This paper presents the results of a field study undertaken all over the Punjab, Pakistan, to evaluate the socio-economic and climatic impact of photovoltaic-operated high-efficiency irrigation systems (HEIS), i.e., drip and sprinkler irrigation systems. Nearly half of the rural population relies on agriculture for a living, and the recent energy crisis has had a negative impact on rural communities. Farmers’ reliance on fossil fuels for the operation of irrigation systems has increased exponentially, resulting in the high costs of agricultural production. Primary data regarding on-farm agriculture and irrigation practices used in this study were collected through an intensive on-farm survey, while secondary data were taken from published reports and statistics. The results of the current investigation show that the installation of PV systems has resulted in the increased adoption of high-efficiency irrigation systems, a reduction in the high operational costs incurred on account of old diesel-powered pumping systems (with an annual saving of 6.6 million liters of diesel), a 100% increase in farmer’s income, a reduction of 17,622 tons of CO₂ emissions per annum, and 41% savings in water. The unit cost of PV-powered HEIS was found to be 0.1219 USD/kWh, which was 4% and 66% less than subsidized electricity cost and diesel cost, respectively.

Induction motors (IMs) are the backbone of industry, and play a vital role in daily life as well. However, induction motors face various faults during their operation, which may cause overheating, energy losses, and failure in the motors. Keeping in mind the severity of the issues associated with fault occurrence, this paper proposes a novel method of fault detection in induction motors by using “Machine Vision (MV)” along with “Infrared Thermography (IRT)”. It is worth mentioning that the timely prevention of faults in the IM ensures the motor’s safety from failures, and provides longer service life. In this work, a dataset of thermal images of an induction motor under different conditions (i.e., normal operation, overloaded, and fault) was developed using an infrared camera without disturbing the working condition of the motor. Then, the extracted thermal images were effectively used for the feature extraction and training by local octa pattern (LOP) and support-vector machine (SVM) classifiers, respectively. In order to enhance the quality of feature extraction from images, the LOP was implemented along with a genetic algorithm (GA). Finally, the proposed methodology was implemented and validated by detecting the faults introduced in an induction motor in real time. In addition to that, a comparative study of the suggested methodology with existing methods also verified the supremacy and effectiveness of the proposed method in comparison to the previous techniques.

Post-harvest loss is a serious issue to address challenge of food security. A solar-grid hybrid cold storage system was developed and designed for on-farm preservation of perishables. Computational Fluid Dynamic analysis was performed to assess airflow and temperature distribution inside the cold chamber. The system comprises a 21.84 m³ cubical cold storage unit with storage capacity of 2 tonnes. A hybrid solar system comprising 4.5 kWp PV system, 5 kW hybrid inverter, and 600 Ah battery bank was used to power the entire system. A vapor-compression refrigeration system (2 tonnes) was employed coupled with three cooling pads (filled with brine solution) as thermal backup to store cooling (−4 °C to 4 °C). Potatoes were stored at 8 °C for a period of three months (May 2019 to July 2019) and the system was tested on grid utility, solar, and hybrid modes. Solar irradiation was recorded in range of 5.0–6.0 kWh/(m² × d) and average power peak was found to be 4.0 kW. Variable frequency drive was installed with compressor to eliminate the torque load and it resulted about 9.3 A AC current used by the system with 4.6 average Coefficient of Performance of refrigeration unit. The average energy consumed by system was found to be 15 kWh with a share of 4.3 kWh from grid and 10.5 kWh from solar, translating to 30% of power consumption from grid and 70% from solar PV modules. Overall, cold storage unit efficiently controlled total weight loss (7.64%) and preserved quality attributes (3.6 ⁰Brix Total soluble solids, 0.83% Titratable acidity, 6.32 PH) of the product during storage time.

Baking is an energy intensive unit operation. The thermal application of solar energy is getting attention in food processes by eliminating the facts of interrupted supply and fluctuated costs of nonrenewable energy sources. This study has been carried out for the design and development of solar bakery unit which comprises of a 10 m² Scheffler reflector focusing all the beam radiations on a secondary reflector that further concentrate the beam radiations toward the heat receiver of solar bakery unit to heat up the air circulated through baking chamber employing a photovoltaic operated fan. Computational fluid dynamic (CFD)-based three-dimensional (3D) simulation was performed to analyze the design for uniform air distribution in the baking chamber. The system designed configurations gave quite good results for airflow distribution. The receiver temperature reached between 300 and 400 °C while temperature at the inlet of baking chamber was in the range of 200–230 °C, sufficient for most of the products to be baked. The maximum available solar power at receiver was calculated to be 3.46 kW having an average efficiency of 63%. A series of experiments were conducted for the baking of cakes and total energy available in baking chamber was about 3.29 kW and cake utilized 0.201 kW energy to be baked. The average value of energy utilization ratio was found to be 45%. As a base, the study would lead to the development of an appropriate and low cost solar baking units for the maximum retention of quality parameters and energy saving.

A novel solution for optimized energy management comprising a microgrid system for industries in Pakistan is proposed. The proposed study considered microgrids based on photovoltaics, wind turbines, power storage systems, and dual-fuel (DF) generators as backup. A heuristic methodology with a cuckoo search algorithm (CSA) is presented for efficient power trading by scheduling machines. The study was conducted to prove that CSA is adaptable and flexible for self-governing choices for the efficient management and scheduling of machines and power trade between the microgrid and commercial grid. A mixed integer linear programming algorithm is introduced to optimize the system design problems that control decision making for the ideal operation management. A real-time pricing scheme is utilized for electricity price figures. The simulation results show the efficient performance of the proposed scheme to maximize profitability, reduction in electricity cost, and peak to average ratio. Furthermore, the proposed optimization technique was compared with a highly in-use strawberry algorithm to prove the supremacy of the proposed technique. The proposed efficient and robust energy management system was implemented in Shafi Dyeing Industry, Faisalabad, to validate the simulated model.