In this paper, technical challenges for realizing offshore multi-terminal HVDC (MTDC) transmission system in Europe is evaluated. An offshore MTDC topology is projected by interconnecting point-to-point HVDC links with the same voltage level and technology found in southeastern part of North Sea. Availability analysis is done to evaluate the feasibility of the proposed offshore MTDC topology. As compared to point-topoint (PtP) HVDC link, MTDC operation gives a more secure and reliable system. This paper shows that the proposed MTDC topology can operate 98.36% of the time.
Abstract-As more and more VSC-HVDC links are planned and commissioned, interconnection of neighboring links or connection to an existing link becomes a feasible step towards secure and reliable multi-terminal DC (MTDC) transmission systems. Multi-vendor MTDC (MV-MTDC) operation is anticipated as an impact of this gradual DC grids development. In order to achieve certain operation conditions, DC grid controller needs to send specific control settings to individual converter station controls. One of the challenges in MV-MTDC transmission systems occurs in coordination of converters since different types of controller might exist in the same system. It is therefore important to identify the parameters required by the converter station controllers to ensure a smooth coordination. In this paper, different converter station controllers available in the literatures are categorized. Challenges of converter station control coordination are also discussed.
The development of electric power systems has become more complex. Consequently, electric power systems are operating closer to their limits and are more susceptible to instability when a disturbance occurs. Transient stability problems are especially prevalent. In addition, the identification of transient stability is difficult to achieve in real time using the current measurement data. This research focuses on developing a convolutional neural network—long short-term memory (CNN-LSTM) model using historical data events to detect transient stability considering time-series measurement data. The model was developed by considering noise, delay, and loss in measurement data, line outage and variable renewable energy (VRE) integration scenarios. The model requires PMU measurements to provide high sampling rate time-series information. In addition, the effects of different numbers of PMUs were also simulated. The CNN-LSTM method was trained using a synthetic dataset produced using the DigSILENT PowerFactory simulation to represent the PMU measurement data. The IEEE 39 bus test system was used to simulate the model under different loading conditions. On the basis of the research results, the proposed CNN-LSTM model is able to detect stable and unstable conditions of transient stability only from the magnitude and angle of the bus voltage, without considering system parameter information on the network. The accuracy of transient stability detection reached above 99% in all scenarios. The CNN-LSTM method also required less computation time compared to CNN and conventional LSTM with the average computation times of 190.4, 4001.8 and 229.8 s, respectively.
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