Abstract-This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and current. The developed approach is validated at room temperature using two types of lithium-ion batteries under a pulse discharge test. In addition, the proposed model is verified under varying temperature settings under EV drive cycles. The experimental results demonstrate that the DSA optimized RFR algorithm achieves RMSE of 0.382% in the HPPC test using LiNMC battery. Besides, the proposed method obtains satisfactory outcomes in EV drive cycles, estimating MAE of 0.193% and 0.346% in DST and FUDS cycles, respectively, at 25°C.

The overhead transmission line system is one of the methods of transmitting electrical energy at a high voltage from one point to another, especially over long distances. The demand for electrical energy is increasing due to the increase in the world population, the evolution of transport technology, and economic expansion, thereby resulting in overloading to the overhead line (OHL) system. In building new infrastructure for transmission lines, several issues need to be addressed. Thus, optimizing existing power by increasing the ampacity of power line is a practical solution to meet energy demand issues. During long-term operation, the temperature of OHL conductors may increase beyond their rated temperature, which is typically 75 °C for conventional conductors such as aluminum-conductor steel-reinforced cable. This condition is defined as thermal stress, which results in lower sag vertical clearance, tensile loss, elongation and creep, and reduced life span of the conductors. This condition must be avoided to ensure that the line is not permanently elongated, which can disrupt the vertical ground clearance, and to expand the conductor’s life. Other factors such as lightning, wildfire, aging, and degradation of the conductor can also cause thermal stress on the conductors and have thermal effects on the conductor’s performance. Therefore, unwanted thermal stress needs to be examined and identified by monitoring the thermal effect and behavior of the lines. This paper presents the state of the art in monitoring technologies that can be used to identify thermal stress on OHL conductors, including the issues and challenges in monitoring. At the end of this paper, a few suggestions are included to address the occurrence and assessment of thermal stress in lines. Ultimately, this work may provide complete information to researchers and maintenance engineers to enable them to make better decisions on condition monitoring, operation, and maintenance of the system.

The converters and their control strategies play an important role in converting, transmitting and improving the performance of high voltage direct current (HVDC) system. There are different types of converter and their control strategies being employed in the HVDC system such as line commutated converter, voltage source converter (VSC) etc. However, the existing converter controllers have still some limitations on certain deficiencies in certain aspects such as in weak AC grid or at high voltage and power level. Thus, an advanced converter control strategy is very much important in order to ensure optimal power transfer with minimal loss and stable voltage. This paper presents a comprehensive review of the advanced control strategies to address the problems and enhance the performance of the VSC based HVDC (VSC-HVDC) transmission system. A detailed study on the overview of VSC-HVDC and their converter classifications are investigated. The main contribution of this paper is to carry out the different types of VSC-HVDC control strategies in controlling voltage, current, power and the control parameters of the HVDC transmission system. This review also highlights several factors, challenges, and problems of the conventional VSC-HVDC controllers. Furthermore, the review provides some suggestions for the advanced control for the future research and development of the HVDC system.

A comprehensive study of a minimized component pencil shaped (PS) 9-level inverter constructed with just two DC supplies is presented in this research. Many of the suggested low component multilevel inverters (MLIs) use DC supplies that are not being used appropriately along with additional conducting switches. Since this proposed MLI has a reduced quantity of power electronic switches, it is more efficient. The architecture may be expanded to a modular higher voltage level inverter, which uses less DC supplies and uses them correctly without the need of an extra H-bridge circuit. To determine their optimum capabilities, the proposed inverter parameters’ simplified formulas are constructed. Furthermore, the extended model of the proposed architecture is used to generate an optimum PSMLI design for lowering the total standing voltage (TSV) of the inverter. To demonstrate its advantages over recent MLIs of similar types, comparison studies are given to justify the proposed inverter. Through proper simulation and laboratory experiment, the MLI obtained a higher efficiency of 95.54%. On the other hand, the optimized 17-level version of PSMLI obtained total harmonic distortions (THD) of only 5.15% which successfully attained IEEE 519 standard performance.

High-voltage direct current (HVDC) has received considerable attention due to several advantageous features such as minimum transmission losses, enhanced stability, and control operation. An appropriate model of HVDC is necessary to assess the operating conditions as well as to analyze the transient and steady-state stabilities integrated with the AC networks. Nevertheless, the construction of an HVDC model is challenging due to the high computational cost, which needs huge ranges of modeling experience. Therefore, advanced dynamic modeling of HVDC is necessary to improve stability with minimum power loss. This paper presents a comprehensive review of the various dynamic modeling of the HVDC transmission system. In line with this matter, an in-depth investigation of various HVDC mathematical models is carried out including average-value modeling (AVM), voltage source converter (VSC), and line-commutated converter (LCC). Moreover, numerous stability assessment models of HVDC are outlined with regard to stability improvement models, current-source system stability, HVDC link stability, and steady-state rotor angle stability. In addition, the various control schemes of LCC-HVDC systems and modular multilevel converter- multi-terminal direct current (MMC-MTDC) are highlighted. This paper also identifies the key issues, the problems of the existing HVDC models as well as providing some selective suggestions for future improvement. All the highlighted insights in this review will hopefully lead to increased efforts toward the enhancement of the modeling for the HVDC system.

The optimal algorithm of Energy Storage System (ESS) has gained remarkable attention in developing a microgrid (MG) system to reduce the intensity of carbon emission in the electricity sector and alleviate the environmental impact by 2050. This article provides a historical background and a comprehensive analysis of the optimal algorithm of ESS in MG applications. A brief search has been directed through the Scopus database with some predefined conditions on the last week of January 2021 over 11 years to select the top-cited articles. This bibliometric study is evaluated in this field over the last decades based on the year of publication, interrelation of co- occurrence keywords, articles type, country of origin, journal, and publisher that published the 120 top-cited articles. A sum of 4995 articles was revealed within the year 2010 to 2020 in the field of the optimal algorithm of ESS in MG applications, and the top-most 120 papers were received in total 23003 citations (mean-119.69; median-157.5). Articles having the highest citation revealed in 30 different journals, 27 different regions and 6 different publishers. This bibliometric approach of ESS in MG applications offers the trends of research, gaps of this field, and knowledge essential for further development and advancement in this area. It is predicted that extracting, evaluating, and investigating the top-most cited articles will support further research in the optimal algorithm of ESS in MG applications.