Energy intense nature of cement kiln demands optimal operation to minimize the energy requirement. Optimal control of cement kiln is achieved by proper tuning of the model predictive controller (MPC), which is addressed in this work. Genetic algorithm (GA) is used to determine the MPC weights that minimize the overall energy utilization with reduced tracking error. Single objective function has been formulated using importance weighted performance metrics like energy utilization and integral absolute error in tracking the desired response. Importance weights are determined in specific to the control scenarios using an interactive decision tree (IDT). It interacts with the operator to detect the weaker metrics and raises the importance level for further improvement. The algorithm terminates after attending all the metrics with the consent from the operator. Five control scenarios that predominantly occur in industrial cement kiln have been considered in this study. It includes tracking, measured, and unmeasured disturbance rejection of pulse and Gaussian type noises. The results illustrate the minimized energy operation with the use of the proposed single objective function as compared with the multi-objective function-based GA tuning procedure.

Fault detection and classification are fundamental requirements of multilevel inverters (MLIs) to ensure constant operation and improved reliability. Nowadays, the machine learning (ML) technique is utilized for fault diagnosis in MLIs due to its inherent features such as high accuracy, reduced computation time, and complexity. However, the rich availability of parts and classifiers in ML techniques demands a tedious investigation of every combination to design an optimal fault classifier. To overcome this problem, a combined optimiser is proposed to automate the ML-based classifier design, which involves selecting optimal features and classifier. Mean, total harmonic distortion, root mean square, and different harmonic orders (upto 12^th order) of the output voltage of MLI are considered as features and four different ML techniques like K-nearest neighborhood, decision tree, Naive Bayesian classifier, and support vector machines are considered. Ant Colony Optimization (ACO) is used to formulate a combinatorial optimization routine to maximize classification accuracy by optimal selection of features and classifier. The proposed technique is used to design a fault classifier for two different MLIs, such as Cascaded H-bridge MLI (CHBMLI) and Packed U cell inverter (PUC), during the fault conditions (open circuit and short circuit) to check the feasibility. Simulation results illustrate classification accuracy of 97.84% and 98.61% for CHBMLI and PUC, respectively. Experimental validation of the designed classifier on the inverter prototype is also carried out and illustrates 95.56% and 94.28% classification accuracy for CHBMLI and PUC, respectively.

Dispersion nature of water droplets over the insulator surface is used for hydrophobicity classification. Stochastic nature of water dispersions makes naive Bayesian classifier a preferable choice, which has been investigated in this work. About 12 features describing the characteristics of water droplets are extracted from the binary image using binary large objects analysis. Ambient light intensity is a significant factor that affects the binary image quality. As these insulators are installed in the outside environment, variations in ambient light intensity are inevitable. An adaptive threshold technique is proposed to compensate for ambient light variations. Six classes of various ambient light intensities have been considered in this study, and the proposed adaptive threshold technique can produce quality binary image consistently. Features extracted from the binary image are ordered according to their principal components (PCs) using PC analysis. Improvement in classification accuracy with the accumulation of ordered features is analysed. Results illustrate the use of the first eight features provides a reliable classification accuracy of 97.6% for test image samples. In comparison to the other existing classifiers, the proposed classifier illustrates optimal performance in terms of classification accuracy and computational time.

Hydrophobicity of polymeric insulator plays a vital role in determining the insulation quality in outdoor overhead electrical transmission and distribution lines. Loss of hydrophobicity increases the leakage current and leads to flashover. Monitoring hydrophobicity becomes a fundamental requirement to ensure continuity of power line operations. Hydrophobicity of polymeric insulator is classified according to STRI (Swedish Transmission Research Institute) guidelines. This paper proposes an intelligent ANFIS (Adaptive Neuro-Fuzzy Inference System) based classifier to determine the hydrophobicity quality using the digital image of the insulator. Ten statistical features are extracted from the digital images. Two stages of feature reduction are employed to reduce the number of features. Pre-design stage uses PCA (Principal Component Analysis) and reduces the number of features to six from ten and the post-design stage analyzes the accumulation effect to reduce the number of features to four. Various ANFIS classifiers are trained using these reduced features extracted from the image. The performance of these ANFIS classifiers is evaluated in both field and laboratory specimens. Results indicate classification accuracy of 96.4% and 93.3% during the training and testing phase when triangular membership function with linear output function is employed in ANFIS. A GUI (Graphical User Interface) has also been designed to facilitate the use of the proposed system by field operators.

Real time traffic control is a main criteria of the urban traffic signal control system, and giving viable ongoing traffic signal control for a substantial complex traffic system is a testing issue. The objective of the work is to find and adjust the timing of signals based on the traffic density. Such a situation arises in a city where outbound vehicles during morning time and inbound vehicles during evening time is more while the vehicular movement in the opposite direction is less. To predict and adjust the timings of the signals on both sides of the road at the same time, Artificial Neural Network technique is used. A real time traffic survey of Light Motor Vehicle, Heavy Motor Vehicle, two and three wheeler vehicular movement in Thanjavur city is done. The number of vehicles (cars, auto, bikes, trucks and buses) and width of the road was given as a input and the output predicted was in terms of timing for the traffic signal at any particular place and for any particular width of the road. The width of the road is also taken into account which is essential in planning a city based traffic consisting of different road widths.