<![CDATA[ IET Generation, Transmission & Distribution - new TOC ]]>
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TOC Alert for Publication# 4082359 2018March 19<![CDATA[Detailed modelling and parameters optimisation analysis on governing system of hydro-turbine generator unit]]>125104510513512<![CDATA[AI-based approach<?show [AQ ID=Q1]?> to identify compromised meters in data integrity attacks on smart grid]]>125105210661513<![CDATA[Travelling waves-based identification of sub-health condition of feeders in power distribution system]]>125106710734178<![CDATA[Short-term ice accretion forecasting model for transmission lines with modified time-series analysis by fireworks algorithm]]>125107410803682<![CDATA[Transmission network expansion planning considering risk level assessment and scenario-based risk level improvement]]>125108110883641<![CDATA[PV inverter reactive power control for chance-constrained distribution system performance optimisation]]>125108910983078<![CDATA[Quasi-oppositional harmony search algorithm based optimal dynamic load frequency control of a hybrid tidal–diesel power generation system]]>125109911084820<![CDATA[Correlation-based synchro-check relay for power systems]]>125110911207364<![CDATA[Novel high-frequency-based diagnostic approach for main contact assessment of high-voltage circuit breakers]]>125112111264059<![CDATA[Optimal operation of aggregated electric vehicle charging stations coupled with energy storage]]>125112711363161<![CDATA[Vulnerable transmission line identification considering depth of <italic>K</italic>-shell decomposition in complex grids]]>K-shell (Ks) decomposition under power transfer (named the DKsPS method), which fully considers the dynamic characteristics of the power transfer and transmission capability after the power grid fault. This method establishes a correlation network based on the correlation matrix of transmission lines under the N - 1 check and then identifies the vulnerable transmission lines by using the modified Ks decomposition. Numerical simulations on both the IEEE-39 bus system and the Northeast China power grid verify the validity and accuracy of the DKsPS method.]]>125113711441814<![CDATA[Approach for identification and classification of HIFs in medium voltage distribution networks<?show [AQ ID=Q1]?>]]>125114511523301<![CDATA[Fuzzy logic gain-tuned adaptive second-order GI-based multi-objective control for reliable operation of grid-interfaced photovoltaic system]]>125115311638076<![CDATA[Methodology for ESS-type selection and optimal energy management in distribution system with DG considering reverse flow limitations and cost penalties]]>125116411702819<![CDATA[Investigation on enhancing breakdown voltages of transformer oil nanofluids using multi-nanoparticles technique]]>125117111762331<![CDATA[Numerical approach of<?show [AQ ID=Q1]?> impressed potential on buried pipelines near high-voltage DC grounding electrodes]]>125117711822261<![CDATA[Ion-flow field calculation of HVDC overhead lines using a high-order stabilisation technique based on Petrov–Galerkin method]]>125118311893873<![CDATA[Investigating the effect of cavity size within medium-voltage power cable on partial discharge behaviour]]>125119011976585<![CDATA[Decentralised control for reactive power sharing using adaptive virtual impedance]]>125119812054272<![CDATA[Using smart meters to estimate low-voltage losses]]>125120612121967<![CDATA[Simple and efficient control of DSTATCOM in three-phase four-wire polluted grid system using MCCF-SOGI based controller]]>125121312225458<![CDATA[Resilience-based framework for switch placement problem in power distribution systems]]>125122312301662<![CDATA[Generalised protection strategy for HB-MMC-MTDC systems with RL-FCL under DC faults<?show [AQ ID=Q1]?>]]>125123112392937<![CDATA[Correlation characteristics between gas pressure and SF<sub>6</sub> decomposition under negative DC partial discharge]]>6) was conducted under different pressures on the basis of constructing a SF_{6} decomposition experimental platform under DC PD. A stainless steel needle-plate electrode was used to simulate the insulation defects of metal protrusions in GIS. SOF_{2}, SO_{2}F_{2}, CO_{2}, SO_{2}, and CF_{4} data generated from SF_{6} decomposition were obtained under different pressures. The variation law of component concentration and effective characteristic ratio with pressure were analysed in detail. In addition, a mathematical formula for pressure and decomposition components was deduced according to gas micro-ionisation theory. The effective content and formation rate were defined to validate the relationship. Results show that the concentration of SF_{6} decomposition components decreased gradually with increasing pressure with a strong regularity. Furthermore, the relationship between pressure and component concentration derived from the theory can explain the relationship between the effective content of SF_{6} decomposition components and the effective formation rate and pressure variations.]]>125124012462127