<![CDATA[ IET Generation, Transmission & Distribution - new TOC ]]>
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TOC Alert for Publication# 4082359 2017December 14<![CDATA[Signal processing for TFR of synchro-phasor data]]>1116388138916376<![CDATA[Comprehensive approach for hybrid AC/DC distribution network planning using genetic algorithm]]>1116389239024861<![CDATA[Capacity withholding assessment in the presence of integrated generation and transmission maintenance scheduling]]>111639033911804<![CDATA[Dynamic and adaptive reconfiguration of electrical distribution system including renewables applying stochastic model predictive control]]>1116391239217607<![CDATA[Demonstration of voltage control in a real distribution system using model predictive control]]>1116392239293435<![CDATA[Hadoop-based framework for big data analysis of synchronised harmonics in active distribution network]]>1116393039372838<![CDATA[Design and implementation of low-cost universal smart energy meter with demand side load management]]>1116393839453882<![CDATA[Two-stage heuristic methodology for optimal reconfiguration and Volt/VAr control in the operation of electrical distribution systems]]>1116394639541560<![CDATA[Fully distributed multi-area dynamic economic dispatch method with second-order convergence for active distribution networks]]>1116395539653246<![CDATA[Robust pole placement for power systems using two-dimensional membership fuzzy constrained controllers]]>1116396639733229<![CDATA[Model predictive control of plug-in hybrid electric vehicles for frequency regulation in a smart grid]]>1116397439834929<![CDATA[New travelling-wave-based protection algorithm for parallel transmission lines during inter-circuit faults]]>1116398439911584<![CDATA[Modelling and optimisation for costly efficiency improvements on residential appliances considering consumer's income level]]>1116399240012891<![CDATA[Adjustment of discrete load changes in feeder databases for improving medium-term demand forecasting]]>DLC adjustment, improve their average performance over 33% compared to the case were this phenomena is not considered.]]>1116400240082961<![CDATA[Fast convergence evolutionary programming for economic dispatch problems]]>1116400940171501<![CDATA[Analytical derivation of the DC-side input admittance of the direct-voltage controlled modular multilevel converter]]>1116401840302848<![CDATA[Robust multi-objective PQ scheduling for electric vehicles in flexible unbalanced distribution grids]]>1116403140402949<![CDATA[Transmission line fault classification using modified fuzzy <italic>Q</italic> learning]]>Q learning (MFQL) for transmission lines. Proposed MFQL fault classifier is able to achieve very high classification accuracy with relatively small number of samples. The authors' is a first attempt at designing a fault identifier using reinforcement learning for fault segregation in transmission lines. The authors' identifier does not assume prior knowledge of transmission line model or target fault information. Raw voltage and current data (supply and load side) is processed using empirical mode decomposition to generate 13 intrinsic mode functions (IMFs'). Classifier employs the J48 algorithm to further prune these 13 IMF's to eight most relevant input variables, which serve as inputs to the MFQL fault classifier. The authors compare performance of the proposed MFQL classifier to other contemporary AI-based classifiers, e.g. neural networks and support vector machines. Simulation results and performance comparison against other AI-based classifiers elucidates that the proposed MFQL-based identifier achieves a significantly higher performance level and could serve as an important tool for transmission line fault diagnosis.]]>1116404140502744<![CDATA[Multi-criteria decision-making methods for grading high-performance transformer oil with antioxidants under accelerated ageing conditions]]>1116405140582571<![CDATA[Linear time complexity sorting algorithms for electromagnetic transient simulation of MMC-HVdc system]]>N-1) comparisons for BE model and (2N-3) comparisons for TR model are required, showing that the proposed sorting algorithms have linear time complexities. Finally, all the proposed approaches are validated by EMT simulations on MATLAB/Simulink.]]>1116405940677555<![CDATA[High-frequency transient comparison based fault location in distribution systems with DGs]]>1116406840774283<![CDATA[Coordination of directional over-current relays in active distribution networks using generalised benders decomposition]]>1116407840862537<![CDATA[Model order reduction analysis of DFIG integration on the power system small-signal stability considering the virtual inertia control]]>1116408740952253<![CDATA[Identification method for power system low-frequency oscillations based on improved VMD and Teager–Kaiser energy operator]]>α is obtained by a large number of tests. The original signal is decomposed into several modes via improved VMD (IMVD) method. Then, Teager-Kaiser energy operator is applied on the fitting of each component to get the amplitude, frequency, and damping factor of it. By the constructed test signals, the proposed method is compared with the methods of non-parametric VMD, empirical mode decomposition, total least-squares estimation of signal parameters via rotational invariance techniques, and Prony on the performance of mode parameter identification. Results show that the IVMD method effectively overcomes the shortcomings of those methods mentioned above in dealing with mode mixing, noise sequence, and non-stationary signals. Finally, the feasibility of the proposed method in extracting the low-frequency oscillation mode parameters of power system is verified by the simulation signals of the IEEE two-area four-generator power system and the New England 39-bus system.]]>1116409641033502<![CDATA[An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations]]>1116410441132813