Infrared Thermography has been used as a tool for predictive and preventive maintenance of Photovoltaic panels. International Electrotechnical Commission provides some guidelines for using thermography to detect defects in Photovoltaic panels. However, the proposed guidelines focus only on the location of the hot spot than diagnosing the types of faults. The long-term reliability and efficiency of panels can be affected by progressive defects such as discolouring and delamination. This paper proposed the new Thermal Pixel Counting algorithm to detect the above faults based on three thermal profile index values. The real-time experimental testing was carried out using FLIR T420bx® thermal imager and results have been provided to validate the proposed method. In this work, the fuzzy rule-based classification system is proposed to automate the classification process. Fuzzy reasoning method based on a single winner rule fuzzy classifier is designed with modified rule weights by particular grade. The performance of the proposed classifier is compared with the conventional fuzzy classifier and neural network model.

The purpose of the Combined Economic Emission Dispatch (CEED) of electric power is to offer the most exceptional schedule for production units, which must run with both low fuel costs and emission levels concurrently, thereby meeting the lack of system equality and inequality constraints. Economic and emissions dispatching has become a primary and significant concern in power system networks. Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations. The optimal power allocation to generators serves as a solution to this problem. Emission dispatch reduces emissions while ignoring economic considerations. A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions. Consequently, this work manages the Substantial Augmented Transformative Algorithm (SATA) to take care of the Combined Economic Emission Dispatch Problem (CEEDP) of warm units while fulfilling imperatives, for example, confines on generator limit, diminish the fuel cost, lessen the emission and decrease the force misfortune. SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms. The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution. The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search. The simulation of the proposed system is developed using MATLAB Simulink software. Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues. The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Simulated Annealing (SA), and Differential Evolution (DE) methods. The fuel cost and gas emission of the proposed system are 128904 $/hr and 138094.4652$/hr.