# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 2 Related Work  
A model that can estimate, simulate, or predict a single-diode PV's energy production must be accurate and efficient while monitoring the MPPT. PV models consist of non-linear characteristics between current (I) and voltage (V). It is of ultimate importance to minimize errors when estimating the I-V curve. The higher the estimation, the better the model. Notwithstanding, even if the PV modeling is accurate, there are many other errors that might occur with the PV systems. The PV system fails due to abnormal module power operation, sensor problems, and other issues. Tracking those errors and reporting them to stakeholders is crucial for a continuous PV operation. As presented in the work of [13], to accurately predict the output of a PV, the estimation must be associated with outdoor environmental parameters. Weather data were periodically retrieved (i.e., every 5 min) for two years. The selected outdoor values monitored are ambient temperature (\(Ta\)), relative humidity (\(RH\)), wind speed (\(WS\)), and global horizontal irradiance (\(GHI\)). Moreover, the respective PV parameters were monitored for the same time interval and period of time and are the maximum PV power output (\(Pmax\)), the temperature of the PV modules (\(Tm\)), and the plane of array irradiance (\(POA\)). \(Pmax\) was correlated with all the weather variables to indicate their association. The lowest error was produced when the model consisted of \(RH\), \(WS\), and \(Ta\). Therefore, it is suggested to forecast PV power using those variables. According to [14], the MPPT may be estimated through the curve of the solar array by comparing power (P) and static conductance (G). Their model generates fluctuating power and is compared to the PV's array of actual instantaneous power. The error produced is used to modify the static conductance while reaching the MPP. Moreover, the generated error is applied to change the power reference until it attains a limit cycle near the MPP. Their model ensures that the MPPT operates accurately between 10% and 100% nominal power. As stated in [15], to estimate the PV parameters for better PV accuracy, the proper circuit model must be indicated. A flexible particle swarm optimization (FPSO) algorithm is exploited. The FPSO's ordinary movement is altered to boost its accuracy. The FPSO is compared to many other widely used algorithms, like the bird mating optimizer and particle swarm optimization. As stated above, the non-linear characteristics of the PV cells result in a continuous fluctuation of their values. Consequently, the proper modeling of the PV and its parameters is of key importance. As addressed in the work of [16], it is crucial that the PV's operations be continuous without faults or disruptions. The MPPT enhances the efficiency of the modules through DC-DC power optimizers. However, there are still many errors that might occur to the PV systems, like single component failure and plants out of service. As a result, PV auditing and monitoring are essential for diagnostic and maintenance purposes. PV monitoring is deployed through a day-ahead PV output prediction while faults are diagnosed. Moreover, a thorough examination is applied to the PV forecast through its performance indexes. In [17], it is reported that the I-V and P-V parameters and characteristics of a single-diode model affect the PV's operation. The authors divide the voltage and PV current output through an intermediate parameter. They simulate the PV's operation and analyze how each parameter affects the PV output and the error degree. The decomposition of voltage and current in the I-V simplifies the estimation of PV parameters while demonstrating the effect on MPP. As far as monitoring PV techniques are concerned in [18], a novel method for smoothing photovoltaic power fluctuations using a hybrid storage system and optimized charging/discharging cycles is presented.