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In high-voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To investigate the classification accuracy under noisy signals, the remaining data were corrupted with artificial noise. The noisy data were classified using the ANN, which had been trained by noise-free PD signals. It is found that Cepstrum-ANN yields the highest classification accuracy for noisy PD signals than the other methods tested. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Fast and effective fault location in distribution system is important to improve the power system reliability. Most of the researches rarely mention about effective fault location consisting of faulted phase, fault type, faulty section and fault distance identification. This work presents a method using support vector machine to identify the faulted phase, fault type, faulty section and distance at the same time. Support vector classification and regression analysis are performed to locate fault. The method uses the voltage sag data during fault condition measured at the primary substation. The faulted phase and the fault type are identified using three-dimensional support vector classification. The possible faulty sections are identified by matching voltage sag at fault condition to the voltage sag in database and the possible sections are ranked using shortest distance principle. The fault distance for the possible faulty sections isthen identified using support vector regression analysis. The performance of the proposed method was tested on an unbalanced distribution system from SaskPower, Canada. The results show that the accuracy of the proposed method is satisfactory. [ABSTRACT FROM AUTHOR]

The main objectives of load frequency control (LFC) are to regulate the electrical power supply in two-area power system and change the system frequency and tie-line load. The performance of LFC has to be tuned properly so that its performance can be optimised. However, most of the tuning processes are performed through trial and error until the best performance is achieved. Therefore, to overcome this situation, in this work, particle swarm optimization (PSO) and evolutionary particle swarm optimization (EPSO) algorithms were employed in a LFC of twoarea power system to optimise the performance of the PID controller. The purpose of using PID controller is to improve the performance of the LFC. Comparison of the performance using PSO and EPSO was made to identify which algorithm is better in controlling the performance of the LFC. It was found that using EPSO, the performance of the LFC is better in terms of settling time and rise time than using PSO. Hence, by implementing an optimisation method, the performance of the LFC can be optimised through optimising the PID controller parameters. [ABSTRACT FROM AUTHOR]

Severe lightning environment has been the major cause of overhead transmission line outages. In order to minimise the impact of lightning overvoltage, it is necessary to further evaluate lightning performance of any line before implementing specific protection methods. Therefore, in this work, a thorough study on back flashover tripping patterns of a 275/132 kV quadruple steel monopole towers in Malaysia is presented. Several design parameters such as lightning current magnitude, tower footing resistance and power frequency voltage were varied to investigate their influences on the line performance associated with lightning events. The results show that each parameter gives a significant effect on the flashover patterns. For each case, no flashover was recorded on the higher voltage circuits (275 kV), even though the line consists of tall structures, which might be more susceptible to lightning strikes at the upper part. Regardless of the effect of tower height, flashovers occur mainly on 132 kV circuits due to difference in the line insulation level. The results obtained were then used in designing appropriate protection scheme to further improve the transmission line lightning performance, mainly in terms of reducing the double circuit line outages. [ABSTRACT FROM AUTHOR]

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]

Modeling of the partial discharge (PD) process allows a better understanding of the phenomena. In this paper, a simulation model for spherical cavities within a homogeneous dielectric material has been developed. The model is implemented using Finite Element Analysis (FEA) software in parallel with a mathematical package. This method provides many advantages over previous PD models because discharge events can be simulated dynamically and the electric field in the cavity can be calculated numerically. The model has been used to study the effect of different amplitudes and frequencies of the applied voltage and simulation results have been compared with experimental measurement results. It is found that certain model parameters are dependent on the applied stress and parameters that clearly affect PD activity can be readily identified, these parameters include; the electron detrapping time constant, the cavity surface conductivity, the initial electron generation rate and the extinction voltage. The influence of surface charge decay through conduction along the cavity wall on PD activity has also been studied. [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]

• Polymer insulators are susceptible to damage and accelerated aging due to contaminants, impacting their performance and structural integrity. • This study conducts comprehensive investigations about discharge behaviors of polymer insulator when exposed to three distinct contaminants: fly ash, salt, and algae. • Measurements of inception and surface breakdown voltages, along with partial discharge patterns, reveal unique responses to each contaminant. Salt intensifies electrical stress under dry conditions, accelerating aging and increasing partial discharge compared to other contaminants. Fly ash alters Partial Discharge Inception Voltage and Surface Breakdown Voltage, enhancing surface discharge performance, while algae's biofouling process deteriorates insulator surfaces, decreasing PDIV and altering PD patterns. • The findings of this study contribute to the enrichment of references related to partial discharge (PD) phenomena caused by salt, fly ash, and algae contaminants. By identifying unique PD patterns associated with each contaminant, this research enhances the ability to diagnose and mitigate insulator degradation in diverse environmental conditions. The performance of polymer insulators can be significantly compromised by the presence of contaminants, resulting in damage, structural alterations, and accelerated aging. Hence, this research aimed to investigate the discharge behavior of polymer insulators when exposed to various types of contaminants. Three distinct contaminants, namely fly ash, salt, and algae, were prepared for the study. In order to assess the characteristics of these insulators, measurements of inception and surface breakdown voltages were conducted for each sample. Additionally, partial discharge measurements were performed at two different applied voltages to observe the patterns of partial discharge under different contaminants. To aid in distinguishing the characteristics of partial discharge, a statistical method based on graphical analysis was employed. Furthermore, visual inspection was carried out after the occurrence of partial discharge activity to provide an explanation for these phenomena. The findings of the study revealed that each contaminant produced a unique pattern, which was influenced by the specific elements present in the respective contaminant. It was observed that contaminants exhibit varying effects on partial discharge depending on their nature. Salt was found to induce faster stress propagation, fly ash inhibited stress propagation, and algae had a degrading effect on the surface of the polymer insulators. [ABSTRACT FROM AUTHOR]

• Preparation of cellulose supported promising magnetic sorbents (CMS) is summarized. • Physical performance of the magnetic nanoparticles is discussed. • Chemical attraction of magnetic nanoparticles towards cellulose substrates is summarized. • CMS in the field of heavy metal, organic pollutants, and protein adoption is discussed. • Advantages, challenges, and future of CMS in magnetic solid-phase extraction are provided. Cellulose with ample hydroxyl groups is considered as a promising supportive biopolymer for fabricating cellulose supported promising magnetic sorbents (CMS) for magnetic solid-phase extraction (MSPE). The easy recovery via external magnetic field, and recyclability of CMS, associated with different types and surface modifications of cellulose has made them a promising sorbent in the field of solid-phase extraction. CMS based sorbent can offer improved adsorption and absorption capabilities due to its high specific surface area, porous structure, and magnetic attraction feature. This review mainly focuses on the fabrication strategies of CMS using magnetic nanoparticles (MNPs) and various forms of cellulose as a heterogeneous and homogeneous solution either in alkaline mediated urea or Ionic liquids (ILs). Moreover, CMS will be elaborated based on their structures, synthesis, physical performance, and chemical attraction of MNPs and their MSPE in details. The advantages, challenges, and prospects of CMS in future applications are also presented. [ABSTRACT FROM AUTHOR]

• The GSST-TFBG has an extended SRI range, and the resonance amplitudes remain strong and detectable beyond the cut-off point. • The s-mode resonances are practically insensitive whereas the p-mode remains sensitive to the changes of SRI. • Refractive index measurement for transformer oil using GSST-coated TFBG. In this work, a tilted fiber Bragg gratings coated with a 130 nm thick Ge-Sb-Se-Te (GSST) layer, a high refractive index soft glass had been fabricated and demonstrated for the refractometric test of transformer oil. The findings indicated that the introduction of the high refractive index coating had enhanced the polarization property and extended the refractive index detection range of the tilted fiber Bragg gratings beyond the cut-off point of the cladding modes. In the experiment with transformer oils, the Ge-Sb-Se-Te -coated tilted fiber Bragg gratings has shown a good linear sensitivity of 9.952 dB/RIU for the refractive index range of 1.472–1.484 RIU. [ABSTRACT FROM AUTHOR]

Cellulose and its forms are widely used in biomedical applications due to their biocompatibility, biodegradability and lack of cytotoxicity. It provides ample opportunities for the functionalization of supported magnetic nanohybrids (CSMNs). Because of the abundance of surface hydroxyl groups, they are surface tunable in either homogeneous or heterogeneous solvents and thus act as a substrate or template for the CSMNs' development. The present review emphasizes on the synthesis of various CSMNs, their physicomagnetic properties, and potential applications such as stimuli-responsive drug delivery systems, MRI, enzyme encapsulation, nucleic acid extraction, wound healing and tissue engineering. The impact of CSMNs on cytotoxicity, magnetic hyperthermia, and folate-conjugates is highlighted in particular, based on their structures, cell viability, and stability. Finally, the review also discussed the challenges and prospects of CSMNs' development. This review is expected to provide CSMNs' development roadmap in the context of 21st-century demands for biomedical therapeutics. • Cellulose utilization for the synthesis of magnetic nanohybrids is reviewed. • The review presents various shapes of cellulose supported magnetic nanohybrids (CSMNs). • Synthesis and physicomagnetic properties of different CSMNs are discussed. • Advances in the biomedical applications of CSMNs are explicated. • Challenges in preparation of CSMNs and future perspectives are expounded. [ABSTRACT FROM AUTHOR]

• This study uses real partial discharge measured from cable joints as the dataset. • Partial discharge data was used as an image to train a convolutional neural network. • 5 networks & 4 image types were tested to find the best noise invariant classifier. • The system was trained using clean data but tested using data overlapped with noise. • The proposed system has 16.90% better performance than traditional methods. Partial discharge (PD) pattern recognition is essential since it can help to identify the nature of the insulation defect. Numerous machine learning models have been utilized for PD classification applications in the past. However, traditional machine learning models rely on manual feature extraction to obtain training data. They are usually trained using clean PD data measured in the laboratory but are expected to work on-site where some degree of interference or noise is expected. When tested using clean PD data, most machine learning models can easily achieve above 90% accuracy. However, when tested using PD data overlapped with noise, classification accuracy reduces significantly. In this work, the development of a convolutional neural network (CNN)-based PD classification system using transfer learning was proposed. In order to achieve a more practical performance evaluation, a modified 10-fold cross-validation procedure was used where the CNN-based PD classifier was trained using clean PD data but tested using PD data that has been overlapped by noise. The results showed that CNN-based PD classifier was able to achieve up to 16.90% higher classification accuracy under noise contamination compared to traditional machine learning with manual feature extraction. This shows that the proposed method was able to retain higher classification accuracy in the presence of noise. [ABSTRACT FROM AUTHOR]