Parameters Identification of Solar PV Using Hybrid Chaotic Northern Goshawk and Pattern Search

Abstract

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

“…The accurate estimation of ARX parameters reflecting the LD-Didactic temperature process plant model further validates the better performance of ICGWO. Future studies can extend the application of the proposed scheme to solve problems such as PV solar panels, constraint-preserving mixers, and real-time estimation of harmonics in nonlinear loads [ 92 , 93 , 94 , 95 , 96 ].…”

An efficient, fast, and robust algorithm for single diode model parameters estimation of photovoltaic solar cells

“…Metaheuristic methods, on the other hand, are algorithms inspired by nature to solve optimization problems for model parameter estimation. Artificial Bee Colony (ABC) algorithm is in [12], Rat swarm optimizer (RSO) in [13], adaptive differential evolution (DE) algorithm in [14], a performance-guided JAYA (PGJAYA) algorithm in [15], a cat swarm optimization (CSO) algorithm in [16], a flexible particle swarm optimization (FPSO) in [17], a modified simplified swarm optimization (MSSO) algorithm in [18], an improved chaotic whale optimization (CWO) algorithm in [19], an improved opposition-based whale optimization algorithm (WOA) in [20], an improved cuckoo search algorithm (ImCSA) in [21], an improved sine cosine algorithm (ISCA) in [22], an improved Lozi-map chaotic optimization algorithm in [23], an improved teaching-learning-based optimization (TLO) in [24], a coyote optimization algorithm (COA) in [25], a slime mould algorithm (SMA) in [26], a grey wolf optimizer (GWO) in [27] and [28], a sine cosine algorithm (SCA) in [29], an ant lion optimizer (ALO) in [30] and [31], an improved moth-flame optimization (MFO) algorithm in [32] and [33], an improved lion swarm optimization in [34], northern goshawk optimization algorithm (NGO) [10], a multiverse optimizer (MVO) in [35], modified whale optimization algorithm (MWOA) [36], an enhanced adaptive butterfly optimization algorithm (EABOA) in [37], and a manta ray foraging optimization (MRFO) in [38] have been applied for solving the PV parameter estimation.…”

“…The models of PV solar cells or PV arrays are required in real climatic operations where temperature and irradiation vary non-linearly during the daytime and The absolute error (AE ) and mean absolute error (MAE ) are related to the difference between the measured and estimated currents at one measured voltage value and the mean of the differences for all measured voltage values, as shown in (8) and (9). For the relative error (RE ) relation and mean relative error (MRE ), unexpected large errors can be handled and are formulated in (10) and (11), respectively. The RMSE is more accurate as it deals with the squares of errors, as shown in (12).…”

“…Metaheuristic optimization algorithms mainly define the solution vector and regions of the solution parameters. In most cases, the objective function is formulated to minimize the root‐mean‐square error ($$RMSE$$) between the estimated and measured current values of a given PV solar cell or module [9, 10].…”

Earthquake detection in mountainous homes using the internet of things connected to photovoltaic energy supply

“…So that the application of PIR sensors and LDR sensors can detect human movement which causes the house door to activate an emergency lock that can be controlled by the home owner. Additional technology to support the control power so that it is always stable using photovoltaic (PV) technology with the aim of fulfilling a home security system [27]- [29].…”