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

Hybrid microgrid energy management and control based on metaheuristic-driven vector-decoupled algorithm considering intermittent renewable sources and electric vehicles charging lot

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels

A new active power controller in dual active bridge DC-DC converter with a minimum-current-point-tracking technique

This article proposes a new controller for power regulation in dual active bridge (DAB) dc-dc converter based on a new scheme that tracks minimum RMS current to ensure minimum losses. The proposed controller is based on an implementation of perturb and observe (PO) tracking method that enables minimum current point tracking (MCPT) at any desired level of active power transfer and dc voltage ratio. The PO is embedded in a closed-loop control scheme which simultaneously regulates active power in DAB converter. The nonlinear I - V characteristic of DAB presents the basis for this proposed controller and the rationale of using PO algorithm. The proposed controller does not require complex nonlinear converter modeling and is not circuit parameter dependent. Design procedure of the proposed controller is presented, and extensive simulation is carried out using MATLAB/Simulink to validate the effectiveness of the proposed MCPT closed-loop controller. An experimental prototype also substantiates the results achieved

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

Frequency Control of Microgrid Network using Intelligent Techniques – ANN, PSO and ANFIS

The electric grid is a complex system that transmits electricity from the point of generation to the point of consumption. According to the IEA, worldwide energy-related carbon emissions in 2021 will be 36.3Gt, 60% greater than at the start of the industrial revolution. Researchers have used intelligent solutions for power system frequency regulation to ensure that the system\u27s frequency is maintained. A proper frequency control of the microgrid necessitates the modeling and study of the systems. To emulate the operation of the human brain, frequency control employs a variety of artificial intelligence-based computer algorithms. This thesis generates a complete state space model of a microgrid composed of solar power plants, wind turbines, battery storage systems, and backup generators. The system frequency control was created for this system and analyzed against a benchmark PID controller utilizing several intelligent controllers such as PSO optimized PID, ANN, and ANFIS. The suggested intelligent frequency controllers were be simulated and validated using MATLAB/ Simulink

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller

Optimized PID Controller of DC-DC Buck Converter based on Archimedes Optimization Algorithm

This research assesses the suitability of the Archimedes Optimization Algorithm (AOA) as a metaheuristic technique to fine-tune a PID controller in a closed-loop DC-DC buck converter. The converter's core function is to regulate output voltage, ensuring stability despite load fluctuations and input voltage changes.  The operational effectiveness of the converter hinges significantly on the gain settings of the PID controller and determining the optimal gain setting for the PID controller is a non-trivial task. For robust performance, the PID controller necessitates optimal gain settings, attainable through metaheuristic optimization. The algorithm aids in identifying ideal proportional, integral, and derivative gains based on varying load conditions. Leveraging the metaheuristic algorithm, the PID controller is optimized to minimize voltage errors, reduce overshoot, and enhance response time. The proposed PID controller, optimized using AOA, is contrasted with PID controllers tuned via alternative algorithms including the hybrid Nelder-Mead method (AEONM), artificial ecosystem-based optimization (AEO), differential evolution (DE), and particle swarm optimizer (PSO). Performance evaluation involves injecting a voltage disturbance into the buck converter with load changes of up to 20%. Results demonstrate the superiority of the AOA-optimized PID controller in voltage recovery.  It demonstrates a faster response time and outstanding voltage regulation performance, while also exhibiting minimal performance degradation during load changes. This study concludes that the AOA optimization algorithm surpasses other methods in tuning the PID controller for closed-loop DC-DC buck converters