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The penetration of photovoltaic systems (PVs) to existing power grids is increasing as they are considered attractive options for electricity generation in distribution networks. This paper focuses on estimating the total power generated by a group of neighbouring PVs, spread over a distribution network using a single pyranometer for measuring the solar irradiance. A new empirical-based model that employs the Gene Expression Programming (GEP) technique is proposed to correlate the distribution of the PVs and the irradiance measured by the pyranometer and estimate the total power generated by the PVs. The geographic variability reduction index has been considered in developing the proposed model that also employs a Wavelet Transform technique to enhance its accuracy. The effective performance of the proposed model is validated using real data collected by the Solar Project at the University of Queensland, Brisbane, Australia. Results reveal that the proposed technique yields more accurate results when compared with other existing approaches in the literature.

Current industry practice to identify fault location in transmission lines is based on visual inspection, travelling waves, and line impedance measurement. Unfortunately, these techniques are only developed to detect the fault location upon its occurrence without the ability to predict abnormal events that usually precede major faults and issue a timely warning signal to avoid potential consequences for power line failures. Furthermore, the current fault locating techniques exhibit some drawbacks that limit their wide practical implementation. This includes cost, access to required data, and low accuracy when employed for specific power line topologies. This study is aimed at presenting and validating a new cost-effective technique based on the line voltage–current characteristics to predict and identify the location of various abnormal and fault events in real-time. By measuring the currents and voltages at both ends of the line, a unique line fingerprint can be identified. Any change in this fingerprint can be detected and analysed by a software installed in the control centre in real-time to identify the location, type, and level of the abnormal events or emerging faults. Robustness of the proposed technique is assessed through simulation analysis conducted on various case studies along with a practical case study.

Due to the low converters rating and cost of doubly fed induction generator (DFIG) along with its ability to function under variable wind speed, DFIG has been widely employed in wind energy conversion systems (WECSs). Unfortunately, the performance of DFIG is sensitive to the variation in the operating conditions and disturbance events at the grid side. This includes wind gust, voltage fluctuation and faults at the point of common coupling of the DFIG and the grid. In this study, a model-free adaptive control (MFAC) is developed for a unified power flow controller (UPFC) in order to improve the overall dynamic performance of a DFIG-based WECS during wind gusts and enhance the fault ride through capability of the DFIG during various disturbance events. The effective performance of the proposed controller is assessed through a comparison with a conventional proportional–integral (PI) controller optimised by a modified flower pollination algorithm. Results reveal the superiority of the proposed UPFC-MFAC technique over the conventional PI controller currently used in most of the UPFC-WECS applications.

With the growing pool of aged power transformers, application of the sweep frequency response analysis (SFRA) to assess power transformers' mechanical integrity has been given much attention. One of the research gaps in this field is the lack of reliable and automated techniques to interpret SFRA signatures. Conventional interpretation technique relies on visual inspection and personnel level of expertise, which may lead to inconsistent interpretation for the same signature. Furthermore, current SFRA technique fails in detecting transformer incipient mechanical deformations of low levels. To overcome these limitations, this paper presents a new three-dimensional (3D)-SFRA signature that comprises frequency, magnitude and phase angle in one plot. In contrary to the current interpretation practice that relies only on the magnitude plot, the proposed 3D signature exhibits more features, which can improve the SFRA identification accuracy. To automate and standardise the fault identification process, a digital image processing code is developed to extract some unique features from the proposed signature. The proposed technique is validated through finite element simulation analysis to detect short-circuit turns, axial displacement and radial deformation of a three-phase 40 MVA transformer and practical feasibility is assessed through its application to detect short-circuit turns of a three-phase 45 MVA transformer.

This study explores how the voltage control of a remote part of the utility gird can be improved using more sophisticated voltage control on wind-solar farms equipped with fast communication platforms. The idea is to make renewable plant the master voltage controller during large disturbance events in the grid. This is done by proposing an enhanced voltage droop control strategy based on instantaneous reactive power consumption by monitoring the virtual impedance of the point of connection using a new customised data class model of IEC 61850 communication protocol. The conventional centralised voltage droop control strategy and the proposed instantaneous direct voltage control method are both implemented on the White Rock Solar Wind Farm in NSW, Australia and their performances are compared using both MATLAB Simulink simulations under 5% voltage step disturbances, single-phase-to-ground and three-phase-to-ground faults as well as some tests conducted in the field.

One of the main drawbacks of the frequency response analysis (FRA) technique that is widely accepted as the most reliable tool to detect transformer internal mechanical deformations is the inconsistent interpretation of the measured signature because of its reliance on personal expertise more than standard codes. Moreover, conventional FRA signature has a very low accuracy in detecting incipient and low mechanical fault levels. In order to avoid inconsistent interpretation for the transformer FRA signatures and improve its accuracy to detect minor fault levels, a reliable automated technique has become essential. This study investigates the feasibility of utilising FRA polar plot to detect minor radial deformation levels within two, three-phase power transformers of different ratings and winding configurations simulated using three-dimensional finite-element analysis software. Simulation results are validated through experimental measurements. Results of this study are also compared with the results obtained for other types of transformer winding deformations that are published in the literature in order to identify unique impact for each fault type on the proposed method. Findings reveal the superiority of the proposed approach over existing conventional technique in terms of accurate identification and quantification for minor transformer winding deformations.

Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of the equipment. Although several papers about wind turbine condition monitoring can be found in the literatures, adopting a reliable and cost-effective technique for wind turbines that usually experience severe mechanical stress due to the harsh weather conditions they are exposed to is still challenging. Although statistical studies show that gearbox failure rate is low, the resulted downtime and the replacement or repairing cost is substantial. This study introduces an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses. In this context, a test rig that emulates real wind turbine operation has been developed to analyse the behaviour of wind turbine under various mechanical fault levels. Shaft torque and mechanical vibration signals are detected using high-resolution sensors and are analysed using two signal processing techniques: wavelet and order analyses to examine the impact of investigated fault levels on the wind turbine drive train. Both signal processing techniques are compared based on their sensitivity to detect incipient fault levels.

In this study, a new circuit topology of a three-phase half-bridge multilevel inverter (MLI) is proposed. The proposed MLI that consists of a cascaded half-bridge structure along with a modified full-bridge structure requires less number of dc-power supplies and power semiconductor devices, e.g. insulated gate bipolar transistors and diodes when compared with the existing MLI topologies, which significantly reduces the size and cost of the inverter. Two different structures: isolated and non-isolated dc-power supply-based three-phase half-bridge MLIs are investigated. A number of generalised methods are proposed to determine the magnitude of the input dc-power supplies that has a great impact on the number of levels of the output voltage waveform. To verify the feasibility of the proposed MLI topology, a scaled down laboratory prototype three-phase half-bridge MLI is developed and the experimental results are analysed and compared with the simulation results. Experimental and simulation results reveal the feasibility and excellent features of the proposed inverter system.

Furans are the major degradation of insulating study in transformer oil. Hence, the concentration of furans in oil can be used as a good indicator of study deterioration. Furan concentration in transformer oil is currently measured using high-performance liquid chromatography or gas chromatography–mass spectrometry. Both methods provide accurate and reliable results in detecting furan concentration. However, the two methods need very expensive equipment and take a long time to perform the test on oil samples that should be chemically treated before conducting the test. Moreover, both methods require an expert to perform the test and to interpret its results. This study introduces a novel technique for detecting furan concentration in transformer oil through measuring oil spectral response. The ultraviolet-to-visible spectral response of transformer oil can be measured instantly with relatively cheap equipment and does not need an expert person to conduct the test. Results show that there is a good correlation between oil spectral response and its furan contents. The study introduces two approaches using expert systems to estimate the relationship between furan concentration and spectral response of transformer oil; they are gene expression programming and fuzzy logic.

Application of distributed wind energy conversion systems (WECS) in modern power systems and smart grids has significantly increased during the last decade. In particular, wind turbines equipped with doubly fed induction generator (DFIG) have dominated the market since 2004 as they represented about 55% of the worldwide total wind capacity during this year. This study investigates the application of superconducting magnetic energy storage (SMES) units to improve the dynamic performance of DFIG-based WECS during small and large disturbances in the grid side. A new control approach for the SMES unit using hysteresis current regulator and fuzzy logic is introduced. Detailed simulation results show that the proposed SMES controller is very effective in improving the dynamic performance of the WECS and is able to maintain its parameters within the safety margins of various grid codes during disturbance events considered in this study.

New smart load management (SLM) approach for the coordination of multiple plug-in electric vehicle (PEV) chargers in distribution feeders is proposed. PEVs are growing in popularity as a low emission and efficient mode of transport against petroleum-based vehicles. PEV chargers represent sizeable and unpredictable loads, which can detrimentally impact the performance of distribution grids. Utilities are concerned about the potential overloads, stresses, voltage deviations and power losses that may occur in distribution systems from domestic PEV charging activity as well as from newly emerging charging stations. Therefore this study proposes a new SLM control strategy for coordinating PEV charging based on peak demand shaving, improving voltage profile and minimising power losses. Furthermore, the developed SLM approach takes into consideration the PEV owner preferred charging time zones based on a priority selection scheme. The impact of PEV charging stations and typical daily residential loading patterns are also considered. Simulation results are presented to demonstrate the significant performance improvement offered by SLM for a 1200 node test system topology consisting of several low-voltage residential networks populated with PEVs.