and its application in typical process control systems and multi-area interconnected power systems from a new perspective of real-coded population extremum optimization. The main research work and innovation of this paper are as follows: (1) A real coded population extremum optimization algorithm based on multipoint nonuniform mutation is proposed. By dividing several typical continuous optimization test functions and voltage automatic control systems, a real coded population extremum optimization algorithm is proposed. Simulation study on Parameter Optimization of Multi-order PID Controller, It is verified that RCEO has less adjustable parameters and better optimization performance than genetic algorithm based on real number coding, particle swarm optimization (PSO) and extreme value optimization based on other mutation operations. A constrained generalized predictive control algorithm (CGPC-RCEO) based on RCEO and a constrained dynamic matrix control algorithm (CDMC-RCEOO) are proposed. The simulation results of heat exchanger circulating water temperature control and continuous stirred tank reactor temperature control are verified. Compared with the classical predictive control algorithm, the CGPC-RCEO and CDMC-RCEO algorithms are proposed in this paper. The predictive control method based on GA and PSO has better control performance such as system output response and control increment response. Based on the research work, the basic idea of CDMC-RCEO is extended to multi-area interconnected power system. A distributed load frequency predictive control method for multi-area interconnected power systems based on CDMC-RCEO is proposed. The simulation results for two and three regions of interconnected power systems show that: compared with the traditional integral control algorithm, the proposed method in this paper is better than the traditional integrated control algorithm. The proportional integration algorithm CGPC-RCEO. CDMC algorithm based on GA and PSO has better dynamic and steady-state control performance and stronger robustness.
【學(xué)位授予單位】：溫州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP13;TP18

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