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In recent decades, flash floods have become more common because of climate change and are considered a substantial risk for many cities worldwide. This catastrophic natural hazard presents a significant threat to critical infrastructure in urban areas, particularly the power distribution system. As modern societies are much more dependent on electrical energy these days, it is essential and imperative to make existing distribution systems resilient against flash flooding. Although researchers in this area have proposed various algorithms to impart resilience to a distribution system, however, the focus in these works is on wind‐related events such as hurricanes, cyclones, and windstorms. Therefore, here, the spatiotemporal effects of a flash flood on the distribution system are modelled using a grid‐based hydrodynamic model. The evolving line faults are then included in the proposed resilience‐oriented time horizon‐based service restoration model that also considers dynamic load demand, heavy uncertainties related to renewable generation, and interdependence among critical loads. Finally, the resilience of the distribution system's response is assessed using an operational resilience metric. The efficacy of the proposed framework is evaluated on IEEE 33‐bus and 69‐bus systems and the results show that the model provides an efficient restoration solution despite increased complexity caused by varying conditions. [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]

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]

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]

Growing environmental concerns due to emission of greenhouse gas from the use of nonrenewable resources can be reduced with the aid of renewable resources, which are considered as an alternative fuel in the absence of fossil fuel in the future. Biomass, one of the renewable resources, is supposed to play an important role in energy sectors because it is the second cheapest energy source among the renewable resources. Apart from generating electricity by using the current biomass technology, such as combustion, gasification, or pyrolysis, a combination of binary and gasification can be one of the effective ways to harvest energy from biomass and to secure the energy production. In term of biomass, Malaysia is blessed with a plenty of renewable energy resources including solar, biomass, and hydro. Hence, a combined binary and biomass power generation can be a promising source of energy generation. In this work, a techno-economic feasibility study on a binary and gasifier-based power generation system from biomass and municipal waste is conducted. The study is conducted to determine the suitability of the system development in Malaysia based on the current resources available. From the results obtained, it is found that the estimated amount of electricity generated from palm empty fruit bunch and municipal waste is 369.65 GWh/yr and 21 262.327 84 GWh/yr, respectively, using combined binary and gasifier-based power generation. Thus, a combined binary and gasifier-based power generation from biomass and biowaste is feasible to be developed in Malaysia. [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 aim of this article is to present a review of the development and application of superhydrophobic insulation for outdoor high-voltage applications. The methods of preparing insulation exhibiting superhydrophobic properties to mitigate the pollution problem under high voltages in the outdoors are explored. In addition, the existing evaluation techniques at the material development stage are reviewed and potential new modifications are discussed. The self-cleaning and anti-icing properties, the electrical performance, and prospects of superhydrophobic insulators are also addressed. Although superhydrophobic insulators may show promising behavior in small-scale laboratory conditions that do not use electrical stress, the effect of surface micro–nano structure responsible for the superhydrophobicity on the performance under the actual outdoor and weathering conditions under voltage remains to be fully understood and verified. Therefore, there is a need to verify the feasibility of superhydrophobic insulators for outdoor high-voltage applications with a solid evaluation protocol, while using material screening methods in the laboratory, and with reference to field experience. [ABSTRACT FROM AUTHOR]

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques. [ABSTRACT FROM AUTHOR]

New and emerging demand for polyurethane (PU) continues to rise over the years. The harmful isocyanate binding agents and their integrated PU products are at the height of environmental concerns, in particular PU (macro and micro) pollution and their degradation problems. Non‐isocyanate poly(hydroxy urethane)s (NIPUs) are sustainable and green alternatives to conventional PUs. Since the introduction of NIPU in 1957, the market value of NIPU and its hybridized materials has increased exponentially in 2019 and is expected to continue to rise in the coming years. The secondary hydroxyl groups of these NIPU′s urethane moiety have revolutionized them by allowing for adequate pre/post functionalization. This minireview highlights different strategies and advances in pre/post‐functionalization used in biobased NIPU. We have performed a comprehensive evaluation of the development of new ideas in this field to achieve more efficient synthetic biobased hybridized NIPU processes through selective and kinetic understanding. [ABSTRACT FROM AUTHOR]

Integration between supplies for stationary power and vehicles is potentially useful for increasing the efficiency and the reliability of energy generation systems. Solid oxide fuel cell is one matured technology, which is suitable for a polygeneration system and provides an integration of supply for stationary power and vehicles. However, a combination of solid oxide fuel cell with photovoltaic thermal and thermoelectric generation increases the complexity of a polygeneration system. The system needs a management strategy for dispatching the energies produced. Therefore, in this work, a fuzzy energy management strategy was applied for this polygeneration system by considering two different configurations: an off-grid system with electric vehicle supply and an on-grid system with hydrogen vehicle supply. A two-stage fuzzy energy management strategy considering optimization and management of multi-parameters of the polygeneration components was considered. The evaluation of the optimum fuzzy was analyzed based on energy, economic, and environmental criteria. From the results obtained, the optimal strategy increased the reliability, energy, and system cost savings by 22.05%, 22.4%, and 32.58%, respectively. Moreover, the optimum management reduced the power loss of the polygeneration system by about 48.82%, which was achieved by the configuration with electric vehicles supply and off-grid connection. [ABSTRACT FROM AUTHOR]

Recent studies have proven that a three-coil wireless power transfer (WPT) design shows better performance and has higher efficiency compared to the two-coil designs, especially when the source resistance and transmission distance between the primary and receiver coil increases. The three-coil WPT system, similar to a two-coil system, is capable of achieving constant output current (CC) and constant output voltage (CV) with ZPA. However, in the CV mode of the conventional three-coil design, the efficiency of the light-load system dramatically decreases as the load becomes smaller. In this article, a new series–series–LCLCC (S–S–LCLCC) compensation design for the three-coil WPT system with load-independent output voltage, which is capable of realizing ZPA characteristics during the entire process of the charging process, is proposed. The new design is capable of significantly improving the energy efficiency stiffness against the load variation, misalignment, increasing the flexibility to optimize the system efficiency, reducing the voltage stress, and increasing the power delivery to load compared to the conventional topology. The experimental design shows that the overall trend of efficiency of the proposed design is higher than the conventional design as the load decreases and the new system has approximately 10% higher efficiency when the battery equivalent load resistance reaches 222 Ω. [ABSTRACT FROM AUTHOR]

Modern societies these days are more dependent on electrical energy and they expect a continuous supply as per demand. In this regard, the complex power system is designed to supply electrical energy with a certain level of quality and continuity though it is still susceptible to vandalism, natural disasters, and extreme weather. The black sky event where the power grid goes down is more of a possibility nowadays than ever due to more frequent severe weather events. This in turn has increased the need to study resilience in the context of the power system. This study presents a comprehensive review of the literature on power system resilience from various perspectives. First, well-developed power system safety concepts are discussed and critically reviewed in view of large-scale power outages. Then, the various definitions and confounding features of resilience in the power system domain are presented. Subsequently, several frameworks, resilience curves, and quantitative metrics proposed in recent years for power system resilience are investigated, followed by a summary of hardening strategies. Next, a case study is presented to illustrate how the resilience of a 69-bus system is assessed against a hurricane. Finally, the study highlights challenges and proposes several future works to achieve a resilient power grid. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) is commonly used to identify the fault type in power transformers. However, the available DGA methods have certain limitations because every method depends on the concentration of the dissolved gases. Therefore, in this work, hybrid feature selection-artificial intelligence-gravitational search algorithm (GSA) techniques were proposed to determine the fault type of power transformers based on DGA data. The artificial intelligence (AI) methods applied include support vector machine and artificial neural network. Both AI methods were optimised by GSA to enhance the accuracy of the results. Feature selections using stepwise regression and robust regression were applied to utilise only significant gases. The accuracy of the results was tested with various ratios of testing and training data. Comparison of the results using the proposed method with other optimisation methods and the previous works was performed to validate the performance of the proposed technique. It was observed that the proposed hybrid feature selection-AI-GSA technique yields reasonable accuracy although fewer types of dissolved gases were used. Therefore, the proposed method can be recommended for the application of automated power transformer fault type detection based on DGA data in practice. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is a tool for evaluating mechanical integrity of transformer's core and winding. However, several studies have reported that FRA is also sensitive to other parameters such as temperature, moisture content and presence of oil. Since some of these parameters are critical in the insulation degradation process, it is interesting to further investigate the possibility of using FRA for insulation assessment. In this paper, the insulation conditions of three field transformers are investigated using FRA. This is performed after statistical analysis on the response suggested no mechanical damage on the transformers. The responses are further analyzed to determine the percentage of change of winding capacitance. This is achieved by comparing resonance frequencies between the responses. The percentage of change represents the amount of degradation in the insulation. This is because as the insulation condition degrades, the response is shifted towards lower frequencies. This new investigation demonstrates the applicability of FRA to estimate the insulation degradation. [ABSTRACT FROM AUTHOR]

Microgrid (MG) with optimal operating (OC) and capital cost (CC) is highly required. This study presents dual‐mode energy management system (DM‐EMS) operation with the objective of minimising the overall OC of the MG. DM‐EMS determines the best mode to operate battery and fuel cell at a particular time and its duration to operate MG with minimum cost. The CC of an MG is mainly depending on battery size and battery life. In this study, novel battery sizing method by using the proposed life cycle cost (LCC) function is introduced. Considering the impact of battery size and life in the OC and CC of an MG, the proposed DM energy management strategy and novel battery sizing are concurrently optimised by using the proposed LCC function. The proposed strategy is validated using an MG system consisting of wind, battery storage and fuel cell. The results show that better optimal OC and CC with an accurate optimal battery capacity of an MG are achieved. In addition, analysis on the optimal battery size variations for different battery level [state of charges (SOCs)] is conducted. Changes in OCs and CC for the different range of initial battery SOC are also assessed. [ABSTRACT FROM AUTHOR]

Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified using a directed acyclic graph SVM. The possible faulty sections are identified by estimating the fault resistance using support vector regression and matching the voltage sag data in the database with the actual voltage sag data. The most possible faulty sections are ranked using ranking analysis. The fault distance for the possible faulty sections is then identified using support vector regression analysis and its overfitting or underfitting issues are addressed by the proper selection of a regularization parameter. The feasibility of the proposed method was tested on an actual Malaysian distribution system. The results of faulty phase, fault type, faulty section, and fault distance are analyzed. The performance of the proposed method is compared with various other intelligent methods such as the artificial neural network, deep neural network, extreme learning machine, and kriging method. The test results indicate that the faulty phase and fault type yield 100% accurate results. All the faulty sections are identified and the proposed method obtains reliable fault location. [ABSTRACT FROM AUTHOR]

Reconfiguring the link between buses is a crucial task to enhance the distribution system performance. Reconfiguration is a complex combinatorial process due to numerous feasible solutions. Therefore, to consistently find global optimum solutions within a short span of time is a challenging task. One of the factors that cause time consumption in finding optimal network configurations is the elimination of non‐radiality network solutions during the optimisation process. To address this issue, this work proposes to store pre‐determined network radiality solutions in a database. These sets of solutions are used in the network reconfiguration optimisation by a discrete evolutionary programming and a discrete evolutionary particle swarm optimisation techniques. These optimisation methods are based on a multi‐objective problem which minimises power loss, voltage deviation, and a number of switching actions. Moreover, the quality of the solutions is measured in terms of computational time and consistency. To demonstrate the efficiency of the proposed technique, a comparative assessment is carried out on 33‐bus and 118‐bus distribution systems. It is found that the proposed technique outperforms other existing methods in terms of quality of the solutions. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a novel machine for data analysis and has advantageous characteristic of good generalization. Because of this characteristic, SVM is used in this work for fault classification and diagnosis in distribution systems. This work proposes an effective fault location method using SVM to identify the fault type, faulty section, and fault distance. The classification and regression analysis of the SVM are performed to locate a fault. The proposed method utilizes the voltage sag magnitude and angle measured at the primary substation of a distribution system. First, the fault type is identified using one- versus-one concept of support vector classification. The next step identifies the faulty section by calculating fault resistance, finding possible faulty sections and ranking the possible sections. Finally, the fault distance is identified using support vector regression analysis. The performance of the proposed method is tested using SaskPower distribution system from Canada having 20 line sections. Test cases are carried out under various fault scenarios considering the fault type and fault resistance. The results of fault distance are compared for different kernel functions, and the most accurate kernel is chosen. Test results show that the proposed method obtains reliable fault location. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. [ABSTRACT FROM AUTHOR]

Islanding detection is important for the protection of any distribution system supported by distributed energy resources. In this work, a number of islanding and non-islanding events are simulated to prioritise 16 different passive parameters on the basis of performance analysis. Among the parameters tested, the rate of change of frequency over reactive power (df/dq) attains the highest accuracy and consequently, a new passive technique is proposed that employs df/dq as discriminative feature. An extended study is carried out to further test the ability of the technique to differentiate the islanding from the non-islanding events of similar signatures, such as load variation, capacitor switching, faults and induction motor starting. The simulation results confirm that the technique is proficient enough to identify islanding from non-islanding events that cause minute disturbances in the power supply network. [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]

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location. [ABSTRACT FROM AUTHOR]

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]

Cable joints are the weakest point in cross-linked polyethylene (XLPE) cables and are susceptible to insulation failures. Partial discharge (PD) analysis is a vital tool for assessing the insulation quality in cable joints. Although many works have been done on PD pattern classification, it is usually performed in a noise-free environment. Also, works on PD pattern classification are mostly done on lab fabricated insulators, where works on actual cable joint defects are less likely to be found in literature. Therefore, in this work, classifications of cable joint defect types from partial discharge measurement under noisy environment were performed. Five XLPE cable joints with artificially created defects were prepared based on the defects commonly encountered on-site. A high noise tolerance principal component analysis (PCA)-based feature extraction was proposed and compared against conventional input features such as statistical and fractal features. These input features were used to train the classifiers to classify each defect type in the cable joint samples. Classifications were performed using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). It was found that the proposed PCA features displayed the highest noise tolerance with the least performance degradation compared to other input features under noisy environment. [ABSTRACT FROM AUTHOR]

Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five cross-linked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination. [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]

This study analyse the impact of different distributed generation (DG) operating modes towards the system performance when network reconfiguration, DG generation and tap changer setting are simultaneously configure to optimal value. The main consideration in the optimal configuration process is to minimise daily power losses. For the purpose of optimisation, imperialist competitive algorithm is applied. Analysis is conducted based on safety margin of total DGs penetration, photovoltaic generation based on daily irradiance and daily load profile. The analysis of different DG mode of operations is conducted using IEEE 33 having five tie‐switches and one on‐load tap changer. [ABSTRACT FROM AUTHOR]

In power transformer fault diagnosis, dissolved gas analysis (DGA) has been widely used to identify the type of the fault. The common methods of DGA are IEC 60599 method, Doenenberg’s ratio method and Roger’s ratio method. The accuracy of the DGA diagnosis will determine the cost, duration and workload of the maintenance since it can influence the error in the maintenance. Although DGA methods have been used widely, sometimes they still yield incorrect diagnosis results. Thus, many works on transformer fault diagnosis have been proposed previously, which include artificial intelligence methods, to improve the accuracy of transformer fault diagnosis. However, the accuracy of the previously reported works is believed to have rooms for improvement. Therefore, in this work, hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC)-artificial neural network (ANN) was proposed for transformer fault diagnosis based on dissolved gas data. This is due to these two methods have never been proposed for transformer fault diagnosis in the past. The performance of the ANN was optimised through the proposed MEPSO-TVAC. The superiority of the proposed method was demonstrated through comparison with the existing DGA methods, unoptimised ANN and previously reported methods in literatures. The comparison shows that the proposed hybrid MEPSO-TVAC-ANN obtained the highest accuracy among all methods, which can then be used for power transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

The problems related to electromagnetic waves transmitted by lightning strikes can be studied through physical lightning models based on laboratory results. The main concern of these models is determining the striking distance between the leader tip and the lightning rod during lightning occurrences. The striking distance is a significant factor in designing the lightning protection system. However, models using finite element analysis (FEA) for this purpose are less likely to be found in the literature. Therefore, in this work, a geometry model of a lightning rod and leader was developed using available FEA software. The model was used to determine the striking distance for different lightning rod heights. The results obtained were compared with those of previous research using different methods to validate the models developed using FEA software. From the comparison, the striking distance obtained from the FEA software as a function of lightning rod height was in good agreement compared to other methods from previous research. The use of FEA software also enables the effect of different tip radii of curvature and shapes of the lightning rod on striking distance to be studied, which can further enhance our understanding of the relationship between striking distance and different rod parameters. [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]

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]