本文選題：群集智能　切入點：大規(guī)模優(yōu)化　出處：《武漢大學(xué)》2016年博士論文

【摘要】：“創(chuàng)新、協(xié)調(diào)、綠色、開放、共享”是“十三五”時期乃至中長期指導(dǎo)我國能源電力行業(yè)科學(xué)發(fā)展的新理念。在著力推進(jìn)能源電力行業(yè)創(chuàng)新發(fā)展與綠色發(fā)展的進(jìn)程中,大量亟待優(yōu)化與創(chuàng)新的技術(shù)問題相繼涌現(xiàn),且隨著電力系統(tǒng)規(guī)模的日益增長、技術(shù)要求的不斷提升,這類技術(shù)問題呈現(xiàn)出規(guī)模化、復(fù)雜化的發(fā)展趨勢。本文依托實際科研課題,以群集智能思想的應(yīng)用為出發(fā)點,結(jié)合專業(yè)背景,圍繞電力系統(tǒng)建設(shè)、電力系統(tǒng)運(yùn)營中的兩類典型復(fù)雜優(yōu)化問題展開研究：大規(guī)模光伏系統(tǒng)復(fù)雜光照下最大功率點跟蹤以及電能計量設(shè)備運(yùn)維作業(yè)動態(tài)優(yōu)化,抽象出一類具有大規(guī)模、多極值、變量耦合等特性的復(fù)雜優(yōu)化問題,并建立基于群集智能的求解模型。在此基礎(chǔ)上,針對不同問題的屬性與特點研究基于群集智能的求解方法,并最終回歸實際問題的求解與優(yōu)化。具體地講,本文主要研究內(nèi)容及創(chuàng)新成果如下：對于具有多極值特性的復(fù)雜優(yōu)化問題,由于群集智能算法易出現(xiàn)因個體陷入局部極值且難以擺脫而導(dǎo)致的“早熟”收斂現(xiàn)象,極大程度地限制了算法對于這類問題的求解性能。本文以粒子群算法為例,分析其“早熟”現(xiàn)象的形成原因,并從增強(qiáng)粒子個體智能屬性的角度出發(fā)提出若干防“早熟”策略以及HSPSO、 HSPSO-FI算法,通過為個體引入仿人腦的智能屬性以增強(qiáng)其擺脫局部極值點束縛的能力。仿真實驗表明,通過引入仿人智能屬性,粒子個體能夠有效克服局部極值點的束縛,算法優(yōu)化性能得以顯著提升。對于具有大規(guī)模特性的優(yōu)化問題,由于問題復(fù)雜度隨變量維數(shù)的增加呈指數(shù)上漲,這一“維數(shù)災(zāi)難”的出現(xiàn)將導(dǎo)致常規(guī)優(yōu)化算法失效。尤其當(dāng)大規(guī)模優(yōu)化問題同時具有變量耦合特性時,問題的求解將變得極為復(fù)雜。為拓展群集智能的應(yīng)用領(lǐng)域,提升其對各類大規(guī)模優(yōu)化問題的求解性能,本文研究并提出一類通用的多參考向量自適應(yīng)協(xié)同進(jìn)化(AM-CC)算法框架,并以粒子群算法為例提出AM-CCPSO算法。仿真實驗表明,AM-CC框架對于具有變量可分割以及變量不可分割等特性的1000維大規(guī)模問題具有良好的求解性能。AM-CC框架的提出為群集智能應(yīng)用于求解大規(guī)模問題,尤其對于具有變量耦合特性的大規(guī)模問題求解提供了一種通用、有效的解決方案。在上述理論研究的基礎(chǔ)上,針對電力系統(tǒng)建設(shè)中的典型復(fù)雜優(yōu)化問題展開應(yīng)用研究：圍繞大規(guī)模光伏系統(tǒng)復(fù)雜光照下的“熱斑效應(yīng)”與最大功率點跟蹤問題,研究并提出了基于群集智能的求解方案�！盁岚咝�(yīng)”對光伏系統(tǒng)局部遮陰環(huán)境下的穩(wěn)定工作構(gòu)成嚴(yán)重威脅,現(xiàn)有方法普遍存在系統(tǒng)輸出功率額外損失、成本較高或難以在大規(guī)模系統(tǒng)中應(yīng)用等缺陷。針對這一問題,本文研究了基于光伏電池控制裝置與支路穩(wěn)壓裝置的大規(guī)模光伏陣列拓?fù)浣Y(jié)構(gòu),為實現(xiàn)單塊電池板(或最小控制單元)級的最大功率點跟蹤提供了硬件基礎(chǔ)。此外,建立了以大規(guī)模優(yōu)化問題為描述形式的大規(guī)模光伏系統(tǒng)最大功率點跟蹤數(shù)學(xué)模型,并將本文理論研究部分提出的各算法應(yīng)用于模型求解。仿真實驗表明,通過拓?fù)浣Y(jié)構(gòu)、數(shù)學(xué)模型與求解算法的相互配合,大規(guī)模光伏系統(tǒng)各電池板(或最小控制單元)在復(fù)雜光照環(huán)境下能夠穩(wěn)定工作于各自理論最大功率點,使“熱斑效應(yīng)”得以有效解決的同時保證了系統(tǒng)的最大輸出功率。此外,針對電力系統(tǒng)運(yùn)營中的典型復(fù)雜優(yōu)化問題展開應(yīng)用研究：圍繞電能計量設(shè)備運(yùn)維作業(yè)動態(tài)優(yōu)化問題,分析電網(wǎng)企業(yè)相關(guān)管理工作的實際需求,并建立基于群集智能的運(yùn)維作業(yè)動態(tài)優(yōu)化模型,以實現(xiàn)對任務(wù)點數(shù)量、實時路況、運(yùn)維人員屬性與數(shù)量、決策者偏好等外部條件的實時響應(yīng)。在此基礎(chǔ)上,采用本文理論研究部分提出的各算法完成對模型的求解。仿真實驗表明,提出的模型與算法能夠?qū)﹄娋W(wǎng)企業(yè)關(guān)于運(yùn)維作業(yè)的各項要求予以實時響應(yīng),實現(xiàn)電能計量設(shè)備運(yùn)維作業(yè)的高維度實時、動態(tài)優(yōu)化,提升電網(wǎng)企業(yè)日常運(yùn)維工作管理效率。
[Abstract]:"Innovation, harmony, green, open, sharing" is a new concept of "13th Five-Year" period and long-term guidance of scientific development of China's energy and power industry. In order to promote the development of innovation and development of green energy power industry in the process, many technical problems need to be optimized and innovation have emerged, and with the increasing scale of power system the technical requirements of the continuous upgrading, the technical problems showing a large-scale, complex trend. Based on the practical research project, by using the swarm intelligence theory as the starting point, combined with professional background, focus on the construction of the power system, power system operation in the two typical complex optimization problem is studied: mass photovoltaic system under complex illumination maximum power point tracking and metering equipment operation and maintenance of dynamic optimization, abstract a class has a large-scale, multi peak, variable coupling etc. The complex optimization problems, and establish a model based on swarm intelligence. On this basis, according to the characteristics of different problem solving method based on swarm intelligence, solving and optimization and finally returns to the practical problems. Specifically, the main contents and innovations are as follows: for the complex optimization problem with multi extremum the characteristics, due to swarm intelligence algorithm is prone to fall into local extremum and because the individual is difficult to get rid of the "premature convergence" phenomenon, greatly limits the performance of algorithms for solving this kind of problems. This paper uses the particle swarm algorithm as an example, the cause of formation of the "premature" phenomenon, and starting from the enhanced particle individual intelligence attribute aspect puts forward some anti "premature" strategy and HSPSO, HSPSO-FI algorithm, through the intelligent properties into humanoid brain for the individual to enhance their escape from local The ability of extremum bound. Simulation results show that through the introduction of intelligent property, the individual particles can effectively overcome the constraints of local extremum, algorithm optimization performance can be significantly improved. For the optimization problem with large scale property, due to the complexity of the increase with the dimension of the exponential rise, the "curse of dimensionality" will lead to the failure of the conventional optimization algorithm. Especially when the large-scale optimization problems with variable coupling characteristics, solving the problem will become extremely complex. For the expansion of the application of swarm intelligence, to enhance its various types of large-scale optimization problem solving performance, a generic multi reference vector adaptive co evolution is studied in this paper and put forward (AM-CC) the algorithm framework, and particle swarm algorithm as an example. Simulation results show that the proposed AM-CCPSO algorithm, the AM-CC framework for variable segmentation and variable cannot be divided The 1000 dimensional cutting characteristics of large-scale problem solving performance has good.AM-CC framework for the application of swarm intelligence to solve large-scale problems, especially provides a general for solving large-scale problems with variable coupling characteristic, effective solution. Based on the above theoretical research, applied research on typical complex optimization problems in power system construction: focusing on the large-scale photovoltaic system under complex illumination "hot spot effect" and the maximum power point tracking problem, study and put forward the solution scheme based on swarm intelligence. "Hot spot effect" poses a serious threat to the stability of the photovoltaic system partial shade environment, the existing methods are additional system output power loss, high cost or difficult in large scale applications and other defects. To solve this problem, this paper studies the control based on photovoltaic cell Large scale photovoltaic array device topology and branch voltage stabilizing device, to achieve single panels (or minimum control unit) provides hardware based maximum power point tracking level. In addition, to establish a large-scale optimization problem described in the form of large-scale photovoltaic system maximum power point tracking model, and some theoretical research of this paper is put forward the algorithm is applied to solve the model. Simulation results show that the topological structure, interaction mathematical model and solution algorithm, the system of large scale photovoltaic panels (or minimum control unit) in complex illumination can work stably in the respective theoretical maximum power point under the environment, the "hot spot effect" can also effectively solve the maximum output power of the system. In addition, the application of typical complex optimization problems in power system operation: around the power metering equipment. The dynamic optimization problem of dimension operation, analysis of the actual needs of the relevant management of power grid enterprises, and set up the model of dynamic optimization of operation and maintenance based on swarm intelligence, the number, in order to achieve the task of real-time traffic, the number of attributes and operation and maintenance personnel, real-time response preference and other external conditions. On this basis, using the algorithm in the part of theory to study and put forward the solution of the model. Simulation results show that the proposed model and algorithm can be real-time response to the power grid enterprises on the operation and maintenance requirements, implementation of electric energy metering equipment operation and maintenance of the high dimension of real-time, dynamic optimization, enhance the daily maintenance work efficiency of management of power grid enterprises.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP18

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