Dynamic performance evaluation and improvement of PV energy generation systems using Moth Flame Optimization with combined fractional order PID and sliding mode controller

In this work, a survey is carried out on six MPPT algorithms which include conventional and artificial intelligence based approaches. Maximum Power Point Tracking (MPPT) algorithms are used in PV systems to extract the maximum power in varying climatic conditions. The following most popular MPPT techniques are being reviewed and studied: Hill Climbing (HC), Perturb and Observe (P&O), Incremental Conductance (INC), Open-Circuit Voltage (OCV), Short Circuit Current (SCC), and Fuzzy Logic Control (FLC). The algorithms are evaluated, analyzed, and interpreted using a Matlab-Simulink environment to show the performance and limitations of each algorithm

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“…The components shown in Fig. 1 are connected to form the PV system [8] which are composed as follows:…”

Section: Pv Energy Generation Systemmentioning

confidence: 99%

Survey of Six Maximum Power Point Tracking Algorithms under Standard Test conditions

Bouakkaz

Boukadoum

Boudebbouz

et al. 2021

AJRESD

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“…It must be a better selection to study the practical application of a fractional order controller, which shows stronger robustness in related industrial fields. Bouakkaz [22] proposed an adapted control strategy where the PV voltage is regulated by a fractional order PID controller in various regions and it can be seen that the proposed dynamic performance improvement strategy has excellent transient responses in different operating scenarios and can well improve the PV system dynamics. Kommula [23] put forward a Firefly Algorithm (FA) based Fractional Order PID (FOPID) Controller for Brushless DC (BLDC) motor to achieve an effective control of torque and speed and it is obvious that the FOPID torque controller controls the motor torque effectively with a very low ripple from simulation results.…”

Section: B Design Of Fractional Order Controllermentioning

confidence: 99%

Fractional Controller Design of a DC-DC Converter for PEMFC

Tang

Pei

et al. 2020

IEEE Access

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Proton Exchange Membrane Fuel Cell (PEMFC) is difficult for application because of its high current and wide voltage output. A fractional order PID controller is introduced to a Four-Switch Buck-Boost DC/DC converter to stabilize the power output in this paper. To reduce the inductor current ripple, a kind of dual-triggered switch strategy is employed in detail, and an accurate dynamic model of DC/DC converter is proposed for fuel cell measurement and control system. Then a fractional order PID controller is designed for voltage module compensation, in which a stochastic inertia weight PSO algorithm is employed to optimize the parameters of this controller. The results of simulation and experiment indicate that the compensation effect of fractional order PID controller has better performance than the integer order controller, which is robust to the variation of fuel cell system dynamics and power request.

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“…Nowadays, the MFO algorithm is deeply studied to solve multi-objective problems, unconstrained optimization problems, global optimization problems, and so on [13][14][15][16][17][18]. The MFO algorithm has been proved to be effective in networks [19,20], manufacturing [21], power systems [22][23][24], control [25], energy [26][27][28], reliability analysis [29][30][31], autonomous robot navigation [32], testing [33], photovoltaic modules [34], biomedical science [35][36][37], and so on. The MFO algorithm can then be utilized to optimize and improve the accuracy of the classification algorithm for the purpose of network intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

Przystupa

Fang

et al. 2020

Electronics

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With the widespread use of the Internet, network security issues have attracted more and more attention, and network intrusion detection has become one of the main security technologies. As for network intrusion detection, the original data source always has a high dimension and a large amount of data, which greatly influence the efficiency and the accuracy. Thus, both feature selection and the classifier then play a significant role in raising the performance of network intrusion detection. This paper takes the results of classification optimization of weighted K-nearest neighbor (KNN) with those of the feature selection algorithm into consideration, and proposes a combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN, in order to improve the performance of network intrusion detection. Experimental results show that the weighted KNN can increase the efficiency at the expense of a small amount of the accuracy. Thus, the proposed combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN can then improve both the efficiency and the accuracy of network intrusion detection.

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Dynamic performance evaluation and improvement of PV energy generation systems using Moth Flame Optimization with combined fractional order PID and sliding mode controller

Cited by 51 publications

References 29 publications

Survey of Six Maximum Power Point Tracking Algorithms under Standard Test conditions

Survey of Six Maximum Power Point Tracking Algorithms under Standard Test conditions

Fractional Controller Design of a DC-DC Converter for PEMFC

A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

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