Control Of Hybrid Power System Research Articles

Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance.

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

A state of art review on the opportunities in automatic generation control of hybrid power system

A state of art review on the opportunities in automatic generation control of hybrid power system

Statement of Retraction: Torque Coordinated Control of Hybrid Power System Based on Iterative Algorithm in Industry 4.0

Statement of Retraction: Torque Coordinated Control of Hybrid Power System Based on Iterative Algorithm in Industry 4.0

Dynamic modelling, simulation, and control of hybrid power systems for escort tugs and shuttle tankers

The maritime industry targeted shipping decarbonization over the past few years for achieving stakeholders’ expectations and also to abide by the environmental global agenda. For that, increasing energy efficiency and reducing pollutant gas emissions are key objectives. Thus, alternatives, such as hybrid power systems and the use of alternative fuels, emerge as promising solutions to meet stakeholders’ and society’s expectations. This paper proposes hybrid power system architectures for an escort tug and for a shuttle tanker, which are vessels largely used in the oil and gas sector. We analyze different control strategies of these systems, namely Rule-Based method, Equivalent Consumption Minimization Strategy (ECMS), Adaptive-ECMS and Model Predictive Control (MPC), using numerical modeling and simulations, comparing their performance with those of traditional power systems. The most efficient designs showed reductions in fuel consumption of up to 18.9% for tugs, and 2% for shuttle tankers.

Design and Implementation of Node-Red Based Open-Source SCADA Architecture for a Hybrid Power System

At present, hybrid renewable power systems (HRPS) are considered reliable combinations for power generation under various conditions. The challenge facing researchers and engineers today is designing and implementing a reliable, efficient, and applicable SCADA system for adequate monitoring and control of hybrid power systems. In order to analyze, observe, and control the essential parameters of an HRPS, a SCADA system is crucial. As part of this study, a low-cost, low-power, open-source SCADA (Supervisory, Control, and Data Acquisition) system for hybrid renewable energy systems is presented. The system utilizes two remote terminal units (RTUs), an Arduino Mega2560 and a Wio terminal, to communicate with all actuators and measure vital system characteristics such as voltage, current, and power. Using the Firmata protocol, a laptop serves as the main terminal unit (MTU) to communicate with the Arduino. In addition to being the system’s central component, Node-Red is utilized for processing, analyzing, storing, and displaying data. In contrast, a Wio terminal is used to display the values of all sensors in real-time on its LCD screen. As a whole, the proposed SCADA system is designed to keep the HRPS running smoothly and safely by displaying vital parameters, reporting any significant faults, and controlling the generator so that the batteries can be charged and discharged correctly. This article presents a complete description of all algorithms, experimental setups, testing, and results.

Optimal Control of Hybrid Power System Integrated with Distributed Generation and Electric Vehicle

This study presents load-frequency-control of a two-area hybrid power system (HPS) incorporating distributed generation, geo-thermal plant, thermal plant and electric-vehicle. Such a complex system with non-linearities leads the power system towards more complexity and accordingly requires a resilient controller to handle such complexity. In this regard, optimal cascaded proportional integral – multistage proportional integral derivative (cascaded PI-MSPID) is explored for developed power system. The efficiency of optimal controller is validated by comparing it with other controllers. The influence of flywheel energy storage on system dynamics is demonstrated. Further, the electric vehicle’s participation in enhancing the system stability is presented. Moreover, the developed system is subjected to an intense load deviation which is a practical data taken from one of the literature. It is demonstrated that responses achieved for proposed cascaded PI-MSPID controller effectively handles the system disturbances, which reflects the strength of proposed cascaded PI-MSPID controller.

Fuzzy Logic Based Load Frequency Control of Hybrid Power System Integrated with SMES

The use of a Fuzzy logic-based controller for automatic generation control (AGC) and load frequency control (LFC) of two area power plants in an interconnected power system is proposed in this paper. In addition, a superconducting magnetic Energy Storage (SMES) unit is being considered in two areas. After a sudden load disturbance, the implementation of SMES combination arrests the initial fall in frequency deviation and tie-line power deviation. The simulation results show that the proposed SMES unit improves system performance significantly. The system's sensitivity analysis is carried out by altering the parameters and operating load conditions from their nominal values. Even when the system is subjected to wide variations in loading conditions and system parameters, the optimised gains of the proposed fuzzy logic- based controller and fuzzy controller along with SMES controllers do not need to be reset. Finally, different types of load patterns are used to test the effectiveness of the proposed controller design. The simulation results show that the Fuzzy PID controller combined with SMES outperforms the Fuzzy PID controller alone. The simulations were run using the MATLAB software.

Automatic Load Frequency Control of Renewable Energy Integrated Hybrid Power System

Frequency aberration, power quality, and system instability may be caused by the general perception of Renewable Energy (RE). To control the frequency with the tolerable limit, load frequency control is being performed. Automatic Load Frequency Control (ALFC) must be provided with a proper controller. Ziegler-Nichols method is being used to tune the parameters of the Proportional-Integral-Derivative (PID) controller for Load Frequency Control of Hybrid Power System (HPS). Traditional PID controllers are capable of handling a larger varieties of rapid changes in load variations in renewable energy hybrid power systems. This work considers the HPS of 2000 MW power system including RE resources. The OP4510 is utilized for hardware-in-loop (HIL) simulation to test the accomplished controller's real-time applicability. The MATLAB simulation and the Real-Time simulator provide identical results.

RETRACTED ARTICLE: Torque Coordinated Control of Hybrid Power System Based on Feedback Iterative Algorithm in Industry 4.0

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Yunlong Wang, Hongtian Zhang & Yuantao Sun (2022) Torque Coordinated Control of Hybrid Power System Based on Feedback Iterative Algorithm in Industry 4.0, IETE Journal of Research, DOI: 10.1080/03772063.2022.2069605 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Efficient Load Frequency Control of Renewable Integrated Power System: A Twin Delayed DDPG-Based Deep Reinforcement Learning Approach

Power systems have been evolving dynamically due to the integration of renewable energy sources, making it more challenging for power grids to control the frequency and tie-line power variations. In this context, this paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning. By incorporating intermittent renewable energy sources, variable loads and electric vehicles, the complexity of the interconnected power system is escalated for a more realistic approach. The proposed method tunes the proportional-integral-derivative (PID) controller parameters using an improved twin delayed deep deterministic policy gradient (TD3) based reinforcement learning (RL) agent, where a non-negative fully connected layer is added with absolute function to avoid negative gain values. Multi deep reinforcement learning agents are trained to obtain the optimal controller gains for the given two-area interconnected system, and each agent uses the local area control error information to minimize the deviations in frequency and tie-line power. The integral absolute error (IAE) of area control error is used as a reward function to derive the controller gains. The proposed approach is tested under random load-generation disturbances along with nonlinear generation behaviors. The simulation results demonstrate the superiority of the proposed approach compared to other techniques presented in the literature and show that it can effectively cope with nonlinearities caused by load-generation variations.

Analysis of Gaussian fuzzy logic‐sliding model control and flexible AC transmission systems controllers for automatic generation control of hybrid power system under chaotic‐water cycle algorithm approach

Analysis of Gaussian fuzzy logic‐sliding model control and flexible AC transmission systems controllers for automatic generation control of hybrid power system under chaotic‐water cycle algorithm approach

Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment

Aiming at promoting the intelligent development of control technology for new energy vehicles and showing the outstanding advantages of deep reinforcement learning (DRL), this paper trained a VGG16-based road recognition convolutional neural network (CNN) at first. Lots of high-definition images of five typical roads are collected by the racing game Dust Rally 2.0, including dry asphalt, wet asphalt, snow, dry cobblestone, and wet cobblestone. Then, a time-varying driving environment model was established, involving driving images, road slope, longitudinal speed, and the number of passengers. Finally, a stereoscopic control network suitable for nine-dimensional state space and three-dimensional action space was built, and for parallel hybrid electric vehicles (HEVs) with the P3 structure, a deep q-network (DQN)-based energy management strategy (EMS) achieving multi-objective control was proposed, including the fine-tuning strategy of the motor speed to maintain the optimal slip rate during braking, the engine power control strategy and the continuously variable transmission (CVT) gear ratio control strategy. Simulation results show under the influence of some factors such as tree shade and image compression, the road recognition network has the highest accuracy for snow roads and wet asphalt roads. Three types of control strategies learned simultaneously by the stereoscopic control network not only maintain the near-optimal slip rate in the braking period but also achieve a fuel consumption of 4788.93 g, while dynamic programming (DP)-based EMS gets a fuel consumption of 4295.61 g. Moreover, even DP-based EMS only contains three states and two actions, the time consumed for DP-based EMS and DQN-based EMS to run the speed cycle of 3602s is about 4911s and 10s, respectively. Therefore, the optimization and real-time performance of DRL-based EMS can be guaranteed.

Sine cosine adopted Harris' hawks optimization for function optimization and power system frequency controller design

A considerable number of intermittent renewable resources like photovoltaic generation and wind energy when integrated into the conventional grid technology causes serious issues in the power systems like frequency instability. So an intelligent controller is desired for steady and reliable operation of the electric grid. This paper deals with the frequency control of hybrid power system (HPS) employing a sine cosine adopted Harris' hawks optimization (SCaHHO) technique. The proposed technique aims at improving the performance of the original Harris' hawks optimization (HHO) algorithm by incorporating a sine and cosine function in the calculation of escaping energy and chances of escaping of prey respectively to emphasize the exploitation process of hawks. The effectiveness of the SCaHHO technique is validated with the novel HHO technique as well as moth flame optimization, sine cosine algorithm, grey wolf optimization, salp swarm algorithm and gravitational search algorithm using 23 standard test functions. A non-parametric statistical investigation is also carried out to verify the effectiveness of the suggested technique. Later the SCaHHO technique is utilized to tune a newly proposed adaptive fuzzy proportional integral derivative controller (AFPID) for frequency control of HPS. The suggested SCaHHO-tuned AFPID controller achieves an improved control action by moderating the frequency fluctuations as compared to the PID controller. Hardware-in-loop validation of the proposed load frequency control scheme is also carried out using OPAL-RT to measure its fidelity with the numerical simulation results.

Control of hybrid power system based renewable energy generations using PID controller

This paper addresses to integrate an optimal Proportional-Integrator-Derivative controller for frequency regulation in an isolated microgrid power system based renewable generation. This autonomous microgrid system is composed of Distributed Energy Sources like wind, solar, Diesel Engine Generator, Fuel Cells system, and two different storage devices such as Battery Energy Storage System and Flywheel Energy Storage System. Optimal tuning of the investigated controller is considered as the main problem to be resolved using the Krill Herd algorithm through an objective function. The obtained results are also accomplished with and without the battery energy storage system. The comparison of system performance shows that the proposed control scheme based Krill Herd Algorithm is better than the Genetic Algorithm in the improvement of system performance.

Frequency control of hybrid power system by sine fu nction adapted improved whale optimisation technique

In this study, a sine adapted improved whale optimisation algorithm (SiWOA) is proposed where sine function is incorporated in the Whale Optimization Algorithm (WOA) for the choice of algorithm parameter and scaling factors are engaged in WOA technique to adjust the travel of search agents in the search procedure. The performance of SiWOA is analysed by testing on benchmark functions and equated with similar methods. Then frequency control of distributed power system (DPS) is performed by designing an Adaptive Fuzzy PID (AFPID) controller employing the proposed SiWOA technique. It is demonstrated that SiWOA technique outperforms the WOA technique in controller design problem. It is noticed that SiWOA tuned AFPID controller is more active in controlling the frequency relative to PID. Lastly, the outcomes of AFPID controller is equated with PID controller under different serious uncertain scenarios to demonstrate the advantage of the suggested frequency control approach.

Global Vision-Based Formation Control of Soft Robotic Fish Swarm.

Possessing the attributes of high adaptability and low cost, soft robotic individuals can further coordinate and form into a swarming system, enhancing the performances as well as functions in practical applications. However, the formation control of soft robotic swarm remains challenging mainly due to the limitation in relatively low precision and slow response of the soft actuators. In this work, a soft robotic fish swarm system with global vision positioning was studied. The soft robotic fish used in the project is driven by a hybrid power-control system, in which the soft dielectric elastomers and the rigid electrical servo provide forward propulsion and controllable steering function, respectively. Results show that soft robotic fish swarm can quickly shift their formations, mimicking three typical swarming behaviors of natural creatures: highly parallel group, encircling, and torus. The system design and controlling principles of the soft robotic fish swarm may guide the future research of soft robots and robotic swarms, specifically for underwater applications.

Densely-Packed Construction for Hybrid Power Control System for Unmanned Aerial Vehicles

This paper examines the creation of the hybrid power control system with dense layout of units for the various vehicles. The relevance of the topic is proved by the several publications of Toyota Motors, Subaru Corporation, Texas Instruments and others, who have implemented similar decisions in their electronic control units. The mathematical model and the developed prototype are suitable for gasoline internal combustion engine, where the main feature of this development is the support and control of energy generation. Due to the dense layout of particular vehicles it is necessary to resolve the issues of electromagnetic compatibility, based on protection and decoupling of various elements among themselves. The controlling device for the internal combustion engine of vehicle carries out a complete decoupling of control and signal circuits, and protection of power for integrated circuits. These constructive decisions include the installation of optically decoupled devices, protective diodes and resistances for bypassing the high voltage surges. The proposed concept includes the control of the engine cooling, ignition, engine rate and throttle, generation current, engine startup and cut-off. The prototype working on this concept has an increased uptime and working distance. These features ensure the stability of the engine and its primary characteristics of rate support and generation current control. Processes of installation prototype modeling, technical decisions and reliability issues have been described. This work can be used to develop the engine control units with high reliability and diverse set of functions.

PSO Based Dispatch and Control of Hybrid Power System with PV-Diesel Generator- A Prototype

A micro-grid is a small-scale power grid that can operate independently or in conjunction with the area’s main electrical grid. Micro-grid paves way to power remote locations and effectively integrate various sources of distributed generation, including renewable. In this paper, an economical way of dispatching PV power and power generated by generators in islanded micro-grid is proposed. Here, part of the micro-grid demand is supplied by PV power station. The demand which cannot be supplied by the PV panel, are dispatched between DGs by solving unit commitment and economic dispatch problems. Unit commitment is carried out using priority list method and economic dispatch particle Swarm optimization. A hardware prototype for the proposed micro-grid dispatch methodology has been developed. Here, commitment and de­commitment of the sources and demands is carried out by generating control signals denoting the ON/OFF status to the sources for different intervals.

Design of Parallel Braking Control System for Port Hybrid Electric Vehicle

ABSTRACT Zhang, Z., 2018. Design of parallel braking control system for port hybrid electric vehicle. In: Liu, Z.L. and Mi, C. (eds.), Advances In Sustainable Port And Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 513–519. Coconut Creek (Florida), ISS N 0749-0208. Conventional port hybrid vehicles can't effectively recycle tremendous kinetic energy brought by car brake due to the limitation of energy storage element and energy conversion form in energy recovery braking system. A parallel hydraulic hybrid power control system is designed to solve the contradiction between braking safety stability and realizing the maximum energy recovery. The linear proportional electromagnetic valve is adopted to control the hydraulic pressure of the hydraulic cylinder of the brake. The numerical control of the frictional braking force is realized, and the braking force control strategy can be realized with the braking control system. The experimental results show that the system improves the s...