# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 1 Introduction  
Photovoltaic (PV) systems are a remarkable breakthrough that dazzle with their unparalleled benefits. The energy harnessed by these systems is devoid of any pernicious pollutants and does not impinge on natural resources or threaten the well-being of living beings. In addition, the noiseless and highly reliable PVs are entirely self-sufficient, requiring only nominal maintenance costs [1]. It is no wonder that solar PVs are considered a preeminent source of clean energy, and their prevalence continues to rise because of their groundbreaking advances in both technology and marketability. These strides include an impressive array of cell types, PV efficiency, and electronic capabilities that elevate their prominence and secure their position as a pinnacle of modern technology [2]. Although photovoltaic systems offer a multitude of benefits, it should be noted that their output is subject to fluctuations that are dependent on several factors, including prevailing outdoor conditions such as solar irradiance and temperature, as well as the characteristics of the PV modules, such as photovoltaic cell temperature and connected load [3]. To ensure optimal performance, the connected load must operate at a specific juncture, known as the maximum power point (MPP), where the PV produces the maximum power output. This feat is accomplished by accurately correlating the PV's characteristics with the environmental variables to achieve maximum point power tracking (MPPT). The exhaustive testing of all commercially available PV models under different environmental conditions is an arduous and cost-intensive task. Consequently, researchers have exploited mathematical and physical models to simulate, estimate, and forecast the PV's electrical characteristics based on diverse parameters [4]. The most commonly used models are lumped parameter models with varying numbers of diodes. The single-diode model of the PV cell reigns supreme as the most prevalent model employed today [5]. However, determining the current-voltage correlation of the single-diode photovoltaic is an intricate process that involves solving complex transcendental and nonlinear equations, rendering it a laborious undertaking. Establishing all pertinent parameters is imperative to accurately model a single-diode photovoltaic model [6]. However, not all parameters and specifications are readily available in the manufacturer's manual. While data regarding voltage at MPP (\(V_{mpp}\)), open circuit voltage (\(V_{oc}\)), current at MPP (\(I_{mpp}\)), and short circuit current (\(I_{sc}\)) are commonly provided, other critical PV parameters, such as diode ideality factor (\(a\)), parallel resistance (\(R_{p}\)), reverse saturation current (\(I_{s}\)), and photogenerated current (\(I_{ph}\)), may not be easily obtained [6]. Consequently, estimation or modeling using alternative means becomes necessary to determine these unknown parameters. Given the complexity of the process, accurately modeling PV characteristics is essential to ensuring model robustness and reliability. Estimating photovoltaic parameters is a multifaceted endeavor that relies on diverse numerical, statistical, analytical, and intelligent techniques. Although numerical models rely solely on manufacturer-provided data to estimate PV parameters, analytical and statistical approaches utilize complex non-linear equations to estimate the I-V curve parameters [7]. Intelligent models leverage meta-heuristic algorithms to extract PV parameters, making use of sophisticated techniques to tackle the challenge of PV modeling [8]. Indeed, the extensive literature [9] on extracting PV parameters attests to the formidable nature of this task. From the perspective of PV estimation, the paramount and pivotal factors are PV modeling and its parameters, as well as the MPPT. In addition, it is crucial to ensure the uninterrupted and continuous functioning of photovoltaics.