Particle Swarm Optimization Control Research Articles

A thermal management control using particle swarm optimization for hybrid electric energy system of electric vehicles

A metaheuristic algorithm, Particle Swarm Optimization (PSO), was employed for developing the optimal control strategies for an innovative hybrid thermal management system (IHTMS) in a proton exchange membrane fuel cell (PEMFC)/battery electric vehicles (EVs). The goals were to shorten the period of low-efficiency temperatures during the initial startup of EVs, and to maintain temperatures of PEMFCs and batteries at their optimal-efficiency zones, where significantly enhances the traveling range and power output of EVs. Prior to simulation for benefit analysis, eight IHTMS subsystems were mathematically constructed. For the multi-input-multi-output PSO control strategy, two inputs were the fuel cell and battery coolant temperatures; while two outputs were the coolant mass flow rate and the flow rate ratio between two energy sources. A rule-based (RB) control strategy for four actuators was designed as the baseline case. Another RB using the PSO to derive the initial conditions (PSOi) was developed as well. In this research, the IHTMS was tested under two driving patterns, WLTP and NEDC, where outstanding thermal management performance was exhibited. The results demonstrate that: in WLTP driving cycle, to compare PSO and PSOi-RB with the RB strategies, the rise time of optimal temperature decreased 13.655 % and 9.505 % for the PEMFC; while 8.77 % and 4.385 % for the battery. For the NEDC driving cycle, the rise time of optimal temperature decreased 8.908 % and 7.318 % for the PEMFC, while 5.226 % and 3.136 % for the battery. The improvements of average temperature errors of the PEMFC were 19.759 % and 11.023 %; the improvements of the average temperature errors of the battery were 57.027 % and 3.67 %. For NEDC driving cycle, the improvements of average temperature errors of the PEMFC were 18.879 % and 9.551 %; the improvements of the average temperature errors of the battery were 29.144 % and 20.221 %. In the future work, the IHTMS will be integrated to a hybrid-energy EV for experimental verification.

PSO and NNPC-based integrative control allocation for dynamic positioning ships with thruster constraints

In order to overcome the issues of thrust saturation, uneven energy consumption and suboptimal control associated with the traditional hierarchical control strategy employed in ship dynamic positioning (DP) systems, an integrative control allocation strategy is proposed by combining the low-level thrust allocation into the high-level motion controller. The proposed scheme is implemented via the neural network-based nonlinear predictive control (NNPC) and solved via an improved particle swarm optimization (PSO) algorithm. A data-driven recurrent neural network (RNN) ship motion and thrust allocation model is off-line identified based only on the current and historical measured output and thrust input data. Utilizing the established RNN model to predict the future multi-step outputs, and combining the motion control, the thruster energy consumption and the thruster wear and tear as an integrative control objective, the integrated NNPC online optimization problem with thruster constraints is established and solved by an improved evolution algorithm of PSO. Simulations are respectively carried out for setpoint regulation and trajectory tracking. Simulation results well demonstrate the feasibility and superiority of the proposed control strategy.

Optimization of Sliding Mode Control for Doubly Fed Induction Generator Systems Using Particle Swarm and Grey Wolf Algorithms

This study proposes the utilization of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to optimize the gains in sliding mode control (SMC) for a doubly fed induction generator (DFIG) system. The primary aim is to enhance reference tracking, improve overall system performance, and ensure system stability. The controllers’ performance is assessed by evaluating tracking performance and stability through the analysis of noise signals in the stator’s active and reactive power controllers. The experiments involve 10 search agents in both PSO and GWO algorithms, with a maximum of 100 iterations. Statistical results reveal that the PSO algorithm demonstrates better convergence stability and yields lower fitness function values, particularly in the case of IAE. Simulations demonstrate that SMC tuning with PSO leads to satisfactory dynamics for active and reactive powers, characterized by fast response and no overshoot. Performance metrics, including IAE and ISE indices, are employed to assess the control strategies, consistently showing that the SMC with PSO controller outperforms the SMC with GWO controller in terms of criteria values. The proposed control strategy utilizing PSO optimization effectively enhances reference tracking and overall performance in DFIG systems.

A novel virtual inertia-based damping stabilizer for frequency control enhancement for islanded microgrid

The Inverter-based generator lacks natural inertia, which leads to instability problems in Microgrid frequency. Virtual inertia is a promising control concept that rapidly injects/absorbs active power to/from the power system to ensure the frequency stability of low-inertia systems. This paper proposes an innovative virtual damping stabilizer (VDS) integrated into battery energy storage system (BESS) based virtual inertia and damping control loops. The proposed VDS adds supplementary damping and stabilizing capability to the BESS by properly correcting and controlling its inertia pumped/drawn power. The VDS aims to enhance frequency stability, decrease the necessary BESS large capacity, and lower the initial expense. Furthermore, the proposed VDS helps operate the BESS efficiently by smoothing the output power profile during transient disturbances, consequently elongating the lifetime. The Particle Swarm Optimization (PSO) method is utilized to get the optimal parameters of the proposed VDS. A frequency control model of an isolated microgrid with the proposed controller is presented, considering various operating conditions. Time-domain simulations and experimental validation using OPAL-RT verify the efficacy of the proposed VDS scheme. Sensitivity and eigenvalues analyses assess the VDS's dynamic stability enhancement and robustness against inherent uncertainties in Microgrid parameters. The results demonstrate a superior frequency response and a reduction in the BESS size compared to PSO and model predictive controller (MPC) based virtual inertia and damping strategies.

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.

Robust Control of SEDCM by Fuzzy-PSO

Industries have many rotational operations that are used for design, transport, lift, drilling, rolling, robotics, and many other applications. These rotating applications require a proper controller for accurate control of the operation. Separately excited DC motors (SEDCMs) are versatile and have various industrial operations because of their specific speed control characteristics. So, for smooth and accurate operation of an SEDC motor, controllers should be used. PI and PID controllers are used in many cases, but they are ineffective for nonlinear load operation. A fuzzy controller is a heuristic controller and can provide automatic control of the operation. Its operation depends on the selection of the correct membership values. This work proposes a novel particle swarm optimization (PSO) technique that would provide the optimum value of the membership for fuzzy controllers for optimum control of the industrial processes. To obtain SEDC results, MATLAB simulation was performed, and the fuzzy controller with novel PSO was implemented. A fuzzy PSO controller used for motor speed control operation obtains a rise time of 0.00026 s, settling time of 0.000214 s, maximum overshoot of zero, and delay time of 0.016 s, which are the best values when compared to PID and PID-Fuzzy controllers. It is observed that the results obtained from the separately excited DC motor using a fuzzy PSO controller improve the dynamic behavior of the motor that so it smoothly tracks the required speed without any more overshoot or oscillation than the PID controller. Such dynamic, stable operation of the motor makes it perfect for industrial as well as household operations.

Performance Analysis of Three MPPT Controls Under Partial Shading: P&O, PSO and ANN

Photovoltaic energy has gained an important place among renewable energies. Given its weather-dependent efficiency, Maximum Power Point Tracking (MPPT) techniques are essential to maximize the energy extracted from the panels. Partial shading is a phenomenon that degrades the efficiency of photovoltaic panels, by distorting the power vs. voltage curve, which consequently has several peaks, one of which is a global maximum. The aim of this work is to evaluate the ability of MPPT methods to find the global maximum power point in partial shading conditions. To achieve this, the work focuses on simulating and discussing the performance of three MPPT methods, the Perturb and Observe (P&O) command, a command based on particle swarm optimization (PSO), and a command based on artificial neural networks (ANN). By comparing the results of simulations of the three controllers under different partial conditions, we can see that the P&O controller loses accuracy in partially shaded conditions, while the PSO controller is more accurate but with a very slow response time, and lastly, the ANN-based controller offers the best precision and speed performance.

Intelligent Control for Voltage Regulation in the Distribution Network Equipped with PV Farm

A combined PSO-ANN control is proposed in this work to achieve the best voltage regulation in a distribution network, based on quick response and minimum average voltage deviation. The Jordanian Sabha Distribution Network (JSDN) with PV Farms is used as a real case study to examine a voltage variation issue. Two STATCOMs are used to solve the voltage fluctuation problem on the network’s three buses. The required reactive powers of STATCOMs for voltage regulation during load variation are calculated in offline mode using a particle swarm optimization (PSO) algorithm. Despite its high performance in solving voltage issue in the JSDN network, the PSO controller is unable to react promptly to dynamic changes in the network. An artificial neural network (ANN) is therefore suggested as an online mode controller for quick and efficient voltage regulation. The offline dataset is used to train the ANN for online voltage regulation utilizing the MATLAB-Tool Box. At an average voltage deviation (AVD) of 1.168%; (whereas an acceptable one is 6%), the results revealed the proposed ANN controller’s competence for voltage regulation in the distribution network. Moreover, to find the best position based on an efficient voltage regulation, many sites for STATCOMs are taken into consideration.

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller.

The effect of an evolutionary algorithm's rapid convergence on improving DC motor response using a PID controller

Mobile robots experience a lot of challenges with path tracking because of the significant problem with DC motor speed control. The particle swarm optimization (PSO) tuned proportional integral derivative (PID) controller is commonly used to control DC motor speed. However, it fails to reach the optimal solution quickly. Therefore, this paper demonstrates the application of the artificial bee colony (ABC) algorithm to tune a PID controller for speed regulation of a DC motor. The proposed approach was used to obtain the optimal PID controller gains (proportional, integral, and derivative) by minimizing the integral of time absolute error (ITAE) as the objective function. The analysis of statistical tests, transients, and frequency responses was conducted to test the rapid convergence of the proposed approach. Also, the performance of the proposed ABC-tuned PID controller has been compared with three variants of the PSO-tuned PID controller. The conducted simulation results and comparison with the proposed ABC controller and variant PSO controllers have shown that the proposed ABC-PID controller is more effective and robust than the variant PSO-PID controllers for the speed control of a DC motor.

Designing and Execution of 3-Level Stabilizing unit in Hybrid Photovoltaic and Wind Energy Systems

The fundamental concept of control for a three-legged IGBT-based voltage source regulator-based stabiliser was thoroughly discussed in this paper. The proposed technique's effectiveness and efficiency in a two-machine based model is demonstrated by a comparative analysis in a grid integrated hybrid system. The hybrid solar wind system produces 400 volts of voltage. A comparative analysis of the various stabilisation control algorithms is currently underway. Three algorithms have been used to implement the system. Particle swarm optimization (PSO) control, hysteresis control, and neural network modified PSO control are all possible with PI..

PSO Self-Tuning Power Controllers for Low Voltage Improvements of an Offshore Wind Farm in Taiwan

A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller.

Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System Under Partial Shading Conditions

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

Trajectory control of electro-hydraulic position servo system using improved PSO-PID controller

This paper presents an improved particle swarm optimization (PSO) algorithm to optimize the coefficients of the proportional integral derivative (PID) controller for trajectory tracking accuracy problems. Nonlinear adaptive methods are adopted on the inertia weight to built a balance between the local and global search areas, asynchronous change strategies are introduced into the learning coefficients to rationally use individual and group experience, an elite mutation method is designed to mutate the global best position in each iteration. Basic PSO (BPSO-PID) and Ziegler-Nichols PID controller (ZN-PID) are implemented with step, sinusoidal and slope references on a co-simulation platform. The simulation results illustrate that high accuracy and fast convergence can be obtained based on the improved PSO algorithm PID controller (IPSO-PID). Experimental results demonstrated that the IPSO-PID method can achieve the highest tracking accuracy, compared with the BPSO-PID method, this method can improve the tracking accuracy by 37.14% and 50.32%, respectively.

A multi-objective particle swarm for constraint and unconstrained problems

Multi-objective particle swarm optimization algorithms (MOPS) are used successfully to solve real-life optimization problems. The multi-objective algorithms based on particle swarm optimization (PSO) have seen various adaptations to improve convergence to the true Pareto-optimal front and well-diverse non-dominated solution. In some cases, the values of the MOPS control parameters need to be fine-tuned while solving a specific multi-objective optimization problem. It is challenge to correctly fine-tune the value of the PSO control parameters when the true non-dominated solutions are not known as in case of a real-life optimization problem. To address this challenge, a multi-objective particle swarm optimization algorithm that uses constant PSO control parameters was developed. The new algorithm called NF-MOPSO is capable of solving different multi-objective optimization problems without the need of fine-tuning the value of the PSO control parameters. The NF-MOPSO enhances the convergence to the true Pareto-optimal front and improves the diversity of Pareto-optimal using the same fixed values for all the PSO control parameters. The NF-MOPSO uses constant values of the PSO control parameters such as acceleration coefficients $$c_{1}$$ and $$c_{2}$$ , and inertia weight $$\omega$$ . A Gaussian mutation is applied to the position of particles to increase diversity while a penalty function is used as constraint mechanism. The algorithm has been tested on 45 well-known benchmark test functions using four performance metrics. The test results demonstrate the capability of the NF-MOPSO to solve different multi-objective optimization problems using the same value of the PSO control parameters. The capability of the NF-MOPSO was demonstrated in real-life optimization problem by solving a multi-objective optimization problem of a neutron radiography collimator. The results of collimator optimization showed that the optimizer was able to provide a set of Pareto optimal solutions from which the geometrical design parameters of a collimator could be retrieved for given application.

Performance evaluation of PI controller for positive output Luo converter

The aim of this work is to design and analyze a Proportional Integral (PI) controller for Positive Output Luo Converter applications. Positive Output Luo Converter is a developed DC-DC converter. It is respected as a right choice for most industrial application where the rate of the output load voltage must be varying between the low and high values of the input value of voltage, output voltage rise and fall is smaller. This converter involve Power electronics switches (Diodes and MOSFET) since these elements are non-linear. The detailed model includes high-frequency switching that is introducing discontinuities into the model. PI controller coefficients (kp, ki) are calculated by particle swarm optimization (PSO) to provide optimal PI as hybrid PI by PSO controller with simple design procedure .Transient and steady state responses requirement of the system are considered in designing the proposed PI controller. The consequences show that the time of performing characteristics of PSO-PI controller established on integral squared error (ISE) performance index has the best time performing characteristics, line disturbance, load disturbance and set point variation.

Optimal Tuning of Linear Quadratic Regulator Controller Using Ant Colony Optimization Algorithm for Position Control of a Permanent Magnet DC Motor

This paper presents the design of an optimal Linear Quadratic Regulator (LQR) controller using Ant Colony Optimization (ACO) and particle swarm optimization (PSO) methods for position control of a permanent magnet DC (PMDC) motor. In this work, Ant Colony control and particle swarm control algorithms have been utilized to set the optimal elements of the weighting matrices subjected to a proposed cost function. The proposed cost function is a combination of the quadratic performance index and integral square error. The proposed design can overcome the difficulty in setting the weighting matrices with the suitable elements. The simulation results using (Matlab Package) show that the optimal LQR controller using ACO algorithm can give excellent performance in terms of obtaining smooth and unsaturated state voltage control action that will stabilize the DC motor system performance and minimize the position tracking error of the system output. In addition, the rising time and settling time is decreased in comparison with the LQR based PSO controller performance.

Rigorous reduction of partial shading condition in grid connected solar PV system using discrete time-based PSO controller

Solar industry has seen tremendous development over the past decade and in particular, the PV (Photovoltaic) system which has an imperative advancement almost in all fields of science. Envisaging various faults in the PV system will significantly enhance the efficiency, reliability, and life of the PV system. The main vulnerable element in the PV system which is subjected under different weather condition causes total damage to the system. Proper monitoring and maintenance are needed to increase the life of the system. Several investigations were made so far to predict the faults in the PV system such as the Visual method, the Thermal method, the Electrical detection method, the Machine learning techniques, the Arc fault detection technique and the Protection device-based techniques were used generally. But more effective fault diagnosis techniques are required for PV arrays. The proposes a novel method for reducing the partial shading condition in solar PV system connected to a grid which consists of a discrete time-based particle swarm optimization (PSO) controller that controls the irradiation or partial shading as well as any short circuits in PV cell. Hence, this proposed work enhances with producing efficient energy by achieving high predictive accuracy of about 99%, high efficiency of about 98.9% and low THD (0.9) under partial shading conditions as well as harmonics.

Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization

This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was 46.8 mpg, which is a 9.4% improvement over the base control model. The PSO control strategy could quickly converge and that feature makes PSO a good fit to be used in real-time control applications.

MPPT control of PV array based on PSO and adaptive controller

In general, photovoltaic (PV) array is not able to generate maximum power automatically, because some partial shading caused by trees, clouds, or buildings. Irradiation imperfections received by the PV array are overcome by applying maximum power point tracking (MPPT) to the output of the PV array. In order to overcome these partial shading problems, this system is employing particle swarm optimization (PSO) as MPPT method. It optimizes the output power of the solar PV array by Zeta converter. Output voltage of MPPT has high rate such that it needs stepdown device to regulate certain voltage. Constant voltage will be the input voltage of buck converter and controlled using adaptive PID. Adaptive control based on model reference adaptive control (MRAC) has design that almost same as the conventional PID structure and it has better performance in several conditions. The proposed system is expected to have stable output and able to perfectly emulate the response of the reference model. From the simulation results, it appears that PSO have high tracking accuracy and high tracking speed to reach maximum power of PV array. In the output voltage regulation, adaptive control does not have a stable error status and consistently follows the set point value.