Solar photovoltaic (PV) panels are the best solution to reduce greenhouse gas emissions by fossil fuel combustion, with global capability now exceeding 714 GW due to rapid technological advances in solar panels (SPs). However, SPs’ efficiency and lifespan remain limited due to the absence of advanced fault-detection systems, and they are prone to short circuits (SC), open circuits (OC), and power degradation. Therefore, this large-scale production requires reliable, real-time fault diagnosis to maintain panel performance. However, traditional diagnostic methods implemented using MPPT, neural networks, or microcontroller-based systems often rely on complex computational algorithms and are not cost-effective. So, this paper proposes a diagnostic system composed of six functional blocks to address this issue. The proposed system was initially verified using an Intel DE-10 Lite FPGA board. Once its functionality was confirmed, an ASIC design was proposed for mass production, offering a significantly lower implementation cost and reduced hardware complexity than prior methods. Different circuit designs were developed for each of the six blocks. All designs were created using Cadence software and TSMC 180 nm technology files. The basic components used in these designs include PMOS transistors with 300 nm channel length and 2 µm width, NMOS transistors with 350 nm channel length and 2 µm width, as well as resistors and capacitors. Differential amplifiers with a gain of 40 dB were used for voltage and current sensing from the SP. The chip activation signal generator circuit was designed with an adjustable frequency and generated 120 MHz and 100 MHz signals in this work. The decision-making block, Logic Driver Circuit, was innovatively implemented using a reduced number of transistors. A custom memory block with a reset switch was also implemented to store the fault value detected at the SP. Finally, the proposed ASIC was implemented for fabrication, which is highly cost-effective in mass production and does not require complex computational stages.

The greenhouse effect, responsible for trapping heat in Earth’s atmosphere, has a parallel thermal phenomenon at the indoor scale known as the Room-Level Greenhouse Effect (RGHE), where solar radiation elevates room temperatures and increases energy consumption. The RGHE contributes to indoor temperature increases of 4–10 °C and elevates energy demands by 15–30% in high solar exposure zones, the effect being even worse in tropical zones. To address this problem, an innovative analog microarchitecture is proposed for real-time RGHE detection by sensing the sunlight intensity radiation factor (SIR). A compact analog system is introduced, comprising three stages: a Sensing Circuit Stage (SCS) that isolates the dynamic sunlight signal f (r) from static room condition factors (RCFs), an Amplification Stage (AS) that shifts and boosts the signal, and a Stabilized Peak Detection Stage (SPDS) that captures the peak solar intensity. The microsystem was tested across fixed f (m) levels of 0.75 V, 1.0 V, and 1.5 V, and varying f (r) values of 3 mV, 4 mV, and 5 mV. It successfully detects peak voltages ranging from 1.69 V to 1.92 V, with stabilization achieved within 60 µs, enabling accurate detection of the f (r) signal. The proposed microarchitecture offers a scalable approach to localized thermal monitoring in smart building environments using fully analog circuitry, designed and simulated in Cadence Virtuoso using the TSMC 180 nm technology library.