H. Shayeghi, A. Younesi,
Volume 13, Issue 4 (12-2017)
Abstract
This paper presents an online two-stage Q-learning based multi-agent (MA) controller for load frequency control (LFC) in an interconnected multi-area multi-source power system integrated with distributed energy resources (DERs). The proposed control strategy consists of two stages. The first stage is employed a PID controller which its parameters are designed using sine cosine optimization (SCO) algorithm and are fixed. The second one is a reinforcement learning (RL) based supplementary controller that has a flexible structure and improves the output of the first stage adaptively based on the system dynamical behavior. Due to the use of RL paradigm integrated with PID controller in this strategy, it is called RL-PID controller. The primary motivation for the integration of RL technique with PID controller is to make the existing local controllers in the industry compatible to reduce the control efforts and system costs. This novel control strategy combines the advantages of the PID controller with adaptive behavior of MA to achieve the desired level of robust performance under different kind of uncertainties caused by stochastically power generation of DERs, plant operational condition changes, and physical nonlinearities of the system. The suggested decentralized controller is composed of the autonomous intelligent agents, who learn the optimal control policy from interaction with the system. These agents update their knowledge about the system dynamics continuously to achieve a good frequency oscillation damping under various severe disturbances without any knowledge of them. It leads to an adaptive control structure to solve LFC problem in the multi-source power system with stochastic DERs. The results of RL-PID controller in comparison to the traditional PID and fuzzy-PID controllers is verified in a multi-area power system integrated with DERs through some performance indices.
R. Samanth, S. G. Nayak, P. B. Nempu,
Volume 19, Issue 1 (3-2023)
Abstract
In the CMOS circuit power dissipation is a major concern for VLSI functional units. With shrinking feature size, increased frequency and power dissipation on the data bus have become the most important factor compared to other parts of the functional units. One of the most important functional units in any processor is the Multiply-Accumulator unit (MAC). The current work focuses on the development of MAC unit bus encoders as well as the identification of an improved architecture for image processing applications. To reduce the power consumption in these functional units, two bus encoding architectures were developed by encoding data before it was sent on the data buses. One is MSB reference encoding, and another is Fourth and Fifth bit ANDing (FFA) without the need for an extra bus line with fewer transitions by using gray codes. The comparison of the proposed encoding architectures with the existing encoding architectures from the literature revealed an 8% to 36% significant improvement in power dissipation. The simulation was done with Xilinx ISE, and the Cadence RTL Compiler tool was utilized for the synthesis, which was done with the 180nm technology library. And also, the image filtering is analyzed using MATLAB.
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
Zahra Mobini-Serajy, Mehdi Radmehr, Alireza Ghorbani,
Volume 20, Issue 0 (12-2024)
Abstract
Micro-Grids harness the benefits of non-inverter and inverter based Distributed Energy Resources (DER) in grid-connected and island environments. Adoption them with the various type of electric loads in modern MGs has led to stability and power quality issues. In this paper, two-level control approach is proposed to overcome these problems. A state-space dynamic model is performed for Micro-Grids, for this goal, the state-space equations for generation, network and load components are separately developed in a local DQ reference frame, and after linearization around the set point, then combining them into a common DQ reference frame. In the first level, the control of inverter-based DERs and some types of loads with fast response are activated, and in the second level, the control of synchronous diesel generator resources with slower response are used. In order to validate and effectiveness evaluation of proposed control approach, numerical studies have been stablished on a standard test MG under normal and a symmetrical three-phase fault conditions. Finally, the simulations results are summarized.
