Agricultural Control Research Articles

A unified optimization control of wind farms considering wake effect for grid frequency support

This paper proposed a unified active power optimization control of wind farms under wake effect. It takes the sum of the kinetic energy variation and pitch angle variation as the optimization objective and used the particle swarm algorithm to achieve the optimization results. The main feature of the proposed method is that it unifies the kinetic energy optimization under a low wind speed area, the pitch angle and kinetic energy trade-off optimization under a medium wind speed area, and the pitch angle optimization under a high wind speed area. Combined with the de-loaded power constraint, it can flexibly reach various optimal operating states of the wind farm. The simulation results show that the proposed method optimizes the rotor speed and pitch angle in different wind speed areas, and releases kinetic energy and/or increases the output power of the wind farm to provide frequency support by switching the operating states.

End-to-end wind turbine wake modelling with deep graph representation learning

Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data, demonstrating its ability to rapidly make accurate 3D flow field predictions for various inlet conditions and turbine yaw angles. The specific graph neural network model employed here is shown to generalise well to unseen data and is less sensitive to over-smoothing compared to common graph neural networks. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation. Moreover, the proposed graph neural network framework is flexible and highly generic and as formulated here can be applied to any steady state computational fluid dynamics simulations on unstructured meshes.

Power train degradation modelling for multi-objective active power control of wind farms

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated.

A Novel Power Injection Priority Optimization Strategy for Voltage Support Control of PMSG-based Wind Farm

This article presents a novel power injection priority optimization-based control strategy for a PMSG-based wind farm. The proposed strategy seeks to provide voltage support while mitigating the frequency excursion resulting from voltage dip during the fault. To achieve this objective, the conventional fixed reactive power injection priority plan is replaced by an adaptive power injection priority plan in the proposed method. The control problem is formulated as a multi-objective function optimization problem, where the power injection priority plan is calculated by minimizing the shortfall of WF active power and maximizing the reactive power injection of WF. During the fault period, the optimized result is directly applied to the outer control of machine-side converter and grid-side converter to regulate the active/reactive power generation. The simulation system model and proposed optimal model are built in EMTDC/PSCAD and MATLAB, respectively, to verify the effectiveness of the proposed strategy. Test results demonstrate that the proposed control strategy is applicable to mitigate frequency excursion while complying with the voltage support requirement during the fault.

In Vitro Investigation of the Antibacterial Activity of Nine Commercial Water Disinfectants, Acidifiers, and Glyceride Blends against the Most Important Poultry Zoonotic Bacteria.

Identifying and monitoring the efficiency of alternative biocides that are presently used in livestock is gaining vast attention. The objective of this study was to determine, in vitro, the antibacterial activity of nine commercial water disinfectants, acidifiers, and glyceride blends against clinical isolates or reference strains of zoonotic pathogens belonging to the genera Escherichia spp., Salmonella spp., Campylobacter spp., Listeria spp., and Staphylococcus spp. For each product, the antibacterial activity was tested in concentrations ranging from 0.002 to 1.136% v/v and expressed as the minimum concentration of the product that inhibits bacterial growth (MIC). Water disinfectants Cid 2000™ and Aqua-clean® recorded MICs ranging from 0.002 to 0.142% v/v, while the lowest MICs were recorded at two strains of Campylobacter (0.002-0.004% v/v). Virkon® S displayed various MICs (0.013-0.409% w/v) and was highly effective at suppressing the growth of Gram-positive bacteria such as S. aureus (0.013-0.026% w/v). The MICs of water acidifiers (Agrocid Super™Oligo, Premium acid, and Ultimate acid) and glyceride blends (CFC Floramix, FRA®LAC34, and FRA®Gut Balance) ranged from 0.036 to 1.136% v/v, and for most of these products, MICs were closely correlated by their ability to modify the pH of the culture medium close to 5. In conclusion, most of the tested products showed promising antibacterial activity; as a result, they would be good candidates for pathogen control in poultry farms and for reducing the emergence of antimicrobial resistance. However, further in vivo studies are recommended to provide relevant information for the underlying mechanisms, as well as for the establishment of the optimal dosage scheme for each product and their possible synergies.

Factors Influencing Disease Prevention and Control Behaviours of Hog Farmers

Animal diseases are a serious threat to animal husbandry production and diet health, and effective prevention and control measures need to be explored. This study investigates the factors influencing the adoption of biosecurity prevention and the control behaviours of hog farmers towards African swine fever and provides appropriate recommendations. Using research data from Sichuan, Hubei, Jiangsu, Tianjin, Liaoning, Jilin, and Hebei, we employed a binary logistic model to empirically analyse these factors. Regarding individual farmer characteristics, male farmers emphasised biosecurity prevention and control in farms, with higher education actively influencing the adoption of prevention and control measures. Farmers who received technical training were actively inclined to adopt such behaviours. Furthermore, the longer the duration of farming, the more probable the farmers were to neglect biosecurity prevention and control. However, the bigger and more specialised the farm, the more inclined they were to adopt prevention and control behaviours. With respect to disease prevention and control awareness, the more risk-averse the farmers were, the more they actively adopted epidemic prevention behaviours. As the awareness of epidemic risk increased, the farmers tended to adopt active epidemic prevention behaviours by reporting suspected outbreaks. The following policy recommendations were made: learning about epidemic prevention and improving professional skills; large-scale farming, specialised farming; and timely dissemination of information to raise risk awareness.

Wind energy-harvesting technologies and recent research progresses in wind farm control models

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment.

Mindin Activates Autophagy for Lipid Utilization and Facilitates White Spot Syndrome Virus Infection in Shrimp.

Mindin is a secreted extracellular matrix protein that is involved in regulating cellular events through interacting with integrin. Studies have demonstrated its role in host immunity, including phagocytosis, cell migration, and cytokine production. However, the function of Mindin in the host-virus interaction is largely unknown. In the present study, we report that Mindin facilitates virus infection by activating lipid utilization in an arthropod, kuruma shrimp (Marsupenaeus japonicus). Shrimp Mindin facilitates white spot syndrome virus infection by facilitating viral entry and replication. By activating autophagy, Mindin induces lipid droplet consumption, the hydrolysis of triglycerides into free fatty acids, and ATP production, ultimately providing energy for virus infection. Moreover, integrin is essential for Mindin-mediated autophagy and lipid utilization. Therefore, by revealing the mechanism by which Mindin facilitates virus infection through regulating lipid metabolism, the present study reveals the significance of Mindin in the host-virus interaction. IMPORTANCE White spot syndrome virus (WSSV) is an enveloped double-stranded DNA virus that has had a serious influence on worldwide shrimp farming in the last 30 years. We have demonstrated that WSSV hijacks host autophagy and lipid metabolism for reproduction in kuruma shrimp (Marsupenaeus japonicus). These findings revealed the mechanism by which WSSV exploits host machinery for its infection and provided serial targets for WSSV prevention and control in shrimp farming.

Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the challenges associated with designing an efficient control scheme for wind farms; (iii) a breakdown of the different types of AI and ML algorithms used in wind farm controllers and control schemes; (iv) AI and ML for wind speed prediction; (v) AI and ML for wind power prediction; (vi) AI and ML for mechanical component monitoring and fault detection; and (vii) AI and ML for electrical fault prevention and detection. This paper will offer researchers and engineers in the wind energy generation field a comprehensive review of the application of AI and ML in the control methodology of offshore and onshore wind farms so that more efficient and robust control schemes can be designed for future wind turbine controllers.

DecoWind: Development of low-noise and cost-effective wind farm control technology

DecoWind is a 3-year Danish research project whose goal is to devise advanced control strategies for wind turbines and farms for minimizing their acoustic impact. Noise propagation models (Nord2000 and WindStar) are verified through dedicated measurement campaigns onshore. Long-range offshore measurements are also conducted to understand these specific conditions. A Parabolic Equation method for noise propagation, which have mostly been restricted to academic use in the past, is integrated into an engineering context for wind farm control. This framework can be used to define a wind farm optimal control strategy. The energy production is maximized while limiting the noise impact at dwellings, depending on the considered site, by operating the turbines using their different noise operational modes. This framework is demonstrated through numerical test-cases. In addition, a public survey is conducted to assess the socio-acoustical impact of wind turbine noise, looking at several factors. The ultimate goal is to produce a set of recommendations regarding wind turbine noise regulations that would connect the new engineering design capabilities and the findings regarding public annoyance. The project is a collaboration between DTU, Siemens-Gamesa Renewable Energy, FORCE Technology, and EMD International. In this contribution, the main achievements of the project are summarized.

RISIKO USAHATANI UBI ALABIO (DIOSCOREA ALATA L.) DI DESA SUNGAI AMBANGAH KECAMATAN SUNGAI RAYA KABUPATEN KUBU RAYA

Alabio sweet potato (Dioscorea alata L.) is a palawija crop that has potential for alternative food. Risk is the possibility of loss arising from conditions beyond the farmer's control. This study aims to identify the risk level of Alabio sweet potato (Dioscorea alata L.) farming and formulate efforts to reduce the risk of Alabio sweet potato (Dioscorea alata L.) farming. Respondents were 25 farmers of Alabio sweet potato (Dioscorea alata L.) in Sungai Ambangah Village, Sungai Raya District, Kubu Raya Regency. The analytical tool used is the Coefficient of Variation Analysis to measure the level of risk faced by farmers and the Fishbone Diagram to identify problems to find solutions. The results of this study are that the level of production risk and the level of income risk have a coefficient of variation (CV) > 0.5, meaning that the risks faced by farmers are large. The sources of risks that occur are high rainfall, plant pests and diseases, fluctuating input prices, inactive agricultural extension assistance, limited farming capital and no farmer groups.

A Critical Review of Current and Future Options for Wind Farm Participation in Ancillary Service Provision

This paper presents a critical review, from a wind farm control perspective, of different methodologies in the open literature that enable wind farms to participate in ancillary service provision. Firstly, it considers the services currently provided in power systems with high levels of wind generation (specifically, Denmark, Ireland, and Great Britain), reviewing current regulatory frameworks and recommendations. Secondly, it reviews the ancillary service markets that wind farms do not currently participate in, considering the barriers to entry and discussing potential solutions using a proper control-enabled framework. Thirdly, it also considers the future perspective for wind farm participation in ancillary service provision, including a review of the body of published academic research on wind farm participation in ancillary service provision. Finally, this review concludes by suggesting where the gaps are in the academic literature, and subsequently suggests future work. Two examples are the disconnect between the mechanical and farm side approaches with power-system-based modelling, and how much wind farm modelling is very-low-fidelity-omittedkey aspects such as wake effects and component fatigue analysis.

New design of a wind farm frequency control considering output uncertainty and fatigue suppression

Frequency response capability is necessary for wind farms (WFs) in the power system with a high proportion of renewable energy. In frequency control, WFs need to meet the grid’s power demand and avoid obvious additional damage to wind turbines (WTs) due to frequency control. In terms of meeting the grid power demand, the actual power characteristics of WTs need to be accurately described. An improved Gaussian Mixture Model (GMM) is used to model the actual measured output of WTs. The wind farm control center can accordingly issue achievable effective power commands to the WTs in the frequency response. In terms of avoiding the structural fatigue increase of WTs, a two-degree-of-freedom control strategy is designed, which simultaneously controls the generator torque and pitch angle and achieves fatigue suppression through multi-objective model predictive control (MOMPC). The simulation results show that the proposed control system can improve the frequency response capability of the WF and reduce the fatigue load of WTs, which has obvious application prospects.

Heteroscedastic Bayesian Optimisation for Active Power Control of Wind Farms*

Active power control of wind farms remains an open challenge due to inherent noise in wind power that arises from uncertain wind speed measurements and plant/model mismatch. To leverage the heteroscedastic nature of the wind power noise, heteroscedastic Bayesian optimisation (BO) is used for active power control of wind farms. BO utilises closed-loop performance data to tune the parameters of a stochastic model predictive controller (SMPC) in a systematic and data-efficient manner. This, in turn, allows for enhancing the closed-loop performance of the controller intended to decrease the power tracking error. A case study with 9 turbines in a 3 × 3 wind farm shows that the heteroscedastic BO approach achieves a reduced closed-loop power tracking error in terms of root-mean-square by 8.89% compared to one that relies on nominal BO and a decrease by 64.99% compared to a nominal model predictive controller (MPC) whose performance is not tuned using closed-loop data and BO.

A SURVEY OF THE DISTRIBUTION OF SYNANTHROPIC COCKROACHES IN ANIMAL FARMS AND FOOD PROCESSING PLANTS IN BULGARIA

Cockroaches are pests of veterinary and public health importance with their role as pathogen spreaders. The aim of the present study was to determine the distribution of synanthropic species of cockroaches in animal farms (AFs) and food processing plants (FPPs). The survey was carried out in 52 sites (42 AFs and 10 FPPs), by questionnaire-visual survey and monitoring catch method. The presence of cockroaches was found in 78.57% of AFs and 0% of FPPs. The industrial pig farms are the most frequently and highly affected – all of them were infested in high density. The oriental cockroach (Blatta orientalis L.) was the dominant species. An inhomogeneous distribution was found in invased pig farms with preferred areas of localization in the new-born sectors and sanitary filters. The obtained results are proof of the need to strengthen insect monitoring and pest control in animal farming, especially in pig farms.

Two-Stage Optimal Re/Active Power Control of Large-Scale Wind Farm to Mitigate Voltage Dip Induced Frequency Excursion

A two-stage optimal active and reactive power coordinated control strategy of large-scale wind farm (WF) to mitigate voltage dip induced frequency excursion (VDIFE) is proposed in this letter. Before the voltage fault occurs, the power reference look-up table of each wind turbine (WT) are calculated. When voltage fault occurs, the power reference values of each WT are optimized based on model predictive control. Distinguished from existing works, the proposed optimal power control can better mitigate VDIFE and balance the variation of rotor speed of each WT. Case studies on a doubly-fed-induction-generator based WF built in MATLAB/Simulink are carried out to demonstrate the effectiveness of the proposed control scheme.

Synchrophasor Data Based Q-V Droop Control of Wind Farm Integrated Power Systems

With increasing scalability of grid-integrated wind farms (WFs), reactive power ancillary service through Q-V droop control of doubly-fed inducton generators (DFIGs) is one of the prime interests of power system operators. However, variable and uncertain availability of wind-resource at the wind turbines creates challenge against allocating reactive power reserve at the WFs for maintaining voltage stability of the grid. This issue can be resolved through online coordination among the WFs. This paper proposes a synchrophasor data based Q-V droop (SQVD) control technique for the WFs integrated to the grid through different point of common couplings (PCCs) in the power system transmission network. The data from the phasor measurement units (PMUs) at the WF-connected PCCs are used to obtain synchronized Q-V droop gain for each WF. Further, distributed coordination among DFIGs within each WF is performed to allocate optimum reactive power share at the converters. The total reserve of each WF is employed by their synchronized droop, in improving voltage stability at both the local PCC and neighboring PCCs. Performance of SQVD is verified at 418-bus practical Indian power system, in different dynamic situations of the grid, and found to be more effective as compared to the constant Q-V droop and variable Q-V droop control methods.

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Coordinated control of wind farm and supercapacitor energy storage system for dynamic performance reinforcement of multi-area power systems

Review of the Applications of Satellite Remote Sensing in Organic Farming (Part I)

Organic farming is a much more sustainable farming system than conventional farming. It is part of humanity's efforts to preserve biodiversity and provides healthy and safe food to humans. Remote sensing methods are widely used in agriculture. Their use will help the transition from conventional to organic farming. They can help farmers choose the most suitable place to build an organic farm. Remote sensing methods are a very powerful tool for weed control in organic farming. They can be used to determine the level of stress that crops experience. They provide a good opportunity to forecast yields on organic farms. Remote sensing methods can optimize fertilization on organic farms. They can be used to distinguish between organic and conventional agriculture, as well as to monitor biodiversity in agricultural areas. Remote sensing methods can help organic farmers make timely and adequate decisions in managing their farms.

Distributionally robust model predictive control for wind farms

In this paper, we develop a distributionally robust model predictive control framework for the control of wind farms with the goal of power tracking and mechanical stress reduction of the individual wind turbines. We introduce an ARMA model to predict the turbulent wind speed, where we merely assume that the residuals are sub-Gaussian noise with statistics contained in an moment-based ambiguity set. We employ a recently developed distributionally model predictive control scheme to ensure constraint satisfaction and recursive feasibility of the control algorithm. The effectiveness of the approach is demonstrated on a practical example of five wind turbines in a row.