Low-frequency oscillations (LFOs) in interconnected power systems threaten stability, and various damping devices such as Power System Stabilizers (PSSs) often fail to mitigate inter-area modes effectively. High-Voltage Direct Current (HVDC) transmission systems, with independent control of active and reactive power at both converter ends, provide a flexible platform for damping oscillations. This research introduces a hybrid Neuro-Fuzzy Wavelet Controller (NFWC) optimized via the Levenberg–Marquardt Algorithm (LMA) to enhance HVDC system stability. The NFWC combines fuzzy inference with Wavelet Neural Networks (WNNs) to provide a damping current signal to the master control of the HVDC control system. The proposed algorithm utilizes the LMA rather than conventional optimization techniques, thereby avoiding the issue of getting stuck in local minima and effectively damping LFOs. Simulation results on single-machine and multi-machine power systems under varied loading and fault scenarios demonstrate superior transient and steady-state damping performance of the proposed controller. Based on qualitative and quantitative results, it is found that the proposed NFWC significantly improves performance in both transient and steady-state regions.

Conventional methods like Automatic Voltage Regulators (AVRs) and Power System Stabilizers (PSSs) fall short of effectively damping Low-Frequency Oscillations (LFOs), necessitating the exploration of advanced solutions. This paper addresses the challenge of power system stability by utilizing advanced soft computing techniques in Supplementary Damping Control (SDC) for Static Synchronous Compensator (STATCOM) to mitigate low frequency oscillations in multi-machine power system. The proposed Type-II NeuroFuzzy Wavelet Controls (NFWC) employ Gaussian Type-II membership functions, with uncertainties in mean and standard deviation parameters, in the antecedent part and Wavelet Neural Networks (WNNs) in the consequent part. A multi-machine power system with STATCOM is considered for the performance evaluation of the proposed controllers. The proposed Type-II NFWC-1 and Type-II NFWC-2 outperform Adaptive NeuroFuzzy TSK Control (ANFTSKC) in terms of convergence speed and damping performance. Type-II NFWC-2 exhibits and maintains its superior performance over Type-II NFWC-1 and ANFTSK, highlighting the effectiveness of incorporating additional uncertainty in both mean and standard deviation parameters.

Modern power systems face growing stability challenges due to rising network complexity and dynamic operating conditions. Traditional control mechanisms often struggle to effectively mitigate Low-Frequency Oscillations (LFOs), underscoring the need for more advanced and adaptive damping strategies. Flexible AC Transmission Systems (FACTS), especially Static Synchronous Compensators (STATCOMs), have shown considerable promise in strengthening system stability under such challenging conditions. However, their performance is highly dependent on the quality of the Supplementary Damping Controller (SDC) strategy, and conventional methods may fall short under nonlinear and dynamic conditions. To tackle these issues, this paper presents a novel Indirect Adaptive Polynomial Wavelet-based Neuro-Fuzzy Control (ANFWC) framework designed to damp LFOs in STATCOM applications. The ANFWC includes three controllers, each employing a distinct Orthogonal Polynomial Wavelet-based Neural Network (PWNN) within an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Takagi-Sugeno-Kang (TSK) controller: the Legendre Wavelet-based Controller (ANFLWC), the Hermite Wavelet-based Controller (ANFHWC), and the Chebyshev Wavelet-based Controller (ANFCWC). These controllers enhance ANFIS learning and nonlinear mapping by leveraging PWNNs in the consequent layer. The performance of these controllers is evaluated through MATLAB simulations on the Single-Machine Infinite Bus (SMIB) and IEEE 9-bus Western System Coordinating Council (WSCC) test systems under various fault and disturbance conditions. Comparative analyses show that ANFLWC achieves the best performance, followed by ANFCWC and ANFHWC. All proposed controllers significantly outperform the conventional ANFIS-based TSK controller (ANFTSKC) and Lead-Lag Control (LLC), demonstrating the effectiveness of the ANFWC approach in improving power system damping and stability.

Power system stability continues to be a major challenge as modern grids grow more complex, uncertain, and increasingly reliant on renewable energy sources. This paper presents two new Neuro-Fuzzy controllers for Static Synchronous Compensators (STATCOMs): the Direct Chebyshev Wavelet-Based Neuro-Fuzzy Controller (DNF-CW), which adapts parameters online using fixed-structure rules, and the Indirect Chebyshev Wavelet-Based Neuro-Fuzzy Controller (IDNF-CW), which uses an online identifier to measure plant sensitivity. Chebyshev wavelet-based neural networks are utilized in the consequent part of both controllers to enable accurate local modeling and improved damping performance. The proposed methods are evaluated using a Single Machine Infinite Bus (SMIB) system and the IEEE 9-bus multi-machine system under a variety of fault and loading conditions. Benchmark comparisons include a conventional Indirect Adaptive Neuro-Fuzzy Takagi–Sugeno–Kang (IDNF-TSK) based controller, a configuration without STATCOM (No STATCOM), and a configuration with STATCOM but without auxiliary control (No Control). In the SMIB scenario, the IDNF-CW achieves a 40% reduction in settling time compared to the IDNF-TSK. In the more demanding multi-machine setup, the IDNF-CW restores stability within 3 seconds after a sequence of faults, outperforming DNF-CW and IDNF-TSK. Additionally, reductions of over 53% in the Integral of Time-weighted Absolute Error (ITAE) and 36% in the Integral of Absolute Error (IAE) are observed. These tests under multiple fault conditions and 10% measurement noise confirm stable operation, with overshoot limited to 3.57%–4.7% and minimal impact on settling time. These findings highlight the effectiveness of combining Chebyshev wavelets, adaptive control, and indirect architectures for enhancing power system stability.

In this paper, a self-excited topology by utilizing third harmonic field excitation is proposed for realizing the brushless operation of the Wound Rotor Vernier Machine (WRVM). The proposed topology consists of two windings housed inside the stator periphery connected in series with the three-phase diode rectifier. The two windings on the stator periphery are the 12-pole excitation and 4-pole armature winding. The 12-pole excitation winding is linked to the open-end star winding of 4-pole armature winding with the help of a three-phase diode rectifier. When the current is supplied by the inverter, a 4-pole, and a 12-pole Magneto Motive Force (MMF) is developed in the machine air gap. The 4-pole fundamental MMF component develops the main stator field and a 12-pole MMF component develops the third harmonic field. The third harmonic field tends to induce a harmonic current in the harmonic winding which is housed inside the rotor periphery. The induced harmonic current is then rectified by the means of an H-bridge rectifier to excite the rotor field winding, and this realizes the brushless operation of a WRVM. The Two-dimensional Finite Element Analysis (2D-FEA) is carried out in a JMAG designer software to validate the operation of the proposed BL-WRVM. Furthermore, the proposed topology is analyzed for the comparative analysis with the sub-harmonic model after the verification of FEA results. The comparative analysis shows that the third harmonic topology results in improved average torque when compared to the subharmonic wound-rotor vernier machine.

Since their inception, fuzzy logic and its variants involving neural networks have witnessed tremendous applications in the area of identification and control of nonlinear dynamic plants. Fuzzy logic being the universal approximator becomes more powerful when combined with inherent learning capability of Neural Networks (NNs). This research presents a novel adaptive fuzzy control based on Functional Link NNs (FLNNs). The Laguerre orthogonal polynomials have been used for functional expansion of FLNNs. The parameter adaptation and thus the shape of the membership functions and weights of the polynomials of FLNNs are adapted online based on gradient descent optimization technique. Finally, the proposed control scheme has been checked for its performance using comparative evaluation with conventional control schemes applied to different nonlinear plants. The nonlinear time domain simulation results and their quantitative analysis validate the superior performance of the proposed adaptive fuzzy FLNN control.

Voltage instability in a power system produces low-frequency oscillations (LFOs), causing adverse effects in power distribution. Intelligent control schemes can overcome the limitations of fixed-parameter structures in power system stabilizers (PSS). Flexible alternating current transmission system (FACTS) control along with some supplementary control have remarkable potential in damping the oscillations. This paper proposes an adaptive neurofuzzy based recurrent wavelet control (ANRWC) scheme to enhance the power system stability. The proposed scheme utilizes recurrent Gaussian as antecedent part’s membership function and recurrent wavelet function in consequent parts. Our scheme uses gradient descent, adadelta, adaptive moment estimation (ADAM) and proximal gradient descent algorithms for optimization in which parameters of the scheme are updated using a back-propagation algorithm. A multi-machine power system is used for testing the controller. We evaluate the proposed control scheme in comparison to conventional lead-lag control and an adaptive neurofuzzy takagi sugeno kang (ANFTSK) control scheme. For comparison, we calculate the performance indices (PIs) for different controllers. Both quantitative and qualitative evaluations assert the effectiveness of the proposed control as compared to other schemes.

The main objective of this research is to use a Flexible AC Transmission System (FACTS) controller to increase the stability of AC grid. Power system stability is a major concern for reliable and secure operation of the system. FACTS controllers are mainly used for voltage regulation and power flow control; however, they can be very effective for power stability when equipped with efficiently designed auxiliary control. In this work, an artificial intelligence approach based on NeuroFuzzy Hermite wavelet based direct adaptive control has been used as an auxiliary controller for shunt-type FACTS controller. The performance of the proposed controller has been checked using Single Machine Infinite Bus and IEEE 9 BUS multimachine systems installed with a Static Synchronous Compensator (STATCOM), a shunt-type FACTS controller. Finally, the comparative evaluation of the proposed controller has been made in terms of local and inter-area modes of oscillations using nonlinear time domain simulations for different faults and operating conditions.

This paper presents a proposed model of a multi-stack slotted stator axial-flux type permanent magnet synchronous machine (AFPMSM) specifically for reducing torque ripple. The proposed AFPMSM model uses pentagon-shaped permanent magnets (PMs). It has a low value of cogging torque and torque ripples compared to the conventional model with a trapezoidal magnet shape. Additionally, it has increased internal generated voltage (Ef) as compared to the conventional model. To further enhance Ef phases and minimize cogging torque of the proposed model, the proposed AFPMSM model was optimized by varying different sides of PMs using a genetic algorithm (GA). A time-stepped three-dimensional (3D) finite element analysis (FEA) was performed for the comparative analysis of conventional, proposed, and optimized AFPMSM models. From this comparative performance analysis, it is observed that torque ripples and cogging torque of the optimized AFPMSM are significantly decreased, while output average torque is appreciably increased. Ef and output power are also enhanced.

The aim of this paper is to design, analyze and optimize the “Multi-Stack Slotless Axial Flux Switching Permanent Magnet Machine”. In the design process, mathematical models are implemented, and the Finite Element Method (FEM) is performed to analyze the machine performances. The paper aims to minimize the occurrence of cogging torque and torque ripple in the multi-stack slotless stator AFPM machine. As a consequence, it reduces the vibrations in the machine and increases its life span. Multi-stack slotless stator AFPM machine with a right-angled trapezoid-shaped PM is proposed and comparison is done with conventional shape AFPM machine. In order to examine the performance of multi-stack slotless stator AFPM machine Finite Element Analysis (FEA) is used. To further enhance the characteristics of the designed machine with the proposed right-angled PM shape, optimization is done by considering inner and outer pole pitch as the design variables. In optimization process, krigging method assigned with Latin Hyper-cube Sampling and a genetic algorithm (GA) is performed due to suitability with non-linear data. Then, finite element analysis by JMAG-Designer is performed to verify the results. It is determined that optimized model has achieved 65% reduction in torque ripples as compared with the conventional design. Hence, this work attempts to optimize the performance of the AFPM machine.

The immense emergence of plug-in hybrid electric vehicles (PHEVs) is envisioned in the future. The rapid proliferation of PHEVs and their charging triggers intense surges in the load during load peak hours. A sophisticated controlled charging station is developed for PHEVs to alleviate grid load during peak demand hours. A novel feedback linearization embedded full recurrent adaptive NeuroFuzzy Legendre wavelet control (FBL-FRANF-Leg-WC) technique is employed to control the charging of PHEVs. The antecedent part of the NeuroFuzzy framework is based on recurrent Gaussian membership function while the consequent part comprises of recurrent Legendre wavelet. The charging station is integrated into a grid-connected microgrid hybrid power system. The charging station consists of five different PHEVs with seven different modes of operation. The performance of the control scheme is tested for various power quality and power system stability parameters. The effectiveness of the suggested control scheme is validated through simulation results by comparing with adaptive NeuroFuzzy, adaptive PID, and conventional PID control scheme.

The study of power flow and contingency is essential for planning future power system expansion. It is necessary to determine the optimal way to operate existing power systems. D-FACTS devices are used to eliminate transmission line infractions. The aim of this paper is to investigate the optimal placement of FACTS devices in transmission lines. For this, a 37-bus benchmark test case has been selected for testing. The results show violations in transmission lines and no other path to install new transmission lines between these buses. After getting results for optimal placement of FACTS from the contingency analysis tool in PowerWorld Simulator, D-FACTS devices are placed on overloaded transmission lines. The number of violations decreased from 98 to 53 after placing FACTS at an appropriate location in the transmission line in the power system.

Permanent magnet vernier machines (PMVMs) are becoming progressively attentive because of their high efficiency and high torque density and can thus be utilized for direct-drive applications such as wind power and electric vehicles etc. This paper presents performance improvement of multi-rotor axial flux vernier permanent magnet (MR-AFVPM) machine with a proposed two-stage parallelogram-shaped PM. The proposed shaped PM reduces the cogging torque and torque ripples due to its skew effect. Furthermore, it also presents a comparative analysis of the conventional and proposed shape PM. Then 3D finite element analysis (FEA) is used for comparative analysis. Genetic algorithm (GA) associated with kriging method based on LHS is introduced and is used to optimize the proposed shaped PM for further performance improvement in terms of cogging torque, back EMFs, torque ripples, VTHD, output torque, flux density distributions, power factor and output power of the analyzed machines which are validated by 3D-FEA.

The research article presents a novel maximum power point tracking (MPPT) control scheme to extract maximum power from a grid-connected wind energy conversion system (WECS). The control scheme is based on adaptive feedback linearization embedded full-recurrent adaptive Neuro-Fuzzy hybrid B-Spline wavelet control (FBL-FRANF-HBs-WC). FRANF-HBs-WC architecture estimates the nonlinear functions of FBL using B-Spline membership function and the Morlet wavelet network in the antecedent and the consequent parts, respectively. The MPPT performance using FBL-FRANF-HBs-WC is validated for the permanent magnet synchronous generator (PMSG) based WECS through simulation testbed implemented in MATLAB/Simulink. The performance parameters are compared against adaptive PID (aPID) for varying wind speed and grid-connected load for 24 hours.

A smart city is a dynamic and sustainable urban system that provides a great quality of service to its residents by optimally managing its resources. In smart cities, low-frequency oscillations are a serious concern to the power system as they adversely affect system’s stability. Moreover, power system stabilizers are inefficient due to their fixed-parameter architecture. Flexible ac transmission system controller performs effective damping of low-frequency oscillations when provided with suitable supplementary damping control like a Static synchronous series compensator. In this context, the paper proposes an adaptive neuro-fuzzy recurrent wavelet control for smart cities that uses the recurrent Gaussian membership function and recurrent wavelet neural network in antecedent and consequent parts respectively. The paper applies the gradient descent optimization with a back-propagation algorithm to update the parameters of suggested adaptive neuro-fuzzy recurrent wavelet control. Simulations are performed on two test systems, both showing the effectiveness of the recommended control scheme as compared to traditional lead-lag control and artificial neuro-fuzzy Takagi Sugeno Kang control. Calculations of performance index for various fault scenarios lead to the conclusion that the control scheme can damp oscillations effectively and enhances the power system’s transient stability.

In the modern world, only conventional energy resources cannot fulfil the growing energy demand. Electricity is a fundamental building block of a technological revolution. Today, most of the electricity demand is met by the burning of fossil fuels but at the cost of adverse environmental impact. In order to bridge the gap between electricity demand and supply, nonconventional and eco-friendly means of energy generation are considered. Renewable energy systems (RESs) offer an adequate solution to mitigate the challenges originated due to greenhouse gasses (GHG). However, they have an unpredictable power generation with specific site requirements. Grid integration of RESs may lead to new challenges related to power quality, reliability, power system stability, harmonics, subsynchronous oscillations (SSOs), power quality, and reactive power compensation. The integration with energy storage systems (ESSs) can reduce these complexities that arise due to the intermittent nature of RESs. In this paper, a comprehensive review of renewable energy sources has been presented. Application of ESSs in RESs and their development phase has been discussed. Role of ESSs in increasing lifetime, efficiency, and energy density of power system having RESs has been reviewed. Moreover, different techniques to solve the critical issues like low efficiency, harmonics, and inertia reduction in photovoltaic (PV) systems have been presented. Unlike most of the available review papers, this article also investigates the impact of FACTS technology in RESs-based power system using multitype flexible AC transmission system (FACTS) controllers. Three simulation models have been developed in MATLAB/Simulink. The results show that FACTS devices help to maintain the stability of RESs integrated power system. This review paper is believed to be of potential benefit for researchers from both the industry and academia to develop better understanding of challenges and solution techniques for REs-based power systems and future research dimensions in this area.

The state-of-charge (SoC) of an energy storage system (ESS) should be kept in a certain safe range for ensuring its state-of-health (SoH) as well as higher efficiency. This procedure maximizes the power capacity of the ESSs all the times. Furthermore, economic load dispatch (ELD) is implemented to allocate power among various ESSs, with the aim of fully meeting the load demand and reducing the total operating cost. In this research article, a distributed multi-agent consensus based control algorithm is proposed for multiple battery energy storage systems (BESSs), operating in a microgrid (MG), for fulfilling several objectives, including: SoC trajectories tracking control, economic load dispatch, active and reactive power sharing control, and voltage and frequency regulation (using the leader-follower consensus approach). The proposed algorithm considers the hierarchical control structure of the BESSs and the frequency/voltage droop controllers with limited information exchange among the BESSs. It embodies both self and communication time-delays, and achieves its objectives along with offering plug-and-play capability and robustness against communication link failure. Matlab/Simulink platform is used to test and validate the performance of the proposed algorithm under load disturbances through extensive simulations carried out on a modified IEEE 57-bus system. A detailed comparative analysis of the proposed distributed control strategy is carried out with the distributed PI-based conventional control strategy for demonstrating its superior performance.

Motivated by the synergistic integration of soft computing paradigms this paper introduces a fully adaptive multiple-input-multiple-output NeuroFuzzy control for multi-type Flexible AC Transmission Systems (FACTS) to damp low frequency oscillations. The novel control strategy integrates the complementary features of locally controllable fuzzy Bspline membership functions and robust wavelet neural networks in NeuroFuzzy structure. The gradient decent based back-propagation mechanism used for parameters update has been optimized using online Adaptive Learning Rates (ALRs). The stability of the proposed algorithm has been ensured by deriving an upper bound on ALRs using Lyapunov stability criteria. The application of this controller to provide damping signals to various FACTS controllers like Static Synchronous Series Compensator (SSSC) and Static Synchronous Compensator (STATCOM) can effectively enhance the dynamic stability of the system. A benchmark multi-machine power system has been used for performance validation of the controller by applying various faults under different loading scenarios. Conventional Lead-Lag and NeuroFuzzy controls have been considered for comparative evaluation using nonlinear time and frequency domain techniques to reveal that the proposed control performs better in different operating regions. Furthermore, the graphical results obtained from time and frequency domain simulations have been quantified numerically using different performance indices and Energy Spectral Density (ESD), respectively. The temporal, spectral and numerical analysis confirms the superior performance of the proposed control scheme.

Intelligent Transport Systems (ITS) require accurate information to be shared among vehicles and infrastructure nodes for applications including accident information or pre-crash warnings, to name a few. Due to its sensitive nature, ITS applications are vulnerable against data integrity attacks where nodes transmit false information that results in wrong decision making by the applications. A characteristic of such attacks is that the false transmitted information is significantly different than the actual information. In this paper, we propose an Outlier Detection, Prioritization and Verification (ODPV) protocol that efficiently isolates false data and improves traffic management decisions. ODPV uses the isolation forest algorithm to detect outliers, fuzzy logic to prioritize outliers and C-V2X communications to verify the outliers. Extensive simulation results verify the effectiveness of the proposed protocol to isolate the outliers.

The research article presents an advanced controlled charging station (CS) for plug-in hybrid electric vehicles (PHEVs) in a grid-coupled microgrid. The proposed control scheme mitigates intense load surges caused by the charging of PHEVs during peak-load demand. Morlet wavelet-based full recurrent NeuroFuzzy structure is employed to design an adaptive feedback linearization control(FBL-FRANF-Mor-WC) that relieves the stressed power system and ensures its stable operation. The effectiveness of the FBL-FRANF-Mor-WC scheme is endorsed for different modes of operation of a CS and various types of connected PHEVs. The control scheme is benchmarked against conventional PID control and adaptive PID control that validates its effectiveness in the smooth operation of grid-connected PHEVs charging station.