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Dielectric loss has long been recognized as one of the most important indicators of cable insulation health. Although huge efforts have been made in the past to measure the dielectric loss of cable circuits, there has been no report of any successful on-line techniques for the purpose. This paper proposes a new on-line insulation dielectric loss monitoring method, based on separation of currents collected from the co-axial cables connecting the cable sheathes and the cable link boxes. The principle and theoretical foundation of the method are demonstrated. MATLAB simulations, based on the real cable parameters in a 110 kV cable tunnel in China, indicate the error of the proposed method is less than 1 × 10 −3 %. The criteria for determining the relative dielectric loss in cable segments, based on the leakage current separation results, are demonstrated at the end. Initial results and analysis of implementation of the proposed method in the real world 110 kV cable tunnel are presented.

The fast growing wind industry requires a more sophisticated fault detection approach in pitch-regulated 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 min 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 h and 13 h 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.

Domestic electricity load profile is essential for energy planning and renewable energy system design. This paper presents analysis of domestic electric load characteristics and a method to model domestic and regional load profile. Multiple Gaussian functions are used to express the load characteristics in the proposed model. This is done by associating the Gaussian function parameters with the peak load changes, e.g. changing height parameters to reflect the peak magnitude. The result of the load curve represented with multiple Gaussian functions allows the model to generate a regional load profile using the number of homes, the number of bedrooms (Nr) and the number of occupants (Np). The proposed model simulates domestic load profile by its load demand change characteristics instead of its appliance ownership and use pattern, etc. Data requirement for the proposed method is significantly lower than the previous top-down and bottom-up approaches. Seasonal change is not included in the present paper, but the method is capable of including seasonal changes if each season’s load demand changes in relation to Np and Nr is available. A demonstration of modelling England and Wales’s national hourly load profile in 2001 and 2011 is presented in this paper. Comparison is made of the proposed method with two other published domestic load profile models. Results show that the proposed method improves the mean percentage errors by at least 5.7% on average hourly load profile.

This paper develops a methodology to determine the impacts of high penetration level of fully electric vehicles (FEVs) charging loads on the thermal ageing of power distribution transformers. The method proposed in this paper is stochastically formulated by modelling the transformer life consumption due to FEVs charging loads as a function of ambient temperature, start time of FEVs charging, initial state-of-charge and charging modes. FEVs loads are modelled using the results from an analytical solution that predicts a cluster of FEVs chargers. A UK generic LV distribution network model and real load demand data are used to simulate FEVs’ impacts on the thermal ageing of LV power distribution transformers. Results show that the ambient temperature, FEVs penetration level, and start time of charging are the main factors that affect the transformer life expectancy. It was concluded that the smart charging scenario generally shows the best outcome from the loss of life reduction perspective. Meanwhile, public charging which shifts a large percentage of charging load to commercial and industrial areas can significantly alleviate the residential transformer loading thus has little impact on the loss of life of transformers. The proposed method in this paper can be easily applied to the determination of the optimum charging time as a function of existing loads, and ambient temperature.

Distributed Generation (DG) is gaining in significance due to the keen public awareness of the environmental impacts of electric power generation and significant advances in several generation technologies which are much more environmentally friendly (wind power generation, micro-turbines, fuel cells, and photovoltaic) than conventional coal, oil and gas-fired plants. Accurate assessment of energy losses when DG is connected is gaining in significance due to the developments in the electricity market place, such as increasing competition, real time pricing and spot pricing. However, inappropriate modelling can give rise to misleading results. This paper presents an investigation into the effect of load models on the predicted energy losses in DG planning. Following a brief introduction the paper proposes a detailed voltage dependent load model, for DG planning use, which considers three categories of loads: residential, industrial and commercial. The paper proposes a methodology to study the effect of load models on the assessment of energy losses based on time series simulations to take into account both the variations of renewable generation and load demand. A comparative study of energy losses between the use of a traditional constant load model and the voltage dependent load model and at various load levels is carried out using a 38-node example power system. Simulations presented in the paper indicate that the load model to be adopted can significantly affect the results of DG planning.

Non-intrusive load monitoring (NILM) is a convenient method to determine the electrical energy consumption and operation of individual appliances based on analysis of composite load measured at the entry of a building. It avoids installation of parallel sensors for monitoring individual appliances and could be applied in the smart metering system to obtain useful information for load management. This paper presents an improved NILM method that is capable of recognizing Electric Vehicle Battery (EVB) charging as a load type. Based on the proposed framework, a special pattern recognition algorithm is used to perform load disaggregation. A random switching simulator is developed to examine the performance of the improved NILM under various scenarios. The results demonstrate that the EVB charging load is recognized as well as other traditional appliances. The overall success rate of the disaggregation reaches 94.5% at typical circumstance. Through sensitivity analysis it is also shown that the EVB charging load makes a small impact on the overall performance.