Virtual synchronous generator (VSG) is an important concept toward frequency stabilisation of the modern power system. The penetration of power electronic-based power generation in power grid reduces the total inertia, and thus increases the risk of frequency instability when disturbance occurs in the grid. VSG produces virtual inertia by injecting appropriate active power value to the grid when needed. This virtual inertia can stabilise the grid frequency in case of a power imbalance between generation and loads or any disturbances that affected frequency stability. Its intensive research can see the importance of VSG in inertia control and various intelligent controller techniques. Owing to the importance of VSG in the modern power grid, this study provides a comprehensive review on the control and coordination of VSG toward grid stabilisation in terms of frequency, voltage and oscillation damping during inertia response. A review on the type of energy storage system used for VSG and their benefits is also presented. Finally, perspective on the technical challenges and potential future research related to VSG is also discussed in this study. Nomenclature P g measured active power Q g measured reactive power P * active power reference Q* reactive power reference V g * output voltage reference V g output voltage ω o reference angular frequency ω g grid Frequency ω r rotor angular frequency J moment of inertia constant D damping coefficient constant δ VSG phase angle X output reactance of the VSG K w governor proportional control gain K q reactive power proportional control gain T Q reactive power first-order lag time constant

In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. The main goal of the ELD is to meet the load demand at minimum operating cost by determining the output of the committed generating unit while satisfying system equality and inequality constraints. The problem formulation is based on a multiobjective model in which the multiobjective are defined as fuel cost minimization and carbon emission minimization. MOCSA is based on the inspiration from the brooding parasitism of cuckoo species in nature. Three cases are considered to test the effectiveness of the proposed technique which are fuel cost minimization, carbon emission minimization and multiobjective function with fixed weighted sum. The effectiveness of the MOCSA’s performances are illustrated through comparative study with other techniques such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Particle Swarm Optimization (MOPSO) in terms of fitness functions. The proposed study was conducted on three generating unit system at various loading condition. The result proved that MOCSA provide better solution in minimizing fuel cost and carbon emission usage as compared to other techniques.

<span>Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. </span>

Recently, renewable energy (RE) has become a trend in power generation. It is slowly evolving from an alternative energy source into the main energy source. The technology is currently working as an auxiliary to the existing generators. Demands for electricity is expanding rapidly nowadays, which require generators to run near its operation limit. This activity put grieve risk to the generators. Nonetheless, the extensive analysis should be conducted upon RE integration into the existing power system. This paper assesses its economic impact on the power system. Setting up RE technology such as photovoltaic and wind turbine are costly, yet may reduce generator's fuel cost in the long run. Thus, economic load dispatch (ELD) is conducted to compute the operating cost of power system with the integration of RE system. In this study, the operating cost represents the fuel cost of conventional fossil-fuel generators. Furthermore, a novel optimization technique namely Differential Evolution Immunized Ant Colony Optimization is proposed as the optimization engine. Comparative studies are conducted to assess the performance of the proposed approach. Keywords-economic load dispatch (ELD); renewable energy (RE); differentia evolution immunized ant colony optimization (DEIANT)

Geomagnetically induced current (GIC) has been a significant concern for the electrical power grid in high latitudes for decades. Its origin starts in the Sun; during extreme space weather, the magnetic field of the Earth varies rapidly. This variation induces electric fields at the Earth's surface and leads to GICs in manmade technologies. Power systems are the most affected by this induced current, which causes halfcycle saturation of power transformers and other issues. Understanding the behaviours and chain effects of this phenomenon is the key consideration in modelling the hazards to technological systems from space weather. In this paper, a comprehensive review of space weather, geomagnetic disturbances (GMDs) and GICs and their impacts on the power systems in both high and mid-low latitude regions is presented. Additionally, we highlight the most commonly used methods to model and calculate geoelectric fields at the Earth's surface and GIC in the power systems with respect to DC and AC analysis. In addition, we have classified the GIC effects on the different power system components. Moreover, the possible solutions and mitigation techniques to eliminate or reduce these effects based on different GIC blocking devices are reviewed in this work. This work provides researchers and power system operators a shortcut road path to understanding GIC phenomena, modelling and calculations, effects, and mitigation of these effects.

Voltage instability in power distribution systems can result in voltage collapse throughout the grid. Today, with the advanced of power generation technology from renewable sources, concerns of utility companies are much being focused on the stability of the grid when there is an integration of distributed generation (DG) in the system.  This paper presents a study on DG units placement and sizing in a radial distribution network by using a pre-developed index called Voltage Stability Condition Index (VSCI). In this paper, VSCI is used to determine DG placement candidates, while the value of power losses is used to identify the best DG placement. The proposed method is tested on a standard 33-bus radial distribution network and compared with existing Ettehadi and Aman methods. The effectiveness of the method is presented in terms of reduction in power system losses, maximization of system loadability and voltage quality improvement. Results show that VSCI can be utilized as the voltage stability indicator for DG placement in radial distribution power system. The integration of DG is found to improve voltage stability by increasing the system loadability and reducing the power losses of the network.

Abstract

This paper presents a statistical algorithm for classification of fault causes on power transmission lines. The proposed algorithm is based upon the root mean square (RMS) current duration, voltage dip, and discrete wavelet transform (DWT) measured at the sending end of a line and the decision tree method, a commonly accessible measurable method. Fault duration of RMS current signal, voltage dip, and DWT gives concealed data of a fault signature as a contribution to decision tree calculation which is utilized to classify various fault causes. The proposed method was carried out in the MATLAB/SIMULINK programming platform based upon the information made with the fault analysis of the 275 kV sample transmission line considering wide variations in the operating conditions. The classifier performance of different parameters was also compared in a confusion matrix form to obtain the best classification results of the decision tree.