Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

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Road traffic accidents, the inadvertent crash involving at least one motor vehicle

Smart city adoption and deployment has taken the centre stage worldwide with its realisation clearly hinged on energy efficiency, but its planning is threatened by the vulnerability of smart grids (SGs). Adversaries launch attacks with various motives, but the rampaging electricity theft menace is causing major concerns to SGs deployments and consequently, energy efficiency. Smart electricity meters deployments via the advanced metering infrastructure present promising solutions and even greater potential as it provides adequate data for analytical inferences for achieving proactive measures against various cyberattacks. This study suggests the sources of threats as the first step of such proactive measures of curbing electricity thefts. It provides a framework for monitoring, identifying and curbing the threats based on factors indicative of electricity thefts in a smart utility network. The proposed framework basically focuses on these symptoms of the identified threats indicative of possible electricity theft occurrence to decide on preventing thefts. This study gives a useful background to smart city planners in realising a more reliable, robust and secured energy management scheme required for a sustainable city.

<p>For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO and WT algorithms is efficient for solar radiation forecasting even though the hybrid WT-ANFIS gives more accurate results.</p>

The continuous increase in the penetration of renewable energy (RE) based distributed generations (DGs) in the power system network has created a great concern on the stability of the existing grid. Traditional bulk power plants, which are dominated by synchronous machines (SMs) can easily support system instability, due to the inherent rotor inertia and damping characteristic, as well as voltage (reactive power) control ability. Nevertheless, converter based RE has some special characteristics, such as stochastic real and reactive power output, quick active and reactive power response, small output impedance, and little or no inertia and damping property thereby causing frequency and voltage instability in the system. To solve this problem, virtual synchronous generator (VSG) concept was proposed to emulate some of the features of conventional SG through converter control strategy in order to provide additional inertia virtually. Different control schemes for VSG has been proposed in literature. Surprisingly, an overview of these schemes is yet to be efficiently presented. This paper presents an overview of the VSG control schemes. It provides the concepts, the features of the control schemes and the applications of VSG. Finally, the crucial issues regarding VSG control schemes and the necessary improvement that need to be addressed are highlighted.

<p><span>In this study, a hybrid approach combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is examined for solar radiation prediction in Nigeria. Meteorological data obtained from NIMET Nigeria comprising of </span><span lang="EN-MY">monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours were used as inputs to the model and monthly mean solar radiation was used as the model output. The data used was divided into two for training and testing, with 70% used during the training phase and 30% during the testing phase. The hybrid model performance is assessed using three statistical evaluators, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of determination </span><span lang="EN-SG">(R²). According to the results obtained, a very accurate prediction was achieved by the WT- ANFIS model by improving the value of (R²) by at least 14% and RMSE by at least 78% when compared with other existing models. And a MAPE of 2% is recorded using the proposed approach. The obtained results prove the developed WT-ANFIS model as an efficient tool for solar radiation prediction.</span></p>

Non-technical Losses mainly Electricity theft has been a main concern for power utilities from last many years. Power utilities are estimated to lose billion dollars annually because of illegal usage of electricity by fraudulent consumers. Researchers are trying different methods for proficiently recognizing fraudster costumers. This research suggests a new approach based on C5 algorithm for efficiently identifying consumers involved in electricity theft. The C5.0 algorithm is a modified form of the C4.5 algorithm. It is also one of the decision tree algorithms but with a much-improved classification rate. The C5.0 algorithm relies on monthly energy consumption data to identify any anomaly in consumer energy usage data associated with NTL behavior. There are many types of fraud committed by fraudulent consumers but this research is focused on fraudulent consumers who have a unexpected deviation from their usual load profile. The motivation of this research is to aid Power distribution companies in Pakistan to decrease there NTL’s due to pilfering in energy consumption by fraudulent consumers. The accuracy of the C5.0 algorithm is 94.61% which is much higher when compared to some state of the art machine learning algorithms like Random forest, Support Vector Machine, K-NN and other decision trees.