The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.

<span lang="EN-GB">Electricity theft has caused huge losses over the globe and the trend of its perpetuation constantly evolve even as smart technologies such as smart meters are being deployed. Although the smart meters have come under some attacks, they provide sufficient data which can be analysed by an intelligent strategy for effective monitoring and detection of compromised situations. So many techniques have been employed but satisfactory result is yet to be obtained for a real-time detection of this electrical fraud. This work suggests a framework based on Universal Anomaly Detection (UAD) utilizing Lempel-Ziv universal compression algorithm, aimed at achieving a real-time detection in a smart grid environment. A number of the network parameters can be monitored to detect anomalies, but this framework monitors the energy <a name="_Hlk725881"></a>consumption data, rate of change of the energy consumption data, its date stamp and time signatures. To classify the data based on normal and abnormal behaviour, Lempel-Ziv algorithm is used to assign probability of occurrence to the compressed data of the monitored parameters. This framework can learn normal behaviours of smart meter data and give alerts during any detected anomaly based on deviation from this probability. A forced aggressivemeasure is also suggested in the framework as means of applying fines to fraudulent customers.</span>

Homogeneous charge compression ignition (HCCI) engine has emerged as a promising combustion technology. Theoretically, an HCCI engine can reduce both NOx and soot emissions significantly down to almost zero levels. This is possible as a result of two fundamental processes that occur in the HCCI engine, i.e. the homogeneous mixture and its autoignition characteristics. Neither spark plug nor injector is used in the HCCI engine. The autoignition of the homogeneous mixture is solely influenced by its chemical reactions inside the combustion chamber. However, this is where the problems start to occur. At low loads or too lean mixtures, misfire may occur, thus increasing the HC and CO emissions. At high loads or too rich mixtures, soot emissions and knocking tendency may increase. Moreover, an undesirable pressure rise due to knocking will increase the combustion temperature and potentially increase the probability of NOx formation. Therefore, the operating range of HCCI engine is very limited only to part loads. Controlling its combustion phasing play an important role to extend the narrow operating range of the HCCI engine. Despite numerous review articles have been published, classification of the approaches to achieve HCCI combustion in diesel engines were rarely presented clearly. Therefore, this review article aims to provide a concise and comprehensive classification of HCCI combustion so that the role and position of each strategy found in the literature could be understood distinctively. In short, two important questions must be solved to have successful HCCI combustion; (1) how to form a homogeneous mixture? and (2) how to control its auto-ignition?

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