Bio: Chitralekha Jena is an academic researcher from KIIT University. The author has contributed to research in topics: Computer science & Differential evolution. The author has an hindex of 2, co-authored 18 publications receiving 49 citations.
••01 Feb 2016
TL;DR: Gaussian mutation in DE is proposed which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the simplicity of the structure of DE.
Abstract: This paper presents differential evolution with Gaussian mutation to solve the complex non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. Valve-point loading and prohibited operating zones of conventional thermal generators are taken into account. Differential evolution (DE) is a simple yet powerful global optimization technique. It exploits the differences of randomly sampled pairs of objective vectors for its mutation process. This mutation process is not suitable for complex multimodal optimization. This paper proposes Gaussian mutation in DE which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the simplicity of the structure of DE. The effectiveness of the proposed method has been verified on five test problems and three test systems. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed differential evolution with Gaussian mutation-based approach is able to provide better solution.
••01 Jan 2020
TL;DR: Comparison of dynamic responses corresponding to above controllers reveals that FOPID outperforms better than conventional PID controller, and a comparison has performed with and without PEV applied to four-area systems.
Abstract: This study reveals the load frequency control of an unequal four-area thermal system with HVDC link considering suitable generation rate constraint. Performances of controllers such as proportional–integral–derivative (PID) and fractional-order PID (FOPID) are separately evaluated in the system. At first, plug-in electric vehicle (PEV) is applied to the thermal unit system to provide the stability for fluctuated load demand, which is widely expected from customer side as a spinning reserve. For the better quality of solution and improvement of convergence property, a hybrid differential evolution particle swarm optimization (DEPSO) technique is used here. DC tie-line is introduced here which improves the stability of system as compared to AC link. Comparison of dynamic responses corresponding to above controllers reveals that FOPID outperforms better than conventional PID controller. Also, a comparison has performed with and without PEV applied to four-area systems. The simulation is carried out by using MATLAB/SIMULINK software with step load perturbation (SLP).
TL;DR: In this article , a non-dominated sorting genetic algorithm-II (NSGA-II) was proposed to solve multiobjective scheduling of generation for fixed head hydro-thermal system integrating pumped hydro energy storage and sources of renewable energy taking into consideration the outage and uncertainty in presence of DSM.
Abstract: Atmospheric pollutants mainly produced by thermal power plants compel to utilize green energy sources such as renewable energy sources and hydroelectric plants in a power system. But due to blinking behavior of sources of renewable energy and due to very high rate of outages, it has a detrimental consequence on overall grid. Demand side management (DSM) programs decrease cost and improve power system security. This study proposes non-dominated sorting genetic algorithm-II (NSGA-II) to solve multiobjective scheduling of generation for fixed head hydro-thermal system integrating pumped hydro energy storage and sources of renewable energy taking into consideration the outage and uncertainty in presence of DSM. Numerical results of the test system attained using the proposed technique were compared with strength pareto evolutionary algorithm 2 (SPEA 2).
TL;DR: Numerical results for two test systems have been presented to demonstrate the performance of the proposed group search optimization method, which has been compared with those obtained from differential evolution and evolutionary programming.
Abstract: This paper presents group search optimization for optimal scheduling of thermal plants in coordination with fixed head hydro units. Numerical results for two test systems have been presented to demonstrate the performance of the proposed method. Results obtained from the proposed group search optimization method have been compared with those obtained from differential evolution and evolutionary programming.
••01 Dec 2016
TL;DR: In this paper, a three phase symmetrical cascaded multi-level inverter topology based on switched capacitor basic units is presented, which has inherent capability to boost the input voltage and can generate multilevel output voltage with reduced number of power supplies and switching devices compared to that for other recently developed three phase topologies.
Abstract: This paper presents a novel three phase symmetrical cascaded multi-level inverter topology based on switched capacitor basic units. The proposed topology has inherent capability to boost the input voltage and can generate multilevel output voltage with reduced number of power supplies and switching devices compared to that for other recently developed three phase topologies. Further, the presented topology is modular in nature such that damage of any modules, the topology can supply the load with reduced number of voltage levels. In addition, by employing simple switching strategy, the capacitor voltage balancing can be achieved. The operating principle and the expressions for different parameters of the inverter are presented in detail. A multi-carrier phase opposition pulse width modulation technique is developed for switching the inverter. To prove the effectiveness and merits of the proposed inverter, a 17 levels line to line voltage topology is simulated in MATLAB/Simulink. In addition, a phase of the proposed inverter has been practically developed and the corresponding pole voltage and capacitor voltages are experimentally investigated at no load condition.
TL;DR: The most popular heuristic and meta-heuristic optimization algorithms are studied in this paper, and implementation of the optimization procedures for the solution of CHPED problem taking into account the objective functions and different constrains are discussed.
Abstract: Combined heat and power economic dispatch (CHPED) aims to minimize the operational cost of heat and power units satisfying several equality and inequality operational and power network constraints. The CHPED should be handled considering valve-point loading impact of the conventional thermal plants, power transmission losses of the system, generation capacity limits of the production units, and heat-power dependency constraints of the cogeneration units. Several conventional optimization algorithms have been firstly presented for providing the optimal production scheduling of power and heat generation units. Recently, experience-based algorithms, which are called heuristic and meta-heuristic optimization procedures, are introduced for solving the CHPED optimization problem. In this paper, a comprehensive review on application of heuristic optimization algorithms for the solution of CHPED problem is provided. In addition, the most popular heuristic and meta-heuristic optimization algorithms are studied in this paper, and implementation of the optimization procedures for the solution of CHPED problem taking into account the objective functions and different constrains are discussed. The main contributions of the reviewed papers are studied and discussed in details. Additionally, main considerations of equality and inequality constraints handled by different research studies are reported in this paper. Five test systems are considered for evaluating the performance of different optimization techniques. Optimal solutions obtained by employment of multiple heuristic and meta-heuristic optimization methods for test instances are demonstrated and the introduced methods are compared in terms of convergence speed, attained optimal solutions, and constrains. The best optimal solutions for five test systems are provided in terms of operational cost by employment of different optimization methods.
TL;DR: The proposed improved Whale Optimization Algorithm (WOA) is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches.
Abstract: This paper presents an improved Whale Optimization Algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a ‘shrinking’ strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well.
TL;DR: Experimental results show that the proposed IGA-NCM algorithm outperforms the other ones according to computation accuracy and runtime, and is a potential alternative for the CHPED problems with or without prohibited operating zones.
Abstract: This paper presents an improved genetic algorithm using novel crossover and mutation (IGA-NCM) to solve the combined heat and power economic dispatch (CHPED) problems. The basic genetic algorithm (GA) has been augmented in three aspects. First, the selection operation is excluded from GA in order to avoid excessive losses of population diversity. Second, two kinds of adaptive crossover operations are used to sufficiently excavate the information of parents and yield potential offsprings. Third, a novel mutation operation is used to replace a few genes of each crossed offspring by those of the other crossed offsprings’ parents, which can further improve their quality. Furthermore, a new constraint handling method is proposed to repair the mutated offsprings and enable them to enter feasible regions easily. Experimental results show that our proposed IGA-NCM algorithm outperforms the other ones according to computation accuracy and runtime. Therefore, it is a potential alternative for the CHPED problems with or without prohibited operating zones.
TL;DR: In this article, a real coded genetic algorithm with improved Muhlenbein mutation (RCGA-IMM) was proposed for solving the combined heat and power economic dispatch (CHPED) optimization problem.
Abstract: The combined heat and power economic dispatch (CHPED) is a complicated optimization problem which determines the production of heat and power units to obtain the minimum production costs of the system, satisfying the heat and power demands and considering operational constraints. This paper presents a real coded genetic algorithm with improved Muhlenbein mutation (RCGA-IMM) for solving CHPED optimization task. Muhlenbein mutation is implemented on basic RCGA for speeding up the convergence and improving the optimization problem results. To evaluate the performance features, the proposed RCGA-IMM procedure is employed on six benchmark functions. The effect of valve-point and transmission losses is considered in cost function and four test systems are presented to demonstrate the effectiveness and superiority of the proposed method. In all test cases the obtained solutions utilizing RCGA-IMM optimization method are feasible and in most instances express a marked improvement over the provided results by recent works in this area.
TL;DR: In this article, the authors reviewed the energy consumption targeting methodologies via total site heat integration for estimating and designing the capacity of the utility have been reviewed in this work, including both insight-based pinch analysis and mathematical modelling approaches.
Abstract: There has been growing interest in developing Locally Integrated Energy Sectors (LIES) as a Process (Heat) Integration approach for synergising the industrial thermal energy systems that include renewable energy resources with urban (i.e. civic, residential, business and service complexes). The aim is to enhance the regional energy efficiency and minimise greenhouse gas (including carbon) emissions. However, a comprehensive planning and design framework is crucial at the onset of its development, which is accounting for supply and demand sides, but there have been limited works directed to this scope to date. For the development of such framework, this paper reviews the energy consumption targeting methodologies via Total Site Heat Integration for estimating and designing the capacity of the utility have been reviewed in this work, inclusive of both insight-based Pinch Analysis and mathematical modelling approaches. As a final outcome of the review, suggestions are provided for investigating key factors for integration of industrial, residential, commercial, institutional and service energy systems, maximising the integration and reuse of waste and low potential heat, including renewables to boost sustainability aspects. The review of methodologies for energy system integration is followed by identification of research directions that deserve future attention, refinement and development.