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In modern railway traffic systems, direct current (DC) electrification is a prevalent choice, with numerous traction networks adopting a variety of voltage levels to accommodate varying load current dynamics. These dynamics are influenced by passenger density, aggregate demand for electrical power, headways, and frequency of locomotive operations. Load currents are prone to surges during periods of dense traffic and transient phases such as acceleration, deceleration, and the start–stop sequences of trains. Such surges hold the potential to precipitate fault currents within the traction system, which are similar to those engendered by external anomalies. Conventional protection systems, such as the Détection Défaut Ligne’—French for ’Line Fault Detection), may not always effectively identify remote faults or prolonged overcurrent situations. These scenarios necessitate an advancement beyond the traditional fault detection methodologies, which primarily rely on fixed thresholds and may not account for the dynamic nature of the railway system’s electrical load. This paper addresses the limitations inherent in the existing DDL protection mechanisms by focusing on the feeder attributes specific to the DC Traction System. In pursuit of this objective, we introduce an innovative adaptive current DDL algorithm to refine the rigid threshold paradigm inherent in the conventional approach. To facilitate a pragmatic assessment, the Rapid Rail network of Malaysia serves as a reference for emulating the railway’s electrical system. This comprehensive analysis yields insights that are potentially useful for safety protocols in DC electrified railroad traffic systems.

In this research, prediction of crude oil cuts from the first stage of refining process field is laid out using rough set theory (RST) based adaptive neuro-fuzzy inference system (ANFIS) soft sensor model to enhance the performance of oil refinery process. The RST was used to reduce the fuzzy rule sets of ANFIS model, and its features in the decision table. Also, discretisation methods were used to optimise the continuous data’s discretisation. This helps to predict the two critical variables of light naphtha product: Reid Vapor Pressure (RVP) and American Petroleum Institute gravity (API gravity), which detect the cut’s quality. Hence, a real-time process of Al Doura oil refinery is examined and the process data of refining crude oil from these two sources improve the knowledge provided by the data. The response variables represent the feedback measured value of cascade controller in the top of the splitter in crude distillation unit (CDU) in the rectifying section, which controls the reflux liquid’s flow towards the splitter’s head. The proposed adaptive soft sensor model succeeded to fit the results from laboratory tests, and a steady-state control system was achieved through an embedded virtual sensor. The predictive control system has been employed using cascade ANFIS controller in parallel with the soft sensor model to keep the purity of the distillate product in the stated range of the quality control of oil refinery. The results obtained from the proposed ANFIS based cascade control have no over/undershoots, and the rise time and settling time are improved by 26.65% and 84.63%, respectively than the conventional proportional-integral-derivative (PID) based cascade control. Furthermore, the results of prediction and control model are compared with those of other machine learning techniques.

Solar Photovoltaic (PV) system is one of the most significant forms of renewable energy resources, and it requires accuracy to assess, design, and extraction of its parameters. Several methods have been extensively applied to mimic the nonlinearity and multi-model behavior of the PV system. However, there is no method to date that can guarantee the extracted parameter of the PV model is the most accurate one. Therefore, this paper presents a unique approach known as Hybridized Arithmetic Operation Algorithm based on Efficient Newton Raphson (HAOAENR) to experimentally extract the parameters of the single-diode and double-diode PV models at the variability of the climatic changes. Firstly, the objective function is efficiently designed to roughly predict the initial root values of the PV equation. Secondly, the Lévy flight and Brownian strategies are integrated in the four operators of AOA to thoroughly analyze the feature space of this problem. Additionally, the four operators of the AOA is divided into two phases to equilibrium between the exploration and exploitation tendencies. Furthermore, the chaotic map and robust mutation techniques are systematically employed in the beginning and halves of generations to ensure the algorithm can reach globally at few numbers iterations. Finally, a nonlinearly adjustable damping parameter of the Levenberg-Marquardt technique is linked with the NR method to replicate the fluctuation behaviours of the PV models. The experimental findings revealed that the proposed HAOAENR outperformed all other methods found in the literature, with average RMSE values close to zero values for both PV models.

Modern power utilization has been forced to operate close to their stability limits, which may lead to voltage instability or collapse owing to the stressed operating conditions of networks. This raises the need to develop a new technique to predict the level of voltage security under different operating conditions to detect critical lines or buses that operate close to its stability limit. This study proposes a new line voltage stability index (BVSI) to identify weak lines and buses for various loading conditions and network configurations. Comparative studies between the existing voltage stability indices (VSIs) and the proposed index have been performed to comprehensively highlight the indices’ foundation, performance, and overall behavior for several different IEEE benchmark test systems; 14-bus, 30-bus, 118-bus test systems and 33-bus radial distribution system. The sensitivity towards different loading conditions due to variables such as active power, reactive power, angular difference between sending and receiving bus voltage, line resistance, and shunt admittance, which have been neglected in the formulation of VSIs, are briefly discussed. The findings of this work are aimed to provide a general guideline for other researchers in selecting the appropriate VSIs for various applications, particularly in solving optimization problems such as distributed generation (DG) and reactive power compensation (RPC) placement, optimal power flow, optimal reactive power dispatch, optimal network reconfiguration for various loading occasions, and different networks. Lastly, the application of the proposed index has been adopted as an analytical approach for solving optimal location and sizing of DG(s) problems. The results prove the effectiveness of the proposed index in accessing the voltage stability state and stress condition of lines. Besides, the proposed approach also capable of determining the potential candidate bus for single DG location and sizing effectively.

The communication requirement for integrating Distributed Renewable Generation (DRG) into Smart Grid (SG) is not strict, where the reliability and critical demand of data delivery are compromised due to the low-data rate and power of ZigBee. However, the presence of various dielectric constant materials in the DRG can cause transmission impairments of the electromagnetic wave. In this paper, we have analysed the performance of the ZigBeePRO network by applying the shortest path algorithm while delivering energy data from the solar DRG to the SG. The DRG architecture is created by considering a real test-bed of 35 kW solar DRG at Universiti Putra Malaysia (UPM). The numbers of nodes are calculated from specifications of the ZigBeePRO enabled Waspmote embedded board, inverters, and electrical parameters of a Photovoltaic (PV) module. The results of the obtained propagation path loss model indicate that the Transverse Electric (TE) and Transverse Magnetic (TM) polarizations are proportional to the loss of the propagation path at different incident angles (α); however, an exception is observed for the TM polarization at α = 55 o. Due to this polarization effect, the brick-built type cabin at the DRG site is a consequence of a higher propagation path loss than the Iron (III)-made cabin. The other performance parameters, including network throughput, data loss, and ZigBeePRO collision, are also evaluated.

Standalone photovoltaic (PV) systems are crucial for providing reliable, cost-effective, and sustainable energy to remote or off-grid locations with limited or no access to the traditional electrical grid. In this context, the sizing of a standalone PV system for a remote health clinic involves determining the clinic’s power requirements and matching them with an appropriate PV system’s capacity. Maximum power point tracking (MPPT) is a critical aspect of standalone PV systems, as it optimizes the PV system’s power output by adjusting the PV array’s operating point to the maximum power point (MPP) of the PV module. However, the non-linear behavior of PV systems due to exposure to changing climate conditions and partial shading can hide some faults, making traditional protection devices incapable of detecting them. Therefore, the design of an optimal simulation model of a properly sized standalone PV system is important for simulating and collecting the necessary data to diagnose faults. In this study, a standalone PV system for a hypothetical clinic in Malaysia is proposed, and it is sized based on peak sun hours. Its simulation model is built using MATLAB/Simulink (R2018b). The proposed simulation system’s performance was evaluated considering the proper design of the PV system’s boost converter within the changing conditions, and its ability to harvest maximum power points is considered a criterion for evaluating the performance. Various scenarios of uniform irradiance, uniform temperatures, shading effects, and sequential partial shading scenarios were implemented to evaluate the proposed simulation model. Results show that the proposed simulation model is suited to simulating different tests as it can harvest maximum power in all test scenarios.

This study proposes SVM based Random Subspace (RS) ensemble classifier to discriminate different Power Quality Events (PQEs) in a photovoltaic (PV) connected Microgrid (MG) model. The MG model is developed and simulated with the presence of different PQEs (voltage and harmonic related signals and distinctive transients) in both on-grid and off-grid modes of MG network, respectively. In the pre-stage of classification, the features are extracted from numerous PQE signals by Discrete Wavelet Transform (DWT) analysis, and the extracted features are used to learn the classifiers at the final stage. In this study, first three Kernel types of SVM classifiers (Linear, Quadratic, and Cubic) are used to predict the different PQEs. Among the results that Cubic kernel SVM classifier offers higher accuracy and better performance than other kernel types (Linear and Quadradic). Further, to enhance the accuracy of SVM classifiers, a SVM based RS ensemble model is proposed and its effectiveness is verified with the results of kernel based SVM classifiers under the standard test condition (STC) and varying solar irradiance of PV in real time. From the final results, it can be concluded that the proposed method is more robust and offers superior performance with higher accuracy of classification than kernel based SVM classifiers.