Generic closed loop controller for power regulation in dual active bridge DC-DC converter with current stress minimization

This paper presents a comprehensive and generalized analysis of the bidirectional dual active bridge (DAB) DC/DC converter using triple phase shift (TPS) control to enable closed loop power regulation while minimizing current stress. The key new achievements are: a generic analysis in terms of possible conversion ratios/converter voltage gains (i.e. Buck/Boost/Unity), per unit based equations regardless of DAB ratings, and a new simple closed loop controller implementable in real time to meet desired power transfer regulation at minimum current stress. Per unit based analytical expressions are derived for converter AC RMS current as well as power transferred. An offline particle swarm optimization (PSO) method is used to obtain an extensive set of TPS ratios for minimizing the RMS current in the entire bidirectional power range of - 1 to 1 per unit. The extensive set of results achieved from PSO presents a generic data pool which is carefully analyzed to derive simple useful relations. Such relations enabled a generic closed loop controller design that can be implemented in real time avoiding the extensive computational capacity that iterative optimization techniques require. A detailed Simulink DAB switching model is used to validate precision of the proposed closed loop controller under various operating conditions. An experimental prototype also substantiates the results achieved

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors

In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of imperfect communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the environment. In order to minimize the mean square estimation error of parameter tracking, we propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light and only relies on the second-order statistics of the system parameters. For this, we first consider an offline non-convex optimization problem, which is solved exactly using semidefinite programming. Based on the offline solution, we design an online power allocation policy that requires minimal online computation and satisfies the dynamics of energy flow at each sensor. We prove that the proposed online policy is asymptotically equivalent to the optimal offline solution and show its convergence rate and robustness. We empirically show that the estimation performance of the proposed online scheme is better than that of the online scheme when channel state information about the dynamical system is available in the low SNR regime. Numerical results are conducted to demonstrate the effectiveness of our approach

Design and analysis of current stress minimalisation controllers in multi-active bridge DC-DC converters.

Multi active bridge (MAB) DC-DC converters have attracted significant research attention in power conversion applications within DC microgrids, medium voltage DC and high voltage DC transmission systems. This is encouraged by MAB's several functionalities such as DC voltage stepping/matching, bidirectional power flow regulation and DC fault isolation. In that sense this family of DC-DC converters is similar to AC transformers in AC grids and are hence called DC transformers. However, DC transformers are generally less efficient compared to AC transformers, due to the introduction of power electronics. Moreover, the control scheme design is challenging in DC transformers, due to its nonlinear characteristics and multi degrees of freedom introduced by the phase shift control technique of the converter bridges. The main purpose of this research is to devise control techniques that enhance the conversion efficiency of DC transformers via the minimisation of current stresses. This is achieved by designing two generalised controllers that minimise current stresses in MAB DC transformers. The first controller is for a dual active bridge (DAB). This is the simplest form of MAB, where particle swarm optimisation (PSO) is implemented offline to obtain optimal triple phase shift (TPS) parameters, for minimising the RMS current. This is achieved by applying PSO on DAB steady-state model, with generic per unit expressions of converter AC RMS current and transferred power under all possible switching modes. Analysing the generic data pool generated by the offline PSO algorithm enabled the design of a generic real-time closed-loop PI-based controller. The proposed control scheme achieves bidirectional active power regulation in DAB over the 1 to -1 pu power range with minimum-RMS-current for buck/boost/unity modes, without the need for online optimisation or memory-consuming look-up tables. Extending the same controller design procedure for MAB was deemed not feasible, as it would involve a highly complex PSO exercise that is difficult to generalise for N number of bridges; it would therefore generate a massive data pool that would be quite cumbersome to analyse and generalise. For this reason, a second controller is developed for MAB converter without using a converter-based model, where current stress is minimised and active power is regulated. This is achieved through a new real-time minimum-current point-tracking (MCPT) algorithm, which realises iterative-based optimisation search using adaptive-step perturb and observe (P&O) method. Active power is regulated in each converter bridge using a new power decoupler algorithm. The proposed controller is generalised to MAB regardless of the number of ports, power level and values of DC voltage ratios between the different ports. Therefore, it does not require an extensive look-up table for implementation, the need for complex non-linear converter modelling and it is not circuit parameter-dependent. The main disadvantages of this proposed controller are the slightly slow transient response and the number of sensors it requires

Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles

We present a novel, realtime algorithm to compute the trajectory of each pedestrian in moderately dense crowd scenes. Our formulation is based on an adaptive particle filtering scheme that uses a multi-agent motion model based on velocity-obstacles, and takes into account local interactions as well as physical and personal constraints of each pedestrian. Our method dynamically changes the number of particles allocated to each pedestrian based on different confidence metrics. Additionally, we use a new high-definition crowd video dataset, which is used to evaluate the performance of different pedestrian tracking algorithms. This dataset consists of videos of indoor and outdoor scenes, recorded at different locations with 30-80 pedestrians. We highlight the performance benefits of our algorithm over prior techniques using this dataset. In practice, our algorithm can compute trajectories of tens of pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per second). To the best of our knowledge, our approach is 4-5 times faster than prior methods, which provide similar accuracy

Secure Clustering in DSN with Key Predistribution and WCDS

This paper proposes an efficient approach of secure clustering in distributed sensor networks. The clusters or groups in the network are formed based on offline rank assignment and predistribution of secret keys. Our approach uses the concept of weakly connected dominating set (WCDS) to reduce the number of cluster-heads in the network. The formation of clusters in the network is secured as the secret keys are distributed and used in an efficient way to resist the inclusion of any hostile entity in the clusters. Along with the description of our approach, we present an analysis and comparison of our approach with other schemes. We also mention the limitations of our approach considering the practical implementation of the sensor networks.Comment: 6 page