电网技术
電網技術
전망기술
POWER SYSTEM TECHNOLOGY
2011年
8期
104-108
,共5页
吴敬兵%罗安%杨晓峰%马伏军%曾灿林
吳敬兵%囉安%楊曉峰%馬伏軍%曾燦林
오경병%라안%양효봉%마복군%증찬림
有源电力滤波器%比例-积分-微分控制器%迭代%学习控制
有源電力濾波器%比例-積分-微分控製器%迭代%學習控製
유원전력려파기%비례-적분-미분공제기%질대%학습공제
active power filter (APF)%proportion-integration-differentiation (PID) controller%itemtive leaming control
将混合型有源电力滤波器应用于电网谐波抑制与无功功率补偿,在传统比例一积分(proportion—integration,PI)控制迭代学习算法基础上加入关于电流误差信号的微分项,提出一种新型比例-积分-微分(proportion—integration-differentiation,PID)控制迭代学习控制算法,并推导出了算法收敛的条件,该控制算法改进了传统迭代学习算法的不足,不依赖于初始电流输入信号的选取。采用一种改进的齐格勒一尼柯尔斯方法实现对PID控制器参数的优化,以提高系统的控制精度。仿真和实验结果证明了该控制方法的可行性和有效性,与传统迭代学习算法比较,其具有响应速度快、控制精度高和易于实现的特点。
將混閤型有源電力濾波器應用于電網諧波抑製與無功功率補償,在傳統比例一積分(proportion—integration,PI)控製迭代學習算法基礎上加入關于電流誤差信號的微分項,提齣一種新型比例-積分-微分(proportion—integration-differentiation,PID)控製迭代學習控製算法,併推導齣瞭算法收斂的條件,該控製算法改進瞭傳統迭代學習算法的不足,不依賴于初始電流輸入信號的選取。採用一種改進的齊格勒一尼柯爾斯方法實現對PID控製器參數的優化,以提高繫統的控製精度。倣真和實驗結果證明瞭該控製方法的可行性和有效性,與傳統迭代學習算法比較,其具有響應速度快、控製精度高和易于實現的特點。
장혼합형유원전력려파기응용우전망해파억제여무공공솔보상,재전통비례일적분(proportion—integration,PI)공제질대학습산법기출상가입관우전류오차신호적미분항,제출일충신형비례-적분-미분(proportion—integration-differentiation,PID)공제질대학습공제산법,병추도출료산법수렴적조건,해공제산법개진료전통질대학습산법적불족,불의뢰우초시전류수입신호적선취。채용일충개진적제격륵일니가이사방법실현대PID공제기삼수적우화,이제고계통적공제정도。방진화실험결과증명료해공제방법적가행성화유효성,여전통질대학습산법비교,기구유향응속도쾌、공제정도고화역우실현적특점。
To use hybrid active power filter (HAPF) for power system harmonic suppression and reactive power compensation, by means of adding differential item of current error to traditional PI iterative learning algorithm a novel PID control iterative learning control algorithm is proposed, and the convergence condition of the proposed algorithm is derived. This novel control algorithm remedies the insufficiency of traditional iterative learning control algorithm and is independent of the selection of initial current input signal. An improved Ziegler-Nichols method is adopted to optimize parameters of PID controller to improve control accuracy of the system. Results of experiments and simulation verify the feasibility and effectiveness of the proposed control method. The proposed control algorithm is easy to implement and possesses faster response speed and higher control accuracy than those of traditional iterative learning algorithm.