Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

WVAELET TRANSFORM IN TANDEM WITH AUTOREGRESSIVE TECHNIQUE FOR MONITORING AND DIAGNOSIS OF MACHINERY

A new approach is advanced based on wavelet transform in tandem with autoregressive technique. Wavelets and wavelet packets decompose dynamic signal into different bands and pro- vide multiresolution or multiscale view of signal. Autoregressive technique possesses effective function for short data processing. The approach is abopted to analyse nonstationary vibration sig- nals of mining excavator. The results indicate that nonstationary operating condition can be moni- tored and diagnosed. Transient toothmeshing frequency and rotating speed are measured. Weak vibration signal of defective ball bearing is extracted from strong vibration of gearbox and diag- nosed successfully. This approach is effective for nonstationary condition monitoring and early di- agnosis of latent fault.