Showing 50 results for Optimization
N. Sayyadi Shahraki, S. H. Zahiri,
Volume 16, Issue 2 (6-2020)
Abstract
In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new and comprehensive proposed criterion, and also meet different design specifications such as DC gain, Gain-Band Width product (GBW), Phase Margin (PM), Slew Rate (SR), Common Mode Rejection Ratio (CMRR), Power Supply Rejection Ratio (PSRR), etc. The proposed MOLA contains several automata and each automaton is responsible for searching one dimension. The workability of the proposed approach is evaluated in comparison with the most well-known category of intelligent meta-heuristic Multi-Objective Optimization (MOO) methods such as Particle Swarm Optimization (PSO), Inclined Planes system Optimization (IPO), Gray Wolf Optimization (GWO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The performance of the proposed MOLA is demonstrated in finding optimal Pareto fronts with two criteria Overall Non-dominated Vector Generation (ONVG) and Spacing (SP). In simulations, for the desired application, it has been shown through Computer-Aided Design (CAD) tool that MOLA-based solutions produce better results.
A. N. Patel, B. N. Suthar,
Volume 16, Issue 3 (9-2020)
Abstract
Optimization of specific power of axial flux permanent magnet brushless DC (PMBLDC) motor based on genetic algorithm optimization technique for an electric vehicle application is presented. Double rotor sandwiched stator topology of axial flux permanent magnet brushless DC motor is selected considering its best suitability in electric vehicle applications. Rating of electric motor is determined based on vehicular dynamics and application needs. Double rotor sandwiched stator axial flux PMBLDC motor is designed considering various assumed design variables. Initially designed axial flux PMBLDC motor is considered as a reference motor for further analysis. Optimization of the specific power of electric motor for electric vehicle applications is a very important design issue. The Genetic Algorithm (GA) based optimization technique is proposed for optimization of specific power of axial flux permanent magnet brushless DC motor. Optimization with an objective of maximum specific power with the same torque rating is performed. Three-dimensional finite element analysis is performed to validate the proposed GA based specific power optimization. Close agreement between results obtained from finite element analysis and analytical design establishes the correctness of the proposed optimization technique. The performance of the improved motor is compared with the initially designed reference motor. It is analyzed that the specific power of axial flux PMBLDC motor is enhanced effectively with the application of GA based design optimization technique.
A. Bahmanyar, H. Borhani-Bahabadi, S. Jamali,
Volume 16, Issue 3 (9-2020)
Abstract
To realize the self-healing concept of smart grids, an accurate and reliable fault locator is a prerequisite. This paper presents a new fault location method for active power distribution networks which is based on measured voltage sag and use of whale optimization algorithm (WOA). The fault induced voltage sag depends on the fault location and resistance. Therefore, the fault location can be found by investigation of voltage sags recorded throughout the distribution network. However, this approach requires a considerable effort to check all possible fault location and resistance values to find the correct solution. In this paper, an improved version of the WOA is proposed to find the fault location as an optimization problem. This optimization technique employs a number of agents (whales) to search for a bunch of fish in the optimal position, i.e. the fault location and its resistance. The method is applicable to different distribution network configurations. The accuracy of the method is verified by simulation tests on a distribution feeder and comparative analysis with two other deterministic methods reported in the literature. The simulation results indicate that the proposed optimized method gives more accurate and reliable results.
A. Rajabi, H. Lexian,
Volume 17, Issue 1 (3-2021)
Abstract
One of the important requirements in projectiles is to design a power supply for fuse consumption. In this study, an optimum design for the power supply, which includes a Miniaturized Inertia Generator (MIG), was introduced. The main objective of this research was to optimize the dimensions of the MIG with the aim of increasing energy. To achieve this, the design of experiment (DOE) was carried out through RSM-BBD to optimize six parts of the MIG. Numerical simulations were performed using Maxwell’s software. After analyzing of results by ANOVA and extracting the optimum result from the RSM, a Miniaturized Inertia Generator was fabricated with optimum dimensions. The results showed that the MIG with optimum dimensions at an acceleration of 800’g could generate 15.25V and stores the generated energy using an RLC circuit within 1ms. The experimental results which were obtained by the shock test system showed that 14.75V was charged on a capacitor within 1.1ms which has good conformity with the numerical results. The results indicated that the proposed design not only increased the MIG efficiency, but also determined the effect of each parameter on the produced energy and efficiency.
A. Jabbari, F. Dubas,
Volume 17, Issue 2 (6-2021)
Abstract
In this paper, we present a mathematical model for determining the optimal radius of the iron pole shape in spoke-type permanent-magnet (PM) machines (STPMM) in order to minimize the pulsating torque components. The proposed method is based on the formal resolution of the Laplace’s and Poisson’s equations in a Cartesian pseudo-coordinate system with respect to the relative permeability effect of iron core in a subdomain model. The effect of PM width on the optimal radius of the iron pole has been investigated. In addition, for initial and optimal machines, the effect of the iron core relative permeability on the STPMM performances was studied at no-load and on-load conditions considering three certain PM widths. Moreover, the effect of iron pole shape on pulsating torque components with respect to certain values of iron core relative permeability was studied by comparing cogging torque, ripple and reluctance torque waveforms. In order to validate the results of the proposed analytical model, three motors with different PM widths were considered as case studies and their performance results were compared analytically and numerically. Two prototype spoke-type machines were fabricated and the experimental results were compared to analytical results. It can be seen that the analytical modeling results are consistent with the numerical analysis and experimental results.
M. Ahmadinia, J. Sadeh,
Volume 17, Issue 4 (12-2021)
Abstract
In this paper, an accurate fault location scheme based on phasor measurement unit (PMU) is proposed for shunt-compensated transmission lines. It is assumed that the voltage and current phasors on both sides of the shunt-compensated line have been provided by PMUs. In the proposed method, the faulted section is determined by presenting the absolute difference of positive- (or negative-) sequence current angles index, firstly. After determining faulted section, the voltage phasor at the shunt-compensator terminal is estimated via the sound section. The faulted section can be assumed as a perfect transmission line that synchronized voltage and current phasors at one end and voltage phasor at the other end are available. Secondly, a new fault location algorithm is presented to locate the precise fault point in the faulted section. In this algorithm, the location of the fault and the fault resistance are calculated simultaneously by solving an optimization problem, utilizing the heuristic Particle Swarm Optimization (PSO) method. The simulation results in MATLAB/SIMULINK platform demonstrate the high performance of the proposed method in finding the fault location in shunt-compensated transmission lines. The proposed scheme has high accuracy for both symmetrical and asymmetrical fault types and high fault resistance.
A. Mansoori, A. Sheikhi Fini, M. Parsa Moghaddam,
Volume 18, Issue 1 (3-2022)
Abstract
In recent years, the increasing of non-dispatchable resources has posed severe challenges to the operation planning of power systems. Since these resources are random in nature, the issue of flexibility to cover their uncertainty and variability has become an important research topic. Therefore, having flexible resources to cover changes in the generation of these resources during their operation can play an essential role in eliminating node imbalances, system reliability, providing the required flexible ramping capacity, and reducing system operating costs. Among flexibility resources, there are quick-act generation units such as gas units that can play an important role in covering net load changes. Also, on the demand side, the optimal design of demand response programs as responsive resources to price and incentive signals, by modifying the system load factor can prevent severe ramps at net load, especially during peak load hours, and as a result, increase system flexibility while decreasing operational cost of the power system. In this paper, unlike the existing literature, the effect of the mentioned flexibility resources (both on the generation side and the demand side) in day-ahead operation planning under high penetration of wind generation units has been studied on the IEEE RTS 24-bus test system. Also, for this scheduling, a mixed-integer, two-stage, and tri-level adaptive robust optimization have been used, which is solved by column-and-constraint generation decomposition-based algorithm to clear the energy and ramping capacity reserve jointly.
A. Saffari, S. H. Zahiri, M. Khishe,
Volume 18, Issue 1 (3-2022)
Abstract
In this paper, multilayer perceptron neural network (MLP-NN) training is used by the grasshopper optimization algorithm with the tuning of control parameters using a fuzzy system for the big data sonar classification problem. With proper tuning of these parameters, the two stages of exploration and exploitation are balanced, and the boundary between them is determined correctly. Therefore, the algorithm does not get stuck in the local optimization, and the degree of convergence increases. So the main aim is to get a set of real sonar data and then classify real sonar targets from unrealistic targets, including noise, clutter, and reverberation, using GOA-trained MLP-NN developed by the fuzzy system. To have accurate comparisons and prove the GOA performance developed with fuzzy logic (called FGOA), nine benchmark algorithms GOA, GA, PSO, GSA, GWO, BBO, PBIL, ES, ACO, and the standard backpropagation (BP) algorithm were used. The measured criteria are concurrency speed, ability to avoid local optimization, and accuracy. The results show that FGOA has the best performance for training datasets and generalized datasets with 96.43% and 92.03% accuracy, respectively.
T. Barforoushi, R. Heydari,
Volume 18, Issue 2 (6-2022)
Abstract
Curtailment of the production of wind resources due to uncertainty can affect the expansion of the transmission networks. The issue that needs to be addressed is how to expand the transmission network, which is accompanied by increasing wind energy utilization. In this paper, a new framework is proposed to solve the transmission expansion planning (TEP) problem in the presence of wind farms, considering wind curtailment cost. The proposed model is a risk-constrained stochastic bi-level problem that, the difference between the expected social welfare and investment cost is maximized at the upper level where optimal decisions on expansion plans are adopted by the independent system operator (ISO). To make the best use of wind generation resources, a new term called wind power curtailment cost is added to the upper level. Also, the risk index is included in expansion decisions. The market-clearing is considered at the lower level, aiming at maximizing social welfare. Uncertainties relating to wind power and the forecasted demand are modeled by sets of scenarios. Using duality theory, the proposed framework is modeled as mixed-integer linear programming (MILP) problem. The model is examined using the classical Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The results show that by considering the wind curtailment cost, the transmission network is expanded in a way that increases the wind energy utilization factor from 92.05% to 95.17%.
M. Soruri, S. M. Razavi, M. Forouzanfar,
Volume 18, Issue 3 (9-2022)
Abstract
Power amplifier is one of the main components in the RF transmitters. It must provide various stringent features that can lead to complicating the design. In this paper, a new optimizing method based on the inclined planes system optimization algorithm is presented for the design of a discrete power amplifier. It is evaluated in a 2.4-3 GHz power amplifier, which is designed based on “Cree’s CGH40010F GaN HEMT”. The optimization goals are input and output return losses, Power Added Efficiency, and Gain. Large signal simulation of the optimized power amplifier shows a good performance across the bandwidth. In this frequency range, the input and output return losses are about lower than -10 dB, the Power Added Efficiency is greater than 51%, while the Gain is higher than 13.5 dB. A two-tone test with a frequency space of 1 MHz is applied for the linearity evaluation of the designed power amplifier. The obtained result shows that the power amplifier has good linearity with a low memory effect.
Hamid Salarvand, Meysam Doostizadeh, Farhad Namdari,
Volume 18, Issue 4 (12-2022)
Abstract
Owing to the portability and flexibility of mobile energy storage systems (MESSs), they seem to be a promising solution to improve the resilience of the distribution system (DS). So, this paper presents a rolling optimization mechanism for dispatching MESSs and other resources in microgrids in case of a natural disaster occurrence. The proposed mechanism aims to minimize the total system cost based on the updated information of the status of the DS and transportation network (TN). In addition, the characteristics of the protection system in DS (i.e., relays with fixed protection settings), the constraints related to the protection coordination are examined under pre- and post-event conditions. The coordinated scheduling at each time step is formulated as a two-stage stochastic mixed-integer linear program (MILP) with temporal-spatial and operation constraints. The proposed model is carried out on the Sioux Falls TN and the IEEE 33-bus test system. The results demonstrate the effectiveness of MESS mobility in enhancing DS resilience due to the coordination of mobile and stationary resources.
G. Hamza, M. Sofiane, H. Benbouhenni, N. Bizon,
Volume 19, Issue 2 (6-2023)
Abstract
In this paper, a wind power system based on a doubly-fed induction generator (DFIG) is modeled and simulated. To guarantee high-performance control of the powers injected into the grid by the wind turbine, five intelligent super-twisting sliding mode controllers (STSMC) are used to eliminate the active power and current ripples of the DFIG. The STSMC controller is a high-order sliding mode controller which offers high robustness compared to the traditional sliding mode controller. In addition, it reduces the phenomenon of chattering due to the discontinuous component of the SMC technique. However, the simplicity, ease of execution, durability, and ease of adjusting response are among the most important features of this control compared to some other types. To increase the robustness and improve the response of STSMC, particle swarm optimization method is used for this purpose, where this algorithm is used for parameter calculation. The simulation results obtained using MATLAB software confirm the characteristics of the designed strategy in reducing chattering and ensuring good power control of the DFIG-based wind power.
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
Mohamed Khalaf, Ahmed Fawzi, Ahmed Yahya,
Volume 20, Issue 1 (3-2024)
Abstract
Cognitive radio (CR) is an effective technique for dealing with scarcity in spectrum resources and enhancing overall spectrum utilization. CR attempts to enhance spectrum sensing by detecting the primary user (PU) and allowing the secondary user (SU) to utilize the spectrum holes. The rapid growth of CR technology increases the required standards for Spectrum Sensing (SS) performance, especially in regions with low Signal-to-Noise Ratios (SNRs). In Cognitive Radio Networks (CRN), SS is an essential process for detecting the available spectrum. SS is divided into sensing time and transmission time; the more the sensing time, the higher the detection probability) and the lower the probability of a false alarm). So, this paper proposes a novel two-stage SS optimization model for CR systems. The proposed model consists of two techniques: Interval Dependent De-noising (IDD) and Energy Detection (ED), which achieve optimum sensing time, maximum throughput, lower and higher. The Simulation results demonstrated that the proposed model decreases the, achieves a higher especially at low SNRs ranging, and obtains the optimum sensing time, achieving maximum throughput at different numbers of sensing samples (N) and different SNRs from -10 to -20 dB in the case of N = 1000 to 10000 samples. The proposed model achieves a throughput of 5.418 and 1.98 Bits/Sec/HZ at an optimum sensing time of 0.5ms and 1.5ms respectively, when N increases from 10000 to 100000 samples. The proposed model yields an achievable throughput of 5.37 and 4.58 Bits/Sec/HZ at an optimum sensing time of 1.66ms and 13ms respectively. So, it enhances the SS process than previous related techniques.
Priyanka Handa, Balkrishan Jindal ,
Volume 20, Issue 1 (3-2024)
Abstract
The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios. The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory.
Ali Riyadh Ali , Rakan Khalil Antar, Abdulghani Abdulrazzaq Abdulghafoor ,
Volume 20, Issue 3 (9-2024)
Abstract
Artificial intelligence-based optimization algorithm was used to compute the switching angle values. In order to run the inverter with the lowest possible Total Harmonic Distortion (THD) value, it is suggested in this study to use an algorithm such as the Practical Swarm Algorithm (PSA). The multilevel inverter and optimization algorithm were created and simulated in this study using a MATLAB software. A frequency spectrum analysis was also conducted and found to be consistent with the theoretical analysis of the system. To provide practical results, the FPGA generates PWM signals that are appropriate for the inverter switches. On the Spartan-3E Starter set, the suggested control schemes were developed and put it into practice. Xilinx-ISE 12.1i design software and VHDL hardware description language were used to create the FPGA software. The suggested approaches have a number of benefits over conventional digital PWM techniques, including straightforward hardware implementation, minimum scaling of digital circuits, easy digital design, reconfigurable, and flexibility in adaptability. The outcomes of the experiment and the simulation agreed rather well.
Farhad Amiri, Mohammad H. Moradi,
Volume 21, Issue 1 (3-2025)
Abstract
Low inertia is one of the most important challenges for frequency maintenance in islanded microgrids. To address this issue, the innovative concept of Virtual Inertia Control (VIC) has emerged as a promising solution for enhancing frequency stability in such systems. This paper presents an advanced controller, the PD-FOPID, as a highly effective technique for improving the efficiency of VIC in islanded microgrids. By leveraging the Rain Optimization Algorithm (ROA), this approach enables precise fine-tuning of the controller's parameters. A key advantage of the proposed method is its inherent resilience to disruptions and uncertainties caused by parameter fluctuations in islanded microgrids. To evaluate its performance and compare it with alternative control methods, extensive assessments were conducted across various scenarios. The comparison includes VIC based on an H-infinity controller (Controller 1), VIC based on an MPC controller (Controller 2), Adaptive VIC (Controller 3), VIC based on an optimized PI controller (Controller 4), conventional VIC (Controller 5), and systems without VIC (Controller 6). The results demonstrate that the proposed methodology significantly outperforms existing approaches in the field of VIC. The simulations were conducted using MATLAB software.
Syazwan Ahmad Sabri, Siti Rafidah Abdul Rahim, Azralmukmin Azmi, Syahrul Ashikin Azmi, Muhamad Hatta Hussain, Ismail Musirin,
Volume 21, Issue 2 (6-2025)
Abstract
The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.
Ying Foo Leong, Nizaruddin M. Nasir, Suliana Ab-Ghani, Norazila Jaalam, Nur Huda Ramlan,
Volume 21, Issue 2 (6-2025)
Abstract
This paper focuses on the application of a cascaded multilevel inverter, specifically the 5-level multilevel inverter, utilizing a proposed controller known as the FLC-PSO-PI controller. The primary challenge addressed in this research is the precise regulation of output voltage in the multilevel inverter during load variations while meeting voltage harmonic and transition requirements as per industry standards, which are the 10 % voltage limit recommended by IEC and 8 % of total harmonic distortion (THD) by IEEE. An innovative solution is proposed by integrating PSO and FLC to dynamically adapt the controller in real-time, ensuring stable and accurate output voltage regulation. The proposed controller is designed and simulated using MATLAB/Simulink, and its performance is compared with PSO-PI and no controller under various load conditions. The results demonstrate that the FLC-PSO-PI controller significantly enhances output voltage regulation were achieving the desired peak voltage and low THD across different load scenarios, including half load to full load (0.8 %) and no load to full load (0.89 %). Furthermore, the FLC-PSO-PI controller exhibits superior transient response characteristics, such as reduced overshooting (2.89 %), faster rise time at 36.946 µs, and satisfactory settling time at 151.014 µs. This research contributes to the advancement of multilevel inverter technology and its potential applications in renewable energy systems, motor drives, and grid-connected devices. The proposed FLC-PSO-PI controller offers a promising solution for precise voltage regulation in multilevel inverters, enhancing their performance and enabling widespread adoption in various industrial sectors.
Murni Nabila Mohd Zawawi, Zainuddin Mat Isa, Baharuddin Ismail, Mohd Hafiz Arshad, Ernie Che Mid, Md Hairul Nizam Talib, Muhammad Fitra Zambak,
Volume 21, Issue 2 (6-2025)
Abstract
This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge multilevel inverters (CHB MLI) by employing the Henry Gas Solubility Optimization (HGSO) algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the optimization of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the optimization process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex optimization challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems.
