Flexible HVDC transmission systems small signal modelling: a case study on CIGRE Test MT-HVDC grid

Future Flexible HVDC Transmission Systems will consist of several active and passive infrastructures including DC Power Flow Controller (DC-PFC) device. The addition of the DC-PFC to the future Multi-Terminal HVDC (MT-HVDC) grids arises some key concerns such as stability, system interoperability, and possible adverse interactions. Hence, a suitable model is necessary to conduct deep frequency domain analysis. In this context, this paper proposes a linearized model for small-signal stability analysis of flexible MT-HVDC grids in state space framework that can be straightforwardly utilized in the process of control design. In this paper, a spick-and-span, systematic and step-by-step process to derive the small signal model of all flexible MT-HVDC grid components is presented such that each sub-system is modeled individually and then all are integrated together. The derived model is cross-verified by time domain simulations of a nonlinear system model in a MATLAB/SIMULINK platform for CIGRE DCS3 MT-HVDC test grid.Peer ReviewedPostprint (author's final draft

Analysis on impacts of the shunt conductances in multi-terminal HVDC grids optimal power-flow

This study deals with impacts of the shunt conductances associated with HVDC cables and VSC-HVDC stations on optimal operation of Multi-Terminal HVDC (MT-HVDC). In this study, for the first time, shunt conductances are integrated to HVDC Optimal Power-Flow (OPF) program that is executed at the Power Dispatch Center (PDC) of the MT-HVDC grid. With the objective of losses minimization, optimal reference operation points of the VSC-HVDC stations are derived. The operating points of the power converter stations are adjusted based on the calculations performed in the dispatch center. CIGRE DCS3 MT-HVDC grid, structured by CIGRE B4 working group, is taken as the test platform. Test results have revealed the optimum voltages and loss pattern change. Moreover, the findings are compared with the case of neglecting the shunt conductances.Peer ReviewedPostprint (author's final draft

Adaptation of VSC-HVDC Connected DFIG Based Offshore Wind Farm to Grid Codes: A Comparative Analysis

Lack of synchronism between VSC-HVDC (Voltage Source Converter - High Voltage Direct Current) connected offshore wind farm and onshore grid leads to immunity of wind turbines to grid contingencies. Focusing on DFIG (Doubly Fed Induction Generator) based wind farms; this paper has presented a univalent control structure based on inertial and primary frequency response in which DC link voltage is utilized as synchronization interface. Based on the presented structure, four approaches based on the communication system, frequency, voltage and combined frequency and voltage modulation are utilized and compared to inform the onshore grid status to individual wind turbines. Considering Kondurs two area power system, results have revealed that all four approaches have similar ability (with negligible error) in offering inertial and primary frequency response to improve slow network oscillations. On the other hand, voltage and combined frequency and voltage modulation approaches have the ability to satisfy Fault Ride Through (FRT) requirements thanks to superior dynamics. However, communication and frequency modulation approaches lose that ability as communication and frequency measurement delays increase respectively. It has been concluded that combined frequency and voltage modulation, as the superior approach, has advantages like minimum FRT DC voltage profile increase and deviation from operating point after the fault, the minimum imposition of electrical and mechanical stress on DFIG and preservation of prevalent control structure thanks to appropriate dissociation between slow and fast dynamics. ©2019. CBIORE-IJRED. All rights reserved Article History: Received Dec 8th 2017; Received in revised form July 16th 2018; Accepted December 15th 2018; Available online How to Cite This Article: Yazdi, S.S.H., Milimonfared, J. and Fathi, S.H. (2019). Adaptation of VSC-HVDC Connected DFIG Based Offshore Wind Farm to Grid Codes: A Comparative Analysis. Int. Journal of Renewable Energy Development, 8(1), 91-101. https://doi.org/10.14710/ijred.8.1.91-10

Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

This paper proposes an efficient learning based approach to detect the faults of an industrial oil pump. The proposed method uses the wavelet transform and genetic algorithm (GA) ensemble for an optimal feature extraction procedure. Optimal features, which are dominated through this method, can remarkably represent the mechanical faults in the damaged machine. For the aim of condition monitoring, we considered five common types of malfunctions such as casing distortion, cavitation, looseness, misalignment, and unbalanced mass that occur during the machine operation. The proposed technique can determine optimal wavelet parameters and suitable statistical functions to exploit excellent features via an appropriate distance criterion function. Moreover, our optimization algorithm chooses the most appropriate feature submatrix to improve the final accuracy in an iterative method. As a case study, the proposed algorithms are applied to experimental data gathered from an industrial heavy-duty oil pump installed in Arak Oil Refinery Company. The experimental results are very promising

Should cross-border banking benefit from the financial safety net?

Using bank-level data from 84 countries, we find that a higher degree of bank internationalization is associated with higher interest expenses. Internationalization is proxied by a bank's share of foreign liabilities in total liabilities or a Herfindahl index of international liability concentration. Bank interest expenses rise relatively more with internationalization if the bank is underperforming or headquartered in a country with weak public finances, and especially at times of weak world output growth. These results suggest that liability holders of distressed internationalized banks expect less from the financial safety net since lack of an efficient recovery and resolution regime for international banks can make their insolvency very costly to deal with.post-prin

Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics

The fault diagnosis of photovoltaic (PV) arrays aims to increase the reliability and service life of PV systems. Line-Line (LL) faults may remain undetected under low mismatch level and high impedance due to low currents of faults, resulting in power losses and fire potential disaster. This paper proposes a novel and intelligent fault diagnosis method based on an ensemble learning model and Current-Voltage (I-V) characteristics to detect and classify LL faults at the DC side of PV systems. For this purpose, first, the key features are extracted via analyzing I-V characteristics under various LL fault events and normal operation. Second, a feature selection algorithm has been applied to select the best features for each learning algorithm in order to reduce the amount of data required for the learning process. Third, an ensemble learning model is developed that combines several learning algorithms based on the probabilistic strategy to achieve superior diagnostic performance. Here, we find an excellent agreement between simulation and experimental results that the proposed method can obtain higher accuracy in detecting and classifying the LL faults, even under low mismatch levels and high fault impedances. In addition, the comparison results demonstrate that the performance of the proposed method is better than individual machine learning algorithms, so that the proposed method precisely detects and classifies LL faults on PV systems under the different conditions with an average accuracy of 99% and 99.5%, respectively

Fault Detection and Classification for Photovoltaic Systems Based on Hierarchical Classification and Machine Learning Technique

Line-Line (LL) and Line-Ground (LG) faults may not be detected by common protection devices in PV arrays due to these faults are not detectable under high fault impedance and low mismatch level. In recent years, many efforts have been devoted to overcome these challenges using intelligent methods. However, these methods could not classify the type of faults and diagnose their severity. This paper proposes a novel and intelligent fault monitoring method to detect and classify LL and LG faults at the DC side of PV systems. For this purpose, the main features of Current-Voltage (I-V) curves under different fault events and normal conditions are extracted. The faults are categorized using the Hierarchical Classification (HC) platform. Later, the LL and LG faults are detected and classified by Machine Learning (ML) methods. The proposed method aims to reduce the amount of dataset which is required for the learning process and also obtain a higher accuracy in detecting and classifying the fault events at low mismatch levels and high fault impedance compared to other fault diagnostic methods. The experimental results verify that the proposed method precisely detects and classifies LL and LG faults on PV systems under the different conditions and severity with the accuracy of 96.66% and 91.66%, respectively