SummaryDamping of low‐frequency oscillations due to the unpredictable perturbations of a power network has always been a challenging task. In an interconnected power network, power system stabilizers (PSSs) are in practice to damp out these low‐frequency oscillations by providing a necessary control signal to the automatic voltage regulator unit based on the deviation in generator speed/power output. This article proposes a novel approach of hybrid modified grey wolf optimization‐sine cosine algorithm for tuning the parameters of PSS of an interconnected multimachine power system. The optimal parameter tuning of PSS with the proposed algorithm is achieved by considering a multiobjective function comprises of improving the damping and eigenvalue characteristics of the consolidated multimachine system. A benchmark model of two area four machine system is adopted to investigate the performance achieved with the proposed algorithm in the simultaneous damping of the local and interarea mode of oscillations in a multimachine power system. The system study has been carried out under a self‐clearing fault condition, and the detailed analysis is presented by analyzing the eigenvalues, and their corresponding natural frequencies, damping ratios. The damping nature achieved for the system states under system uncertainties with the proposed algorithm is also presented. The performance obtained from the proposed hybrid algorithm has been compared with the standalone and state‐of‐the‐art optimization methods.

An emerging challenge for software engineering is the development of the methods and tools to aid design and analysis of concurrent and distributed software. Over the past few years, a number of analysis methods that focus on Ada tasking have been developed. Many of these methods are based on some form of reachability analysis, which has the advantage of being conceptually simple, but the disadvantage of being computationally expensive. We explore the effectiveness of various Petri net-based techniques for the automated deadlock analysis of Ada programs. Our experiments consider a variety of state space reduction methods both individually and in various combinations. The experiments are applied to a number of classical concurrent programs as well as a set of “real-world” programs. The results indicate that Petri net reduction and reduced state space generation are mutually beneficial techniques, and that combined approaches based on Petri net models are quite effective, compared to alternative analysis approaches.

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Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis.

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