| 引用本文: | 王毅,刘恒,侯兴哲,等.基于改进强跟踪UKF的电压暂态扰动检测[J].电力系统保护与控制,2017,45(19):109-116. |
| WANG Yi,LIU Heng,HOU Xingzhe,et al.Transient voltage disturbance detection based on modified strong tracking UKF[J].Power System Protection and Control,2017,45(19):109-116 |
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| 基于改进强跟踪UKF的电压暂态扰动检测 |
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王毅1,2,3,刘 恒1,2,侯兴哲2,李松浓2,叶 君2,孙洪亮2,郑 可2
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(1.重庆邮电大学，重庆 400065;2.国网重庆市电力公司电力科学研究院，重庆 401123; 3.国网重庆市电力公司博士后科研工作站，重庆 401123)
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| 摘要: |
| 针对目前强跟踪滤波器在电压暂态扰动检测方面，在强非线性系统下存在参数估计精度不够，高维滤波器模型下计算复杂等问题，结合STF和UKF提出一种基于改进的强跟踪无迹卡尔曼滤波器(MSTUKF)的电能质量扰动检测方法。在状态变量发生突变时，通过次优渐消因子自适应调节过程噪声协方差矩阵的权重，在满足强跟踪滤波器不同时刻残差序列正交条件下，推导MSTUKF成立的充分条件。该算法较传统的STF方法改善了滤波器的估计精度，无需求解雅可比矩阵，只需一次UT变换，计算复杂度降低，且保留了STF在模型失配情况下的强鲁棒性。将所提方法与传统STF进行对比，仿真实验结果表明:所提方法更能快速、准确地检测到电压暂降、暂态脉冲及暂态谐波信号发生的起止时刻，跟踪到突变幅值和突变相位，验证了改进的强跟踪UKF是电能质量扰动检测的一种好的解决方案。 |
| 关键词: 强跟踪 渐消因子 电能质量扰动 无迹卡尔曼滤波 |
| DOI：10.7667/PSPC161472 |
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| 基金项目:中国博士后科学基金资助项目(2015T80961);重庆市自然科学基金项目(cstc2016jcyjA0214) |
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| Transient voltage disturbance detection based on modified strong tracking UKF |
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WANG Yi1,2,3,LIU Heng1,2,HOU Xingzhe2,LI Songnong2,YE Jun2,SUN Hongliang2,ZHENG Ke2
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(1. Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2. Chongqing Electric Power Research Institute, Chongqing 401123, China;3. Postdoctoral Workstation of the Chongqing Electric Power Corporation, Chongqing 401123, China)
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| Abstract: |
| Strong tracking filter has some problems in terms of power quality disturbances detection, such as inadequate accuracy in strong nonlinear system, calculation complexity in high dimensional filter model and poor filtering performance. This paper combines strong tracking filter with unscented Kalman filter and proposes a new method which is based on modified strong tracking Kalman filter (MSTUKF). When the sudden changes of the state variables occur, it derives the sufficient conditions for the establishment of the MSTUKF by the suboptimal fading factor which adaptively adjusts the weights of process noise covariance matrix on the condition that filter residual sequence is orthogonal at different time. The proposed method improves the filter estimation accuracy of the filter without calculating the Jacobian matrix when compared with the traditional STF method. Besides, it just needs one UT transformation. Therefore, the computational complexity is reduced, what’s more, it retains the strong robustness of STF under model mismatch. Simulation results show that the proposed method is more rapid and has a higher accuracy detection of mutation starting and ending time of voltage sag, transient pulse and transient harmonic signal, tracking the mutation and mutant amplitude phase, which verifies the modified MSTUKF is a good solution for power quality disturbance detection. This work is supported by China Postdoctoral Science Foundation (No. 2015T80961) and Natural Science Foundation of Chongqing (No. cstc2016jcyjA0214). |
| Key words: strong tracking filter fading factor power quality disturbance unscented Kalman filter |
