Reactive Control Strategy Research Articles

Research on reactive power voltage control strategy of distribution network based on information sharing technology

Abstract In view of the fact that the traditional voltage control of the distribution network is suitable for local voltage control and affects the economy of the operation of the distribution network, a reactive power voltage control strategy of the distribution network based on information-sharing technology is proposed. First, the overall architecture of voltage control in the distribution network based on information-sharing technology is analyzed, and secondly, an algorithm based on the Jacobian sensitivity matrix is established with the minimum voltage RMS as the control goal. The model is solved, and finally, the effectiveness of the proposed voltage control strategy is verified by combining actual examples. The results show that the proposed voltage control strategy has a better voltage regulation effect and effectively reduces the overall voltage deviation of the distribution network.

Research on reactive power voltage control strategy of distribution networks containing distributed PV

Abstract With the large-scale increase in photovoltaic power generation, its integration into the grid will affect the system voltage of the distribution network. This paper presents a reactive voltage control strategy for the distribution network based on deep reinforcement learning. Firstly, the objective function model is established based on the node voltage and network loss of the distribution network. Secondly, each photovoltaic inverter is modeled as an agent of the enhanced learning environment. The collaborative control problem of the photovoltaic inverter is transformed into an observable Markov decision-making process through the distribution network partition. The paper uses a double delay depth deterministic gradient algorithm to solve the problem. Finally, combined with the improvement of 33 nodes for verification, the proposed strategy has the stability of improving voltage, presenting an ability to effectively reduce network loss and verify the effectiveness of the proposed strategy.

Observation-Based Diagnostics of Reactive Nitrogen Recycling through HONO Heterogenous Production: Divergent Implications for Ozone Production and Emission Control.

Understanding of nitrous acid (HONO) production is crucial to photochemical studies, especially in polluted environments like eastern China. In-situ measurements of gaseous and particulate compositions were conducted at a rural coastal site during the 2018 spring Ozone Photochemistry and Export from China Experiment (OPECE). This data set was applied to investigate the recycling of reactive nitrogen through daytime heterogeneous HONO production. Although HONO levels increase during agricultural burning, analysis of the observation data does not indicate more efficient HONO production by agricultural burning aerosols than other anthropogenic aerosols. Box and 1-D modeling analyses reveal the intrinsic relationships between nitrogen dioxide (NO2), particulate nitrate (pNO3), and nitric acid (HNO3), resulting in comparable agreement between observed and simulated HONO concentrations with any one of the three heterogeneous HONO production mechanisms, photosensitized NO2 conversion on aerosols, photolysis of pNO3, and conversion from HNO3. This finding underscores the uncertainties in the mechanistic understanding and quantitative parametrizations of daytime heterogeneous HONO production pathways. Furthermore, the implications for reactive nitrogen recycling, ozone (O3) production, and O3 control strategies vary greatly depending on the HONO production mechanism. On a regional scale, the conversion of HONO from pNO3 can drastically enhance O3 production, while the conversion from NO2 can reduce O3 sensitivity to NOx changes in polluted eastern China.

Research on reactive voltage control strategy for substations connected to photovoltaic systems

Abstract The reactive voltage control of conventional substations is mainly realized by adjusting the switching of capacitors or reactors and changing the transformer ratio, which can only be controlled discretely. The PV inverter can carry out continuous reactive power control, and the reactive voltage control strategy of the conventional substation does not consider the reactive power control of the PV. In this paper, the nine-zone diagram control strategy of the traditional substation is used as a reference, and the reactive voltage optimization control strategy of a substation with a photovoltaic power generation system is constructed considering the characteristics that photovoltaic inverter can continuously regulate reactive power. Taking the internal power grid of an enterprise as an example, the simulation analysis verifies the effectiveness of the control strategy, which can ensure that the voltage and power factor of the grid-connected access point are qualified. At the same time, the voltage fluctuation of the grid-connected point can be reduced, and the network consumption can be reduced.

Experimental validation of data-driven reactive control strategy for wave energy converters: A Gaussian process regression approach

Wave energy is a renewable energy source that can generate electricity without emitting carbon dioxide using wave energy converters (WECs). WECs face several challenges, such as effectively harnessing wave energy, ensuring operational safety, and implementing cost-saving measures. In this study, a data-driven reactive control strategy for WECs using Gaussian process regression is experimentally validated. The control strategy aimed to maximize energy absorption while satisfying physical constraints without a WEC dynamic model. In this study, the Gaussian process regression model represents a WEC dynamic model using an approximation function based on the input/output data. Experimental validation results confirmed that the proposed data-driven reactive control strategy effectively controlled the WEC and improved its performance using the Gaussian model. Moreover, performance was improved under different wave conditions compared to those used during training. The control strategy can be useful in situations where conventional methods cannot realize ideal control parameters, such as when substantial model errors exist or the WEC dynamic model changes over time. The data-driven approach that uses Gaussian process regression reduces learning costs compared to other machine-learning methods. It also has stable control performance because it is independent of the training dataset. The potential of data-driven approaches for optimizing WEC control strategies was highlighted.

Internal control systems of SMEs in Nigeria: a proactive or reactive strategy against employees’ fraud?

Purpose The purpose of this study is to determine the strategy adopted by small- and medium-scale enterprise (SME) owners in setting up internal control systems, examine the relationship between the numbers of employees’ fraud and strategy adopted in setting up internal control and evaluate the difference between proactive and reactive strategies in employee’s fraud prevention among SMEs. Design/methodology/approach A survey research approach was adopted to determine whether proactive or reactive strategies were adopted by the SME owners. Specifically, a survey questionnaire was used to gather primary data from selected respondents in Lagos and Oyo States. Descriptive statistics, Spearman correlation and the Mann–Whitney test were used to analyse the primary data. Findings The study found that most of the SME owners used reactive internal control strategies in setting up their internal control systems after they suffered financial losses occasioned by employees’ fraud. Also, the study found a positive relationship between the number of employees’ fraud and reactive strategy. Finally, the study found a significant difference in the number of employees’ fraud occurrences between proactive and reactive internal control strategies in SMEs. Research limitations/implications The study provides further confirmation that where internal control is properly set up and strengthened, a lower number of employee frauds will occur. Thus, giving credence to the fraud theory. The study was, however, conducted in six selected local government areas in two states. Practical implications The study provides recommendations on the adoption of a proactive strategy for curbing employees’ fraud at the onset of business operations and not until devastating events of employees’ fraud become a reality. Originality/value The study is original, as it focuses on the strategy adopted by SME owners in setting up internal control systems, which is rare in fraud empirical studies, particularly for studies conducted in emerging markets like Nigeria. It provides the need for the sustainability of SMEs as engine of growth and employment through the adoption of appropriate strategies in setting up internal control systems.

Generalizability of foot placement control strategies during unperturbed and perturbed gait.

Control of foot placement is an essential strategy for maintaining balance during walking. During unperturbed, steady-state walking, foot placement can be accurately described as a linear function of the body's centre of mass (CoM) state at midstance. However, it is uncertain if this mapping from CoM state to foot placement generalizes to larger perturbations that could potentially cause falls. Recovery from these perturbations may require reactive control strategies not observed during unperturbed walking. Here, we used unpredictable changes in treadmill belt speed to assess the generalizability of foot placement mappings identified during unperturbed walking. We found that foot placement mappings generalized poorly from unperturbed to perturbed walking and differed for forward perturbation versus backward perturbation. We also used the singular value decomposition of the mapping matrix to reveal that people were more sensitive to backward versus forward perturbations. Together, these results indicate that a single linear mapping cannot describe the foot placement control during both forward and backward losses of balance induced by treadmill belt speed perturbations. Better characterization of human balance control strategies could improve our understanding of why different neuromotor disorders result in heightened fall risk and inform the design of controllers for balance-assisting devices.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

Reactive control of velocity fluctuations using an active deformable surface and real-time PIV

This study demonstrates an experimental realization of turbulence control strategies previously explored by Choi et al. (J. Fluid Mech., vol. 262, 1994, pp. 75–110) through numerical simulations. To conduct the experiments, a deformable surface with a streamwise array of 16 independently controlled actuators was developed. A real-time particle image velocimetry (RT-PIV) system was also created for flow measurements. The objective of the control strategy was to target the sweep and ejection motions of the vortex shedding from a spherical cap placed in a laminar boundary layer. Reactive control strategies consisted of wall-normal surface deformations that opposed or complied with the wall-normal (v) or streamwise (u) velocity fluctuations obtained from the RT-PIV. The results showed two primary outcomes of the control approach. Firstly, it effectively hindered the advancement of sweep motions towards the wall. Secondly, it disrupted the periodic shedding of vortices. The v-control with opposing wall motions and u-control with compliant wall motions exhibited strong inhibition of sweep motions, while the v-control with compliant and u-control with opposing wall motions showed weaker inhibition. All reactive control cases resulted in the disruption of vortex shedding. In some instances, this disruption was accompanied by increased turbulent kinetic energy due to the generation of additional flow motions. However, the v-control with opposing wall motions significantly reduced the vortex-shedding energy while maintaining total turbulent kinetic energy close to or below that of the unforced flow. Overall, the experiments show the effectiveness of reactive control strategies in mitigating sweep motions and disrupting vortical structures, offering insights for developing reactive control strategies.

Proactive Versus Reactive Control Strategies Differentially Mediate Alcohol Drinking in Male Wistars and P Rats.

Problematic alcohol consumption is associated with deficits in decision-making and alterations in prefrontal cortex neural activity likely contribute. We hypothesized that the differences in cognitive control would be evident between male Wistars and a model of genetic risk: alcohol-preferring P rats. Cognitive control is split into proactive and reactive components. Proactive control maintains goal-directed behavior independent of a stimulus, whereas reactive control elicits goal-directed behavior at the time of a stimulus. We hypothesized that Wistars would show proactive control over alcohol seeking whereas P rats would show reactive control over alcohol seeking. Neural activity was recorded from the prefrontal cortex during an alcohol seeking task with two session types. On congruent sessions, the conditioned stimulus (CS+) was on the same side as alcohol access. Incongruent sessions presented alcohol opposite the CS+. Wistars, but not P rats, made more incorrect approaches during incongruent sessions, suggesting that Wistars utilized the previously learned rule. This motivated the hypothesis that neural activity reflecting proactive control would be observable in Wistars but not P rats. While P rats showed differences in neural activity at times of alcohol access, Wistars showed differences prior to approaching the sipper. These results support our hypothesis that Wistars are more likely to engage in proactive cognitive control strategies whereas P rats are more likely to engage in reactive cognitive control strategies. Although P rats were bred to prefer alcohol, the differences in cognitive control may reflect a sequela of behaviors that mirror those in humans at risk for an AUD.

Building energy performance metamodels for district energy management optimisation platforms

Reactive control strategies lack the flexibility necessary to optimize the operational costs of buildings and district systems. To overcome this limitation and to enable the transition to model predictive control strategies (MPC), the development of dedicated control platforms and models is required. Predictive models for district systems management should provide supply and demand side integrated modelling, high accuracy, generalization capacity and reduced computational times. However, traditionally available MPC solutions do not meet these requirements as simplified models offer short computational times but lack the required accuracy; detailed physics-based models provide satisfactory generalization but at the expense of high computational costs; and the generalization capacity of data models is constrained by the quality and availability of data. In contrast, metamodels developed through the combined use of physics-based models and machine learning techniques offer a powerful alternative at reduced computational cost. This paper describes an upgraded Integrated District Model concept developed through co-simulation coupling metamodels of buildings with a district heating infrastructure Modelica model. Furthermore, the process to produce the metamodels and optimization engine required to generate demand flexibility optimization functionalities for the buildings of the Stepa Stepanovic subnetwork (Belgrade) is depicted. Starting from the development of metamodels of instances of specific buildings (residential and educational use) the process was expanded to provide additional generalization to define, (1) a generic metamodel with the capacity to reproduce the behaviour of any instance of building of the residential typology, and (2) metamodels with generalization capacity in relation to operational settings. As part of this process the potential of several machine learning algorithms (e.g Support Vector Machines, etc) was evaluated including the latest ensemble boosting methods (e.g. Adaboost, Gradient Boosting and Extreme Gradient Boosting) with comparatively low use in the building simulation community. Finally, a virtual test bed consisting in metamodels coupled to an optimization engine based on genetic algorithms, was implemented, and compared to a traditional Physics-based model MPC solution (EnergyPlus-GENOPT), to evaluate the potential of the developed building level optimization functionalities. The metamodels and optimization engine were able to reproduce the optimized settings identified by the EnergyPlus-GENOPT MPC solution with cost savings potentials of 5–10%.

Reactive and Predictive Control Scheme for Evasive Maneuvers in Aerial Robots

Reactive and Predictive Control Scheme for Evasive Maneuvers in Aerial Robots

Balancing power production and coastal protection: A bi-objective analysis of Wave Energy Converters

Wave Energy Converters (WECs) have the potential to serve dual purposes, generating power and protecting coastlines. Although traditionally the focus has been on maximizing power generation for cost-effectiveness, growing impacts of climate change have made coastal protection increasingly imperative. However, power production and coastal protection have been addressed separately, missing potential synergies. This paper addresses this gap by conducting a bi-objective analysis to investigate the interactions between power extraction and wave attenuation for a single Oscillating Surge Wave Converter (OSWC) and WEC farms of three and five units.A linear Power Take-Off (PTO) system, with passive and reactive control strategies, is examined. By varying the PTO parameters, we assess their influence on both power production and wave field attenuation. Results demonstrate a significant impact of the PTO choice on wave attenuation, with a similar trend observed for power production. This finding highlights the potential for a trade-off, where maximizing wave attenuation may come at the cost of moderate energy output. Furthermore, the interactions observed within the WEC farms enhance this trend.The study emphasizes the importance of a holistic approach to WEC technology development, promoting sustainable and resilient harnessing of wave energy resources, considering both power generation and coastal protection.

Research on Reactive Power Coordination Control Strategies of Multi-Infeed Line-Commutated Converter–High-Voltage Direct Current Systems

For a receiving-end power grid with multi-infeed LCC-HVDC systems, simultaneous commutation failures may seriously threaten the safe and stable operation of the system. To evaluate the impact of commutation failure and improve the voltage stability of the commutation buses in multi-infeed HVDC systems, this paper proposes a method for evaluating the voltage stability of commutation buses and a reactive power coordination control (RPCC) method for commutation failure of multiple HVDC systems. Firstly, three indicators and the entropy weight method are adopted to comprehensively evaluate the voltage stability of commutation buses. Then, an RPCC method is proposed to resist commutation failure. The proposed RPCC method uses the voltage interaction factor (VIF) to screen out DC systems that are strongly related to dynamic reactive power compensation devices and activates the devices to provide RPCC to the DC systems through an auxiliary controller. Finally, the effectiveness of the proposed method is verified through a practical example of the Jiangsu power grid.

Active flow control on airfoils by reinforcement learning

Active flow control is a widespread practice for airfoil aerodynamic performance enhancement. Within active flow control, reactive strategies are very effective, but the adequate design of these strategies is often complex. This study proposes a reactive control strategy based on a Reinforcement Learning (RL) agent to effectively govern the motion of a rotating flap implemented on a NACA0012 airfoil. With this objective, first different Computational Fluid Dynamics (CFD) simulations are conducted to gather data about the tested case. Then, a numerical model based on Artificial Neural Networks (ANN) is developed to model the discussed case. Finally, the RL agent is trained and tested under different conditions. The results show that the trained RL agent is able to provide a fast and reliable response for every tested condition, setting the adequate position of the flap and obtaining an appropriate aerodynamic performance of the airfoil for all the tested conditions. In comparison with the optimum conditions, the absolute error in the position of the flap set by the agent is below 2.2 ° for all the angles of attack, resulting in an aerodynamic performance very close to the optimum, being only 0.39%–3.05% lower, depending on the case.

Collaborative Active and Reactive Power Control of DERs for Voltage Regulation and Frequency Support by Distributed Event-Triggered Heavy Ball Method

The increasing integration of distributed energy resources (DERs) into distribution networks draws great attention to the advanced voltage control. Meanwhile, DERs scattered in distribution networks also show potential to provide frequency support. This paper proposes a novel coordinated active and reactive control strategy of DERs by using a distributed event-triggered heavy ball method, aiming to allow DERs to offer voltage regulation and frequency support in a unified framework. In the meantime, the proposed control strategy can effectively save communication cost and accelerate the convergence rate simultaneously. Specifically, for achieving a faster convergence rate, the dual-based heavy ball method is incorporated into a traditional dual iteration scheme by adding extra momentum terms. To further reduce the communication burden, the control scheme is modified by designing event-triggered conditions on dual variables. The convergence results are provided based on a linearized power flow model to facilitate the controller design and theoretical analysis. The proposed control strategy is also numerically tested on a modified IEEE 123-bus network with a nonlinear power flow model. Finally, the simulation results illustrate that the event-triggered heavy ball approach can greatly reduce the communication cost and achieve an improved convergence rate compared with the existing methods.

A Power-Aware Control Strategy for an Elbow Effort-Compensation Device

This work presents a reactive control strategy for loading and sudden unloading of an elbow effort-compensation device controlled in force. Through this control strategy, in addition to an individual's forearm weight, an external load can be detected and adaptively compensated via a feed-forward force reference, facilitating the execution of arbitrary movements by the wearer. In case of a sudden contact/load loss, a power-aware strategy is implemented to immediately eliminate the portion of external loading in the force reference. The adaptive compensation of the external loads is achieved through an electromyography interface. Instead, to react to sudden load releases, we set a power limit on the tendon, and continuously measure it through an encoder and a load cell connected with the cable. Two sets of experiments are designed to test the proposed load-releasing method on a bench-top setup with 2 kg, and 3.9 kg, and a human subject with 0.5 and 1 kg. Next, the overall scenario including load-compensation and load-releasing are carried out on eight human subjects with 0.5 and 1 kg loads to evaluate the release and compensation time, and the effort reduction with respect to non-powered exoskeleton case. Results show that the average compensation/release time (payload) among subjects is measured as 0.98/0.91 seconds (0.5 kg), and 1/0.86 seconds (1 kg). The average effort reduction among the subjects are also reported as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$66.4\%$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$67.11\%$</tex-math></inline-formula> for 0.5 kg, and 1 kg, respectively.

Research on the simulation of SVG for online de-icing on distribution lines

The distribution network connects with families, more branch circuit breakers, more and more users rely on power in winter, which requires higher and higher reliability requirements for power supply. The power supply department is facing greater pressure to stop distribution line for de-icing. In view of the above reasons, this paper first briefly introduces several measures of de-icing without power failure, and selects the technical route of injecting reactive power at the end of the line for online de-icing; secondly, it introduces the principle of SVG device generating reactive power, which uses reactive power to generate heat loss in the distribution network line for on-line de-icing, and then proposes an improved reactive power outer loop control strategy to make it equivalent to current source De-icing device; Finally, a simulation model is built in PSCAD. Taking a 5km ACSR (LGJ-120) line with 5.4Mvar reactive power injected at the end of the line as an example, it can be seen that the line current reaches more than 500A due to the injected reactive power. The simulation results prove the effectiveness of the method. Considering its economy, it provides a feasible way for the distribution network with important loads Electric deicing scheme.

Development of model-based and model-free reactive control scheme: considering copper loss and movable-floater-displacement constraint for a wave energy converter

The utilization of wave energy is expected since ocean wave energy has a high potential. The improvement of the feasibility of wave energy converters requires control that maximizes the electric output energy, including the copper loss under a displacement constraint. Several model-based and model-free reactive controls have been developed. Although model-based reactive control attains high performance, it struggles to deal with modeling errors and forecasting wave excitation forces. On the other hand, the model-free reactive control can adapt to dynamic modeling, including modeling errors; however, it requires a vast amount of learning data and considerable time and effort to consider the displacement constraint. Model-based and model-free reactive controls each have advantages and disadvantages. Combined model-based and model-free reactive controls are desirable to freely switch between the model-based and model-free reactive controls based on various ocean situations. In this study, two equivalent model-based and model-free reactive controls that can consider the copper loss and displacement constraints without forecasting the wave excitation forces were proposed. The model-free reactive control was compared with the model-based reactive control and a conventional control using numerical simulations in irregular waves. The results of the simulation show that the proposed model-based reactive control achieves superior performance compared to that of the conventional control. The proposed model-free reactive control achieved comparable performance to that of the proposed model-based reactive control under various wave conditions. Moreover, the proposed model-free reactive control decreased the required training trials. The development of the two equivalent control schemes will lead to the proposal of combined model-based and model-free reactive controls in the future.

Different Control Strategies Drive Interlimb Differences in Performance and Adaptation during Reaching Movements in Novel Dynamics.

Humans exhibit lateralization such that most individuals typically show a preference for using one arm over the other for a range of movement tasks. The computational aspects of movement control leading to these differences in skill are not yet understood. It has been hypothesized that the dominant and nondominant arms differ in terms of the use of predictive or impedance control mechanisms. However, previous studies present confounding factors that prevented clear conclusions: either the performances were compared across two different groups, or in a design in which asymmetrical transfer between limbs could take place. To address these concerns, we studied a reach adaptation task during which healthy volunteers performed movements with their right and left arms in random order. We performed two experiments. Experiment 1 (18 participants) focused on adaptation to the presence of a perturbing force field (FF) and experiment 2 (12 participants) focused on rapid adaptations in feedback responses. The randomization of the left and right arm led to simultaneous adaptation, allowing us to study lateralization in single individuals with symmetrical and minimal transfer between limbs. This design revealed that participants could adapt control of both arms, with both arms showing similar performance levels. The nondominant arm initially presented a slightly worst performance but reached similar levels of performance in late trials. We also observed that the nondominant arm showed a different control strategy compatible with robust control when adapting to the force field perturbation. EMG data showed that these differences in control were not caused by differences in co-contraction across the arms. Thus, instead of assuming differences in predictive or reactive control schemes, our data show that in the context of optimal control, both arms can adapt, and that the nondominant arm uses a more robust, model-free strategy likely to compensate for less accurate internal representations of movement dynamics.