Performances of Six-leg DSTATCOM Topology under Various SRF Algorithms

Six-leg DSTATCOM can be employed for improving the superiority of power in a distribution system. DSTATCOM is the most valuable current harmonic reduction device because of its excellent compliance in active situations. The DSTATCOM has a shunt voltage source inverter (VSI) attached to the common coupling point of power system via the interfacing inductor. In its controllers, the various SRF algorithms such as Conventional SRF, DCC based SRF and ICC based SRF have been adopted due to its easy execution aspects. These algorithms create balanced and sinusoidal supply current, compensate the reactive power, improving the power factor near unity on common coupling point and mitigate harmonic in the supply current. The reference signal for PWM generator is generated using SRF algorithms. A 3-phase 4-wire power system with rectified RC/RL load has been taken for the detailed simulation study. The performances of DSTATCOM topology have been compared under conventional SRF, DCC based SRF and ICC based SRF control strategies in terms of power quality improvement.&nbsp

Comparisons of PI and ANN controllers for shunt HPF based on STF-PQ Algorithm under distorted grid voltage

This paper proposes a shunt hybrid power filter (HPF) for harmonic currents and reactive power compensation under a distorted voltage and in a pollutedenvironment. For this purpose, the reference current of the shunt HPF is computed based on the instantaneous reactive power (p-q) theory with self tuning filter (STF). In order to adjust the dc voltage as a reference value, PI and ANN controllers have been utilized. Moreover, the system has been implemented and simulated in a MATLAB-SIMULINK platform, and selected results are presented. Therefore, the results verified the good dynamic performance, transient stability and strong robustness of the ANN controller. Furthermore, the shunt HAPF with ANN controller has been found to be in agreement with the IEEE 519-1992 standard recommendations on harmonic levels

Comparisons of pi and ann controllers for shunt hpf based on stf-pq algorithm under distorted grid voltage

This paper proposes a shunt hybrid power filter (HPF) for harmonic currents and reactive power compensation under a distorted voltage and in a polluted environment. For this purpose, the reference current of the shunt HPF is computed based on the instantaneous reactive power (p-q) theory with self-tuning filter (STF). In order to adjust the dc voltage as a reference value, PI and ANN controllers have been utilized. Moreover, the system has been implemented and simulated in a MATLAB-SIMULINK platform, and selected results are presented. Therefore, the results verified the good dynamic performance, transient stability and strong robustness of the ANN controller. Furthermore, the shunt HAPF with ANN controller has been found to be in agreement with the IEEE 519-1992 standard recommendations on harmonic levels

Study and RTDS implementation of some controllers for performance and power quality improvement of an induction motor drive system

The present research work is directed to study of some controllers for design, modelling, simulation and RTDS implementation of induction motor (IM) drive system to identify suitable controller for high performance.Initially dynamic modelling and simulation of a feedback linearization scheme for high performance IM drive is carried out. The flux measurement required in this scheme is achieved using flux estimator rather sensor to simplify the system. The complexity and calculation involved in reference frame transformation is taken care by implementing the scheme in stationary reference frame. Two linear and independent subsystems: (i) Electrical and (ii) Mechanical are created by linearizing control scheme. The systematic design of closed loop control scheme using Proportional Integral (PI) controller is developed for implementation. To take care of uncertainties in the system the Fuzzy controller is added to speed controller. Sliding Mode (SM) controller considered to be a robust control strategy is designed and developed for IM drive. A procedure of finding gain and bandwidth of the controller is developed to take care of model inaccuracies, load disturbances and rotor resistance variation. During practical implementation of this controller for IM leads to oscillations and of state variable chattering due to presence of limiter and PWM inverter in the system. Iterative Learning controller (ILC) introduced in recent time is gaining popularity due to capability to take care of short comings of Sliding Mode controller. Feedback and feed forward Iterative Learning controller combining fuzzy logic is designed and developed. The MATLAB/SIMULINK model of IM drive with controllers designed are simulated under various possible operating conditions. A comparative study of three controllers is carried out in similar situation and the response of the drive system is presented.Normally we neglect stability aspect of IM while investigating procedure for performance improvement of IM drive. Stability study of IM in open loop and closed vii loop conditions using Lyapunov criteria and also considering the power balance equation are presented

Planning and Management of a solar power-based distribution system

This thesis is aimed at the response of the power system network to the integration of solar photovoltaic (PV) generation and battery energy storage systems (BESS). Any solar power–based system integrated into a grid has voltage fluctuations that must be controlled through adaptive and robust control algorithms. The siting of battery in a distribution system affects system performance, including voltage regulation, system losses and cost minimization. In particular, here the aim is to analyse how the present-day schemes and technologies affect voltages, and their control, in the network. Another focus is on the optimal placement of BESS to facilitate system loss minimisation and cost reduction in the system. The battery placement optimisation is achieved through the minimisation of the losses in, and the cost of, the system. The voltage regulation is achieved through two control algorithms: Synchronous Reference Frame theory (SRFT) and adaptive linear neural network (ADALINE), which are subsequently modified by incorporation of fuzzy logic into the control system. Both battery placement optimisation and improvements to voltage regulation are shown to improve performance of the system. A further aim of this work is to improve cooperation between present day grid regulation equipment and schemes and the conventional methods through advancements in the control techniques. The aims of this thesis are as follows: 1. It is essential to place BESSs optimally. The aim of the thesis is to study and enhance the method of the optimal siting of battery energy storage in the presence of renewable energy–based power generating sources (RES)– such as solar PV – in a low-voltage power system network. A model for optimisation is developed to potentially find the battery site that enhances the hosting capability of the RES of the power system network. Among the essential points of this technique are its accuracy and robust nature. The fitness function includes the minimisation of the cost of operation and of system losses. 2. The second research objective is to examine the power control techniques of the inverter that might be leading to the voltage quality issues during unbalanced voltage scenarios, especially with solar PV–based generation in the power system. As such, after the implementation of the suggested coordination of the control mechanism into the grid under study, the variations in the voltage due to the solar PV variability dynamics are regulated more quickly and more precisely compared with the control schemes employed in the past. This substantially minimises the voltage fluctuations in time and amplitude, helps in mitigating hunting phenomena in voltage and provides alternative to the unnecessary control operations existing in the system

Intelligent Learning Control System Design Based on Adaptive Dynamic Programming

Adaptive dynamic programming (ADP) controller is a powerful neural network based control technique that has been investigated, designed, and tested in a wide range of applications for solving optimal control problems in complex systems. The performance of ADP controller is usually obtained by long training periods because the data usage efficiency is low as it discards the samples once used. Experience replay is a powerful technique showing potential to accelerate the training process of learning and control. However, its existing design can not be directly used for model-free ADP design, because it focuses on the forward temporal difference (TD) information (e.g., state-action pair) between the current time step and the future time step, and will need a model network for future information prediction. Uniform random sampling again used for experience replay, is not an efficient technique to learn. Prioritized experience replay (PER) presents important transitions more frequently and has proven to be efficient in the learning process. In order to solve long training periods of ADP controller, the first goal of this thesis is to avoid the usage of model network or identifier of the system. Specifically, the experience tuple is designed with one step backward state-action information and the TD can be achieved by a previous time step and a current time step. The proposed approach is tested for two case studies: cart-pole and triple-link pendulum balancing tasks. The proposed approach improved the required average trial to succeed by 26.5% for cart-pole and 43% for triple-link. The second goal of this thesis is to integrate the efficient learning capability of PER into ADP. The detailed theoretical analysis is presented in order to verify the stability of the proposed control technique. The proposed approach improved the required average trial to succeed compared to traditional ADP controller by 60.56% for cart-pole and 56.89% for triple-link balancing tasks. The final goal of this thesis is to validate ADP controller in smart grid to improve current control performance of virtual synchronous machine (VSM) at sudden load changes and a single line to ground fault and reduce harmonics in shunt active filters (SAF) during different loading conditions. The ADP controller produced the fastest response time, low overshoot and in general, the best performance in comparison to the traditional current controller. In SAF, ADP controller reduced total harmonic distortion (THD) of the source current by an average of 18.41% compared to a traditional current controller alone

Review of fundamental active current extraction techniques for SAPF

The field of advanced digital signal processing methods is one of the fastest developing scientific and technical disciplines, and is important in the field of Shunt Active Power Filter control methods. Shunt active power filters are highly desirable to minimize losses due to the increase in the number of nonlinear loads (deformed power). Currently, there is rapid development in new adaptive, non-adaptive, and especially hybrid methods of digital signal processing. Nowadays, modern methods of digital signal processing maintain a key role in research and industrial applications. Many of the best practices that have been used to control shunt active power in industrial practice for decades are now being surpassed in favor of new progressive approaches. This systematic research review classifies the importance of using advanced signal processing methods in the field of shunt active power filter control methods and summarizes the extant harmonic extraction methods, from the conventional approach to new progressive methods using genetic algorithms, artificial intelligence, and machine learning. Synchronization techniques are described and compared as well.Web of Science2220art. no. 798