## 4 PV Systems--Implementation and Remote-Control Methods
### Use-Cases
The experimental setup comprised a photovoltaic system, a device for monitoring current and voltage, a controller, a signal transmission system, and an upper computer. The suggested monitoring solution is applicable to all types of photovoltaic systems and is based solely on the monitoring and evaluation of electrical parameters, including open-circuit voltage, short-circuit current, voltage, and current at the maximum power point. The solution outlined in this research is more cost-effective and efficient than other existing solutions. It employs a smaller number of sensors and is capable of detecting a wider range of faults, including open-circuit, short-circuit, aging, and shadow shading faults, with high accuracy even in the presence of significant shading. Case 5: [PERSON] et al. [27] developed a conventional hybrid photovoltaic system comprising 10 photovoltaic panels (with a 100 Wp capacity each), batteries, loads, inverters, maximum power point tracking (MPPT), PLN grids, sensors, and a monitoring system. The sensors were capable of connecting to a cloud server via Internet of Things (IoT) applications, utilizing the Wi-Fi communication protocol. The Internet of Things (IoT) functionality was responsible for the transfer of data from the sensors to the cloud. This process consisted of an Arduino IoT Node MCU microcontroller, RTC DS 1302, and sensors; namely, a DC 200A current sensor, an AC current sensor, a 0-24 V DC voltage sensor, and a 0-225 V AC voltage sensor. The monitoring application had more dashboards which displayed the data collected from solar panels, battery parameters, PLN panels, and load panels. The data collected were stored in a cloud database, but the research was limited because the data were not used for other functionalities, such as intelligence services, prediction of energy generated by solar panels, or the detection of grid performance failures. The research did not present clearly if the system could be controlled from a distance, if the data used in Internet communications were encrypted, or if the main principles of cybersecurity were respected over Internet connections. According to paper [28], the inverters from on-grid systems are vulnerable to cyber-attacks: malicious actors can attack photovoltaics by providing false measurements of sensed signals, such as the power generated, or the inverter control system could be sabotaged by the grid current. Case 6: [PERSON] et al. [29] used a predefined dataset for training (20%) and testing (80%) a long short-term memory (LSTM) parameter for a proposed dynamic dipper-throated optimization stochastic fractal search (DDTOSFS) algorithm for photovoltaic systems. The algorithm developed could be used for predicting energy consumption using advanced machine learning concepts. This functionality could be integrated in a monitoring system, because it is important to know how much electrical energy is stored in batteries for stand-alone photovoltaic systems [4] and what could be the financial impact (electricity invoices) for on-grid and hybrid photovoltaic systems. The dataset could contain wrong values for parameters, and this affects the integrity of the data used during the algorithm training and testing processes. Automatically, the output when the real values of the photovoltaic system's parameters are used will be affected. Case 7: [PERSON] et al. [30] presented a microgrid system which had 15 kW, bi-directional inverters, and was connected to the grid, and for the storage system, Li batteries and supercapacitors (SC) were used. The paper proposed the development of an optimized and efficient failure-detection method by using machine learning and analysis of big data (BD) and a new power smoothing method using SC and batteries for on-grid systems. It was estimated that the system was able to function properly for between 2 and 5 days based on the weather conditions, especially on cloudy days.