The modern electrical power system relies on digital communications to detect, react, and ideally prevent power outages and other disruptions. In this study, an artificial neural network is trained on both complete and incomplete data to make a prediction about the voltage stability of a power grid network consisting of four nodes arranged in a star configuration. In addition, the missing input data are then forecasted and applied to the problem of maintaining the Voltage stability of the power grid. Accuracy, mean squared error, and mean absolute error are some of the performance evaluation metrics that have been utilized in the context of binary classification. Other metrics include mean absolute error. The evaluation procedure was completed successfully and the model delivers a result of 98.73% precision after going through the testing process.

The antlion optimizer (ALO) algorithm is used in this article for maximum power point tracking (MPPT) of a solar array. The solar array consists of a single module, while there are 20 cells in the module. The voltage and current ratings of each cell are 2 V and 2.5 A, making a 100 W array in ideal condition. However, the voltage and current characteristics of the PV cell are unable to achieve maximum power. Therefore, the ALO was used for MPPT. The results of the ALO are compared with the traditional metaheuristic approaches, perturb and observe ($P\&O$) and flower pollination (FP) algorithms. Comparison of the ALO with the stated algorithms is conducted for two cases: when solar irradiance is 1000 W/m${}^2$ and when it drops to 200 W/m${}^2$ at first then reaches 1000 W/m${}^2$. The change of irradiance is performed to simulate the partial shading condition. The simulation results depict that maximum power for the first case using the ALO reaches 91.3 W in just 0.05 s, while the $P\&O$ and PFA reach 90 W after 0.64 and 2 s, respectively. For the case of partial shading, maximum power using the ALO drops to 55 W when irradiance decreases to 200 W/m${}^2$ and then increases with the increase in irradiance reaching 91.3 W which clearly shows that the ALO outperforms the P&O and FPA.

This work aims to identify cancerous (malignant) and non‐malignant (non‐cancerous) cells in a breast cancer database. Wisconsin breast cancer data (WBC) was utilized and obtained from the University of California, Irvine's machine learning repository. The proposed approach involves the Naive bayes algorithm with Gaussian distribution of the function in combination with Chi‐squared‐based attributes selection approach. This experimentation has been done after reducing the dimensional space of the used data with extended Kernel Principal Component Analysis (K‐PCA). Five different kernels in K‐PCA have been tested after the implementation of necessary pre‐processing techniques. The performance assessment of the proposed system has been evaluated based on confusion matrix‐based accuracy, precision, sensitivity, and specificity. Our proposed methodology with six selected feature and sigmoid K‐PCA attained the best accuracy of 99.28%. This result outer performs many state‐of‐the‐art studies recently published on the identical dataset.

The stability of the operation of the power system is essential to ensure a continuous supply of electricity to meet the load of the system. In the operational process, voltage stability (VS) should be recognized and predicted as a basic requirement. In electrical systems, deep learning and machine learning algorithms have found widespread applications. These algorithms can learn from previous data to detect and predict future scenarios of potential instability. This study introduces long short-term memory (LSTM) technology to predict the stability of the nominal voltage of the power system. Based on the results, the recommended LSTM technology achieved the highest accuracy target of 99.5%. In addition, the LSTM model outperforms other machine learning (ML) and deep learning techniques, i.e., support vector machines (SVMs), Naive Bayes (NB), and convolutional neural networks (CNNs), when comparing the accuracy of the VS forecast. The results show that the LSTM method is useful to predict the voltage of an electrical system. The IEEE 33-bus system indicates that the recommended approach can rapidly and precisely verify the system stability category. Furthermore, the proposed method outperforms conventional assessment methods that rely on shallow learning.

The maintenance of an uninterrupted electricity supply to meet demand is of paramount importance for maintaining the stable operation of an electrical power system. Machine learning and deep learning play a crucial role in maintaining that stable operation. These algorithms have the ability to acquire knowledge from past data, enabling them to efficiently identify and forecast potential scenarios of instability in the future. This work presents a hybrid convolutional long short-term memory (ConvLSTM) technique for training and predicting nodal voltage stability in an IEEE 14-bus microgrid. Analysis of the findings shows that the suggested ConvLSTM model exhibits the highest level of precision, reaching a value of 97.65%. Furthermore, the ConvLSTM model has been shown to perform better compared to alternative machine learning and deep learning models such as convolutional neural networks, k-nearest neighbors, and support vector machine models, specifically in terms of accurately forecasting voltage stability. The IEEE 14-bus system tests indicate that the suggested method can quickly and accurately determine the stability status of the system. The comparative analysis obtained the results and further justified the efficiency and voltage stability of the proposed model.

For the boiler-turbine unit in power systems, a coordinated control structure plays a crucial role in maintaining the balance in supply and demand of energy, reducing pollutant emissions and optimizing energy efficiency. The matching requirements of the turbine and boiler, the wide range of load changes, and the cooperative operation of many devices in the power system pose many challenges to designing a coordinated control system for the boiler turbine system, thus making the control design a difficult task. In this paper, iterative learning control (ILC) is used to maintain the throttle pressure and megawatt output power of a boiler turbine at the desired set points by controlling the hybrid pattern design structure. Simulation results show that the proposed approach can maintain the desired set points, and the desired response can also be obtained faster by using the proposed approach compared to the ones available in the literature.