MOPSO-based optimal fuzzy logic control strategy for standalone hybrid diesel-wave energy conversion system

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81°, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort.

We investigate the detection and classification of buried radioactive materials of interest using data collected by a sodium iodide (NaI) detector with a short sensor dwell time (i.e., less than or equal to 1 s). The objective of detection is to detect a target from background and nontarget materials, while the objective of classification is to classify targets buried at different depths. Binned energy windows can reduce data dimensionality, help alleviate the negative impact from background, and suppress trivial spectral variations. However, the performance is sensitive to bin partition parameters including the number of bins and their bin widths. We have developed a particle swarm optimization (PSO)-based automatic system to determine these parameters. We also propose to apply a multiobjective PSO to optimize both the detection and classification accuracy simultaneously. The experimental results show that the PSO-based algorithm can outperform the Powell's direction set optimization method. The multiobjective PSO can achieve the balance between the two objectives, and it may provide even better individual accuracy than a single-objective PSO.

Self tuning fuzzy PID type load and frequency controller

Integrating renewable resources in microgrids (MGs) poses a substantial challenge: ensuring stability with low inertia. This paper provides control techniques for the AC frequency and the DC voltage for an isolated/islanded hybrid AC/DC MG using intelligent virtual synchronous generators (VSGs) and intelligent virtual capacitors (VCs). The suggested approaches take advantage of VSG's adaptive damping coefficient and VC's adaptive virtual resistance as defined by intelligent controls. The controllers operate independently, relying solely on local DC voltage and AC frequency measurements without additional communication infrastructure. Compared to the traditional VSG and VC, the hybrid MG with intelligent VSG and VC controls performs better in suppressing AC frequency and DC voltage deviations, regardless of operating mode (rectifier or inverter). Simulation studies in MATLAB/Simulink validate these findings, showing that the artificial neural network (ANN) approach reduces DC voltage deviation by 34.4% and AC frequency deviation by 26.3%, while the fuzzy logic control (FLC) approach further reduces these deviations, particularly DC voltage deviation by 41.9% and AC frequency deviation by 23.2%. ANN excels in reducing deviations for AC frequency, while FLC is more effective in lowering deviations for DC voltage. The proposed controllers' positive impact on system stability is assessed using small‐signal stability analysis.

AUTO-TUNING OF FUZZY PID CONTROLLERS

The deployment of electric vehicle charging stations degrades the quality of power in the distribution grid. This article proposes an interval type-2 fuzzy logic controlled shunt converter coupled novel high-quality charging scheme for electric vehicles. This system includes three-phase bidirectional front-end ac-dc pulsewidth modulation (PWM) converter, backend dc-dc PWM converter and three-phase three-wire distribution static compensator. The bidirectional converters help to perform both grid to vehicle and vehicle to grid mode of operations. The combination of dc-link voltage with decoupled current control technique is exploited for ac-dc converter. A multistep constant current control technique is proposed for the dc-dc converter to charge and discharge the battery. A fuzzy logic controller based instantaneous reactive power theory control method is proposed for shunt converter. The performance of type-1, interval type-2, and real coded genetic algorithm optimized fuzzy logic controllers are evaluated by the shunt converter dc-link voltage and the total harmonic distortion of the source current. Lithium-ion batteries are utilized as an energy storage device for electric vehicles in the proposed system. The entire system is modeled and evaluated in the MATLAB/Simulink environment. An interval type-2 and real coded genetic algorithm optimized fuzzy logic controller affords the better performance during V2G and G2V operations, respectively.

Genetic synthesis of fuzzy logic controllers in turning

This paper presents a novel approach for designing a decentralized controller for load frequency control of interconnected power areas.The proposed fuzzy logic load frequency controller (FLFC) has been designed to improve the dynamic performance of the frequency and tie line power under a sudden load change in the power areas.The effect of generation rate constraint (GRC) for both areas has been considered in the controller design.The proposed FLFC consists of two internal fuzzy logic controllers namely, the PDlike fuzzy logic controller and the PI-like fuzzy logic controller.The FLFC has been co-coordinated with the conventional integral controller.Time-domain simulations using MATALB/SIMULINK program has been performed to demonstrate the effectiveness of the proposed FLFC.The simulation results show that the proposed FLFC can provide good damping and reduce the overshoot even in the presence of the GRC.

Design studies of the most suitable controller for the speed of induction motor (SIM) and peak DC-link voltage (PDV) of high performance z-source inverter (HP-ZSI) greatly affects performance of hybrid electric vehicles. This paper is to give comprehensive analyses comparison and evaluations of the different control techniques as PI controller, self-tuning fuzzy PI controller (SFP) and genetic algorithms optimal tuning gains of the PI controller for controlling the SIM based on criteria of the integral of the absolute value of speed error. With control PDV, this paper also presents comparisons of the different techniques as PI controller, fuzzy logic controller, SFP based on observation of its response and total harmonic distortion of current. All methods are applied to the closed loop of the SIM which relies on direct torque control combined with modified space vector modulation and closed loop of PDV in HP-ZSI. These methods are verified by Matlab software. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2445 Full Text: PDF

Proportional and Integral (PI) controllers are usually accustomed for the control operation of Distribution Static Compensator (DSTATCOM) for the distribution system. However, since the system is very nonlinear and subject to numerous disturbances, the PI controlled DSTATCOM cannot offer optimum performance for various operation points. Additional controllers like the Instant Power Theory (IPT) supported fuzzy logic approach is needed for the DSTATCOM to supply adequate dynamic current control and to enhance power quality and stability of the distribution system. This paper presents the planning of fuzzy logic Controllers (FLC) of a DSTATCOM for improving power quality for a distribution system. The IPT based FLC for the DSTATCOM is used for improving the power quality and the dynamic performance of a distribution system is compared with a DSTATCOM controlled IPT based PI controller. The performances of the DSTATCOM controllers are evaluated throughout grid-side currents with linear and non-linear loads of the distribution system. Simulation results show that the DSTATCOM with IPT based FLC will improve the power quality by reducing reactive currents and harmonic currents with higher system dynamic performance than the DSTATCOM with IPT based standard PI controller.

Power converters play an essential role in Photovoltaic (PV) system to maximize the power transfer to the electrical grid. However, the generated harmonics in the grids due to these power converters and nonlinear loads are considered one of the encountered problems to overcome. This paper presents a decoupled control of PV field real power and reactive power injected to the high voltage network via a PWM inverter by using fuzzy logic controllers. Elsewhere, a procedure based on a coordinated active and LCL filtering is proposed to mitigate the harmonic current introduced by a nonlinear load and the inverter itself in such a way to enhance the power quality injected into the grid. The results obtained in the present study show the good performance of the suggested hybrid filtering approach and demonstrate that almost all harmonics orders of the grid current are well mitigated; the current Total Harmonic Distortion (THD) meets its standard and consequently the power quality is considerably enhanced.

In this paper, a comparison between fuzzy logic controller (FLC) and conventional proportional-integral controller (PIC) is presented for a connected PV array system to utility grid via a three-phase line-commutated inverter. The main objective of the controller is providing maximum power for utility grid in different solar radiation conditions. The simulink model of the studied schemes is built using MATLAB/PSB for generating firing pulses of the thyristors in the inverter by applying PIC and FLC methods separately. Three different radiation conditions are examined on both controller which are constant solar radiation, step solar radiation and a combination of step and ramp scheme. According to simulations, FLC dominates PIC for all radiation types and in many important aspects. Some of these aspects could be named as, extracting power from PV array, providing active power for grid, shape of output current of inverter, shape of grid current waveform and total harmonic distortion (THD) of these currents.

This paper presents an application of Single-input Fuzzy Logic Controller (SFLC) to determine the control signal of a Static Compensator (STATCOM) synchronous for improvement of power system stability. This compensation scheme is relevant to Flexible AC Transmission systems (FACTSs) technology which is used worldwide to improve system dynamic performance. STATCOM improves the damping of electromechanical oscillations when used in transmission systems. The SFLC uses only one input variable which is called as signed distance. The SFLC has the advantages of reduced number of rules. Thus, generation, estimation and tuning of control rules are much easier while comparing with the existing conventional Fuzzy Logic Controllers (FLCs). The proposed control method is applied to control the AC and DC bus voltage of a STATCOM connected at a load bus in a Single Machine Infinite Bus (SMIB) System and also to improve the power system stability.

Renewable generation units, along with storage systems, are being integrated into shipboard microgrids (SHMGs) for enhanced operational efficiency and cost-effective benefits. However, the direct current (DC) voltage profiles of SHMGs are affected by sudden changes in load, intermittent renewable inputs, and unmodeled dynamics, which necessitate advanced control strategies to address them. To address the above problem, an adaptive data-driven controller has been proposed for voltage regulation of SHMGs with hybrid energy storage units (HESUs) to preserve robust stability across a wide range of operational situations. The proposed data-driven voltage regulator is developed in two stages: (i) an ultra-local model control (ULMC) is designed to stabilize the voltage outcomes of SHMG using the input/output (I/O) data. In this stage, a regularized actor-critic (RAC) using deep neural networks is adopted to adaptively adjust coefficients of ULMC, and (ii) in the next stage, the un-modeled phenomena and disturbances included in the marine power system are approximated by non-integer extended state observer (NIESO). In this approach, deep neural networks of RAC learning are trained in such a way that they obtain an optimal policy using a reward function that is defined based on the regulation requirements of SHMG. The proposed control framework provides this possibility for the system to have a robust estimation and compensation to tackle the largely unknown nonlinear disturbances and unmodeled dynamics. Hardware-in-the-loop (HiL) simulation using OPAL-RT has been employed as a powerful real-time testbed to evaluate the feasibility and applicability of the proposed voltage compensator under realistic operations of SHMG. HiL outcomes reveal that the proposed controller based on RAC achieves significant enhancement in performance indexes, with improvements of 44.08% over the fuzzy logic controller and 36.85% over the model predictive controller (MPC).