In this paper, electromagnetic compatibility estimator is proposed using artificial neural network with scaled conjugate gradient algorithm. Neural networks are trained with the help of seven different optimisation algorithm in MATLAB. Their performance is evaluated on the basis of number of neurons, desired output and mean squared error in offline mode in MATLAB. Among seven algorithms, scaled conjugate gradient algorithm is found to be the best choice. Hence, it is implemented in LabVIEW for online assessment of electromagnetic compatibility issues. Voltage dip, swell and harmonics are generated with the help of experimental set up. It consists of 230 V, 50 Hz input voltage supply, microcontroller, variac and solid state relays. It is interfaced to the LabVIEW software with the help of NI USB 6361 data acquisition system. It enabled the continuous online monitoring of various signals. Along with voltage dip and swell, harmonics are also evaluated with the help of spectrum analyser in LabVIEW. The detailed description of hardware setup and mathematical modelling of trained network is given in this paper.

An electromagnetic field and artificial intelligence-based transmission line protection system for smart grid is proposed in this paper. Bayesian regularization neural network is used as an intelligence technique. Instead of conventional current transformers, the use of magnetic field sensors for monitoring the current in transmission lines is proposed. A mathematical model of the magnetic field due to the current in transmission line is developed by considering two positions of the magnetic field sensors. The developed model is simulated for extensive scenarios. Line to ground, line to line, double line to ground and three-phase faults are created. The fault current values are transformed into magnetic field values and given to the dyadic analysis filter bank for feature extraction process. The extracted features are given as an input to the neural network for the training. The Bayesian regularization algorithm is used as a training algorithm. The performance of the proposed system is compared with other algorithms. It shows that the proposed system with Bayesian regularization have 96.11% of accuracy with the combination of different faults. The results obtained show that the proposed system outperforms the existing approaches.

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM_2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting.

Residential microgrids (MGs) increasingly rely on decentralized energy sources and peer-to-peer energy trading mechanisms to maintain uninterrupted power distribution. However, ensuring concurrency between dynamic energy demands and supply responses remains a critical challenge, especially under fluctuating load and availability conditions. This study proposes a novel Demand Response Optimizer Model (DROM), leveraging Concurrent Extreme Learning (CEL), and blockchain (BC)-based verification to enhance fairness, responsiveness, and efficiency in energy allocation within residential MGs. The proposed DROM incorporates a feed-forward neural network architecture, wherein demand biasing and trading weights are adaptively computed to optimize energy dispatch. A BC framework is employed for decentralized storage and validation of transactional records, preserving system transparency, data integrity, and facilitating real-time energy trading decisions. The model operates across two user categories—Type 1 (building/peer-level) and Type 2 (residential individual)—and dynamically balances demand and response by minimizing bias while maximizing weight assignments across energy requests. The system is evaluated using real-world MG simulation data. Empirical results demonstrate that the proposed model achieves a 14.62% increase in optimal energy trading efficiency and a 14.77% improvement in demand coverage for Type 2 users. Furthermore, the framework delivers a 16.11% enhancement in power distribution and a 14.01% gain in trading reliability compared to benchmark methods. These findings underscore the effectiveness of the CEL-based BC-integrated framework in addressing concurrency, demand suppression, and equitable energy sharing in decentralized smart energy networks.