Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm

Application of an optimal tilt controller in a partial loading schedule of multi-area power system considering HVDC link and virtual inertia

Maintaining the reliability of the modern power system is becoming increasingly challenging due to the continuous growth in its size and complexity. One of the most significant challenges PS faces is frequency regulation, which is essential for maintaining a stable and reliable operation of PS. This article investigates the automatic generation control (AGC) of a multi-area PS considering partial loading scheduled in a hydro and thermal generating unit. A bird swarm algorithm (BSA) based two-degree of freedom tilt integral derivative (2DOF-TID) controller has been suggested. The superiority of the optimal 2DOF-TID controller has been selected by comparing the system dynamics with other conventional controllers. Also, the ascendancy of suggested BSA has been shown over the grasshopper algorithm and particle swarm optimization techniques. The investigation on partial loading schedules for the hydro-thermal system showed that the system's dynamic response deteriorates when loading on hydro units exceeds their nominal loading. Also, system dynamics are enhanced when thermal units are loaded beyond their capacity. The investigations are also conducted to analyse the effect of high voltage direct current (HVDC) link and virtual inertia emulation with an energy storage system. The studies with alternating current (AC)/HVDC and virtual inertia revealed that dynamic responses are better with the latter when compared with AC and HVDC links alone.

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis

Trunk water has an important influence on the metabolism and ecological balance of living trees, which affects the vegetation growth and moisture cycle of the whole forest ecosystem. The accurate and real-time measurement of moisture content (MC) is of vital guiding meaning to living tree cultivation and forest management. In this paper, a water content diagnosis system based on a wireless acoustic emission sensor network (WASN) was designed and implemented with the aim of the nondestructive detection of water content in living wood trunks. Firstly, the acoustic emission (AE) signal of the trunk epidermis was sampled at high speed; then, its characteristic parameters were calculated and transmitted wirelessly to the gateway. Furthermore, the optimal characteristic wavelet sequence was decomposed by the adaptive chirp mode decomposition (ACMD), and the improved grey wolf optimizer (IGWO) optimization XGBoost established the MC prediction model, which was improved by the multi-strategy joint optimization. Finally, field monitoring was carried out on Robinia Pseudoacacia, Photinia serrulata, Pinus massoniana and Toona sinensis. The average diagnostic accuracy reached 96.75%, which shows that the diagnosis system has excellent applicability in different working conditions.

Deep Reinforcement Learning for Parameter Tuning of Robot Visual Servoing

Robot visual servoing controls the motion of a robot through real-time visual observations. Kinematics is a key approach to achieving visual servoing. One key challenge of kinematics-based visual servoing is that it requires time-varying parameter configuration throughout the entire process of one task. Parameter tuning is also necessary when applying to different tasks. The existing work on parameter tuning either lacks adaptation or cannot automate the tuning of all parameters. Meanwhile, the transferability of existing methods from one task to another is low. This work develops a Deep Reinforcement Learning (DRL) framework for robot visual servoing, which can automate all parameters tuning for one task and across tasks. In visual servoing, forward kinematics focuses on motion speed, while inverse kinematics focuses on the smoothness of motion. Therefore, we develop two separate modules in the proposed DRL framework. One tunes time-varying Forward Kinematics (FK) parameters to accelerate the motion, and the other tunes the Inverse Kinematics (IK) parameters to ensure smoothness. Moreover, we customize a knowledge transfer method to generalize the proposed DRL models to various robot tasks without reconstructing the neural network. We verify the proposed method on simulated robot tasks. The experimental results show that the proposed method outperforms the state-of-the-art methods and manual parameter configuration in terms of movement speed and smoothness in one task and across tasks.

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

In this paper, a review of Metaheuristic Optimization Techniques (MOT) which are currently in use for optimization in a vast range of problems, is presented. MOT are known for their simplicity and stochastic nature and successfully applied to solve complex engineering problems. Although there exist various categories of MOT, the techniques from swarm intelligence is reviewed in this paper. An explanation of the theoretical foundation upon which each algorithm is based is provided, along with the relevant mathematical models that explain how an algorithm attempts to obtain the best solution to a problem. The paper also reviews the applications of swarm-based MOT to the control of the doubly fed induction generator (DFIG). Particular attention is given to control of the DFIG for wind energy applications. Control of the DFIG is generally realized via the use of PI controllers. While various PI controller tuning methods are well established (such as the Ziegler–Nichols and Cohen–Coon methods), these methods produce satisfactory results, and often fail to meet the stringent levels of control presently required. Due to this fact, as well as the current success of MOT in engineering, the application of MOT to the control of the DFIG could be promising area of research. The results of the study show that although the various swarm-based MOT differ from each other in terms of aspects such as complexity and advantages, they are all based on the concept of randomness, and always attempt to produce the best possible solution. It was also observed that various swarm-based MOT displays the demerit of getting easily trapped in the local optimum, however various advancements have been proposed to correct such an issue. Based on the results of the application of these techniques to other engineering problems, their application to the DFIG could yield exceptional results.

Parameter optimization of PID controller for water and fertilizer control system based on partial attraction adaptive firefly algorithm

Proportional Integral Derivative (PID) control is the main control method in the process of agricultural water and fertilizer regulation, and its parameter setting directly affects the control effect of water and fertilizer regulation. However, the traditional PID parameters are adjusted manually such as using the critical proportionality method, which is time-consuming and difficult to achieve optimal control effects. To solve the optimal combination of PID control parameters and improve the control effect of water and fertilizer regulation, a partial attraction adaptive firefly algorithm (PAAFA) is proposed in this paper. Specifically, a partial attraction strategy is designed to speed up the convergence of the PAAFA and reduce the oscillation problem at the late stage of the algorithm. In addition, an adaptive inertia weight operator is proposed to balance the global search capability and local search capability of PAAFA and avoid the algorithm from trapping in the local optimum. Subsequently, to test the performance of PAAFA, the algorithm is subjected to a series of simulation experiments and bench tests with the latest methods, i.e., genetic algorithm (GA), Adaptive genetic algorithm (AGA), and firefly algorithm (FA) applied to PID parameter optimization problems. The simulation results demonstrate that the regulation times of the response curve of PAAFA-based PID control are reduced by 22.75%,10.10%and 20.61%, respectively, compared with GA, AGA, and FA. The bench test results show that the PAAFA-based PID control has the smallest relative error, and best control accuracy compared to GA, AGA, and FA, with an average relative error reduction of 3.99, 2.42, and 3.50 percentage points respectively.

A Fast Firefly Algorithm for Function Optimization: Application to the Control of BLDC Motor

Firefly Algorithm (FA) is a recent swarm intelligence first introduced by X.S. Yang in 2008. It has been widely used to solve several optimization problems. Since then, many research works were elaborated presenting modified versions intending to improve performances of the standard one. Consequently, this article aims to present an accelerated variant compared to the original Algorithm. Through the resolving of some benchmark functions to reach optimal solution, obtained results demonstrate the superiority of the suggested alternative, so-called Fast Firefly Algorithm (FFA), when faced with those of the standard FA in term of convergence fastness to the global solution according to an almost similar precision. Additionally, a successful application for the control of a brushless direct current electric motor (BLDC) motor by optimization of the Proportional Integral (PI) regulator parameters is given. These parameters are optimized by the FFA, FA, GA, PSO and ABC algorithms using the IAE, ISE, ITAE and ISTE performance criteria.

Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview

Automatic generation control (AGC) is primarily responsible for ensuring the smooth and efficient operation of an electric power system. The main goal of AGC is to keep the operating frequency under prescribed limits and maintain the interchange power at the intended level. Therefore, an AGC system must be supplemented with modern and intelligent control techniques to provide adequate power supply. This paper provides a comprehensive overview of various AGC models in diverse configurations of the power system. Initially, the history of power system AGC models is explored and the basic operation of AGC in a multi-area interconnected power system is presented. An in-depth analysis of various control methods used to mitigate the AGC issues is provided. Application of fast-acting energy storage devices, high voltage direct current (HVDC) interconnections, and flexible AC transmission systems (FACTS) devices in the AGC systems are investigated. Furthermore, AGC systems employed in different renewable energy generation systems are overviewed and are summarized in tabulated form. AGC techniques in different configurations of microgrid and smart grid are also presented in detail. A thorough overview of various AGC issues in a deregulated power system is provided by considering the different contract scenarios. Moreover, AGC systems with an additional objective of economic dispatch is investigated and an overview of worldwide AGC practices is provided. Finally, the paper concludes with an emphasis on the prospective study in the field of AGC.

Highly Robust Observer Sliding Mode Based Frequency Control for Multi Area Power Systems with Renewable Power Plants

This paper centers on the design of highly robust observer sliding mode (HROSM)-based load frequency and tie-power control to compensate for primary frequency control of multi-area interconnected power systems integrated with renewable power generation. At first, the power system with external disturbance is model in the state space form. Then the state observer is used to estimate the system states which are difficult or expensive to measure. Secondly, the sliding mode control (SMC) is designed with a new single phase sliding surface (SPSS). In addition, the whole system asymptotic stability is proven with Lyapunov stability theory based on the linear matrix inequality (LMI) technique. The new SPSS without reaching time guarantees rapid convergence of high transient frequency, tie-power change as well as reduces chattering without loss of accuracies. Therefore, the superiority of modern state-of-the-art SMC-based frequency controllers relies on good practical application. The experimental simulation results on large interconnected power systems show good performance and high robustness against external disturbances when compared with some modern state of art controllers in terms of overshoots and settling time.