Unlike synchronous generators, wind turbines cannot directly respond to large disturbances, which may cause transient instability, due to their power electronic-based interface and maximum power control strategy. To effectively monitor the influence of wind turbines, this paper proposes an approach that combines decision trees (DTs), and a newly developed variant of the Mean-Variance Mapping Optimization (MVMO) algorithm, to simultaneously tackle the problem of selecting the key variables that properly reflect the transient stability performance of a system dominated by wind power, and designing the DTs for reliable online assessment of transient stability. The notion of key variables refers to the set of variables that are closely related to the modified power system transient stability performance as a consequence of the replacement of conventional power plants by wind generators. The selection of key variables is formulated as a non-linear optimization problem with weight factors as decision variables and is tackled by MVMO. A weight factor is assigned to each key variable candidate, and its value is considered to reflect the degree of influence of the key variable candidate on the splitting property and estimation accuracy of the DTs. The samples of the key variable candidates and the initialized weight factors are used to build the first group of DTs. Then, MVMO iteratively evolves the weight factors according to its special mapping function with minimizing DTs' estimation error. According to the final list of optimized weight factors, system operators can select a reduced set of variables with the largest weight factors as key variables, depending on the resulting accuracy of the DTs. Meanwhile, DTs built by using key variables are considered as the optimal performance trees for transient stability estimation. In this way, the selection of key variables and the development of DTs are made jointly and automatically, without the interference of the users of the DTs. Test results on the modified IEEE 9 bus system and a synthetic model of a real power system show that the proposed method can correctly identify the set of key variables related to wind turbine dynamics, as well as its ability to provide a reliable estimation of the transient stability margin.

Due to the power electronic converter based interface and maximum power control strategy, wind generators cannot directly respond to power system transients. This brings new challenges on power system transient stability. Taking wind turbine type 4 (WT4) as one example, this paper analyses its influence on transient stability with respect to locations, low voltage ride through parameters, wind power plant installation capacity and penetration levels. Based on the sensitivity analysis carried out for the influence of WT4, a supplementary transient stability control is proposed. The results on a 3-area system show that this supplementary control can improve transient stability of power systems with high penetration of wind power.

The deployment of variable renewable energy based power plants is increasing all over the world, however, unlike conventional power plants these are mostly connected to the grid via power electronic interfaces. High penetration of power electronic interfaced generation (PEIG) has an important impact on the inertia of the system, which is of major concern for frequency and large disturbance rotor angle (transient) stability. Therefore, it is desirable to study the effectiveness of widely used approaches to assess the stability of a system with high penetration of PEIG. This paper concerns with the modelling and control aspects of a power system for the evaluation of the most widely used metrics (indicators) to assess the dynamics of the power system related to frequency and rotor angle stability. The functionalities of Python are used to automate the generation of operational scenarios, the execution of time domain simulations, and the extraction of signal records to compute the aforesaid indicators. The paper also provides a discussion about possible improvements in the application of these indicators in monitoring tasks.

The CMS Hadron Calorimeter in the barrel, endcap and forward regions is fully commissioned. Cosmic ray data were taken with and without magnetic field at the surface hall and after installation in the experimental hall, hundred meters underground. Various measurements were also performed during the few days of beam in the LHC in September 2008. Calibration parameters were extracted, and the energy response of the HCAL determined from test beam data has been checked.