Improved Rat Swarm Optimizer Algorithm-Based MPPT Under Partially Shaded Conditions and Load Variation for PV Systems

Mohammed, Karam Khairullah; Mekhilef, Saad; Buyamin, Salinda

doi:10.1109/tste.2022.3233112

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…The chromosomal length of 10, maximum iteration rate of 100 and a population size of 10 were used for the experimental investigation. The existing techniques such as Multi-Layer Perceptron (MLP) [31], Gated Recurrent Unit (GRU) [32], Long Short-Term Memory (LSTM) [33] and Fuzzy [7] and the heuristic strategies like Eurasian Oystercatcher Optimizer (EOO) [27], Rat Swarm Optimizer (RSO) [28], Red Deer Algorithm (RDA) [29], and RPO [26] were used for the experimental study.…”

Section: A Experimental Setupmentioning

confidence: 99%

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An Attentive LSTM with Fuzzy Network for Intelligent Optimal Routing in Wireless Sensor Networks using Enhanced Red Piranha-based Optimal Path Selection Mechanism

2024

Preprint

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Several agricultural processes can be greatly improved by data mining and sensor networks approaches. This action is controlling the amount of water used in agricultural crops. Furthermore, the Wireless Sensor Networks (WSN) have emerged as a more important technology in forming field. Utilizing energy and extending the lifespan of the sensor nodes is a key component of WSN design. The enormous problem of energy consumption reduction is used to increase the lifetime of the entire framework. Smart sensing IoT devices typically have low batteries and are not usually rechargeable. The effective protocols seek to extend structure life span while reducing the power consumption to prevent disruptions to the nodes installed for environment controlling and data collection. An intelligent deep learning-based routing in WSN is developed to enhance the network lifetime. This model provided efficient optimal routing, in which the routing decisions are taken by Attentive LSTM with Fuzzy (ALSTMFuzzy). Here, the routing decisions are selected based on the security, malicious node, link availability and scalability, etc. The effective routing is performed by the optimal path selection using Mutated Fitness-based Red Piranha Optimization (MFRPO). The path selection for intelligent routing is performed based on several multiple constrains like distance, delay, PDR, energy consumption and throughput. The developed intelligent routing in WSN system outcome is compared to existing models with effectiveness measures.

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Section: A Experimental Setupmentioning

confidence: 99%

“…STATISTICAL INVESTIGATION OF DESIGNED OPTIMAL ROUTING IN WSN SYSTEM AMONG DIFFERENT HEURISTIC STRATEGIESTerms EOO-ALSTMFuzzy[27] RSO-ALSTMFuzzy[28] RDA-ALSTMFuzzy[29] ERPO-ALSTMFuzzy[30] MFRPO-ALSTMFuzzy…”

mentioning

confidence: 99%

An Attentive LSTM with Fuzzy Network for Intelligent Optimal Routing in Wireless Sensor Networks using Enhanced Red Piranha-based Optimal Path Selection Mechanism

2024

Preprint

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“…Their method not only drastically decreases the tracking time and reduces the number of operations per iteration but also saves up to 67% of the tracking time, as compared with the original FA. Authors of [30] proposed an improved Rat Swarm Optimizer algorithm by initializing with detecting and skips method and employing an adaptive step size method for varying load. Overall, improving MHAs is an ongoing research topic for MPPT that requires a deep understanding of the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading

Fan,

Lian,

Lin

2023

Energies

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Solar photovoltaic (PV) systems often encounter a problem called partial shading condition (PSC), which causes a significant decrease in the system’s output power. To address this issue, meta-heuristic algorithms (MHAs) can be used to perform maximum power point tracking (MPPT) on the system’s multiple-peak P-V curves due to PSCs. Particle swarm optimization was one of the first MHA methods to be implemented for MPPT. However, PSO has some drawbacks, including long settling time and sustained PV output power oscillations during tracking. Hence, some improved MHA methods have been proposed. One approach is to combine a MHA with a deterministic approach (DA) such as P & O method. However, such a hybrid method is more complex to implement. Also, the transition criteria from a DA to a MHA and vice versa is sometimes difficult to define. Another approach, as adapted in this paper is to modify the existing MHAs. This includes modifying the search operators or the parameter settings, to enhance exploration or exploitation capabilities of MHAs. This paper proposed to incorporate the stooping behaviour in the golden eagle optimization (GEO) algorithm. Stooping is in fact a hunting technique frequently employed by golden eagles. Inclusion of stooping in the GEO algorithm not only truly model golden eagles’ hunting behaviour but also yields great performance. Stooping behavior only requires one extra parameter. Nevertheless, on average, the proposed method can reduce tracking time by 42.41% and improve dynamic tracking accuracy by 1.95%, compared to GEO. Moreover, compared to PSO, GWO, and BA, the proposed method achieves an improvement of 2.66%, 3.56%, and 4.24% in dynamic tracking accuracy, respectively.

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“…Tey et al [31] modified the differential evolution algorithm by adding a converging direction to the mutation process. Modifications focusing on FPPT include the current skipping technique, adaptive scaling factor beta method, and 0.8 V oc model [32][33][34][35][36][37][38]. Merchan et al [39] also argued that the computer-sciencebased searching algorithms conduct a nonlinear search and avoids waving around the final value, reducing the steadystate error compared to sequential searching algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…[31] modified the differential evolution algorithm by adding a converging direction to the mutation process. Modifications focusing on FPPT include the current skipping technique, adaptive scaling factor beta method, and 0.8 V oc model [32‐38]. Merchan et al.…”

Section: Introductionmentioning

confidence: 99%

A novel global FPPT strategy for PV system under partial shading condition

Xing,

Jia,

Yang

2023

IET Renewable Power Gen

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Flexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions.

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Improved Rat Swarm Optimizer Algorithm-Based MPPT Under Partially Shaded Conditions and Load Variation for PV Systems

Cited by 12 publications

References 22 publications

An Attentive LSTM with Fuzzy Network for Intelligent Optimal Routing in Wireless Sensor Networks using Enhanced Red Piranha-based Optimal Path Selection Mechanism

An Attentive LSTM with Fuzzy Network for Intelligent Optimal Routing in Wireless Sensor Networks using Enhanced Red Piranha-based Optimal Path Selection Mechanism

A New Golden Eagle Optimization with Stooping Behaviour for Photovoltaic Maximum Power Tracking under Partial Shading

A novel global FPPT strategy for PV system under partial shading condition

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