Optimizing PI controller of the single-phase inverter based on FOA

DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems

Comment and improvement on “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example”

This study offers a new Red Deer Algorithm (RDA) optimization technique for total harmonic distortion (THD) minimization in multilevel inverters (MLIs). RDA belongs to the class of swarm-based biologically inspired optimization methods. To test the angles determined via RDA optimization, a single-phase seven-level cascade multi-level inverter with symmetrical DC sources was used in this study. The switching angles for the optimally minimized total harmonic distortion (OMTHD) were computed via RDA optimization. RDA optimization was compared to meta-heuristic algorithms like the Improved Whale Optimization Algorithm (IWOA), Whale Optimization Algorithm (WOA), Sunflower Optimizer (SFO), Particle Swarm Optimization (PSO), Krill Herd (KH), Grey Wolf Optimizer (GWO), Galactic Swarm Optimization (GSA), Genetic Algorithm (GA), Fruit Fly Optimization Algorithm (FFO) and Artificial Greyctric Field Algorithm (AEFA). The RDA optimization outperformed the ten algorithms used as benchmark frameworks in this study as it can find angles with minimum THD for the modulation index in 0–1.

The lack of control in voltage overshoot, transient response, and steady state error are major issues that are frequently encountered in a grid-connected photovoltaic (PV) system, resulting in poor power quality performance and damages to the overall power system. This paper presents the performance of a control strategy for an inverter in a three-phase grid-connected PV system. The system consists of a PV panel, a boost converter, a DC link, an inverter, and a resistor-inductor (RL) filter and is connected to the utility grid through a voltage source inverter. The main objective of the proposed strategy is to improve the power quality performance of the three-phase grid-connected inverter system by optimising the proportional-integral (PI) controller. Such a strategy aims to reduce the DC link input voltage fluctuation, decrease the harmonics, and stabilise the output current, voltage, frequency, and power flow. The particle swarm optimisation (PSO) technique was implemented to tune the PI controller parameters by minimising the error of the voltage regulator and current controller schemes in the inverter system. The system model and control strategies were implemented using MATLAB/Simulink environment (Version 2020A) Simscape-Power system toolbox. Results show that the proposed strategy outperformed other reported research works with total harmonic distortion (THD) at a grid voltage and current of 0.29% and 2.72%, respectively, and a transient response time of 0.1853s. Compared to conventional systems, the PI controller with PSO-based optimization provides less voltage overshoot by 11.1% while reducing the time to reach equilibrium state by 32.6%. The consideration of additional input parameters and the optimization of input parameters were identified to be the two main factors that contribute to the significant improvements in power quality control. Therefore, the proposed strategy effectively enhances the power quality of the utility grid, and such an enhancement contributes to the efficient and smooth integration of the PV system.

A hybrid optimization approach for PI controller tuning based on gain and phase margin specifications

Integration of photovoltaic (PV) power to the grid is achieved using three-phase inverters with high-quality current waveforms. The new grid codes impose a limit on the total harmonic distortion (THD) value of the inverter’s current waveforms. Due to simple structure and ease of design, proportional integral (PI) controllers are the most widely adopted methods to control three-phase grid-connected inverters; however under abnormal grid conditions, PI controllers may suffer from instability problems. To address the aforementioned problem, this article proposes an expert knowledge-based proportional resonant (PR) control for the integration of PV power to the grid under abnormal grid conditions. The proposed control method adjusts the control parameters based on rules generated from expert knowledge. Initially, parameters of the proposed control methods are optimized using particle swarm optimization (PSO) method. The proposed control is tested with a 100 kW grid-tied three-phase inverter unit under normal and abnormal grid conditions and its performance are compared with PI and fixed gain PR control methods. Moreover, the proposed control is tested experimentally with a scaled down 300 W grid-tied inverter lab prototype module. From the simulation and experimental results, it is verified that the proposed adaptive PR control technique is capable of providing low THD in the injected current under abnormal grid conditions compared with fixed gained PR controllers.

Dissolved oxygen directly affects the growth status of fishes in intensive aquaculture, thus we set up a prediction model to determine the future changing trend of dissolved oxygen. The dissolved oxygen prediction model we proposed was based on the least squares support vector regression (LSSVR) model with fruit fly optimization algorithm (FOA) to find optimal parameters (γ and σ) of LSSVR. Because these two parameters can significantly affect the performance of the LSSVR, we studied the other two parameter optimization methods the particle swarm optimization (PSO) algorithm, the genetic algorithm (GA) and immune genetic algorithm(IGA) to compare them with the FOA algorithm. The calculated mean absolute percentage errors of the results of the four prediction models were 0.35%, 1.3%, 2.03% and 1.33%, respectively. The FOA-LSSVR model has a higher prediction accuracy and more reliable performance than the other models. When the predicted values of dissolved oxygen fall below the safety level, the farmer can start an oxygen increasing machine in advance to maintain the safety of fishes. The prediction model was used in Yangzhong, Jiangsu province, China, and it performed well and helped farmers make decisions and reduce aquaculture risks.

Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN

The primary function of microgrid (MG) is to share the power accurately among different distributed generation (DG) units according to the load demands. The droop control methods are normally used for appropriate power sharing. However, these control techniques suffer badly due to the feeder impedances and uncontrollable power coupling issues. Many techniques have been proposed to share power accurately, most of them rely on communication link. In this work a localized/communication free signal generating mechanism is proposed. The mechanism injects the transient reactive power in frequency droop characteristics by observing the changes in microgrid. This injection disturbs the actual active power sharing which is helpful for the computation of reactive power sharing errors. These errors are minimized by utilizing the proportional integral (PI) controller. The PI controller modifies the y-intercept and the slop of Q-E (Reactive Power –Voltage) droop characteristic for the correction of reactive power sharing error. Moreover, the parameters of droop and PI controller of inner control loop are optimized by Particle Swarm Optimization (PSO) to obtain high precision and reliability. The proposed technique has the following salient features:1. Accurately shares the reactive power without the information of feeder line impedances2. This accurate reactive power sharing scheme has been started locally without the need of communication signal from the central controller. Thus, there is no delay in monitoring the notable changes in microgrid caused by DGs. 3. The careful selection of PI & droop controller parameters through PSO makes the system more stable as compared to the conventional methods in terms of transients and steady state responses.

The distortion produced in the voltage waveform of the inverter hinders its operation at high power. To overcome this issue, this paper presents the modeling of the nine-level quadruple boost (NLQB) inverter and closed-loop control of the same using a proportional-integral (PI) controller. The single DCsourced NLQB inverter uses 10 insulated gate bipolar transistor switches and two switched capacitors. A cascaded PI controller is designed (for voltage and current control) in the aforementioned NLQB inverter to maintain its output voltage at a desirable level. Further, the performance of this inverter after employing a PI controller is studied with harmonic analysis using a fast Fourier transform. Compared with the open-loop operation of this NLQB inverter, the suggested cascaded PI-based closed-loop system produces lesser harmonics and total harmonic distortion.

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

This paper presents a modern approach for speed control of an induction motor (IM) using the particle swarm optimization (PSO) method for determining the optimal parameters, Kp and Ki, of the integral proportional (IP) controller. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. Comparison between different controllers is achieved, using an IP controller which is tuned by two methods, firstly manually and secondly using the PSO technique. Integral time absolute error, integral absolute error and integral square error performance indices are considered to satisfy the required criteria in output speed of an IM. From the simulation results it is observed that the IP controller designed with PSO yields better results when compared to the traditional method in terms of performance index.

A Static Synchronous Compensator (STATCOM) is one of the Flexible AC Transmission System (FACTS) device which plays an vital role in power system network in alleviation of small and large transient disturbances and better voltage regulation. This paper proposed a self tuning Proportional-Integral (PI) controller for STATCOM which provided a voltage support at point of common coupling (PCC). Self tuning of PI controller was achieved by adapting PI controller gains with system parameters by using Bats Echolocation controller which mimics the Bats Echolocation behavior. In the proposed controller, the system voltage (parameter) at point of common coupling compared with reference voltage and then Bats Echolocation Controller updates the PI controller gains according to the variation obtained between them. This paper also proposed a Cascaded Multilevel Inverter (CMLI) instead of conventional Voltage Source Inverter (VSI) for STATCOM to obtain better voltage regulation at point of common coupling. The voltage imbalance between H-bridge cells in CMLI was rectified by prosing Multicarrier Pulse Width Modulation (PWM) techniques, The proposed controller was validated by comparing the simulation results with fixed gain PI controller and Self tuning PI controller using Neural controller and Particle Swarm Optimization.

Two-stage inverters have been commonly applied in the distribution systems, where Total Harmonic Distortion (THD) is important for the judgement of the tacking effects. This paper focuses on the current tracking of the grid-tied two-stage inverter composed by the front buck-boost converter and the rear single-phase inverter. A cascaded Proportional-Integral (PI) Model Predictive Control (MPC) is applied for the intermediate voltage tracking of the front stage. Meanwhile, an adaptive MPC scheme is adopted for the output current tracking of the single-phase inverter stage by introducing an time-varying observer for the filter inductance and load resistance identification. By regulating the intermediate voltage, there will be a corresponding voltage which minimizes THD under the same other parameters. Hence, a reference voltage generator based on the circuit parameters, the input voltage, the reference current and grid voltage is employed for the voltage regulation to obtain lower THD. The simulation results show the effectiveness of the control scheme for the two-stage inverters applied in this paper.

To design a variety of algorithms and problem-solving techniques, including different ways of searching, designing, and optimization. Evolutionary computation gets the principles or inspirations by observing the nature, organisms, and biological phenomena. Nowadays, many different algorithms, like genetic algorithm (GA), ant algorithm (AA) , particle swarm optimization(PSO), artificial fish swarm algorithm(AFSA), and the latest New fruit fly optimization algorithm(NewFOA), have been developed. In this paper, we adopted newer algorithms, which are PSO, AFSA and NewFOA, to optimize the parameters of general regression neural network(GRNN). Then, by applying some data mining techniques like General Regression Neural Network, we constructed the logistic flow service satisfaction detection model for electronic commerce enterprises. The results indicated that the one hundred and twentieth interviewee showed the highest and the thirteenth the lowest satisfaction in the data envelopment analysis(DEA). We chose four principle components to be the independent variables for our models. In the three data mining techniques, we found out that the root mean square error (RMSE) value of GRNN model which was optimized by NewFOA showed good convergent result and good capability of classification and forecasting.