Particle Swarm Optimization Technique Research Articles

Optimal Design and Operation of an Off-Grid Hybrid Renewable Energy System in Nigeria’s Rural Residential Area, Using Fuzzy Logic and Optimization Techniques

This study focuses on a technical and economic analysis of designing and operating an off-grid hybrid renewable energy system (HRES) in a rural community called Olooji, situated in Ogun state, Nigeria, as a case study. First, a size optimization model is developed on the basis of the novel metaheuristic particle swarm optimization (PSO) technique to determine the optimal configuration of the proposed off-grid system on the basis of the minimization of the levelized cost of electricity, by factoring in the local meteorological and electricity load data and details on the technical specification of the main components of the HRES. Second, a fuzzy-logic-controlled energy management system (EMS) is developed for the dynamic power control and energy storage of the proposed HRES, ensuring the optimal energy balance between the different multiple energy sources and the load at each hour of operation. The result of the size optimization model showed that an LCOE for implementing an HRES in the community would be 0.48 USD/kWh in a full-battery-capacity scenario and 1.17 USD/kWh in a half-battery-capacity scenario. The result from this study is important for quick decision-making and effective feasibility studies on the optimal technoeconomic synopsis of implementing minigrids in rural communities.

Design and Development of Polymer-Based Optical Fiber Sensor for GAIT Analysis

In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared.

Optimization of input parameters of ANN–driven plasma source through nature-inspired evolutionary algorithms

The plasma sources are a device that produces energetic ion streams for applications in diversified fields. However, operating the ion sources with repeatable performance remains challenging and time-consuming due to its nonlinear nature and the complex relation between the input and output parameters. This maiden study examines the efficacy of nature-inspired evolutionary algorithms (NIEAs) for optimizing the input parameters of an ion source model based on an artificial neural network (ANN) that generates high-density negative hydrogen ions. Hence, it requires the development of a predictive ion source model based on ANNs and further optimized using NIEA. This work optimizes the magnetic field and gas pressure as input parameters using the particle swarm optimization (PSO) technique, simulated annealing (SA), and genetic algorithm (GA). The input parameters for different plasma densities and ion saturation currents are adjusted for a constant Radio Frequency (RF) power (900 watts) at 6 mTorr, 7.5 mTorr, 30 mTorr gas pressures and magnetic fields of 40G, 55G, and 86G. The number of iterations, the computation time, and the Root Mean Squared Error (RMSE) defines the performance of the algorithms. SA, GA, and PSO optimized the input parameters by averaging 569, 63, and 32 iterations, while the average computation time remained at 54 seconds, 27 seconds, and 4 seconds, respectively. The RMSEs for SA, GA, and PSO are 1.57, 1.56, and 0.54. The comparison shows that the PSO approach is the most efficient regarding the number of iterations, computation time and RMSE.

Swarm Optimization of Fuzzy Systems for Mobile Robots with Remote Control

This paper is dedicated to the development and research of the advanced approach for optimization of fuzzy control systems (FCS) for mobile robots (MR) with remote control based on bioinspired swarm techniques. The proposed approach makes it possible to create effective intelligent control systems for MRs based on the principles of hierarchical multi-level control, remote IoT-based control, fuzzy logic control, and intelligent optimization of fuzzy control devices. The applied hybrid particle swarm optimization (PSO) techniques with elite strategy allow effectively optimizing various parameters of FCSs, finding the optimal solution to the problem, and, at the same time, have a higher convergence rate compared with the basic PSO algorithms. To evaluate the effectiveness of the obtained advanced approach based on hybrid swarm techniques, the optimization process of the FCS for the speed control of the multi-purpose caterpillar MR, which can move on inclined and vertical ferromagnetic surfaces, is carried out. The presented research results fully confirm the high efficiency of the proposed approach, as well as the expediency of its application for the optimization of fuzzy control systems for various remotely controlled mobile robots.

Well Placement Optimization through the Triple-Completion Gas and Downhole Water Sink-Assisted Gravity Drainage (TC-GDWS-AGD) EOR Process

Gas and downhole water sink-assisted gravity drainage (GDWS-AGD) is a new process of enhanced oil recovery (EOR) in oil reservoirs underlain by large bottom aquifers. The process is capital intensive as it requires the construction of dual-completed wells for oil production and water drainage and additional multiple vertical gas-injection wells. The costs could be substantially reduced by eliminating the gas-injection wells and using triple-completed multi-functional wells. These wells are dubbed triple-completion-GDWS-AGD (TC-GDWS-AGD). In this work, we design and optimize the TC-GDWS-AGD oil recovery process in a fictitious oil reservoir (Punq-S3) that emulates a real North Sea oil field. The design aims at maximum oil recovery using a minimum number of triple-completed wells with a gas-injection completion in the vertical section of the well, and two horizontal well sections—the upper section for producing oil (from above the oil/water contact) and the lower section for draining water below the oil/water contact. The three well completions are isolated with hydraulic packers and water is drained from below the oil–water contact using the electric submersible pump. Well placement is optimized using the particle swarm optimization (PSO) technique by considering only 1 or 2 TC-GDWS-AGD wells to maximize a 12-year oil recovery with a minimum volume of produced water. The best well placement was found by considering hundreds of possible well locations throughout the reservoir for the single-well and two-well scenarios. The results show 58% oil recovery and 0.28 water cut for the single-well scenario and 63.5% oil recovery and 0.45 water cut for the two-well scenario. Interestingly, the base-case scenario using two wells without the TC-GDWS-AGD process would give the smallest oil recovery of 55.5% and the largest 70% water cut. The study indicates that the TC-GDWS-AGD process could be more productive by reducing the number of wells and increasing recovery with less water production.

An An Optimal Placement of STATCOM Controller on 14-Bus IEEE Standard Test Transmission Network Using Particle Swarm Optimization

The bulk transport of electrical energy from a generating station to an electrical substation is facilitated by transmission network. Transmission network suffered from unavoidable power loss and there is need to minimize the loss so as to improve the overall efficiency of the network. By strategically placing Static Synchronous Compensator (STATCOM) controller into appropriate location, power loss can be reduced. Factually, one of the most crucial Flexible Alternating Current Transmissions (FACTS) device that control electrical power and increase system power transfer capability is considered as STATCOM. STATCOM is typically installed on the transmission network to reduce power losses and improve the voltage profile of the system. To significantly reduce power losses and improve the system voltage profile, which will ensure the security and dependability of the power system, this research looks into the optimal placement of STATCOM controller on transmission system using Particle Swarm Optimization (PSO) technique. A steady state mathematical model of STATCOM-based Power Injection Mode (PIM) through voltage source representation was derived. The mathematical model was implemented into Newton-Raphson (NR) load flow and PSO technique was used to optimize the exact location of STATCOM in objective to minimize power losses, improve voltage magnitude profile and provide adequate voltage stability to the power system. The voltage magnitude, active and reactive power loss were achieved for load flow solution on 14-bus IEEE test system using Power System Analysis Toolbox (PSAT) in MATLAB Simulink. Results revealed that, all the terminal voltages are within the voltage limits of 1.040 p.u and 1.035 p.u., respectively. The total active and reactive power losses were able to reduce by 6.90 % and 9.13 % respectively. The STATCOM based PSO optimal placement gave a stronger compensatory impact because it helped to reduce power losses and improved the overall network STATCOM-based profile.

Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization

Predicting telemetry data is vital for the proper operation of orbiting spacecraft. The Grey–Markov model with sliding window (GMSW) combines Grey model (GM (1, 1)) and Markov chain forecast model, which allows it to describe the fluctuation of telemetry data. However, the Grey–Markov model with sliding window does not provide better predictions of telemetry series with the pseudo-periodic phenomenon. To overcome this drawback, we improved the GMSW model by applying particle swarm optimization (PSO) algorithm a sliding window for better prediction of spacecraft telemetry data (denoted as PGMSW model). In order to produce more accurate predictions, background-value optimization is specially carried out using the particle swarm optimization technique in conventional GM (1, 1). For verifying PGMSW, it is utilized in the prediction of the cyclic fluctuation of telemetry series data and exponential variations therein. The simulation results indicate that the PGMSW model provides accurate solutions for prediction problems similar to the pseudo-periodic telemetry series.

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

<span lang="EN-US">Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.</span>

Development of a stable and optimised voltage and frequency controller for a self-excited induction generator system

ABSTRACT A self-excited induction generator (SEIG) has the inability to maintain its terminal frequency and voltage with prime-mover speed and load perturbations. To address this issue, a generalised impedance controller (GIC) is attached to SEIG with feedback voltage processed through a proportional–integral (PI) controller. One algorithm is proposed to evaluate control gains of the PI controller to make the GIC-based SEIG system stable. As the conventional design of the PI controller performs poorly under different perturbations, this study is extended using the advanced computational intelligence-based particle swarm optimisation (PSO) technique to tune the coefficients of the PI controller and enhance the performance. The obtained THD of generator and load voltages and currents are within 0.23%. To validate the proposed technique, an experiment on a GIC-based SEIG system is carried out using a commercially available TMS320F2812 DSP processor.

An environmental optimal power flow framework of hybrid power systems with pumped hydro storage

The novelty of this study is to present the combined operational strategy of wind-solar- thermal (WST) system with pumped hydro storage (PHS) facility while mitigating the intermittency of wind and solar power. The design problem has been formulated in an environmental optimal power flow (EOPF) framework while addressing issues like emission minimization, operating cost reduction and voltage security maximization. The major challenge in the present hybrid power system (HPS) operating scenario is the evaluation of optimal scheduling and dispatching of electrical power without any violation of real-time operational constraints. Voltage secure and cost-effective power system operations with the integration of hybrid renewable power sources become extremely challenging in achieving reliable and environmental friendly solutions. The optimal operational paradigm for various hybrid configurations is evaluated using the particle swarm optimization (PSO) technique. This study performs a comparative performance analysis among three different hybrid configurations are (1) WST; (2) wind-hydro-thermal (WHT); and (3) WST-PHS in fulfilment of multiple operational objectives. Results show that the optimized schedule of WST-PHS configuration presents a remarkable savings (i.e., 3.835×105 US$/y to 8.307×105 US$/y) in the system operating cost, reduction of emission from 9.636 Ton/y to 20.148 Ton/y and voltage security enhancement by 1.51%–3.57% compared to other HPS. The trade-off curve among conflicting objectives is proposed using bi-objective optimization and fuzzy membership functions. The superior results obtained with WST-PHS configuration are validated in the IEEE-30 bus benchmark power system.

Load Frequency Control Assessment of a PSO-PID Controller for a Standalone Multi-Source Power System

The performance of load frequency control (LFC) for isolated multiple sources of electric power-generating units with a proportional integral derivative (PID) controller is presented. A thermal, hydro, and gas power-generating unit are integrated into the studied system. The PID controller is proposed as a subordinate controller to stabilize system performance when there is a sudden demand on the power system. The particle swarm optimization (PSO) algorithm is used to obtain optimal gain values of the proposed PID controller. Various cost functions, mainly integral time absolute error (ITAE), integral absolute error (IAE), integral squared error (ISE), and integral time squared error (ITSE) were used to optimize controller gain parameters. Furthermore, the enhancement of the PSO technique is proven by the performance comparison of conventional, differential evolution (DE) algorithm- and genetic algorithm (GA)-based PID controllers for the same system. The results show the PSO-PID controller delivers a faster settled response and the percentage improvement of the proposed technique over the conventional method is 79%, over GA is 55%, and over DE is 24% in an emergency in a power system.

Performance enhancement of duobinary modulation-based DWDM system using particle swarm optimization

Abstract The next-generation light wave communication DWDM networks can accommodate large number of channels to efficiently utilize the bandwidth (BW). However, in the deployment of DWDM system mixing of two or more waves limits the performance due to four wave mixing (FWM) nonlinearity in fiber. This article, presents improved performance analysis of DWDM using a proposed improved duobinary modulation (IDBM), modified duobinary modulation (MDBM) and duobinary modulation (DBM) model for mitigation of FWM nonlinearities has been estimated experimentally through interference of noise on optical signal, FWM efficiency, FWM power etc. In IDBM, a unit delay in addition to time delay parameter is also utilized for generation of electrical duobinary pulses. The parameters of both delay blocks are tuned through particle swarm optimization (PSO) technique to improve the performance. The overall proposed IDBM effectively decreases the FWM. The results obtained for IDBM in terms of Q-factor 8.9, OSNR 18 dB, FWM power −87 dBm and FWM efficiency −70 dB shows substantial improvement with the recommended IDBM technique in comparison to DBM and MDBM schemes.

Carbon emission controlled investment and warranty policy based production inventory model via meta-heuristic algorithms

Over the last few years, manufacturing firms are required to take more action in order to control the carbon emissions from their activities. Again, the warranty of a product is an important factor for both buyers and manufacturers. However, due to uncertain market situations and fluctuations of customers’ demand, to conduct a detailed analysis of a manufacturing system is a complicated task. Primarily addressing these concepts together, in this work, an interval valued production inventory model is formulated under carbon taxation regulation in which demand is dependent on warranty period of the products. The main objective of this work is to determine the effect of warranty period of the products on the optimal policy of the production firm. Parallelly, this work also navigates the effect of carbon taxation regulation on the revenue of the manufacturer. There arises an interval optimization problem that is solved by centre-radius optimization technique. Further, to illustrate the validity of the model, a numerical example is considered and solved by different variants of quantum behaved particle swarm optimization (QPSO) techniques, grey-wolf optimizer algorithm (GWOA), teaching learning based optimizer algorithm (TLBOA), sparrow search algorithm (SSA). From the findings of numerical solution, it is observed that all the algorithms are equally efficient. Sensitivity analysis indicates that the centre of the average profit of the system increases most significantly as the initial demand rate of the products increases. Further, the warranty period of the products affects the optimal policy of the system in a suffice way and carbon taxation affects the revenue of the system less significantly. Finally, as a practical illustration of the model, another numerical example is taken into account considering the manufacturing and business strategies of LED monitor in a local manufacturing firm of Kolkata (India).

Queueing and Reliability Analysis of Unreliable Multi-server Retrial queue with Bernoulli feedback

The study consists of an unreliable multiserver retrial queue with feedback where clients may resist to join the orbit and the service provider is erratic. In this model we have considered c service providers at a time and if all the c servers are busy, the incoming clients wait in the retrial orbit. The model is unfolded using Matrix Geometric Method (MGM) to obtain the rate matrix and stationary probabilities. The system with retrials is modelled and the performance measures and reliability indices are procured. These derived quotients are then visualised and validated with the help of tables and graphs. Further, the cost analysis of the model is carried out and the optimal cost for the system is obtained using Particle Swarm Optimization (PSO) algorithm for matrix method. The optimal repair and retrial rates are obtained using PSO technique which helps the computer and manufacturing industry to work towards better decision policies.

Scaled and nonlinear multi-objective model for Downlink and Uplink exposure in massive MIMO

When designing wireless networks with a large number of wireless broadband devices, it is important to take into account the induced Uplink (UL) and Downlink (DL) exposure from base station broadcasting. This work focuses on the reduction of power consumption, low downlink and uplink electromagnetic exposure, and the optimization of locations and power levels of large Multiple Input–Multiple Output-Long Term Evolution base stations (MIMO-LTE). Additionally, it anticipates achieving maximum user coverage. In order to handle the power consumption and Electro Magnetic Field (EMF) exposure, a Nonlinear and Scaled Weight objective model (NLSW) is defined (DL and UL exposure). An enhanced Particle Swarm Optimization (PSO) technique is employed to resolve this optimization issue. Finally, DL exposure, UL exposure, and power performance of a NLSW are validated compared to the traditional linear model. Increased antenna leads to a 25% reduction in DL dosage. The minimum BS deployed in the area with the number of antenna elements until it reaches the ideal value is related to the UL exposure maximization.

Analysis of Price Based Demand Response Program Using Load Clustering Approach

The K-Medoids algorithm is adopted along with the proposed elephant herd optimization (EHO) algorithm in this peculiar work to cluster the consumers according to their required load consumption patterns and for price-based demand response (DR) program implementation. Consumer baseline load (CBL) estimation is done using the clustered load data. The novel approach of clustering the data analysis in this proposed work is done by collecting the load data from the transformer centers under the control of the 33/11 kV substation in the state of Goa, India, as well as the price data from the state load dispatch center and Indian energy exchange. Further the aggregated load data obtained for the 11 kV feeder is used to estimate the reference consumer load consumption by using regression and an artificial neural network model. Consumer response towards load change with price is modeled based on price elasticity and the consumer benefit function. An EHO algorithm is proposed for implementing the time of use (TOU) DR program for domestic consumers. The algorithm’s effectiveness and robustness is demonstrated by comparing the results with those of the genetic algorithm and particle swarm optimization technique. The proposed work of CBL estimation, consumer segregation, and optimum evaluation of the TOU DR program using the EHO algorithm assists in the implementation of DR programs. The proposed research can be used for analyzing the demand reduction potential, deployment of alternative energy sources, reduction in the use of conventional energy sources, promotion of energy efficiency, and the reduction of CO2 emissions.

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.

Optimal Placement and Sizing of Distributed Generation for Power Loss Minimization in Distribution Network using Particle Swarm Optimization Technique

Electrical power distribution network is the final stage in the delivery of electric power to consumers. It suffers from high power losses along its radial lines, and there is a need to minimize these losses. One of the technologies used in reducing losses is the application of Distributed Generation (DG). However, inappropriate sizing or placement of DG could inadvertently increase losses in the network. Therefore, this study carries out Optimal Placement and Sizing of DG (OPSDG) in the distribution system using Particle Swarm Optimization (PSO) in reducing the total power loss of the distribution network. A mathematical model of distribution system without and with DG was developed from one voltage source representation to generate a set of equations using Bus Injection to Branch Current (BIBC) and Branch Current to Bus Voltage (BCBV) load flow technique. The model was optimized using PSO and implemented with MATLAB. In each case, the Loss Reduction Index (LRI) was computed. The approach was used on a Nigeria Distribution network 11 kV 34-bus Ayepe feeder of the Ibadan Electricity Distribution Company (IBEDC). The total LRI obtained using analytical technique for sizing and placement of DG is 0.1808 p.u. With the incorporation of DG using PSO, the total LRI is 0.2636 p.u. The best location and size of the DG unit in the network after optimization is at bus 14 with an active power of 5.00 MW. The results established that using PSO for DG placement and sizing significantly reduced the distribution network total power loss.

Damage-based optimal control of steel moment-resisting frames equipped with tuned mass dampers

The efficacy of a tuned mass damper (TMD) in dissipating the seismic energy transmitted to a structure depends on the TMD’s and structure’s characteristics and the structural response(s) that the TMD is intended to control. This study presents an approach to designing TMDs considering seismic damage as a representative of structural response. Damage is quantified by the Park–Ang damage index, which integrates deformation-induced damages with the cumulative hysteretic energy dissipated by structural members. The design problem is formulated into an optimization framework. The objective function is assumed to be the maximum value of story damage averaged over an ensemble of independently applied, scaled historical ground motions, and constraints are defined such that an adjustably uniform distribution of the mean story damage along the height of the structure is achieved. The approach is demonstrated by finding the optimum design of a TMD for a 10-story inelastic steel moment-resisting frame (SMRF) using the passive congregation particle swarm and grey wolf optimization techniques and showing that the optimized TMD substantially mitigates local damage in beams and columns, achieves a more uniform damage distribution in stories, yields an improved balance of reduced drifts and accelerations in the structure, and reduces the seismic fragility of the SMRF at different performance levels.

Design optimization of Spoke IPM motor for improving efficiency using PSO and Rao-1 algorithm based FEA

The rate of demand for permanent magnet synchronous motors (PMSMs) in all types of applications is elevated day by day. Many topologies of PMSM have been developed and utilized in different sectors. Many software are available for primary analysis of motors, which have an inbuilt finite element solver. The study presented in the paper aims to optimize a spoke shape interior permanent magnet synchronous motors (IPMSMs) design parameters using particle swarm optimization technique (PSO), and Rao-1 algorithm. In this research, a 4 pole 24 slots spoke shape IPMSM motor is considered to optimize its parameters such as outer, inner stator diameter, rotor outer diameter, the height of magnet, stator tooth width, and the base width of the slot. Results obtained, and the effectiveness of optimization are validated by comparing with the results reported in the literature. Efficiency, torque, magnetic field, and other electromagnetic parameters are calculated and reported for the optimized IPMSM parameters. For improving the efficiency of IPMSM, an analysis of its different design factors is focused. In this study, efficiency improvement is achieved by optimizing stator slot featuring, and rotor parts designing. The corresponding results are reported. The complete operational analysis has been conducted in ANSYS Maxwell and MATLAB environment. Here total excitation of the motor highly depends on the magnetic material used in the rotor part of the motor.