Sakthivel V.P., Suman M. and Sathya P.D.
Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel…
Abstract
Purpose
Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants.
Design/methodology/approach
The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately.
Findings
Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems.
Practical implications
This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning.
Originality/value
To the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.
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V.P. Sakthivel and S. Subramanian
The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging…
Abstract
Purpose
The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady‐state equivalent circuit parameters of the three‐phase induction motor using a set of manufacturer data.
Design/methodology/approach
The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double‐cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio‐inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized.
Findings
Comparisons of the results of GA, PSO, BFO and IEEE Std. 112‐F (using no‐load, locked‐rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio‐inspired optimization algorithms over the classical one. Besides, BFO‐based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods.
Practical implications
The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system‐level studies.
Originality/value
The most significant contribution of the research is the potential to determine the equivalent circuit parameters of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio‐inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112‐F method.
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V.P. Sakthivel, R. Bhuvaneswari and S. Subramanian
The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor.
Abstract
Purpose
The purpose of this paper is to present the application of an adaptive bacterial foraging (BF) algorithm for the design optimization of an energy efficient induction motor.
Design/methodology/approach
The induction motor design problem is formulated as a mixed integer nonlinear optimization problem. A set of nine independent variables is selected, and to make the machine feasible and practically acceptable, six constraints are imposed on the design. Two different objective functions are considered, namely, the annual active material cost, and the sum of the annual active material cost, annual cost of the active power loss of the motor and annual energy cost required to supply such power loss. A new adaptive BF algorithm is used for solving the optimization problem. A generic penalty function method, which does not require any penalty coefficient, is employed for constraint handling.
Findings
The adaptive BF algorithm is validated for two sample motors and benchmarked with the genetic algorithm, particle swarm optimization, simple BF algorithm, and conventional design methods. The results show that the proposed algorithm outperforms the other methods in both the solution quality and convergence rate. The annual cost of the induction motor is remarkably reduced when designed on the basis of minimizing its annual total cost, instead of minimizing its material cost only.
Originality/value
To the best of the knowledge, none of the existing work has applied the BF algorithms for electrical machine design problems. Therefore, the solution to this problem constitutes the main contribution of the paper. According to the huge number of induction motors operating all over the world, the BF techniques used in their design, on minimum annual cost basis, will lead to a tremendous saving in global energy consumption.
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Mine Sertsöz, Mehmet Fidan and Mehmet Kurban
Improvements on the energy efficiency of the induction motors bear on not only these motors but also on the whole industry as a result of preference of these types of motors. In…
Abstract
Purpose
Improvements on the energy efficiency of the induction motors bear on not only these motors but also on the whole industry as a result of preference of these types of motors. In recent projects, energy efficiency of the induction motors is approaching to 90 per cent. The first necessary condition of the efficiency improvements is an accurate estimation of energy efficiency. This study aims to estimate the energy efficiency of induction motors by using three innovative estimation methods.
Design/methodology/approach
Data for 307 motors were taken from three different companies and their torque, power, power factor and speed data were used. Three hybrid models were created by estimating the error of three autoregressive (AR)-based efficiency estimation models with the back-propagation artificial neural network (ANN) structure. In these proposed hybrid models, the AR models were supported with artificial neural networks to obtain a minimum estimation error. These three hybrid models were called as AR1-ANN, AR4-ANN and residual-ANN.
Findings
Without hybridization of AR models by back-propagation ANNs, the best estimation result was obtained by residual model. On the other hand, for the proposed hybrid models, the best estimation was obtained by AR1-ANN, followed by AR4-ANN and finally the residual-ANN according to ME values.
Practical implications
Proposed AR-ANN hybrid models relieve of longtime experiments for the energy efficiency measurement of induction motors. Furthermore, these AR-ANN models give more accurate results than the available methods in the literature. Engineering value of this research is three different issues in finding energy efficiency. The first one is minimizing of the test cost, the second one is no requirement the test equipment and the third one is not interrupting the motor. Every company that needs motors can use these estimation methods due to the advantages.
Originality/value
Novel three AR-ANN hybrid models for energy efficiency estimation were studied. These novel methods give better response than the other methods which were used for estimation of induction motors in the literature.
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Martin Marco Nell, Georg von Pfingsten and Kay Hameyer
Traction applications, e.g. the IMs are mainly operated by field-oriented control (FOC). This control technique requires an accurate knowledge of the machine’s parameters, such as…
Abstract
Purpose
Traction applications, e.g. the IMs are mainly operated by field-oriented control (FOC). This control technique requires an accurate knowledge of the machine’s parameters, such as the main inductance, the leakage inductances and the stator and rotor resistance. The accuracy of the parameters influences the precision of the calculated rotor flux and the rotor flux angle and the decoupling of the machine’s equations into the direct and quadrature coordinate system (dq-components). Furthermore, the parameters are used to configure the controllers of the FOC system and therefore influence the dynamic behavior and stability of the control.
Design/methodology/approach
In this paper, three different methods to calculate the machine’s parameters, in an automated and rapid procedure with minimal measuring expenditure, are analyzed and compared. Moreover, a method to configure a control that reduces the overall Ohmic losses of the machine in every torque speed operation point is presented. The machine control is configured only with the identified machine parameter.
Findings
Simulations and test bench measurements show that the evolutionary strategy is able to identify the electrical parameters of the machine in less time and with low error. Moreover, the controller is able to control the torque of the machine with a deviation of less than 2 per cent.
Originality/value
The most significant contribution of the research is the potential to identify the machine parameter of an induction motor and to configure an accurate control with these parameters.
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Hossine Guermit, Katia Kouzi and Sid Ahmed Bessedik
This paper aims to present a contribution to improve the performance of vector control scheme of double star induction motor drive (DSIM) by using an optimized synergetic control…
Abstract
Purpose
This paper aims to present a contribution to improve the performance of vector control scheme of double star induction motor drive (DSIM) by using an optimized synergetic control approach. The main advantage of synergetic control is that it supports all parametric and nonparametric uncertainties, which is not the case in several control strategies.
Design/methodology/approach
The suggested controller is developed based on the synergistic control theory and the particle swarm optimization (PSO) algorithm which allow to obtain the optimal parameter of suggested controller to improve the performance of control system.
Findings
To show the benefits of proposed controller, a comparative simulation results between conventional PI controller, sliding mode controller and suggested controller were carried out.
Originality/value
The obtained simulation results illustrate clearly that synergetic controller ensures a rapid response, asymptotic stability of the closed-loop system in the all range operating condition and system robustness in presence of parameter variation in all range of operating conditions.
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Datta Bharadwaz Y., Govinda Rao Budda and Bala Krishna Reddy T.
This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of particle…
Abstract
Purpose
This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of particle swarm optimization (PSO) with Derringer’s desirability.
Design/methodology/approach
The performance parameters such as brake thermal efficiency (BTHE), brake specific fuel consumption (BSFC), CO, HC, NOx and smoke are considered as objectives with compression ratio, blend percentage and load as input factors. Optimization is carried out by using PSO coupled with the desirability approach.
Findings
From results, the optimum operating conditions are found to be at compression ratio of 18.5 per cent of fuel blend and 11 kg of load. At this input’s parameters of the engine, outputs performance parameters are found to be 34.84 per cent of BTHE, 0.29 kg/kWh of BSFC, 2.86 per cent of CO, 13 ppm of HC, 490 ppm of NOx and 26.25 per cent of smoke.
Originality/value
The present study explores the abilities of both particle swarm algorithm and desirability approach when used together. The combined approach resulted in faster convergence and better prediction capability. The present approach predicted performance characteristics of the variable compression ratio engine with less than 10 per cent error.
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Anwar Zorig, Ahmed Belkheiri, Bachir Bendjedia, Katia Kouzi and Mohammed Belkheiri
The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter…
Abstract
Purpose
The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA).
Design/methodology/approach
The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors.
Findings
The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests.
Originality/value
After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.
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Slawomir Golak and Mirosław Kordos
The attractiveness of functionally graded composites lies in the possibility of a gradual spatial change of their properties such as hardness, strength and wear resistance. The…
Abstract
Purpose
The attractiveness of functionally graded composites lies in the possibility of a gradual spatial change of their properties such as hardness, strength and wear resistance. The purpose of this paper is to discuss the use of electromagnetic buoyancy to separate the reinforcement particles during the casting process of such a composite.
Design/methodology/approach
The basic problem encountered in the process of casting composites is to obtain electromagnetic buoyancy and simultaneously to avoid a flow of the liquid metal which destroys the desired composite structure. In this paper the authors present the methodology of numerical optimization of inductor geometry in order to homogenize the electromagnetic force field distribution.
Findings
The optimization method based on searching the solution subspace created by applying knowledge of the modelled process physics proved better than the universal local optimization methods. These results were probably caused by the complex shape of the criterion function hypersurface characterized by the presence of local minima.
Practical implications
Due to their characteristics, functionally graded composites are of great interest to the automotive, aerospace and defense industries. In the case of metal matrix composites casting techniques (as the presented one) are the most effective methods of producing functionally graded materials.
Originality/value
The paper presents the optimization of a new process of casting functionally graded composites in a low-frequency alternating electromagnetic field. The process involves problems that did not occur previously in the area of electromagnetic processing of materials. The paper proposes the use of special design of inductors to homogenize the electromagnetic force field.
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Sharad Asthana and Rachana Kalelkar
This paper's purpose was to examine the impact of geomagnetic activity (GMA) on the timing and valuation of earnings information disclosed by firms every quarter.
Abstract
Purpose
This paper's purpose was to examine the impact of geomagnetic activity (GMA) on the timing and valuation of earnings information disclosed by firms every quarter.
Design/methodology/approach
The authors start the analyses with a sample of 112,669 client firms from 1989 to 2018. To analyze the impact of GMA on the earnings response coefficient (ERC), the authors use the three-day cumulative abnormal returns and cumulative abnormal returns for the extended post-earnings announcement window [2, 75] as the dependent variables. The authors interact unexpected earnings (UE) with the C9 Index, an index commonly used to measure GMA and study how GMA affects the pricing of new public information. To examine the effect of GMA on the timing of disclosure of earnings news, the authors regress a variant of the GMA index on the propensity to disclose bad earnings news.
Findings
The authors find significantly lower earnings response coefficients during periods of high GMA. This effect is permanent and stock prices do not correctly incorporate the implications of earnings information over time. The authors also show that managerial behavior is affected by GMA as well and the managers are more (less) likely to release bad (good) news during periods of higher activity. Finally, the authors also find that in situations where stakeholders are likely to rely on modern technology that depends minimally on humans, the adverse impact of GMA on the pricing of earnings information is mitigated.
Originality/value
The literature on the effect of GMA on the capital market is very limited and focuses primarily on stock returns, while the behavioral finance literature focuses on circumstances like weather, temperature and sporting outcome to study how the investors' mood affects their capital market behavior. The authors add to both the literature by investigating how GMA influences investors' and managers' behaviors in the capital market.