## 4 PV Systems--Implementation and Remote-Control Methods
### Use-Cases  
Case 1: [PERSON] and [PERSON] [21] proposed an installed a stand-alone solar photovoltaic (SA-SPV) system on the roof of a residential building and presented its results. The system was situated 12 m above the ground and had a tilt angle of 21\({}^{\circ}\). The proposed system worked for three seasons specific to the India (Goa) region: southwest monsoon (June-September); post-monsoon (October-January), and summer (February-May). In addition, the system had a battery pack with a capacity of 300 Ah. The photovoltaic system proposed had two modes of operation: stand-alone and on-grid. Sometimes the consumers used the electrical energy from the grid, but in the summer months the system injected electrical energy into the grid. The system's behavior was beneficial for the house's consumers as they always had the necessary electrical energy for consumption. The main risk of the proposed photovoltaic system is that the batteries have been placed in the building and the risk of the building being on fire increases [6]. The data acquisition was done remotely using the Combo platform which registered and stored the data in a memory card or in a cloud solution. Case 2: [PERSON] et al. [22] considered data collected from a grid-connected photovoltaic system (GCPVS) plant in Algiers, consisting of 90 photovoltaic modules with a total power output of 9.54 kWp. The behavior of the GCPVS was simulated in MATLAB in order to obtain the anticipated evolution of the DC power produced by the photovoltaic arrays and the AC power at the output of the photovoltaic inverter. In the suggested approach, the temperature readings were not obtained through the monitoring system; instead, they were generated through a simulation process. Following a comparison of the simulation results with the measured parameters of the photovoltaic system, it was established that the Nominal Operating Cell Temperature (NOCT) model was an empirically validated and suitable representation of the system's behavior. The main purpose of the paper is to present two ways for PV system monitoring: exponentially weighted moving average (EWMA) and double exponentially weighted moving average (DEWMA). Both proposed solutions detect faults of PV systems, especially anomalies occurring in the DC and AC outputs. Therein were discussed and investigated six types of electrical and environmental faults of photovoltaic systems. The proposed procedures are efficient, but they need improvements using modern techniques such deep learning-driven models [23]. Case 3: [PERSON] et al. [24] designed a stand-alone photovoltaic system for testing and analyzing the performance of an Intelligent Monitoring System (IMS). The photovoltaic panels powered three sensors, for temperature, humidity, and irradiance, which were installed on the panels. The data collected from these sensors were used for system monitoring and prediction of the output power. The research presented a complex mode of operation and monitoring for photovoltaic systems. The monitoring system had many capabilities, such as using IoT applications, remote access, detecting (96.56%) and classifying (96.89%) faults, and power generation prediction in photovoltaic systems. The paper presented a comparison between the results obtained by the proposed model with other existing models (convolutional layer--Conv1d, multi-layer perceptron--MLP, LSTM) using two different metrics (root mean squared error--RMSE and mean absolute error--MAE). The data transfer and user access were implemented by using cloud or Internet of Things (IoT) applications. The user's identity is not verified during the authenticationprocess, and this could lead to leaking data to unauthorized persons. To avoid unauthorized access and leaked data, a MFA (Multi Factor Authentication) solution for user authentication could be implemented [25]. Case 4: [PERSON] et al. [26] presented an experimental configuration for the observation of parameter values in photovoltaic modules under four distinct fault conditions and in the absence of faults.