The Optimal Power Flow is essential for ensuring the reliable and cost-effective operation in modern power systems. However, solving the AC Optimal Power Flow problem is difficult because it is highly non-convex and computationally demanding, particularly when the power system operates under stressed conditions. Convex relaxation methods, such as Second-Order Cone Programming, have been widely used to obtain faster solutions, although their accuracy can be reduced in meshed transmission networks. This paper evaluates the performance of the strengthened SOCP-III relaxation for solving AC-OPF problems under stressed loading conditions. The study is carried out using the IEEE 30, 57 and 118 bus test systems respectively. Where the system load is gradually increased to represent different levels of operating stress. The obtained solutions are compared with the results of conventional AC-OPF to assess the effectiveness of the approach. The evaluation focuses on generation cost and computational efficiency. In addition, the approach preserves acceptable voltage profiles even at higher loading levels. These findings demonstrate that SOCP-III provides a good balance between computational speed and solution accuracy. The method is using increasing load conditions (100%–125%) especially suitable for real-time and large-scale power system optimization.
Aditi Ramteke, Kshitija A. Gaikwad, Shubh Arekar et al.· International Conference on...· 0 citations
Unplanned failures of induction motors impose serious operational and financial penalties on industrial facilities, yet the fault signatures that precede such failures are detectable well in advance through careful sensor instrumentation and data-driven analysis. This paper presents an end-to-end Internet-of-Things (IoT) predictive maintenance scheme based on two off-the-shelf sensors: a DS18B20 one-wire digital thermometer and 2 piezo vibration sensor modules, with an ESP32 edge device for running the full machine learning pipeline offline, independent from any cloud services. Four operating scenarios are considered: healthy condition, BPFO (bearing outer race fault), misaligned shaft, and rotor imbalance. Based on 1-second sampling intervals, 17 descriptors are derived, including statistics in the time domain, Fourier harmonic peaks, energy ratios between different frequency bands, and temperature gradients measured across all sensors. A dual-stage feature selection method using mutual information (MI) score and Random Forest mean decrease impurity (MDI) ranking reduces the number of features to the 10 most relevant descriptors, reducing the computational complexity by 41% at the expense of 5.6% F1-macro. On a balanced 600-sample synthetic dataset, the resulting Random Forest classifier attains 87.3% hold-out accuracy, 91.0±1.9% five-fold cross-validation accuracy, and a macro area-under-the-ROC-curve of 0.980. End-to-end inference takes just 39 ms on the ESP32, easily meeting the 200 ms requirement for real-time alerting.
Akash Mastud, Dhiraj Vaidya, Azaroddin Sayyed et al.· International Conference on...· 0 citations
