Maximum Power Point Tracking for stand-alone Photovoltaic system using Evolutionary Programming

IET Renewable Power Generation

Eltamaly

et al. 2020

This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × V oc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications.

Section: Evolutionarymentioning

confidence: 99%

Recent developments of MPPT techniques for PV systems under partial shading conditions: a critical review and performance evaluation

Kermadi

IET Renewable Power Generation

Eltamaly

et al. 2020

“…At the reproduction stage, a new population known as offspring is generated from the parent population through a uniquely formulated equation, according to the various distinctive SC algorithms. Five conventional SC algorithms are chosen for the optimization case study which are Particle Swarm Algorithm (PSO) [12,13], Evolutionary Programming (EP) [14,15], Whale Optimization Algorithm (WOA) [16], Elephant Herding Optimization (EHO) [17] and Butterfly Optimization Algorithm (BOA) [18,19]. The reproduction equations and input parameters for each algorithm are tabulated in Table 1.…”

Section: Overview Of Soft Computing (Sc) Algorithmsmentioning

confidence: 99%

New deterministic initialization method for soft computing global optimization algorithms

Hashim

Ismail

et al. 2020

IJEECS

<span>The initialization stage12 of a Soft Computing (SC) algorithm is vital as it affects the success rate of algorithms in solving multi-peak global optimization problems. The individuals in an initial population, which are known as search agents, are often generated randomly using pseudo-random number generator (PRNG) due to unavailability of prior information on the location of global peak (GP). The random nature of the generated search agents causes uneven distribution of the initial population over the search space (SS), which may lead the search towards unpromising regions from the very beginning. This paper proposes a new deterministic initialization method (DIM) for SC algorithms where search agents are evenly fixed in the SS by using a simple deterministic formulation. The performance of the proposed DIM is then compared to the conventional PRNG and more recent quasi-random number generator (QRNG). An optimization case study is carried out using two popular SC algorithms which are the Particle Swarm Algorithm (PSO) and the Evolutionary Programming (EP), and three relatively new SC algorithms which are the Whale Optimization Algorithm (WOA), the Elephant Herding Optimization (EHO), and the Butterfly Optimization Algorithm (BOA). The optimization is done on various one-dimensional (1D) benchmark functions, as well as practical problems such as partial shading condition (PSC). Simulation results show that the proposed DIM successfully improved the performance of each SC algorithm under study in solving almost all tested functions with 99% success rate compared to 88.7% and 80.2% for the QRNG and PRNG approaches respectively. Furthermore, the WOA is the most reliable and robust among the five SC techniques under study with a success </span>

“…In this paper, classical EP, which uses a Gaussian distribution function for updating the offspring, has been selected because of its ease of use and provides comparatively good results. The reproduction operator of the EP algorithm can be described as follows [35][36][37][38][39]:…”

Section: Evolutionary Programming (Ep)mentioning

confidence: 99%

Critical evaluation of soft computing methods for maximum power point tracking algorithms of photovoltaic systems

Hashim

2019

IJPEDS

<span lang="EN-US">With the proliferation of numerous soft computing (SC)–based maximum power point tracking (MPPT) algorithms for photovoltaic (PV) systems, determining which algorithm performs better than others is becoming increasingly difficult. This is primarily due to the absence of standardized methods to benchmark their performances using consistent and systematic procedures. Moreover, the module technology, power ratings, and environmental conditions reported by numerous publications all differ. Based on these concerns, this paper presents a critical evaluation of the five most important and recent SC-based MPPTs, namely, genetic algorithm (GA), cuckoo search (CS), particle swarm optimization (PSO), differential evolution (DE), and evolutionary programming (EP). To perform a fair comparison, the initialization, selection, and stopping criteria for all methods are fixed in similar conditions. Thus, the performance is determined by its respective reproduction process. Simulation tests are performed using the MATLAB/SIMULINK environment. The performance of each algorithm is compared and evaluated based on its speed of convergence, accuracy, complexity, and success rate. The results indicate that EP appears to be the most promising and encouraging SC algorithm to be used in MPPT for a PV system under the multimodal partial shading condition.</span>