The state of cellulosic solid kraft paper (CSKP) insulation, to a large extent, is an indication of a transformer’s health. It not only reflects the condition of transformer but also diagnose its residual life. The quantity of 2-furfuraldehyde (2-FAL), carbon dioxide (CO₂), and carbon monoxide (CO) dissolved in the transformer oil are useful diagnostic indicators to predict the state of the CSKP insulation. In this work, the current physical state of the CSKP is determined with the help of easily measurable parameters, like temperature, moisture, and the aging time. Here, the degree of deterioration of CSKP insulation has been determined using an integrated insulation health assessment system. This technique integrates a two-stage system comprising of a neural network (NN) model followed by a Smart Life Prediction Approach (SLPA). A thermo-moisture-aging multi-layer feed-forward NN model has been developed to predict the concentrations of 2-FAL, CO₂, and CO, which are further correlated to estimate the Degree of Polymerization (DP) values adopting an SLPA. The advantage of the proposed integrated system is that it provides an alternative means of paper health assessment based on Dissolved Gas Analysis (DGA) without estimating dissolved gas concentrations in oil, thereby avoiding the use of sophisticated measuring instruments. The optimal configuration of the NN model has been achieved at minimum iterations with an average cross-validation mean square error of 3.78 × 10⁻⁷. The proposed system thereby avoids destructive and offline measurement of DP and facilitates real-time condition monitoring of oil-immersed transformers. The test results of the developed system show considerable reliability in determining insulation health using easily measurable parameters. Furthermore, the system’s performance is compared with reported work and has been found to provide encouraging outcomes.

The rapid growth of technologies like the Internet of Things (IoT) has thrown newer challenges to resource management, making it one of the crucial tasks in dynamic application scenarios of IoT. Service-Oriented Architecture (SOA) plays an important role in this respect by proffering reference architecture towards better positioning of infrastructural software and hardware components, mostly comprehending from a service-oriented perspective. In this direction, we propose Cone Model that deals with the performance and handling issues with SOA system. Service integration is an all-important feature of SOA that supports communication and interaction between multiple service components (Service Containers). In addition to discussing the related work for critical resource allocation algorithms, this paper proposes Cone Model for assigning an available set of resources to requested services, explained through mathematical expressions and algorithms of the model. These algorithms justify how different services from various devices in IoT can be provisioned on the adoption of this proposed cone model. It is illustrated through the estimated utilization of resources by the services. The proposed model aligns with the applications in multifarious domains, primarily in the IoT and in areas of resource sharing for cloud computing and massive data centers.

In recent years, Multi-level Inverter Technology is preferred for medium and high voltages and power applications. In electrical power system most loads require ac power as a source. In cases where dc power is an input, we require an inverter to convert dc to ac. Previously, the inverter technology was limited to two level voltages, which causes more distortions and noise. With the demand of better quality of power, continuous research on inverter technology has resulted in the development of multilevel inverter. In this work, the focus is to increase the fundamental output voltages and also to minimize the total harmonics distortion (THD). A three -level “diode clamped inverter” has been analysed and observed under different electrical loadings, considering a PV system as a separate dc source [6]. Simulations have been carried out in MATLAB using third harmonic injection PWM technique (THIPWM) to control the 3 level inverter in order to reduce the harmonics distortion and boost fundamental voltage level.