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Monitoring the partial discharge (PD) activity of power equipment insulation is crucial to ensure uninterrupted power system operation. PD occurrence is highly correlated to weakened insulation strength. If PD occurrences are left unchecked, unexpected insulation breakdowns may occur. The comprehensive PD diagnostic process includes the detection, localization, and classification of PD. Accurate PD source localization is necessary to locate the weakened insulation segment. As a result, rapid and precise PD localization has become the primary focus of PD diagnosis for power equipment insulation. This paper presents a review of different approaches to PD localization, including conventional, machine learning (ML), and deep learning (DL) as a subset of ML approaches. The review focuses on the ML and DL approaches developed in the past five years, which have shown promising results over conventional approaches. Additionally, PD detection using conventional, unconventional, and a PCB antenna designed based on UHF techniques is presented and discussed. Important benchmarks, such as the sensors used, algorithms employed, algorithms compared, and performances, are summarized in detail. Finally, the suitability of different localization techniques for different power equipment applications is discussed based on their strengths and limitations. [ABSTRACT FROM AUTHOR]

Ensuring a stable and efficient transformer operation is a very crucial task nowadays, especially with the integration of modern and sensitive electrical equipment and appliances down the line. However, transformer maloperation still cannot be completely avoided, particularly with the existence of inrush current that possess similar characteristics as the fault currents when a fault occurred. Thus, this paper proposes an enhanced method for inrush current identification based on a backpropagation (BP) network, optimized using genetic and simulated annealing algorithms. The proposed method has the ability to find the global optimal solution while avoiding local optima, with increased solution accuracy and low calculation complexity. Through extensive simulations, it was found that the inrush and fault currents have differences in their harmonic contents, which can be exploited for the identification of those currents using the proposed identification method. The proposed genetic simulated annealing–BP (GSA-BP) algorithm make use of 200 current samples to improve the detection accuracy of the inrush current from 80% to 97.5%. Comparative studies performed against the existing identification methods show that the GSA-BP network has superior efficiency and accuracy while being practical for real-life application to improve the transformer protection system. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market." The correction addresses errors in the original publication, specifically changing certain phrases. The correction also includes a revised section on the Adaptive Neuro-Fuzzy Inference System (ANFIS), explaining the differences between the Mamdani-based FIS approach and the Sugeno approach. The authors state that the scientific conclusions of the article are unaffected, and the correction has been approved by the Academic Editor. [Extracted from the article]

The installation of a corona ring on an insulator string on a transmission line is one of the solutions to reduce the electric field stress surrounding the energised end of the insulator string. However, installing a corona ring with an optimum design to reduce the electric field magnitude on an insulator string is a challenging task. Therefore, in this work, a method to achieve the optimum design of a corona ring for 132 kV composite non-ceramic insulator string was proposed using two optimisation methods: the Imperialist Competitive Algorithm (ICA) and Grey Wolf Optimisation (GWO). A composite non-ceramic insulator string geometry with and without a corona ring was modelled in finite element analysis and used to obtain the electric field distribution in the model geometry. The electric field distribution was evaluated using a variation in the corona ring's dimensions, i.e., the ring diameter, the ring tube diameter and the vertical position of the ring along the insulator string. From the results achieved, a comparison of the minimum electric field magnitude along the insulator string with a corona ring design shows that the minimum electric field magnitude is found to be lower using optimisation techniques compared to without using optimisation techniques by between 3.724% and 3.827%. Hence, this indicates the capability and effectiveness of the proposed methods in achieving the optimum design of a corona ring on an insulator string. [ABSTRACT FROM AUTHOR]

Wind energy is one of the most common types of renewable energy resource. Due to its sustainability and environmental benefits, it is an emerging source for electric power generation. Rapid and random changes of wind speed makes it an irregular and inconsistent power source when connected to the grid, causing different technical problems in protection, power quality and generation dispatch control. Due to these problems, effective intermittent smoothing approaches for wind power output are crucially needed to minimize such problems. This paper reviews various intermittent smoothing approaches used in smoothing the output power fluctuations caused by wind energy. Problems associated with the inclusion of wind energy resources to grid are also briefly reviewed. From this review, it has been found that battery energy storage system is the most suitable and effective smoothing approach, provided that an effective control strategy is available for optimal utilization of battery energy system. This paper further demonstrates different control strategies built for battery energy storage system to obtain the smooth output wind power. [ABSTRACT FROM AUTHOR]

In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods. [ABSTRACT FROM AUTHOR]

This study uses natural fibre, which is moisture absorbent, as an innovative and economical filler for insulating oil. Rice husk (RH) is a natural fibre known to have water-absorbing properties and used as a supplementary cementitious material. This research utilises the water-absorbing properties of RH to improve the physicochemical and dielectric properties of insulating oil. RH was refined into a fine powder at a diameter of less than 63 µm. Palm oil (PO) was synthesised with RH at concentrations of 0 g/L, 0.01 g/L, and 0.1 g/L. The moisture-absorbent properties of RH were analysed by using Fourier-transform infrared (FTIR) spectroscopy. The particle size and distribution of RH in PO were also obtained using a scanning electron microscope (SEM) and a Zeta particle analyser. The breakdown voltage (BDV) strength of PO with RH was measured according to IEC 60156, and the dielectric frequency response was investigated in the range of 102–105 Hz. RH is proven to absorb moisture from PO, as the O–H band intensity at the 3350 cm−1 is increased from the experimental results. Therefore, the mean BDV and dielectric insulation properties of PO dispersed with RH also increased accordingly. [ABSTRACT FROM AUTHOR]

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization. [ABSTRACT FROM AUTHOR]

In zinc oxide (ZnO) surge arresters, leakage current usually flows across the arrester under normal operating condition. Leakage current is one of the factors which contribute towards degradation of surge arresters and therefore, it is very important to monitor the condition of surge arrester. In this work, the behaviour of leakage current in a ZnO surge arrester during normal operation, under different voltage amplitudes, wetness and pollution conditions was analysed. An 11 kV surge arrester model in three-dimensional space was subjected to finite element analysis (FEA) to determine the leakage current under different conditions. The results from the FEA model were compared with the measurement results to validate the model that has been developed. From comparison between the measurement and simulation results, physical parameters of a surge arrester that influence the leakage current under different conditions of the surge arrester were identified from the model. Through this work, a better understanding of leakage current behaviour can be attained, which may help in condition monitoring analysis on surge arrester in electrical utilities. [ABSTRACT FROM AUTHOR]