基于Kalman滤波与神经网络的高精度同步时钟算法
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摘要
大规模分布式电源、储能与电动汽车的接入对配电网状态监测与运行控制带来了挑战。基于配电网同步相量测量单元(phasor measurement unit,PMU)的广域量测系统被认为是解决这一问题的有效方式。然而,采用低成本晶振时,现有的同步时钟算法难以满足配电网PMU对同步时钟高精度、高稳定性、低成本的要求。为满足配电网PMU应用的需求,提出一种基于Kalman滤波器与BP神经网络的授时/守时算法。基于卫星信号误差与晶振频率数学模型,利用Kalman滤波器对卫星信号的随机误差进行滤除,提高授时精度,并提供准确的晶振状态数据。利用此数据训练BP神经网络模型,刻画出晶振频率的老化规律,提高守时性能。在卫星信号正常接入与失锁场景下,基于实际时钟装置量测数据进行测试验证。测试结果显示,文中所提算法在不提高现有硬件成本的基础上,有效提高了同步时钟的算法性能。
Access of large-scale distributed power supplies, energy storage and electric vehicles brings challenges to state monitoring and operation control of distribution network. The wide area measurement system based on synchronized phasor measurement unit(PMU) of distribution network is considered as an effective way to solve the problem. However, existing synchronization clock algorithm using low-cost crystal oscillator can hardly meet the requirements of high accuracy, high stability and low cost for the PMU of distribution network. In order to meet the demand of PMU application in distribution network, this paper proposes a timing/time keeping algorithm based on Kalman filter and BP neural network. Based on the mathematical models of satellite signal error and crystal oscillator frequency, Kalman filter is used to eliminate the error of satellite signal to improve timing accuracy and provide accurate crystal oscillator state data. The data are used to train a BP neural network model, describing the aging rule of crystal vibration frequency and improving the time keeping performance. In the scenario of normal access and losing lock of satellite signal, the tests are carried out based on the measured data of actual clock device. Results show that the algorithm proposed in this paper can effectively improve the performance of synchronous clock without increasing cost of existing hardware.
Access of large-scale distributed power supplies, energy storage and electric vehicles brings challenges to state monitoring and operation control of distribution network. The wide area measurement system based on synchronized phasor measurement unit(PMU) of distribution network is considered as an effective way to solve the problem. However, existing synchronization clock algorithm using low-cost crystal oscillator can hardly meet the requirements of high accuracy, high stability and low cost for the PMU of distribution network. In order to meet the demand of PMU application in distribution network, this paper proposes a timing/time keeping algorithm based on Kalman filter and BP neural network. Based on the mathematical models of satellite signal error and crystal oscillator frequency, Kalman filter is used to eliminate the error of satellite signal to improve timing accuracy and provide accurate crystal oscillator state data. The data are used to train a BP neural network model, describing the aging rule of crystal vibration frequency and improving the time keeping performance. In the scenario of normal access and losing lock of satellite signal, the tests are carried out based on the measured data of actual clock device. Results show that the algorithm proposed in this paper can effectively improve the performance of synchronous clock without increasing cost of existing hardware.
引文
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[12]文超,钟俊.一种适用于GPS信号异常情况的高精度主时钟设计方法[J].电力系统保护与控制,2016,44(3):103-108.Wen Chao,Zhong Jun.A high-precision main clock design method suitable for GPS signal anomaly[J].Power System Protection and Control,2016,44(3):103-108(in Chinese).
[13]黄翔,江道灼.GPS同步时钟的高精度守时方案[J].电力系统自动化,2010,34(18):74-77.Huang Xiang,Jiang Daoshao.High precision timekeeping scheme of GPS synchronous clock[J].Automation of Electric Power Systems,2010,34(18):74-77(in Chinese).
[14]韩玉强.智能电网卫星同步时钟的误差分析与修正方法研究[D].北京:华北电力大学,2016.
[15]薛冲.基于晶体振荡器的老化建模及其智能补偿[D].西安:西安电子科技大学,2014.
[16]杨少尘,胡昌华,周志杰,等.一种基于ARMA模型的GPS校频系统频率保持方法[J].电光与控制,2016(12):31-35.Yang Shaochen,Hu Changhua,Zhou Zhijie,et al.A frequency maintenance method for GPS frequency calibration system based on ARMA model[J].Electronics Optics&Control,2016(12):31-35(in Chinese).
[17]Zhao S,Shmaliy Y S,Liu F.Fast Kalman-like optimal unbiased FIRfiltering with applications[J].IEEE Transactions on Signal Process,2016,64(9):2284-2297.
[18]Partovibakhsh M,Liu G.An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots[J].IEEE Transactions on Control Systems Technology,2015,23(1):357-363.
[19]Wang S Y,Neubig B,Wu J H,et al.Extension of the frequency aging model of crystal resonators and oscillators by the Arrhenius factor[C]//IEEE International Frequency Control Symposium,Xi’an,China,2017:269-272.
[2]李鹏,宿洪智,王成山,等.基于PMU量测的智能配电网电压-功率灵敏度鲁棒估计方法[J].电网技术,2018,42(10):3258-3267.Li Peng,Su Hongzhi,Wang Chengshan,et al.Robust estimation method of voltage to power sensitivity for smart distribution networks based on PMU measurements[J].Power System Technology,2018,42(10):3258-3267(in Chinese).
[3]王少芳,刘广一,黄仁乐,等.多采样周期混合量测环境下的主动配电网状态估计方法[J].电力系统自动化,2016,40(19):30-36.Wang Shaofang,Liu Guangyi,Huang Renle,et al.Active distribution network state estimation method in multi-sampling cycle mixed measurement environment[J].Automation of Electric Power Systems,2016,40(19):30-36(in Chinese).
[4]李泽文,曾祥君,黄智伟,等.基于高精度晶振的GPS秒时钟误差在线修正方法[J].电力系统自动化,2006,30(13):55-58.Li Zewen,Zeng Xiangjun,Huang Zhiwei,et al.GPS second clock error online correction method based on high precision crystal vibration[J].Automation of Electric Power Systems,2006,30(13):55-58(in Chinese).
[5]Tian M,Bo Y,Chen Z,et al.Improved GPS system performance estimation algorithm based on Gaussian sum filter[C]//2017 3rd IEEEInternational Conference on Computer and Communications(ICCC),Chengdu,2017:263-268.
[6]李泽文,舒磊,邓丰,等.基于全数字锁相环的电力系统高精度同步时钟[J].电力自动化设备,2015,35(7):32-36.Li Zewen,Shu Lei,Deng Feng,et al.Power system high precision synchronous clock based on full digital phase-locked loop[J].Electric Power Automation Equipment,2015,35(7):32-36(in Chinese).
[7]Arceo-Miquel L,Ibarra-Manzano O,Shmaliy Y S.Unbiased predictive steering of local clocks utilizing GPS 1PPS time signals[Z].Recent Researches in Telecommunications,Informatics,Electronics and Signal Processing-TELE-INFO',2011,1:65-70.
[8]Shmaliy Y S,Khan S H,Zhao S,et al.General unbiased FIR filter with applications to GPS-based steering of oscillator frequency[J].IEEE Transactions on Control Systems Technology,2017,PP(99):1-8.
[9]Zhou H,Kunz T,Schwartz H.Adaptive correction method for an OCXO and investigation of analytical cumulative time error upper bound[J].IEEE Transactions on Ultrasonics Ferroelectrics&Frequency Control,2011,58(1):43-50.
[10]冯雪阳.基于GPS秒脉冲的恒温晶振驯服和自适应保持技术研究与实现[D].成都:电子科技大学,2014.
[11]田鸣.GPS校准的数字式守时钟研究[D].武汉:华中科技大学,2011.
[12]文超,钟俊.一种适用于GPS信号异常情况的高精度主时钟设计方法[J].电力系统保护与控制,2016,44(3):103-108.Wen Chao,Zhong Jun.A high-precision main clock design method suitable for GPS signal anomaly[J].Power System Protection and Control,2016,44(3):103-108(in Chinese).
[13]黄翔,江道灼.GPS同步时钟的高精度守时方案[J].电力系统自动化,2010,34(18):74-77.Huang Xiang,Jiang Daoshao.High precision timekeeping scheme of GPS synchronous clock[J].Automation of Electric Power Systems,2010,34(18):74-77(in Chinese).
[14]韩玉强.智能电网卫星同步时钟的误差分析与修正方法研究[D].北京:华北电力大学,2016.
[15]薛冲.基于晶体振荡器的老化建模及其智能补偿[D].西安:西安电子科技大学,2014.
[16]杨少尘,胡昌华,周志杰,等.一种基于ARMA模型的GPS校频系统频率保持方法[J].电光与控制,2016(12):31-35.Yang Shaochen,Hu Changhua,Zhou Zhijie,et al.A frequency maintenance method for GPS frequency calibration system based on ARMA model[J].Electronics Optics&Control,2016(12):31-35(in Chinese).
[17]Zhao S,Shmaliy Y S,Liu F.Fast Kalman-like optimal unbiased FIRfiltering with applications[J].IEEE Transactions on Signal Process,2016,64(9):2284-2297.
[18]Partovibakhsh M,Liu G.An adaptive unscented Kalman filtering approach for online estimation of model parameters and state-of-charge of lithium-ion batteries for autonomous mobile robots[J].IEEE Transactions on Control Systems Technology,2015,23(1):357-363.
[19]Wang S Y,Neubig B,Wu J H,et al.Extension of the frequency aging model of crystal resonators and oscillators by the Arrhenius factor[C]//IEEE International Frequency Control Symposium,Xi’an,China,2017:269-272.
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