The contribution of solar photovoltaics (PV׳s) in generation of electric power is continually increasing. PV cells are commonly modelled as circuits. Finding appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model parameters of solar PV cells is referred to as “PV cell model parameter estimation problem,” and is highly attracted by researchers. In this paper, the existing research works on PV cell model parameter estimation problem are classified into three categories and the research works of those categories are reviewed. Based on the conducted review, some recommendations for future research are provided.

Parameter estimation of solar photovoltaic (PV) cells: A review

Habitat selection and compensation for Doppler effect are incorporated into algorithm.Algorithm possesses the quantum search operator and mechanical search operator.Self-adaptive local search is proposed.Algorithm shows significant performance in comparison with more than 20 methods. A novel bat algorithm (NBA) is proposed for optimization in this paper, which focuses on further mimicking the bats' behaviors and improving bat algorithm (BA) in view of biology. The proposed algorithm incorporates the bats' habitat selection and their self-adaptive compensation for Doppler effect in echoes into the basic BA. The bats' habitat selection is modeled as the selection between their quantum behaviors and mechanical behaviors. Having considered the bats' self-adaptive compensation for Doppler effect in echoes and the individual's difference in the compensation rate, the echolocation characteristics of bats can be further simulated in NBA. A self-adaptive local search strategy is also embedded into NBA. Simulations and comparisons based on twenty benchmark problems and four real-world engineering designs demonstrate the effectiveness, efficiency and stability of NBA compared with the basic BA and some well-known algorithms, and suggest that to improve algorithm based on biological basis should be very efficient. Further research topics are also discussed.

A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization

DSTATCOM allocation in distribution networks considering load variations using bat algorithm

Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.

https://www.mdpi.com/2227-7390/8/9/1415/pdf

Elephant Herding Optimization: Variants, Hybrids, and Applications

Precise model parameters being the prerequisite for realizing accurate PV models, parameter identification techniques have gained immense interest over the years among the researchers specializing in PV systems. The application of various promising metaheuristic algorithms to optimize the model parameters have lightened up the scope of further enhancements in this field. Ever since, numerous metaheuristic algorithms have deployed for this purpose. With handful of techniques available in this regard, this paper takes up an initiative to review the existing metaheuristic algorithms based parameter extraction techniques with an emphasis on their compatibility, accuracy, convergence speed, range of parameters set and their validating environment. Based on the analysis conducted, accurate models available for 17 different industrial solar cells/modules are identified. Inspired by this review, an unidentified gateway between parameter extraction and fault detection in PV systems have been identified; and has further extended this review to differentiate some models that can help the researchers to achieve accurate, efficient and rapid fault detection. This review is a valuable gathering of statistics from the various researches carried out in PV parameter extraction which can assist enhanced researches for fault detection in PV systems as well.

Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems

Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm

In this paper, the design of fuzzy logic power system stabilizer (FPSS) is carried out using a harmony search algorithm (HSA) to optimize the input–output scaling factors of the fuzzy logic controller. The optimization problem is considered minimization of integral square error as an objective function with single-machine and multimachine power system. The performance of the HSA optimized FPSS with both systems is compared that of with robust FPSS; HSA tuned input scaling factors of FPSS, and particle swarm optimization tuned input–output FPSS for a wide range of operating conditions. The speed response performance for both systems is compared in terms of performance indices, and superiority of the proposed HSFPSS is validated.

Optimal Tuning of Fuzzy Logic Power System Stabilizer Using Harmony Search Algorithm

Power system stabilizers (PSSs) are added to excitation system to enhance the damping during low frequency oscillations. This paper presents a study of fuzzy logic power system stabilizer (PSS) for stability enhancement of a multi-machine power system. In order to accomplish the stability enhancement, speed deviation (Deltaomega) and acceleration (Delta macromega) of the rotor of synchronous generator of multi machine power system were taken as the input to the fuzzy logic controller. These variables take significant effects on damping on damping of the generator shaft mechanical oscillations. The stabilizing signals were computed using the fuzzy membership functions depending on these variables. The simulations were tested under different de-fuzzyfication methods. The simulation results are quite encouraging and satisfactory.

Fuzzy Logic based Robust Power System Stabilizer for Multi-Machine Power System

Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm

This paper presents an evaluation of membership functions on a single-machine infinite-bus and two-area four-machine ten-bus power system with power system stabilizers (PSSs). The PSS is added to an excitation system to enhance the damping during low-frequency oscillations. In this paper, the system is analysed for fuzzy logic power system stabilizer (FPSS) with different membership functions (MFs). The speed deviation (
 $$\Delta \omega$$
 ) and acceleration (
 $$\Delta \dot{\omega }$$
 ) of the rotor of a synchronous generator are taken as the input to the fuzzy logic controller to improve small signal stability by enhancing damping. The effect of these variables on damping at the generator shaft mechanical oscillation is very significant. The stabilizing signals were computed using the different fuzzy membership functions in the Mamdani inference system. The general membership functions under consideration are triangular, trapezoidal, Gaussian, bell, sigmoid and polynomial types. The performance of the fuzzy logic PSS with different membership functions is compared to get best-suited MF to design an FPSS. The best-performing MF is found based on simulation study with both power systems and with varying no. of linguistic variables as well. It is found that, If the number of linguistic variables is 3 or 5, the preferred best-suited membership function appears as the Gaussian type, while with increased linguistic variables as 7 or above, then the triangular MF is preferable as the performance is better in comparison with Gaussian MF.

Dhanesh Kumar Sambariya

Papers

Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm

Optimal Tuning of Fuzzy Logic Power System Stabilizer Using Harmony Search Algorithm

Fuzzy Logic based Robust Power System Stabilizer for Multi-Machine Power System

Design of optimal input–output scaling factors based fuzzy PSS using bat algorithm

Selection of Membership Functions Based on Fuzzy Rules to Design an Efficient Power System Stabilizer