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Detecting transformer faults is critical to avoid the undesirable loss of transformers from service and ensure utility service continuity. Transformer faults diagnosis can be determined based on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key gas, Rogers' ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis. Therefore, recent research has been developed to diagnose transformer fault and the diagnostic accuracy using combined traditional methods of DGA with artificial intelligence and optimization methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper Throated Optimization Algorithms (GSDTO), to enhance the transformer faults' diagnostic accuracy, which was considered a novelty in this work to reduce the misinterpretation of the transformer faults. The robustness of the constructed GSDTO-based model was addressed by the statistical study using Wilcoxon's rank-sum and ANOVA tests. The results revealed that the constructed model enhanced the diagnostic accuracy up to 98.26% for all test cases. [ABSTRACT FROM AUTHOR]

A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). In the MMPO, the predator's strategies are merged to consider the possibilities for variation in the environmental and climatic circumstances. The suggested MMPO is contrasted with the standard marine predators optimizer (MPO) and genetic, harmony search, fireworks, firefly and improved sine–cosine optimizers. The proposed MMPO is validated on single and multiple objectives using 33- and 69-bus distribution systems at light, nominal and heavy loading levels. The results obtained by the proposed MMPO are compared with those obtained by the original MPO and other optimizers. The achieved simulation outputs reveal a great improvement over the standard MPO and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation. [ABSTRACT FROM AUTHOR]

A new procedure to optimally identifying the prediction equation of oil breakdown voltage with the barrier parameters’ effect is presented. The specified equation is built based on the results of experimental works to link the response with the barrier parameters as the inputs for hemisphere-hemisphere electrode gap configuration under AC voltage. The AC HV is applied using HV Transformer Type (PGK HB-100 kVAC) to the high voltage electrode in the presence of a barrier immersed in Diala B insulating oil. The problem is formulated as a nonlinear optimization problem to minimize the error between experimental and estimated breakdown voltages (VBD). Comprehensive comparative analyses are addressed using three recent innovative marine predators, grey wolf, and equilibrium optimization algorithms to reduce the error between the experimental and estimated breakdown voltages. In addition, the experimental results are expressed in two different models via grey and real coding. Finally, simulation results are conducted with statistical indices that show the effectiveness of the proposed models for experimental verification. The total percentage errors of the tested samples between the observed and estimated VBD are 1.207, 1.222, and 1.207 for EO, GWO, MPA, respectively. The marine predator algorithm has the best performance compared with the other two competitive algorithms for grey and actual codes. [ABSTRACT FROM AUTHOR]

This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the "particle swarm optimization method" and the "grey wolf optimization method". These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified. [ABSTRACT FROM AUTHOR]

The prediction of fault type in transformers at an early stage is an important aspect for power system reliability. Checking the transformer status begins with the dissolved gas analysis (DGA) test. Rogers' four-ratio and IEC 60599 standard methods are commonly DGA-based techniques that used to detect the transformer faults. These methods are very easy to implement, but their diagnostic accuracies are very poor. The target of this work is to build a new PSO-FS optimization platform identifying the optimal limits of gas ratios and corresponding rules for each fault type to enhance the diagnostic accuracies of Rogers' four-ratio and IEC 60599 code methods. In the proposed platform, PSO optimization technique specifies the gas ratio limits and corresponding rules where the fuzzy system identifies the fault types. Based on the results of the optimization process, the new codes that used to diagnose the fault types were developed. The performance of the proposed approach investigated using 481 collected datasets. The overall accuracies of the Rogers' four-ratio and IEC code models enhanced from 47.19 and 55.09 to 85.65 and 85.03, respectively. However, when datasets due to mixtures of faults (samples with undetermined or misidentified faults) are removed from the calculations, the accuracies of the PSO-FS methods are not significantly better than when using existing DGA methods. [ABSTRACT FROM AUTHOR]

The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies' co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot's response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters' uncertainty compared with other techniques. [ABSTRACT FROM AUTHOR]