Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules

Premkumar, M.; Jangir, Pradeep; Sowmya, R.; Elavarasan, Rajvikram Madurai; Kumar, B. Santhosh

doi:10.1016/j.isatra.2021.01.045

Cited by 101 publications

(51 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To resolve the issues faced in the DESE, [8] presents the blurkeyword seek over encoded cloud information and furthermore keeping up the keyword security in which the umpteen keywords are encoded and decoded accordingly. The study [9] highlights the test of secure positioned keyword look over encoded cloud information, additionally Ranked pursuit using KNN calculation is forecasted whichincredibly upgrades the ergonomics by empowering query output significance positioning as opposed to sending constant outcomes, and further guarantees the record recovery exactness.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

Rao¹

2021

TURCOMAT

View full text Add to dashboard Cite

The data owner administers their data to the public cloud due to regulatory. Even this data encoded once it easily transmitted to the cloud. In order to ensure the privacy and security, cloud subscribers need a very different type of online data. This specific information must be germane to the recipient's query. The user to inspect the query too in the cloud with umpteen keywords can accomplish this. With the intention to defend the privacy of data, confidential data encoded before subcontracting by the metadata owner, attempting to make the traditional and efficient plaintext keyword search tactic pointless. Therefore, it is crucial to explore a secure data search service for encrypted user data. Due to the growing of massive number of digital users and legal documents in the cloud, umpteen keywords forced in the search request and documents returned in the order of their relevance to these keywords.Cloud assistance end-user demands cloud information by various paternoster inquiry, whichlabelled as umpteenkeywordstratified exploration over scrambled information. In the cloud server, all the client inquiries exchanged. Server looks through the significant matter by utilizing the harmonic equivalency and sends the applicable outcomes to the client. Information got from cloud server is in the scrambled configuration. To get to control to the client,Information proprietor furnishes with key for unscrambling of information. Message Authentication Code(MAC) calculation is utilized to check and confirm trustworthiness of information. Thusly this paper depicts the method of giving security to redistribute information on cloud and checking the trustworthiness of information using blowfish algorithm. Comparison results shows the improvement in efficiency of Data Retrieval and Time efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

Rao¹

2021

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…Recently, many optimization algorithms depended on chaos theory to improve their performance such as the chaotic differential bee colony [26], chaotic bat algorithm [27], modified artificial bee colony [28], chaotic krill herd [29], modified artificial bee colony based on the chaos [30] and hybrid PSO and GSA integrated with chaotic maps (CPSOGSA) [31] and Enhanced chaotic JAYA algorithm [32]. However, these algorithms have been applied for solving different optimization problem such as the economic dispatch, optimal reactive power dispatch, nonconvex emission/economic dispatch, optimal power flow with stochastic wind and FACTS devices, parameter estimation of photovoltaic, and dynamic economic dispatch with valve-point effects problems in power systems.…”

Section: Introductionmentioning

confidence: 99%

Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch

et al. 2021

View full text Add to dashboard Cite

In this paper, Chaotic Artificial Ecosystem-based Optimization Algorithm (CAEO) is proposed and utilized to determine the optimal solution which achieves the economical operation of the electrical power system and reducing the environmental pollution produced by the conventional power generation. Here, the Combined Economic Emission Dispatch (CEED) problem is represented using a max/max Price Penalty Factor (PPF) to confine the system's nonlinearity. PPF is considered to transform a four-objective problem into a single-objective optimization problem. The proposed modification of AEO raises the effectiveness of the populations to achieve the best fitness solution by well-known 10 chaotic functions and this is valuable in both cases of the single and multi-objective functions. The CAEO algorithm is used for minimizing the economic load dispatch and the three bad gas emissions which are sulfur dioxide (SO2), nitrous oxide (NOx), and carbon dioxide (CO2). To evaluate the proposed CAEO, it is utilized for four different levels of demand in a 6-unit power generation (30-bus test system) and 11-unit power generation (69-bus test system) with a different value of load demand (1000, 1500, 2000, and 2500MW). Statistical analysis is executed to estimate the reliability and stability of the proposed CAEO method. The results obtained by CAEO algorithm are compared with other methods and conventional AEO to prove that the modification is to boost the search strength of conventional AEO. The results display that the CAEO algorithm is superior to the conventional AEO and the others in achieving the best solution to the problem of CEED in terms of efficient results, strength, and computational capability all over study cases. In the second scenario of the bi-objective problem, the Pareto theory is integrated with a CAEO to get a series of Non-Dominated (ND) solutions, and then using the fuzzy approach to determine BCS.

show abstract

“…These structures are influenced by natural events, such as swarming activities, mechanisms focused on nature, and physics. Genetic algorithm (GA) [18], [19], particle swarm optimization [20]- [22], enhanced leader particle swarm optimization algorithm (PSO) [23], niche particle swarm optimization in parallel computing algorithm [24], several versions of differential evolution (DE) [25]- [28], penalty-based DE algorithm [29], sunflower optimizer [30], grey wolf optimizer (GWO) [31], whale optimizer algorithm (WOA) [32], harris-hawk optimizer (HHO) [33], improved salp swarm algorithm (ISSA) [34], several version of JAYA algorithm [35], multiple learning backtracking search algorithm [36], coyote optimization algorithm [37], teaching-learning-based optimization and its various versions [38]- [44], political optimizer (PO) [4], evolutionary shuffled frog leaping algorithm [45], slime-mould optimizer (SMO) [46], [47], marine predator algorithm (MPA) [48], equilibrium optimizer (EO) [49], ions motion optimization (IMO) [50], improved PSO (IPSO) [51], Forensic-based investigation algorithm [52], and improved learning-search algorithm [53] are among good heuristic-based structures. Some studies have endeavored to hybridize a few of these strategies to boost their performance, such as hybrid grey wolf optimizer with cuckoo search algorithm [54], hybrid firefly with pattern search algorithms [55], hybrid grey wolf optimizer with particle swarm algorithm [56], hybrid WO with DE algorithm [26], hybrid GA with simulated annealing algorithm [18], etc.…”

Section: Introductionmentioning

confidence: 99%

“…The authors introduced a logistic-chaotic map in [60] to solve updating process of the JAYA algorithm. The authors of [35] introduced chaotic adaptive weight factors with the JAYA algorithm to estimate the parameters of various PV models. In [61], different PV model parameters are obtained under multiple climatic conditions based on the fractional chaotic ensemble PSO.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Premkumar¹,

Jangir²,

Ramakrishnan³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

When discussing the commercial applications of photovoltaic (PV) systems, one of the most critical problems is to estimate the efficiency of a PV system because current (I)voltage (V) and power (P) voltage (V) characteristics are highly non-linear. It should be noted that most of the manufacturer's datasheets do not have complete information on the electrical equivalent parameters of PV systems that are necessary for simulating an effective PV module. Compared to conventional approaches, computational optimization and global research strategies are more acceptable as an effective alternative to parameter estimation of solar PV modules. Recently, a Gradient-based optimizer (GBO) is reported to solve the engineering design optimization problems. However, the basic GBO algorithm is stuck in local optima when handling complex non-linear problems. In this sense, this paper presents a new optimization technique called the Chaotic-GBO (CGBO) algorithm to derive the parameters of PV modules while offering precise I-V and P-V curves. To this end, the CGBO algorithm is based on a chaotic generator to obtain the PV parameters combined with the GBO algorithm. There are five case studies considered to validate the performance of the proposed CGBO algorithm. A quantitative and qualitative performance evaluation reveals that the proposed CGBO algorithm has improved results than other state-of-the-art algorithms in terms of accuracy and robustness when obtaining PV parameters. The average RMSE values and runtime of five case studies are equal to 9.8427E-04, 2.3700E-04, 2.4251E-03, 4.3524E-03 and 1. 8349E-03, and 18.44, 17.78, 18.18, 18.28 and 17.97, respectively. The results proved the superiority of the proposed CGBO algorithm over the different selected algorithms. For future research, this study will be backed up with external support at https://premkumarmanoharan.wixsite.com/mysite.

show abstract

Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules

Cited by 101 publications

References 70 publications

Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

Enhanced Effective and Privacy Preserving Multi Keyword Search over Encrypted Data in Cloud Storage Using Blowfish Algorithm

Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch

Identification of Solar Photovoltaic Model Parameters Using an Improved Gradient-Based Optimization Algorithm With Chaotic Drifts

Contact Info

Product

Resources

About