Hierarchical Control Technique Research Articles

Innovative hierarchical control of multiple microgrids: Cheetah meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques. This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights. HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities. The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600 V-bus According to IEEE519–2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 s. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

Further results on optimal local and remote control with packet losses and transmission delay

In this article, the finite horizon optimal local and remote control (LRC) problem is further investigated for the case that both the packet losses (PL) and the transmission delays (TD) occur over the unreliable uplink channel from the local controller to the remote controller. In terms of the variational technique and the dynamic programming approach, the LRC problem with PL and TD is solved for the first time. The results derived in this article are innovative in the following aspects: Firstly, the necessary and sufficient solvability conditions are developed using the convex variational technique, illustrating that the solvability of the LRC problem is equivalent to the solvability of the forward and backward stochastic differential equations (FBSDEs). Secondly, by proposing an innovative hierarchical control technique and using the optimality principle of dynamic programming, the explicit optimal linear LRC is derived in terms of the completing square approach. Finally, the derived results of this article are illustrated by a numerical example.

Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

Graded Control Technology of Oil Stability and Water Control in Fracture-Pore Carbonate Reservoirs

In view of the prominent problems of extremely strong heterogeneity, fracture development of different scales, and serious rapid water channelling in the process of water injection development in fracture-pore carbonate reservoirs, a comprehensive treatment method of oil stabilizing and water controlling is proposed, which includes fracture classification control, combination of adjustment and plugging, and wettability change. Based on the static and dynamic test data, the characteristics of reservoir fracture development and fracture scales are described in detail. By means of reservoir engineering and numerical simulation, the water flooding rule under different grade fracture reservoir conditions is studied, the influence of different levels of fractures on water injection development is evaluated, and the improvement effect of different control methods on water drive sweep is demonstrated. Research shows that a large amount of remaining oil trapped in pores is affected by fracture water channelling, which makes it difficult to use effectively. The water flooding sweep characteristics of reservoirs with different scales of fractures are quite different, and large-scale fractures are the main reason for ineffective water injection. The hierarchical control techniques of plugging large-scale fractures, intervening and adjusting small- and medium-scale fractures, and changing the wettability of microscale pore media can significantly extend the water flooding spread range and effectively improve oil well production. The research results have been applied in the field and achieved good results. This technology can provide theoretical guidance and a technical basis for the comprehensive treatment of oil stabilization and water control in fractured-porous carbonate oil and gas reservoirs.

Frequency and voltage coordinated control of a grid of AC/DC microgrids

This paper proposes a hierarchical control scheme based on a distributed controller design for a multi-microgrid system. Thus, a proposed control approach of ac and dc microgrid interfaces is presented, based on virtual synchronous generators to control the power exchange of the interconnected microgrids, and provide frequency support, voltage regulation, and virtual inertia for the individual microgrids and ac host grid. A hierarchical distributed control technique is proposed, with primary controls of interfacing virtual synchronous generators providing adaptive inertia for all ac systems, while a secondary distributed control regulates the frequency and the voltages of the ac host grid and the interconnected ac and dc microgrids, based on a consensus technique with limited information about the overall system. The proposed controller shares the total system load among the grid and microgrids, while minimizing the overall frequency and dc-voltage deviations in all interconnected systems. The presented interface and the controller are implemented, tested, and validated using time-domain simulations for a multi-microgrid test system, which is based on a CIGRE benchmark medium voltage system and consists of both ac and dc microgrids.

Hierarchical Control of DC Motor Coupled with Cuk Converter Combining Differential Flatness and Sliding Mode Control

This paper proposes a hierarchical control law for DC motor fed by DC–DC power Cuk Converter. The control is divided into two parts: Firstly, the property of differential flatness associated with the mathematical model of the DC motor is studied to design a robust control that achieves the task of tracking the reference angular speed trajectory for the motor. It also gives the voltage profile $$\vartheta $$ which must be followed by the Cuk converter. The second independent controller, based on cascade control, is proposed for the Cuk converter, which allows the converter output voltage to follow the specified trajectory $$\vartheta $$ . Sliding mode control is used in the inner loop, whereas proportional integral control is used in the proposed cascade controller’s outer loop. Numerical simulation of the hierarchical control technique is carried out in MATLAB/Simulink, and results under parametric variation show robustness. Finally, a comparison is drawn for speed trajectory tracking by the DC motor coupled with DC–DC buck and Cuk converter to show the performance improvement in case of using Cuk converter for angular speed trajectory tacking of a DC motor.

A Comprehensive Review on Control Techniques for Power Management of Isolated DC Microgrid System Operation

The present research investigations in power management fraternity are focused towards the realization of smart microgrid (MG) technologies. Due to intrinsic advantages of Direct Current (DC) system in terms of compatibility with power generation sources, modern loads and storage devices, DC MG has becoming popular over Alternate Current (AC) system. A secondary voltage and current control schemes of DC MG system are mainly based on the distributed consensus control of Multi-agent system (MAS) to balance generation and the demand. The basic concern of the cooperative control of MAS is consensus, which is to design a suitable control law such that the output of all agents can achieve synchronization. The distributed consensus algorithm requires much less computational power and information exchange in between the neighbor’s agent. Meanwhile the other controllers such as model predictive control (MPC) requires accurate dynamic models with high computational cost and it suffers from lack of flexibility. The hierarchical consensus control technique is classified into three levels according to their features, namely primary, secondary, and tertiary. MAS is a popular distributed platform to efficiently manage the secondary control level for synchronization and communication among the power converters in autonomous MGs. In this article, various primary control techniques for local voltage control, voltage restoration in the secondary control level and tertiary control for energy management techniques are discussed. With this, the key emphasis to reduce the voltage deviation and disturbances in a heterogeneous DC MG network solutions are discussed. Furthermore, to analyze the system response and the charging and discharging characteristics of the battery unit, the developed second order heterogeneous consensus controller is compared with the traditional homogeneous consensus control and droop control methods. Finally a detailed discussion on simulation case study using heterogeneous consensus control method over the traditional methods are provided using MATLAB/Simulink platform.

Distributed Operation of Microgrids Considering Secondary Frequency Restoration Based on the Diffusion Algorithm

This paper proposes a distributed control of the microgrid (MG) system based on the diffusion algorithm. Unlike the existing decentralized strategy that focuses on the economic operation of the MG system, the proposed strategy performs secondary frequency regulation in addition to the optimization of the MG system. The hierarchical control technique is employed in this study, where the primary layer is responsible for power control and the secondary layer is responsible for the frequency control and economic operation of the MG system. A tested MG system with four distributed generations (DGs) is considered. Three types of communication topologies are evaluated in this study, which are line, ring, and full topologies. The proposed controller is compared to the conventional consensus controller to show the effectiveness of the proposed diffusion controller. Simulation results show that the proposed diffusion strategy improves the convergence speed of the distributed control, resulting in the improvement of power responses and frequency quality of the MG system. The tested system is implemented in the MATLAB/Simulink environment to show the feasibility of the proposed diffusion controller.

Hierarchical control technique-based harmony search optimization algorithm versus model predictive control for autonomous smart microgrids

This paper proposes a robust hierarchical control technique for islanded smart microgrids (SMGs). The proposed technique is composed of three control levels to optimally adjust the microgrid (MG) voltage and frequency and improve its power quality by reducing the total harmonic distortion (THD) in islanded mode. The primary control level is based on adjusting the MG voltage and frequency to an acceptable limit after the system load variations using the droop controller. The secondary control level is depending on retaining the MG voltage and frequency to their nominal values after any load changes by proposing a multi-stage H-infinity (H∞) controller. The tertiary control level is proposed to optimally evaluate the multi-stage H∞ controller weighted parameters based on the harmony search (HS) optimization algorithm. To verify the effectiveness of the proposed hierarchical control technique, a model predictive control (MPC) method is designed and simulated. For the sake of comparison between the two methods, different indices are used including maximum frequency deviation, integral of square error (ISE), THD, and controller response time. Robustness analysis is performed based on the impact of line impedance changes and filter parameters variations. Moreover, the system stability is analyzed based on the system step response, singular value decomposition (SVD), and the bode diagram. The proposed control technique and the MPC method are modeled and simulated using MATLAB/Simulink program. The results are reported and analyzed to prove the effectiveness of the proposed control technique.

Optimal Multiobjective Control of Low-Voltage AC Microgrids: Power Flow Regulation and Compensation of Reactive Power and Unbalance

The presence of single-phase distributed generators unevenly injecting active power in three-phase microgrids may create undesired upstream current unbalance. Consequently, voltage asymmetry and even active power curtailment may occur in such networks with negative economic impact. Thus, this paper proposes an optimal multiobjective approach to regulate the active and reactive power delivered by distributed generators driven by a three-layer hierarchical control technique in low-voltage microgrids. This method does not require previous knowledge of network parameters. The multiobjective algorithm is implemented in the secondary level achieving optimal dispatch in terms of maximizing the active power generation, as well as minimizing the reactive power circulation and current unbalance. By the existence of a utility interface three-phase converter placed at the point-of-common-coupling, the proposed control can regulate the power circulating among the microgrid phases, and the microgrid structure can withstand grid-connected and islanded operating modes. The path for interphase power circulation through the DC-link of the utility interface allows the multiobjective algorithm to achieve better results in terms of generation and compensation compared to the system without utility interface. The proposed method is assessed herein by computational simulations in a three-phase four-wire microgrid under realistic operational conditions.

Control Techniques in AC, DC, and Hybrid AC–DC Microgrid: A Review

The microgrid concept is gaining popularity with the proliferation of distributed generation. Control techniques in the microgrid are an evolving research topic in the area of microgrids. A large volume of survey articles focuses on the control techniques of the microgrid; however, a systematic survey of the hierarchical control techniques based on different microgrid architectures is addressed very little. The hierarchy of control in microgrid comprises three layers, which are primary, secondary, and tertiary control layers. A review of the primary and secondary control strategies for the ac, dc, and hybrid ac–dc microgrid is addressed in this paper. Furthermore, it includes the highlights of the state-of-the-art control techniques and evolving trends in the microgrid research.

Approximately Optimal Teaching of Approximately Optimal Learners

We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks [1] , e.g., teaching students the meanings of words by showing images that exemplify their meanings a la Rosetta Stone [2] and Duo Lingo [3] . The approach is grounded in control theory and capitalizes on recent work by [4] , [5] that frames the “teaching” problem as that of finding approximately optimal teaching policies for approximately optimal learners (AOTAOL). Our work expands on [4] , [5] in several ways: (1) We develop a novel student model in which the teacher's actions can partially eliminate hypotheses about the curriculum. (2) With our student model, inference can be conducted analytically rather than numerically, thus allowing computationally efficient planning to optimize learning. (3) We develop a reinforcement learning-based hierarchical control technique that allows the teaching policy to search through deeper learning trajectories. We demonstrate our approach in a novel ITS for foreign language learning similar to Rosetta Stone and show that the automatically generated AOTAOL teaching policy performs favorably compared to two hand-crafted teaching policies.

Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation

Abstract This paper presents a novel coordinated path following system (PFS) and direct yaw-moment control (DYC) of autonomous electric vehicles via hierarchical control technique. In the high-level control law design, a new fuzzy factor is introduced based on the magnitude of longitudinal velocity of vehicle, a linear time varying (LTV)-based model predictive controller (MPC) is proposed to acquire the wheel steering angle and external yaw moment. Then, a pseudo inverse (PI) low-level control allocation law is designed to realize the tracking of desired external moment torque and management of the redundant tire actuators. Furthermore, the vehicle sideslip angle is estimated by the data fusion of low-cost GPS and INS, which can be obtained by the integral of modified INS signals with GPS signals as initial value. Finally, the effectiveness of the proposed control system is validated by the simulation and experimental tests.

Disturbance observer based hierarchical control of coaxial-rotor UAV

This paper propose an hierarchical controller based on a new disturbance observer with finite time convergence (FTDO) to solve the path tracking of a small coaxial-rotor-typs Unmanned Aerial Vehicles (UAVs) despite of unknown aerodynamic efforts. The hierarchical control technique is used to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the thrust force acting on the vehicle. The new disturbance observer with finite time convergence is intergated to online estimate the unknown uncertainties and disturbances and to actively compensate them in finite time.The analysis further extends to the design of a control law that takes the disturbance estimation procedure into account. Numerical simulations are carried out to demonstrate the efficiency of the proposed control strategy.

Master–Slave Based Hierarchical Control for a Small Power DC-Distributed Microgrid System with a Storage Device

In this paper, we analyze one of the main drawbacks of droop control-based DC microgrid systems, and propose a novel control method to overcome this problem. Typically, DC microgrid systems use droop control techniques to enable communication independency and expandability. However, as these advantages are based on bus quality and regulation abandonment, droop-based schemes have limitations in terms of high bus impedance and bus regulation. This paper proposes a novel master–slave based hierarchical control technique for a DC distribution system, in which a DC bus signaling method is used to overcome the communication dependency and the expandability limitations of conventional master–slave control methods. The concept and design considerations of the proposed control method are presented, and a 1 kW simulation under a Powersim (PSIM) environment and hardware prototype—built to verify the system—is described.

Generalized hierarchical control

Multi-objective control systems for complex robots usually have to handle multiple prioritized tasks. Most existing hierarchical control techniques handle either strict task priorities by using null-space projectors or a sequence of quadratic programs; or non strict task priorities by using a weighting strategy. This paper proposes a novel approach to handle both strict and non-strict priorities of an arbitrary number of tasks. It can achieve multiple priority rearrangements simultaneously. A generalized projector, which makes it possible to completely project a task into the null-space of a set of tasks, while partially projecting it into the null-space of some other tasks, is developed. This projector can be used to perform priority transitions and task insertion or deletion. The control input is computed by solving one quadratic programming problem, where generalized projectors are adopted to maintain a task hierarchy, and equality or inequality constraints can be implemented. The effectiveness of this approach is demonstrated on a simulated robotic manipulator in a dynamic environment.

Implementation of Hierarchical Control in DC Microgrids

DC microgrids are becoming popular in low-voltage distribution systems due to the better compatibility with photovoltaic panels, electric vehicles, and DC loads. This paper presents a practical dc microgrid developed in the Water and Energy Research Laboratory (WERL) in the Nanyang University of Technology, Singapore. The coordination control among multiple dc sources and energy storages is implemented using a novel hierarchical control technique. The bus voltage essentially acts as an indicator of supply-demand balance. A wireless control is implemented for the reliable operation of the grid. A reasonable compromise between the maximum power harvest and effective battery management is further enhanced using the coordination control based on a central energy management system. The feasibility and effectiveness of the proposed control strategies have been tested by a DC microgrid in WERL.

Hierarchical optimal contour control of motion systems

Many motion control applications utilize multiple axes to traverse complex trajectories. The hierarchical contour control methodology proposed in this paper treats each axis as an individual subsystem and combines the Internal Model Principle with robust tracking and optimal hierarchical control techniques to track a desired trajectory. In this method the objectives are divided into two levels. Measurable goals of each subsystem are included in the bottom level and unmeasurable goals, which are estimated using the bottom level states, are considered in the top level where the subsystems are synchronized. The proposed methodology reduces system complexity while greatly improving tracking performance. The tracking error for each axis is considered in the bottom level where the Internal Model Principle is used to compensate for unmodeled nonlinear friction and slowly varying uncertainties. The top level goal (i.e., zero contour error) is propagated to the lower level by an aggregation relationship between contour error and physical linear axis variables. A controller is designed at the bottom level which simultaneously satisfies the bottom level goals (i.e., individual axis tracking) and the top level goal. Experimental results implemented on a table top CNC machine for diamond and freeform contours illustrate the performance of the proposed methodology. While this methodology was implemented for a two-axis motion system, it can be extended to any motion system containing more than two axes.

Sequence Stratigraphy of Jurassic Succession in Central Junggar Basin, China

Sequence stratigraphic features of the Jurassic succession in Central Junggar Basin were investigated by integrating multiple materials including cores, well and seismic data. Due to their unique formation mechanism, sequences of the target interval were analyzed with a binary systems tract mode (each complete sequence contains a transgressive systems tract and a highstand systems tract) rather than the traditional one. Basic principles and analytical methods of high-resolution sequence stratigraphy were also applied to this research. By adopting cyclic correlation and hierarchical control techniques, eight sequences (Sq1-Sq8) were identified in the target interval. Especially, coal seams are of great significance in the sequence identification and they were considered to be closely relevant to maximum flooding surfaces (mfs).