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Table Of Integrals Series And Products

An adaptive Cuckoo search algorithm for optimisation

A survey on applications and variants of the cuckoo search algorithm

The state of health (SOH) of lithium-ion batteries (LIBs) is a critical parameter of the battery management system. Because of the complex internal electrochemical properties of LIBs and uncertain external working environment, it is difficult to achieve an accurate SOH determination. In this paper, we have proposed a novel SOH estimation method by using a prior knowledge-based neural network (PKNN) and the Markov chain for a single LIB. First, we extract multiple features to capture the battery aging process. Due to its effective fitting ability for complex nonlinear problems, the neural network with a prior knowledge-based optimization strategy is adopted for the battery SOH prediction. The Markov chain, with the advantageous prediction performance for the long-term system, is established to modify the PKNN estimation results based on the prediction error. Experimental results show that the maximum estimation error of the SOH is reduced to less than 1.7% by adopting the proposed method. By comparing with the group method of data handling and the back-propagation neural network in conjunction with the Levenberg–Marquardt algorithm, the proposed estimation method obtains the highest SOH accuracy.

A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain

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.

A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives

Cuckoo search algorithm for combined heat and power economic dispatch

An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems

This paper proposes a novel effective optimization algorithm called enhanced coyote optimization algorithm (ECOA). This proposed method is applied to optimally select the position and capacity of distributed generators (DGs) in radial distribution networks. It is a multi-objective optimization problem where properly installing DGs should simultaneously reduce the power loss, operating costs as well as improve voltage stability. Based on the original coyote optimization algorithm (COA), ECOA is developed to be able to expand the search area and retain a good solution group in each generation. It includes two modifications to improve the efficiency of the original COA approach where the first one is replacing the central solution by the best current solution in the first new solution generation technique and the second focuses on reducing the computation burden and process time in the second new solution generation step. In this research, various experiments have been implemented by applying ECOA, COA as well as salp swarm algorithm (SSA), Sunflower optimization (SOA) for three IEEE radial distribution power networks with 33, 69 and 85 buses. Obtained results have been statistically analyzed to investigate the appropriate control parameters and to verify the performance of the proposed ECOA method. In addition, the performance of ECOA is also compared to various similar meta-heuristic methods such as genetic algorithm (GA), particle swarm optimization (PSO), hybrid genetic algorithm and particle swarm optimization (HGA-PSO), simulated annealing, bacterial foraging optimization algorithm, backtracking search optimization algorithm, harmony search algorithm, whale optimization algorithm (WOA) and combined power loss index-whale optimization algorithm (PLI-WOA). Detailed comparisons show that ECOA can determine more effective location and size of DGs with faster speed than other methods. Specifically, the improvement levels of the proposed method over compared to SFO, SSA, and COA can be up to 2.1978%, 0.7858% and 0.2348%. Furthermore, as compared to other existing methods in references, ECOA achieves the significant improvements which are up to 31.7491%, 20.2143% and 22.7213% for the three test systems, respectively. Thus, the proposed method is a favorable method in solving the optimal determination of DGs in radial distribution networks.

Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

This paper proposes a cuckoo search algorithm (CSA) using different distributions for solving the short-term hydrothermal scheduling (ST-HTS) problem with reservoir storage constraint on hydropower plants. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. In the proposed CSA, three distributions have been used including Levy distribution, Gaussian distribution and Cauchy distribution. The proposed method has been tested on two test systems and the obtained results have been compared to that from other methods available in the literature. The result comparisons have indicated that the proposed method is a very favorable method for solving the short term hydrothermal scheduling problems with reservoir constraint.

http://www.ijeei.org/docs-101277519571471ff28c76.pdf

Cuckoo Search Algorithm Using Different Distributions for Short-Term Hydrothermal Scheduling with Reservoir Volume Constraint

In this research, we proposed and investigated the physical layer system called the full-duplex (FD) power beacon-assisted (PB) energy harvesting (EH) relaying cooperative network. The system model has one PB node, one destination (D), one source (S), and one relay (R) node. We investigated the system performance in terms of outage probability (OP) and system throughput (ST) with the power-splitting (PS) protocol in both delay-tolerant (DTT) and delay-limited (DLT) transmission modes. Moreover, the optimal power splitting (PS) factor in both DDT and DLT modes is proposed and derived. Finally, the mathematical closed-form expressions of the OP and ST are derived by using the Monte Carlo simulation with the help of MATLAB software. From the results, it can be observed that the analytical values and simulation values are the same in the effect of the main system parameters.

https://www.mdpi.com/2073-8994/12/1/106/pdf

Bach Hoang Dinh

Papers

Cuckoo search algorithm for combined heat and power economic dispatch

An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems

Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm

Cuckoo Search Algorithm Using Different Distributions for Short-Term Hydrothermal Scheduling with Reservoir Volume Constraint

Power Beacon-Assisted Energy Harvesting Wireless Physical Layer Cooperative Relaying Networks: Performance Analysis