Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

Development and implementation of a LabVIEW based SCADA system for a meshed multi-terminal VSC-HVDC grid scaled platform

This project is oriented to the development of a Supervisory, Control and Data Acquisition (SCADA) software to control and supervise electrical variables from a scaled platform that represents a meshed HVDC grid employing National Instruments hardware and LabVIEW logic environment. The objective is to obtain real time visualization of DC and AC electrical variables and a lossless data stream acquisition. The acquisition system hardware elements have been configured, tested and installed on the grid platform. The system is composed of three chassis, each inside of a VSC terminal cabinet, with integrated Field-Programmable Gate Arrays (FPGAs), one of them connected via PCI bus to a local processor and the rest too via Ethernet through a switch. Analogical acquisition modules were A/D conversion takes place are inserted into the chassis. A personal computer is used as host, screen terminal and storing space. There are two main access modes to the FPGAs through the real time system. It has been implemented a Scan mode VI to monitor all the grid DC signals and a faster FPGA access mode VI to monitor one converter AC and DC values. The FPGA application consists of two tasks running at different rates and a FIFO has been implemented to communicate between them without data loss. Multiple structures have been tested on the grid platform and evaluated, ensuring the compliance of previously established specifications, such as sampling and scanning rate, screen refreshment or possible data loss. Additionally a turbine emulator was implemented and tested in Labview for further testing

Global Tracking Passivity--based PI Control of Bilinear Systems and its Application to the Boost and Modular Multilevel Converters

This paper deals with the problem of trajectory tracking of a class of bilinear systems with time--varying measurable disturbance. A set of matrices {A,B_i} has been identified, via a linear matrix inequality, for which it is possible to ensure global tracking of (admissible, differentiable) trajectories with a simple linear time--varying PI controller. Instrumental to establish the result is the construction of an output signal with respect to which the incremental model is passive. The result is applied to the boost and the modular multilevel converter for which experimental results are given.Comment: 9 pages, 10 figure

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

Real-time estimation and damping of SSR in a VSC-HVDC connected series-compensated system

Infrastructure reinforcement using high-voltage direct-current (HVDC) links and series compensation has been proposed to boost the power transmission capacity of existing ac grids. However, deployment of series capacitors may lead to subsynchronous resonance (SSR). Besides providing bulk power transfer, voltage source converter (VSC)-based HVDC links can be effectively used to damp SSR. To this end, this paper presents a method for the real-time estimation of the subsynchronous frequency component present in series-compensated transmission lines-key information required for the optimal design of damping controllers. A state-space representation has been formulated and an eigenvalue analysis has been performed to evaluate the impact of a VSC-HVDC link on the torsional modes of nearby connected thermal generation plants. Furthermore, the series-compensated system has been implemented in a real-time digital simulator and connected to a VSC-HVDC scaled-down test-rig to perform hardware-in-the-loop tests. The efficacy and operational performance of the ac/dc network while providing SSR damping is tested through a series of experiments. The proposed estimation and damping method shows a good performance both in time-domain simulations and laboratory experiments