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Abstract This contribution presents a two‐step hybrid diagnostic approach, combining k‐means clustering for subset formation, followed by subset analysis conducted by human experts. As the feature input vector has a significant influence on the performance of unsupervised machine learning algorithms, seven feature input vectors derived from traditional methods, including Duval pentagon method, Rogers ratio method, three ratios technique, Denkyoken method, ensemble gas characteristics method, Duval triangle method, and Gouda triangle method were explored for the subset formation stage. The seven proposed individual methods, corresponding to the seven feature input vectors, were implemented using a dataset of 595 DGA samples and tested on an additional 254 DGA samples. Furthermore, a combined technique based on a support vector machine was introduced, utilising the diagnostic results of the individual methods as input features. From training and testing, with diagnostic outcomes of 91.09% and 90.94%, the combined technique demonstrated the highest overall diagnostic accuracies. Using the IEC TC10 database, the diagnosis accuracies of the proposed diagnostic methods were compared to existing methods of literature. From the results obtained, the combined technique outperformed the proposed individual methods and existing methods used for comparison.

Abstract A key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.

Abstract A new de‐excitation system for advanced brushless excitation based on a synchronous generator is presented. The proposed system uses a discharge resistor in series with a freewheeling diode of a buck chopper. It improves the reliability of the entire power plant and can be used on several excitation systems. It can be used in case of programmed shutdown of the generator and also shows good results in voltage regulation without destroying the efficiency of the excitation system under nominal operation. An analysis of the system has allowed us to suggest a methodology to obtain the appropriate value of the discharge resistance to be used. The validation of the proposed system has been done through simulations in the Matlab/Simulink environment, supported by experimental tests.

The tremendous diversity of Hymenoptera is commonly attributed to the evolution of parasitoidism in the last common ancestor of parasitoid sawflies (Orussidae) and wasp-waisted Hymenoptera (Apocrita). However, Apocrita and Orussidae differ dramatically in their species richness, indicating that the diversification of Apocrita was promoted by additional traits. These traits have remained elusive due to a paucity of sawfly genome sequences, in particular those of parasitoid sawflies. Here, we present comparative analyses of draft genomes of the primarily phytophagous sawfly Athalia rosae and the parasitoid sawfly Orussus abietinus. Our analyses revealed that the ancestral hymenopteran genome exhibited traits that were previously considered unique to eusocial Apocrita (e.g., low transposable element content and activity) and a wider gene repertoire than previously thought (e.g., genes for CO2 detection). Moreover, we discovered that Apocrita evolved a significantly larger array of odorant receptors than sawflies, which could be relevant to the remarkable diversification of Apocrita by enabling efficient detection and reliable identification of hosts.

Abstract In this contribution, a new dissolved gas analysis (DGA) method combining key gases and ratio approaches for power transformer fault diagnostic is presented. It is based on studying subsets and uses the five main hydrocarbon gases including hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). The proposed method uses 475 samples from the dataset divided into subsets formed from the maximum and minimum(s) concentrations of the whole dataset. It has been tested on 117 DGA sample data and validated on the International Electrotechnical Commission (IEC) TC10 database. The performance of the proposed diagnostic method was evaluated and compared with the following diagnostic methods: IEC ratios method, Duval's triangle (DT), three ratios technique (TRT), Gouda's triangle (GT), and self‐organizing map (SOM) clusters. The results found were analysed by computer simulations using MATLAB software. The proposed method has a diagnosis accuracy of 97.42% for fault types, as compared to 93.16% of TRT, 96.58% of GT method, 97.25% of SOM clusters method and 98.29% of DT method. However, in terms of fault severity, the proposed method has a diagnostic accuracy of 90.59% as compared to 78.90% of SOM clusters method, 83.76% of TRT, 88.03% of DT method, and 89.74% of GT method.

Fish are ectotherm, which rely on the external temperature to regulate their internal body temperature, although some may perform partial endothermy. Together with photoperiod, temperature oscillations, contribute to synchronizing the daily and seasonal variations of fish metabolism, physiology and behavior. Recent studies are shedding light on the mechanisms of temperature sensing and behavioral thermoregulation in fish. In particular, the role of some members of the transient receptor potential channels (TRP) is being gradually unraveled. The present study in the migratory Atlantic salmon, Salmo salar, aims at identifying the tissue distribution and abundance in mRNA corresponding to the TRP of the vanilloid subfamilies, TRPV1 and TRPV4, and at characterizing their putative role in the control of the temperature-dependent modulation of melatonin production—the time-keeping hormone—by the pineal gland. In Salmo salar, TRPV1 and TRPV4 mRNA tissue distribution appeared ubiquitous; mRNA abundance varied as a function of the month investigated. In situ hybridization and immunohistochemistry indicated specific labeling located in the photoreceptor cells of the pineal gland and the retina. Additionally, TRPV analogs modulated the production of melatonin by isolated pineal glands in culture. The TRPV1 agonist induced an inhibitory response at high concentrations, while evoking a bell-shaped response (stimulatory at low, and inhibitory at high, concentrations) when added with an antagonist. The TRPV4 agonist was stimulatory at the highest concentration used. Altogether, the present results agree with the known widespread distribution and role of TRPV1 and TRPV4 channels, and with published data on trout (Oncorhynchus mykiss), leading to suggest these channels mediate the effects of temperature on S. salar pineal melatonin production. We discuss their involvement in controlling the timing of daily and seasonal events in this migratory species, in the context of an increasing warming of water temperatures.

In this article, we propose a strategy for finding the maximum power point (MPP) of a hybrid plant (solar and wind), in order to maximize the power extracted from this production plant. This strategy exploits the perturbation and observation method, based on fuzzy logic coupled with the Duty Cycle Modulator (DCM). The main objective of this study is to extract the maximum power from this hybrid power plant, while ensuring the precision and speed of convergence towards this point of maximum power. This method, tested under the Matlab / Simulink environment for a 160 kW hybrid power plant, gave results that we compared to those obtained with the Fuzzy - PWM (Pulse Width Modulator) strategy. It emerges that the Fuzzy-DCM strategy gives better precision (around 2.6 times) and a speed (around 2 times) of convergence compared to the Fuzzy-PWM strategy.