OPTIMAL PERFORMANCE OF SELF EXCITED INDUCTION GENERATOR USING TEACHING LEARNING-BASED OPTIMIZATION ALGORITHM AND STATIC VAR COMPENSATOR

An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems

This study presents the dynamic performance of stand-alone self-excited induction generator driven by a variable speed wind turbine using direct torque control strategy. The system consists of wind turbine, self-excited induction generator, voltage source converter and battery with capacitor. The input to the induction generator is wind whose velocity is not constant. The magnitude and frequency of the generator terminal voltage varies with wind velocity. To maintain the voltage and frequency of the induction generator terminal constant; the direct torque control strategy is used. This study investigates the dynamic and steady state performance of self-excited induction generator for varying load and wind speed conditions and short circuit at stator terminals. The proposed system is designed and simulated in MATLAB/SIMULINK environment. The simulation results show the effectiveness of the proposed system.

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

Teaching Learning Based Optimization (TLBO) algorithm simulate the teaching learning peculiarity of a classroom to solve multidimensional, linear and nonlinear problems with appreciable efficiency. In order to accelerate the execution time of software implementation, the TLBO algorithm is implemented on hardware. Then the TLBO hardware is developed as TLBO Intellectual Property and it is interfaced as a peripheral to the System on Chip platform. We compared the performance of the floating point TLBO IP is realized by solving the benchmark functions, results 183–224X times faster than the software implementation of the same algorithm. As a case study, the same TLBO IP is used to solve the spectrum allocation problem by optimizing Max-Sum-Reward (MSR) function and it results 69–78X times faster than the software implementation of the same algorithm.

An improved teaching–learning-based optimization algorithm with a modified learner phase and a new mutation-restarting phase

Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

In this article, an attempt is made to design the fractional order proportional integral derivative (FO-PID) controller for multi-input multi-output (MIMO) system using teaching learning-based optimization (TLBO) algorithm. The control of MIMO system always offers a complicated control paradigm due to its structure. It is realized that dynamic systems can be effectively represented by fractional order dynamics which is based on fractional calculus. The PID controller is still the most reliable controller due to its uncomplicated structure, and the FO-PID controller is predicted to have better performance as compared to conventional PID controller due to increment of two more parameters. The performance of non-integer PID controller depends upon the five constants; hence, the tuning of these five constants is most important for the optimum performance. In the presented work, TLBO technique is adopted for the tuning of the non-integer PID controller. The designed controller is implemented on the four tank MIMO system in MATLAB. The performance of the designed controller is also compared from the previous results available in literature.KeywordsFour tank MIMO systemFO-PIDTLBO

A high bypass turbofan aero-engine delivers compressed air from the low-pressure to the high-pressure compressor through a compressor transition duct. Weight and design space limitations impel to its S-shaped design. Despite that, the compressor transition duct has to guide the flow very carefully to the high-pressure compressor without disturbances and flow separations. Hence, the present paper is devoted to elaborate on the application of the proposed heuristic optimization technique known as multi-objective teaching–learning-based optimization (MO-TLBO) algorithm in the area of S-shaped transition compressor duct. The present algorithm is applied for obtaining the optimum design of the S-shaped duct while minimizing the total pressure loss coefficient and non-uniformity index at the S-shaped duct outlet. These objectives are also optimized independently and as a multi-objective consideration as well. Because of the conflicting nature of the objective functions, a Pareto-optimal curve is also presented for a successful trade-off between the objectives. Pareto-optimal curve gives flexibility to the designer for selecting the best set of the parameters based on the objectives. Successful execution of the proposed MO-TLBO algorithm is examined by comparing the optimum results acquired by the multi-objective genetic algorithm and comparisons divulge that the TLBO algorithm can be successfully practiced to obtain the optimized design of compressor transition S-shaped duct. Optimized duct shows the reduction in total pressure loss and non-uniformity by 28.80% and 36.67%, respectively, despite the reduction of 14.74% in overall length.

Teaching---learning-based optimisation (TLBO) is an emerging gradient-free optimisation algorithm inspired by interactions between students and teacher in classrooms. TLBO has no control parameter to be tuned by user. This property makes it popular in research community. It has been successfully applied to challenging optimisation problems in different areas. In this study, TLBO is assisted to find optimal setting of thyristor-controlled series compensators in electric power systems. The experiments have been done for both N-1 and N-2 line outage contingencies. The results show that TLBO performs well in solving this problem.

This paper presents the optimization and analysis of machining parameters on machining of Al6061 alloy using magnetic field-assisted powder-mixed electrical discharge machining (MFAPM-EDM) process. For performance analysis, peak current (IP), spark on time (SON), spark off time (SOFF), powder concentration (PC), and magnetic field (MF) are considered as machining parameters and tool wear rate (TWR) as performance measures. The experimental design based on Box Behnken Design is used for conducting the experiments and single objective optimization is performed using teaching–learning-based optimization (TLBO) algorithm. Peak current is observed as the most significant parameters followed by spark on time, magnetic field, and powder concentration. Model to predict TWR is developed in terms of machining parameters. The optimum set of machining parameters for minimum TWR using TLBO algorithm is obtained at IP-1 A, SON-30 µs, SOFF-51 µs, PC-10 g/l, and MF-0.45 T.

Multi-Objective parametric optimization during micro-EDM drilling of Ti-6Al-4 V using teaching learning Based optimization algorithm