Biblio

This paper proposes a power network vulnerability analysis method based on topological approach considering of uncertainties from high-penetrated wind generations. In order to assess the influence of the impact of wind generation owing to its variable wind speed etc., the Quasi Monte Carlo based probabilistic load flow is adopted and performed. On the other hand, an extended stochastic topological vulnerability method involving Complex Network theory with probabilistic load flow is proposed. Corresponding metrics, namely stochastic electrical betweenness and stochastic net-ability are proposed respectively and applied to analyze the vulnerability of power network with wind generations. The case study of CIGRE medium voltage benchmark network is performed for illustration and evaluation. Furthermore, a cascading failures model considering the stochastic metrics is also developed to verify the effectiveness of proposed methodology.

This paper introduces an application of machine learning algorithms. In fact, support vector machine and decision tree approaches are studied and applied to compare their performances in detecting, classifying, and locating faults in the transmission network. The IEEE 14-bus transmission network is considered in this work. Besides, 13 types of faults are tested. Particularly, the one fault and the multiple fault cases are investigated and tested separately. Fault simulations are performed using the SimPowerSystems toolbox in Matlab. Basing on the accuracy score, a comparison is made between the proposed approaches while testing simple faults, on the one hand, and when complicated faults are integrated, on the other hand. Simulation results prove that the support vector machine technique can achieve an accuracy of 87% compared to the decision tree which had an accuracy of 53% in complicated cases.

Renewable energy resource plays an important role due to increasing energy claim. Power generation by PV technology is one of the fastest growing renewable energy sources due to its clean, economical and sustainable property. Grid integrated PV systems plays an important role in power generation sector. As the energy demand is increasing day by day, the power transfer capability of transmission line is increasing which leads various problems like stability, increase in fault current, congestion etc. To overcome the problem, we can use either FACTS device or battery storage or construct additional lines which is cost effective. This paper deals with grid connected PV system, which functions as PV-STATCOM. Voltage and damping control are used to elevate the power transfer capacity and to achieve regulated voltage within the limits at the point of common coupling (PCC). The studies are performed on SMIB and the simulation is carried out in MATLAB/SIMULINK environment.

Cyber-attacks detection in modern power systems is undoubtedly indispensable to enhance their resilience and guarantee the continuous production of electricity. As the number of attacks is very small compared to normal events, and attacks are unpredictable, it is not obvious to build a model for attacks. Here, only anomaly-free measurements are utilized to build a reference model for intrusion detection. Specifically, this study presents an unsupervised intrusion detection approach using the k-nearest neighbor algorithm and exponential smoothing monitoring scheme for uncovering attacks in modern power systems. Essentially, the k-nearest neighbor algorithm is implemented to compute the deviation between actual measurements and the faultless (training) data. Then, the exponential smoothing method is used to set up a detection decision-based kNN metric for anomaly detection. The proposed procedure has been tested to detect cyber-attacks in a two-line three-bus power transmission system. The proposed approach has been shown good detection performance.

Energy security program which becomes the part of energy management must ensure the high reliability of the electric power transmission system so that the customer can be served very well. However, there are several problems that can hinder reliability achievement such as the long duration of fault recovery. On the other side, the prediction of fault recovery duration becomes a very challenging task. Because there are still few machine learning-based solution offer this paper proposes a machine learning-based strategy by using Naive-Bayes Classifier (NBC) and Support Vector Machine (SVM) in predicting the fault recovery duration class. The dataset contains 3398 rows of non-temporary-fault type records, six input features (Substation, Asset Type, Fault Category, Outage Start Time, Outage Day, and Outage Month) and single target feature (Fault Recovery Duration). According to the performance test result, those two methods reach around 97-99% of accuracy, average sensitivity, and average specificity. In addition, one of the advantages obtained in field of fault recovery prediction is increasing the accuracy of likelihood level calculation of the long fault recovery time risk.

This paper considers a framework of electrical cyber-physical systems (ECPSs) in which each bus and branch in a power grid is equipped with a controller and a sensor. By means of measuring the damages of cyber attacks in terms of cutting off transmission lines, three solution approaches are proposed to assess and deal with the damages caused by faults or cyber attacks. Splitting incident is treated as a special situation in cascading failure propagation. A new simulation platform is built for simulating the protection procedure of ECPSs under faults. The vulnerability of ECPSs under faults is analyzed by experimental results based on IEEE 39-bus system.

Smart grids utilize computation and communication to improve the efficacy and dependability of power generation, transmission, and distribution. As such, they are among the most critical and complex cyber-physical systems. The success of smart grids in achieving their stated goals is yet to be rigorously proven. In this paper, our focus is on improvements (or lack thereof) in reliability. We discuss vulnerabilities in the smart grid and their potential impact on its reliability, both generally and for the specific example of the IEEE-14 bus system. We conclude the paper by presenting a preliminary Markov imbedded systems model for reliability of smart grids and describe how it can be evolved to capture the vulnerabilities discussed.