Power quality issues are handled very well by filter technologies. In recent years, the advancement of hybrid active power filters (HAPF) has been enhanced due to ease of control and flexibility as compared to other filter technologies. These filters are a beneficial asset for a power producer that requires a smooth filtered output of power. However, the design of these filters is a daunting task to perform. Often, metaheuristic algorithms are employed for dealing with this nonlinear optimization problem. In this work, a new hybrid metaheuristic algorithm (Marine Predator Algorithm and Sine Cosine Algorithm) has been proposed for selecting the best parameters for HAPF. The comparison of different algorithms for obtaining the HAPF parameters is also performed to show case efficacy of the proposed hybrid algorithm. It can be concluded that the proposed algorithm produces robust results and can be a potential tool for estimating the HAPF parameters. The confirmation of the performance of the proposed algorithm is conducted with the results of fitness statistical results, boxplots, and different numerical analyses.

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A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter

The perception of automatic generation control (AGC) has a massive part in delivering eminence power in an interrelated structure. To acquire eminence power by monitoring the fluctuations of frequency and tie-link power, an appropriate controller strategy is essential. This work explores AGC learning under the traditional situation. In this study, we employ a cascade controller with proportional amalgamation with a tilt-integral-derivative with a filter (TIDN) and fractional order integral-derivative (FOID), named TIDN-FOID. In order to acquire the controller’s attributes, a meta-heuristic optimization algorithm spotted hyena optimizer (SHO) is employed. Several investigations express the excellency of the TIDN-FOID controller over other controllers from outlook regarding the lessened level of peak_overshoot, peak_undershoot, and settling_time for the considered structure. The structure comprises thermal, biodiesel units in area 1, thermal, and geothermal units in area-2, and hydrothermal units in area-3. Both biodiesel and GPP have a better effect on system dynamics even in the presence of time delay. Action in the redox flow battery is also examined, providing a noteworthy outcome. Eigenvalue assessment is carried out to comment on the stability of the system. TIDN-FOID parameter values at nominal conditions are appropriate for a higher disturbance value without the need for optimization.

/pdf/dynamic-stability-evaluation-of-an-integrated-biodiesel-1wsn31q8.pdf

Dynamic Stability Evaluation of an Integrated Biodiesel-Geothermal Power Plant-Based Power System with Spotted Hyena Optimized Cascade Controller

Optimal parameter assessment of Solar Photovoltaic module equivalent circuit using a novel enhanced hybrid GWO-SCA algorithm

ABSTRACT The development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the African Vultures Optimization Algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models’ accuracies were notably improved with RMSE scores of and respectively for the RTC France cell, and for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices.

A twofold hunting trip African vultures algorithm for the optimal extraction of photovoltaic generator model parameters

Economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm

The research on deriving accurate equivalent circuit of solar photovoltaic (PV) modules is increasing due to the necessity of constructing efficient energy conversion devices. The PV panel manufacturers provide data on three essential points on current–voltage (I–V) characteristics for standard temperature conditions (STC). Hence, the research on PV modules for different environmental/operational conditions with equivalent mathematical models are quite complex. Therefore, there is a necessity to derive accurate PV equivalent circuit parameters using novel AI-based approaches. This work proposes a novel hybrid meta-heuristic algorithm, hybrid Chimp-Sine cosine algorithm (HCSCA), for PV panel equivalent circuit parameter extraction. A well-known single- and double-diode PV models have been investigated with the proposed algorithm for different categories of PV modules, namely monocrystalline, polycrystalline, and thin film. The parameters derived from the proposed approach result in minimum error over different executions in the order of less than 10−10, which recommends better implementation in the present scenario. The nature of extracted parameters and I–V characteristics of considered PV panels are examined over different runs, which provided satisfactory performance characteristics with the proposed algorithm and recommended for its practical implementation. 

Solar photo voltaic module parameter extraction using a novel Hybrid Chimp-Sine Cosine Algorithm

In this paper an improved optimization strategy is proposed to address issues related to uncertainty of the optimum power flow (OPF) considering the cost analysis. Elephant Herd Optimization (EHO) ...

Bali Sravan Kumar

Papers

Solar photo voltaic module parameter extraction using a novel Hybrid Chimp-Sine Cosine Algorithm

Optimal power flow solution of an integrated power system using elephant herd optimization algorithm incorporating stochastic wind and solar power