To mitigate the problems of demand-supply mismatch in the future grid the solution of renewable energy source (RES) integration results in a bidirectional flow of information and transactions, which are prone to different kinds of cyber attacks, especially in energy trading where the security of financial transactions is of most concern. Electric vehicle (EV) having the advantage of mobility can play a significant role in maintaining demand-supply balance at any location unlike their peers (conventional compensator). For deciding entire system security, securing EVs charging-discharging transactions at all charging stations or connecting points is most important. The system can be made more secure against cyber-attacks with the introduction of the blockchain framework. Hence, in view of secured transactions, the paper focuses on the energy trading process between EVs and distribution network (DN) in a Byzantine based blockchain consensus framework. During peak load period DN initiates the energy trading process by demanding additional power from the EVs. This process of energy trading results in energy and information exchange which needs to be secured through blockchain from vulnerable attacks and threats. Possible scenarios of various cyber-attacks on different nodes of the system are visualized in the form of false data. To highlight the application of blockchain, the Byzantine general problem framework is used which states that for successful attack 33% of information is to be manipulated, in other words, decreasing the probability of attack confirms the system security. Numerical results based on various operating scenarios for the standard IEEE 33 bus system are in agreement with the Byzantine consensus problem indicating improvement in system security.

In this paper, angles-only target tracking (AoT) problem is investigated in the non Gaussian environment. Since the conventional minimum mean square error criterion based estimators tend to give poor accuracy in the presence of large outliers or impulsive noises in measurement, a maximum correntropy criterion (MCC) based framework is presented. Accordingly, three new estimation algorithms are developed for AoT problems based on the conventional sigma point filters, termed as MC-UKF-CK, MC-NSKF-GK and MC-NSKF-CK. Here MC-NSKF-GK represents the maximum correntropy new sigma point Kalman filter realized using Gaussian kernel and MC-NSKF-CK represents realization using Cauchy kernel. Similarly, based on the unscented Kalman filter, MC-UKF-CK has been developed. The performance of all these estimators is evaluated in terms of root-mean-square error (RMSE) in position and % track loss. The simulations were carried out for 2D as well as 3D AoT scenarios and it was inferred that, the developed algorithms performed with improved estimation accuracy than the conventional ones, in the presence of non Gaussian measurement noise.

This research focuses on tracking a moving target using only bearing measurements obtained by a moving observer, commonly termed bearings-only tracking (BoT) or target motion analysis (TMA). The accuracy of popular nonlinear state estimators such as the unscented Kalman filter (UKF) is affected by non-Gaussian noises in the measurements and uncertainties in the observer path. To address this, maximum correntropy (MC) and minimum error entropy (MEE) criteria have been recently reported in the literature. Even though the MEE criteria have the potential to produce more accurate estimates, the estimation framework tends to become unstable. Hence, to deal with the complexities of noise in the model and the instability in numerical computations, a robust mixture minimum error entropy criterion (MMEE) with UKF is proposed in this paper. In addition to MMEE, a robust centered error entropy-based estimator (RCEE) is also proposed. The RCEE-UKF and MMEE-UKF methods are compared with MEE-UKF and MC-UKF estimators through simulations, and it is found that the RCEE-UKF provides the best estimation accuracy.

This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.