电力系统自动化
電力繫統自動化
전력계통자동화
AUTOMATION OF ELECTRIC POWER SYSTEMS
2014年
8期
56-60
,共5页
董飞飞%刘涤尘%吴军%岑炳成%宋春丽%马文媛
董飛飛%劉滌塵%吳軍%岑炳成%宋春麗%馬文媛
동비비%류조진%오군%잠병성%송춘려%마문원
次同步振荡%改进生物地理学优化算法%静止无功补偿器%次同步阻尼控制器%电力系统
次同步振盪%改進生物地理學優化算法%靜止無功補償器%次同步阻尼控製器%電力繫統
차동보진탕%개진생물지이학우화산법%정지무공보상기%차동보조니공제기%전력계통
subsynchronous oscillation (SSO)%improved biogeography-based optimization (IBBO) algorithm%static var compensator (SVC)%subsynchronous damping controller%power systems
针对常用的次同步振荡控制器不能较好地适应电力系统时变非线性的特点,提出了一种引入余弦迁移模型、早熟判断机制、变尺度混沌变异策略及排重操作的改进生物地理学优化算法。基于该算法结合静止无功补偿器(SVC)抑制次同步振荡的机理,对次同步阻尼控制器进行优化设计,并采用特征值分析和时域仿真验证了控制系统的有效性。锦界电厂算例分析表明:经改进生物地理学算法优化的SVC次同步阻尼控制器能较好地提高机组扭振的模态阻尼,可有效抑制次同步振荡,进而保证机组和电网的安全稳定运行;与传统的生物地理学优化算法、粒子群算法及遗传算法相比,改进生物地理学优化算法在搜索最优控制参数时具有较快的搜索速度和较高的搜索精度。
針對常用的次同步振盪控製器不能較好地適應電力繫統時變非線性的特點,提齣瞭一種引入餘絃遷移模型、早熟判斷機製、變呎度混沌變異策略及排重操作的改進生物地理學優化算法。基于該算法結閤靜止無功補償器(SVC)抑製次同步振盪的機理,對次同步阻尼控製器進行優化設計,併採用特徵值分析和時域倣真驗證瞭控製繫統的有效性。錦界電廠算例分析錶明:經改進生物地理學算法優化的SVC次同步阻尼控製器能較好地提高機組扭振的模態阻尼,可有效抑製次同步振盪,進而保證機組和電網的安全穩定運行;與傳統的生物地理學優化算法、粒子群算法及遺傳算法相比,改進生物地理學優化算法在搜索最優控製參數時具有較快的搜索速度和較高的搜索精度。
침대상용적차동보진탕공제기불능교호지괄응전력계통시변비선성적특점,제출료일충인입여현천이모형、조숙판단궤제、변척도혼돈변이책략급배중조작적개진생물지이학우화산법。기우해산법결합정지무공보상기(SVC)억제차동보진탕적궤리,대차동보조니공제기진행우화설계,병채용특정치분석화시역방진험증료공제계통적유효성。금계전엄산례분석표명:경개진생물지이학산법우화적SVC차동보조니공제기능교호지제고궤조뉴진적모태조니,가유효억제차동보진탕,진이보증궤조화전망적안전은정운행;여전통적생물지이학우화산법、입자군산법급유전산법상비,개진생물지이학우화산법재수색최우공제삼수시구유교쾌적수색속도화교고적수색정도。
In view of the common subsynchronous oscillation (SSO) controller incapability of suiting the time-varying and nonlinear characteristics of power system,the cosine migration model,the premature judging mechanism,the mutative scale of chaos mutation strategy, and re-scheduling operations are introduced into the improved biogeography-based optimization (IBBO) algorithm to design subsynchronous damping controller(SSDC)optimally based on the mechanism of suppressing SSO by static var compensator (SVC).Finally,eigenvalue analysis and electromagnetic simulation are conducted to verify the effectiveness of the controller developed.The simulation analysis of Jinjie Plant indicates that SVC-SSDC optimized by the IBBO algorithm can greatly improve the damping of the three torsional modes and thus effectively depress the multimodal SSO, ensuring stability of the system and safety of the generator shafts.Moreover,The IBBO algorithm has a faster search speed and higher search accuracy in searching for the optimal control parameters compared with the traditional biogeography-based optimization (BBO) algorithm,the particle swarm optimization (PSO) algorithm,as well as the genetic algorithm (GA).