The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R²), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties.

Detection of high impedance arcing faults (HIAFs) in an active distribution network is challenging due to limited fault current contribution from power electronic interfaced distributed energy resources. It may also happen that during the switching operation of any system, component like a transformer, capacitor bank, nonlinear load, and lines, the protection function may fail to maintain security. The secure operation of any detection algorithm depends on the proper selection of key parameters and threshold settings. To enhance the HIAF detection process, the residual current is preprocessed using OTET. The cumulative sum of the fault detection index from the energy coefficients of OTET is used to take a suitable decision. Optimal parameters of the basis function in OTET are obtained using particle swarm optimization algorithm with a wide variety of fault and no-fault datasets. Further, the optimal value of a drift parameter is designed using OTET investigations and utilized in the fault detection index calculation. The selection mechanism incorporated in the method increases both dependability and security. It can be used for grid-connected/islanded modes of the system, different switching events, and grounding connections. The method is also reliable for noise and harmonic contaminated signal and unbalanced system.

In this paper, cascade PI-PD secondary control scheme is presented in automatic generation control (AGC) application under a deregulated environment. Along with conventional electric power generating units, solar is integrated in one of the areas which influences the frequency control process of the entire grid. Differential Evaluation (DE) algorithm is used to obtain optimal parameter gains of the PI-PD controller of each area simultaneously in order to control the frequency oscillations of the system. Centralized area controller concept is adopted to provide this control action. Further, communication delay effect is also investigated in this paper under deregulated environment when the power system integrated with solar and supervised by the PI-PD controller to minimize the frequency oscillations.