This paper presents a methodology of optimizing power systems demand due to electric vehicle (EV) charging load. Following a brief introduction to the charging characteristics of EV batteries, a statistical model is presented for predicting the EV charging load. The optimization problem is then described, and the solution is provided based on the model. An example study is carried out with error and sensitivity analysis to validate the proposed method. Four scenarios of various combinations of EV penetration levels and charging modes are considered in the study. A series of numerical solutions to the optimization problem in these scenarios are obtained by serial quadratic programming. The results show that EV charging load has significant potential to improve the daily load profile of power systems if the charging loads are optimally distributed. It is demonstrated that flattened load profiles may be achieved at all EV penetration levels if the EVs are charged through a fast charging mode. In addition, the implementation of the proposed optimization is discussed with analyses on the impact of travel pattern and the willingness of customers.Index Terms-Electric vehicle (EV), load modeling, power demand, quadratic programming.

Partial discharge (PD) measurement is an established condition monitoring technique used to facilitate the detection of incipient faults in high voltage electrical insulation systems such as gas-insulated switchgear and power transformers. By simultaneously recording partial discharge using both the IEC60270 'apparent charge' measurement technique and the more recent Radio Frequency method, which measures the energy radiated from the discharge, it is thought that more information can be gained about the nature of the PD source. Combined measurement has been carried out on various common PD source topologies recreated under laboratory conditions using PD test cells in a range of insulating media including SF6, oil and epoxy resin. Test cell geometries include floating electrodes, free particles and protrusions. By plotting apparent charge against the energy of the RF signal for a large number of PD pulses, the relationship between the two techniques can be quantified for a given test configuration. It has been found that the correlation between the two techniques produces characteristic patterns specific to each defect type. It is envisioned that combined RF/IEC measurements will contribute to a more widespread acceptance of the RF technique in terms of its ability to quantify PD severity by relating the RF energy to the more widely accepted IEC60270 pC level

It is a great challenge to differentiate Partial Discharge (PD) induced by different types of insulation defects in high voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to be differentiate, even for the most experienced specialists. T o overcome the challenge, a Convolutional Neural Network (C NN) based deep learning methodology for PD pattern recognition is presented in this paper. Firstly, PD testing for five types of artificial defects in E thylene-Propylene-R ubber (E PR) cables was carried out in the High V oltage (HV) laboratory to generate signals containing PD data. Secondly, 3500 sets of PD transient pulses were extracted and then 33 kinds of PD features were established. T he third stage applies a C NN to the data: typical C NN architecture and the key factors which affect the C NN based pattern recognition accuracy, are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. T he paper presents a flowchart of the C NN based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the C NN based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e. Support V ector Machine (SV M) and Back Propagation Neural Network (BPNN). T he results show that the proposed C NN method has higher pattern recognition accuracy than SV M and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications. Index Terms-C onvolutional neural network, deep learning, high voltage cables, partial discharge, pattern recognition.

Concerns over changes to the global environment and the growing need for energy have increased the penetration of renewable energy (RE) generation into low voltage distribution networks. The introduction of Energy Storage Systems (ESS) into distribution networks has been proposed to improve the reliability and performance of power systems. Energy storage systems will also be important in helping to regulate the supply from intermittent RE sources that feed into variable load demand. The focus of this paper is to provide an overview of the state of the art of ESS, concentrating on the distinct characteristics and applications of the different types. The analysis includes comparison and evaluation of different storage preferences with regard to integration of these technologies with an electric grid at the distribution network level that may lead to benefits such as distribution upgrade deferral and improvements in power quality and reliability.

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The fast growing wind industry requires a more sophisticated fault detection approach in pitchregulated wind turbine generators (WTG), particularly in the pitch system that has led to the highest failure frequency and downtime. Improved analysis of data from Supervisory Control and Data Acquisition (SCADA) systems can be used to generate alarms and signals that could provide earlier indication of WTG faults and allow operators to more effectively plan Operation and Maintenance (O&M) strategies prior to WTG failures. Several data-mining approaches, e.g. Artificial Neural Network (ANN), and Normal Behaviour Models (NBM) have been used for that purpose. However, practical applications are limited because of the SCADA data complexity and the lack of accuracy due to the use of SCADA data averaged over a period of 10 minutes for ANN training. This paper aims to propose a new pitch fault detection procedure using performance curve (PC) based NBMs. An advantage of the proposed approach is that the system consisting of NBMs and criteria, can be developed using technical specifications of studied WTGs. A second advantage is that training data is unnecessary prior to application of the system. In order to construct the proposed system, details of WTG operational states and PCs are studied. Power-generator speed (P-N) and pitch angle-generator speed (PA-N) curves are selected to set up NBMs due to the better fit between the measured data and theoretical PCs. Six case studies have been carried out to show the prognosis of WTG fault and to demonstrate the feasibility of the proposed method. The results illustrate that polluted slip rings and the pitch controller malfunctions could be detected by the proposed method 20 hours and 13 hours earlier than by the AI approaches investigated and the existing alarm system. In addition, the proposed approach is able to explain and visualize abnormal behaviour of WTGs during the fault conditions.

Cross-bonded metal sheath connection is applied in sectioned single-core power cables to reduce or eliminate the voltages which are induced in the sheath over long distances. However, cross-bonded cables present an opportunity as well as a challenge for on-line measurement and diagnosis of cable conditions. In this paper, a methodology to identify cable faults through analysis of the sheath system currents in a cross-bonded cable system is presented. Firstly, a numerical model is established to simulate the sheath currents in cross-bonded cable systems. Secondly, analyses of several faults, which happen frequently with serious consequences, are presented on the basis of current measurement at the link cable. Simulations of normal and fault conditions are given to determine the feasibility of fault diagnosis. A case study using field data from a cable tunnel in China considering the normal condition is presented to verify the numerical model. Results in normal condition show good consistency with field data with error less than 5%. Simulation results of fault conditions show that analysis of readings from 6 current sensors can distinguish different fault types and fault positions using the method proposed. Based on the analyses, criteria are established for sheath loop fault type diagnosis.

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