The Role of Single End-Users and Producers on GHG Mitigation in Pakistan—A Case Study

Photovoltaic Panels Classification Using Isolated and Transfer Learned Deep Neural Models Using Infrared Thermographic Images

Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system's memory, resulting in savings in the PV investment.

Defects Impact on PV System GHG Mitigation Potential and Climate Change

Solar photovoltaic (PV) systems are widely used to mitigate greenhouse gases (GHG), due to their green renewable nature. However, environmental factors such as bird drops, shade, pollution, etc., accommodation on PV panels surface reduce photons transmission to PV cells, which results in lower energy yield and GHG mitigation potential of PV system. In this study, the PV system’s energy and GHG mitigation potential loss is investigated under environmental stresses. Defects/hotspots caused by the environment on PV panel surface have unknown occurrence frequency, time duration, and intensity and are highly variable from location to location. Therefore, different concentrations of defects are induced in a healthy 12 kWp PV system. Healthy PV system has the potential to avoid the burning of 3427.65 L of gasoline by 16,157.9 kWh green energy production per annum. However, in 1% and 20% defective systems, green energy potential reduces to 15,974.3 and 12,485.6 kWh per annum, respectively. It is equivalent to lesser evasion burning of 3388.70, and 2648.64 L of gasoline, respectively. A timely solution to defective panels can prevent losses in the PV system to ensure optimal performance.

Influence of load-shedding and night on SiR insulator environmental aging under AC test voltages

Insulators play a critical role in healthy transmission under the influence of operational stresses (OSs). OSs include environmental stresses (ultraviolet-radiations, seasonal heat and humidity variations, and pollution (inert and active), etc.) and electrical stresses (electric field distribution, corona, etc.). OSs can deteriorate insulators, jeopardizing the healthy transmission. In this experimental study, four silicon rubber (SiR) insulators with different concentrations of micro and nano-silica fillers are tested in a specially designed and fabricated compact environmental chamber for 1920 h under OSs, un-scheduled load shedding, and night factors for more realistic field insulators deterioration characteristics. Insulators' potential distribution, electrical field distribution, hydrophobicity, and leakage current characteristics are considered. Experimental results reveal OSs affected insulators' leakage current profile due to hydrophobicity loss and electric field distribution variations. Moreover, SiR insulators under OSs regained insulation characteristics due to heavier inner molecule replacement to the dissipated and degraded outer lower weight insulating molecules. This property provides a reusability opportunity for SiR insulators in power system networks, unlike traditional insulators.