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This work presents a machine-learning (ML) algorithm for maximum power point tracking (MPPT) of an isolated photovoltaic (PV) system. Due to the dynamic nature of weather conditions, the energy generation of PV systems is non-linear. Since there is no specific method for effectively dealing with the non-linear data, the use of ML methods to operate the PV system at its maximum power point (MPP) is desirable. A strategy based on the decision-tree (DT) regression ML algorithm is proposed in this work to determine the MPP of a PV system. The data were gleaned from the technical specifications of the PV module and were used to train and test the DT. These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature. The boost converter duty cycle was determined using predicted values. The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m2 irradiance and a temperature of 25°C. The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such as β-MPPT, cuckoo search and artificial neural network results. From the proposed algorithm, efficiency has been improved by >93.93% in the steady state despite erratic irradiance and temperatures.

Photovoltaic panels use the sun’s radiation on their surface to convert solar energy into electricity. This process is dependent on the temperature of the surface and the intensity of the sun's radiation. To escalate the energy transformation, the solar system must be functioned at its maximum power point (MPP). Every maximum power point tracking (MPPT) technique has a distinct mechanism for tracking maximum power point (MPP). The support vector machine (SVM) regression algorithm is used in this work to develop a novel method for tracking the MPP of a PV panel. The solar panel technical parameters were used to prepare the data for training and testing the SVM model. The SVM algorithm predicts the PV panel's maximum power and relevant voltage for specific irradiation and temperature. The duty cycle of the boost converter corresponding to the maximum power was evaluated using the predicted values. The result of the simulation shows that the proposed control strategy forces the solar panel to work near the predicted MPP. The SVM regression control strategy gives the MPP tracking efficiency of more than 94% for the solar PV system despite variable climatic conditions during its stable state operation. In addition, a comparative analysis of the proposed method was carried out with the existing approaches to confirm the effective tracking of the proposed technique.

The present day systems are increasing in complexity in terms of both the size and functionality. Also society demands these systems to be ultra-reliable. Reliability evaluation and optimization techniques play a major role in these regards. However reliability evaluation & optimization techniques do not give any idea about maintenance, risk involved and related cost incurred and criticality of system components or subsystems. Important measures (IM) exist in literature that identify the weak components i.e critical components and give ranking to them. Recently some work has appeared on Cost Importance Measure (CIM). There are number of mistakes/short comings in the paper Cost-related importance measure by Ming et.al. Definition of CIM given in general and the same used for computation of CIM of component x i have appeared differently (Different definitions for CIM). PD(x i),Partial derivative of component x i obtained for most of the components are either inexact or are faulty in expression and computations are wrong. All other mistakes also have not only been pointed but have been corrected also. A New CIM (NCIM) proposed, which highlights the above issues and have done desired calculations. The new CIM which has been advanced is computationally simpler and yields the desired ranking of components.

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