<![CDATA[ IET Generation, Transmission & Distribution - new TOC ]]>
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TOC Alert for Publication# 4082359 2016September26<![CDATA[Thermal behaviour analyses of gas-insulated switchgear compartment using thermal network method]]>101228332841960<![CDATA[Multi-objective approach for distribution network reconfiguration with optimal DG power factor using NSPSO]]>101228422851678<![CDATA[Distribution network reconfiguration validation with uncertain loads – network configuration determination and application]]>101228522860617<![CDATA[Effects of QV curves in the dynamic behaviour of power systems]]>101228612870639<![CDATA[Algorithm for transformer differential protection based on wavelet correlation modes]]>1012287128791048<![CDATA[Hierarchical hybrid control strategy for micro-grid switching stabilisation during operating mode conversion]]>101228802890667<![CDATA[Disturbance propagation mechanism based on the electromechanical wave theory]]>101228912898622<![CDATA[Combinational scheme for voltage and frequency recovery in an islanded distribution system]]>101228992906852<![CDATA[Power system energy stability region based on dynamic damping theory]]>101229072914756<![CDATA[Comparative study on the performance of many-objective and single-objective optimisation algorithms in tuning load frequency controllers of multi-area power systems]]>101229152923673<![CDATA[Circulating current derivation and comprehensive compensation of cascaded STATCOM under asymmetrical voltage conditions]]>1012292429321299<![CDATA[Probabilistic assessment of state estimation capabilities for grid observation]]>observation grades. With a view to performing a reliable SE, these are defined as ratings capable of indicating that a measurement system (devoted to observing the state of a power grid under many different conditions), has a seal of approval, i.e. relatively low risk of being unsuccessful. The methodology proposed to express observation grades is based on the Monte Carlo simulation approach. The availability of measurement units and grid branches are adequately considered. Numerical results of a proof of concept study performed on the 24- and 118-bus benchmark systems illustrate the application and expected benefits of the proposed methodology.]]>101229332941263<![CDATA[Identification of critical generating units for maintenance: a game theory approach]]>101229422952898<![CDATA[Dynamic load shedding for an islanded microgrid with limited generation resources]]>101229532961567<![CDATA[Successive power flows with adaptive step-length increments for fast approximation of the maximum loading point]]>101229622971691<![CDATA[Numerical polynomial homotopy continuation method to locate all the power flow solutions]]>embarrassingly parallelisable. The authors demonstrate the technique performance by solving several test cases up to the 14 buses. Finally, they discuss possible strategies for scaling the method to large size systems, and propose several applications for security assessments.]]>101229722980314<![CDATA[Comprehensive power transfer distribution factor model for large-scale transmission expansion planning]]>101229812989473<![CDATA[Detection of high impedance faults using current transformers for sensing and identification based on features extracted using wavelet transform]]>101229902998996<![CDATA[Non-cooperative game theory based energy management systems for energy district in the retail market considering DER uncertainties]]>1012299930091248<![CDATA[Conditional abnormality detection based on AMI data mining]]>101230103016546<![CDATA[Application of high-order Levenberg–Marquardt method for solving the power flow problem in the ill-conditioned systems]]>σ and μ) can decrease the number of iterations and the computation time in solving the power flow problem of the ill-conditioned power systems. The proposed formulations and algorithm are tested on the 11-bus, 57-bus, 118-bus and 2383-bus ill-conditioned test systems. The simulation results show that the proposed method can significantly reduce the computation time and the number of iterations.]]>101230173022366<![CDATA[MILP branch flow model for concurrent AC multistage transmission expansion and reactive power planning with security constraints]]>101230233032653<![CDATA[Per-unit power costs of traditional and innovative OHLs: a multi-criteria comparison]]>per-unit-of-transportable-power cost of each solution. This parameter provides, for any given line length, a total cost normalised in terms of the theoretical transmission capability of each solution and, thus, an interesting comparison tool among different solutions.]]>101230333040717<![CDATA[Output feedback dynamic tracking excitation control of synchronous generators]]>101230413049539<![CDATA[Probabilistic power flow calculation using the Johnson system and Sobol's quasi-random numbers]]>101230503059772<![CDATA[Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production]]>101230603067492<![CDATA[Real-time verification of new controller to improve small/large-signal stability and fault ride-through capability of multi-DER microgrids]]>1012306830841719<![CDATA[PQ state space representation and its application to electromagnetic compatibility/incompatibility degree, influence degree, and PQ performance assessment]]>101230853092441<![CDATA[Three-phase probabilistic load flow for power system with correlated wind, photovoltaic and load]]>101230933101484<![CDATA[Operation of a hybrid modular multilevel converter during grid voltage unbalance]]>101231023110760