Effective Control Actions Research Articles

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

Occupancy dynamics of free ranging American mink (Neogale vison) in Greece.

Identifying the environmental factors that determine the occurrence of invasive species is essential in defining and implementing effective control campaigns. Here, we applied multi-season occupancy models to analyze American mink (Neogale vison) track data collected using 121 floating rafts, as a function of factors occurring at multiple spatial scales. Our overall aim was to identify those factors that determine the use, colonization or abandonment of rafts by free ranging individuals found in western Macedonia, Greece. We found that increasing values of shrubs and rock cover at the micro-habitat scale were positively associated with the species' probability of raft use, as was the density of medium-sized rivers at the landscape scale. Colonization was found to increase with increasing amounts of shrub and reed cover; however, both variables were not informative. Conversely, the distance from the nearest fur farm was highly informative in predicting raft abandonment by the species. Effective control actions may require removal by trapping along rocky or densely vegetated riverbanks or lake shores located in the vicinity of the established fur farms in the area. Habitat management, although possible, may be difficult to implement due to the ability of the species to adapt. Finally, fur farms should maximize security and establish an early warning and rapid eradication system in case of future escapes.

A mobile application for building smart water consumption habits

The article substantiates the necessity of forming environmental culture in the field of water consumption using software products. The paper creates a concept of a smart information system that combines a mobile application and technical devices that provide real-time control of water consumption and the occurrence of abnormal or emergency situations that significantly increase water consumption. Algorithms realizing the functional capabilities allow collecting and processing data from these devices and promptly recommend effective control actions. In addition, an interactive game is available to the user, which implies periodic performance of tasks related to household environmental literacy. Using object-oriented design methods, a user graphical interface has been created and a fragment of such a game is presented in the paper.

Seroprevalence and risk factors for Taenia spp infection in pigs in Kongwa and Songwe districts, Tanzania: A cross-sectional study

Taenia solium porcine cysticercosis (PCC) is widespread in many low- and middle-income countries (LMICs) where free-range pig rearing is common and hygienic standards are subpar. A cross-sectional survey was conducted in 42 villages between June and September 2019 (14 in Songwe district, southwest Tanzania, and 28 in Kongwa district, central Tanzania). Using a commercial Ag-ELISA kit (apDia, Belgium), circulating antigens of Taenia spp in pig serum were identified and used to calculate the PCC seroprevalence. The study recruited 692 randomly selected households, sampling one pig per household. The relationship between each risk factor and the seroprevalence of PCC at the household and village levels was analysed using mixed logistic regression models. The findings showed that approximately 28% of the pigs were reared in free-range settings, the proportion of households with latrines across the districts was 92%. Twenty-seven percent of households with latrines had water and soap available for hand washing. Sixty-seven (9.7%) tested positive for PCC based on Ag-ELISA. The overall seroprevalence in Kongwa and Songwe districts was 7.3% and 14.0% respectively. In addition, the overall village Ag-ELISA positivity was 9.3%, with an interquartile range (IQR) of 4.6% – 14.1%. Increasing the age of the pig (OR = 3.13 95% CI = 1.48 – 6.60; p = 0.003), pig originating from outside the household (OR = 0.5 95% CI = 0.25 – 0.99; p = 0.05), and pigs kept in a household that practised deworming (OR = 2.23 95% CI = 1.08 – 4.61; p = 0.03) were important risk factors associated with PCC positivity. Therefore, the high seroprevalence of PCC, up to 14%, calls for rapid and effective control actions such as vaccination and treatment of pigs against PCC, and public health education emphasises on indoor pig rearing, hygienic practices and regular use of latrines. Our findings also point to a potential danger of Taenia. spp infection indicating the possibility of people carrying the adult parasite Taenia solium not only in the rural communities of Kongwa and Songwe districts but also in the urban areas of Tanzania, where pigs from these areas are transported for consumption. To develop effective management measures, further research on taeniasis and cysticercosis in the human population is required.

A review of plant disease detection and classification methods

ABSTRACT The majority of agricultural yields are affected by plant diseases leading to economic losses. Hence, it is necessary to evaluate plant diseases at the very beginning to enhance crop production by making effective control actions. Therefore, automatic plant disease detection models are required to reduce the huge laborious tasks. In recent times, deep learning and computer vision models have been established for the automated detection of plant disease which functions by analyzing the symptoms on plant leaves. Thus, this paper reviews a set of articles that utilizes deep learning and image processing in the field of detecting plant and crop diseases. This survey ensures a comprehensive view of all research works regarding plant disease detection for exploring the effectiveness of the existing models for detecting diseases in diverse crops. This paper aims to analyze various machine learning and deep learning approaches regarding crop disease identification. This review gives an introduction to plant leaf diseases, the conventional methods for detecting plant diseases, the dataset used, the performance metrics utilized for analyzing the existing detection frameworks, and the implementation platforms. Finally, this review provides the limitations in this field of plant leaf detection and the future research trends for further enhancement.

Формирование программы сбалансированного развития ТЭК: методические аспекты

The article considers the methodological approach to the formation of the balanced development program of the Fuel and Energy Complex. The relevance of the study is due to the cause-and-effect relationship between the level of economic security of the country, ensuring socio-economic development of society through the bal-anced development of the Fuel and Energy Complex. According to statistics, the share of the oil and gas sector in Russia’s GDP is still significant, and the fuel and energy complex makes a great contribution to the formation of the country’s budget revenues. In addition, imbalances in the development of individual subsystems of the fuel and energy complex are identified in the approved Energy Security Doctrine of the Russian Federation as internal threats to the economic security of the state, hence, there is a demand to find ways to reduce them. Therefore, the optimal development of the FEC complex can be ensured through, on the one hand, effective control actions on the fuel and energy balance and its structure, and, on the other hand, the target balance of all subsystems included in the fuel and energy complex and consideration of their interests. Due to the complexity of the research object, the expediency of using systematic and integrated approaches is justified. On the meth-odological aspect, the proposed algorithm for creating a program of balanced development of the FEC implies the use of the method of hierarchy analysis, program-targeted technologies and multi-criteria approach. Using these methodological frameworks in practice will allow to achieve a synergistic effect and improve the quality of programs aimed at achieving targets in the field of ensuring balanced development of the FEC.

The Program for the Control of Visceral Leishmaniasis in Brazil: The Effect of the Systematic Euthanasia of Seropositive Dogs as a Single Control Action in Porteirinha, a Brazilian City with an Intense Transmission of Visceral Leishmaniasis.

Porteirinha is endemic for visceral leishmaniasis (VL), with intense disease transmission of the disease. We evaluated the impact of canine euthanasia as a single control measure on the incidence of VL in humans and canines. A prospective observational cohort study was carried out over four years (1998-2002) in 8 of the 12 neighborhoods of the city. The dynamics of canine visceral leishmaniasis (CVL) transmission were evaluated for 2 years, before beginning the screening-culling intervention. The comparative morbidity index (CMI) was used to stratify areas with the greatest risk of CVL, and the spatial distribution of human and canine VL cases was compared using univariate and bivariate K-functions. Human cases conglomerated in three neighborhoods. Spatial clusters were detected for CVL in 1998, 2000, and 2001, but not in 1999, when greater spatial dispersion occurred. The screening and culling intervention reduced the number of human VL cases and decreased the incidence of CVL, mainly in neighborhoods with a high CMI. The systematic euthanasia of seropositive dogs was shown to be an effective control action of the Program for Control of Visceral Leishmaniasis (PCLV) in Brazil. The fundamental role of domestic dogs in the epidemiological chain of VL was reaffirmed.

Effective Methods of Estimation of Pathogen Prevalence in Pooled Ticks.

Since tick-borne diseases (TBDs) incidence, both in human and animal populations, is increasing worldwide, there is the need to assess the presence, distribution and prevalence of tick-borne pathogens. Reliable estimates on tick-borne pathogens (TBPs) prevalence represent the public health foundation to create risk maps and take effective prevention and control actions against TBDs. Tick surveillance consists of collecting and testing (usually in pools) thousands of specimens. Construction and analysis of tick pools represent a challenge due to the complexity of tick-borne pathogens and tick-borne diseases ecology. The aim of this study is to provide a practical guideline on appropriate pooling strategies and statistical analysis of infection prevalence through: (i) reporting the different pooling strategies and statistical methodologies commonly used to calculate pathogen prevalence in tick populations and (ii) practical comparison between statistical methods utilising a real dataset of infection prevalence in ticks collected in Northern Italy. Reporting detailed information on tick pool composition and size is as important as the correct TBPs prevalence estimation. Among the prevalence indexes, we suggest using maximum-likelihood estimates of pooled prevalence instead of minimum infection rate or pool positivity rate given the merits of the method and availability of software.

Multiplicative and Additive Error Compensation Techniques for Perfect Reference Tracking

This paper introduces a novel approach to supply reference inputs to feedback systems. In this approach, modified inputs are used to ensure enhancing the capability of the system to achieve the actual desired inputs. Traditionally, the difference between the reference inputs to a system and the outputs are used to decide the suitable correcting actions. In this paper, modified reference inputs are used to cancel out the steady state error of the system. Accordingly, the control effort is evaluated based on a different set of errors. As a result, more effective control actions are implemented to the system. The results show that, the performance specifications of the systems are remarkably enhanced. The enhancement results obtained, include, much limited bounds of the control effort, smaller delay, enhanced transient response, and zero steady state error.

Intelligent Control Methodology for Smart Highway Bridge Structures Using Optimal Replicator Dynamic Controller

Control algorithms are an essential part of effective semi-active vibration control systems used for the protection of large structures under dynamic loading. Adaptive control algorithms, which are data-driven methods, have recently been developed to replace model-based control algorithms, thus improving efficiency. The dynamic parameters of semi-actively controlled infrastructures will change after significant vibration loading. As a result, these structures require real-time, effective control actions in response to changing conditions, which classical controllers are unable to provide. To improve the efficiency of the semi-active controller, the optimal control algorithm was developed in this study. The algorithm is the integration of the replicator dynamics with an improved non-dominated sorting genetic algorithm (NSGA), which is NSGA-II. The optimal parameters of replicator dynamics (total resources, growth rate, and fitness function), which represent the behavior of the actuators, were obtained through a multi-objective optimization process. The new control system was then used to reduce the vibrations of the isolated highway bridge, which is equipped with semi-active control devices known as MR dampers. Moreover, the current study improved the performance of the structural control system with minimum energy consumption by assigning a specific growth rate to each control device. In order to reduce the vibrations of the highway bridge, the results show that the performance of the optimal replicator controller is better than the performance of the classical control algorithms. Doi: 10.28991/CEJ-2023-09-01-01 Full Text: PDF

Distributed Asynchronous Greedy Control of Large Networks of Thermostatically Controlled Loads for Electric Demand Response

This letter illustrates two multi-agent greedy demand-side response control schemes for networks of Thermostatically Controlled Loads. The objective is to provide simple but effective local control actions such that the overall power consumption tracks an aggregated desired profile. Compared with the existing literature the novelties are twofold. Since model-free, our schemes possess certain robustness features to the model deterioration and exogenous disturbances. Since greedy, they are of easy implementation also on cheap development boards which do not support optimization software, moreover because asynchronous do not require any network-wide synchronization event. Specifically, Algorithm 1 is very simple but it is applicable only on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -regular communication topologies. Such prerequisite is then removed in Algorithm 2 by including within its instruction list a dynamic consensus protocol to estimate the mean network power consumption. Performance analysis and numerical simulations confirm the effectiveness of the schemes.

Methodology for Assessment of Congestion Problem

Abstract: Due to increasing in load demand, blackout or unexpected failure in any component of power system including generator, transformer, transmission lines, lead to congestion and over loading in power system this leads the power system to reach on its verse of emergency state ,therefore there should be a proper management or control action for that condition. Several methods are there to control congestion management including Generator Rescheduling (GR),Nodal Pricing, Particle Swarm Optimizer(PSO),Genetic Algorithm (GA) ,Grey Wolf Optimizer (GWO),This paper presents proper congestion management based on Grey Wolf Optimizer (GWO) for reducing load shedding, improve voltage profile, voltage stability, active power loss, this shows optimal load shedding is effective control action for congestion management. The algorithm applied on IEEE 30 bus system considering (n-1) contingency.

Congestion Management in Deregulated Power System Using Different Control Technique

Abstract: In today’s scenario there is a challenge for power companies to meet the expected demand load due to continuous increases in load demand causes unpredictable failure in the components of power system including transmission line, generator, transformer and various other equipment this leads to over loading in the power system and the line become congested if this failure not removed on time the system reaches to emergency state, therefore we are using various techniques to control or manage these situations these methods includes Generator Rescheduling(GR),Load Shedding, Particle Swarm Optimizer(PSO), Grey Wolf Optimization (GWO),Harmony search algorithm etc. Optimal load shedding is effective control action for congestion management. The various algorithm applied on IEEE 30 bus system.

Deep Reinforcement Learning-Based Approach for Autonomous Power Flow Control Using Only Topology Changes

With the increasing complexity of power system structures and the increasing penetration of renewable energy, driven primarily by the need for decarbonization, power system operation and control become challenging. Changes are resulting in an enormous increase in system complexity, wherein the number of active control points in the grid is too high to be managed manually and provide an opportunity for the application of artificial intelligence technology in the power system. For power flow control, many studies have focused on using generation redispatching, load shedding, or demand side management flexibilities. This paper presents a novel reinforcement learning (RL)-based approach for the secure operation of power system via autonomous topology changes considering various constraints. The proposed agent learns from scratch to master power flow control purely from data. It can make autonomous topology changes according to current system conditions to support grid operators in making effective preventive control actions. The state-of-the-art RL algorithm—namely, dueling double deep Q-network with prioritized replay—is adopted to train effective agent for achieving the desired performance. The IEEE 14-bus system is selected to demonstrate the effectiveness and promising performance of the proposed agent controlling power network for up to a month with only nine actions affecting substation configuration.

Control of arboviruses vectors using biological control by Wolbachia pipientis: a short review.

The number of arbovirus cases has increased in recent years, demonstrating a need for investing in effective control actions. Among these actions, are strategies using biological control vectors, a field where Wolbachia pipientis has shown itself as useful. Wolbachia pipientis, an obligatory intracellular Gram-negative bacteria, which parasites arthropods naturally or through laboratory-induced infections, is capable of manipulating the reproduction of its host. A systematic literature review gathering studies on this bacteria over last 10years (2007-2021) was performed given its important role in the reduction of insect disease vectors. 111 articles were found, from which 78 were used in this study. Information on the Wolbachia biology, mechanism of action and potential for the biological control of insect disease vectors was gathered. The present study may contribute to the knowledge surrounding the bacterium, as well as stimulate the production of other studies with the same theme.

Hazard Identification Risk Assessment and Risk Control (HIRARC) for Mengkuang Dam Construction

The construction industry is a main contributor to Malaysia's economy and yet some construction projects are considered being extremely unsafe, unhealthy, and exposing to a lot of hazards and risks to worksites. Hazard Identification, Risk Assessment and Risk Control (HIRARC) is a process commonly used to determine and evaluate both existing and potential hazards on a worksite and an effective control action plan used to reduce the identified hazards. HIRARC risk management is a fundamental to the business operation, planning and management. This study investigates the implementation of the HIRARC Risk Management Plan and Risk Registered Matrix for the construction project of the Mengkuang Dam in Malaysia, which involved the scopes of raising and extension of the existing dam. Construction of the dam upgrading and expansion was subjected to severe risks factors such as potential impact to the existing dam, project schedule delay, exceeding the project budget and contractual dispute. The Risk Register Matrix based on HIRARC was initiated for this project and was also implemented during the construction of the dam. The risk management plan framework and process involved in the risk identification, risk assessment, risk control, and mitigating measures are discussed for further assessments of risk control and action plans. The findings have shown that the construction project risk in term of the relative risks can be reduced by implementing an effective control action plan to mitigate the risks significantly. The HIRARC risk management that applied in this study can also be adopted in other dam construction projects to provide an effective safety management system and reduce the project relative hazard risks.

A kernel-based control approach for multi-period assets allocation based on lower partial moments

In quantitative finance, multi-period portfolio optimization can be reformulated as a stochastic optimal control problem, and standard feedback tools can be employed for its analysis. The performance of the trading solutions strongly depend on the quality of the model of the returns. Therefore, data-driven solutions have been recently proposed to optimize simple-linear allocation policies, based only on a set of possible market scenarios. In this work, kernel-based methods are proposed to design more complex and effective control actions, providing better trade-offs in terms of risk and investment performance with respect to linear ones, by preserving convexity. The proposed approach relies on the minimization of the Lower Partial Moments (LPM) risk measure. The effectiveness of the method is shown on a set of real historical financial data.

A three port bidirectional DC-DC converter for PV – Battery – DC microgrid application using fuzzy logic control

Electric vehicles (EVs) have emerged as cutting-edge technology in mainstream transportation. But, the accessibility of opportunities to charge the battery cells has become a contentious issue. Even though EVs are constituted a dynamic load, regulating available grid electricity or sustainable assisted electricity generation to recharge EVs incorporates a range of variables. This study represents the hierarchy of a DC microgrid, unit concepts and methods. Finally, an effective control action is to maintain the power for charging, depending on multiple energy usage circumstances. The proposed fuzzy operate decentralized to maintain power distribution between the microgrid DC-link and the ESU. The total PV coupled with the DC microgrid charging point control system is investigated under a variety of realistic conditions such as low and full PV array energy production, network overflowing, existing energy storage, and vehicle to vehicle. The laboratory-scale MATLAB/Simulink simulation and experimental of multiple EV charging plug spot DC microgrid has been studied and validated under numerous different microgrid settings, and they coincide with the suggested charging station upshot.

Analysis of CSTR Temperature Control with PID, MPC &amp; Hybrid MPC-PID Controller

This paper presents an analysis of the continuous stirred tank reactor (CSTR) temperature control with the Proportional-Integral-Derivative (PID) Controller, Model Predictive Controller (MPC) and Hybrid-Model Predictive Controller-Proportional Integral Derivative Controller (MPC-PID). It is the main goal of this project to find a suitable improvement strategy for the system’s stability and accuracy to be more stable. By creating a model, the control system is implemented for all the above mentioned control methods and so comparative analysis is carried out to find the best control method for CSTR. Simulation data inspector is used to compare the performance of different types of control systems: PID, MPC and MPC-PID. It has been observed that the hybrid MPC-PID has a more effective control action than a PID controller; with some tuning, the MPC controller can maintain the temperature within a reference or set point range. The control and simulation toolbox is used to construct the model predictive control method in LabVIEW platform. The performance of controllers is measured in terms of settling time, rise time and percentage of overshoot. Finally, a comparative overview of PID, MPC and Hybrid MPC-PID controllers on system performance is presented and discussed.

STATUS OF AIR QUALITY AND NOISE LEVEL WITH ASSOCIATED HEALTH RISK VICINITY TO SHIPBREAKING YARDS OF BANGLADESH

Shipbreaking industry has turned out to be dominant sector in Bangladesh without considering the environmental issues. However, to date, extremely limited studies have been performed to monitor the air and noise level around shipbreaking sites of Bangladesh. This study is conducted to assess the air and noise level with associated health risk vicinity to selected shipbreaking areas of the country. Herein, data on various air pollutants, noise level, and meteorological factors were collected from six prominent shipbreaking yards in Chattogram. Findings of the research work revealed that some air quality parameters and noise level adjacent to shipbreaking areas are exceeded the permissible level set by the WHO. The values of Air Quality Index (AQI) is found in the unhealthy category for human health perspective. The average noise level was also recorded beyond the permissible limit in every sampling sites, following descending order: S3 &gt; S4 &gt; S5 &gt; S6 &gt; S1 &gt; S2. From the statistical analysis, the authors explored that there are significant correlation with poor to strong (0.0.026 to 0.97) among the climatic parameters and air pollutants. Both particulate matters and AQI demonstrate a moderate correlation with the meteorological parameters (humidity and wind speed), and gaseous air pollutants (CO2 and TVOC) also have considerable correlations. The moderate correlation between AQI and noise level is also observed in this study. The authors recommended for effective control actions to alleviate the detrimental environmental pollution arises due to the consequences of shipbreaking activities for ensuring health safety of workers.