Ant Lion Optimization Technique Research Articles

An improved SPWM control approach with aid of ant lion optimization for minimizing the THD in multilevel inverters

This article presents an innovative asymmetric multilevel inverter (MLI) topology that outperforms conventional counterparts. The introduced topology presents a breakthrough in implementing power electronics control by maximizing specific levels while minimizing switching components. A cutting-edge control scheme for optimal operation of the cascaded half-bridge MLI is presented. The ant lion optimization (ALO) algorithm was implemented to optimize the switching control to reduce the total harmonic distortion (THD) and improve power quality. For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. The suggested method showcases a remarkable reduction in the THD compared to SSPWM. The quality of the resulting waveform was enhanced, and both filter size and cost were significantly reduced. To meet stringent IEEE standards, an LC filter has been designed with minimal size and proper requirements. Experimental results validation of the suggested scheme, using a dSPACE R&D controller board unequivocally, confirmed its robustness and effectiveness. This groundbreaking study not only introduces a superior asymmetric MLI topology but also validates its exceptional performance through comprehensive analysis and experimentation. The experimental waveforms showed good matching with the simulation outcomes. The findings hold immense promise for advancing the field of power system control and revolutionizing the designing and implementation of efficient and cost-effective inverter systems.

PR controller-based droop control strategy for AC microgrid using Ant Lion Optimization technique

Microgrids are now an integral part of the modern power system to maintain the stability of the power distribution process. Ensuring the frequency and voltage stability in the autonomous mode is quite a challenging task indeed. These have now reached the extent of utilizing evolutionary intelligence techniques leading to the concept of intelligent microgrids. In this work, a novel droop control strategy is implemented with a proportional resonant controller, whose proportional gain is optimized by the Ant Lion Optimization algorithm (A.L.O). The efficacy of the A.L.O algorithm for its application in the primary level droop control for voltage stability is investigated in this work. The superiority of the A.L.O optimized PR controller over the conventional PI controller is compared in terms of error indices, timed domain specifications, speed of response, voltage regulation and T.H.D levels. It could ensure a higher speed of response, better voltage stability and higher power quality as against its PI counterpart. The performance of the proposed A.L.O optimized PR controller is validated by comparing it with the performance achieved by controllers tuned with two other algorithms namely, particle swarm optimization (P.S.O) and Satin bower bird optimization (S.B.O). The proposed controller could ensure better steady-state stability compared to the other two.

Mitigating Low-Frequency Oscillations and Enhancing the Dynamic Stability of Power System Using Optimal Coordination of Power System Stabilizer and Unified Power Flow Controller

The integration of a flexible alternating current transmission system (FACTS) and a power system stabilizer (PSS) can increase dynamic stability. This paper presents the enhancement of power system dynamic stability through the optimal design of a power system stabilizer and UPFC using an ant lion optimization (ALO) technique to enhance transmission line capacity. The gained damping ratio, eigenvalue and time domain results of the suggested ALO technique were compared with a base case system, ALO-based PSS and ALO-based PSS-UPFC to test the effectiveness of the proposed system in different loading cases. Eigenvalues gained from an ant lion approach-based UPFC with a PSS and a base case system are compared to examine the robustness of the ALO method for various loading conditions. Thus, this paper addresses the mechanism regarding the power system dynamic stability of transmission lines by integrating the optimal size of a PSS and UPFC into the power system. Therefore, the main contribution of this manuscript is the optimal coordination of a power system stabilizer, power oscillation damper and unified power flow using ant lion optimization for the mitigation of low-frequency oscillation.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Meta‐heuristics algorithms for optimization of gains for dynamic voltage restorers to improve power quality and dynamics

AbstractThis article discusses metaheuristic algorithms for optimizing controller gains for dynamic voltage restorers (DVRs) that use an impedance control strategy to compensate for unbalance in source voltages, voltage harmonics, and sag/swell in source voltages. The gains of the proportional‐integral (PI) controllers become critical for proper DVR load voltage extraction. Various techniques for optimization, such as whale optimization technique, gray wolf optimization technique, particle swarm optimization technique, and ant lion optimization technique, are used to obtain DC and AC PI controller gains for DVR. The impedance control strategy employs simple calculations to determine the resistance and reactance of a polluted source voltage, without the use of frame conversions as in synchronous reference theory, instantaneous reference power theory, and so on. The quick calculations of the impedance control scheme improve the power quality and dynamics. The Metaheuristic algorithms are used to calculate the number of iterations required to achieve the best possible controller gains, which further helps to improve power quality and dynamics. Among these optimization techniques, the antlion optimization technique provides fast convergence and the best possible controller gain values to improve the dynamics of the dc‐link voltage of voltage source converter and terminal voltage, thereby improving power quality. The proposed antlion optimization technique‐based DVR model is simulated in MATLAB R2019, and the results are validated with RT‐LAB.

Fuzzy Logic Controlled Based Ant-Lion Optimization Hybridization for Economic Power Dispatch

The demand on the power system rising more rapidly is causing to increase the power system size and capacity. There is a need of interconnection of various generating stations to meet the increased load demand. Economical unit commitment is necessary for plant operation with the advancement in power system integration. The Economical Power Dispatch (EPD) is to find the most favourable combination of generating systems output powers which reduce the fuel cost by satisfying all system constraints. This research involves the fuzzy logic controller (FLC) has been hybridized with Ant-Lion Optimization (ALO) algorithm for EPD. By using this new hybrid technique, minimization of total operating cost by economically dispatch the power to meet the required load and also minimization of system total losses by optimum allocation of DG units were done. Fuel cost function and demand on system are modeled by fuzzy membership functions. The ALO is used to obtain the schedule the committed generating unit’s outputs so as to meet the required load demand. This proposed FLC based ALO technique executed with MATLAB software and applied on IEEE-30 system. Effectiveness of this projected algorithm is determined and evaluated with standalone techniques like conventional ALO, ALO-PSO algorithms.

Enhancement of power management in micro grid system using adaptive ALO technique

Micro grids are drawing in more consideration since they can mitigate the worry of primary transmission systems, reduce feeder losses, enhance system power quality (PQ), and power management. Power management in MG is challengeable utilizing energy storage and generation components like PV, diesel generator, micro-hydro generator, battery bank with a bidirectional inverter. In this paper, the power management of the MG is investigated with the proposed droop controller. The droop controller is intended to get the optimal energy management of MG. The proposed method is the combination of Ant Lion Optimization (ALO) with a Bat algorithm to enhance the power management of MG. The ALO calculation is a nature-inspired algorithm. It imitates the chasing system of ant lions in nature. The Bat algorithm is utilized to refresh the insect lion position of the ALO algorithm. The droop control is sharing the power in the generation side depends upon the load demand of the grid side through the control action of the real and reactive power. The goal of the paper is used to enhance the real and reactive power of MG. With the assistance of the proposed technique, the real and reactive power is enhanced and the voltage is controlled. Based on the droop characteristics, power management is accomplished continuously by way of the upgraded proposed approach. The proposed strategy is executed in the MATLAB/Simulink platform and also analysis of the PV power, wind power, power demand, real power, reactive power, voltage, current, demand values, and grid power. The upgrading proposed framework is compared to current techniques such as CSO and PSO algorithms. Hence, the overall performance of the proposed approach will be effective.

Protocols SMEER (Secure Multitier Energy Efficient Routing Protocol) and SCOR (Secure Elliptic curve based Chaotic key Galois Cryptography on Opportunistic Routing)

Analysis of two protocols “Secure Elliptic curve based Chaotic key Galois cryptography on Opportunistic Routing (SCOR)” and Secure Multitier Energy Efficient Routing Protocol (SMEER) are analysed. The simulation is done on the NS-2 simulation platform and finally performances are evaluated on various simulation parameters. Wireless sensor networks (WSNs), energy efficiency is thought to be an important issue, because of the constrained sensor nodes battery limit. In WSNs, routing protocols are application subordinate and outline objectives change between various applications. Secure Multi-Tier Energy Efficient Routing Protocol uses Ant Lion optimizer technique for selecting cluster head & K-means for doing clustering while this information is transmitted using Spherical grid based multi-curve Elliptical curve cryptographic routing.

An explorative optimization algorithm for sparse scheduling in-home energy management with smart grid

PurposeElectricity utilization at electricity peak hour may differ from every single administration region, for example, mechanical region, business territory and residential zone. This paper introduces a demand-side load management (DSM) strategy, which is one of the utilization of smart grid (SG) that is fit for controlling loads inside the residential working so that the client fulfillment is augmented at least expense.Design/methodology/approachIn this paper, a heuristic algorithms-based energy management controller is intended for a residential region in a SG. Here, Antlion Optimization technique is used for DSM techniques such as load shifting, peak clipping, and valley filling in the residential sectors for 24 h with the help of stochastic function to determine the detection of random distribution of the load.FindingsThis proposed algorithm offered the greatest fulfillment and least expense caused by the consumers when compared to the traditional cost by taking the individual consumer preferences for the loads and the ideal time scheduling for the load, which is obtained from the rebuilding trap.Originality/valueSimulation results demonstrate that the comparison of the cost incurred by the users obtained by the DSM techniques is satisfiable.

Implementation of Grasshopper optimization algorithm based cascaded fuzzy PD-PI controller for frequency stability in a multi-area power system

This article proposed a Grasshopper optimization algorithm (GOA) based cascade controller i.e. fuzzy-PD with PI controller (FPD-PI) for the study of load frequency control. A three unequal-area interconnected power system employed with thermal units has been considered for the present study. Initially, the performance of the GOA technique is verified over the Ant-lion optimization technique with integral square error (ISE) as an objective function. Finally, the supremacy of the proposed FPD-PI controller is verified over other benchmark controllers namely PID and fuzzy-PID with GOA, after ensuring the superiority of integral time multiplied absolute error (ITAE) over ISE.

Kinematic and Dynamic Optimal Trajectory Planning of Industrial Robot Using Improved Multi-objective Ant Lion Optimizer

In this paper, an optimal robotic trajectory planning subjected to kinematic and dynamic constraints has been described. The kinematic parameters like jerk and dynamic parameters like torque rate mainly influence the smoothness of the travel of robot end effector along the trajectory path. Therefore, these parameters are to be constrained for reducing the robot positional error, but it leads to vast increase in total travel time of robot which in the end affects the productivity. Therefore, an improved multi-objective ant lion optimization technique has been applied to obtain the optimal trajectory with minimization time-jerk–torque rate for a 6 axis Kawasaki RS06L industrial robot. After implementation of the algorithm, the torque rate and jerk have been reduced considerably and the total travel time before and after optimization has been found to be 34.38 s and 28.21 s.

Optimal Placement and Capacity of UPFC Using JMFALO Technique to Upgrade Power System Dynamic Stability

This paper proposes an optimal placement and capacitance of UPFC for enhancing the dynamic stability of a power system using a new hybrid optimization technique. The hybrid optimization technique is the joined execution of both the moth flame optimization (MFO) and ant lion optimization (ALO) and hence it is named as Joined moth flame ant lion optimization (JMFALO) technique. Here, the MFO technique optimizes the maximum power loss line as the suitable location of the UPFC by considering the variation available in the bus system power flow parameters like voltage, angle, real power and reactive power. Dependent on the ALO technique the influenced location parameters and dynamic stability constraints are restored into secure limits utilizing the optimum capacity of the UPFC with least voltage deviation, loss of power, and reduced installation cost. Subsequently, to restore the dynamic stability constraints at a secure limit the optimized UPFC capacity is utilized. The dynamic stability constraint of the system is power balance constraint, active and reactive power loss, and UPFC installation cost and bus voltage constraints. The proposed technique is implemented in MATLAB/Simulink working platform. The performance of the proposed technique is assessed by utilizing the comparison analysis with the existing techniques.

Optimization of a Modified Rectangular Patch Antenna Array for X-band Applications

Objectives: To optimize the dimensions of Conventional Rectangular Patch Antenna using ant lion optimization and to design the 2 × 2 patch antenna array with the optimized modified rectangular patch antenna. Methods/Statistical Analysis: Analysis is done in the form of curve fitting equations and objective functions regarding the relations between performance and dimensional parameters of the patch antenna. In this work, length of the patch, width of matching line, height of the substrate and length of the ground plane are analyzed to achieve the desired performance characteristics of the patch antenna. Further, corporate feed method is used to design the different configurations of patch antenna array. Findings: An optimized rectangular patch antenna is designed by using ant lion optimization technique to achieve the required resonant frequency, S11 (dB), bandwidth and gain. Further, optimized design is transformed into modified rectangular patch, 1 × 2 array and 2 × 2 array to enhance the gain and required radiation pattern. The gain and bandwidth of the Modified Rectangular Patch Antenna array is 3.6 dBi and 718 MHz respectively, which shows improvement over the conventional design. Similarly 1 × 2 patch antenna array resonates at two frequencies which are 8.2 GHz and 9.6 GHz with a maximum gain of 8.1 dBi. The final design operates at 8 GHz (X-band) with a bandwidth of 1.65 GHz and a gain of 5.2 dBi. Also, the radiation pattern of the proposed 2 × 2 array consists of two major lobes adjacent to the null on broadside direction. Application/Improvements: The gain as well as bandwidth in the MRPA is improved by 20% and 10% respectively. The fractional bandwidth of the proposed 2 × 2 array is found to be 21.25%. The design covers spectrum of 8.025-8.40 GHz used for earth exploration satellite communication. Keywords: Antenna Array, Ant Lion Optimization, Bandwidth, Gain

A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique

In this paper, a new optimization method is proposed to solve the optimal sizing and siting problem of distributed generation (DG) in a distribution system (DS). The optimization problem is solved using a new ant lion optimizer (ALO) with considering different objectives. These objectives are reduction of purchased energy cost from upstream network (due to DGs’ power generation), reliability improvement, reduction of DGs’ application cost, reduction of DS losses and reduction of buses’ voltage deviation. This problem is solved as a multi-objective optimization (MOO) in addition of a single-objective optimization (SOO). The proposed DGs optimal sizing and siting is implemented on 33 and 69-bus IEEE networks. The obtained results show that the ALO has the best performance in extracting the solution of DGs optimal sizing and siting problem in comparison to particle swarm optimization (PSO) and genetic algorithm (GA).

Hydro-thermal-wind scheduling employing novel ant lion optimization technique with composite ranking index

A solution to the combined hydro-thermal-wind scheduling problem of multi reservoir cascaded hydro plants is presented employing a novel ant lion optimization (ALO) algorithm. Five objectives, cost, various emissions and power loss, are simultaneously optimized. The optimal schedules of thermal, hydro and wind power (WP) units are determined for continuously varying load subject to a large number of practical operational constraints. The effect of reserve and penalty coefficients and WP uncertainty is also investigated for the multi-objective (MO) problem. The newly proposed ALO algorithm has unique features like random walk, roulette wheel, and boundary shrinking. These operations provide a judicious balance between exploration and exploitation, and create a powerful optimization technique for complex real-world problems.Finding the best compromise solution (BCS) is a tedious task when multiple objectives are involved. A composite ranking index (CRI) is proposed as a performance metrics for MO problems. The CRI helps the decision maker in ranking the large number of Pareto-optimal solutions. The developed model is tested on three standard systems, having a mix of hydro, thermal and wind generators. The performance is found to be superior to published results and comparable with established algorithms like artificial bee colony (ABC) and differential evolution (DE).