Recent Advances in Electrical & Electronic Engineering - Volume 17, Issue 10, 2024

This study aims to evaluate the performance of a 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC) fed Electric Vehicle (EV) using an Enriched Switched Parameter Cuk (ESPC) converter and an Elman Back Propagation (EBP) maximum power point tracking algorithm (MPPT). The acceptance of fuel cell-fed EVs in modern society is critical to the development of a pollution-free environment. One of the significant contributors to excessive pollution is transportation on public roads using internal combustion engines powered by crude oil as their primary energy source.

This study identifies suitable high voltage gain DC-DC converters with minimum duty cycle operation for fuel cell-fed electric vehicle systems and develops an intelligent MPPT controller for hybrid electric vehicle applications.

In this study, MATLAB/Simulink environment is used to design a 1.26 kW PEMFC powered electric vehicle. To integrate PEMFC to BLDC motor, an Enriched Switched Parameter Cuk converter is built with a high static converter voltage gain.

The effectiveness and performance of the fuel cell-fed EV system are investigated using perturb and observe method and Elman Back Propagation MPPT approaches for various fuel cell input temperature conditions and intervals.

This study discusses the use of low-voltage fuel cell sources with power electronic converters that are available for various high gains in the literature. The proposed ESPC is designed to reduce stress on power converter components and is intended for low-voltage FC-fed electric vehicle applications.

Consumption of electricity always varies based on demand. The load cluster pattern aims at categorizing periodical changes over a specific time. Predicting the electric load was the initial goal of this study. Additionally, the outcomes of the load prediction were utilized as data for categorizing electrical loads using a descriptive-analytical method.

The study has dealt with a matching of load-side electric demand with the electric supply. To ensure dependable power-generating stability, it is vital to anticipate and categorize loads. Thus, the research presented here has focused on electrical load forecasting and classification.

Alternative algorithms, including Naive Bayes, decision tree, and support vector machine classifier, were employed to address the cluster pattern. The data used for this research presentation was collected from the D Block of the Kamaraj College of Engineering and Technology, K. Vellakulam, India, every 15 minutes. Multiple unsuitable loaded circumstances were ignored during the pre-processing of the dataset. Additionally, other algorithms, like Naive Bayes, decision tree, and support vector machine, were used to categorize the raw data. The processing of data was done by a feature selection approach.

The performance was predicted by comparing the entire machine learning algorithms. Out of the machine learning techniques, an accuracy of 4.2% for Academic Block 4, a precision of 33% for Boys Hostel, a recall score of 4.7% for Academic Block 4, and an F1 score of 5.3% for Academic Block 4, were obtained.

In the study, the decision tree algorithm has shown promising performance than the other machine learning techniques used.

This article presents a novel strategy that utilizes the nature-inspired Spotted Hyena Optimizer Algorithm (SHOA) to optimize the placement of solar and wind-based renewable distributed generation (RDG) and distribution static compensators (DSTATCOMs) in radial distribution systems (RDS).

The proposed technique aims to determine the optimal locations of DSTATCOM and RDGs based on the loss sensitivity factor (LSF), while the appropriate sizes are determined using the newly developed SHOA. To facilitate efficient load flow calculations, a fast and effective backward/forward sweep algorithm (BFSA) is employed.

The primary objective of this method is to minimize overall power losses within the system. The effectiveness of the optimization approach based on SHOA is demonstrated through extensive simulations conducted on a standard IEEE 33-bus test system with diverse load models.

The results of the simulations and comparisons of multiple case studies clearly indicate that the allocation of DSTATCOMs leads to significant reductions in power losses and improvements in voltage profiles.

The utilization of electric vehicles (EVs) is on the rise, which has led to significant challenges for the radial distribution system (RDS). This research aimed to investigate the impact of electric vehicle charging stations (EVCS) on power loss, voltage stability, and reliability within the RDS. Solar-based renewable distributed generation (SRDG) offers a range of benefits in mitigating the impact of EVCS on the distribution system. To simulate the integration of EV charging loads and assess their effects on the RDS, the study has employed the widely used IEEE 69-bus system as a test platform.

A novel approach utilizing the cuckoo search algorithm (CSA) is presented in this article to determine the optimal locations for EVCS and SRDG.

The study has examined the potential power losses resulting from additional EV loads and explored strategies to optimize charging rates and minimize resistive losses. Voltage stability has been assessed by analyzing the voltage drop caused by high charging loads, and the efficiency of voltage regulation devices and control strategies has been evaluated. Moreover, the paper has discussed the reliability implications of EV charging, including localized overloads and the possibility of outages.

Overall, this research has enhanced our understanding of the challenges associated with integrating EVCS into the RDS and offered potential solutions to address these challenges.

The Integration of non-conventional energy systems (NCES), like solar, wind, etc., into the grid with power electronic devices is adapted to meet the demand. Parallel connection of inverters (PCI) is an efficient method to boost power handling capacity, reliability, and system efficiency. However, the main drawback is the unequal power sharing among the inverters while using the conventional droop control technique (CDC). In addition, the circulating currents (CC) flow between these PCI, leading to common mode voltage (CMV), current waveform distortion, and reduction in the system's overall performance.

This work consists of a photovoltaic system (PVS) and battery energy storage system (BES) as the distributed generation (DG) unit to voltage source inverter (VSI) 1 and 2. The multi-objectives of the suggested work are (a) the equal power/load sharing among two inverters, (b) effective minimization of CC and the CMV, (c) maintaining constant DC-link (DCL) voltage during different solar irradiation and constant temperature, and (d) the reduction in total harmonic distortion (THD) of load current.

A novel approach related to the enhanced droop control (EDC) method with an adaptive neuro-fuzzy hybrid controller (ANFHC) was suggested here to overcome the above issues. The performance analysis of the suggested technique was done in the Matlab/ Simulink platform with islanded and grid-connected modes for different loads.

A comparative analysis with the available methods like the proportional integral controller (PIC) and sliding mode controller (SMC) was carried out to exhibit the viability of the developed control technique.

This study focused on the operation and control of a PCI with ANFHC in islanded and grid-connected modes to address issues such as uniform power sharing and reduction of CC and CMV.

One of the key challenges in maximizing the performance of PV systems is the efficient tracking of the maximum power point (MPP) under varying operational conditions, including changes in solar irradiance and temperature. Accurate MPP tracking is essential for achieving optimal energy conversion efficiency and maximizing the electricity generation potential of the PV array, even during partial shading conditions. Traditional maximum power point tracking (MPPT) algorithms, such as the incremental conductance (INC) method, often struggle to efficiently handle partial shading conditions. As a result, there is a need for more sophisticated and robust optimization techniques that can effectively address this challenge. This study presents a novel and innovative Giza Pyramid Construction (GPC) algorithm to solve the partial shading-induced MPP tracking problem.

This study aims to apply the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in photovoltaic systems under partial shading conditions, aiming to enhance energy conversion efficiency and overall system performance.

The methodology involves implementing the Giza Pyramid Construction (GPC) algorithm as the core optimization technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The GPC algorithms are utilized to iteratively adjust the duty cycle of the boost converter, enabling efficient power extraction from the PV array under varying shading conditions. The performance of the GPC algorithm is evaluated through simulations in MATLAB/SIMULINK and compared against conventional MPPT methods like INC and DGO techniques.

The successful application of the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in PV systems under partial shading led to significantly reduced optimization time, faster settling times, and minimized output ripples. With the proposed GPC MPPT, optimization time is reduced to 41ms, settling time is reduced to 93ms, and ripples are minimized to 0.092%.

The Giza Pyramid Construction (GPC) algorithm demonstrates its effectiveness as a robust and efficient maximum power point tracking method in photovoltaic systems, particularly under partial shading conditions. The improved optimization speed, reduced settling times, and minimized output ripples underscore the GPC algorithm's potential to enhance the overall efficiency and reliability of PV systems, paving the way for its practical implementation in real-world renewable energy applications.

The frequency of energy crises due to shortage of fuel or rise in fuel prices has increased in the past decade. This may be due to increasing energy demand, the financially crippled state of the distribution companies, high prices of fuel, shortage of fuel, tension between the countries, etc. Apart from this, climate change, global warming, and rising environmental concerns have necessitated the need to search for more sustainable alternative sources of electricity. It has created a lot of interest in Renewable Energy Sources (RES), which includes solar, wind and hydropower. Even governments all around the world are introducing policies to support the adoption of RES for the generation of electricity and have set the target to achieve the Net-Zero emission level by decarbonizing the power sector.

This paper provides an overview of all the prominent renewable energy technologies, namely solar power plants, wind energy plants, biomass and hydropower, using a systematic review procedure. In addition to this, the importance of Power Electronic Technologies (PET) in the integration of RES in today’s power system has also been presented. Further, a SWOT analysis framework has also been utilized to evaluate the strengths, weaknesses, opportunities and threats associated with the application of PET in renewable energy systems.

The results from the analysis showcases that the implement of PET offers significant strength for the implementation of RES in the power system by providing increased system efficiency, and improved power quality. However, the technologies also face some of the weakness of limited scalability, regulatory concerns, high initial cost etc. Further, the different opportunities for a widespread adoption of PET have been discussed in this paper. Finally, all the threats concerning the implementation of the PET in the RES in the modern power system have also been highlighted.

The purpose of this research work is to classify recent research techniques on renewable energy sources that were published between the years of 2010 to 2022 through a systematic and statistical review framework to showcase the need for power electronic technologies for the implementation of RES.