Overvoltage and Insulation Coordination of Overhead Lines in Multiple-Terminal MMC-HVDC Link for Wind Power Delivery

The voltage-sourced converter-based HVDC link, including the modular multilevel converter (MMC) configuration, is suitable for wind power, photovoltaic energy, and other kinds of new energy delivery and grid-connection. Current studies are focused on the MMC principles and controls and few studies have been done on the overvoltage of transmission line for the MMC-HVDC link. The main reason is that environmental factors have little effect on DC cables and the single-phase/pole fault rate is low. But if the cables were replaced by the overhead lines, although the construction cost of the project would be greatly reduced, the single-pole ground fault rate would be much higher. This paper analyzed the main overvoltage types in multiple-terminal MMC-HVDC network which transmit electric power by overhead lines. Based on ±500 kV multiple-terminal MMC-HVDC for wind power delivery project, the transient simulation model was built and the overvoltage types mentioned above were studied. The results showed that the most serious overvoltage was on the healthy adjacent line of the faulty line caused by the fault clearing of DC breaker. Then the insulation coordination for overhead lines was conducted according to the overvoltage level. The recommended clearance values were given

Recent developments in HVDC transmission systems to support renewable energy integration

The demands for massive renewable energy integration, passive network power supply, and global energy interconnection have all gradually increased, posing new challenges for high voltage direct current (HVDC) power transmission systems, including more complex topology and increased diversity of bipolar HVDC transmission. This study proposes that these two factors have led to new requirements for HVDC control strategies. Moreover, due to the diverse applications of HVDC transmission technology, each station in the system has different requirements. Furthermore, the topology of the AC-DC converter is being continuously developed, revealing a trend towards hybrid converter stations. Keywords: Direct current transmission system, Topology, Control strategy, AC-DC converte

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Providing Virtual Inertia Through Power Electronics

VSC-HVDC (voltage source converter based HVDC) system with its inherent merits for renewable energy integration has captured increasing research attentions. However, compared with AC systems dominated by synchronous generators (SGs), VSC-HVDC systems with general vector control cannot provide inertia for the grid due to lack of kinetic energy. This tends to degrade the safety and stability of the grid with the increasing penetration of renewable energy sources. To cope with this issue, virtual synchronous generator (VSG) has been proposed. In this thesis, firstly, a comprehensive introduction of various typologies of VSG schemes is made to illustrate their deficiencies and merits. The simulation results established in Simulink/Plecs show that VSG can not only participate into the regulation of frequency and voltage in case of power disturbances but guarantee the inertia provision for the grid. Although the integration of VSG control enhances the inertia and damping response of inverts, researches show that plenty of issues relative with VSG should be ameliorated. The fluctuation performances of SGs are introduced into the output active power and current of inverters when incorporates VSG control. This threatens the stability and safety of VSG operation, for power electronic based inverters are more vulnerable during the oscillations of current and frequency. Hence, to solve these issues, various enhanced VSG strategies have been constructed to improve its robustness and output performance. In this thesis, the structures and properties of enhanced VSG schemes are fully discussed. The results show that the dynamic properties of VSG during transient periods are enhanced in comparison of that of normal VSG. Modular multilevel converters (MMC) and alternate arm converters (AAC), as the representatives for enhanced topologies of VSC-HVDC system, have more complicated inner structures in comparison with 2/3 level converters. In this thesis, VSG control is applied into MMC/AAC models to strengthen their power and frequency regulation ability. In addition, a four-terminal multi terminal direct current (MTDC) system is incorporated with VSG control to provide primary frequency and voltage response for the grid. The results show that the integration of VSG improves the stability operation and inertia response of MMC/AAC/MTDC systems