The power oscillation damper (POD) in the doubly-fed induction generator (DFIG) is to damp the low- frequency oscillation (LFO) in the power systems. But the POD will introduce the LFO to the DFIG and intensify the oscillation of the latter. In this paper, open-loop subsystems of the DFIG with the POD and the rest part of power system are derived to describe the interaction between them. The balanced truncation method is used to reduce the transfer function of the DFIG and derive the analytical description to the oscillation of the DFIG. Considering the impact of the POD on the DFIG, the constraint to the DFIG is newly incorporated in the optimization model to the POD parameters, with the aim to alleviate the oscillation of both the DFIG and power system. The simulation verifies the accuracy of the reduced model of the DFIG, and validates the effectiveness of the improved optimization to the POD of the DFIG.

Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data or take into account a single external factor, which results in significant forecasting errors. To improve the forecasting accuracy, this paper proposes a short-term load forecasting method considering multiple contributing factors based on VAR and CEEMDAN-CNN- BILSTM. Firstly, multiple contributing factors strongly correlated with the short-term load are selected based on the Spearman correlation analysis, the vector autoregressive (VAR) model with multivariate input is derived, and the Levenberg–Marquardt algorithm is introduced to estimate the model parameters. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and permutation entropy (PE) criterion are combined to decompose and reconstruct the original load data into multiple relatively stationary mode components, which are respectively input into the CNN-BILTSM network for forecasting. Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. The actual simulation results utilizing the Australian data validate the forecasting accuracy of the proposed model, achieving reduction in the root mean square error by 31.21% and 18.04% compared to the VAR and CEEMDAN-CNN-BILSTM, respectively.

The impact of wind power forecast errors (WPFEs) on power system reliability can be quantified by a sensitivity model, which helps to determine the importance of different wind farms. However, the unknown distribution and correlation of WPFEs make it difficult to calculate the reliability sensitivity. The existing univariate non-standard third-order polynomial normal transformation (NSTPNT) expresses the reliability sensitivity of WPFEs by a normal random variable with explicit distribution, and is not suitable for multiple wind farms with correlated forecast errors. In this paper, the univariate NSTPNT method is extended to the multivariate by deriving the analytical expression of the correlation coefficients before and after the transformation, to establish the transformation between the WPFEs and a normal random vector (RV) with the specific correlation. A reliability sensitivity model to the WPFEs expressed to the normal RV is then proposed. The numerical results validate the accuracy of the proposed multivariate NSTPNT and the sensitivity model. The maximum relative error for using the sensitivity to approximate the change of reliability with distribution parameters of the WPFEs is less than 2.42%. The necessity of considering the correlation of WPFEs is analyzed. The maximum relative error of the sensitivity reaches 83% when the correlation is ignored.

With increasing wind power, interaction of the wind turbine generators (WTGs), e.g., doubly-fed induction generators (DFIGs), may cause oscillation in the wind farm and endanger the power systems, so it is necessary to optimize the parameters of the WTG to mitigate the interaction. The existing study often focuses on the interaction between the wind farm and the system, but ignores the interaction among the WTGs. In this paper, the interaction path and parameter optimization for the wind farm with the DFIGs is studied. The novelties are, 1) The analytical expression for interaction path in the wind farm is newly derived with the proposed expanded effective open-loop process (EEOP), 2) a new indicator is proposed to evaluate the gain of the interaction paths for two-input, two-output characteristics of the DFIGs, 3) analytical expression for the sensitivities of the interaction with respect to the control parameters is newly derived, and 4) a multi-objective model including the sensitivity model to optimize the parameters of the DFIGs is proposed to mitigate the interaction. In the numerical analysis, a wind farm with multi-DFIG is used to verify the interaction paths and the indicator, and validate the mitigation effect with the optimized parameters of the DFIGs.

The increasing wind power decreases power system damping and may intensify low-frequency oscillation (LFO). The LFO are usually damped by the power system stabilizer (PSS) at synchronous generator (SG), and now by the power oscillation damper (POD) at doubly-fed induction generator (DFIG). The existing damping torque analysis (DTA) sets the parameters of the PSS and evaluates its damping capability, but can not be applied to the POD due to the difficulty of finding the damping path related to the DFIG and the coupling between the POD and the DFIG, which are studied in this paper. At first, the analytical expression of the coupling between the POD and DFIG is newly derived with linear fractional transformation (LFT) technique. Then the path damping torque analysis (PDTA) is proposed to reconstruct the damping path of the POD. Thirdly, the damping indicator based on the return difference matrix is proposed to evaluate the contribution of damping path to the LFO. Finally, numerical results of test system are given to validate effectiveness and accuracy of the proposed model, and parameter optimization to the multi-input POD (MIPOD) is performed to show the application value of the proposed model.

At one converter station, compared with the high‐voltage direct current (HVDC) line, the conversion units of the ultra HVDC (UHVDC) line are doubled and controlled independently, possibly yielding the unbalanced operation modes, separate settings to the DC variables of the positive and negative poles, and different equivalent powers and mismatch equations (MEs). Under the hierarchical connection mode (HCM), the inverters connect two AC voltages. Hence, the operation modes are not the same as the UHVDC line, and the parameters of the inverters at the high and low terminals are to be set independently. In this study, the power flow model of the UHVDC line is newly proposed. Considering the unbalanced modes and the HCM, the DC variables, the equivalent powers of the converters, and the MEs are decided. The elements of the extended Jacobian matrix are defined. The initial values and specified values are derived. The modified IEEE RTS system and a provincial system in the eastern China are applied to validate the feasibility and accuracy of the proposed models. The research provides the basis for the operation and economic dispatch of the AC/UHVDC systems.