When operating an equipment or a power system at the high voltage, problems associated with partial discharge (PD) can be tracked down to electromagnetic emission, acoustic emission or chemical reactions such as the formation of ozone and nitrous oxide gases. The high voltage equipment and high voltage installation owners have come to terms with the need for conditions monitoring the process of PD in the equipments such as power transformers, gas insulated substations (GIS), and cable installations. This paper reviews the available PD detection methods (involving high voltage equipment) such as electrical detection, chemical detection, acoustic detection, and optical detection. Advantages and disadvantages of each method have been explored and compared. The review suggests that optical detection techniques provide many advantages in the consideration of accuracy and suitability for the applications when compared to other techniques.

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10−8 and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods.

The steady increase in the energy demand and the growing carbon footprint has forced electricity-based utilities to shift from their use of non-renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2-stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non-identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi-Agent-System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs).

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

No abstract