Teaching-learning Algorithm Research Articles

Improved Teaching Learning Algorithm with Laplacian operator for solving nonlinear engineering optimization problems

Teaching Learning Algorithm (TLA) is a recently developed nature-inspired optimization technique applicable to complex optimization problems. This paper proposes an improved TLA version using the Laplacian operator of the Genetic Algorithm (GA), named LX-TLA. The proposed algorithm is tested on benchmark optimization problems, including unimodal and multimodal problems. The numerical results are obtained in the form of objective function values, and a t-test is applied to compare the performance of LX-TLA and basic TLA. Convergence plots are given to provide insight into the convergence behavior of LX-TLA. The results reveal that proposed algorithm provides effective and efficient performance in solving benchmark test functions. The proposed algorithm is also applied to engineering design problems, such as Tuned Mass Damper (TMD), truss structure, welded beam, tension string, and pressure vessel. The results obtained using LX-TLA are compared with other nature-inspired optimization algorithms. The results demonstrate that the proposed algorithm is a robust and effective tool for solving complex optimization problems.

An optimal placement model of UPFC using Hybrid Taylor series-based spider monkey-teaching learning

Due to the inconsistent nature of the system, the power system uncertainties put forward numerous operational challenges that lead to enhanced risk. Also, minimizing the predictability of the power system makes the operational decisions becomes more challenging and undesirable. Therefore, optimal location, with the real and apparent power flow of a Unified Power Flow Controller (UPFC), is accomplished for enhancing the rate of predictability and minimizing the loss. In this paper, a novel Taylor Spider Monkey-Teaching Learning (TSMO-TL) algorithm is proposed which is the integration of two optimization algorithms namely the Taylor spider monkey as well as the teaching-learning algorithm. Here, the novel TSMO-TL algorithm is employed to optimize the location for situating the UPFC by the objective function (i.e. active power loss and active power flow predictability index). In addition to this, the TSMO-TL approach attains the optimal UPFC placement by considering the input bus voltage, line loss as well as angle. Finally, the proposed TSMO-TL algorithm is utilized in the IEEE-57 bus system to determine its effectiveness. Then the novel TSMO-TL method proved the effectiveness of the system with respect to active power loss, power flow predictability index as well as computational time.

Digital Twin simulation for deep learning framework for predicting solar energy market load in Trade-By-Trade data

Because users behave randomly and in a nonlinear way, predicting electrical loads proves to be a difficult procedure. Due to the development of the smart grid (SG) and advanced metering infrastructure (AMI), humans will be capable of recording, monitoring, and analyzing these non-linear behaviors. The use of electric load projection layouts is a necessity in order to make decisions, plan, and evaluate contracts in electrical systems. Consequently, there have been several load prediction methods in the research that demonstrate trade-offs among prediction precision and runtime (convergence rate). The current paper presents a method for short-term load prediction of market in trade-by-trade data that would be quick and precise. Modified mutual information (MMI) is used to extract abstractive characteristics from historic information. Learning empowers the factored conditional restricted Boltzmann machine (FCRBM) for predicting the electrical loads. Ultimately, the efficiency has been optimized using the suggested modified teaching–learning algorithm (MTLA). The suggested architecture has the advantage of improving prediction precision and convergence rate. The MMI method and FCRBM layout improve prediction precision. In addition, MTLA has been used to enhance the convergence rates. Based on simulation outcomes, the suggested quick and precise layout performs better than conventional layouts when it comes to forecasting precision and convergence rates, including Bi-level, MI-artificial neural network (MI-ANN), and accurate fast converging short-term load forecast (AFC-STLF).

Dynamic optimal distributed resource planning for renewable energy based interconnected microgrids using teaching learning algorithm

Distributed Generating (DG) units, Energy Storage Systems (ESS), Distributed Reactive Sources (DRS), and resilient loads make up the microgrid (MG), which can operate in both connected and isolated modes. Because the amount of power generated by Renewable Energy Sources (RES) such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES) is unpredictable, it becomes difficult for MGs planners to make judgments. In this article, the uncertainties caused by RES are resolved through the successful application of a hybrid optimization approach and the integration of hybrid DGs. The Teaching Learning Algorithm (TLA) is used in this study to determine the best site for DGs and reconfiguration, and heuristic fuzzy has been merged with TLA to handle multi-objectives such as total generation and emission cost minimization, and bus voltage deviation. In addition, the impact of replacing RES with hybrid DGs on RES performance is investigated. The ideal structures are determined by solving four different scenarios with the suggested approach, allowing DSO to make decisions with greater flexibility. The proposed technique is validated using a benchmark IEEE 33 bus system that has been converted into a microgrid. WES, PVES, and hybrid DGs are validated using a 24-hour daily load pattern with 24-hour load dispatching characteristic behaviors.

Solar photovoltaic model parameter identification using robust niching chimp optimization

Researchers are becoming increasingly interested in studying how to accurately estimate the parameters of solar PV models. In this regard, this paper proposes a newly proposed nature-inspired technique named chimp optimization algorithm (ChOA) to create accurate and dependable PV models, such as single diode, double diodes, three diodes, and PV module models. In the PV models’ parameters estimation using optimization algorithms, two significant concerns need to be addressed: classifying various local/global optima and preserving these optimum values until the termination. Since ChOA is a general optimizer, it lacks an operator to address the two issues mentioned above. In order to address the mentioned problems, this paper embeds the niching technique in ChOA that includes the personal best qualities of PSO and a local search technique. In addition, a novel constraint handling approach is utilized to ensure that the algorithm is robust in tackling PV Models’ parameters estimation constraints. The outcome of RN-ChOA is evaluated using seven well-known optimization algorithms, including the whippy Harris hawks optimization algorithm (WHHOA), performance-guided JAYA (PGJAYA), enriched Harris hawks optimization algorithm (EHHOA), improved JAYA (IJAYA), birds mating optimizer (BMO), flexible particle swarm optimization algorithm (FPSO), chaotic biogeography-based optimizer (CBBO), and generalized oppositional teaching-learning algorithm (GOTLA), as well as dynamic Levy flight ChOA (DLF-ChOA) and weighted ChOA (WChOA) as the most recent modified version of ChOA. Furthermore, the performance of the RN-ChOA method has been assessed in a practical application for parameter evaluation of three widely-used commercial modules, namely, multi-crystalline (KC200GT), polycrystalline (SW255), and mono-crystalline (SM55), under a variety of temperature and irradiance conditions that cause changes in the photovoltaic model’s parameters. The findings demonstrate the robustness and excellent performance of the suggested approach.

Resource aware wind farm and D-STATCOM optimal sizing and placement in a distribution power system

<span lang="EN-US">Doubly fed induction generators (DFIG) based wind farms are capable of providing reactive power compensation. Compensation capability enhancement using reactors such as distributed static synchronous compensator (D-STATCOM) while connecting distribution generation (DG) systems to grid is imperative. This paper presents an optimal placement and sizing of offshore wind farms in a coastal distribution system that is emulated on an IEEE 33 bus system. A multi-objective formulation for optimal placement and sizing of the offshore wind farms with both the location and size constraints is developed. Teaching learning algorithm is used to optimize the multi-objective function constraining on the capacity and location of the offshore wind farms. The proposed formulation is a multi-objective problem for placement of the wind generator in the power system with dynamic wind supply to the power system. The random wind speed is generated as the input and the wind farm output generated to perform the optimal sizing and placement in the distributed system. MATLAB based simulation developed is found to be efficient and robust.</span>

New Optimal Control of Permanent Magnet DC Motor for Photovoltaic Wire Feeder Systems

This article aims to improve the permanent magnet DC (PMDC) motor performance for photovoltaic (PV) wire-feeder systems (PVWFSs) of arc welding machines. The considered technique is designed by direct speed control based on optimal Fractional-order Fuzzy PID FO-Fuzzy-PID controller. The purpose is to ensure optimal control of wire feed speed reference to reduce torque ripples and hence, the performance of the WFS is improved. The dynamic reaction of the proposed solar PVWFS relies upon the scaling factors of FO-Fuzzy-PID controller, which are optimized by using teaching-learning algorithm based on Particle Swarm Optimization (PSO) method. The maximum power point tracking (MPPT) is achieved using an intelligent FO-Fuzzy-PID current controller based Perturb and Observe (P&O) MPPT algorithm. The PVWFS system incorporating the proposed method is tested and compared with the conventional PID control scheme under different weather conditions. The simulation of the proposed system by MATLAB\SIMULINK is carried out. The simulation results indicate the effectiveness of the considered control strategy in terms of the reduction in torque oscillations, optimizing electrical power and wire feed speed.

Cost-Effective Outage Management in Smart Grid under Single, Multiple, and Critical Fault Conditions through Teaching–Learning Algorithm

A distribution system becomes the most essential part of a power system as it links the utility and utility customers. Under abnormal conditions of the system, a definitive goal of the utility is to provide continuous power supply to the customers. This demands a fast restoration process and provision of optimal solutions without violating the power system operational constraints. The main objective of the proposed work is to reduce the service restoration cost (SRC) along with the elimination of the out-of-service loads. In addition, this work concentrates on the minimal usage and finding of optimal locations for additional equipment, such as capacitor placement (CP) and distributed generators (DGs). This paper proposes a two-stage strategy, namely, the service restoration phase and optimization phase. The first phase ensures the restoration of the system from the fault condition, and the second phase identifies the optimal solution with reconfiguration, CP, and DG placement. The optimization phase uses the teaching–learning algorithm (TLA) for optimal restructuring and optimal capacitor and DG placement. The robustness of the algorithm is validated by addressing the test cases under different fault conditions, such as single, multiple, and critical. The effectiveness of the proposed strategy is exhibited with the implementation to IEEE 33-bus radial distribution system (RDS) and 83-bus Taiwan Power Distribution Company (TPDC) System.

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

This study presents an advanced Learning Enthusiasm based teaching–learning algorithm (LebTLBO) for optimization of line width compensation, extrusion speed, filling speed, and layer thickness of the Fused Deposition Modeling (FDM) process. These independent parameters have a notable effect on dimensional accuracy, warp deformation, and manufacturing time which further affects the usability of FDM parts. A comparative analysis of LebTLBO has been performed with conventional algorithms for solving a complex optimization problem. This optimization tool simulates the classroom’s teaching–learning process and demands only the common control parameters i.e. number of generations and population size instead of algorithm-specific control parameters. The findings show that LebTLBO is most efficient to chalk out single parametric settings which would maximize each response. The optimum value of parameters predicted by LebTLBO is 0.15 mm layer thickness, extrusion speed of 21.85 mm/s, and 40 mm/s filling speed for acrylonitrile butadiene styrene thermoplastic material. Confirmatory experiments validate the efficiency of LebTLBO as the maximum response is achieved at predicted parametric settings.

Sustainable friction stir spot welding of 6061-T6 aluminium alloy using improved non-dominated sorting teaching learning algorithm

Swept Friction Stir Spot Welding is provoked and fascinated for its captive performance in building space frame structures, ship buildings and panels of automotive vehicles. The aim of the current research is to avoid the wastage in welding of 6061-T6 aluminium alloy and to improve the sustainabilty of the weld process. Al2O3 nanoparticles were added into the guide hole for improving the weld characteristics. The percentage of reinforcement was varied by changing the guide hole diameter as 1.5, 2.0, 2.5 and 3.0 mm. An improved NSTLBO algorithm was proposed and applied. The proposed algorithm predicted the optimum welding parameters as D = 2.8 mm, N = 1387 rpm and F = 17 mm/minute with the predicted wear, lap shear load and microhardness of 197 microns, 6134 N and 94.6 HV, respectively. The validation experiments were further done and the error within 0.5% was found between the actual and predicted results. The results of proposed algorithm was evaluated with other popular meta-heuristic algorithms for the effectiveness. The microscopic examinations on weld zones and fracturerd samples were also presented. Industries can use these best process parameters predicted by the proposed algorithm for earning the best weld characteristics from eco-friendly welding.

Solution of Economically Load Scheduling Problem using Teaching Learning and Bio-Geography Based Hybrid Optimization Algorithm

The article presents a new optimization algorithm view namely hybridization of teaching learning (TLBO) and biogeography based (BBO) optimization algorithm used to solve the convex economic dispatch (ED) problem with non-linear constraints like ramp rate limit, valve point loading effect etc. Hybridization of TLBO and BBO is the mixed combination of superior properties of TLBO and BBO. Teaching learning algorithm (TLBO) is based on teacher learner relationship in class and bio-geography algorithm (BBO) is based on geographical representation of biological species. The main goal of ED is to allocate power allocation economically meeting load demand. The proposed algorithm is tested for 13-unit, 15-unit, 40-unit and 140 unit systems. For proving superiority properties of proposed algorithm, obtained result are compared with recent algorithm. It gives optimum fuel cost compared to other optimization algorithm.

A novel hybrid method based on teaching–learning algorithm and leader progression model for evaluating the lightning performance of launch sites and experimental tests

In this article, a numerical optimization method has been used to study the lightning effect on satellite launch sites. In the conventional direct method, the leader progression model along with charge simulation method is employed for all the lightning leader tip positions and all the possible lightning current values on the equipment. This time-consuming calculation set is reduced in the developed method by using an optimization algorithm. For this purpose, the specifications of a real air termination system have been used for simulation and a system in a laboratory scales has been used for both purposes of simulation and practical tests. Teaching–learning-based optimization (TLBO) algorithm has been employed as an intelligent method to find the number of lightning strikes to the equipment, to detect critical areas on the structure surface and to reduce the execution time. Also, some experimental tests conducted in a high-voltage laboratory are referred on the small-scale system to investigate the conditions of leaders inception and propagation. An appropriate adaptation between the simulation results of TLBO and conventional direct method verifies the application of this method to investigate the performance of other air termination systems.

Optimal restructuring of distorted distribution system by teaching-learning-based optimisation

ABSTRACT Distribution systems are basic connections between utility and utility clients. Distribution system restructuring is a standout among the most critical procedures took after for the control of energy loss. Because of the more utilisation of non-linear loads by the utility clients, more harmonics are being infused into distribution systems, which may prompt high distortion levels. To lessen the distortion level, power quality constraints are incorporated as one among the other working requirements with the primary goal. The essential goal is to limit the power loss cost of the distribution system while fulfilling the power flow, operational and power quality limitations. This paper proposes Teaching-Learning-Based Optimisation (TLBO) to take care of the issue. The backward-forward sweep Harmonic Load Flow (HLF) is utilised to assess the harmonics present in the distribution system, which has been coordinated with Teaching Learning Algorithm (TLA). The proposed hybrid TLA-HLF strategy has been validated with IEEE-33 bus distribution and 83-bus Taiwan Power Distribution Company system.

Impact of battery electric vehicles on low voltage distribution networks

Due to their high energy capacity and potential mass deployment, battery electric vehicles (BEVs) will have a significant impact on power distribution networks. There are issues for the distribution network operator if BEV charging is allowed to take place without any control on the time of day, duration or charging rate. Specifically, the network voltage may fall below prescribed limits at times of peak demand and power flows may cause a thermal overload of assets. The existing literature on scheduling charging/discharging of BEVs makes use of decentralised/centralised control architectures to study the effect of charging/discharging of BEVs on distribution network. This paper presents a teaching-learning algorithm method to optimally charge and discharge the BEVs and hence mitigate the adverse impacts on the distribution network by considering the driving behaviour of car owners. This approach has resulted in reduced transformer loading even when using V2G and G2V modes of operation of the BEVs and hence has prevented transformer aging in low voltage distribution networks.

Impact of battery electric vehicles on low voltage distribution networks

Due to their high energy capacity and potential mass deployment, battery electric vehicles (BEVs) will have a significant impact on power distribution networks. There are issues for the distribution network operator if BEV charging is allowed to take place without any control on the time of day, duration or charging rate. Specifically, the network voltage may fall below prescribed limits at times of peak demand and power flows may cause a thermal overload of assets. The existing literature on scheduling charging/discharging of BEVs makes use of decentralised/centralised control architectures to study the effect of charging/discharging of BEVs on distribution network. This paper presents a teaching-learning algorithm method to optimally charge and discharge the BEVs and hence mitigate the adverse impacts on the distribution network by considering the driving behaviour of car owners. This approach has resulted in reduced transformer loading even when using V2G and G2V modes of operation of the BEVs and hence has prevented transformer aging in low voltage distribution networks.

Teaching learning algorithm based optimization of kerf deviations in pulsed Nd:YAG laser cutting of Kevlar-29 composite laminates

Kevlar is the most popular aramid fiber and most commonly used in different technologically advanced industries for various applications. But the precise cutting of Kevlar composite laminates is a difficult task. The conventional cutting methods face various defects such as delamination, burr formation, fiber pullout with poor surface quality and their mechanical performance is greatly affected by these defects. The laser beam machining may be an alternative of the conventional cutting processes due to its non-contact nature, requirement of low specific energy with higher production rate. But this process also faces some problems that may be minimized by operating the machine at optimum parameters levels. This research paper examines the effective utilization of the Nd:YAG laser cutting system on difficult-to-cut Kevlar-29 composite laminates. The objective of the proposed work is to find the optimum process parameters settings for getting the minimum kerf deviations at both sides. The experiments have been conducted on Kevlar-29 composite laminates having thickness 1.25 mm by using Box-Benkhen design with two center points. The experimental data have been used for the optimization by using the proposed methodology. For the optimization, Teaching learning Algorithm based approach has been employed to obtain the minimum kerf deviation at bottom and top sides. A self coded Matlab program has been developed by using the proposed methodology and this program has been used for the optimization. Finally, the confirmation tests have been performed to compare the experimental and optimum results obtained by the proposed methodology. The comparison results show that the machining performance in the laser beam cutting process has been remarkably improved through proposed approach. Finally, the influence of different laser cutting parameters such as lamp current, pulse frequency, pulse width, compressed air pressure and cutting speed on top kerf deviation and bottom kerf deviation during the Nd:YAG laser cutting of Kevlar-29 laminates have been discussed.

Harmony search with teaching-learning algorithm for nonnegative linear least square

Nonnegative linear least square (NLLS) is an important optimization problem, which often appears in processing the physical data. In this paper, we use a recently proposed algorithm named harmony search with teaching-learning (HSTL) strategy which derived from Teaching-Learning-Based Optimization (TLBO) for solving NLLS problems. Numerical results are reported which demonstrate very good computational performance.

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

A new method for the coordinated design of power system damping controllers

This paper proposes a new Teaching–Learning Algorithm (TLA) that uses the chaotic map to prevent the conventional TLA from getting stuck on local optima and enhancing the convergence characteristics, due to non-repetitions nature and ergodicity of chaotic functions. Some shortcomings are encountered in the original TLA, for instance, it can be trapped in local optima. This work tries to improve it by substituting the random in the initial algorithm with chaotic sequences. At this level, the initial population is chaotically generated and chaotic values are used in both phases. The global solutions are further enhanced by adding a new third chaotic phase. To demonstrate the effectiveness of the improved Teaching–Learning algorithm (ITLA), a fifteen of well-known benchmark functions are used. Experimental results demonstrate that ITLA outperforms significantly the conventional TLA, in terms of the accuracy of the final solution and the speed of convergence. The enhanced optimization algorithm is employed to solve the coordinated design problem of power system stabilizers (PSS) and thyristor-controlled series capacitor (TCSC), in order to investigate the feasibility and effectiveness of the proposed method in power systems. The performance of the proposed controllers is evaluated on a multi-machine power system under large disturbance and for different operating conditions through a nonlinear time-domain simulation. At the end, the results confirm the robustness of the proposed controllers in comparison to PSS designed by ITLA (ITLAPSS) and TCSC designed by ITLA (ITLATCSC).

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm

Multi-objective Optimal Dispatch in Power System Based On a Modified Multi-Objective Teaching-Learning Algorithm