Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybridparticle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line.
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
Economic Load Dispatch for Multi-Generator Systems with Units Having Nonlinea...IJAPEJOURNAL
This document presents an economic load dispatch problem that uses the Gravity Search Algorithm to minimize total generation costs for multi-generator power systems. It discusses how practical constraints like valve point loading, multi-fuel operation, and forbidden zones result in non-ideal, non-continuous generator cost curves. The Gravity Search Algorithm is applied to find the optimal dispatch schedule that accounts for these realistic cost functions and minimizes the total cost of generation while satisfying demand. The algorithm is tested on sample power systems and able to find solutions within acceptable timeframes that outperform traditional optimization methods for large, complex problems.
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
Optimizing location and size of capacitors for power loss reduction in radial...TELKOMNIKA JOURNAL
Power radial distribution systems are increasingly more and more important in transmitting the electric energy from power plants to customers. However, total loss in lines are very high. This issue can be solved by allocating capacitor banks. Determining the suitable allocation and optimal sizing of capacitor banks needs an efficient approach. In this study, the diffusion and update techniques-based algorithm (DUTA) is proposed for such reason. The efficiency of DUTA is inspected on two distribution systems consisting of 15-bus and 33-bus systems with different study cases. The solutions attained by DUTA are competed with recently published methods. As a consequence, the method is more effective than the other methods in terms of the quality of solution.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Stochastic renewable energy resources integrated multi-objective optimal powe...TELKOMNIKA JOURNAL
This document proposes a method for solving single and multi-objective optimal power flow problems that integrate stochastic wind and solar power with traditional coal-based power plants. It models the uncertainties in wind and solar output using probability distribution functions. A multi-objective moth flame optimization technique is used to solve the optimization problems. The results are validated on an adapted IEEE 30-bus test system incorporating wind and solar plants.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
Optimal power flow solution with current injection model of generalized inte...IJECEIAES
1. The document presents a novel ant lion optimization algorithm with Lévy flight operator called ameliorated ant lion optimization (AALO) to solve optimal power flow problems incorporating a current injection model of generalized interline power flow controller (GIPFC).
2. GIPFC can control multiple transmission lines simultaneously and control sending end voltage to improve bus voltages and minimize objectives like fuel cost, emissions, and losses while satisfying constraints.
3. AALO enhances the exploration of the standard ant lion optimization algorithm. It is tested on benchmark functions and the IEEE 30-bus system with GIPFC to optimize power flow.
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
Economic Load Dispatch for Multi-Generator Systems with Units Having Nonlinea...IJAPEJOURNAL
This document presents an economic load dispatch problem that uses the Gravity Search Algorithm to minimize total generation costs for multi-generator power systems. It discusses how practical constraints like valve point loading, multi-fuel operation, and forbidden zones result in non-ideal, non-continuous generator cost curves. The Gravity Search Algorithm is applied to find the optimal dispatch schedule that accounts for these realistic cost functions and minimizes the total cost of generation while satisfying demand. The algorithm is tested on sample power systems and able to find solutions within acceptable timeframes that outperform traditional optimization methods for large, complex problems.
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
Optimizing location and size of capacitors for power loss reduction in radial...TELKOMNIKA JOURNAL
Power radial distribution systems are increasingly more and more important in transmitting the electric energy from power plants to customers. However, total loss in lines are very high. This issue can be solved by allocating capacitor banks. Determining the suitable allocation and optimal sizing of capacitor banks needs an efficient approach. In this study, the diffusion and update techniques-based algorithm (DUTA) is proposed for such reason. The efficiency of DUTA is inspected on two distribution systems consisting of 15-bus and 33-bus systems with different study cases. The solutions attained by DUTA are competed with recently published methods. As a consequence, the method is more effective than the other methods in terms of the quality of solution.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Stochastic renewable energy resources integrated multi-objective optimal powe...TELKOMNIKA JOURNAL
This document proposes a method for solving single and multi-objective optimal power flow problems that integrate stochastic wind and solar power with traditional coal-based power plants. It models the uncertainties in wind and solar output using probability distribution functions. A multi-objective moth flame optimization technique is used to solve the optimization problems. The results are validated on an adapted IEEE 30-bus test system incorporating wind and solar plants.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
Optimal power flow solution with current injection model of generalized inte...IJECEIAES
1. The document presents a novel ant lion optimization algorithm with Lévy flight operator called ameliorated ant lion optimization (AALO) to solve optimal power flow problems incorporating a current injection model of generalized interline power flow controller (GIPFC).
2. GIPFC can control multiple transmission lines simultaneously and control sending end voltage to improve bus voltages and minimize objectives like fuel cost, emissions, and losses while satisfying constraints.
3. AALO enhances the exploration of the standard ant lion optimization algorithm. It is tested on benchmark functions and the IEEE 30-bus system with GIPFC to optimize power flow.
Evolutionary algorithm solution for economic dispatch problemsIJECEIAES
A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints, these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed, the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.
Cuckoo Search Algorithm for Congestion Alleviation with Incorporation of Wind...IJECEIAES
The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Harvesting in electric vehicles: Combining multiple power tracking and fuel-c...IJECEIAES
This document summarizes a research article that proposes a power electronic platform and energy management strategy (EMS) to harvest energy from multiple sources in electric vehicles. The platform allows simultaneous operation of sources like solar panels, fuel cells, energy-generating dampers, and others. The EMS aims to minimize degradation of the battery bank and fuel cell by filtering current transients and ensuring sources operate at their maximum power points. A mathematical model of the platform is presented and stability analysis was performed. Numerical, hardware-in-the-loop, and experimental validations supported the effectiveness of the approach.
PERMANENT MAGNET SYNCHRONOUS GENERATOR BASED WIND ENERGY CONVERSION SYSTEMIRJET Journal
This document describes a simulation study of a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) with maximum power point tracking (MPPT). It includes:
1) A model of a variable speed wind turbine with a PMSG, mechanical, electrical, and aerodynamic components.
2) Two MPPT techniques analyzed - perturb and observe (P&O) and particle swarm optimization (PSO) algorithms.
3) Simulation results in MATLAB/Simulink validating the wind turbine model and MPPT control strategies with constant and variable wind speed inputs.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
This document summarizes literature on economic load dispatch problems. It begins with an introduction to economic load dispatch and its goal of generating required power at minimum cost. It then provides a literature survey summarizing 12 papers on optimization techniques applied to economic load dispatch, including methods like particle swarm optimization and grey wolf optimization. The document also discusses India's overall power generation scenario and Tripura's scenario. It defines the economic load dispatch problem formulation and constraints considered like power balance, generator capacity, and prohibited operating zones. The document concludes that the proposed enhanced colliding bodies optimization technique efficiently solves the economic load dispatch problem.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
Optimal Allocation of Capacitor Bank in Radial Distribution System using Anal...IJECEIAES
In this paper, a novel analytical technique is proposed for optimal allocation of shunt capacitor bank in radial distribution system. An objective function is formulated to determine the optimal size, number and location of capacitor bank for real & reactive power loss reduction, voltage profile enhancement and annual cost saving. A new constant, Power Voltage Sensitivity Constant (PVSC), has been proposed here. The value of PVSC constant decides the candidate bus location and size. The achievability of the proposed method has been demonstrated on IEEE-69 bus and real distribution system of Jamawaramgarh, Jaipur city. The obtained results are compared with latest optimization techniques to show the effectiveness and robustness of the proposed technique.
Optimal design and_model_predictive_control_of_stPremkumar K
This document summarizes a research paper that analyzes the optimal design and model predictive control of a standalone hybrid renewable energy system (HRES) for residential demand side management in Pakistan. It describes using HOMER Pro software to simulate 9 scenarios of PV-wind-diesel-battery-converter systems to determine the optimal system design with the lowest cost. The optimal design was found to be a PV-wind-battery-converter system with a net present cost of $47,398 and levelized cost of energy of $0.309/kWh, providing a 100% reduction in emissions. A MATLAB/Simulink model of the optimal HRES was developed and validated. Model predictive control algorithms were also applied to regulate
Optimal Allocation and Sizing of Distributed Generation using Artificial Bee ...IRJET Journal
This document summarizes a research paper that proposes using an artificial bee colony (ABC) algorithm to optimize the allocation and sizing of distributed generation (DG) units in distribution systems. The goal is to minimize power losses and improve voltage profiles. The ABC algorithm is described as being inspired by honeybee swarm behavior and involving employed, onlooker, and scout bee groups. The algorithm is tested on the IEEE 34 bus test system and results are verified in ETAP and MATLAB software. Key findings are that ABC algorithm provides an efficient, robust method for solving mixed-integer nonlinear optimization problems related to DG unit placement and sizing.
Simultaneous network reconfiguration and capacitor allocations using a novel ...IJECEIAES
Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.
Identification study of solar cell/module using recent optimization techniquesIJECEIAES
This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.
PRACTICAL IMPLEMENTION OF GAOPF ON INDIAN 220KV TRANSMISSION SYSTEMecij
This paper presents the practical implementation of developed genetic algorithm based optimal power flow algorithms. These algorithms are tested on IEEE30 bus system and the results were presented in the paper [8]. The same algorithms now tested on 220KV Washi zone Indian power transmission system . The GAOPF with fixed penalty and Fuzzy based variable penalty tested on 220KV transmission system consists of 52 bus and 88lines. The fuel costs ,computational time and the system condition were studied and the results are presented in this paper .Also the available load transfer capability of the 220KV system for congestion management is also presented
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Optimal Placement of D-STATCOM Using Hybrid Genetic and Ant Colony Algorithm ...IJAPEJOURNAL
In this work, a modern algorithm by hybrid genetic algorithm and ant colony algorithm is designed to placement and then simulated to determine the amount of reactive power by D-STATCOM. Also this method will be able to minimize the power system losses that contain power loss in transmission lines. Furthermore, in this design a IEEE 30-bus model depicted and three D-STATCOM are located in this system according to Economic Considerations. The optimal placement of each D-STATCOM is computed by the ant colony algorithm. In order to optimize placement for each D-STATCOM, two groups of ant are selected, which respectively located in near nest and far from the nest. Moreover, for every output simulation of D-STATCOM that is used to produce or absorb of reactive power, a genetic algorithm to minimizing the total network losses is applied. Finally, the result of this simulation shows net losses reduction about 150% that it verifies the new algorithm performance.
PERFORMANCE EXPLORATION OF SINGLE PHASE DAB DC-DC CONVERTER UNDER LOAD VARIATIONIRJET Journal
The document analyzes the performance of a single phase Dual Active Bridge (DAB) DC-DC converter under varying load conditions. It aims to achieve high efficiency and quick dynamic response. The converter uses phase shift pulse width modulation control of switches. Simulation results in Matlab/Simulink show the load voltage and power performance with and without a proportional-integral voltage controller. The controller helps regulate the voltage and improve the dynamic response during load changes.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints, these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed, the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.
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The issue to alleviate congestion in the power system framework has emerged as an alluring field for the power system researchers. The research conducted in this article proposes a cuckoo search algorithm-based congestion alleviation strategy with the incorporation of wind farm. The bus sensitivity factor data are computed and utilized to sort out the sutiable position for the installation of the wind farm. The generators contributing in the real power rescheduleing process are selected as per the generator sensitivity values. The cuckoo search algorithm is implemented to minimize the congestion cost with the embodiment of the wind farm. The proposed method is tested on 39 bus New England framework and the results obtained with the cuckoo search-based congestion management approach outperforms the results opted with other heuristic optimization techniques in the past research literatures.
Harvesting in electric vehicles: Combining multiple power tracking and fuel-c...IJECEIAES
This document summarizes a research article that proposes a power electronic platform and energy management strategy (EMS) to harvest energy from multiple sources in electric vehicles. The platform allows simultaneous operation of sources like solar panels, fuel cells, energy-generating dampers, and others. The EMS aims to minimize degradation of the battery bank and fuel cell by filtering current transients and ensuring sources operate at their maximum power points. A mathematical model of the platform is presented and stability analysis was performed. Numerical, hardware-in-the-loop, and experimental validations supported the effectiveness of the approach.
PERMANENT MAGNET SYNCHRONOUS GENERATOR BASED WIND ENERGY CONVERSION SYSTEMIRJET Journal
This document describes a simulation study of a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) with maximum power point tracking (MPPT). It includes:
1) A model of a variable speed wind turbine with a PMSG, mechanical, electrical, and aerodynamic components.
2) Two MPPT techniques analyzed - perturb and observe (P&O) and particle swarm optimization (PSO) algorithms.
3) Simulation results in MATLAB/Simulink validating the wind turbine model and MPPT control strategies with constant and variable wind speed inputs.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
The gravitational search algorithm for incorporating TCSC devices into the sy...IJECEIAES
This paper proposes a gravitational search algorithm (GSA) to allocate the thyristor-controlled series compensator (TCSC) incorporation with the issue of reactive power management. The aim of using TCSC units in this study is to minimize active and reactive power losses. Reserve beyond the thermal border, enhance the voltage profile and increase transmission-lines flow while continuing the whole generation cost of the system a little increase compared with its single goal base case. The optimal power flow (OPF) described is a consideration for finding the best size and location of the TCSCs devices seeing techno-economic subjects for minimizing fuel cost of generation units and the costs of installing TCSCs devices. The GSA algorithm's high ability in solving the proposed multi-objective problem is tested on two 9 and 30 bus test systems. For each test system, four case studies are considered to represent both normal and emergency operating conditions. The proposed GSA method's simulation results show that GSA offers a practical and robust highquality solution for the problem and improves system performance.
This document summarizes literature on economic load dispatch problems. It begins with an introduction to economic load dispatch and its goal of generating required power at minimum cost. It then provides a literature survey summarizing 12 papers on optimization techniques applied to economic load dispatch, including methods like particle swarm optimization and grey wolf optimization. The document also discusses India's overall power generation scenario and Tripura's scenario. It defines the economic load dispatch problem formulation and constraints considered like power balance, generator capacity, and prohibited operating zones. The document concludes that the proposed enhanced colliding bodies optimization technique efficiently solves the economic load dispatch problem.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Compromising between-eld-&-eed-using-gatool-matlabSubhankar Sau
Creating a compromising points between economic load dispatch & emission created from the plant to minimising those effects.
these are created by using MATLAB and GATOOL .
taking Weighted Sum Method,also Pareto optimal curve.
created by: SUBHANKAR SAU
Optimal Allocation of Capacitor Bank in Radial Distribution System using Anal...IJECEIAES
In this paper, a novel analytical technique is proposed for optimal allocation of shunt capacitor bank in radial distribution system. An objective function is formulated to determine the optimal size, number and location of capacitor bank for real & reactive power loss reduction, voltage profile enhancement and annual cost saving. A new constant, Power Voltage Sensitivity Constant (PVSC), has been proposed here. The value of PVSC constant decides the candidate bus location and size. The achievability of the proposed method has been demonstrated on IEEE-69 bus and real distribution system of Jamawaramgarh, Jaipur city. The obtained results are compared with latest optimization techniques to show the effectiveness and robustness of the proposed technique.
Optimal design and_model_predictive_control_of_stPremkumar K
This document summarizes a research paper that analyzes the optimal design and model predictive control of a standalone hybrid renewable energy system (HRES) for residential demand side management in Pakistan. It describes using HOMER Pro software to simulate 9 scenarios of PV-wind-diesel-battery-converter systems to determine the optimal system design with the lowest cost. The optimal design was found to be a PV-wind-battery-converter system with a net present cost of $47,398 and levelized cost of energy of $0.309/kWh, providing a 100% reduction in emissions. A MATLAB/Simulink model of the optimal HRES was developed and validated. Model predictive control algorithms were also applied to regulate
Optimal Allocation and Sizing of Distributed Generation using Artificial Bee ...IRJET Journal
This document summarizes a research paper that proposes using an artificial bee colony (ABC) algorithm to optimize the allocation and sizing of distributed generation (DG) units in distribution systems. The goal is to minimize power losses and improve voltage profiles. The ABC algorithm is described as being inspired by honeybee swarm behavior and involving employed, onlooker, and scout bee groups. The algorithm is tested on the IEEE 34 bus test system and results are verified in ETAP and MATLAB software. Key findings are that ABC algorithm provides an efficient, robust method for solving mixed-integer nonlinear optimization problems related to DG unit placement and sizing.
Simultaneous network reconfiguration and capacitor allocations using a novel ...IJECEIAES
Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Combining two of the numerous strategies for solving these problems and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies, proposing a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The network voltage profile was significantly improved as the least voltage at bus 18 was increased to 0.9530 p.u. Furthermore, the overall real power loss was significantly mitigated by 48.87%, which, when compared to the results of other methods, was superior.
Identification study of solar cell/module using recent optimization techniquesIJECEIAES
This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.
PRACTICAL IMPLEMENTION OF GAOPF ON INDIAN 220KV TRANSMISSION SYSTEMecij
This paper presents the practical implementation of developed genetic algorithm based optimal power flow algorithms. These algorithms are tested on IEEE30 bus system and the results were presented in the paper [8]. The same algorithms now tested on 220KV Washi zone Indian power transmission system . The GAOPF with fixed penalty and Fuzzy based variable penalty tested on 220KV transmission system consists of 52 bus and 88lines. The fuel costs ,computational time and the system condition were studied and the results are presented in this paper .Also the available load transfer capability of the 220KV system for congestion management is also presented
Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimiz...IJERA Editor
This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.
Optimal Placement of D-STATCOM Using Hybrid Genetic and Ant Colony Algorithm ...IJAPEJOURNAL
In this work, a modern algorithm by hybrid genetic algorithm and ant colony algorithm is designed to placement and then simulated to determine the amount of reactive power by D-STATCOM. Also this method will be able to minimize the power system losses that contain power loss in transmission lines. Furthermore, in this design a IEEE 30-bus model depicted and three D-STATCOM are located in this system according to Economic Considerations. The optimal placement of each D-STATCOM is computed by the ant colony algorithm. In order to optimize placement for each D-STATCOM, two groups of ant are selected, which respectively located in near nest and far from the nest. Moreover, for every output simulation of D-STATCOM that is used to produce or absorb of reactive power, a genetic algorithm to minimizing the total network losses is applied. Finally, the result of this simulation shows net losses reduction about 150% that it verifies the new algorithm performance.
PERFORMANCE EXPLORATION OF SINGLE PHASE DAB DC-DC CONVERTER UNDER LOAD VARIATIONIRJET Journal
The document analyzes the performance of a single phase Dual Active Bridge (DAB) DC-DC converter under varying load conditions. It aims to achieve high efficiency and quick dynamic response. The converter uses phase shift pulse width modulation control of switches. Simulation results in Matlab/Simulink show the load voltage and power performance with and without a proportional-integral voltage controller. The controller helps regulate the voltage and improve the dynamic response during load changes.
Similar to Quantum behaved artificial bee colony based conventional controller for optimum dispatch (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
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Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
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Quantum behaved artificial bee colony based conventional controller for optimum dispatch
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 1260~1271
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1260-1271 1260
Journal homepage: http://ijece.iaescore.com
Quantum behaved artificial bee colony based conventional
controller for optimum dispatch
Himanshu Shekhar Maharana, Saroja Kumar Dash
Department of Electrical Engineering, GITA Autonomous College, Bhubaneswar, Biju Patnaik University of Technology,
Rourkela, India
Article Info ABSTRACT
Article history:
Received Apr 17, 2022
Revised Oct 8, 2022
Accepted Oct 28, 2022
Since a multi area system (MAS) is characterized by momentary overshoot,
undershoot and intolerable settling time so, neutral copper conductors are
replaced by multilayer zigzag graphene nano ribbon (MLGNR)
interconnects that are tremendously advantageous to copper interconnects
for the future transmission line conductors necessitated for economic and
emission dispatch (EED) of electric supply system giving rise to reduced
overshoots and settling time and greenhouse effect as well. The recent work
includes combinatorial algorithm involving proportional integral and
derivative controller and heuristic swarm optimization; we say it as Hybrid-
particle swarm optimization (PSO) controller. The modeling of two multi
area systems meant for EED is carried out by controlling the conventional
proportional integral and derivative (PID) controller regulated and monitored
by quantum behaved artificial bee colony (ABC) optimization based PID
(QABCOPID) controller in MATLAB/Simulink platform. After the
modelling and simulation of QABCOPID controller it is realized that
QABCOPID is better as compared to multi span double display (MM),
neural network based PID (NNPID), multi objective constriction PSO
(MOCPSO) and multi objective PSO (MOPSO). The real power generation
fixed by QABCOPID controller is used to estimate the combined cost and
emission objectives yielding optimal solution, minimum losses and
maximum efficiency of transmission line.
Keywords:
Chaotic self-adaptive search
Particle swarm optimization
Economic and emission
dispatching
Group search optimization
QABCOPID controller
This is an open access article under the CC BY-SA license.
Corresponding Author:
Saroja Kumar Dash
Department of Electrical Engineering, GITA Autonomous College, Bhubaneswar, Biju Patnaik University
of Technology
Rourkela, 769015, Odisha, India
Email: hodeegita@gmail.com
1. INTRODUCTION
Combined economic emission dispatch (EED) is a difficult problem to obtain most favorable result
for optimum dispatch of electric power with optimum emission level due to insecurity and rigorous time
consuming tests like Newton–Raphson method used by Chen and Chen [1], lambda-iteration method used by
Ramadan et al. [2], linear programming techniques and non-linear combined-integer quadratic rule method
practiced by Palanichamy and Babu [3], Kiani and Pourtakdoust [4], Papageorgiou and Fraga [5], and Abido
[6]. Improved heuristic methods mimicking natural selection processes applied by Bakirtzis [7], followed by
Daryani and Zare [8], in group search optimization (GSO) processes adopted by Bora et al. [9], chaotic
self-adaptive search (CHS) processes, and do not perform satisfactorily for bulky coal fired plants due to
presence of complex varying nonuniformities resulted out of multi-valving criteria of impulse reaction
turbines. Ghoshal [10] in the recent work put forth a modified technique by amalgamating limiting factors of
2. Int J Elec & Comp Eng ISSN: 2088-8708
Quantum behaved artificial bee colony based conventional controller for … (Himanshu Shekhar Maharana)
1261
the quantum behaved artificial bee colony optimization technique and proportional, derivative and integral
controller giving rise to quantum behaved artificial bee colony optimization based proportional, derivative
and integral controller (QABCOPID) where a more balanced approach among regional and global search
capabilities can be used to examine the EED situation and swiftly arrive at the minimum location. More
specifically authors in this article present the utility of the quantum behaved artificial bee colony (ABC)
optimization technique to govern the real time proportional integral and derivative controller to ascertain tie
line response and incremental frequency deviations of two area systems and finally results real power
generations of the alternators. These real power generations solve the EED problem of huge thermal power
plants of western countries having bulky thermal generating units involving cubic cost, emission and
combined objective approach using different training methods for the 30-bus test case system depicted in
Figure 1 with six alternators and are utilized to estimate the limiting factors of transmission line and simulate
the power lines as well. Since the incremental real power generation depends upon the incremental frequency
deviation so by regulating the unbalanced power demand at the demand center and turbine intent and
governor setting at the place of generation and by touching the automatic load-frequency control (ALFC)
adopted by Prasanth and Kumar [11], Juang and Lu [12], automatic voltage regulation (AVR) parameters
through the quantum behaved artificial bee colony optimization proportional–integral–derivative
(QUABCOPID) controller model shown in Figure 2, the optimal values for cost of cost of coal and
greenhouse gas level rate are obtained for multi-area complex bulky thermal power plants connected through a
tie line by exploiting the waggle dance of artificial bees shown in Figure 3 to venture into across-the-board area.
The authors of this work attempted to improve the convergence and across-the-board optimization capabilities of
the QABCOPID controller in order to increase its relevance as a more effective approach for assessing various
EED problems. The advantage of this strategy is that it allows for instantaneous tuning of proportional–integral–
derivative (PID) controllers, as demonstrated by Sahu et al. [13], Sharifi et al. [14], Dhawane and Bichkar [15],
Pradhan and Majhi [16]. The following are the characteristics discussed in this dissertation.
Figure 1. IEEE 30 bus 6-unit test case system Figure 2. Model for the proposed method
Figure 3. Waggle dance of honey bee
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 2, April 2023: 1260-1271
1262
The enhanced particle swarm optimization (PSO) algorithm, namely the QABCOPID technique
established by Xu and Sun [17], is implemented to obtain the most favorable result for EED and to perform
better analyses on the results obtained with the proposed QABCOPID algorithm, which includes a
formulation of the EED problem with losses. The outcomes of EED solutions achieved using QUABCOPID
were compared to those acquired using other heuristic optimization algorithms. The obtained real optimal
powers are used to access the stability of transmission lines using the transmission characteristics determined
using a bench power transmission simulator based on HONALEC.
The following is the structure of this article: The formulation of the combined dispatch problem was
described in section 2. Section 3 demonstrated the conventional PSO technique and the new QUABCOPID
technique, respectively. Section 4 details the aforementioned approach's flow chart and implementation.
Section 5 showed the optimization findings obtained using the suggested technique, including a case study,
and compared them to previously published researches, as well as plotted the results. Section 6 discusses the
methods and results of a simulation of a 252-kilovolt transmission line. Finally, sections 7 and 8 give
findings and future prospects.
2. FORMULATION OF EED PROBLEM
The QUABCOPID optimization approach is introduced in this study to optimize fuel cost and
emission level as objective functions. The alternator units are all thermal and operate continuously on-line,
posing many limitations such as equality, inequality, and including transmission losses to ensure the novel
project's completion. A combinatorial approach based on quantum behaved artificial bee colony optimization
and a conventional PID controller were used by Rahimian and Raahemifar [18], Shankar and Mukherjee
[19], Soni and Bhat [20], Huang et al. [21], Lu et al. [22], to optimize a cubic cost, emission, and combined
objective function for a six-unit thermal power plant.
2.1. Formalization of the fundamental objective function
Taking, 𝑎𝑖, 𝑏𝑖, 𝑐𝑖, 𝑑𝑖, 𝑒𝑖𝑎𝑛𝑑 𝑓𝑖as the price Table 1 of fuel price function 𝑓𝑙𝑖
𝑓1𝑖 = ∑ (𝑎𝑖
𝑛
𝑖=1 + 𝑏𝑖𝑃𝑖 + 𝑐𝑖𝑃𝑖
2
+ |𝑑𝑖𝑠𝑖𝑛{𝑒𝑖(𝑃𝑖
𝑚𝑖𝑛
− 𝑃𝑖)}| + 𝑓𝑖𝑃𝑖
3
Rs/hr (1)
𝑓1𝑖 is fuel price function of thermal units, Pgi is real unit outage of the ith
unit, n is total number of
alternators, 𝑃𝑔𝑖
𝑚𝑖𝑛
is minimum power of 𝑖𝑡ℎ
unit.
𝑓2𝑖 = ∑ (αi + βiPi + γiPi
2
+ i
η exp(δiPi))
n
i=1 + ΓiPi
3
kg/hr (2)
Taking, 𝛼𝑖, 𝛽𝑖, 𝛾𝑖, 𝜂𝑖, 𝛿𝑖𝑎𝑛𝑑 Γ𝑖 as emission coefficients of emission function 𝑓2𝑖. 𝑓2𝑖 is emission level
function in kg/hr. Constraints on equality: In equation, the overall output power outage of the units must
equal the output at the demand center plus transmission line losses in the power lines (3):
∑ 𝑃𝑖 − (𝑃𝐷 + 𝑃𝐿)
𝑁
𝑖=1 = 0 (3)
wherein, 𝑃𝐷 is actual energy demand.
PL denotes power dispatch losses, which are modelled using loss factors (4):
𝑃𝐿 = ∑ ∑ 𝑃𝑖𝐵𝑖𝑗
𝑃𝑗
𝑁
𝑗=1
𝑁
𝑖=1 𝑀𝑊 (4)
where B-coefficients for six-generating units are calculated and denoted (5).
𝐵𝑖𝑗 =
(
0.00010.000010.000010.000010.000020.00002
0.000010.000050.000010.000010.000010.00001
0.000010.000010.000060.000010.000020.00001
0.000010.000010.000010.000060.000020.00002
0.000020.000010.000020.000020.000060.00003
0.000020.000010.000010.000020.000080.00003)
(5)
The inequality constraints imposed on generator real power output are
𝑃𝑖
𝑚𝑖𝑛
≤ 𝑃𝑖 ≤ 𝑃𝑖
𝑚𝑎𝑥
(6)
4. Int J Elec & Comp Eng ISSN: 2088-8708
Quantum behaved artificial bee colony based conventional controller for … (Himanshu Shekhar Maharana)
1263
where, 𝑃1
𝑚𝑖𝑛
is the lower limit and 𝑃1
𝑚𝑎𝑥
is the top limit of generator output [18], [19]
⇒ 𝑃𝐿 = (
𝐵11𝑃1
2
+ (2 ∑ 𝐵1𝑖𝑃𝑖 + 𝐵01)
𝑛
𝑖=2 𝑃1
+ ∑ ∑ 𝑃𝑖𝐵𝑖𝑗𝑃𝑗 + ∑ 𝐵0𝑖
𝑛
𝑖=2
𝑛
𝑗=2
𝑛
𝑖=2 𝑃𝑖
+𝐵00
) (7)
where, 𝐵00, 𝐵01, 𝐵𝑙𝑖, 𝐵0𝑖, 𝑎𝑛𝑑 𝐵𝑖𝑗 are B (transmission loss) coefficients.
Table 1. Cost and emission co-efficient with real power generation bounds for QUABCOPID controller
Units 𝑃𝑖
𝑚
𝑃1 𝑃𝑖
𝑚𝑎𝑥
𝑎𝑖 𝑏𝑖 𝑐𝑖 𝑑𝑖 𝑒𝑖 𝑓𝑖 𝑎𝑖 𝛽𝑖 𝛾𝑖 𝜂𝑖 𝛿𝑖 Γ𝑖
1 10 32.6 40.6 1000 40.54 12 33 0.01 0.001 360 3.98 0.04 0.25 0.01 0.022
2 10 25.5 20.5 950.6 39.58 10 25 0.01 0.002 350 3.95 0.04 0.25 0.01 0.028
3 47 150 158 900.7 36.51 12 32 0.01 0.008 330 3.9 0.04 0.25 0.01 0.022
4 50 147 155 800.7 39.51 12 30 0.01 0.002 330 3.9 0.04 0.25 0.01 0.011
5 50 285 293 756.8 38.53 15 30 0.01 0.017 13.8 0.32 0 0.24 0.01 0.03
6 70 282 290 451.3 46.15 10 20 0.01 0.005 13.8 0.32 0 0.24 0.01 0.02
3. UTILITY OF QUABCOPID OPTIMIZATION TECHNIQUE
To overcome the slow convergence of Newton Raphson method a quantum behaved artificial bee
colony based controller involving ABC technique is implemented vide a Simulink model to control the up
Q-bit and down Q-bit of quantum behaved algorithm which in turn regulates the conventional PID
parameters namely Kp, Ki and Kd to yield optimal values of real power that normally goes erroneous
following mismatch in power demand, equality, inequality constraints, congestion constraints security
constraints, ramp rate constraints involving prohibited operating zones. This optimized real power reduces
frequency mismatches to great extent in timeline power control unit. In the proposed work bio-inspired
algorithms (BIAs) applied to unravel various economic and emission objective issues have shown promising
outcomes that are vital in this severely complex real-world system. QUABCOPID controller algorithm, a
type of BIA that produces enormous results compared to certain other soft computing methods. The purpose
of this study is to offer a replacement-modified algorithm based with the goal of increasing convergence
speed and avoiding convergence rate. To demonstrate the suggested algorithm's robust application, it has
been used to solve the active power optimization problem. The proposed individual's outputs outperform other
ABC variations in terms of fast convergence. Additionally, the suggested method has demonstrated superior
performance and solution of economic-emission objective functions.
4. PROPOSED QUABCOPID TECHNIQUE FOR EED
The proposed method, which utilizes a QUABCO and PID controller, is discussed in this section. It
is used for multi-objective generation dispatching when dealing with severe non-linear load behavior of large
thermal power stations subjected to multiple constraints. It utilizes a cubic cost and emission threshold
objective function. The ABC approach along with incremental fuel costs regulates q-bits of quantum behaved
algorithm and the combinatorial approach of ABC and quantum behaved algorithm regulates the PID
parameters bringing thereby an optimal value of real power that becomes suitable to meet out the cost and
emission objectives of economic load dispatch.
5. ABC TECHNIQUE
The fast convergence process of ABC technique is discussed vividly in the following steps involved
in the algorithm. Typically, spectator bees wait inside the hive and collect food sources by exchanging
information with the hired foragers, and there is a far greater likelihood of watchers identifying more
profitable sources. As follows is a description of the ABC algorithm:
Step 1: The first step is to provide food sources for any and all engaged bees.
Step 2: An engaged bee explores a food source, striking her memory and establishing a familiar source, then
judges the amount of honey produced by conducting a flutter kick within the hive shown in Figure 1.
Step 3: An observer sees engaged bees prance and then approaches one of their sources based on the waggle
prances. She estimates the amount of honey produced by the source after choosing an acquaintance in
the vicinity.
Step 4: Unwanted food habitats are identified and swapped for newly discovered food habitats by vanguards.
Step 5: Till all requirements are met, the smallest food habitat discovered during this procedure is noted.
5. ISSN: 2088-8708
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6. PID TECHNIQUE
Conventional PID controller using Zeigler Nichole’s tuning method involving S shaped curves finds
suitable to some extent for controlling system stability except for multi-constraint and contradictory multi-
objective electrical power system dispatch problem. The traditional controller must be updated with the
assistance of the heuristic controller (QUABCOPID) for meeting out the contradictory fuel cost and emission
objectives for economic dispatch. Simulation studies were performed for a system comprising a 2-area
system associated by a tie line whose real power is updated to obtain the optimal power for meeting out the
cost and emission objective for economic dispatch. Each area of electrical power grid comprises 6 generating
units contributing to 30 busses, is modelled by a group of non-linear differential equations supported Park’s
equations. The prototype comprises typical flow restriction and gain impregnation in power plants. Using the
PID parameters developed by Dash et al. [23] gain values are tabulated in Table 2, the traditional PID
controller metamorphoses to a QUABCOPID controller which is regulated by the hybrid action of the
quantum behaved algorithm and ABC technology to regulate the up Q-bit and down Q-bit states of tie line
power and governor output and regulate the nectar level by appointing more scout bees to interact with
employed bees within the neighborhood to perform the sort of waggle dance inside the hive. These vanguard
bees plan for help in achieving the worldwide optimum solution in economic and emission dispatch
problems. The results shown in Figures 4 to 6 clearly demonstrate that the QUABCOPID controller possesses
much slighter undershoot and quicker settling time to understand the steadiness of a feedback system for all
frequency range. The open-loop frequency response curve must get on the right-hand side and away from the
juncture (-1, jo). This vital objective is fulfilled by using QUABCOPID controller that constantly monitors
Kp, Ki and Kd values following the mismatch in power demand, equality, inequality constraints, congestion
constraints security constraints, ramp rate constraints involving prohibited operating zones.
Table 2. Gain values and time constants of fast acting ALFC loop with QUABCOPID controller
S.L No Component Gain Time constant in Second
1 Governor Gg=1 Tg=0.4
2 Turbine Gt=1 Tt=0.47-0.44
3 Amplifier Ga=0.9 Ta=0.19-0.14
4 Exciter Ke=1 Te=1.5
5 Generator Gp=16.94 Tp=1.6
6 Sensor Kr =1 Tr=0.06
7 Inertia constant H=5 sec
8 Regulation R=0.04 Hz PU MW
9 Proportion controller (QUABCOPID) Kp=0.0041
10 Integral controller (QUABCOPID) Ki =0.25
11 Derivative controller (QUABCOPID) Kd=0.26
12 Step load change for area1 0.03 PU
13 Step load change for area2 0.06 PU
Figure 4. Simulink model for incremental frequency Δ𝐹1 Figure 5. Simulink model for incremental frequency Δ𝐹2
Figure 6. Simulink model for incremental time line power 𝛥𝑃12
Area
1
incremental
frequency
Δ𝐹
1
frequency
in
PU
Area
2
incremental
frequency
Δ𝐹
2
frequency
in
PU
Time T in Sec
Δ𝐹1 vs T
Time
line
power
ΔP
12
for
2
area
system
in
PU
Time T in Sec
Δ𝐹2 vs T
ΔP12 vs T
Time T in Sec
6. Int J Elec & Comp Eng ISSN: 2088-8708
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7. QPSO ALGORITHM FOR OPTIMAL EED SOLUTION
Quantum behaved particle swarm optimization (QPSO) intends to enhance traditional PSO making
use of alternate particle creation formulae. Quantum behaved PSO supported trajectory analysis being a
worldwide convergence technique updates the particle position (8) and (9):
𝑋𝑖
𝑡+1
= 𝑃𝑖
𝑡
+ 𝛼|𝑥𝑖
𝑡
− 𝑚𝑏𝑒𝑠𝑡𝑡|𝑙𝑛
1
𝑢𝑖
𝑡 , 𝑖𝑓𝑟𝑎𝑛𝑑𝑣 ≤ 0.5 (8)
𝑋𝑖
𝑡+1
= 𝑃𝑖
𝑡
− 𝛼|𝑥𝑖
𝑡
− 𝑚𝑏𝑒𝑠𝑡𝑡|𝑙𝑛
1
𝑢𝑖
𝑡 , 𝑖𝑓𝑟𝑎𝑛𝑑𝑣 < 0.5 (9)
where, 𝑃𝑖
𝑡
is local attractor of particle 𝑖 = iteration number, mbestt
denotes magnificent mean positions in the
ith
iteration and 𝛼 is the contraction expansion coefficient for regulating the concurrence rate of the QPSO
adopted at par with Tahyudin and Nambo [24], Giri et al. [25], Sui et al. [26]. Identities 𝑢𝑖
𝑡
and rand v are
functions of uniform probability distribution in the range [0, 1]. The local point is processed (10):
𝑃𝑖
𝑡
= 𝑓𝑡
× 𝑃𝑏𝑒𝑠𝑡𝑡
+ (1 − 𝑓𝑡)𝑔𝑏𝑒𝑠𝑡𝑡
(10)
where, 𝜙𝑡
is a random parameter with a uniform distribution in the interval [0, 1], and are personal best of the
particle and universal best of swarm respectively.
𝑚𝑏𝑒𝑠𝑡𝑡
=
1
𝑁
∑ 𝑃𝑏𝑒𝑠𝑡𝑖
𝑡
𝑁
𝑖=1 (11)
QPSO does not alone become useful for complex engineering problems on EED following more and more
numbers of search iterations. It works together with ABC technique for obtaining fast convergence that is
required solving complex multi-objective nonlinear problems.
8. IMPLEMENTATION OF QABCOPID METHOD
This part of the article describes an improved hybrid algorithm for solving the ED problem. This
hybrid technique recognized by Hullender et al. [27], is a combination of two strategies described through
steps 1-13 that finds use in solving complex multi-objective problems subjected to complex nonlinearities
necessitating fast convergence. First strategy uses incremental fuel cost to decide the real power generations
of the generating units involving the quantum behaved algorithm and the second strategy acknowledges these
real generations as initial values for training the artificial bee colony optimization to ascertain the favorable
value of the initial neighborhood. This hybrid strategy is named as QUABCO. The constraints of ED
experimented by Semlyen and Deri [28] and Deb [29] are transmission losses, power demands and the
practical limits associated with alternators to demonstrate the effectiveness of the QUABCO algorithm as presented.
we take two test cases one 6unit 30 bus system subjected to QUABCO approach and other one a 3-unit 15 bus system
subjected to quantum behaved evolutionary algorithm based PID controller involving MATLAB/Simulation with and
without transmission losses.
The QUABCO algorithm to train the PID controller is summarized as follows:
Step 1: Assume initial values for the key elements of QUABCO as referred in Table 1.
Step 2: Compute the incremental fuel costs (𝜆) differentiating (1) using the Pd value and making use of
mbestt
value from the QPSO algorithm.
Step 3: Compute the real power of the ith
alternator (Pi) using 𝜆 equation as under:
𝜆 − 𝑏𝑖 − 2𝑐𝑖𝑃𝑖 − 3𝑓𝑖𝑃𝑖
2
+ |𝑑𝑖𝑐𝑜𝑠𝑒𝑖(𝑃𝑖
𝑚𝑖𝑛
− 𝑃𝑖)| = 0
Step 4: Pi, lower= Pi (1-Rank) and Pi, upper =Pi (1+Rank)
where, Pi, lower and Pi,upper are the minimum and maximum power output of the ith
generating unit and
rank is the rank ofreal power generation output.
Step 5: Create the population (N) of the real generations satisfying the power balance constraints and express it as;
Pi=Pi, lower+ ((Pi, upper- P i, lower). rand (0, 1))
Step 6: Obtain the population suit abilities in increasing order.
Step 7: Chose best solution groups for the neighborhood search and isolate them into two groups.
Step 8: Obtain the neighborhood size for each feasible solution. Mark neighborhood size equal to NQ for
solution group (Q) and NP for solution group (S-Q)
Step 9: Create solutions within the selected solutions in the neighborhood sizes (NP, NQ) and obtain the
suitability value from individual group. Then, choose the most feasible solution from individual
group.
Step 10: Validate the stopping criteria if no, update the iteration count.
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Step 11: Assign the new population (N-Q) to create new real generation of the ith
alternator unit.
Step 12: If the stopping rule is satisfied for the quantum behaved ABC optimization, then obtain the suitable
solution and utilizing the same create a proportional, derivative and integral signal to control Kp, Kd
and Ki of the proportional integral and derivative controller involving automatic generation controller
and MATLAB/Simulink model.
Step 13: Obtain Δ𝐹1 𝑣𝑠. 𝑇, Δ𝐹2 𝑣𝑠. 𝑇 and Δ𝑃12 𝑣𝑠. 𝑇 characteristics shown in Figure 4 to 6 using
QUABCOPID controller Simulink model shown in Figure 2 exploiting the flow chart of
QUABCOPID controller shown in Figure 7. Finally obtain the variation of tie line power with time as
shown in Figure 8 for the test case 2 comprising 3-unit 15 bus system using quantum behaved
evolutionary algorithm hybridized with artificial bee colony optimization and PID controller to
analyze the EED problem.
Figure 7. Flow chart for QUABCOPID optimization
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Figure 8. Simulation output for 3-unit 15 bus system
9. RESULTS ANALYSIS AND PERFORMANCE CHARACTERISTICS OF PROPOSED
QUABCOPID CONTROLLER
To determine the viability and efficacy of the suggested hybrid quantum-behaved PSO, as well as
the ABC method with a PID controller QUABCOPID, a series of experimental simulation studies, were
conducted on a conflicting multi-objective EED optimization process for two different test cases, one using
the QUABCOPID approach with a 6-unit 30 bus test case system and the other using a quantum behaved
evolutionary computing approach with a PID controller to find the optimal value of the fuel. The second
experiment incorporates a two-crossover method into the standard QEA-based ABC optimization process.
The t characteristic of experiment 2 is illustrated in Figure 8. It is quite obvious that the experimental results
of the QUABCOPID controller showing the variation of fuel cost and emission rate with respect to the real
energy production with and without line loss, as seen in Figures 9 to 12 and tabulated in Table 3, outperform
the QEA-based optimal solution results in Table 4. More precisely, the second experiment exhibits a higher
frequency of transient excursion than the first. QUABCOPID controller, on the other hand, has significantly
superior steady-state performance, which results in the ideal fuel economy and emission level again for
economic-emission dispatch problems. When compared to the results produced using the other heuristic
approaches listed in Table 3, the QUABCOPID controller's outcome is determined to be more promising. Both
experimental approaches were written in MATLAB and run on a 512 MB device running on Windows 8.
Figure 9. Operating cost function versus output
power without transmission loss
Figure 10. Emission level function versus output
power without transmission loss
Figure 11. Operating cost function versus output
with transmission loss
Figure 12. Emission level function versus output
power with transmission loss
Time
line
power
ΔP
12
for
2
area
system
in
PU
for
test
case
2
involving
3-unit
15
bus
system
Time T in Sec
Operating
cost
function
𝑓1𝑖
in
S/hr
Output power in (MW)
Emission
level
function
𝑓1𝑖
in
S/hr
Output power in (MW)
0 50 100 150 200 250 300
Operating
cost
function
𝑓1𝑖
in
S/hr
Emission
level
function
𝑓1𝑖
in
S/hr
Output power in (MW) Output power in (MW)
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Table 3. Numerical and comparative results for QUABCOPID controller (30-bus system)
Time 1 2 3 4 5 6 7 8 9 10
Max 19.7648 19.7613 19.7648 19.7648 19.7649 19.7649 19.7649 19.7680 19.7504 19.7449
Average 19.6740 19.6824 19.6999 19.7048 19.7177 19.7249 19.7249 18.4671 19.4816 19.7849
Time 11 12 13 14 15 16 17 18 19 20
Max 19.6614 19.7794 19.7849 19.7949 19.7949 19.6737 19.6949 19.7362 19.7804 19.8823
Average 19.7432 19.7953 19.8049 19.8049 19.8048 19.6937 19.7947 19.6853 19.7902 19.7926
Average (max)=19.7583
Table 4. Test results of experiment 2
Power generation for a power demand of (492MW) and a transmission loss (TL) of 12.30 MW Fuel
Cost
Emission
rate
Simulation
time
Method P1
(in MW)
P2
(in MW)
P3
(in MW)
P4
(in MW)
P5
(in MW)
P6
(in MW)
FC
(in $/h)
PE (in T/h) T in (sec)
QABCOPID without
transmission loss (TL)
32.6 12.5 1.50 1.47 285 282 6865.07 194.45 5.0
QABCOPID with TL 40.6 20.5 1.58 1.55 293 290 6883.08 194.47 5.1
MM 33.77 12.65 150.56 148.50 296.29 293.63 6900.05 196.98 8.2
NN based PID 32.66 12.7 152 149 287 285.3 6897.07 196.23 7.3
MOCPSO 33.71 12.65 150.56 148.50 296.30 293.72 6890.35 195.12 6.2
10. SIMULATION OF TRANSMISSION LINE
The bench line device analyses single-phase as well as 3 transmission lines operating at varied loads
and subjected to fluctuating power and inserted fault circumstances. A single-phase line is composed of a
series of inductive impedances that are connected asynchronously. The tapings thus provide the user with the
ability to adjust the duration of the replicated line, develop nominal 'Pi' or 'Tee' techniques for identifying
losses by utilizing differing capacitance values, and monitor voltage, current, and output at any point on the
road. Six portions of the three-phase line are depicted diagrammatically in 'per-unit' values. It has provisions
for controlling below-variable balanced or imbalanced resistive, inductive, and capacitive (RLC) masses, as
well as selectable neutral provision for adjusting the length parameters. The electrical or electronic circuit
and tangency variables of a 252 kilometers long unit line with such a litigating electrical circuit wattage of
275 V and a point of intersection voltage of 60 V were determined during this experiment, as well as the
associated voltages of area units. Using those specifications, as well as additional information quantified in
Table 5, the propagation variables of the said predicted line systems are currently are calculated as:
𝐶𝑜𝑝𝑝𝑒𝑟 𝑙𝑜𝑠𝑠 = 𝐼𝑜𝑠
2
𝑅 = (
𝑃𝑠
𝑝ℎ𝑎𝑠𝑒
−
𝑃𝑟
𝑃ℎ𝑎𝑠𝑒
)
𝐼𝑜𝑠
2
=
(
𝑃𝑠
𝑝ℎ𝑎𝑠𝑒
−
𝑃𝑟
𝑃ℎ𝑎𝑠𝑒
)
𝑅
𝑅/𝑘𝑚/𝑃ℎ𝑎𝑠𝑒 = 𝑅/252 = (𝑓𝑜𝑟 7 𝑠𝑒𝑐𝑡𝑖𝑜𝑛𝑠 𝑜𝑣𝑒𝑟 𝑎 𝑠𝑝𝑎𝑛 𝑜𝑓 180 𝑘𝑚).
From Short circuit test, 𝑃𝑠 = 47.49 𝑤, 𝑃𝑟 = 𝑉𝑟 𝑥 𝐼𝑜𝑠 𝑥 𝑐𝑜𝑠Ф𝑟 = 117.2𝑥0.45𝑥0.917 = 45.12 𝑤𝑠 𝐼𝑜𝑠 =
0.456 𝐴, 𝑅 = 12.69 Ω, 𝑅/𝑝ℎ𝑎𝑠𝑒/𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 = 12.69/3𝑥252 = 0.0167. Calculation of the line's
impedance per km section (z). 2𝜋𝑓𝐿 = 0.0569 𝐿, so 𝐿 = 0.00016 𝐻 from data on open circuit. 𝐼0𝑆/𝑉0𝑆 =
2 𝑓𝐶 𝐶 = 7.66 𝑓 (this value should be for all seven sections). 𝐶 = 7.66 𝑓/252 𝐶 = 0.03 𝑓 is the
capacitance/part per kilo meter. As with medium conductors, the long line can be split into similar segments
within the shielded conductor. The series electrical phenomenon is denoted by Z′, and the shunt admittance is
denoted by Y′. As a consequence, the ABCD components of this huge line will be specified analogous to that
of a medium line, i.e. in terms of transport protocol, and will be arranged as:
𝑦 = 2 × 𝜋 × 𝑓 × 𝐶 = 2 × 𝜋 × 49.82 × 0.030 × 10−6
= 9.39 × 10−6
where, y is the capacitive susceptance in Mho/Phase/km
As with the intermediate power line, an analogous representation can be used to approximate the
long string. The series impedance is denoted by Z′ in the long power line's-equivalent, but the shunt
admittance is denoted by Y′.
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where, 𝛾 = √𝑧𝑦, 𝑍𝑐 = √
𝑧
𝑦
, 𝑍 = 𝑧 × 1, 𝑎𝑛𝑑 𝑌 = 𝑦 × 1
As a consequence, the ABCD components of this massive line can be stated in the same way as that of a
medium transmission line:
Z’ = Z_c sinhγl = √(z/y) × sinh√yz × l = zlsinhγl/(l√yz) = (Zsinhγl )/γl (12)
coshγl = 1 + (Z^′ Y^′)/2 = Y′/2 × Z_c sinhγl + 1 (13)
We obtain by rearranging the elements in the preceding (14) to (17).
⇒ Y′/2 = 1/Z_c (coshγl − 1)/sinhγl = √(y/z) tanh yl/2 = yl/2 × (tanh γ_l/2)/
(l/2 √yz) = Y/2 × (tanh γ_l/2)/(l/2 √yz) ÞY′ = Y × (tanh γ_l/2)/(γ_l/2) (14)
⇒ A = D = 1 +
Y′Z′
2
per unit (15)
𝐵 = 𝑍′
𝛺 (16)
𝐶 = 𝑌 (1 +
𝑍′𝑌′
4
) 𝐽 (17)
⇒ A = 1.0160, B = 13.5793 Ω, C = 0.0024 ℧ and D = 1.0160
Table 5. Transmission line test case data and simulation result for simulating a 252 km
HVAC transmission line
Sending
end
voltage on
open
circuit
(V0s) in
Volts
Sending
end
current on
open
circuit
(I0s) in
Ampere
Sending
end
voltage on
short
circuit
(Vsc) in
Volts
Receiving
end
current on
short
circuit
(Isc) in
Ampere
Line
Length
in km
Analys
is
method
Number
of π
sections
utilized
Sending
end
Power Ps
In Watts
per phase
Receiving
end Power
Pr in Watts
per phase
A=D B In
Ω
C In
Mho
175 0.456 60 4.44 252 Nominal π 7 47.49 45.12 1.0160 13.5793 1.0160
11. SCOPE FOR FUTURE WORK
The QUABCOPID optimization algorithm will be applied to realistic systems with many objectives
and constraints. The proposed technique will be hybridized with completely different techniques such as
symbolic logic, DE, EP, PSO, artificial neural network (ANN), and good grid observance to address the
inadequacy of the MOELD solution discovered by Ghasemi et al. [30]. This technology can be implemented
to ship climate routing systems with acceptable initial conditions, as demonstrated by Vettor and Soares [31]
as well as smart grids incorporating distributed generations (DGs) [32].
12. CONCLUSION
A proportional integral and derivative (PID) controller tuned through QUABCO has been hotly
proposed as a solution to the ALFC's multi-area disadvantage. The results revealed that a PID based on
QUABCO is capable of ensuring high stability and performance under a variety of load conditions and
parameter changes for a variety of pricing functions. The proposed controller was successful in dampening
all oscillations, limiting subsidence time, and eliminating overshoot, all of which resulted in decreased wear
on high-speed valves and gates. We will extend the QUABCO rule to renewable energy power plants such as
wind turbines in the future. Additionally, we frequently employ it to set the upper limit on load disruptions
that can result in the ability system's instability downside. The transmitting parameters obtained through
QUABCOPID advancement during this proposed work by combining real power generation, real power
demand, and power loss area unit are really quite adequate for meeting the erection demand for power lines
inside the selected as well as proclaimed great areas and alternate solution busy cities. Thus, these optimized
values of real power generations fulfil the fundamental objective of the simulation of conductor demand for
meeting out the transmission and distribution necessities of varied recent and good cities.
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BIOGRAPHIES OF AUTHORS
Himanshu Shekhar Maharana completed his B. Tech degree in EEE from
JITM, Paralakhemundi under BPUT university, Rourkela, Odisha in 2010 and Completed M.
Tech degree in Power System Engineering from GITA, Bhubaneswar under BPUT university,
Rourkela, Odisha in the year 2014. Prior to it he worked on Java Full Stack Development
Software Projects through online training-based mode while doing PhD research work. At
present he is continuing full time PhD in the Department of Electrical Engineering at GITA,
Bhubaneswar under Biju Patnaik University of Technology (BPUT), Rourkela, Odisha, India
vides the guidance of professor Dr. Saroja Kumar Dash. His research areas are power system
engineering, applications of power system optimization techniques relating to economic load
dispatch of electric power system. He can be contacted at: hsmaharana@gmail.com.
Saroja Kumar Dash received the UG degree in Electrical Engineering from I.E,
India in 1991 and accomplished Master’s Program in Electrical Engineering from VSSUT,
Burla (Sambalpur University), India, in 1998 and the PhD degree from Utkal University,
Odisha, India in the year 2006. Dr. S.K. Dash is working at present as a Professor and Head in
the department of Electrical Engineering at GITA, Autonomous College, Bhubaneswar under
Biju Patnaik University of Technology (BPUT, Rourkela, Odisha, India). Dr. S. K. Dash
received Pundit Madan Mohan Malviya award, Union Ministry of Power Prize and gold
medals thereof for his research papers entitled “Economic load dispatching of generating units
with multi-fuel options” and “Short term generation scheduling with take or pay fuel contract
using evolutionary programming technique” on Multi Objective Generation Dispatch. His
research areas include power system planning, operation, and optimization techniques relating
to economic dispatch of electric power system. He can be contacted at: hodeegita@gmail.com.