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The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding Mode Control (L-SMC) strategy for a DC–DC boost converter, addressing significant uncertainties caused by large variations in system parameters (R and L) and ensuring the tracking of a voltage reference. The proposed control strategy employs the Lyapunov stability theory to build an adaptive law to update the parameters of the sliding surface so the system can achieve global asymptotic stability in the presence of uncertainty in inductance, capacitance, load resistance, and input voltage. The nonlinear sliding manifold is also considered, which contributes to a more robust and faster convergence in the controller. In addition, a logic optimization technique was implemented that minimizes switching (chattering) operations significantly, and as a result of this, increases ease of implementation. The proposed L-SMC is validated through both simulation and experimental tests under various conditions, including abrupt increases in input voltage and load disturbances. Simulation results demonstrate that, whether under nominal parameters (R = 320 Ω, L = 2.7 mH) or with parameter variations, the voltage overshoot in all cases remains below 0.5%, while the steady-state error stays under 0.4 V except during the startup, which is a transitional phase lasting a very short time. The current responds smoothly to voltage reference and parameter variations, with very insignificant chattering and overshoot. The current remains stable and constant, with a noticeable presence of a peak with each change in the reference voltage, accompanied by relatively small chattering. The simulation and experimental results demonstrate that adaptive L-SMC achieves accurate voltage regulation, a rapid transient response, and reduces chattering, and the simulation and experimental testing show that the proposed controller has a significantly lower steady-state error, which ensures precise and stable voltage regulation with time. Additionally, the system converges faster for the proposed controller at conversion and is stabilized quickly to the adaptation reference state after the drastic and dynamic change in either the input voltage or load, thus minimizing the settling time. The proposed control approach also contributes to saving energy for the application at hand, all in consideration of minimizing losses.

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.

Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management.

Introduction. Recently, photovoltaic (PV) systems are increasingly favored for converting solar energy into electricity. PV power systems have successfully evolved from small, standalone installations to large-scale, grid-connected systems. When the nonlinear loads are connected to a grid-tied PV system, the power quality can deteriorate due to the active power supplied by the PV array, there’s a noticeable decline in the quality of power delivered to consumers. Its combination with the shunt active power filter (SAPF) enhances system efficiency. Consequently, this integrated system is adept at not only powering local loads but also at compensating for reactive power and filtering out harmonic currents from the main grid. The novelty of the work describes how an operation of a small scale PV system connected to the low voltage distribution system, and nonlinear load can be achieved, the investigation aims to analyze the system’s behavior and elucidate the advantages of employing various control algorithms. These proposed algorithms are designed to ensure a unity power factor for the utility grid while prioritizing high convergence speed and robustness against load power fluctuations across different levels of solar irradiation affecting the PV modules. The purpose of this work is to enhance the dynamic performance of the SAPF by cooperatively using a self-tuning filter (STF) based instantaneous active and reactive power method (PQ) with a novel predictive current control, enhance the system resilience, ensure optimal management of the total active power between the PV system, the electrical network and the non-linear load by integrating the functionalities of the SAPF under different levels of solar irradiation and maintain the DC-link capacitor voltage constant. Methods. A novel predictive current controller is designed to generate the switching signals piloted the three phase source voltage inverter, also a novel algorithm of instantaneous active and reactive power is developed, based on STF, to extract accurately the harmonic reference under non ideal grid voltage, also the perturb and observe algorithm is used to extract, under step change of solar irradiation, the maximum power point tracking of the PV module and the PI controller is used to maintain constant the DC-link capacitor voltage of the SAPF. Results. The efficacy of the proposed system is primarily centered on the grid side, and the performance evaluation of the control system is conducted using the STF based PQ algorithm and predictive current control. In addition, comprehensive testing encompasses all modes of operation, including scenarios involving distorted voltage sources, step changes in solar radiation, and variations in nonlinear loads. Results highlight superior performance in both transient and stable states, affirming the robustness and effectiveness of the proposed controllers. Practical value. The total harmonic distortion value of the grid current for all tests respects the IEEE Standard 519-1992. References 21, tables 7, figures 25.

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.

A microstrip patch antenna for Global Positioning System (GPS) application is presented in this study. It has a rectangular shape with two notched slots. The notched slots are introduced to improve the adaptation between the microstrip line and the antenna. The antenna was designed and simulated by CST using FR-4 material as the substrate with relative permittivity of 4.3. The proposed microstrip antenna is designed to operate in the GPS band frequency from 1.555 GHz to 1.595 GHz. The performance analysis of the proposed antennas has been carried out in terms of return loss (dB), gain (dB), and VSWR.