Performance Of Control Scheme Research Articles

A probabilistic model-based approach to assess and minimize scaling in geothermal plants

Geothermal installations often face operational challenges related to scaling which can lead to loss in production, downtime, and an increase in operational costs. To accurately assess and minimize the risks associated with scaling, it is crucial to understand the interplay between geothermal brine composition, operating conditions, and pipe materials. The accuracy of scaling predictive models can be impacted by uncertainties in the brine composition, stemming from sub-optimal sampling of geothermal fluid, inhibitor addition, or measurement imprecision. These uncertainties can be further increased for fluid at extreme conditions especially high salinity and temperature. This paper describes a comprehensive method to determine operational control strategies to minimize the scaling considering brine composition uncertainties. The proposed modelling framework to demonstrate the optimization under uncertainty workflow consists of a multiphase flow solver coupled with a geochemistry model and an uncertainty quantification workflow to locally estimate the probability of precipitation potential, including its impact on the hydraulic efficiency of the geothermal plant by increasing the roughness and/or decreasing the diameter of the casings and pipelines. For plant operation optimization, a robust control problem is formulated with scenarios which are generated based on uncertainties in brine composition using an exhaustive search method. The modelling and optimization workflow was demonstrated in a geothermal case study dealing with barite and celestite scaling in a heat exchanger. The results showed the additional insights in the potential impact of brine composition uncertainties (aleatoric uncertainties) in scaling potential and precipitation location. Comparing the outcome of optimization problem for the deterministic and fluid composition uncertainties, a change of up to 2.5% in the temperature control settings was observed to achieve the optimal coefficient of performance.

Applying Machine Learning Approach to Design Operational Control Strategies for a Wastewater Treatment Plant in Typical Scenarios

Applying Machine Learning Approach to Design Operational Control Strategies for a Wastewater Treatment Plant in Typical Scenarios

Perspectives on Soft Actor–Critic (SAC)-Aided Operational Control Strategies for Modern Power Systems with Growing Stochastics and Dynamics

Perspectives on Soft Actor–Critic (SAC)-Aided Operational Control Strategies for Modern Power Systems with Growing Stochastics and Dynamics

Sampled-data self-learning observer based attitude tracking control against sensor-actuator faults

Abstract This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence of sensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy, highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.

An improved SPWM control approach with aid of ant lion optimization for minimizing the THD in multilevel inverters

This article presents an innovative asymmetric multilevel inverter (MLI) topology that outperforms conventional counterparts. The introduced topology presents a breakthrough in implementing power electronics control by maximizing specific levels while minimizing switching components. A cutting-edge control scheme for optimal operation of the cascaded half-bridge MLI is presented. The ant lion optimization (ALO) algorithm was implemented to optimize the switching control to reduce the total harmonic distortion (THD) and improve power quality. For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. The suggested method showcases a remarkable reduction in the THD compared to SSPWM. The quality of the resulting waveform was enhanced, and both filter size and cost were significantly reduced. To meet stringent IEEE standards, an LC filter has been designed with minimal size and proper requirements. Experimental results validation of the suggested scheme, using a dSPACE R&D controller board unequivocally, confirmed its robustness and effectiveness. This groundbreaking study not only introduces a superior asymmetric MLI topology but also validates its exceptional performance through comprehensive analysis and experimentation. The experimental waveforms showed good matching with the simulation outcomes. The findings hold immense promise for advancing the field of power system control and revolutionizing the designing and implementation of efficient and cost-effective inverter systems.

Optimal Control Strategies for Reliable Operation of Electric Vehicle Charging Stations under Fault Tolerant Scenarios

Optimal Control Strategies for Reliable Operation of Electric Vehicle Charging Stations under Fault Tolerant Scenarios

The influence of uncertainty parameters on the double-layer model predictive control strategy for the collaborative operation of chiller and cold storage tank in data center

The inefficient operating states and parameter settings are important factors affecting the high energy consumption of data center cooling systems. The double-layer model predictive control (DMPC) strategy is developed to optimize the operating states and parameter settings of cooling system. The theoretical performance of DMPC strategy is impressive. Meanwhile, the control performance is deeply affected by parameter uncertainties in the model, sensors, and actuator in practical application. Therefore, the influence of uncertain parameters on performance of DMPC strategy is quantified, and the key uncertain parameters (including the wet-bulb temperature, approaching temperature of cooling tower, cooling capacity of water tank, COP, water flow rates of cooling water pump and chilled water pump) are identified. The results show that the DMPC strategy can prevent the chillers from operating at very low loads and reduce the 24-h PUE by more than 0.01. However, compared to traditional control strategy, the DMPC strategy is more sensitive to uncertain parameters. Parameter uncertainty leads to 70 % mode prediction error rate of DMPC strategy. In mechanical cooling mode, the DMPC strategy is more sensitive to COP than approaching temperature and wet-bulb temperature. However, in the hybrid cooling mode, the sensitivity of the three parameters is opposite to mechanical cooling mode. In free cooling mode, the DMPC strategy is most sensitive to water pump flow rates. Therefore, in order to reduce the computational and economic costs of hybrid cooling systems using the DMPC strategy in data center, more efforts should be placed on reducing the uncertainties of key parameters in initial investment and modeling.

Integration of a solid oxide electrolysis system with solar thermal and electrical energy: A testing campaign for operation and control strategy definition

AbstractThe EU project PROMETEO has the scope of testing a 25 kW solid oxide electrolysis system integrated with a concentrated solar power plant via thermal energy storage in a relevant environment. Given the plant layout and the hydrogen demand characteristics, this work aims to identify how to operate the system effectively when renewable electricity is unavailable and how to modulate the load during hydrogen generation, thus defining the system's operation modes and control strategy. A 5 kWe stack has been tested at the FBK facility. The hot standby tests show that feeding a reducing gas at the negative electrode and air at the positive electrode, without polarizing the stack, effectively keeps the stack hot at 750°C and prevents degradation. Conversely, the electric protection approach leads to significant stack degradation (15% voltage drop in 200 h for one cluster). Regarding modulation of hydrogen generation, with low steam flowrates, the stack current and the flow rate of produced hydrogen mainly depend on the steam flow rate, while it is not affected by the stack temperature; conversely, with high steam flowrates, the current depends only on the stack temperature (from 25 A at 670°C to 65 A at 760°C). Based on the results, two hot standby modes and two load control strategies to be implemented and tested in the PROMETEO prototype are proposed.

Development of system analysis code and its application in transient simulation of supercritical CO2 Brayton cycle direct cooled reactors

The supercritical carbon dioxide (s-CO2) Brayton cycle direct cooled reactor system has the characteristics of high cycle efficiency, compact equipment, and simple layout, which has attracted the attention of researchers recently. The use of s-CO2 Brayton cycle and the elimination of intermediate heat exchangers make the transient characteristics of this reactor different from light water reactors which have rich operating experience. Transient simulation and safety analysis with system code is an essential stage in reactor design and safety verification. Therefore, a system code, named NUSAS (NUclear Safety Analysis and Simulation), was developed in this study for the transient simulation of s-CO2 Brayton cycle direct cooled reactor system. This code is developed based on the advanced two-fluid two-pressure seven-equation model and has the capability of modelling the turbomachinery in s-CO2 Brayton cycle direct cooled reactor system. The transient characteristics during core power step and precooler cooling water mass flow rate step were analyzed. The load tracking and operation control strategy of the reactor was designed, and transient simulations of the wide range (100% to 50%) load tracking process were conducted. According to the simulation results of NUSAS, the transient characteristics of s-CO2 Brayton cycle direct cooled reactor were demonstrated when operating conditions change.

Research on Hybrid Heating System in Cold Oilfield Regions

Efficient and clean treatment of wastewater and energy recovery and utilization are important links to realize low-carbon development of oilfields. Therefore, this paper innovatively proposes a multi-energy complementary co-production heating system which fully and efficiently utilizes solar energy resources, oilfield waste heat resources, and biomass resources. At the same time, a typical dormitory building in the oil region was selected as the research object, the system equipment selection was calculated according to the relevant design specifications. On this basis, the simulation system model is established, and the evaluation index and operation control strategy suitable for the system are proposed. The energy utilization rate of the system and the economic, energy-saving, and environmental benefits of the system are simulated. The results show that, under the simulated conditions of two typical days and a heating season, the main heat load of the system is borne by the sewage source heat pump, the energy efficiency is relatively low in the cold period, and the energy-saving characteristics are not obvious. With the increase in heating temperature and anaerobic reactor volume, the energy consumption of the system also increases, and the energy efficiency ratio of each subsystem and the comprehensive energy efficiency ratio of the system gradually decrease. In addition, although the initial investment in cogeneration heating systems is high, the operating costs and environmental benefits are huge. Under the condition of maintaining 35 °C, the anaerobic reactor in the system can reduce carbon emissions by 12.15 t per year, reduce sulfur dioxide emissions by 98.4 kg, reduce dust emissions by 49.2 kg, and treat up to 2700 t of sewage per year, which has broad application prospects.

Cerebellum-Inspired Learning and Control Scheme for Redundant Manipulators at Joint Velocity Level.

Redundant manipulators, as mechanical equipments imitating human arms, have been applied to various areas in recent years from the perspective of control. Different from pure control technologies, the motion capability of a human arm is achieved by a complex and efficient neural system, with the cerebellum playing a pivotal role. Motivated by this fact, we design a cerebellum model based on an echo state network (ESN) for the learning and control of redundant manipulators. In addition, to simulate the skillful control ability of the cerebellum over movements of human arms, the proposed model is constructed at the joint velocity level. Furthermore, to improve the accuracy and applicability, we propose an ESN-based Kalman-filter-incorporated and cerebellum-inspired (KFICI) scheme for the learning and control of redundant manipulators with Kalman filter incorporated. The proposed scheme enables a redundant manipulator to track the desired trajectory at the velocity level and tolerate noises. Finally, simulations and experiments based on a physical redundant manipulator are performed to verify the effectiveness of the proposed control scheme.

Optimal operation control strategy for off-grid photovoltaic hydrogen production system considering hydrogen production efficiency

Off-grid photovoltaic hydrogen production is an effective solution for improving photovoltaic (PV) utilization and obtaining green hydrogen. The main challenge faced by off-grid photovoltaic hydrogen production systems (OGPHPS) is how to deal with the randomness, intermittency, and volatility of PV generation. Therefore, this paper proposes an optimal operation control strategy (OOCS). First, a model of OGPHPS is established, comprising photovoltaics, batteries, and an electrolytic array (ELA). Second, a two-step optimization strategy (TSOS) that integrates both long-term and short-term time scales is proposed. In the first step of TSOS, the day-ahead optimization of the operation state of ELA is conducted according to day-ahead power prediction. A constraint was proposed to balance the operation time of the electrolyzer. In the second step of TSOS, according to the ultra-short time power prediction and the electrolyzer efficiency curve, the rolling optimization of the operating power distribution of ELA is carried out. Through TSOS, PV generation consumption is maximized by ELA, the continuous operational stability of OGPHPS is enhanced, and the lifespan of electrolyzer is extended. Third, to alleviate the influence of instantaneous power fluctuation on the system, an energy management strategy (EMS) for OGPHPS is proposed. Finally, a simulation model of OGPHPS was established, and the simulation results showed that the direct current bus voltage fluctuation was within 3%. Compared to the chain allocation strategy, the OOCS achieves higher hydrogen production under the same solar irradiance conditions. Particularly in low irradiance, OOCS has more sufficient consumption of PV generation and higher hydrogen production efficiency.

Analysis of prevention and control strategies for safe operation of old elevators based on evaluation data statistics

In recent years, the safety problems caused by frequent failures of old elevators have attracted increasing social attention. The safety evaluation of old elevators is an effective method to comprehensively judge the safety status of elevators. Through the statistics of the relevant data of the safety evaluation conclusions of more than 100 old elevators, this paper focuses on the analysis of the typical potential safety risks and puts forward practical and effective measures and suggestions, so as to provide effective guidance for ensuring the safe operation of old elevators.

Research on safety control technology of high-speed railway combined test based on threatening event analysis

PurposeSafety management is a key point and poses a challenge in joint testing. To detect and address potential accidents' hidden dangers early, this paper conducts research on the safety control technology for high-speed railway joint tests by incorporating the concept of hazardous events.Design/methodology/approachAiming at ensuring the safety of high-speed railway combined inspections and trials, this paper starts from the dual prevention mechanism. It introduces the concept of threatening events, defines them and analyzes the differences between threatening events and railway accidents. The paper also proposes a cause model for threatening events in high-speed railway combined inspections and trials, based on three types of hazard sources. Furthermore, it conducts research on the control strategies for these threatening events.FindingsThe research on safety control technology for high-speed railway combined operation and testing, based on the analysis of threatened events, offers a new perspective for safety management in these operations. It also provides theoretical and practical support for the transition from passive prevention to active risk pre-control, which holds significant theoretical and practical value.Originality/valueThe innovation mainly includes the following three aspects: (1) Building on the traditional dual prevention mechanism, which includes risk hierarchical management and control as well as hidden danger investigation and management, a triple prevention mechanism is proposed. This new mechanism adds the management of threatening events as the third line of defense. The aim is to more comprehensively identify and address potential security risks, thereby enhancing the efficiency and effectiveness of security management. (2) In this paper, the definition of a railway threatening event is clarified, and the causative model of a high-speed railway threatening event based on three kinds of danger sources is proposed. (3) This paper puts forward the control strategy of the high-speed railway combined operation and trial, which includes five key links: identification, reporting, analysis, rectification and feedback, which provides a new perspective for the safety management of the high-speed railway combined operation and trial and has important theoretical and application value.

Robust variable step size least mean fourth with quotient form-based control scheme for DVR operation

ABSTRACT A robust variable step size least mean fourth with a quotient form control scheme (RVSSLMFQ) is implemented for addressing power quality concerns and delivering clean voltage to sensitive loads through a dynamic voltage restorer (DVR). This method estimates the fundamental components from distorted grid voltages, featuring an automated process with adaptive time-varying step sizes and utilizing a quotient structure to reduce mean squared error (MSE) effectively. The suggested control scheme overcomes the stability and performance issues of the least mean fourth (LMF) algorithm by improving convergence and adapting well to varying step sizes. Furthermore, RVSSLMFQ achieves remarkable results with reduced settling time and fewer peak overshoots compared to the least mean square (LMS) and LMF control algorithms. For tuning the load voltage and DC-link voltage proportional-integral (PI) controllers, the improved grey wolf optimization technique (I-GWO) is applied. The results are evaluated through MATLAB simulations.

Energy consumption analysis of trains based on multi-mode virtual coupling operation control strategies

Urban rail transit, as a main mode of travel, has always been concerned with the balanced relationship between energy consumption and transport efficiency. The advent of the virtually coupled train set (VCTS) has significantly enhanced the efficiency of existing railway lines. Both energy consumption and comfort during the operation of VCTS control technology have emerged as significant challenges for the development of VCTS since the inception of onsite testing. A review of the current research status of VCTS control systems has revealed that the majority of current formation control systems are focused on the safe operation of formation trains, the stability of train control systems, and safe train separation models. However, a case study has shown there is a strong correlation between train operating intervals, train control strategies, and train operating energy consumption. Therefore, it is necessary to design and plan the operation modes of formation trains for different train operation requirements. This paper presents four distinct control modes, developed through the integration of the most prevalent VCTS control strategies, operational modes, and operational requirements. The design of these control modes is guided by considerations of energy consumption, safety, and comfort during train operation. A case study of Beijing Subway Line 11 revealed a significant correlation between energy consumption, control mode, and safety under different control modes. In particular, the correlation between train running intervals and energy consumption in a high-precision tracking state provides valuable technical support for the future operation of VCTS technology in different periods, scenarios, and environments.

A model predictive control for a multi-chiller system in data center considering whole system energy conservation

To meet the thermal environment requirements for the continuous and stable operation of information technology (IT) equipment in data rooms, the cooling system needs to operate all day, which has become the second largest energy-consuming system in data centers. The absence of an effective operational control strategy in the data center cooling process results in high energy consumption and poor power usage effectiveness (PUE) of the data center. This study proposes a model predictive control (MPC) strategy considering the constraint of whole system energy conservation for a multi-chiller system in a data center. This strategy aims to maintain the data center server room temperature stable in the IT equipment working environment, and at the same time, the energy consumption of the cooling system is significantly reduced, thus the PUE of the data center is lowered. The long and short-term memory (LSTM) neural network prediction models for data center cooling load and server room temperature were constructed using the mechanism-coupled data law method. The MPC algorithmic structure coupled with cooling load prediction model and server room temperature prediction model. It takes the balance between supply and demand of cooling capacity as a constraint. The cost function of multi-chiller system control considers the temperature-controlling error in the server room and the power consumption of the chilled-water system. A particle swarm optimization (PSO) algorithm is used to solve the optimal configuration strategy of the chilled water flow rate and the water supply temperature of each chiller, which realizes the dynamic control of the chilled-water system. A dynamic regulation model was developed for a data center in North China. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. Compared with the operation results of the data center cooling system with proportional integral derivative (PID) control and fuzzy control. The results show that under MPC strategy, the stability of the server room temperature has improved by 27.77% compared to PID control and by 18.08% compared to fuzzy control, demonstrating the superiority of MPC in temperature stability control in the server room. In terms of chilled-water system energy consumption, compared to the PID control and fuzzy control strategies, the MPC strategy has achieved energy savings of 11.81% and 7.58%, respectively, showing significant energy-saving effects.

An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance.

An overview of grid-forming technology and its application in new-type power system

To address the global climate crisis, achieving energy transitions is imperative. Establishing a new-type power system is a key measure to achieve CO2 emissions peaking and carbon neutrality. The core goal is to transform renewable energy resources into primary power sources. The large-scale integration of high proportions of renewable energy sources and power electronic devices will dramatically change the operational mechanisms and control strategies of power systems. Existing wind and solar converters mostly adopt the grid-following control mode, which leads to significant challenges in system security and stability as it is insufficient to support the frequency and voltage of the grid. On the other hand, grid- forming control technology (GFM) can provide voltage and frequency support for the system, and thus becomes an effective measure to improve the inertia and damping characteristics of power systems. This paper illustrates the principles, control strategies, equipment types, application scenarios, and project implementation of grid-forming technology. The simulation and analysis based on a renewable-dominated real new-type power system show that GFM can significantly enhance the frequency and voltage support capacity of the power system, improve renewable energy accommodation capacity and grid transmission capacity under weak grid conditions, and play an important role in enhancing the stability and power supply reliability of renewable-dominated new-type power systems.

Return-Temperature Reduction at District Heating Systems: Focus on End-User Sites

This review presents a comprehensive examination of recent advancements and findings related to return-temperature reduction in District Heating (DH) systems, with a focus on enhancing overall system efficiency at end-user sites. The review categorizes and clarifies various return-temperature reduction techniques, emphasizing aspects such as building energy performance, heat emitters, thermostatic radiator valves, and substation units. One shall note that return temperature is not a parameter that can be directly controlled within a DH system; instead, it is influenced indirectly by adjusting various system parameters throughout the design, commissioning, operation, and control phases. Key insights include the direct impact of heat demand on return temperatures; the pivotal role of indoor heating systems in optimizing thermal energy use in relation to heat demand; the significance of thermostatic radiator valves in regulating heat output and maintaining low return temperatures; the advantages of ventilation radiators and add-on fans in enhancing radiator efficiency; the necessity for effective substation operation to improve system cooling capacity; and the critical role of operational control strategies in achieving optimal system performance. These findings underscore the need for integrated approaches in DH system design and operation to achieve lower return temperatures and improve overall system efficiency.