power transmission
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Abstract: In this paper we attempt to explain and establish certain frameworks that can be assessed for implementing security systems against cyber-threats and cyber-criminals. We give a brief overview of electronic signature generation procedures which include its validation and efficiency for promoting cyber security for confidential documents and information stored in the cloud. We strictly avoid the mathematical modelling of the electronic signature generation process as it is beyond the scope of this paper, instead we take a theoretical approach to explain the procedures. We also model the threats posed by a malicious hacker seeking to induce disturbances in the functioning of a power transmission grid via the means of cyber-physical networks and systems. We use the strategy of a load redistribution attack, while clearly acknowledging that the hacker would form its decision policy on inadequate information. Our research indicate that inaccurate admittance values often cause moderately invasive cyber-attacks that still compromise the grid security, while inadequate capacity values result in comparatively less efficient attacks. In the end we propose a security framework for the security systems utilised by companies and corporations at global scale to conduct cyber-security related operations. Keywords: Electronic signature, Key pair, sequence modelling, hacker, power transmission grid, Threat response, framework.

Abstract We establish the method for estimating the stray elements of the GaN-WPT circuit by measuring the radiated emission around the GaN switching device. By controlling the circuit supply voltage, the spectrum peak shift due to the output capacitance of the GaN-HEMT is observed. It is found that these peak shift characteristics include the influence of both the stray wire inductance and stray capacitance. By the fitting using the series resonance model, the value of the stray inductance and stray capacitance can be estimated in the non-destructive measurement in the GaN-WPT circuit.

The law of free access to the transmission network obliges the transmission network to be in orbit, and on the other hand, the high loads in the transmission network, and economic uncertainties cause that the owners of transmission companies, don’t have sufficient motivation and resources to rebuild and develop the network. The main objective of this paper is the modeling the price of emergency power transmission lines in the reserve markets. This paper presents a method for calculating the reference price that a transmission line owner uses to bid on a price in excess of the nominal capacity of the transmission line under his ownership. For this purpose, first, the effects of operating a transmission line at a power greater than the rated power are described. After that, the reduction rate of the transmission line due to operation in these conditions is calculated, and finally the price determination is calculated based on the reduction rate of the generated life. In the next stage, this excess capacity is entered the two-stage model of energy market and reservation considering renewable energy sources as a price offer function. Numerical results of 6-Shin network show that the entry of renewable energy sources reduces energy costs, but the costs of the reserve market increases due to uncertainty. However, despite the emergency capacity, these costs are reduced due to the use of cheap resources in the network.

Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.