Operation Of Control Algorithm Research Articles

Research progress on operation control and optimal scheduling of irrigation canal systems

AbstractOpen canals are a common water transfer method used in water transfer projects and agricultural irrigation and drainage projects. With the emergence of drawbacks in traditional canal control models and the increasingly severe shortage of water resources, accurate transport and distribution of water in the canal system of the irrigation district, rational allocation of water resources, reduction in water loss, improvement in the efficiency and benefit of water resource utilization, and satisfaction of the water demand of different water users are needed. Many scholars have conducted extensive research on canal operation control and optimal scheduling. This paper systematically reviews and summarizes the relevant research progress, including the theory of unsteady flow in open canals, the operation mode of the canal system, the operation control model and algorithm of canal systems, and the optimization of water distribution in canal systems. By summarizing the research progress already achieved, the existing problems and future development directions are identified according to actual needs, providing a reference for the ongoing modernization of irrigation districts and the research and application of digital twin irrigation district technology.

Extension of a low-tech Model Predictive Control (MPC) algorithm for grid-supportive heat pump operation

This paper presents the further development of a low-tech Model Predictive Control (MPC) for the grid-friendly operation of heat pumps. The developed algorithm is already available in the literature [1] and successfully implemented in multiple buildings. The overall goal is, to predictively optimize comfort parameters taking into account weather forecast data for 48 h. A simple mathematical building model and an optimization function are used to calculate the heating (or cooling) requirement and determine the optimum heating (cooling) output curve over time.Based on the initial results, the algorithm is extended to include an energy price forecast. This modification enables the simultaneous optimization of comfort and heat supply costs. This forecast-based cost & comfort function is designed for the use of heat pumps in buildings with thermally activated components (TAB). The extension enables the MPC to process day-ahead electricity prices or other price signals in addition to weather forecast data. This means that the operation of heat pumps can be shifted to periods that are beneficial to the grid.The algorithm is analysed and validated in a Matlab/Simulink building simulation for a heating period of one sample month. A case study with different price scenarios is the main part of this paper. Depending on the assumed price fluctuation, the simulation leads to cost savings between 6.65 % and 12.5 % for the observation period of one month. The simulation results thus show that heat generation can be shifted to times with lower electricity prices, which leads to a significant reduction in heating costs without any significant loss of comfort. In conclusion, the developed low-tech MPC enables grid-supportive operation of heat pumps.

Realization of ultracapacitor as sole energy storage device in induction motor drive electric vehicle with modified state timing based field weakening control algorithm

This article employs the concept of realizing an electric vehicle (EV) driven by an induction motor (IM) with an ultracapacitor (UC) as a sole energy storage device for a short distance range in city drive. In battery-driven EVs, the performance of batteries will extensively degrade during frequent start, stop, acceleration and deceleration of the vehicle. However, UC can withstand that circumstance without any degradation. High charge/discharge efficiency, high power density, high temperature withstand capabilities and quick charging capability allows UC to run the EV for an extended range, including the regenerative braking technique. Apart from source, EVs also require efficient drive control for a smooth vehicle operation in the entire driving range. To achieve the vehicle characteristics, it is preferred to operate the propulsion drive in the field weakening (FW) region over the constant torque region. This paper also proposes a Modified State Timing SVPWM Based Control Algorithm for the FW region operation of IM for EV applications. The proposed algorithm is designed to overcome the torque ripple issue of the conventional SVPWM Based Control Algorithm in the FW region. The reduction in torque ripple is achieved by imposing current and voltage limits on the motor d and q axis by considering a maximum modulation index of 1.2732 and with an additional current loop for mitigating the transients in the torque due to sudden variation of the load. The simulation/hardware studies show a smooth operation of UC-driven EV for an entire driving range with a 78.57 percent improvement in torque ripple in the FW region by the proposed algorithm compared with the conventional FW control algorithm. The real time Lab prototype of UC driven EV has been developed and the proposed algorithm has been validated in real-time using the STM32F407VGTx ARM Cortex M4 microcontroller.

Remote monitoring and control of the status of transformers in distribution electrical networks

RELEVANCE. One of the most important expected effects of digitalization of objects of the power grid complex of the Russian Federation is to increase the level of reliability of its functioning. In this regard, research and development in this direction is undoubtedly relevant. THE PURPOSE. To propose a mathematical model for monitoring dangerous developing defects in power oil-filled transformers, this would meet the properties of predictability and adaptability. Based on the model, to develop a decision-making algorithm for long-term reliable operation oftransformers. METHODS: Methods of statistical pattern recognition theory, correlation analysis and Bayesian classification will be used to solve the problems to ensure high reliability of diagnostic assessments, validity and effectiveness of operational solutions. RESULTS. A predictive model was obtained and verified in the form of a correlation function of a sign of a faulty state of a transformer from the values of its electrical load. An event tree has been formed that restores the causal relationship between the result of monitoring the transformer, the defect sign and the operational decision being made. Based on the event tree and diagnostic evaluation criteria, a control algorithm is implemented, with the help of which calculations are performed confirming the effectiveness of the proposed approach. CONCLUSION. The possibility of effective application of the developed defect recognition model and operational control algorithm as tools of industrial technology of the Internet of Things is illustrated, in particular, when organizing remote diagnostic monitoring of oil-filled transformer equipment at substations of the distribution grid area.

Modelling and optimal management of renewable energy communities using reversible solid oxide cells

The use of reversible solid oxide cells within a renewable energy community results a promising application which permits to balance the temporal mismatch between renewable energy production and users’ demand through hydrogen as energy vector. Differently from batteries and supercapacitors, this technology is characterized by a high stored energy density and a negligible daily self-discharge. Nevertheless, the system management is more complex requiring cell behaviour optimization and hydrogen storage control. Here this work proposes a control algorithm for reversible solid oxide cell operation coupled to renewable energy sources within a renewable energy community formed by an aggregation of fifteen residential customers. Based on forecasts of loads and renewable energy production, the proposed algorithm, a stochastic model predictive control, can optimize system operation aiming at economic benefit maximization. The transition between the fuel cell mode for power generation and the electrolysis mode for energy storage through hydrogen production was set considering the available renewable energy, the power demand of community members and the energy sell-back price in order to increase the auto-consumed amount as well as to favour the electricity exchange within the renewable energy community. Since the reversible solid oxide cell is the key-point in such a system, SIMFC-SIMEC (SIMulation of Fuel Cells and Electrolysis Cells), a physically based 2D model, allowed an effective prediction of cell behaviour deriving the efficiency of electricity and hydrogen production from local physicochemical feature and working parameter gradients on each stacked cell plane.

Design of Nonlinear Active Disturbance Rejection Controller Based on the Adaptive Particle Swarm Optimization Algorithm for the Maglev Train Traction Control System

This paper focuses on speed tracking control of the maglev train operation system. Given the complexity and instability of the maglev train operation system, traditional speed‐tracking control algorithms demonstrate poor tracking accuracy and large tracking errors. The maglev train is easily affected by external interference, increasing train energy consumption and reducing passengers’ riding comfort. This study proposes a control algorithm called APSO‐NLADRC to address the deficiencies of the automatic train operation control algorithms. The APSO‐NLADRC is based on adaptive particle swarm optimization (APSO) algorithm parameter optimization nonlinear active disturbance rejection controller (NLADRC). The method of population comparison, linear update of learning factors, and adaptive updating of inertia weight values addresses the premature convergence phenomenon that occurs during the parameter optimization of the traditional particle swarm algorithm. The APSO algorithm solves the problem that the parameters of the NLADRC are difficult to adjust. Compared with PID, NLADRC, and NLADRC based on traditional particle swarm optimization algorithms, the proposed control algorithm has higher tracking accuracy and more robust anti‐interference capability and provides better comfort.

A Predictive Operation Controller for an Electro-Thermal Microgrid Utilizing Variable Flow Temperatures

We propose an optimal operation controller for an electro-thermal microgrid. Compared to existing work, our approach increases flexibility by operating the thermal network with variable flow temperatures and in that way explicitly exploits its inherent storage capacities. To this end, the microgrid is represented by a multi-layer network composed of an electrical and a thermal layer. We show that the system behavior can be represented by a discrete-time state model derived from DC power flow approximations and 1d Euler equations. Both layers are interconnected via heat pumps. By combining this model with desired operating objectives and constraints, we obtain a constrained convex optimization problem. This is used to derive a model predictive control scheme for the optimal operation of electro-thermal microgrids. The performance of the proposed operation control algorithm is demonstrated in a case study.

Introducing a novel control algorithm and scheduling procedure for optimal operation of energy storage systems

One of the principal purposes of using energy storage systems (ESS) are peak shaving and load smoothing. In the present study, a novel control algorithm has been developed in order to the optimal use of ESS for peak load shaving. First, by comparing the complex control algorithm of the present study with the control algorithm developed by other researchers (simple control algorithm), the superiority and advantage of the present work become clear. To do this, five different load demand profiles have been considered and the mentioned algorithms have been applied to them. The results show that the implementation of the present study novel algorithm on the load demand profiles number one to five reduces the maximum of the required grid power to 6.477%, 11.81%, 12.60%, 10.25%, and 3.000% of their initial value, respectively. Their variance also reduces their initial value to 14.47%, 14.20%, 28.48%, 39.87%, and 54.96%, respectively. On the other hand, the implementation of the simple control algorithm has reduced the maximum of the required grid power to 0.8542% of its initial value only in the fifth profile. But the variance in the first to fifth profiles is reduced by 9.630%, 5.831%, 14.12%, 21.89%, and 52.00% of their initial value, respectively. In the following, by applying the novel control algorithm on the first sample load demand profile and change of various parameters such as ESS capacity, lower and upper limitation of the grid power, etc. the results are extracted and reported in detail.

Miniaturized Distributed Generation for a Micro Smart Grid Simulator

In this paper, a miniaturization method is proposed for developing micro distributed generation for a micro smart grid simulator. The micro smart grid simulator is a fault simulator that was built to test and verify the new operation control algorithms for smart grids in the laboratory and has a size downscaled to one-thousandth of that of an actual smart grid. The micro distributed generation was designed in a multi-layered structure (dimension: 13 × 20 cm2), in which each function is implemented in several layers, to satisfy the size requirements. Next, the grid synchronization and PQ control algorithms required for the distributed generation were developed. A three-phase 19 V power system was built, and a 19 V–7.5 W three-phase micro distributed generation was realized through experimental verification. In addition, by verifying the effectiveness through grid synchronization and 7.5 W PQ control experiments, it was confirmed that the micro distributed generation based on the proposed miniaturization method can be implemented in a micro smart grid simulator.

Development and research of mathematical models and control algorithms for the separation of bulk materials

Analysis of the separation process showed that this object is multidimensional, stochastic, nonlinear and has a number of other “inconvenient” properties. All stages of the separation system functioning have a significant impact on the properties of the finished product. Therefore, to improve the separation technology, constant automatic control over all stages of this technological process and an effective control system for this object are required. The creation of such control systems requires the development of methods for their identification, modeling of processes occurring during separation, creation of operational control algorithms implemented by software and hardware systems. At present, the resources for increasing the efficiency of control over the separation of bulk materials by means of automating their main stages have been exhausted, and the way out is seen in the creation and implementation of new generation systems - systems of intelligent functioning and control. The intelligence of the control processes for the separation of bulk materials is determined by the general logic of functioning, as well as the efficiency of making managerial decisions, which is significantly increased if the developed mathematical models and control algorithms that correspond to the separation processes under study are used, which make it possible to evaluate the quality of the decision made on the model. The article provides a literary review of works on the development of methods and models for effective management of production systems based on the use of modern information technologies, which showed that there are many unresolved issues. This work is aimed at solving some of them. The relevance of the topic and the objectives of the research are determined. The research carried out made it possible to develop mathematical models of separation processes of various bulk materials and algorithms for identifying these processes. Based on the results obtained, an algorithm for making decisions by an intelligent software and hardware control system was developed.

Simulating operation of power units 1 and 2 at Novovoronezh NPP II with two electrical feed pumps disabled and the backup pump not enabled

Introduction. The article analyzes the operation of Unit 1 and 2 of Novovoronezh Nuclear Power Plant II (equipped with VVER-1200 reactors) with two electrical feed pumps disabled and the backup pump not enabled. These operating conditions are subsequently simulated using the power unit model software-hardware package (PUM SHC) developed by LLC IF SNIIP ATOM. Research objectives. The objective of this work was to check the reliability of the forecasts of changes in the power unit parameters obtained using the PUM SHC, based on operational data. Methods. The simulated power unit parameter changes in transient conditions were in good agreement with the data collected in real tests. During the simulation, the power unit dynamic stability was preserved, i.e., the operational parameters were within the design limits and did not exceed the protection operation set points. Results. The results of the work suggest the possibility of using current NPP power unit simulations: for developing proposals for adjusting the operation control algorithms in case of malfunctions and emergency modes with the main equipment shutdown and power unit protection actuation; and for verifying design solutions for updating the power unit systems, which are associated with the use of new equipment or changes in flow diagrams. Conclusion. Current power unit models can be applied both for existing power units and for new ones that are being commissioned.

Optimisation of an Algorithm for Automatic Control of Transmission in a Wheeled Tractor

The paper considers a digital model of a wheeled agricultural tractor, as well as algorithms for automatic control of the engine and transmission. Simulation of the working conditions was performed in MATLAB and its applications, Simulink, Sim-scape. The tractor model is based on a systemic approach and includes such subsystems as the engine, transmission, undercarriage, hook load, and control actions from the operator, as well as functions of logic and optimisation for gear shifting. With the help of fundamental blocks of the Matlab package, models of tractor physical components are created. The fundamental blocks of the package serve to model the physical components of the tractor: engine, transmission control system, the transmission that includes friction clutches, gears, shafts ties with the necessary characteristics. Simulation of the working processes of a tractor unit is performed with optimisation of the laws of engine and transmission control according to the criterion of fuel efficiency and maximum power usage. The control system parameters were optimised in the transport mode, as well as in the maximum power mode in the traction range. As an illustration of the use of this analysis method, a solution to the problem of optimisation of an agricultural wheeled tractor operating modes is given. The results of this mathematical simulation are presented in this paper. Parameters of tractor operation control algorithm, which provide minimum fuel consumption in tractor operating cycles, are given. The research results showed the effectiveness of the proposed algorithms and the digital model. The presented digital model allows to debug the operating tractor control algorithms and offers an engineering practice for optimising automatic control of the agricultural tractor engine and transmission.

Performance analysis of novel fault‐tolerant multilevel inverter with a pristine methodology for fast and exhaustive real‐time failure of switches

SummaryMultilevel inverter (MLI) provides a high power density, though the number of devices increases the chances of failures increases, reducing the reliability of power converter. It is essential to detect, isolate, and reconfigure the faults which arises in MLI. The research proposed here is capable of accomplishing the complete fault tolerance against single and multiple devices for both open circuit and short circuit faults. The control algorithm for operation of single‐pole‐double‐throw (SPDT) relays. This methodology is capable of completely preserving the normal operating condition in post‐fault condition also. The output voltage waveform matches by voltage sensors connected across load, and the controllers sense the faults originated from the variation in the look‐up‐table (LUT) and also categorize the nature of the faults. Field‐programmable gate array (FPGA)‐based controller sends a command signal through SPDT to isolate the faulty switches by redundant switches. In this way, the output power across the load is preserved. This research article also covers the comparative study that is made with the existing fault‐tolerant structures. The reliability comparison shows the better performance indices over conventional cascaded H‐bridge (CHB) inverter. The simulation and experimental results validate the effectiveness of control methodology to achieve complete fault tolerance.

Research and application of cable pipeline robot for underground pipeline 3D simulation technology

In this paper, an underground cable intelligent pipeline detection robot is designed for the collection of pipeline position information. First of all, its mechanical structure is developed. In addition, the research of operation control algorithm is discussed. Finally, the digital image and pipeline space coordinate acquisition and communication methods are studied. In the design stage, the design scheme of each module is first determined and researched, designed and improved; then the integration and verification are carried out; finally, the field test and improvement are carried out; the prototype is produced and operated after meeting the conditions of trial operation, and the operation data are actively collected for improvement and improvement.

Test and optimization of a control algorithm for demand-oriented operation of CHP units using hardware-in-the-loop

This paper covers test and verification of a forecast-based Monte Carlo algorithm for an optimized, demand-oriented operation of combined heat and power (CHP) units using the hardware-in-the-loop approach. For this purpose, the optimization algorithm was implemented at a test bench at Reutlingen University for controlling a CHP unit in combination with a thermal energy storage, both in real hardware. In detail, the hardware-in-the-loop tests are intended to reveal the effects of demand forecasting accuracy, the impact of thermal energy storage capacity and the influence of load profiles on demand-oriented operation of CHP units. In addition, the paper focuses on the evaluation of the content of energy in the thermal energy storage under practical conditions. It is shown that a 5-layer model allows to determine the energy stored quite accurately, which is verified by experimental results. The hardware-in-the-loop tests disclose that demand forecasting accuracies, especially electricity demand forecasting, as well as load profiles strongly impact the potential for CHP electricity utilization on-site in demand-oriented mode. Moreover, it is shown that a larger effective capacity of the thermal energy storage positively affects demand-oriented operation. In the hardware-in-the-loop tests, the fraction of electricity generated by the CHP unit utilized on-site could thus be increased by a maximum of 27% compared to heat-led operation, which is still the most common modus operandi of small-scale CHP plants. Hence, the hardware-in-the-loop tests were adequate to prove the significant impact of the proposed algorithm for optimization of demand-oriented operation of CHP units.

Effects of Dynamic Model Errors in Task-Priority Operational Space Control

SUMMARYControl algorithms of many Degrees-of-Freedom (DOFs) systems based on Inverse Kinematics (IK) or Inverse Dynamics (ID) approaches are two well-known topics of research in robotics. The large number of DOFs allows the design of many concurrent tasks arranged in priorities, that can be solved either at kinematic or dynamic level. This paper investigates the effects of modeling errors in operational space control algorithms with respect to uncertainties affecting knowledge of the dynamic parameters. The effects on the null-space projections and the sources of steady-state errors are investigated. Numerical simulations with on-purpose injected errors are used to validate the thoughts.

ПЕРИОДИЧЕСКОЕ АДАПТИВНОЕ УПРАВЛЕНИЕ НЕЛИНЕЙНЫМ ДИНАМИЧЕСКИМ ОБЪЕКТОМ С ВХОДНЫМ НАСЫЩЕНИЕМ В УСЛОВИЯХ НЕОПРЕДЕЛЕННОСТИ

The article proposes solution to the problem of synthesizing adaptive control algorithm for dy-namic T-periodic nonlinear plant operating under conditions of structural and parametric uncer-tainty, in the presence of input restrictions and constant bounded disturbances. The hyperstabil-ity criterion, L-dissipativity conditions, fast-acting filter-correctors, and an implicit reference model are used as the methods for synthesis of repetitive adaptive control system.

Intelligent Traction Control Method Based on Model Predictive Fuzzy PID Control and Online Optimization for Permanent Magnetic Maglev Trains

Considering that the speed control system of the suspended permanent magnetic maglev train is more complicated and the parameters are more unstable than those of other trains, the traditional speed-tracking algorithm has large tracking errors, frequent controller output changes, high energy consumption, and decreasing the passengers’ riding comfort. To improve the shortcomings of the traditional automatic train operation (ATO) control algorithm, this paper proposes a predictive fuzzy proportional-integral-derivative control algorithm with weights (WM-F-PID). The main contribution of this work is to propose a cascaded predictive fuzzy PID (F-PID) control algorithm architecture with weights and use an improved steepest descent method to calculate online the weight of the F-PID controller input occupied by the predictive controller output. Compared with the proportional-integral-derivative (PID), F-PID, model predictive control (MPC), and simple cascade predictive fuzzy PID (M-F-PID) control algorithms, this control algorithm effectively improves train tracking accuracy and comfort and reduces train energy consumption and stopping errors.

Distributed economic model predictive control for cooperative supply chain management using customer forecast information

Efficient operation of supply chain systems by means of distributed model predictive control is studied in this work. The main focus is on the exploitation and sharing of predictive information on delivery and demand throughout the supply chain. Based on the availability of customer demand predictions, which are assumed to be reliable to some extent, two distributed model predictive control algorithms for supply chain operation are proposed, analyzed and investigated in numerical simulations. The mechanisms employed for information exchange throughout the supply chain differ in both approaches. The first approach establishes and implements the exchange of semi-accurate predictions, which explicitly requires predicted trajectories to only vary slightly from one time step to the next. In the second approach, information exchange is rather indirect by means of terminal constraints in the local MPC formulations, explicitly relying on the stock and flow nature of the overall system. The two approaches considerably differ in terms of system setup, requirements and corresponding results, and hence provide a flexible framework for leveraging predictive information in supply chain system management. As such, they form a basis for further investigations towards the ultimate goal of quantifying the value of predictive information in supply chain systems.

Operational control algorithm of parameters of high-pressure sodium lamps based on a statistical time series model

The developed operational control algorithm parameters of a high pressure sodium lamp is considered in this work. We used a mathematical model to develop the algorithm. An analytical method was used to describe the operation of a sodium lamp based on differential equations as well as an algorithm for evaluating time series. The mathematical expectation and dispersion were also evaluated. The described models for quality control of sodium lamps have been tested in production. The obtained simulation results coincide with the results of the experiment. Using a series of tests based on the proposed control method we confirmed the adequacy of the developed model.