A machine learning method for locating subsynchronous oscillation source of VSCs in wind farm induced by open-loop modal resonance based on measurement

In recent years, sub-synchronous oscillation incidents have been reported to happen globally, which seriously threatens the safe and stable operation of the power system. It is difficult to locate the oscillation source in practice using the parameterized model of open-loop modal resonance. Therefore, this paper aims at the problem of oscillation instability caused by the interaction between the multiple voltage source converters in the wind farm grid-connected system, proposes a method for locating the oscillation source of a wind farm using measurement data based on the transfer learning algorithm of transfer component analysis. At the same time, in order to solve the problem of the lack of oscillation data and the inability to label in the real system, a simplified simulation system was proposed to generate large batches of labeled training samples. Then, the common features of the samples from simulation system and the real system were learned through the transfer component analysis algorithm. Afterward, a classifier was trained to classify samples with common features. Finally, two grid-connected wind farms with VSC access are used to verify that the proposed method has good locating performance. This has important reference value for the practical application of power grid dispatching and operation using measurement to identify oscillation sources

Adaptive Control of a Virtual Synchronous Generator with Multiparameter Coordination

This paper proposes an adaptive strategy of co-regulating the three parameters—P/ω droop coefficient, virtual inertia, and damping coefficient—for the virtual synchronous generator (VSG). This approach is able to solve the uncoordinated performance between the virtual inertia and the damping using the conventional adaptive control in which the system may experience serious frequency fluctuations. Through the mathematical modeling of the VSG grid-connected system, the segmental analysis of the VSG transient process is carried out, and the parameter adjustment law of each stage is obtained. The VSG angular velocity change and the angular velocity instantaneous change rate are associated with the inertia to realize the adaptive adjustment of the inertia, and the adaptive adjustment of the P/ω droop coefficient is carried out in real time according to the VSG angular velocity change. A functional relationship is established between the P/ω droop coefficient, virtual inertia, and damping coefficient so that the P/ω droop coefficient, virtual inertia, and damping coefficient are coordinated to keep the system in the best damping ratio state all the time. Finally, the superiority of the proposed strategy is proved by simulation comparison

Minimum inertial demand estimation of renewable energy considering new power system frequency constraints using sparrow search algorithm

With the proposal of a ''dual carbon strategy '' and the rising share of renewable energy sources, the resulting low-inertia problem has seriously affected the frequency stability of new power systems. This paper proposes a minimum inertial demand estimation of renewable energy considering new power system frequency constraints to enhance the power system inertia situational awareness and clarify the system operation boundary. Firstly, the frequency response model with multi-source renewable energy units and the time-domain mathematical expressions are established, and the frequency constraints and inertial demand of new power systems are given. The minimum inertial demand estimation model is constructed on this basis, which can be solved by the sparrow search algorithm (SSA). Ultimately, the applicability and practicality of the suggested approach are demonstrated and verified in IEEE 39-bus system. The case study results demonstrate the process can accurately calculate the inertial demand of renewable energy side considering system frequency constraints and give guidance for the stable operation of new power systems

Examination on the parameter stability region of the VSC control system in VSC-HVDC transmission systems

Nowadays, the voltage source converter (VSC)-based high-voltage direct current (HVDC) transmission technology is widely adopted for the integration of renewable power generations. However, the grid-connected VSC systems may also cause power system instability issues. This study focuses on the parameter stability region of VSC control system in VSC-HVDC transmission systems. The small-signal linearised model of the whole power system is firstly established and on the basis of which the modal analysis method is applied to obtain the parameter stability region of VSC control system. The results show that the region is mainly affected by both the selection of the initial operating point of VSC and the dynamic interaction between the AC system and the VSC. From these two perspectives, the parameter stability regions of the VSC control system under different operating conditions are investigated. Meanwhile, it is found in the study that when modal coupling occurs between the oscillation modes corresponding to AC system and VSC, the parameter stability regions of the VSC control system obtained in the previous analysis will be deteriorated. Consequently, in terms of stability region, an evaluated method for VSC control system is proposed and validated through case study

Synthetic load modelling considering the influence of distributed generation

In order to establish a load model with distributed generation which is suitable for large-scale power system transient simulation under the background of high permeability of new energy, firstly a four machine integrated load model structure that describes the mixing of distributed new energy and traditional load is constructed in this paper, then a reduced order equivalent algorithm for distributed new energy clusters is proposed to determine the parameters of the distributed power equivalent model. Finally, an example is given to verify the effectiveness of the proposed method. The simulation results verify that the proposed method can accurately simulate the comprehensive load characteristics of distribution network with distributed generation

Research on the influence of load model with distributed PV generation on the voltage stability of receiving-end power grid

The low voltage ride-through (LVRT) characteristics of DPV are first analyzed in this paper, and then the influence of the LVRT control strategies and parameters of PDGs on the grid voltage stability is studied. Finally, the influence of load model with and without DG on grid voltage stability is studied, the simulation results show that it may be too optimistic when new energy sources are not considered

Ambient data-driven SSO online monitoring of type-3 wind turbine generator integrated power systems based on MMPF-KF method

Abstract Series compensation grids connected with type-3 wind turbine generator (WTG)-based wind farms have suffered numerous subsynchronous oscillation (SSO) events worldwide. For early alerting of SSO and effective development of protection and control strategies, it is critical to monitor and identify SSO accurately and quickly. Ambient data is continuously available, which is useful for online monitoring. This paper proposes an ambient data-driven SSO online monitoring method based on the Kalman filter (KF) combined with the multi-model partitioning filter (MMPF). The KF is utilized to fit the measured ambient data with an auto regressive (AR) model. Then, the damping factor (or damping ratio) and frequency in the SSO mode can be acquired by solving the roots of the characteristic polynomial corresponding to the AR model. Moreover, the MMPF is an effective model order selection method applied to the KF for better identification. The performance of the MMPF-KF method is demonstrated by simulations and real-time experiments. The results of case studies validate the effectiveness of the proposed method under various conditions

Hepatic stellate cells promote tumor progression by enhancement of immunosuppressive cells in an orthotopic liver tumor mouse model

National Key Sci-Tech Special Project of China [2012ZX10002-011-005]; National Natural Science Foundation of China [81171976, 81201894]; Provincial Natural Science Foundation of Fujian, China [2011D003]The immunosuppressive properties of hepatic stellate cells (HSCs) contribute to the occurrence and development of hepatocellular carcinoma (HCC). The accumulation of cells with immune suppressive activities, such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) is a key mechanism for tumor immune evasion. However, the impact of HSCs on immune cell populations in tumor-bearing hosts is unclear. In this study, we established an orthotopic liver tumor mouse model for studying the complex tumor-host interactions in HCC. The activated HSCs promoted HCC growth not only induced tumor angiogenesis and lymphangiogenesis, but also significantly increased the suppressive immune cell population of Tregs and MDSCs in the spleen, bone marrow, and tumor tissues of the tumor-bearing mice. Murine HCC cell line H22-activated HSCs also expanded the expression of Tregs and MDSCs in vitro. In conclusion, our study suggests a novel role for HSCs in the HCC microenvironnnent. HSCs can promote HCC progression by enhancement of the immunosuppressive cell population. Targeting HSCs, which is a new concept in adjuvant immunotherapy, may be introduced in the near future to improve the outcome of patients with HCC