Detection and prevention of faults in overhead electric lines is critical for the reliability and availability of electricity supply. The disadvantages of conventional methods range from cumbersome installations to costly maintenance and from lack of adaptability to hazards for human operators. Thus, transmission inspections based on unmanned aerial vehicles (UAV) have been attracting the attention of researchers since their inception. This article provides a comprehensive review for the development of UAV technologies in the overhead electric power lines patrol process for monitoring and identifying faults, explores its advantages, and realizes the potential of the aforementioned method and how it can be exploited to avoid obstacles, especially when compared with the state-of-the-art mechanical methods. The review focuses on the development of advanced Learning Control strategies for higher manoeuvrability of the quadrotor. It also explores suitable recharging strategies and motor control for improved mission autonomy.

Nowadays, AC distributed power networks are facing many challenges in guaranteeing and improving the required level of power quality indices in power networks with increasing nonlinear, time-variable and unbalanced loads. Power networks can benefit from avoiding and minimizing different AC problems, such as frequency fluctuation and Total Harmonic Distortions (THDs), by using power filters, such as Hybrid Active Power Filters (HAPFs). Therefore, attention towards responsible power quality indices, such as Total Harmonic Distortion (THD), Power Factor (P.F) and Harmonic Pollution (HP) has increased. THD and HP are important indices to show the level of power quality at the network. In this paper, modern optimization techniques have been employed to optimize HAPF parameters, and minimize HP, by using a nature-inspired optimization algorithm, namely, Whale Optimization Algorithm (WOA). The WOA algorithm is compared to the most competitive powerful metaheuristic optimization algorithms: Manta Ray Foraging Optimization (MRFO), Artificial Ecosystem-based Optimization (AEO) and Golden Ratio Optimization Method (GROM). In addition, the WOA, and the proposed modern optimization algorithms, are compared to the most competitive metaheuristic optimization algorithm for HAPF from the literature, called L-SHADE. The comparison results show that the WOA algorithm outperformed all other optimization algorithms, in terms of minimizing harmonic pollution, through optimizing parameters of HAPF; therefore, this paper aims to present the WOA as a powerful control model for HAPF.

Wind disturbances and noise severely affect Unmanned Aerial Vehicles (UAV) when monitoring and finding faults in overhead power lines. Accordingly, we propose repetitive learning as a new solution for the problem. In particular, the performance of Iterative Learning Control (ILC) that are based on optimal approaches are examined, namely (i) Gradient-based ILC and (ii) Norm Optimal ILC. When considering the repetitive nature of fault-finding tasks for electrical overhead power lines, this study develops, implements and evaluates optimal ILC algorithms for a UAV model. Moreover, we suggest attempting a learning gain variation on the standard optimal algorithms instead of heuristically selecting from the previous range. The results of both simulations and experiments of gradient-based norm optimal control reveal that the proposed ILC algorithm has not only contributed to good trajectory tracking, but also good convergence speed and the ability to cope with exogenous disturbances such as wind gusts.

This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems.

Two communication media between meter and server are applicable in Electrical Distribution Company (EDCO) in Jordan, namely, the Power Line Carrier (PLC) and the General Packet Radio Service (GPRS). In this paper, we discuss the implementation of these two communication media in the Automated Meter Reading (AMR) and the Advanced Metering Infrastructure (AMI) project in Jordan; and also point-out the main advantages and dis-advantages of using such grid of AMR/AMI based on these communication media.

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.