Power Control Design Research Articles

Determination of the correlation between soilborne pathogen inactivation and radio wave exposure

Radio wave treatment is considered as an alternative for steam treatment of soil in glasshouse horticulture for pathogen suppression. In principle, radio wave treatment can be selectively focused on localized infestations, as well as accommodate renewable energy sources. It may therefore help to significantly reduce fossil fuel consumption for soil treatment in greenhouse horticulture. A prototype treatment system has been developed prior to this study. Following this system’s development, the next step is to develop optimum treatment strategies for its application. To this end, this study presents the development of an experimental method to perform quantified experimentation at laboratory scale on pathogen infested soil samples. The study is an interdisciplinary approach that merges respective contributions from microwave engineering, control engineering, and phytopathology. The design requirements for the experimental system are outlined. The design of the layout of the apparatus, computer simulations to optimize its geometry, and the design of power and temperature control are discussed. The study is finalized by reporting a demonstration of the experimental method.

Optimized design of solid state power controller (SSPC) operating circuit for unmanned aerial vehicles (UAV) DC power distribution systems

Optimized design of solid state power controller (SSPC) operating circuit for unmanned aerial vehicles (UAV) DC power distribution systems

Power controller design for electrolysis systems with DC/DC interface supporting fast dynamic operation: A model-based and experimental study

The ability of the electrolysis system, powered by fluctuating and intermittent renewable sources, to rapidly and accurately track power signals is crucial for energy management in renewable power-to-hydrogen (ReP2H) plants and for providing grid frequency regulation as a virtual power plant (VPP). However, there is a considerable lag (several seconds or even more than 20 s) between the changes of the stack power compared with the stack current, mainly due to the existence of the electric double-layer (EDL) effect. This characteristic hinders the further application of electrolysis systems as flexible loads in power grids. By designing a suitable power controller in the power-electronics interface directly connected to the stack to replace the traditional current controller, it is expected to improve the fast dynamic response of the stack power. This paper proposes a unified electrical equivalent circuit for alkaline water electrolysis (AWE) systems and proton exchange membrane (PEM) electrolysis system with detailed parallel Buck type DC/DC interface, which considers the EDL effect and nonlinear behaviors of electrolysis systems and is suitable for controller design. A power controller design and robust parameter tuning method without excessive current overshoot based on frequency-domain analysis is proposed. The accuracy and effectiveness of the proposed model and method are verified by the experimental test on the 2 N m3/h(10 kW) AWE system and the 1 N m3/h(5 kW) PEM electrolysis system. With the proposed power controller, the AWE and PEM electrolysis system can change the stack power within 0.266s and 0.21s respectively, meeting the requirements of energy management and frequency regulation. Additionally, the temperature stability and the sensitivity of the proposed method to parameter fluctuations in the stack and DC/DC interface are analyzed.

Wireless Power Transfer System Model Reduction with Split Frequency Matching

Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise mathematical formula. However, the model relies on the assumption of loose coupling and loses accuracy when the coupling becomes stronger. In this paper, a model reduction method with split frequency matching is proposed to improve model accuracy under relatively strong coupling conditions, which is suitable for most short-distance WPT applications, such as wireless electrical vehicle charging. Split frequency matching is achieved through a pair of conjugate equivalent mutual inductances, which are derived from the asymmetry characteristics of the full-order dynamic phasor model in the positive and negative frequency domains. The proposed model retains the advantages of the existing model while significantly improving the accuracy under strong coupling conditions. Its characteristics are verified by comparing the experimental results and model predictions under both large step changes and small-signal perturbations.

Joint Phase Shift Design and Resource Management for a Non-Orthogonal Multiple Access-Enhanced Internet of Vehicle Assisted by an Intelligent Reflecting Surface-Equipped Unmanned Aerial Vehicle

This paper integrates intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAV) to enhance the transmission performance of the Internet of Vehicles (IoV) through non-orthogonal multiple access (NOMA). It focuses on strengthening the signals from cell edge vehicles (CEVs) to the base station by optimizing the wireless propagation environment via an IRS-equipped UAV. The primary goal is to maximize the sum data rate of CEVs while satisfying the constraint of the successive interference cancellation (SIC) decoding threshold. The challenge lies in the non-convex nature of jointly considering the power control, subcarrier allocation, and phase shift design, making the problem difficult to optimally solve. To address this, the problem is decomposed into two independent subproblems, which are then solved iteratively. Specifically, the optimal phase shift design is achieved using the deep deterministic policy gradient (DDPG) algorithm. Furthermore, the graph theory is applied to determine the subcarrier allocation policy and derive a closed-form solution for optimal power control. Finally, the simulation results show that the proposed joint phase shift and resource management scheme significantly enhances the sum data rate compared to the state-of-the-art schemes, thereby demonstrating the benefits of integrating the IRS-equipped UAV into NOMA-enhanced IoV.

Advancing Sustainable Energy Solutions in Uganda: A Comprehensive Exploration for Multi-Source Power Control Design

The advent of Multi-Source Power Control Systems (MSPCS) has revolutionized the field of power management, offering enhanced efficiency, reliability, and flexibility in energy utilization. This paper provides a succinct overview of three key aspects crucial for fostering renewable energy in Uganda. Firstly, this paper outlines the essential materials and methodologies required for designing a Multi-Source Power Control System, a critical component for efficiently integrating diverse renewable energy sources into the national grid. The second section examines the current status, potential, and challenges of renewable energy in Uganda, emphasizing the need for sustainable alternatives to address the country's growing energy demands. The second segment delves into the promising prospects of solar energy as a pivotal component in Uganda's renewable energy landscape. Highlighting the abundant solar resources available, the discussion outlines the potential impact of solar energy on the Ugandans’ power generation. Consequently, by addressing these components comprehensively, this research paper contributes to Uganda’s quest for sustainable energy solutions. Keywords: Energy Management, Solar Energy, Generator, Grid, MSPCS

Joint data power control and LSFD design in distributed cell-free massive MIMO under non-ideal UE hardware

This paper investigates distributed cell-free massive multiple-input multiple-output with non-ideal user equipment hardware under spatially correlated channels. By employing the use-and-then-forget technique, a lower capacity bound is derived based on the established generalized UE hardware impairments model. In addition, maximum ratio combining can be used to derive a closed-form expression of the spectral efficiency (SE), which offers novel insights into the impact of non-ideal UE hardware on network performance. Furthermore, a max–min SE fairness problem with UE hardware impairments is established where the optimization variables are data power and large-scale fading decoding (LSFD) vectors. Since this is a non-convex problem, we devise an iterative alternating optimization algorithm based on the bisection search to acquire the globally optimal solution. Numerical results indicate that the recommended joint data power control and LSFD design algorithm provides higher SE for the weakest UE, thus significantly enhancing the total SE of the network.

Design of Fuzzy Logic based Adaptive Active Power Controller to Enhance Power sharing among DGs in an Autonomous Microgrid

This paper presents the design of an adaptive active power controller to enhance the power-sharing capabilities of distributed generators (DGs) in an autonomous microgrid. Each DG in an autonomous microgrid consists of a droop-controlled inverter to control active and reactive power by regulating frequency and voltage correspondingly. The high droop gain can be used in the power controller to encourage faster power sharing among the DGs. However, high droop gain can cause undamped growing oscillations during load fluctuations or generation losses. During such events, attaining faster power-sharing between DGs using high droop gains is difficult. So, the problem with an autonomous microgrid is the conflict between faster power sharing and stability. Stability needs to be compromised to attain quicker power sharing and vice versa. Hence, to achieve power-sharing swiftly with high droop gain and to diminish the growing oscillations caused, this paper proposes a fuzzy logic-based adaptive active power controller (FLAPC). The proposed FLAPC is adaptive and easy to implement. It offers faster power sharing for different values of droop gains and step change in load. The FLAPC is developed in MATLAB 2018a/Simulink environment, and time domain simulations are performed to see the efficacy of the proposed controller. The results of time domain simulations are compared with a droop controller without any additional controller, conventional lead-lag power system stabilizer, and proposed controller for step change in load at different droop gains. The results show that the proposed controller enhances the power-sharing performance and also ameliorates the system’s stability by reducing the settling time and overshoot in active power responses of DGs.

Design of Electric Power Steering System Identification and Control for Autonomous Vehicles Based on Artificial Neural Network

Design of Electric Power Steering System Identification and Control for Autonomous Vehicles Based on Artificial Neural Network

Enhancing Fault Ride-Through Capacity of DFIG-Based WPs by Adaptive Backstepping Command Using Parametric Estimation in Non-Linear Forward Power Controller Design

Enhancing Fault Ride-Through Capacity of DFIG-Based WPs by Adaptive Backstepping Command Using Parametric Estimation in Non-Linear Forward Power Controller Design

Design of infinite horizon LQR controller for discrete delay systems in satellite orbit control: A predictive controller and reduction method approach

In the realm of satellite orbit control, powerful controller design plays a pivotal role in minimizing fuel consumption and ensuring orbit stability. This article introduces an advanced approach to the design of a Linear Quadratic Regulator (LQR) controller with an infinite horizon, tailored for discrete delay systems. The proposed methodology integrates predictive control with a reduction method, aiming for optimality while addressing performance and system constraints. Formulating the control problem as a quadratic program, the predictive control method generates a sequence of control inputs using a reducing horizon strategy. Stability analysis, employing Lyapunov-Krasovsky functions and linear matrix inequalities, yields delay-independent conditions for exponential convergence. A numerical example showcases the controller's effectiveness in maintaining orbit and reducing fuel consumption, underlining its capacity to achieve control objectives despite uncertainties and time delays. This research contributes to robust control strategies in satellite orbit systems, enhancing control performance and operational efficiency.

Retracted: Wearable Power Assistant Robot Sensor Signal Prediction Algorithm and Controller Design.

[This retracts the article DOI: 10.1155/2022/4605389.].

Dynamic resource management in integrated NOMA terrestrial–satellite networks using multi-agent reinforcement learning

The integration of terrestrial and satellite wireless communication networks offers a practical solution to enhance network coverage, connectivity, and cost-effectiveness. Moreover, in today’s interconnected world, connectivity’s reliable and widespread availability is increasingly important across various domains. This is especially more crucial for applications like the Internet of Things (IoT), remote sensing, disaster management, and bridging the digital divide. However, allocating the limited network resources efficiently and ensuring seamless handover between satellite and terrestrial networks present significant challenges. Therefore, this study introduces a resource allocation framework for integrated satellite–terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial–satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods. This research highlights the importance and addresses the need for efficient resource allocation and cache design in integrated terrestrial–satellite networks, paving the way for enhanced connectivity and improved network performance in various applications.

A distributed routing-aware power control scheme for underwater wireless sensor networks

During the data routing process in underwater wireless sensor networks, the communication ranges of nodes profoundly affect the network performances, such as energy utilization and delay overhead. Thus, in this paper, we design a routing-aware power control scheme for underwater wireless sensor networks, whose primary purpose is to assign a differentiated optimal communication radius for each sensor node. Compared with the existing studies, this scheme pays more attention to the combination of power control and existing opportunity routing mechanisms, the distributed implementation of algorithms, and the specific characteristics of underwater sensor network topology. The scheme consists of two main algorithms, namely, the rough adjustment algorithm and the fine adjustment algorithm. These two algorithms employ the game theory and the flooding mechanism to achieve the trade-off among network connectivity maintenance, routing performance promotion, and energy utilization optimization during the power control process. Based on the theoretical analysis, with the proposed algorithms, each node can adjust its transmitted power in a distributed, convergent, and low-complexity manner. According to our numerical simulation experiment, the designed power control scheme could improve the diverse networking performances, including the network lifetime, the packet delivery ratio, the energy efficiency, the load balance, and the end-to-end delay.

Power Quality Improvement using a New DPC Switching Table for a Three-Phase SAPF

This research focuses on the analysis and design of robust direct power control (DPC) for a shunt active power filter (SAPF). The study proposes a novel switching table design based on an analysis of the impact of inverter switching vectors on the derivatives of instantaneous reactive and active powers. The goal is to reduce the number of commutations by eliminating null vectors while maintaining the desired DC-bus voltage using a PI regulator-based anti windup technique. Additionally, a robust PLL structure-based band pass multivariate filter (BPMVF) is utilized to enhance the network voltage. The research demonstrates the effectiveness of the suggested power control through extensive simulation results, showing high performance in both transient and steady-state conditions. The proposed approach offers the advantages of sinusoidal network current, and unitary power factor, and eliminates the need for current regulators and coordinate transformations or PWM generators. Further research directions could explore the practical implementation and real-world performance of this technique in power systems.

Cross-Layer Design for Energy-Efficient Reliable Multi-Path Transmission in Event-Driven Wireless Sensor Networks.

In event-driven wireless sensor networks (WSNs), a reliable, efficient, and scalable routing solution is required for the reliable delivery of sensory data to the base station (BS). However, existing routing algorithms rarely address the issue of energy efficiency under multi-path conflicts for multi-event-driven scenarios. In order to maximize energy efficiency while maintaining a manageable conflict probability, this paper investigates a cross-layer design of routing and power control for multi-event-driven WSNs. We first develop a mathematical characterization of the conflict probability in multi-path routing, and we then formulate the energy efficiency maximization problem as a non-convex combinatorial fractional optimization problem subject to a maximum conflict probability constraint. By utilizing non-linear fractional programming and dual decomposition, an iterative search algorithm was used to obtain near-optimal power allocation and routing solutions. Extensive results demonstrate that our proposed algorithm achieved a gain of 9.09% to 35.05% in energy efficiency compared to other routing algorithms, thus indicating that our proposed algorithm can avoid unnecessary control overhead from multi-path conflicts with a lower conflict probability and can ensure maximum energy efficiency through routing and power control design.

Design and Stability Analysis of a Digital Automatic Power Control Based on a PI Controller for Laser Drivers

Laser diodes are widely used in research and industrial applications in areas such as measurements, communications and health. In most of these applications, stability in the emitted light power is required. This can be realized by modifying the internal parameters, such as the current supply, by using an analog automatic power control (APC). This research presents the design and analysis of a feedback laser driver (digital APC system) based on a proportionall–integral (PI) controller. The controller’s theoretical design acting on the supply current in a laser was obtained by algebraically solving the general equations of a PI controller over a laser described as a steady-state system. The required steady-state model can be determined from the lightl–current curve obtained either from the laser data sheet or experimentally. A posterior numerical analysis shows that the proportional gain of the PI controller is only limited numerically by the reciprocal of the slope efficiency of the laser when the characteristic time of the system is greater than the sampling period. Finally, the APC model was tested in an experimental setting using a laser diode ADL-65052TL at several temperatures. The results show that the proposed relations for the proportional gain and the integral time are valid, achieving the desired power stability with a drift of less than 0.1%.

Design of an adaptive power controller for improving THD performance of integrated-parallel buck-boost converter via efficient reverse power routing

Parallel buck-boost converters are high-output DC-to-DC converters. It is increased by inverting the voltage and decreasing the circuit current capacity. The duty cycle of switching transistors governs converter voltage. Output current moderates THD in these converters. Additional current losses cause output voltage and current harmonics. Inductors, capacitors, MOSFETs, and other power control filtering models reduce excessive current and THD. Complex models lose scalability and stability. Real-time buck-boost controllers can’t use them. Filtered adaptive power controller blocks reduce buck-boost converter response time and inject improper frequency components at the output. A reverse power routing model with an adaptive power controller is proposed to estimate excessive output power and improve converter performance. Reverse power control lowers THD by 20% and increases power throughput. When output power drops, the adaptive power controller (APC) changes ON/OFF duty cycles. The proposed model reduced THD and maintained high output voltage and current in both continuous and discontinuous modes. Context-aware circuit design measured output ripples, harmonics, smoothness factor, and power loss. The proposed parallel buck-boost converter had 10% lower jitters, 23% better harmonic outputs, 15% better smoothness, and 5% lower total power loss than others. The model handles high-density electrical loads with maximum power throughput.

A Dual Observer for Parameter-Free Encoderless Control of Doubly-Fed Reluctance Generators

A novel, cascaded model reference adaptive system (MRAS) observer design for speed and reactive power ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{Q}$</tex-math></inline-formula> ) control of the emerging brushless doubly-fed reluctance generator (BDFRG) without a shaft position sensor, and no machine parameter knowledge, is proposed. The main advantages of the underlying estimation technique over the existing ones reported in the open literature are the intrinsic versatility, robustness, and accuracy, offering entirely decoupled torque (power) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{Q}$</tex-math></inline-formula> responses. The reference model of the rotor angular velocity and position MRAS observer is purely based on the current measurements of the converter-fed (secondary) winding, and its adaptive counterpart on the measured grid voltages and currents at line frequency. The latter requires the inductance ratio for calculations, the online estimates of which being generated by the supplementary MRAS observer working in parallel. Doing so, a complete machine parameter independence and stability of the scheme have been achieved, and its viability fully experimentally validated under variable loading conditions of the small-scale BDFRG wind turbine emulated in a laboratory environment.

Predictive discrete‐time sliding mode based direct power control for grid‐connected energy conversion systems

AbstractA method for digital direct power control design for grid‐connected energy conversion systems was proposed in this paper. The active and reactive powers are directly controlled with constant switching frequency by selecting an appropriate switching control sequence of voltage source converter as a key part of energy conversion systems. The control design combines a discrete‐time sliding mode control and predictive control. The selection of feasible switching control vectors is a direct result of the discrete‐time sliding mode control approach. These vectors satisfy the existence conditions of the sliding mode, but each of them induces system motion with different properties. The task of predictive control is to select an appropriate switching control sequence to reduce the chattering phenomenon, steady‐state error, and overshoot. The cost function of the prediction process is defined as the average value of both power errors on the prediction horizon of three sampling periods. Simulation results, experimental results, and results of a comparative analysis are presented in order to show the performance and effectiveness of the proposed control design.