In recent years, high voltage direct current (HVDC) transmission system has been an effective scheme for the development of wind power in the deep sea. With the continuous operation of a large number of HVDC transmission systems for the grid connection of wind power in the deep sea, high-frequency oscillation phenomenon frequently occurs in practical projects. However, most of the existing research focuses on the high-frequency oscillation phenomenon generated by the interaction of new energy AC grid-connected system or asynchronous zonal power grid, and the research on the high-frequency oscillation of offshore wind power through flexible direct transmission system is not sufficient. Moreover, the existing modular multilevel converter (MMC) high-band impedance model usually ignores the characteristics of the low frequency band of the converter, so that the influence of the high-frequency oscillation suppression strategy on the low-frequency impedance characteristics cannot be accurately revealed. Therefore, this paper first considers the influence of system time delay, and establishes the impedance model of MMC based on the harmonic state space. On this basis, the high-frequency oscillation mechanism of offshore wind power through HVDC transmission system is analyzed, and the control parameter optimization strategy is designed. Finally, based on Matlab/Simulink, the electromagnetic transient simulation model of the offshore wind power through HVDC transmission system is built to verify the accuracy of the impedance model and the effectiveness of high-frequency oscillation suppression strategy.

对基于双馈异步发动机（doubly-fed induction generator，DFIG）的大规模风电场经串补送电进行了次同步振荡的分析，所建立模型基于IEEE第一标准模型。在建立风电场详细数学模型的基础上进行了小干扰稳定性分析，并在电力系统分析软件 DIgSILENT/Power Factory 中建立电力系统模型，采用时域仿真验证小干扰特征值分析的结果，验证了引起风电场次同步振荡的原因是感应发电机效应而不是扭转相互作用。同时分析了控制器参数对次同步振荡特性的影响。

This paper analyzes the subsynchronous resonance （SSR） phenomena in DFIG-based （doubly-fed induction generator） wind farms interconnected with series compensated networks. The study system was derived from IEEE first benchmark model. Small-signal stability analysis was conducted on the basis of detailed mathematical model of wind farm. The model of power system was constructed by power system analysis software DIgSILENT/Power Factory, and time domain simulations were performed to confirm the results of small-signal eigenvalue analysis. The results show that IGE （induction generator effect） instead of TI （torsional interaction） is the major reason for SSR in such systems. Finally, this paper analyzes the influence of controller parameters on the characteristics of SSR.

新型电力系统中次同步振荡能量特性与运行工况、控制参数、外部环境等息息相关，因此基于不同场景下的量测数据进行次同步振荡能量特性提取及主导因素辨识，可以有效解决实际工程中次同步振荡问题。提出一种数据驱动的次同步振荡能量特性影响因素辨识方法。首先，基于变分模态分解(variational mode decomposition, VMD)解决模态混叠问题，从而提取准确的次同步振荡模态；其次，推导基于端口能量的次同步振荡能量函数表达式，利用模态提取结果进行能量函数计算；最后，采用主客观赋权的方法综合考虑主客观权重建立评估模型，辨识次同步振荡能量特性的主导因素。基于PSCAD/EMTDC平台搭建的风电场并网次同步振荡仿真结果验证了所提方法的有效性。

The energy characteristics of sub-synchronous oscillations in new power systems are closely related to operating conditions, control parameters, and the external environment. Therefore, the extraction of the energy characteristics of sub-synchronous oscillations and the identification of the dominant factors based on the measured data under different scenarios can effectively solve the sub-synchronous oscillation problems in practical engineering. In this study, we propose a data-driven method to identify the factors influencing sub-synchronous oscillation energy characteristics. First, it solves the modal mixing problem based on the variational modal decomposition to extract the accurate sub-synchronous oscillation modes； second, it derives the expression of sub-synchronous oscillation energy function based on the port energy and uses the modal extraction results to calculate the energy function； finally, it uses the subjective and objective weighting to establish an evaluation model to identify the dominant factors of sub-synchronous oscillation energy characteristics by considering the subjective and objective weights. The simulation results of the wind farm grid-connected sub-synchronous oscillations built based on the PSCAD/EMTDC platform verified the effectiveness of the proposed method.

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.

精准的多元负荷短期预测是综合能源系统调度和运行的基础。综合能源系统中的多种负荷之间存在较强的耦合作用,目前已有的单一负荷预测难以挖掘不同负荷之间复杂的内在联系。对此,提出一种基于多头概率稀疏自注意力模型的多元负荷短期预测方法。首先,采用皮尔逊相关系数分析多元负荷之间的相关性,并提取多元负荷之间的耦合特征;然后,使用改进位置编码的多头概率稀疏自注意力机制学习长序列输入的依赖关系,并且采用多元预测任务的参数软共享机制,通过不同子任务对共享特征的差异化选择,实现多元负荷的联合预测;最后,在亚利桑那州立大学Tempe校区的多元负荷数据集上对所提模型的性能进行验证,结果表明所提预测方法相较于其他预测模型能够有效提高预测精度。

Accurate short-term forecasting of multienergy loads is the basis for the dispatch and operation of integrated energy systems. There is a strong coupling between multiple loads in an integrated energy system, and the existing single load forecasting is challenging to explore the complex internal relationship between multiple loads. Therefore, a short-term forecasting method for multienergy loads based on a multihead probabilistic sparse self-attention (MPSS) model was proposed. First, the Pearson correlation coefficient was used to analyze the correlation between multiple loads, the coupling features between multiple loads were extracted, a multihead probabilistic sparse self-attention mechanism with improved location coding was used to learn the dependencies of long-sequence inputs, and the parameter soft sharing mechanism of multivariate prediction tasks was adopted. The sharing mechanism realizes the joint prediction of multiple loads through a differentiated selection of shared features using different subtasks. Finally, the performance of the proposed model was verified using the multiple-load dataset of the Tempe Campus of Arizona State University. Compared with other forecasting models, the results show that the proposed multivariate load forecasting method can effectively improve forecasting accuracy.

针对微震信号具有高噪声、突变快、随机性强等特点,基于经验模态分解(EMD)及独立成分分析(ICA)提出一种微震信号降噪方法.首先,对含噪信号进行EMD分解,获得一系列按频率从高到低的内蕴模态函数(IMF),利用原信号与各IMF之间的互相关系数辨识出噪声与信号的分界,将分界之上的高频噪声滤除;其次,为有效去除分界IMF中的模态混叠噪声,基于ICA算法对分界IMF进行盲源分离,提取其中的微震有效信号,并将其与剩余的IMF累加重构,从而得到降噪后的微震信号;最后,利用快速傅里叶变换(FFT)时频谱对比分析降噪前后的信号特征,定性说明本文方法的有效性;引入信噪比和降噪后信号占原信号的能量百分比两个参数,定量说明本文方法能充分保留微震信号的瞬态非平稳特征,降噪效果明显.