Parameters of the solar cell equivalent circuit models have a significant role in assessing the solar cells’ performance and tracking operational variations. In this regard, estimating solar cell parameters is a difficult task because cells have nonlinear current-voltage characteristics. Thus, a fast and accurate optimization algorithm is usually required to solve this engineering problem effectively. This paper proposes two hybrid variants of honey badger algorithm (HBA) and artificial gorilla troops optimizer (GTO) to estimate solar cell parameters. The proposed algorithms minimize the root mean square error (RMSE) between measurement and simulation results. In the first variant, GTO is used to determine the initial population of HBA, while in the second variant, HBA is used to determine the initial population of GTO. These variants can efficiently improve convergence characteristics. The proposed optimization algorithms are applied for parameter estimation of different equivalent circuit models of solar cells and various photovoltaic (PV) modules. Namely, the proposed algorithms test three solar cell equivalent models: single-diode, double-diode, and triple-diode equivalent circuit models. Different photovoltaic modules are investigated, such as the RadioTechnique Compelec (RTC) France solar cell, Solarex’s Multicrystalline 60 watts solar module (MSX 60), and the Photowatt, France solar panel (Photo-watt PWP 201). In addition, the applicability of the proposed optimization algorithms is verified using obtained results from a commercial solar module called Shell Monocrystalline PV module (SM55) with different irradiation and temperature levels. The good results of the proposed algorithms show that they can efficiently improve convergence speed and the accuracy of the obtained results than other algorithms used for parameter estimation of PV equivalent circuit models in the literature, particularly in terms of the values of the RMSE and statistical tests. In addition, the parameters estimated by the proposed methods fit the simulation data perfectly at different irradiance and temperature levels for the commercial PV module.

There are three standard equivalent circuit models of solar cells in the literature—single-diode, double-diode, and triple-diode models. In this paper, first, a modified version of the single diode model, called the Improved Single Diode Model (ISDM), is presented. This modification is realized by adding resistance in series with the diode to enable better power loss dissipation representation. Second, the mathematical expression for the current–voltage relation of this circuit is derived in terms of Lambert’s W function and solved by using the special trans function theory. Third, a novel hybrid algorithm for solar cell parameters estimation is proposed. The proposed algorithm, called SA-MRFO, is used for the parameter estimation of the standard single diode and improved single diode models. The proposed model’s accuracy and the proposed algorithm’s efficiency are tested on a standard RTC France solar cell and SOLAREX module MSX 60. Furthermore, the experimental verification of the proposed circuit and the proposed solar cell parameter estimation algorithm on a solar laboratory module is also realized. Based on all the results obtained, it is shown that the proposed circuit significantly improves current–voltage solar cell representation in comparison with the standard single diode model and many results in the literature on the double diode and triple diode models. Additionally, it is shown that the proposed algorithm is effective and outperforms many literature algorithms in terms of accuracy and convergence speed.

Switched capacitor multilevel inverter (SCMLI) with reduced components is attractive for higher number of voltage levels due to less implementation complexity and low cost. In this study, a new family of hybrid SCMLI for high frequency power distribution system is presented to eliminate the intermediate power conversion. Firstly, a five‐level SCMLI employing a single voltage source is proposed, which is further extended to nine‐level (9L) with its operation. Further extension/enhancement of the proposed 9L‐SCMLI for generating a higher number of voltage levels with reduced number of components is achieved on the basis of structural modification. The mathematical analysis for determination of capacitance, power loss analysis and comparative analysis has been provided in detail. A comprehensive comparison with other similar topologies is also provided to highlight the merits of the proposed topology. Simulation and experimental results are discussed for various dynamic load conditions with different output frequencies to validate the suitability of the proposed SCMLI for various high‐frequency AC applications, such as renewable energy systems, microgrids, electric vehicles and so on.

In this paper, a new single-phase hybrid multilevel inverter (MLI) is proposed. Compared to other existing MLI topologies, the proposed circuit is capable of producing a higher number of output voltage levels using fewer power switches and dc sources. The levels are synthesized by switching the dc voltage sources in series/parallel combinations. An auxiliary circuit is introduced to double the number of levels by creating an intermediate step in between two levels. In addition, a zero level is introduced to overcome the inherent absence of this level in the original circuit. To improve the total harmonic distortion, a hybrid modulation technique is utilized. The operation and performance of the circuit are analyzed and confirmed using MATLAB/Simulink simulation. To validate the workability of the proposed idea, a 300 W, a thirteen level MLI (including the zero level) is designed and constructed. The circuit is tested with a no-load, resistive load and resistive-inductive load. The experimental results match very closely with the simulation and mathematical analysis.

Electricity demand forecasting remains a challenging issue for power system scheduling at varying stages of energy sectors. Short Term load forecasting (STLF) plays a vital part in regulated power systems and electricity markets, which is commonly employed to predict the outcomes power failures. This paper presents an intelligent machine learning with evolutionary algorithm based STLF model, called (IMLEA-STLF) for power systems which involves different stages of operations such as data decomposition, data preprocessing, feature selection, prediction, and parameter tuning. Wavelet transform (WT) is used for the decomposition of the time series and Oppositional Artificial Fish Swarm Optimization algorithm (OAFSA) based feature selection technique to elect an optimal set of features. In order to improvise the convergence rate of AFSA, oppositional based learning (OBL) concept is integrated into it. Then, the water wave optimization (WWO) with Elman neural networks (ENN) model is employed for the predictive process. Finally, inverse WT is applied and obtained the hourly load forecasting data. To validate the effective predictive outcome of the IMLEA-STLF model, an extensive set of simulations take place on benchmark dataset. The resultant values ensured the promising results of the IMLEA-STLF model over the other compared methods.

This paper proposes an extended model based predictive flux control (MPFC) with modified disturbance observer speed loop for induction motors. The main advantages of the proposed method are the improvement of load estimation, the suppression of current overshoot at step changes in reference speed and the removal of weighting factor in the newly formulated cost function. Weighting factor is removed by using extended reference transformation which translates reference torque, generated by the speed controller, into equivalent stator flux vector eliminating the challenging task of gain tuning at different points of operation. Then, the load torque is considered as an unknown disturbance and the accuracy of load estimation during speed jumps is improved by using a reduced order PI observer (ROPIO) with low-pass filter (LPF) for improved integration. The observer is combined with disturbance rejection based control to design a composite speed controller replacing conventional PI loop. The effectiveness of the proposed method is validated on a two-level three-phase inverter fed induction motor drive using dSpace DS1104 controller board. The dynamic response of the proposed method is compared to previously proposed disturbance observer based controller (DOBC) for predictive torque control method. The load estimation error of the proposed method at speed jumps is reduced by 66% while current surges are also suppressed effectively.